Patent Grant
11905468
The present disclosure relates to assemblies and methods to enhance control of fluid catalytic cracking (FCC) processes and, more particularly, to assemblies and methods to enhance control of FCC processes using one or more spectroscopic analyzers during one or more FCC processes.
Fluid catalytic cracking (FCC) processes may be used to produce desired petroleum-based intermediate and final products from hydrocarbon feeds. FCC processes are inherently complex because they involve a large number of variables and processing parameters associated with the hydrocarbon feeds and operation of FCC processing units and downstream processing units. Optimization, design, and control of fluid catalytic cracking (FCC) processing units may benefit from analytical models that describe conversion of hydrocarbon feeds to products. Analytical models, however, may only be useful if provided with timely and accurate information. If the information lacks sufficient accuracy, the analytical model may provide inaccurate outputs, for example, relating to hydrocarbon feedstock monitoring and control, and/or control of FCC and related processing units, and resulting products may lack desired properties. If the information is not provided to the analytical model in a sufficiently responsive manner, desired changes based on the information and model outputs may be delayed, resulting in extending the time during which the FCC processes are performed below optimum efficiency. Conventional laboratory analysis of the hydrocarbon feeds and related materials or processes may suffer from insufficiently responsive results to provide effective monitoring and control of the FCC process and related materials. For example, off-line laboratory analysis and related modeling studies may involve response times of hours, days, or even weeks, during which processing parameters are not optimized. As a result, the value of such monitoring and control may be reduced when used to monitor and control FCC processes in during operation.
Although some FCC processes may include devices and processes for monitoring and controlling the FCC process, Applicant has recognized that such devices and processes may suffer from delayed acquisition of useful information and/or inaccuracies due to the nature of the devices or processes. As a result, Applicant has recognized that there may be a desire to provide assemblies and methods for more accurately monitoring and/or controlling FCC processes and/or for more responsively determining properties and/or characteristics of hydrocarbon feeds, processing unit product materials, intermediate materials, FCC effluent, and/or upstream materials or downstream materials related to the FCC processes. Such assemblies and methods may result in enhanced control of FCC processes for more efficiently producing FCC products and/or downstream products.
The present disclosure may address one or more of the above-referenced considerations, as well as other possible considerations.
Monitoring and control of FCC processes may be important for producing FCC-related products having certain characteristics or properties to meet industry and/or marketing standards. Using current systems and processes, it may be difficult to achieve desired standards because the systems and methods may suffer from delayed acquisition of useful information and/or inaccuracies due to the nature of the devices or processes. At least some embodiments of the present disclosure may advantageously provide assemblies and/or methods for monitoring and/or controlling FCC processes, such that the resulting FCC-related products have desired characteristics or properties that may be achieved more efficiently. In some embodiments, the assemblies and/or methods disclosed herein may result in acquisition of useful information and/or provide more accurate information for monitoring and/or controlling FCC processes. This, in turn, may result in producing FCC-related products having desired characteristics or properties in a more efficient manner. For example, in at least some embodiments, at least some of the acquired information may be used to monitor and prescriptively control FCC processes, resulting in producing FCC-related products having desired characteristics or properties in a more economically efficient manner. For example, prescriptively controlling the FCC process assembly and/or the FCC process, according to some embodiments, may result in enhancing accuracy of target content of one or more of the intermediate materials, the unit product materials, or downstream materials produced by one or more downstream processing units, thereby to more responsively control the FCC processing assembly to achieve material outputs that more accurately and responsively converge on target properties.
According to some embodiments, a method to enhance control of a fluid catalytic cracking (FCC) processing assembly associated with a refining operation, may include supplying a hydrocarbon feedstock to one or more first processing units associated with the refining operation. The one or more first processing units may include an FCC processing unit. The method further may include operating the one or more first processing units to produce one or more corresponding unit materials, and the one or more corresponding unit materials may include one or more of intermediate materials or unit product materials including FCC effluent. The method also may include conditioning a hydrocarbon feedstock sample to one or more of filter the hydrocarbon feedstock sample, change a temperature of the hydrocarbon feedstock sample, dilute the hydrocarbon feedstock sample in solvent, or degas the hydrocarbon feedstock sample. The method still further may include analyzing the hydrocarbon feedstock sample via a first spectroscopic analyzer to provide hydrocarbon feedstock sample spectra. The method also may include conditioning a unit material sample to one or more of filter the unit material sample, change a temperature of the unit material sample, dilute the unit material sample in solvent, or degas the unit material sample. The method further may include analyzing the unit material sample via one or more of the first spectroscopic analyzer or a second spectroscopic analyzer to provide unit material sample spectra. One or more of the first spectroscopic analyzer or the second spectroscopic analyzer may be calibrated to generate standardized spectral responses. The method further may include predicting one or more hydrocarbon feedstock sample properties associated with the hydrocarbon feedstock sample based at least in part on the hydrocarbon feedstock sample spectra, and predicting one or more unit material sample properties associated with the unit material sample based at least in part on the unit material sample spectra. The method also may include prescriptively controlling, via one or more FCC process controllers based at least in part on the one or more hydrocarbon feedstock sample properties and the one or more unit material sample properties, one or more of: (i) a feedstock parameter associated with the hydrocarbon feedstock supplied to the one or more first processing units; (ii) content of the intermediate materials produced by one or more of the first processing units; (iii) operation of the one or more first processing units; (iv) content of the unit material; or (v) operation of one or more second processing units positioned downstream relative to the one or more first processing units, so that the prescriptively controlling results in enhancing accuracy of target content of one or more of the intermediate materials, the unit product material, or downstream materials produced by the one or more second processing units, thereby to more responsively control the FCC processing assembly to achieve material outputs that more accurately and responsively converge on target properties.
According to some embodiments, a fluid catalytic cracking (FCC) control assembly to enhance control of a fluid catalytic cracking (FCC) process associated with a refining operation, may include a first spectroscopic analyzer positioned to: receive a hydrocarbon feedstock sample of a hydrocarbon feedstock positioned to be supplied to one or more first processing units associated with the refining operation, the one or more first processing units including an FCC processing unit; and analyze the hydrocarbon feedstock sample to provide hydrocarbon feedstock sample spectra. The FCC control assembly further may include a second spectroscopic analyzer positioned to: receive on-line a unit material sample of one more unit materials produced by one or more first processing units, the one or more unit materials including one or more of intermediate materials or unit product materials including FCC effluent. The first spectroscopic analyzer and the second spectroscopic analyzer may be calibrated to generate standardized spectral responses, and analyze the unit material sample to provide unit material sample spectra. The FCC control assembly also may include a sample conditioning assembly positioned to one or more of: (i) condition the hydrocarbon feedstock sample, prior to being supplied to the first spectroscopic analyzer, to one or more of filter the hydrocarbon feedstock sample, change a temperature of the hydrocarbon feedstock sample, dilute the hydrocarbon feedstock sample, or degas the hydrocarbon feedstock sample; or (ii) condition the unit material sample, prior to being supplied to the second spectroscopic analyzer, to one or more of filter the unit material sample, change a temperature of the unit material sample, dilute the unit material sample in solvent, or degas the unit material sample. The FCC control assembly still further may include an FCC process controller in communication with the first spectroscopic analyzer and the second spectroscopic analyzer. The FCC process controller may be configured to predict one or more hydrocarbon feedstock sample properties associated with the hydrocarbon feedstock sample based at least in part on the hydrocarbon feedstock sample spectra, and predict one or more unit material sample properties associated with the unit material sample based at least in part on the unit material sample spectra. The FCC process controller further may be configured to prescriptively control, based at least in part on the one or more hydrocarbon feedstock sample properties and the one or more unit material sample properties, one or more of: (i) a feedstock parameter associated with the hydrocarbon feedstock supplied to the one or more first processing units; (ii) content of the intermediate materials produced by one or more of the first processing units; (iii) operation of the one or more first processing units; (iv) content of the unit material; or (v) operation of one or more second processing units positioned downstream relative to the one or more first processing units, so that the prescriptively controlling results in enhancing accuracy of target content of one or more of the intermediate materials, the unit product material, or downstream materials produced by the one or more second processing units, thereby to more responsively control the FCC processing assembly to achieve material outputs that more accurately and responsively converge on target properties.
According to some embodiments, a fluid catalytic cracking (FCC) process controller to enhance control of a fluid catalytic cracking (FCC) processing assembly associated with a refining operation, the FCC process controller being in communication with one or more spectroscopic analyzers and one or more first processing units, may be configured to predict one or more hydrocarbon feedstock sample properties associated with a hydrocarbon feedstock sample based at least in part on hydrocarbon feedstock sample spectra generated by the one or more spectroscopic analyzers. The FCC process controller further may be configured to predict one or more unit material sample properties associated with a unit material sample based at least in part on unit material sample spectra generated by the one or more spectroscopic analyzers. The FCC process controller also may be configured to prescriptively control, based at least in part on the one or more hydrocarbon feedstock sample properties and the one or more unit material sample properties, one or more of: (i) a feedstock parameter associated with the hydrocarbon feedstock supplied to the one or more first processing units; (ii) content of the intermediate materials produced by one or more of the first processing units; (iii) operation of the one or more first processing units; (iv) content of the one or more unit materials; or (v) operation of one or more second processing units positioned downstream relative to the one or more first processing units, so that the prescriptively controlling results in enhancing accuracy of target content of one or more of the intermediate materials, the unit product materials, or downstream materials produced by the one or more second processing units, thereby to more responsively control the FCC process to achieve material outputs that more accurately and responsively converge on target properties.
According to some embodiments, a fluid catalytic cracking (FCC) assembly associated with a refining operation may include one or more first FCC processing units associated with the refining operation including one or more of an FCC reactor or an FCC regenerator. The FCC processing assembly may further include a first spectroscopic analyzer positioned to receive a hydrocarbon feedstock sample of a hydrocarbon feedstock positioned to be supplied to the one or more first FCC processing units, and analyze the hydrocarbon feedstock sample to provide hydrocarbon feedstock sample spectra. The FCC processing assembly further may include a second spectroscopic analyzer positioned to receive on-line a unit material sample of one more unit materials produced by the one or more first FCC processing units, the one or more unit materials including one or more of intermediate materials or unit product materials including FCC effluent. The first spectroscopic analyzer and the second spectroscopic analyzer may be calibrated to generate standardized spectral responses, and analyze the unit material sample to provide unit material sample spectra. The FCC processing assembly also may include a sample conditioning assembly positioned to one or more of (i) condition the hydrocarbon feedstock sample, prior to being supplied to the first spectroscopic analyzer, to one or more of filter the hydrocarbon feedstock sample, change a temperature of the hydrocarbon feedstock sample, dilute the hydrocarbon feedstock sample, or degas the hydrocarbon feedstock sample; or (ii) condition the unit material sample, prior to being supplied to the second spectroscopic analyzer, to one or more of filter the unit material sample, change a temperature of the unit material sample, dilute the unit material sample in solvent, or degas the unit material sample. The FCC processing assembly also may include an FCC process controller in communication with the first spectroscopic analyzer and the second spectroscopic analyzer. The FCC process controller may be configured to predict one or more hydrocarbon feedstock sample properties associated with the hydrocarbon feedstock sample based at least in part on the hydrocarbon feedstock sample spectra, and predict one or more unit material sample properties associated with the unit material sample based at least in part on the unit material sample spectra. The FCC process controller also may be configured to prescriptively control, based at least in part on the one or more hydrocarbon feedstock sample properties and the one or more unit material sample properties, one or more of: (i) a feedstock parameter associated with the hydrocarbon feedstock supplied to the one or more first FCC processing units; (ii) content of the intermediate materials produced by one or more of the first FCC processing units; (iii) operation of the one or more first FCC processing units; (iv) content of the unit material; or (v) operation of one or more second processing units positioned downstream relative to the one or more first FCC processing units, so that the prescriptively controlling results in enhancing accuracy of target content of one or more of the intermediate materials, the unit product materials, or the downstream materials produced by the one or more second processing units, thereby to more responsively control the FCC processing assembly to achieve material outputs that more accurately and responsively converge on target properties.
Still other aspects, examples, and advantages of these exemplary aspects and embodiments are discussed in more detail below. It is to be understood that both the foregoing information and the following detailed description are merely illustrative examples of various aspects and embodiments, and are intended to provide an overview or framework for understanding the nature and character of the claimed aspects and embodiments. Accordingly, these and other objects, along with advantages and features of the present disclosure herein disclosed, may become apparent through reference to the following description and the accompanying drawings. Furthermore, it is to be understood that the features of the various embodiments described herein are not mutually exclusive and may exist in various combinations and permutations.
The accompanying drawings, which are included to provide a further understanding of the embodiments of the present disclosure, are incorporated in and constitute a part of this specification, illustrate embodiments of the present disclosure, and together with the detailed description, serve to explain principles of the embodiments discussed herein. No attempt is made to show structural details of this disclosure in more detail than may be necessary for a fundamental understanding of the exemplary embodiments discussed herein and the various ways in which they may be practiced. According to common practice, the various features of the drawings discussed below are not necessarily drawn to scale. Dimensions of various features and elements in the drawings may be expanded or reduced to more clearly illustrate the embodiments of the disclosure.
Referring now to the drawings in which like numerals indicate like parts throughout the several views, the following description is provided as an enabling teaching of exemplary embodiments, and those skilled in the relevant art will recognize that many changes may be made to the embodiments described. It also will be apparent that some of the desired benefits of the embodiments described may be obtained by selecting some of the features of the embodiments without utilizing other features. Accordingly, those skilled in the art will recognize that many modifications and adaptations to the embodiments described are possible and may even be desirable in certain circumstances. Thus, the following description is provided as illustrative of principles of the embodiments and not in limitation thereof.
The phraseology and terminology used herein is for the purpose of description and should not be regarded as limiting. Any examples of operating parameters and/or environmental conditions are not exclusive of other parameters/conditions of the disclosed embodiments. Additionally, it should be understood that references to “one embodiment,” “an embodiment,” “certain embodiments,” or “other embodiments” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. When introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” “the,” and “said” are intended to mean that there are one or more of the elements. As used herein, the term “plurality” refers to two or more items or components. A multi-component sample may refer to a single (one) sample including a plurality of components, such as two or more components. The terms “comprising,” “including,” “carrying,” “having,” “containing,” and “involving,” whether in the written description or the claims and the like, are open-ended terms, in particular, to mean “including but not limited to,” unless otherwise stated. Thus, the use of such terms is meant to encompass the items listed thereafter, and equivalents thereof, as well as additional items. The transitional phrases “consisting of” and “consisting essentially of,” are closed or semi-closed transitional phrases, respectively, with respect to any claims. Use of ordinal terms such as “first,” “second,” “third,” and the like in the claims to modify a claim element does not necessarily, by itself, connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish claim elements.
Certain terminology used herein may have definitions provided for the purpose of illustration and not limitation. For example, as used herein, the “sampling circuit” may refer to an assembly for facilitating separation of a sample of a material, a sample of a composition of material, and/or a sample of an FCC product, for example, for processing and/or analysis of the sample.
As used herein, the term “sample conditioner” may refer to an assembly for facilitating preparation of a sample for analysis, for example, to improve the accuracy of analysis of the sample and/or to provide consistency and/or repeatability of the analysis of the sample or more than one sample.
As used herein, the term “spectroscopic analyzer” may refer an analyzer that may be used to measure or predict one or more properties of a sample of, for example, a material, a composition of materials, and/or an FCC product. In some embodiments, the spectroscopic analyzers may be used on-line or in a laboratory setting. “Spectroscopic analyzer” may refer in some instances to a spectroscopic analyzer assembly, which may include a spectroscopic analyzer and an analyzer controller in communication with one or more spectroscopic analyzers. The analyzer controller may be configured for use with a corresponding spectroscopic analyzer for pre-processing and/or post-processing steps or procedures related to a spectroscopic analysis, as will be understood by those skilled in the art. In some embodiments, the analyzer controller may be physically connected to the spectroscopic analyzer. In some such embodiments, the spectroscopic analyzer may include a housing, and at least a portion of the analyzer controller may be contained in the housing. In some embodiments, the analyzer controller may be in communication with the spectroscopic analyzer via a hard-wired communications link and/or wireless communications link. In some embodiments, the analyzer controller may be physically separated from the spectroscopic analyzer and may be in communication with the spectroscopic analyzer via a hard-wired communications link and/or a wireless communications link. In some embodiments, physical separation may include being spaced from one another, but within the same building, within the same facility (e.g., located at a common manufacturing facility, such as a refinery), or being spaced from one another geographically (e.g., anywhere in the world). In some physically separated embodiments, both the spectroscopic analyzer and the analyzer controller may be linked to a common communications network, such as a hard-wired communications network and/or a wireless communications network. Such communications links may operate according to any known hard-wired communications protocols and/or wireless communications protocols, as will be understood by those skilled in the art.
As used herein, the term “sample introducer” may refer to a component or assembly that may be used to facilitate the provision of a conditioned sample (portion or stream) to one or more spectroscopic analyzers for analysis.
As used herein, the term “sample stream” may refer to a portion of a sample stream supplied to one or more spectroscopic analyzers for spectroscopic analysis by the one or more spectroscopic analyzers.
As used herein, the term “predicting” may refer to measuring, estimating, determining, and/or calculating one or more properties of a material, a composition of materials, and/or an FCC product based on, for example, a mathematical relationship, a correlation, an analytical model, and/or a statistical model.
As used herein, the term “sample probe” may refer to a component or an interface used to facilitate collection of a sample for analysis by, for example, one or more spectroscopic analyzers.
As used herein, the term “analyzer probe” may refer to a component of one or more spectroscopic analyzers that facilitates direction of electromagnetic radiation (e.g., light energy) from a source through a sample stream (e.g., a conditioned sample stream) to detect and/or measure one or more of absorbance, transmittance, reflectance, transflectance, or scattering intensity associated with the sample stream.
As used herein, the term “sample cell” may refer to a receptacle or cell for receipt of samples for analysis or measurement, for example, by a spectroscopic analyzer.
As used herein, the term “on-line” may refer to equipment and/or processes that are physically located at or adjacent to processing assemblies during operation and, for at least some embodiments, may be capable of providing real-time and/or near real-time analysis and/or data capable of real-time and/or near real-time analysis. For example, in some embodiments, an on-line spectroscopic analyzer may receive one or more sample streams directly from a processing assembly or process and analyze the one or more sample streams in real-time or near real-time to provide results that may, in some embodiments, be used to at least partially control operation of one or more processing assemblies and/or one or more processes in real-time or near real-time. In some embodiments, the on-line spectroscopic analyzer or analyzers may be physically located in a laboratory setting. This may be either extractive (e.g., a sample stream is drawn off of a processing unit and supplied to a spectroscopic analyzer and/or to one or more sensors) or in situ (e.g., a probe of a spectroscopic analyzer or one or more sensors is present in a conduit associated with the processing assembly).
As used herein, the term “at-line” may refer to equipment and/or processes that are physically located at or adjacent to processing assemblies during operation, but which, for at least some embodiments, are not capable of providing real-time and/or near real-time analysis and/or are not capable providing data capable of real-time and/or near real-time analysis. For example, in an “at-line” process, a “field analyzer” located physically at or adjacent a processing assembly may be used to analyze a sample withdrawn from the processing assembly or process and manually taken to the field analyzer for analysis. In some embodiments, the on-line spectroscopic analyzer or analyzers may be physically located in a laboratory setting. For example, in some embodiments, an at-line spectroscopic analyzer would not receive a sample stream directly from processing assemblies, but instead, would manually receive a sample manually withdrawn from a processing unit by an operator and manually taken or delivered by the operator to the at-line spectroscopic analyzer.
In some embodiments, the hydrocarbon feed/charge 18 may include FCC feedstocks including a fraction of crude oil having boiling points ranging from about 650 degrees F. to about 1,000 degrees F., which, in some embodiments, may be relatively free of coke precursors and/or heavy metal contamination, such as, for example, feedstock sometimes referred to as “vacuum gas oil” (VGO), which, in some instances, may be generally obtained from crude oil by distilling off the fractions of the feedstock having boiling points below 650 degrees F. at atmospheric pressure and thereafter separating by further vacuum distillation from the heavier fractions a cut having boiling points ranging from about 650 degrees F. to about 900 degrees to 1,025 degrees F., for example, as will be understood by those skilled in the art. Fractions of the feedstock having boiling points ranging from above about 900 degrees F. to about 1,025 degrees F. may be used for other purposes, such as, for example, asphalt, residual fuel oil, #6 fuel oil, and/or marine Bunker C fuel oil. In some embodiments, some of the cuts having higher boiling points may be used, for example, as feedstock in association with FCC processes that use carbo-metallic oils formed by reduced crude conversion (RCC), for example, using a progressive flow-type reactor having an elongated reaction chamber. In some embodiments, the hydrocarbon feed/charge 18 may be selected to increase or optimize production of propylene by an FCC processing assembly, such as, for example, the hydrocarbon feedstock/charge 18 may be selected to contain feedstocks having a particular aromatics content, a particular hydrogen content, and/or other particular feedstock characteristics known to those skilled in the art to increase, enhance, or optimize propylene production by an FCC processing assembly.
As schematically shown in
In some embodiments, the one or more hydrocarbon feed/charge 18 sample properties and/or the one or more unit material sample properties may include a content ratio indicative of relative amounts of one or more hydrocarbon classes present in one or more of the hydrocarbon feed/charge 18 sample and/or the unit material samples. Other hydrocarbon feed/charge 18 sample properties and/or unit material sample properties are contemplated. Although many embodiments described herein use more than one spectroscopic analyzer, it is contemplated that a single spectroscopic analyzer may be used for at least some embodiments of the FCC processes described herein. One or more of the spectroscopic analyzers 20A through 20N may include one or more near-infrared (NIR) spectroscopic analyzers, one or more mid-infrared (mid-IR) spectroscopic analyzers, one or more combined MR and mid-IR spectroscopic analyzers, and/or one or more Raman spectroscopic analyzers. In some embodiments, one or more of the spectroscopic analyzer(s) 20A through 20N may include a Fourier Transform near infrared (FTNIR) spectroscopic analyzer, a Fourier Transform infrared (FTIR) spectroscopic analyzer, or an infrared (IR) type spectroscopic analyzer. In some embodiments, one or more of the spectroscopic analyzers 20A through 20N may be ruggedized for use in an on-line analyzing process and/or in a laboratory setting, and in some embodiments, one or more of the spectroscopic analyzers 20A through 20N may be at least partially housed in a temperature-controlled and/or explosion-resistant cabinet. For example, some embodiments of the one or more spectroscopic analyzers 20A through 20N may be configured to withstand operating conditions, such as, for example, temperature, pressure, chemical compatibility, vibrations, etc., that may be present in an on-line environment and/or in a laboratory setting. For example, the one or more spectroscopic analyzers 20A through 20N may be designed to be operated in a particular environment of use and/or an environment that meets area classifications, such as, for example, a Class 1, Division 2 location. In some embodiments, a photometer with present optical filters moving successively into position, may be used as a type of spectroscopic analyzer.
As shown in
In some embodiments, the FCC process controller(s) 24 may be configured to prescriptively control, based at least in part on the one or more hydrocarbon feedstock sample properties and the one or more unit material sample properties: (i) a feedstock parameter associated with the hydrocarbon feed/charge 18 supplied to the one or more FCC processing units 22; (ii) content of the intermediate materials produced by one or more of the FCC processing units 22; operation of the one or more FCC processing units 22; (iii) content of the one or more unit materials; and/or operation of one or more processing units positioned downstream relative to the one or more FCC processing units 22 such as, for example, a fractionator 26 configured to separate various hydrocarbon products of FCC effluent received from the FCC reactor 12. In some embodiments, the prescriptive control may result in enhancing accuracy of target content of one or more of the intermediate materials, the unit product materials, or downstream materials produced by the one or more processing units downstream from the one or more FCC processing units, for example, thereby to more responsively control the FCC processing assembly 10 to achieve material outputs that more accurately and responsively converge on target properties.
In some embodiments, the FCC processing assembly 10 further may include a sample conditioning assembly 28 configured to condition the hydrocarbon feed/charge 18, for example, prior to being supplied to the one or more spectroscopic analyzer(s) 20A through 20N. In some embodiments, the sample conditioning assembly 28 may be configured to filter samples of the hydrocarbon feed/charge 18, change (e.g., control) the temperature of the samples of the hydrocarbon feed/charge 18, dilute samples of the hydrocarbon feed/charge 18 with a solvent (e.g., on-line and/or in a laboratory setting), and/or degas the samples of the hydrocarbon feed/charge 18. In some embodiments, one or more sample conditioning procedures may be performed without using the sample conditioning assembly 28, for example, in a laboratory setting. In some embodiments, the sample conditioning assembly 28 also may be configured to condition samples of the unit materials, for example, prior to being supplied to the one or more spectroscopic analyzer(s) 20A through 20N, to filter the samples of the unit materials, to change (e.g., control) the temperature of the samples of the unit materials, to dilute samples of the unit materials in solvent, and/or degas the samples of the unit materials. With respect to diluting samples, for example, in some embodiments, this may include diluting samples of the hydrocarbon feed/charge 18 and/or the unit materials, such dilution may be used for analysis in a laboratory setting, and in some embodiments, the dilution may be performed in a laboratory setting. In some such embodiments, the resulting spectra of the diluted sample may be manipulated, for example, to back out account for the infrared absorption or the Raman scattering due to the presence of the solvent used. In some embodiments, sample conditioning by the sample conditioning assembly 28 may result in more accurate, more repeatable, and/or more consistent analysis of the hydrocarbon feed/charge 18 and/or the one or more unit materials, which may in turn result in improved and/or more efficient control and/or more accurate control of the FCC process. Example embodiments of a sample conditioning assembly 28 are described herein, for example, with respect to
As shown in
For example, according to some embodiments, the assemblies and processes described herein may be used to produce propylene. In some such embodiments, the one or more process parameters may include, for example, residence time in the reactor, reaction temperature, catalyst-to-oil ratio, hydrocarbon partial pressure, and/or other process parameters associated with the production of propylene by an FCC processing assembly known to those skill in the art.
In some embodiments, a feedstock parameter associated with the hydrocarbon feed/charge 18 supplied to the one or more FCC processing units may include content, temperature, pressure, flow rate, API gravity, UOP K factor, carbon residue content, nitrogen content, sulfur content, single-ring aromatics content, dual-ring aromatics content, triple-ring aromatics content, and/or quad-ring aromatics content.
In some embodiments, one or more of the FCC process controller(s) 24 may be configured to prescriptively control at least a portion of the FCC process by, for example, operating an analytical cracking model, which may be executed by one or more computer processors. In some embodiments, the analytical cracking model may be configured to improve the accuracy of: predicting (or determining) the content of the hydrocarbon feed/charge 18 supplied to the one or more FCC processing units 22; predicting (or determining) the content of intermediate materials produced by the one or more FCC processing units 22; controlling the content of the hydrocarbon feed/charge 18 supplied to the one or more FCC processing units 22; controlling the content of the intermediate materials produced by the one or more FCC processing units; controlling the content of the FCC effluent produced by the one or more FCC processing units; the target content of the unit product materials produced by the one or more FCC processing units; and/or the target content of downstream materials produced by one or more of the downstream processing units, such as, for example, the fractionator 26 and/or processing units associated with operation of the fractionator 26.
In some embodiments, the analytical cracking model may include or be a machine-learning-trained model. In at least some such embodiments, the FCC process controller(s) 24 may be configured to: (a) provide, to the analytical cracking model, catalytic cracking processing data related to: (i) material data including one or more of: feedstock data indicative of one or more parameters and/or properties associated with the hydrocarbon feed/charge 18; unit material data indicative of one or more unit material properties associated with the one or more unit materials; and/or downstream material data indicative of one or more downstream material properties associated with one or more downstream materials produced by the one or more downstream processing units 36; and/or (ii) processing assembly data including: first processing unit data indicative of one or more operating parameters 32 associated with operation of the one or more processing units 34, such as, for example, the one or more FCC processing units 22; second processing unit data indicative of one or more operating parameters associated with operation of the one or more of the processing units 34 (collectively), such as, for example, the one or more downstream processing units 36; and/or conditioning assembly data indicative of operation of a sample conditioning assembly 28 configured to one or more of control a sample temperature of a material sample, remove particulates from the material sample, dilute the material samples in solvent, or degas the material sample; and/or (b) prescriptively controlling, based at least in part on the catalytic cracking processing data: one or more hydrocarbon feedstock parameters and/or properties associated with the hydrocarbon feed/charge 18; one or more first operating parameters associated with operation of the one or more FCC processing units 22; one or more properties associated with the one or more unit materials; content of the one or more unit materials; one or more second operating parameters associated with operation of the one or more downstream processing units 36 positioned downstream relative to the one or more FCC processing units; one or more properties associated with the one or more downstream materials produced by the one or more downstream processing units 36; content of the one or more downstream materials; and/or one or more sample conditioning assembly operating parameters associated with operation of the sample conditioning assembly 28.
In some embodiments, the analytical cracking model may include one or more cracking algorithms. The cracking algorithms may be configured to determine, based at least in part on the catalytic cracking data, target material properties for one or more of the hydrocarbon feed/charge 18, the unit materials, or the downstream materials. In some embodiments, the cracking algorithms further may be configured to prescriptively control operation of one or more of the FCC processing units 22 and/or the one or more downstream processing units 36, for example, to produce one or more of unit materials having unit material properties within a first predetermined range of target unit material properties for the unit materials, or one or more of downstream materials having downstream material properties within a second predetermined range of target material properties for the downstream materials. Within range may include within a range above (but not below) the target unit material properties or the target material properties of the downstream materials, within a range below (but not above) the target unit material properties or the target material properties of the downstream materials, or within a range surrounding (on either or both sides of) the target unit material properties or the target material properties of the downstream materials. The cracking algorithms also may be configured to determine one or more of actual unit material properties for the unit materials produced by the one or more FCC processing units 24 or one or more of actual downstream material properties for the downstream materials produced by the one or more downstream processing units 36. The cracking algorithms, in some embodiments, further may be configured to determine one or more of unit material differences between the actual unit material properties and the target unit material properties or downstream material differences between the actual downstream material properties and the target downstream material properties. In some embodiments, the cracking algorithms further still may be configured to change, based at least in part on one or more of the unit material differences or the downstream material differences, the one or more cracking algorithms to reduce the one or more of the unit material differences or the downstream material differences. In some embodiments, the cracking algorithms may result in more responsively controlling the FCC processing assembly 10, the FCC processing unit(s) 22, and/or the downstream processing unit(s) 36 to achieve material outputs that more accurately and responsively converge on the target properties.
In some embodiments, the one or more FCC process controller(s) 24 may be configured to prescriptively control by one or more of (i) generating, based at least in part on the target unit material properties, one or more first processing unit control signals configured to control at least one first processing parameter associated with operation of the one or more FCC processing unit(s) 22 to produce one or more unit materials having unit material properties within the first preselected range of the target unit material properties; or (ii) generating, based at least in part on the target downstream material properties, a second processing unit control signal configured to control at least one second processing parameter associated with operation of the one or more downstream processing unit(s) 36 to produce one or more downstream materials having downstream material properties within the second preselected range of the target downstream material properties. In some embodiments, the FCC process controller(s) 24 still further may be configured to prescriptively control operation of the sample conditioning assembly 28, for example, by generating, based at least in part on the catalytic cracking data, a conditioning control signal configured to control at least one conditioning parameter related to operation of the sample conditioning assembly 28.
In some embodiments, the FCC process controller(s) 24 may be configured to predict the one or more hydrocarbon feed/charge 18 sample properties, for example, by mathematically manipulating a feedstock spectra signal indicative of the hydrocarbon feedstock sample spectra to provide a manipulated feedstock signal, and communicating the manipulated feedstock signal an analytical property model configured to predict, based at least in part on the manipulated feedstock signal, the one or more hydrocarbon feedstock sample properties. In some examples, the FCC process controller(s) 24 may be configured to predict the one or more unit material sample properties by mathematically manipulating a unit material spectra signal indicative of the unit material sample spectra to provide a manipulated unit material signal, and communicating the manipulated unit material signal to an analytical property model configured to predict, based at least in part on the manipulated unit material signal, the one or more unit material sample properties. In some embodiments, the mathematical manipulation may be performed, for example, for an individual wavelength and/or a plurality of wavelengths over a range of wavelengths, and the mathematical manipulation may be based on, for example, a mathematical relationship, which may include one or more of a ratio, a correlation, an addition, a subtraction, a multiplication, a division, taking one or more derivatives, an equation, or a combination thereof, and/or other mathematically-derived relationships.
In some embodiments, the one or more FCC process controller(s) 24 may be configured to prescriptively control one or more aspects of the FCC process by, for example, generating, based at least in part on one or more of the hydrocarbon feed/charge 18 sample properties or one or more of the unit material sample properties, the one or more processing unit control signal(s) 30 to control on-line one or more of the processing parameter(s) 32 related to operation of one or more of the FCC processing unit(s) 22 and/or one or more of the downstream processing unit(s) 36. For example, in some embodiments, the one or more unit sample properties may include reaction effluent yield, and the prescriptive control may include controlling a riser outlet temperature based at least in part on the reaction effluent yield and/or riser lift velocity based at least in part on the reaction effluent yield. In some embodiments, the one or more unit material sample properties may include FCC product yield (e.g., gasoline yield and/or propylene yield), and the prescriptive control may include, for example, controlling riser lift steam rate based at least in part on the FCC product yield. In some embodiments, the one or more unit material sample properties may include riser stripper effluent, and the prescriptive control may include, for example, controlling FCC catalyst stripping based at least in part on the riser stripper effluent. In some embodiments, the one or more unit material sample properties may include one or more of an amount of butane free gasoline, an amount of total butane, an amount of dry gas, an amount of coke, an amount of propylene (e.g., propylene yield), an amount of gasoline, octane rating, an amount of light fuel oil, an amount of heavy fuel oil, an amount of hydrogen sulfide, an amount of sulfur in light fuel oil, or an aniline point of light fuel oil.
In some embodiments, the one or more unit material sample properties may include one or more reaction effluent properties, and the FCC process controller(s) 24 may further be configured to on-line model, based at least in part on the one or more reaction effluent properties, operation of the one or more FCC processing unit(s) 22. In some embodiments, the one or more FCC process controller(s) 24 may be configured to prescriptively control, real-time for improvement or optimization of the FCC process. The FCC process controller(s) 24 may be configured, in at least some embodiments, to provide the one or more hydrocarbon feed/charge 18 sample properties and/or the one or more unit material sample properties to fluid catalytic cracking (FCC) simulation software, for example, to model FCC processing unit material yields and/or FCC unit material characteristics. For example, the one or more FCC process controller(s) 24 may be configured to determine, via the FCC simulation software, based at least in part on the one or more hydrocarbon feed/charge 18 sample properties and/or the one or more unit material sample properties, one or more processing unit control parameters to achieve the FCC processing unit material yields and/or the FCC unit material characteristics.
As shown in
In some embodiments, the unit sample properties may include one or more properties associated with reactor dilute vapors, and the FCC process controller(s) 24 may be configured to prescriptively control riser outlet conditions based at least in part on the reactor dilute vapors, and/or vapor quench based at least in part on the reactor dilute vapors. The one or more unit material properties may include one more unit material yields, and, in some embodiments, the FCC process controller(s) 24 may be configured to tune, based at least in part on the one or more unit material yields, a fluid catalytic cracking (FCC) simulation model, and/or benchmark, based at least in part on the one or more unit material yields, refinery linear program predicted yields.
As shown in
As shown in
As shown in
As shown in
For example, the one or more spectroscopic analyzers 20A through 20N may be configured to receive (e.g., on-line and/or in a laboratory) a sample of the hydrocarbon feed/charge 18 to be supplied to the one or more FCC processing units 22 associated with the refining operation via the feed/charge sample conduit 44. The one or more spectroscopic analyzers 20A through 20N may be configured to analyze the sample of the hydrocarbon feed/charge 18 to provide hydrocarbon feedstock sample spectra.
In some embodiments, for example, as shown
For example, as shown in
As shown in
In some embodiments, one or more of the spectroscopic analyzers 20A through 20N may be configured to analyze sample of the reaction mixture 54 taken at the outlet of the riser 50, and another one of the spectroscopic analyzers 20A through 20N may be configured to analyze the FCC effluent 38 taken at the outlet of the FCC reactor 12, the sample of the reaction mixture 54 and the sample of the FCC effluent 38 may be analyzed substantially concurrently. In some embodiments, one or more of the spectroscopic analyzers 20A through 20N may be configured to receive on-line two or more reaction mixture samples 54 taken from two or more respective different locations of the cross section of the riser 50 (e.g., form two or more respective different locations of the diameter), and the two or more samples of the reaction mixture 54 may be analyzed to generate two or more respective sets of reaction mixture spectra. In some embodiments, one or more of the spectroscopic analyzers 20A through 20N may be configured to receive on-line a sample of the reaction mixture 54 taken from the inlet of the riser 50 via a riser inlet conduit 76, and the sample taken from the inlet of the riser 50 may be analyzed to generate one or more riser inlet sample spectra.
As shown in
As shown in
As shown in
As shown in
In some embodiments, the FCC process controller(s) 24 may be configured to prescriptively control, based at least in part on the one or more hydrocarbon feedstock sample properties and the one or more unit material sample properties: (i) one or more feedstock parameters and/or properties associated with the hydrocarbon feed/charge 18 supplied to the one or more FCC processing units 22; (ii) content of the intermediate materials 106 produced by one or more of the FCC processing units 22; operation of the one or more FCC processing units 22; (iii) content of the one or more unit product materials 102; and/or operation of one or more downstream processing units 36 positioned downstream relative to the one or more FCC processing units 22, such as, for example, a fractionator 26 (see
A shown in
As shown in
In some embodiments, a feedstock parameter associated with the hydrocarbon feed/charge 18 supplied to the one or more FCC processing units may include content, temperature, pressure, flow rate, API gravity, UOP K factor, carbon residue content, nitrogen content, sulfur content, single-ring aromatics content, dual-ring aromatics content, triple-ring aromatics content, and/or quad-ring aromatics content.
In some embodiments, one or more of the FCC process controllers 24 may be configured to prescriptively control at least a portion of the FCC process by, for example, operating an analytical cracking model, which may be executed by one or more computer processors. In some embodiments, the analytical cracking model may be configured to improve the accuracy of: predicting the content of the hydrocarbon feed/charge 18 supplied to the one or more FCC processing unit(s) 22; predicting the content of intermediate materials produced by the one or more FCC processing unit(s) 22; controlling the content of the hydrocarbon feed/charge 18 supplied to the one or more FCC processing unit(s) 22; controlling the content of the intermediate materials produced by the one or more FCC processing unit(s) 22; controlling the content of the FCC effluent produced by the one or more FCC processing unit(s) 22; the target content of the unit product materials produced by the one or more FCC processing unit(s) 22; and/or the target content of downstream materials produced by one or more of the downstream processing unit(s) 36, such as, for example, the fractionator 26 and/or processing units associated with operation of the fractionator 26.
As shown in
As shown in
As shown in
In some embodiments, as shown in
As shown in
As shown in
As shown in
As shown in
As shown in
Spectroscopic analyzers may be used to non-destructively predict (or determine) properties associated with materials. For example, a sample of material may be fed to a spectroscopic analyzer for analysis, and a beam of electromagnetic radiation may be transmitted into the material sample, resulting in the spectroscopic analyzer measuring a spectral response representative of the chemical composition of the sample material, which may be used to predict (or determine) properties of the sample material via the use of modeling. The spectral response may include a spectrum related to the absorbance, transmission, transflectance, reflectance, or scattering intensity caused by the material sample over a range of wavelengths, wavenumbers, or frequencies of the electromagnetic radiation.
Applicant has recognized that over time the results of analysis using a spectroscopic analyzer may change, for example, due to changes or degradation of the components of the spectroscopic analyzer, such as its lamp, laser, detector, or grating. Changing or servicing components of the spectroscopic analyzer may alter its spectral responses relative to the spectral responses outputted prior to the changes, necessitating recalibration. Further, for some applications (e.g., as described herein), more than one spectroscopic analyzer may be used in association with analysis of materials at, for example, a production facility (e.g., a refinery), and it may be desirable for two or more of the spectroscopic analyzers to generate results that are reproducible and consistent with one another to enhance control of the production process, such as an FCC process and/or related upstream processes and/or downstream processes. Due to the complex nature, sensitivity, and principle of operation of spectroscopic analyzers, however, two spectroscopic analyzers may not be likely to provide equivalent results within the variability of the primary test method with which calibration models were made without additional activity (e.g., extensive testing), even when analyzing the same sample of material. This may result in a lack of reproducibility or consistency of results across different spectroscopic analyzers, potentially rendering comparisons between the results outputted by two or more spectroscopic analyzers of little value, unless the spectroscopic analyzers have been calibrated to achieve the same spectral responses.
In some embodiments, methods and assemblies described herein may be used for determining and using standardized spectral responses for calibration (or recalibration) of spectroscopic analyzers. For example, in some embodiments, the methods and assemblies may be used to calibrate or recalibrate a spectroscopic analyzer when the spectroscopic analyzer changes from a first state to a second state, for example, the second state being defined as a period of time after a change to the spectroscopic analyzer causing a need to calibrate the spectroscopic analyzer. In some embodiments, the recalibration may result in the spectroscopic analyzer outputting a standardized spectrum, for example, such that the spectroscopic analyzer outputs a corrected material spectrum for an analyzed material, including one or more of an absorption-corrected spectrum, a transmittance-corrected spectrum, a transflectance-corrected spectrum, a reflectance-corrected spectrum, or an intensity-corrected spectrum and defining the standardized spectrum. In some embodiments, the corrected material spectrum, output when the calibrated or recalibrated spectroscopic analyzer is in the second state, may include a plurality of signals indicative of a plurality of material properties of an analyzed material (e.g., a sample of the material) based at least in part on the corrected material spectrum, the plurality of material properties of the material being substantially consistent with a plurality of material properties of the material outputted by the spectroscopic analyzer in the first state. This may enhance the accuracy, reproducibility, and/or consistency of results outputted by the second-state recalibrated spectroscopic analyzer prior to recalibration relative to results outputted by the first-state spectroscopic analyzer.
In some embodiments, using calibration of a first spectroscopic analyzer to calibrate one or more additional spectroscopic analyzers may include using standardized analyzer spectra for calibration of a spectroscopic analyzer, for example, such that each of the one or more spectroscopic analyzers outputs a corrected material spectrum, including a plurality of signals indicative of a plurality of material properties of an analyzed material based at least in part on the corrected material spectrum, such that the plurality of material properties of the material are substantially consistent with a plurality of material properties of the material outputted by the first spectroscopic analyzer. In some embodiments, this may result in achieving desired levels of accuracy, reproducibility, and/or consistent results from a plurality of spectroscopic analyzers, potentially rendering comparisons between the results outputted by two or more of the spectroscopic analyzers more valuable, for example, when incorporated into a complex process including a plurality of different material altering processes, such as, for example, an FCC process and/or related upstream processes and/or downstream processes.
According to some embodiments, a method for determining and using standardized analyzer spectral responses to enhance a process for calibration of a plurality of spectroscopic analyzers, such that for a given material each of the plurality of spectroscopic analyzers outputs a plurality of signals indicative of a plurality of material properties of the material, the plurality of material properties of the material output by each of the plurality of spectroscopic analyzers being substantially consistent with one another, may include transferring one or more spectral models to each of the plurality of spectroscopic analyzers. Each of the one or more spectral models may be indicative of relationships between a spectrum or spectra and one or more of the plurality of material properties of one or more materials. The method also may include analyzing, via the first spectroscopic analyzer when in a first state, a selected one or more first-state portfolio samples to output a standardized analyzer spectra portfolio for the selected one or more first-state portfolio samples. The standardized analyzer spectra portfolio may include a first-state portfolio sample spectrum for each of the first-state portfolio samples. The method further may include analyzing, via each of a remainder of the plurality of spectroscopic analyzers when in a second state a selected one or more second-state portfolio samples to output second-state portfolio sample spectra for the selected one or more second-state portfolio samples. Each of the second-state portfolio sample spectra may be associated with a corresponding second-state portfolio sample. The analysis of the selected one or more second-state portfolio samples may occur during a second-state time period. The multi-component samples may include a significantly greater number of samples than a number of samples included in the second-state portfolio samples, and the second-state time period for analyzing the second-state portfolio samples may be significantly less than the first-state time period. The method also may include comparing one or more of the second-state portfolio sample spectra for the selected plurality of portfolio samples to the first-state sample spectra of a selected plurality of corresponding first-state multi-component samples. The method still further may include determining, based at least in part on the comparison, for the one or more of the selected plurality of portfolio samples of the second-state portfolio sample spectra, a variance at one or more of a plurality of wavelengths or over a range of wavelengths between the second-state portfolio sample spectra output by each of the remainder of the plurality of spectroscopic analyzers when in the second state and the first-state sample spectra corresponding to the selected one or more first-state multi-component material samples output by the first spectroscopic analyzer in the first state.
In some embodiments, the method still further may include analyzing, via one or more of the remainder of the plurality of spectroscopic analyzers when in the second state, a material received from a material source to output a material spectrum. The method also may include transforming, based at least in part on the standardized analyzer spectra portfolio, the material spectrum to output a corrected material spectrum for the material when in the second state, the corrected material spectrum including one or more of an absorption-corrected spectrum, transmittance-corrected spectrum, a transflectance-corrected spectrum, a reflectance-corrected spectrum, or an intensity-corrected spectrum and defining a standardized spectrum, for example, and/or a mathematical treatment of the material spectrum, such as, for example, a second derivative of the material spectrum.
In the example embodiments shown in
In some embodiments, each of the analyzer controllers 174 may be in communication with a respective one of the spectroscopic analyzers 172. For example, the analyzer controllers 174 may each be physically connected to the respective spectroscopic analyzer 172. In some such embodiments, the spectroscopic analyzers 172 may each include a housing and at least a portion of the respective analyzer controller 174 may be contained in the housing. In some embodiments, the respective analyzer controllers 174 may be in communication with the respective spectroscopic analyzers 172 via a hard-wired and/or wireless communications link. In some embodiments, the respective analyzer controllers 174 may be physically separated from the respective spectroscopic analyzers 172 and may be in communication with the respective spectroscopic analyzers 172 via a hard-wired communications link and/or a wireless communications link. In some embodiments, physical separation may include being spaced from one another, but within the same building, within the same facility (e.g., located at a common manufacturing facility, such as a refinery), or being spaced from one another geographically (e.g., anywhere in the world). In some physically separated embodiments, both the spectroscopic analyzer 172 and/or the respective analyzer controller 174 may be linked to a common communications network, such as a hard-wired communications network and/or a wireless communications network. Such communications links may operate according to any known hard-wired and/or wireless communications protocols as will be understood by those skilled in the art. Although
In some embodiments, using the standardized analyzer spectra may include transferring one or more spectral models of the first spectroscopic analyzer 172A when in the first state to one or more of the second through Nth spectroscopic analyzers 172b through 172N with respective analyzer controllers 174B through 174N after a change to the second through Nth spectroscopic analyzers 172B through 172N, such that, when in the second state, analysis by the second through Nth spectroscopic analyzers 172B through 172N of multi-component materials results in generation of second through Nth material spectra 208B through 208N (
As shown in
In some embodiments, the plurality of different multi-component samples 176 may include a relatively large number of samples. For example, in some embodiments, in order to calibrate the first spectroscopic analyzer 172A with the first analyzer controller 174A to a desired level of accuracy and/or reproducibility, it may be necessary to analyze hundreds or thousands of multi-component samples 176 that have corresponding material data 179. Due to the relatively large number of multi-component samples 176 used for calibration, the first-state time period T1, which may generally correspond to the time period during which the multi-component samples 176 are analyzed, may take a significant amount of time to complete. For example, in some embodiments, in order to calibrate the first spectroscopic analyzer 172A with the first analyzer controller 174A to a desired level of accuracy and/or reproducibility, due to the relatively large number of samples analyzed, the first-state time period T1 may take dozens of hours or longer to complete.
Following calibration of the first spectroscopic analyzer 172A with the first analyzer controller 174A, the spectral responses of the first spectroscopic analyzer 172A with the first analyzer controller 174A may be standardized, for example, by analyzing one or more first-state portfolio sample(s) 183 to output a standardized analyzer spectra portfolio 184 including one or more first-state portfolio sample spectra 185. For example, the first spectroscopic analyzer 172A with the first analyzer controller 174A, when in the first state, may be used to analyze one or more first-state portfolio sample(s) 183 to output a first-state portfolio spectrum 185 for each of the one or more first-state portfolio sample(s) 183. In some embodiments, the respective first-state portfolio sample spectrum 185 associated with a respective first-state portfolio sample 183 may be stored to develop the standardized analyzer spectra portfolio 184, which may be used to reduce a variance between a second-state portfolio sample spectrum (outputted during a second state) and a corresponding first-state portfolio sample spectrum 185 of the standardized analyzer spectra portfolio 184.
As shown in
For example, as shown in
Upon analysis of the multi-component materials, which may be a feed to a processing unit and/or an output from a processing unit, the first spectroscopic analyzer 172A with the first analyzer controller 174A, using the analyzer calibration 182, may output a plurality of material spectra 188A and, based at least in part on the material spectra 188A, predict a plurality of material properties associated with the multi-component materials. In some embodiments, the material spectra 188A and the associated predicted or determined material properties may be stored in a database as predicted (or determined) material data 190A. It is contemplated that additional material data associated with the multi-component materials analyzed may also be included in the database to supplement the predicted or determined material properties. For example, the database may define a library including material data including correlations between the plurality of material spectra and the plurality of different material properties of the corresponding material.
In some embodiments, the analysis of the multi-component materials may occur during a first material time period T1, as shown in
As referenced above, in some embodiments, the first analyzer controller 174A may be configured to use the first-state-portfolio sample spectra 185 of the standardized analyzer spectra portfolio 184 to calibrate or recalibrate one or more of the plurality of spectroscopic analyzers 172A through 172N with the respective analyzer controllers 174A through 174N. For example, as shown in
In some embodiments, using respective portfolio sample-based correction(s) 200B through 200N (see
For example, as shown in
As shown in
Thus, in some embodiments, the plurality of spectroscopic analyzers 172B through 172N with the respective analyzer controllers 174B through 174N may be configured to be calibrated or recalibrated to achieve substantially the same accuracy and/or reproducibility of analysis as the first spectroscopic analyzer 172A with first analyzer controller 174A, while using significantly fewer samples to calibrate or recalibrate each of the plurality of spectroscopic analyzers 172B through 172N with the respective analyzer controllers 174B through 174N, as compared to the number of multi-component samples 176 used to calibrate or recalibrate the first spectroscopic analyzer 172A with the first analyzer controller 174A for the development of spectral model(s) 180 and analyzer calibration 182, thus requiring significantly less time for calibration or recalibration. In some embodiments, the calibrated or recalibrated plurality of spectroscopic analyzers 172B through 172N and/or the plurality of analyzer controllers 174B through 174N, calibrated or recalibrated in such a manner, may be capable of generating substantially the same spectra following calibration or recalibration as outputted by the first spectroscopic analyzer 172A with the first analyzer controller 174A, which may result in improved accuracy and/or reproducibility by the first spectroscopic analyzer 172A and each of the plurality of spectroscopic analyzers 172B through 172N. Such accuracy and/or reproducibility may provide the ability to compare analysis results outputted by either the first spectroscopic analyzer 172A or the plurality of spectroscopic analyzers 172B through 172N, which may result in the first spectroscopic analyzer 172A and the plurality of spectroscopic analyzers 172B through 172N being relatively more useful, for example, when incorporated into a manufacturing process involving the processing of multi-component materials received from material sources, such as shown in
As shown in
In some embodiments, each of the plurality of analyzer controllers 174B through 174N may be configured to determine respective variances 212B through 212N by determining a mean average variance, one or more ratios of variances at respective individual wavelengths, or a combination thereof, for a plurality of wavelengths, wavenumbers, and/or frequencies over a range of wavelengths, wavenumbers, and/or frequencies, respectively. In some embodiments, each of the plurality of analyzer controllers 174B through 174N may be configured to determine a relationship for a plurality of wavelengths, wavenumbers, and/or frequencies over the range of wavelengths, wavenumbers, and/or frequencies, respectively, between the respective second-state portfolio sample spectra 198B through 198N and the first-state portfolio sample spectra 185, and the relationship may include one or more of a ratio, an addition, a subtraction, a multiplication, a division, one or more derivatives, or an equation.
As shown in
As shown in
In some embodiments, each of the plurality of spectroscopic analyzers 172B through 172N with each of the respective analyzer controllers 174B through 174N may be configured to analyze, when in the second state, the multi-component materials received from the respective material sources and output a material spectrum corresponding to the respective multi-component materials, for example, as described previously herein with respect to
In some embodiments, this may render it possible to directly compare the results of analysis by the plurality of spectroscopic analyzers 172B through 172N with the respective analyzer controllers 174B through 174N with results of analysis by the first spectroscopic analyzer 172A with the first analyzer controller 174A. In some embodiments, this may render it possible to directly compare the results of analysis by each of the plurality of spectroscopic analyzers 172B through 172N with each of the respective analyzer controllers 174B through 174N with one another. In addition, as noted above, in some embodiments, using the portfolio sample-based correction(s) 200B through 200N to calibrate or recalibrate of the plurality of spectroscopic analyzers 172B through 172N with the respective analyzer controllers 174B through 174N to achieve the standardization may require the analysis of significantly fewer samples (e.g., the second-state portfolio samples 198) as compared to the original calibration of the first spectroscopic analyzer 172A with first analyzer controller 174A during the first state. This may also significantly reduce the time required to calibrate or recalibrate each of the plurality of spectroscopic analyzers 172B through 172N with each of the respective analyzer controllers 174B through 174N.
Upon analysis of the multi-component materials from the material source(s), which may be feed(s) to one or more processing units and/or an output(s) from one or more processing units, the plurality of spectroscopic analyzers 172B through 172N with the respective analyzer controllers 174B through 174N may establish a plurality of corrected material spectra 208B through 208N and, based at least in part on the corrected material spectra 208B through 208N, predict a plurality of material properties associated with the multi-component materials. In some embodiments, the corrected material spectra 208B through 208N and the associated predicted or determined material properties may be stored in a database as respective predicted (or determined) material data 210B through 210N. It is contemplated that additional material data associated with the multi-component materials analyzed may also be included in the database to supplement the predicted or determined material properties. For example, the database may define a library including material data and/or including correlations between the plurality of material spectra and the plurality of different material properties of the corresponding materials.
As shown in
In some embodiments, as explained herein, using the portfolio sample-based correction(s) 200B through 200N to calibrate or recalibrate the plurality of spectroscopic analyzers 172B through 172N may result in the plurality of spectroscopic analyzers 172B through 172N with the respective analyzer controllers 174B through 174N generating analyzed material spectra and/or predicting corresponding material properties in a manner substantially consistent with a plurality of material properties outputted by the first spectroscopic analyzer 172A with the first analyzer controller 174A.
Although not shown in
At 504, the example process 500 may include determining whether the hydrocarbon feedstock is within a target temperature range, a target pressure range, and/or a target flow rate, for example, as described herein.
If, at 504, it is determined that the temperature, pressure, or target flow rate is not within one or more of the target ranges, at 506, the example method 500 may include adjusting the temperature, the pressure, and/or the flow rate of the hydrocarbon feedstock to be within the target ranges and returning to 504 to repeat the determination.
If, at 504, it is determined that the temperature, pressure, or target flow rate are within the target ranges, at 508, the example method 500 may include conditioning, via a sample conditioning assembly, a sample of the hydrocarbon feedstock for analysis by a spectroscopic analyzer, for example, as described herein.
At 510, the example method 500 may include determining whether the conditioned hydrocarbon feedstock sample is within target parameters for analysis. This may include determining whether water, particulates, and/or other contaminates have been removed from the conditioned hydrocarbon feedstock sample, and/or whether the conditioned sample is within a desired predetermined temperature range for improving the accuracy of the analysis by the spectroscopic analyzer(s).
If, at 510, it is determined that the conditioned hydrocarbon feedstock sample is not within target parameters for analysis, the example method 500, at 512, may include adjusting one or more parameters associated with operation of the sample conditioning assembly, such that the conditioned hydrocarbon feedstock sample is within the target parameters and returning to 510 to repeat the determination.
If, at 510, it is determined that the conditioned hydrocarbon feedstock sample is within target parameters for analysis, the example method 500, at 514, may include supplying the conditioned hydrocarbon feedstock sample to the spectroscopic analyzer(s) for analysis, for example, as described herein.
The example method 500, at 516, may include analyzing, via the spectroscopic analyzer(s), the conditioned hydrocarbon feedstock sample to predict (or determine) hydrocarbon feedstock properties, for example, as described herein.
At 518 (
If, at 518, it is determined that the hydrocarbon feedstock properties are not within the desired ranges of the property targets for the hydrocarbon feedstock, the example method 500, at 520, may include altering the hydrocarbon feedstock toward the target properties to be within the desired ranges of property targets for the hydrocarbon feedstock and returning to 518 to repeat the determination.
If, at 518, it is determined that the hydrocarbon feedstock properties are within the desired ranges of the property targets for the hydrocarbon feedstock, the example method 500, at 522, may include supplying the hydrocarbon feedstock to a riser of the FCC processing assembly, for example, as described herein.
At 524, the example method 500 may include determining whether the riser is operating within a desired range of a predetermined target riser temperature.
If, at 524, it is determined that the riser is not operating within the desired range of the predetermined target riser temperature, the example method 500, at 526, may include altering the riser temperature toward the target riser temperature and returning to 524 to repeat the determination.
If, at 524, it is determined that the riser is operating within the desired range of the predetermined target riser temperature, the example method 500, at 528, may include supplying catalyst to the riser to provide a reaction mixture including the hydrocarbon feedstock and catalyst, for example, as described herein.
At 530 (
If, at 530, it is determined that the FCC reactor is not operating within the desired ranges of the predetermined target FCC reactor parameters, the example method 500, at 532, may include altering the FCC reactor operating parameters toward the predetermined target FCC reactor parameters and returning to 530 to repeat the determination.
If, at 530, it is determined that the FCC reactor is operating within the desired ranges of the predetermined target FCC reactor parameters, the example method 500, at 534, may include supplying the reaction mixture to an FCC reactor to produce FCC effluent, for example, as described herein.
At 536, the example method 500 may include conditioning, via a sample conditioning assembly, a reaction mixture sample and/or an FCC effluent sample for analysis by one or more spectroscopic analyzers. In some embodiments, the one or more spectroscopic analyzers may be calibrated to generate standardized spectral responses, for example, as described herein.
At 538, the example method 500 may include determining whether the conditioned reaction mixture sample and/or the FCC effluent sample is/are within desired ranges of target parameters for analysis. This may include determining whether water, particulates, and other contaminates have been removed from the conditioned reaction mixture sample and/or the FCC effluent sample, and/or whether the conditioned sample is within a predetermined temperature range for improving the accuracy of the analysis by the spectroscopic analyzer.
If, at 538, it is determined that the conditioned reaction mixture sample and/or the FCC effluent sample is/are not within the desired ranges of the target parameters for analysis, the example method 500, at 540, may include adjusting one or more parameters associated with operation of the sample conditioning assembly such that the conditioned reaction mixture sample and/or the FCC effluent sample is/are within the target parameters and returning to 538 to repeat the determination.
If, at 538, it is determined that the conditioned reaction mixture sample and/or the FCC effluent sample is/are within the desired ranges of the target parameters for analysis, the example method 500, at 542, may include supplying the conditioned reaction mixture sample and/or the FCC effluent sample to the one or more spectroscopic analyzers for analysis, for example, as described herein.
At 544 (
At 546, the example method may include determining whether the reaction mixture properties and/or the FCC effluent properties is/are within desired ranges of respective property targets.
If, at 546, it is determined that the reaction mixture properties and/or the FCC effluent properties is/are not within the desired ranges of the respective property targets, the example method 500, at 548, may include altering one or more of the hydrocarbon feedstock, the riser operating parameters, or the FCC reactor operating parameters according to differences between the reaction mixture properties and/or the FCC effluent properties and the property targets, and returning to 546 to repeat the determination.
If, at 546, it is determined that the reaction mixture properties and/or the FCC effluent properties is/are within the desired ranges of the respective property targets, the example method 500, at 550, may include supplying the FCC effluent to one or more downstream processing units to separate the FCC effluent into downstream products, for example, as described herein.
At 552, the example method 500 may include conditioning, via a sample conditioning assembly, one or more downstream product samples for analysis by one or more spectroscopic analyzers, for example, as described herein.
At 554 (
If, at 554, it is determined that the conditioned one or more downstream product samples is/are not within the desired ranges of the target parameters for analysis, the example method 500, at 556, may include adjusting one or more parameters associated with operation of the sample conditioning assembly such that the conditioned one or more downstream product samples is/are within the desired ranges of the target parameters, and returning to 554 to repeat the determination.
If, at 554, it is determined that the conditioned one or more downstream product samples is/are within the desired ranges of the target parameters for analysis, the example method 500, at 558, may include supplying the conditioned one or more downstream product samples to the one or more spectroscopic analyzers for analysis, for example, as described herein.
At 560, the example method 500 may include analyzing, via the one or more spectroscopic analyzers, the conditioned one or more downstream product samples to predict the properties of the one or more downstream products, for example, as described herein. In some embodiments, the one or more spectroscopic analyzers may be calibrated to generate standardized spectral responses, for example, as described herein.
At 562, the example method 500 may include determining whether the properties of the one or more downstream products are within desired ranges of property targets.
If, at 562, it is determined that the properties of the one or more downstream products are not within the desired ranges of the property targets, the example method 500, at 564, may include altering one or more of the hydrocarbon feedstock, the riser operating parameters, the FCC reactor operating parameters, or the downstream processing units operating parameters according to differences between the properties of the one or more downstream products and the property targets, for example, as described herein. Thereafter, at 566, the example method may include returning to 502 and continuing to alter the hydrocarbon feedstock and/or operating parameters to drive the FCC process toward target properties.
If, at 562, it is determined that the properties of the one or more downstream products are within the desired ranges of the property targets, the example method 500, at 566, may include returning to 502 and continuing to monitor and/or control the FCC process according to the method 500.
Different hydrocarbon feedstocks will result in different yields from an FCC process. If an FCC processing unit is operating against a constraint or constraints, the FCC process may need to adjust to avoid exceeding equipment limitations. Typical process parameters or process variables for an FCC process may include feed rate, reactor temperature, feed preheat, and/or pressure. Process responses from each of the process parameters or variables may be non-linear. The optimum set of conditions to increase process and/or economic efficiency in view of unit constraints may depending on, for example, feed quality. Table 1 below provides example feed properties, process conditions, equipment constraints, and product yields, that may be adjusted to increase or optimize process and/or economic efficiency, for four test conditions: normal FCC process operation, new feed with multivariable optimization, new feed with only feed rate varied, and new feed with only real time optimization.
The results in Table 1 show that the application of real time optimization using spectroscopic analyzers may facilitate the FCC process to automatically adjust processing conditions, for example, to maximize processing as feedstock quality changes. Without determining feedstock quality using spectroscopic analyzers and real time optimization, the FCC process may operate at a non-optimum condition until a model optimizer is run and the results implemented. In some embodiments, advanced process control and on-line material analysis by spectroscopic analyzers may be used to manipulate multiple FCC processing variables (e.g., one or more of the variables shown in Table 1 and/or any variables and/or parameters described herein) to push the FCC processing unit against unit operational constraints, for example, to improve or maximize economic and/or processing efficiency associated with the FCC process. In some embodiments, on-line real time optimization may be used to choose a set of operating conditions to improve or maximize economic and/or processing efficiency.
Example 3 illustrates the effect of riser temperature on FCC processes. One common type of process variable study completed via the use of reaction effluent testing is the impact of riser outlet temperature on FCC reactor yields. This set of data may be used, for example, to optimize the performance of an FCC processing unit. Table 2 below includes results from a series of riser temperature tests. For this study, reactor effluent samples were collected at three different riser outlet temperatures, with the maximum riser temperature constrained by the ability to handle gas volumes in the FCC gas plant.
Example 4 illustrates the effect of riser lift velocity on FCC product yields. In order to improve or optimize performance of an FCC processing unit, it may be important to understand the interaction of one or more (e.g., all) of the reaction process variables on FCC product yields. Reaction effluent testing was used to determine the effect of riser lift steam rate on FCC processing unit yields. In the test, the riser steam rate was increased by 165%, with riser velocity increasing from 10 feet per second (fps) to about 17 fps. Table 3 below shows the effects of increasing the riser steam rate injection while holding the feed rate constant.
The heat requirement on the reactor side increases because of the need to heat the steam from the injection temperature to the riser outlet temperature. This results in a need for increased catalyst circulation to maintain the riser outlet temperature constant. The increased catalyst-to-oil ratio results in a greater degree of catalytic cracking relative to thermal cracking. This is evident because of the higher yield of catalytically cracked gasoline and the lower yield of ethane and lighter gas, which is generally the result of thermal cracking in the riser and reactor. There is a reduction in the feed residence time in the riser because of the increased riser volumetric vapor flow resulting from increased amount of riser lift steam. The increased velocity creates a more uniform distribution of catalyst near the wall of the riser where feed is injected. This improves feed contacting allowing for improved riser hydrodynamics. The FCC processing unit used in this test utilized a J-bend with modern feed nozzles.
Example 5 illustrates an example technique for on-line sampling of reactor effluent sometimes referred to as “reaction mix sampling.” One potential advantage of reaction mix sampling testing is that the technique may be used for collection of reaction mixture or vapor stream samples from points in the reactor other than the overhead line or outlet.
When collecting effluent samples from the reactor overhead line to downstream processing units, such as a fractionator, a collection probe is open on the sampling end because the reaction effluent has already been disengaged from the catalyst via the riser termination device and the reactor cyclones. The reaction mix sampling probe may be configured such that a sintered metal filter may be installed on the collection end of the probe. This facilitates collection of samples from areas of high catalyst density, such as the FCC riser, the reactor, and/or the stripper. In some embodiments, sampling locations for collecting reaction mix sampling vapor samples may include one or more of the following: (i) the riser at multiple elevations above the feed injection point to monitor the extent of reaction as the oil/catalyst mixture flowed up the riser and to determine the impact of reaction contact time; (ii) the outlet of the riser to determine the cracking yields before the effects of any thermal cracking that would take place in the reactor vessel; (iii) the outlet of the riser termination device; (iv) substantially concurrently or simultaneously, at the riser outlet and the reactor effluent line to determine the degree of conversion and thermal cracking that occurs in the reactor vessel; (v) the riser at multiple points across the cross-section or diameter to determine the consistency of oil/catalyst distribution above the feed injectors; (vi) the riser at the transition from horizontal to vertical flow to determine the impact of the transition on oil/catalyst distribution; or the catalyst stripper bed and stripper transition into the spent catalyst standpipe to determine stripper vapor composition and determine stripping efficiency.
There may be interest in determining FCC catalyst stripping efficiency. The reaction mix sampling technique may be used to extract vapor samples from the stripper bed and the transition from the stripper into the spent catalyst standpipe. Table 4 below includes the results from testing including reaction mix sampling of three strippers. As shown in in Table 4, one of these strippers operated very poorly, and one of them operated very efficiently.
This data may thereafter be used in conjunction with an engineering calculation to estimate the amount of hydrocarbon flowing from the stripper to the catalyst regenerator. The stripper vapor composition may provide significant information as to processes occurring in the stripper. A first factor is the percentages of water and total hydrocarbon in the recovered vapor stream. As stripper efficiency increases, the percentage of water in the stripper vapor sample will increase as well. A second factor is the distribution of hydrocarbon in the reaction mix sampling sample. There are three potential sources of hydrocarbon in the FCC stripper: (i) reaction effluent in interstitial spaces between catalysts; (ii) reaction effluent in pores of the catalyst; and (iii) unreacted (and/or unvaporized) oil on the catalyst surface and in the catalyst pores.
If the stripper outlet vapor contains more than a very small percentage of gasoline on a total sample weight basis, then it is likely that most or all of the reaction effluent trapped with catalyst does not disengage and flow upward into the reactor. For example, the Unit A stripper vapor sample contained 24 wt % gasoline. The Unit A stripper had no internals of any type, and it was clear that some significant portion of reaction effluent flowing to the Unit A stripper was reaching the regenerator. At the opposite end of the spectrum, the Unit C stripper contained only 0.9 wt % gasoline in the stripper vapor, which probably represents reaction effluent diffused out of the catalyst pores.
Another factor in reviewing stripper results is the content of ethane and lighter gas in the stripper vapor. The production of light gases in an FCC process, especially methane and hydrogen, is recognized as a by-product of thermal cracking. For these samples, the weight percentage of sample including ethane and lighter gas ranged from a low of 2.8 wt % to a high of 14.8 wt %. The production of light gases in the FCC stripper is believed to result from the thermal decomposition of heavy, unvaporized hydrocarbon molecules on the surface of the catalyst, along with condensation and polymerization of multi-ring aromatics. High levels of light gas in the absence of C5+ hydrocarbon in the FCC stripper effluent may indicate a problem with the pore feed atomization and vaporization, not a stripper mechanical problem. If an oil molecule will not vaporize in the presence of 1250 degrees F. catalyst at the bottom of the riser, it will not vaporize in an FCC stripper at 960 degrees F. to 980 degrees F.
An FCC process model, according to some embodiments, may be used to determine improved or optimum operating parameters to maximize processing and/or economic efficiency, and push the FCC processing unit to multiple constraints. Processing variables and/or parameters may include feed rate, reactor temperature, feed preheat, and catalyst activity. The ability of any model to estimate improved or optimum conditions may be dependent on its ability to accurately predict a true process response. Some models may be configured to allow a user to adjust one or more “tuning” factors, for example, to match the model response with commercial data. FCC processing unit technology may affect yield shifts associated with process changes. A unit with a poor stripper, such as Unit A above, may have a substantially different response due to feed preheat changers than a unit with a modern stripper, such as Unit C above. This may be a result of the effect of entrained hydrocarbon on delta coke with changes in catalyst circulation. Another example is reactor temperature. Modern rise termination devices have reduced the post-riser contact time and minimized secondary reactions changing the yield response to process variables.
FCC reaction effluent testing may facilitate an FCC revamp analysis study. Reaction effluent testing may be used as a method for developing FCC base operating case data, for example, by isolating reactor section yields. This may be performed when modifications are anticipated for the FCC product recovery section. Reaction effluent testing may be suitable for exploring conditions at various points in the FCC reaction section where catalyst is present.
An example FCC revamp study was performed to determine the impact of upgrading riser termination to an available advanced design. Reaction effluent testing was performed immediately prior and immediately after completion of the revamp. Careful planning was undertaken to ensure that there were minimal differences in feed quality between the two test runs. Results from the two test runs are presented in Table 5 below. As shown in Table 5, the results indicate that there was significant yield benefit achieved with the revamp.
Accurate yield determination and process variable response may be important to FCC unit optimization. Reaction mix sampling may provide an efficient method to define such parameters or variables. In some embodiments, the results may be used to tune an analytical FCC process model, update LP vectors, audit revamp or catalyst changes, and/or determine optimum process conditions to improve or maximize unit economic or processing efficiency against multiple constraints.
It should be appreciated that at least some subject matter presented herein may be implemented as a computer process, a computer-controlled apparatus, a computing system, or an article of manufacture, such as a computer-readable storage medium. While the subject matter described herein is presented in the general context of program modules that execute on one or more computing devices, those skilled in the art will recognize that other implementations may be performed in combination with other types of program modules. Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types.
Those skilled in the art will also appreciate that aspects of the subject matter described herein may be practiced on or in conjunction with other computer system configurations beyond those described herein, including multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, handheld computers, mobile telephone devices, tablet computing devices, special-purposed hardware devices, network appliances, and the like.
The memory 802 may be used to store program instructions that are loadable and executable by the processor(s) 800, as well as to store data generated during the execution of these programs. Depending on the configuration and type of the FCC process controller 24, the memory 802 may be volatile (such as random access memory (RAM)) and/or non-volatile (such as read-only memory (ROM), flash memory, etc.). In some examples, the memory devices may include additional removable storage 804 and/or non-removable storage 806 including, but not limited to, magnetic storage, optical disks, and/or tape storage. The disk drives and their associated computer-readable media may provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data for the devices. In some implementations, the memory 802 may include multiple different types of memory, such as static random access memory (SRAM), dynamic random access memory (DRAM), or ROM.
The memory 802, the removable storage 804, and the non-removable storage 806 are all examples of computer-readable storage media. For example, computer-readable storage media may include volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. Additional types of computer storage media that may be present may include, but are not limited to, programmable random access memory (PRAM), SRAM, DRAM, RAM, ROM, electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile discs (DVD) or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium which may be used to store the desired information and which may be accessed by the devices. Combinations of any of the above should also be included within the scope of computer-readable media.
The FCC process controller 24 may also include one or more communication connection(s) 808 that may facilitate a control device (not shown) to communicate with devices or equipment capable of communicating with the FCC process controller 24. The FCC process controller 24 may also include a computer system (not shown). Connections may also be established via various data communication channels or ports, such as USB or COM ports to receive cables connecting the FCC process controller 24 to various other devices on a network. In some examples, the FCC process controller 24 may include Ethernet drivers that enable the FCC process controller 24 to communicate with other devices on the network. According to various examples, communication connections 808 may be established via a wired and/or wireless connection on the network.
The FCC process controller 24 may also include one or more input devices 810, such as a keyboard, mouse, pen, voice input device, gesture input device, and/or touch input device. It may further include one or more output devices 812, such as a display, printer, and/or speakers. In some examples, computer-readable communication media may include computer-readable instructions, program modules, or other data transmitted within a data signal, such as a carrier wave or other transmission. As used herein, however, computer-readable storage media may not include computer-readable communication media.
Turning to the contents of the memory 802, the memory 802 may include, but is not limited to, an operating system (OS) 814 and one or more application programs or services for implementing the features and embodiments disclosed herein. Such applications or services may include remote terminal units 816 for executing certain systems and methods for controlling operation of the FCC processing assembly 10 (e.g., semi- or fully-autonomously controlling operation of the FCC processing assembly 10), for example, upon receipt of one or more control signals generated by the FCC process controller 24. In some embodiments, one or more remote terminal unit(s) 816 may be located in the vicinity of the FCC processing assembly 10. The remote terminal unit(s) 816 may reside in the memory 802 or may be independent of the FCC process controller 24. In some examples, the remote terminal unit(s) 816 may be implemented by software that may be provided in configurable control block language and may be stored in non-volatile memory. When executed by the processor(s) 800, the remote terminal unit(s) 816 may implement the various functionalities and features associated with the FCC process controller 24 described herein.
As desired, embodiments of the disclosure may include an FCC process controller 24 with more or fewer components than are illustrated in
References are made to block diagrams of systems, methods, apparatuses, and computer program products according to example embodiments. It will be understood that at least some of the blocks of the block diagrams, and combinations of blocks in the block diagrams, may be implemented at least partially by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, special purpose hardware-based computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functionality of at least some of the blocks of the block diagrams, or combinations of blocks in the block diagrams discussed.
These computer program instructions may also be stored in a non-transitory computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means that implement the function specified in the block or blocks. The computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions that execute on the computer or other programmable apparatus provide task, acts, actions, or operations for implementing the functions specified in the block or blocks.
One or more components of the systems and one or more elements of the methods described herein may be implemented through an application program running on an operating system of a computer. They may also be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, mini-computers, mainframe computers, and the like.
Application programs that are components of the systems and methods described herein may include routines, programs, components, data structures, etc. that may implement certain abstract data types and perform certain tasks or actions. In a distributed computing environment, the application program (in whole or in part) may be located in local memory or in other storage. In addition, or alternatively, the application program (in whole or in part) may be located in remote memory or in storage to allow for circumstances where tasks can be performed by remote processing devices linked through a communications network.
This U.S. Non-Provisional Patent Application is a continuation-in-part of U.S. Non-Provisional application Ser. No. 17/652,431, filed Feb. 24, 2022, titled “METHODS AND ASSEMBLIES FOR DETERMINING AND USING STANDARDIZED SPECTRAL RESPONSES FOR CALIBRATION OF SPECTROSCOPIC ANALYZERS,” which claims priority to and the benefit of U.S. Provisional Application No. 63/153,452, filed Feb. 25, 2021, titled “METHODS AND ASSEMBLIES FOR DETERMINING AND USING STANDARDIZED SPECTRAL RESPONSES FOR CALIBRATION OF SPECTROSCOPIC ANALYZERS,” and U.S. Provisional Application No. 63/268,456, filed Feb. 24, 2022, titled “ASSEMBLIES AND METHODS FOR ENHANCING CONTROL OF FLUID CATALYTIC CRACKING (FCC) PROCESSES USING SPECTROSCOPIC ANALYZERS,” the disclosures of which are incorporated herein by reference in their entireties; and further claims priority to and the benefit of U.S. Provisional Application No. 63/268,456, filed Feb. 24, 2022, titled “ASSEMBLIES AND METHODS FOR ENHANCING CONTROL OF FLUID CATALYTIC CRACKING (FCC) PROCESSES USING SPECTROSCOPIC ANALYZERS”; U.S. Provisional Application No. 63/268,827, filed Mar. 3, 2022, titled “ASSEMBLIES AND METHODS FOR OPTIMIZING FLUID CATALYTIC CRACKING (FCC) PROCESSES DURING THE FCC PROCESS USING SPECTROSCOPIC ANALYZERS”; and U.S. Provisional Application No. 63/268,875, filed Mar. 4, 2022, titled “ASSEMBLIES AND METHODS FOR ENHANCING CONTROL OF HYDROTREATING AND FLUID CATALYTIC CRACKING (FCC) PROCESSES USING SPECTROSCOPIC ANALYZERS,” the disclosures of all three of which are incorporated herein by reference in their entireties.
Having now described some illustrative embodiments of the disclosure, it should be apparent to those skilled in the art that the foregoing is merely illustrative and not limiting, having been presented by way of example only. Numerous modifications and other embodiments are within the scope of one of ordinary skill in the art and are contemplated as falling within the scope of the disclosure. In particular, although many of the examples presented herein involve specific combinations of method acts or system elements, it should be understood that those acts and those elements may be combined in other ways to accomplish the same objectives. Those skilled in the art should appreciate that the parameters and configurations described herein are exemplary and that actual parameters and/or configurations will depend on the specific application in which the systems, methods, and/or aspects or techniques of the disclosure are used. Those skilled in the art should also recognize or be able to ascertain, using no more than routine experimentation, equivalents to the specific embodiments of the disclosure. It is, therefore, to be understood that the embodiments described herein are presented by way of example only and that, within the scope of any appended claims and equivalents thereto, the disclosure may be practiced other than as specifically described.
Furthermore, the scope of the present disclosure shall be construed to cover various modifications, combinations, additions, alterations, etc., above and to the above-described embodiments, which shall be considered to be within the scope of this disclosure. Accordingly, various features and characteristics as discussed herein may be selectively interchanged and applied to other illustrated and non-illustrated embodiment, and numerous variations, modifications, and additions further may be made thereto without departing from the spirit and scope of the present disclosure as set forth in the appended claims.
This U.S. Non-Provisional Patent Application is a continuation-in-part of U.S. Non-Provisional application Ser. No. 17/652,431, filed Feb. 24, 2022, titled “METHODS AND ASSEMBLIES FOR DETERMINING AND USING STANDARDIZED SPECTRAL RESPONSES FOR CALIBRATION OF SPECTROSCOPIC ANALYZERS,” which claims priority to and the benefit of U.S. Provisional Application No. 63/153,452, filed Feb. 25, 2021, titled “METHODS AND ASSEMBLIES FOR DETERMINING AND USING STANDARDIZED SPECTRAL RESPONSES FOR CALIBRATION OF SPECTROSCOPIC ANALYZERS,” and U.S. Provisional Application No. 63/268,456, filed Feb. 24, 2022, titled “ASSEMBLIES AND METHODS FOR ENHANCING CONTROL OF FLUID CATALYTIC CRACKING (FCC) PROCESSES USING SPECTROSCOPIC ANALYZERS,” the disclosures of which are incorporated herein by reference in their entireties; and further claims priority to and the benefit of U.S. Provisional Application No. 63/268,456, filed Feb. 24, 2022, titled “ASSEMBLIES AND METHODS FOR ENHANCING CONTROL OF FLUID CATALYTIC CRACKING (FCC) PROCESSES USING SPECTROSCOPIC ANALYZERS”; U.S. Provisional Application No. 63/268,827, filed Mar. 3, 2022, titled “ASSEMBLIES AND METHODS FOR OPTIMIZING FLUID CATALYTIC CRACKING (FCC) PROCESSES DURING THE FCC PROCESS USING SPECTROSCOPIC ANALYZERS”; and U.S. Provisional Application No. 63/268,875, filed Mar. 4, 2022, titled “ASSEMBLIES AND METHODS FOR ENHANCING CONTROL OF HYDROTREATING AND FLUID CATALYTIC CRACKING (FCC) PROCESSES USING SPECTROSCOPIC ANALYZERS,” the disclosures of all three of which are incorporated herein by reference in their entireties.
| Number | Name | Date | Kind |
|---|---|---|---|
| 981434 | Lander | Jan 1911 | A |
| 1043563 | Bishop | Nov 1912 | A |
| 1044300 | Tryon et al. | Nov 1912 | A |
| 1146047 | Boots | Jul 1915 | A |
| 1163464 | Roovers | Dec 1915 | A |
| 1526301 | Stevens | Feb 1925 | A |
| 1572922 | Govers et al. | Feb 1926 | A |
| 1867143 | Fohl | Jul 1932 | A |
| 2401570 | Koehler | Jun 1946 | A |
| 2498442 | Morey | Feb 1950 | A |
| 2516097 | Woodham et al. | Jul 1950 | A |
| 2686728 | Wallace | Aug 1954 | A |
| 2691621 | Gagle | Oct 1954 | A |
| 2691773 | Lichtenberger | Oct 1954 | A |
| 2731282 | Mcmanus et al. | Jan 1956 | A |
| 2740616 | Walden | Apr 1956 | A |
| 2792908 | Glanzer | May 1957 | A |
| 2804165 | Blomgren | Aug 1957 | A |
| 2867913 | Faucher | Jan 1959 | A |
| 2888239 | Slemmons | May 1959 | A |
| 2909482 | Williams et al. | Oct 1959 | A |
| 2925144 | Kroll | Feb 1960 | A |
| 2963423 | Birchfield | Dec 1960 | A |
| 3063681 | Duguid | Nov 1962 | A |
| 3070990 | Stanley | Jan 1963 | A |
| 3109481 | Yahnke | Nov 1963 | A |
| 3167305 | Backx et al. | Jan 1965 | A |
| 3188184 | Rice et al. | Jun 1965 | A |
| 3199876 | Magos et al. | Aug 1965 | A |
| 3203460 | Kuhne | Aug 1965 | A |
| 3279441 | Lippert et al. | Oct 1966 | A |
| 3307574 | Anderson | Mar 1967 | A |
| 3364134 | Hamblin | Jan 1968 | A |
| 3400049 | Wolfe | Sep 1968 | A |
| 3545411 | Vollradt | Dec 1970 | A |
| 3660057 | Ilnyckyj | May 1972 | A |
| 3719027 | Salka | Mar 1973 | A |
| 3720601 | Coonradt | Mar 1973 | A |
| 3771638 | Schneider et al. | Nov 1973 | A |
| 3775294 | Peterson | Nov 1973 | A |
| 3795607 | Adams | Mar 1974 | A |
| 3838036 | Stine et al. | Sep 1974 | A |
| 3839484 | Zimmerman, Jr. | Oct 1974 | A |
| 3840209 | James | Oct 1974 | A |
| 3841144 | Baldwin | Oct 1974 | A |
| 3854843 | Penny | Dec 1974 | A |
| 3874399 | Ishihara | Apr 1975 | A |
| 3901951 | Nishizaki | Aug 1975 | A |
| 3906780 | Baldwin | Sep 1975 | A |
| 3912307 | Totman | Oct 1975 | A |
| 3928172 | Davis et al. | Dec 1975 | A |
| 3937660 | Yates et al. | Feb 1976 | A |
| 4006075 | Luckenbach | Feb 1977 | A |
| 4017214 | Smith | Apr 1977 | A |
| 4066425 | Nett | Jan 1978 | A |
| 4085078 | McDonald | Apr 1978 | A |
| 4144759 | Slowik | Mar 1979 | A |
| 4149756 | Tackett | Apr 1979 | A |
| 4151003 | Smith et al. | Apr 1979 | A |
| 4167492 | Varady | Sep 1979 | A |
| 4176052 | Bruce et al. | Nov 1979 | A |
| 4217116 | Seever | Aug 1980 | A |
| 4260068 | McCarthy et al. | Apr 1981 | A |
| 4299687 | Myers et al. | Nov 1981 | A |
| 4302324 | Chen et al. | Nov 1981 | A |
| 4308968 | Thiltgen et al. | Jan 1982 | A |
| 4328947 | Reimpell et al. | May 1982 | A |
| 4332671 | Boyer | Jun 1982 | A |
| 4340204 | Heard | Jul 1982 | A |
| 4353812 | Lomas et al. | Oct 1982 | A |
| 4357603 | Roach et al. | Nov 1982 | A |
| 4392870 | Chieffo et al. | Jul 1983 | A |
| 4404095 | Haddad et al. | Sep 1983 | A |
| 4422925 | Williams et al. | Dec 1983 | A |
| 4434044 | Busch et al. | Feb 1984 | A |
| 4439533 | Lomas et al. | Mar 1984 | A |
| 4468975 | Sayles et al. | Sep 1984 | A |
| 4482451 | Kemp | Nov 1984 | A |
| 4495063 | Walters et al. | Jan 1985 | A |
| 4539012 | Ohzeki et al. | Sep 1985 | A |
| 4554313 | Hagenbach et al. | Nov 1985 | A |
| 4554799 | Pallanch | Nov 1985 | A |
| 4570942 | Diehl et al. | Feb 1986 | A |
| 4601303 | Jensen | Jul 1986 | A |
| 4615792 | Greenwood | Oct 1986 | A |
| 4621062 | Stewart et al. | Nov 1986 | A |
| 4622210 | Hirschberg et al. | Nov 1986 | A |
| 4624771 | Lane et al. | Nov 1986 | A |
| 4647313 | Clementoni | Mar 1987 | A |
| 4654748 | Rees | Mar 1987 | A |
| 4661241 | Dabkowski et al. | Apr 1987 | A |
| 4673490 | Subramanian et al. | Jun 1987 | A |
| 4674337 | Jonas | Jun 1987 | A |
| 4684759 | Lam | Aug 1987 | A |
| 4686027 | Bonilla et al. | Aug 1987 | A |
| 4728348 | Nelson et al. | Mar 1988 | A |
| 4733888 | Toelke | Mar 1988 | A |
| 4741819 | Robinson et al. | May 1988 | A |
| 4764347 | Milligan | Aug 1988 | A |
| 4765631 | Kohnen et al. | Aug 1988 | A |
| 4771176 | Scheifer et al. | Sep 1988 | A |
| 4816137 | Swint et al. | Mar 1989 | A |
| 4820404 | Owen | Apr 1989 | A |
| 4824016 | Cody et al. | Apr 1989 | A |
| 4844133 | von Meyerinck et al. | Jul 1989 | A |
| 4844927 | Morris et al. | Jul 1989 | A |
| 4849182 | Luetzelschwab | Jul 1989 | A |
| 4854855 | Rajewski | Aug 1989 | A |
| 4875994 | Haddad et al. | Oct 1989 | A |
| 4877513 | Haire et al. | Oct 1989 | A |
| 4798463 | Koshi | Nov 1989 | A |
| 4901751 | Story et al. | Feb 1990 | A |
| 4914249 | Benedict | Apr 1990 | A |
| 4916938 | Aikin et al. | Apr 1990 | A |
| 4917790 | Owen | Apr 1990 | A |
| 4923834 | Lomas | May 1990 | A |
| 4940900 | Lambert | Jul 1990 | A |
| 4957511 | Ljusberg-Wahren | Sep 1990 | A |
| 4960503 | Haun et al. | Oct 1990 | A |
| 4963745 | Maggard | Oct 1990 | A |
| 4972867 | Ruesch | Nov 1990 | A |
| 5000841 | Owen | Mar 1991 | A |
| 5002459 | Swearingen et al. | Mar 1991 | A |
| 5008653 | Kidd et al. | Apr 1991 | A |
| 5009768 | Galiasso et al. | Apr 1991 | A |
| 5013537 | Patarin et al. | May 1991 | A |
| 5022266 | Cody et al. | Jun 1991 | A |
| 5032154 | Wright | Jul 1991 | A |
| 5034115 | Avidan | Jul 1991 | A |
| 5045177 | Cooper et al. | Sep 1991 | A |
| 5050603 | Stokes et al. | Sep 1991 | A |
| 5053371 | Williamson | Oct 1991 | A |
| 5056758 | Bramblet | Oct 1991 | A |
| 5059305 | Sapre | Oct 1991 | A |
| 5061467 | Johnson et al. | Oct 1991 | A |
| 5066049 | Staples | Nov 1991 | A |
| 5076910 | Rush | Dec 1991 | A |
| 5082985 | Crouzet et al. | Jan 1992 | A |
| 5096566 | Dawson et al. | Mar 1992 | A |
| 5097677 | Holtzapple | Mar 1992 | A |
| 5111882 | Tang et al. | May 1992 | A |
| 5112357 | Bjerklund | May 1992 | A |
| 5114562 | Haun et al. | May 1992 | A |
| 5121337 | Brown | Jun 1992 | A |
| 5128109 | Owen | Jul 1992 | A |
| 5128292 | Lomas | Jul 1992 | A |
| 5129624 | Icenhower et al. | Jul 1992 | A |
| 5138891 | Johnson | Aug 1992 | A |
| 5139649 | Owen et al. | Aug 1992 | A |
| 5145785 | Maggard et al. | Sep 1992 | A |
| 5149261 | Suwa et al. | Sep 1992 | A |
| 5154558 | McCallion | Oct 1992 | A |
| 5160426 | Avidan | Nov 1992 | A |
| 5170911 | Della Riva | Dec 1992 | A |
| 5174250 | Lane | Dec 1992 | A |
| 5174345 | Kesterman et al. | Dec 1992 | A |
| 5178363 | Icenhower et al. | Jan 1993 | A |
| 5196110 | Swart et al. | Mar 1993 | A |
| 5201850 | Lenhardt et al. | Apr 1993 | A |
| 5203370 | Block et al. | Apr 1993 | A |
| 5211838 | Staubs et al. | May 1993 | A |
| 5212129 | Lomas | May 1993 | A |
| 5221463 | Kamienski et al. | Jun 1993 | A |
| 5223714 | Maggard | Jun 1993 | A |
| 5225679 | Clark et al. | Jul 1993 | A |
| 5230498 | Wood et al. | Jul 1993 | A |
| 5235999 | Lindquist et al. | Aug 1993 | A |
| 5236765 | Cordia et al. | Aug 1993 | A |
| 5243546 | Maggard | Sep 1993 | A |
| 5246860 | Hutchins et al. | Sep 1993 | A |
| 5246868 | Busch et al. | Sep 1993 | A |
| 5248408 | Owen | Sep 1993 | A |
| 5250807 | Sontvedt | Oct 1993 | A |
| 5257530 | Beattie et al. | Nov 1993 | A |
| 5258115 | Heck et al. | Nov 1993 | A |
| 5258117 | Kolstad et al. | Nov 1993 | A |
| 5262645 | Lambert et al. | Nov 1993 | A |
| 5263682 | Covert et al. | Nov 1993 | A |
| 5301560 | Anderson et al. | Apr 1994 | A |
| 5316448 | Ziegler et al. | May 1994 | A |
| 5320671 | Schilling | Jun 1994 | A |
| 5326074 | Spock et al. | Jul 1994 | A |
| 5328505 | Schilling | Jul 1994 | A |
| 5328591 | Raterman | Jul 1994 | A |
| 5332492 | Maurer et al. | Jul 1994 | A |
| 5338439 | Owen et al. | Aug 1994 | A |
| 5348645 | Maggard et al. | Sep 1994 | A |
| 5349188 | Maggard | Sep 1994 | A |
| 5349189 | Maggard | Sep 1994 | A |
| 5354451 | Goldstein et al. | Oct 1994 | A |
| 5354453 | Bhatia | Oct 1994 | A |
| 5361643 | Boyd et al. | Nov 1994 | A |
| 5362965 | Maggard | Nov 1994 | A |
| 5370146 | King et al. | Dec 1994 | A |
| 5370790 | Maggard et al. | Dec 1994 | A |
| 5372270 | Rosenkrantz | Dec 1994 | A |
| 5372352 | Smith et al. | Dec 1994 | A |
| 5381002 | Morrow et al. | Jan 1995 | A |
| 5388805 | Bathrick et al. | Feb 1995 | A |
| 5389232 | Adewuyi et al. | Feb 1995 | A |
| 5404015 | Chimenti et al. | Apr 1995 | A |
| 5416323 | Hoots et al. | May 1995 | A |
| 5417843 | Swart et al. | May 1995 | A |
| 5417846 | Renard | May 1995 | A |
| 5423446 | Johnson | Jun 1995 | A |
| 5431067 | Anderson et al. | Jul 1995 | A |
| 5433120 | Boyd et al. | Jul 1995 | A |
| 5435436 | Manley et al. | Jul 1995 | A |
| 5443716 | Anderson et al. | Aug 1995 | A |
| 5446681 | Gethner et al. | Aug 1995 | A |
| 5452232 | Espinosa et al. | Sep 1995 | A |
| RE35046 | Hettinger et al. | Oct 1995 | E |
| 5459677 | Kowalski et al. | Oct 1995 | A |
| 5472875 | Monticello | Dec 1995 | A |
| 5474607 | Holleran | Dec 1995 | A |
| 5475612 | Espinosa et al. | Dec 1995 | A |
| 5476117 | Pakula | Dec 1995 | A |
| 5490085 | Lambert et al. | Feb 1996 | A |
| 5492617 | Trimble et al. | Feb 1996 | A |
| 5494079 | Tiedemann | Feb 1996 | A |
| 5507326 | Cadman et al. | Apr 1996 | A |
| 5510265 | Monticello | Apr 1996 | A |
| 5532487 | Brearley et al. | Jul 1996 | A |
| 5540893 | English | Jul 1996 | A |
| 5549814 | Zinke | Aug 1996 | A |
| 5556222 | Chen | Sep 1996 | A |
| 5559295 | Sheryll | Sep 1996 | A |
| 5560509 | Laverman et al. | Oct 1996 | A |
| 5569808 | Cansell et al. | Oct 1996 | A |
| 5573032 | Lenz et al. | Nov 1996 | A |
| 5584985 | Lomas | Dec 1996 | A |
| 5596196 | Cooper et al. | Jan 1997 | A |
| 5600134 | Ashe et al. | Feb 1997 | A |
| 5647961 | Lofland | Jul 1997 | A |
| 5652145 | Cody et al. | Jul 1997 | A |
| 5675071 | Cody et al. | Oct 1997 | A |
| 5684580 | Cooper et al. | Nov 1997 | A |
| 5699269 | Ashe et al. | Dec 1997 | A |
| 5699270 | Ashe et al. | Dec 1997 | A |
| 5712481 | Welch et al. | Jan 1998 | A |
| 5712797 | Descales et al. | Jan 1998 | A |
| 5713401 | Weeks | Feb 1998 | A |
| 5716055 | Wilkinson et al. | Feb 1998 | A |
| 5717209 | Bigman et al. | Feb 1998 | A |
| 5740073 | Bages et al. | Apr 1998 | A |
| 5744024 | Sullivan, III et al. | Apr 1998 | A |
| 5744702 | Roussis et al. | Apr 1998 | A |
| 5746906 | McHenry et al. | May 1998 | A |
| 5758514 | Genung et al. | Jun 1998 | A |
| 5763883 | Descales et al. | Jun 1998 | A |
| 5800697 | Lengemann | Sep 1998 | A |
| 5817517 | Perry et al. | Oct 1998 | A |
| 5822058 | Adler-Golden et al. | Oct 1998 | A |
| 5834539 | Krivohlavek | Nov 1998 | A |
| 5837130 | Crossland | Nov 1998 | A |
| 5853455 | Gibson | Dec 1998 | A |
| 5856869 | Cooper et al. | Jan 1999 | A |
| 5858207 | Lomas | Jan 1999 | A |
| 5858210 | Richardson | Jan 1999 | A |
| 5858212 | Darcy | Jan 1999 | A |
| 5861228 | Descales et al. | Jan 1999 | A |
| 5862060 | Murray, Jr. | Jan 1999 | A |
| 5865441 | Orlowski | Feb 1999 | A |
| 5883363 | Motoyoshi et al. | Mar 1999 | A |
| 5885439 | Glover | Mar 1999 | A |
| 5892228 | Cooper et al. | Apr 1999 | A |
| 5895506 | Cook et al. | Apr 1999 | A |
| 5916433 | Tejada et al. | Jun 1999 | A |
| 5919354 | Bartek | Jul 1999 | A |
| 5935415 | Haizmann et al. | Aug 1999 | A |
| 5940176 | Knapp | Aug 1999 | A |
| 5972171 | Ross et al. | Oct 1999 | A |
| 5979491 | Gonsior | Nov 1999 | A |
| 5997723 | Wiehe et al. | Dec 1999 | A |
| 6015440 | Noureddini | Jan 2000 | A |
| 6025305 | Aldrich et al. | Feb 2000 | A |
| 6026841 | Kozik | Feb 2000 | A |
| 6047602 | Lynnworth | Apr 2000 | A |
| 6056005 | Piotrowski et al. | May 2000 | A |
| 6062274 | Pettesch | May 2000 | A |
| 6063263 | Palmas | May 2000 | A |
| 6063265 | Chiyoda et al. | May 2000 | A |
| 6070128 | Descales et al. | May 2000 | A |
| 6072576 | McDonald et al. | Jun 2000 | A |
| 6076864 | Levivier et al. | Jun 2000 | A |
| 6087662 | Wilt et al. | Jul 2000 | A |
| 6093867 | Ladwig et al. | Jul 2000 | A |
| 6099607 | Haslebacher | Aug 2000 | A |
| 6099616 | Jenne et al. | Aug 2000 | A |
| 6102655 | Kreitmeier | Aug 2000 | A |
| 6105441 | Conner et al. | Aug 2000 | A |
| 6107631 | He | Aug 2000 | A |
| 6117812 | Gao et al. | Sep 2000 | A |
| 6130095 | Shearer | Oct 2000 | A |
| 6140647 | Welch et al. | Oct 2000 | A |
| 6153091 | Sechrist et al. | Nov 2000 | A |
| 6155294 | Cornford et al. | Dec 2000 | A |
| 6162644 | Choi et al. | Dec 2000 | A |
| 6165350 | Lokhandwala et al. | Dec 2000 | A |
| 6169218 | Hearn | Jan 2001 | B1 |
| 6171052 | Aschenbruck et al. | Jan 2001 | B1 |
| 6174501 | Noureddini | Jan 2001 | B1 |
| 6190535 | Kalnes et al. | Feb 2001 | B1 |
| 6203585 | Majerczak | Mar 2001 | B1 |
| 6235104 | Chattopadhyay et al. | May 2001 | B1 |
| 6258987 | Schmidt et al. | Jul 2001 | B1 |
| 6271518 | Boehm et al. | Aug 2001 | B1 |
| 6274785 | Gore | Aug 2001 | B1 |
| 6284128 | Glover et al. | Sep 2001 | B1 |
| 6296812 | Gauthier et al. | Oct 2001 | B1 |
| 6312586 | Kalnes et al. | Nov 2001 | B1 |
| 6315815 | Spadaccini | Nov 2001 | B1 |
| 6324895 | Chitnis et al. | Dec 2001 | B1 |
| 6328348 | Cornford et al. | Dec 2001 | B1 |
| 6331436 | Richardson et al. | Dec 2001 | B1 |
| 6348074 | Wenzel | Feb 2002 | B2 |
| 6350371 | Lokhandwala et al. | Feb 2002 | B1 |
| 6368495 | Kocal et al. | Apr 2002 | B1 |
| 6382633 | Hashiguchi et al. | May 2002 | B1 |
| 6395228 | Maggard et al. | May 2002 | B1 |
| 6398518 | Ingistov | Jun 2002 | B1 |
| 6399800 | Haas et al. | Jun 2002 | B1 |
| 6420181 | Novak | Jul 2002 | B1 |
| 6422035 | Phillippe | Jul 2002 | B1 |
| 6435279 | Howe et al. | Aug 2002 | B1 |
| 6446446 | Cowans | Sep 2002 | B1 |
| 6446729 | Bixenman et al. | Sep 2002 | B1 |
| 6451197 | Kalnes | Sep 2002 | B1 |
| 6454935 | Lesieur et al. | Sep 2002 | B1 |
| 6467303 | Ross | Oct 2002 | B2 |
| 6482762 | Ruffin et al. | Nov 2002 | B1 |
| 6503460 | Miller et al. | Jan 2003 | B1 |
| 6528047 | Arif et al. | Mar 2003 | B2 |
| 6540797 | Scott et al. | Apr 2003 | B1 |
| 6558531 | Steffens et al. | May 2003 | B2 |
| 6589323 | Korin | Jul 2003 | B1 |
| 6609888 | Ingistov | Aug 2003 | B1 |
| 6622490 | Ingistov | Sep 2003 | B2 |
| 6644935 | Ingistov | Nov 2003 | B2 |
| 6660895 | Brunet et al. | Dec 2003 | B1 |
| 6672858 | Benson et al. | Jan 2004 | B1 |
| 6733232 | Ingistov et al. | May 2004 | B2 |
| 6733237 | Ingistov | May 2004 | B2 |
| 6736961 | Plummer et al. | May 2004 | B2 |
| 6740226 | Mehra et al. | May 2004 | B2 |
| 6772581 | Ojiro et al. | Aug 2004 | B2 |
| 6772741 | Pittel et al. | Aug 2004 | B1 |
| 6814941 | Naunheimer et al. | Nov 2004 | B1 |
| 6824673 | Ellis et al. | Nov 2004 | B1 |
| 6827841 | Kiser et al. | Dec 2004 | B2 |
| 6835223 | Walker et al. | Dec 2004 | B2 |
| 6841133 | Niewiedzial et al. | Jan 2005 | B2 |
| 6842702 | Haaland et al. | Jan 2005 | B2 |
| 6854346 | Nimberger | Feb 2005 | B2 |
| 6858128 | Hoehn et al. | Feb 2005 | B1 |
| 6866771 | Lomas et al. | Mar 2005 | B2 |
| 6869521 | Lomas | Mar 2005 | B2 |
| 6897071 | Sonbul | May 2005 | B2 |
| 6962484 | Brandl et al. | Nov 2005 | B2 |
| 7013718 | Ingistov et al. | Mar 2006 | B2 |
| 7035767 | Archer et al. | Apr 2006 | B2 |
| 7048254 | Laurent et al. | May 2006 | B2 |
| 7074321 | Kalnes | Jul 2006 | B1 |
| 7078005 | Smith et al. | Jul 2006 | B2 |
| 7087153 | Kalnes | Aug 2006 | B1 |
| 7156123 | Welker et al. | Jan 2007 | B2 |
| 7172686 | Ji et al. | Feb 2007 | B1 |
| 7174715 | Armitage et al. | Feb 2007 | B2 |
| 7194369 | Lundstedt et al. | Mar 2007 | B2 |
| 7213413 | Battiste et al. | May 2007 | B2 |
| 7225840 | Craig et al. | Jun 2007 | B1 |
| 7228250 | Naiman et al. | Jun 2007 | B2 |
| 7244350 | Kar et al. | Jul 2007 | B2 |
| 7252755 | Kiser et al. | Aug 2007 | B2 |
| 7255531 | Ingistov | Aug 2007 | B2 |
| 7260499 | Watzke et al. | Aug 2007 | B2 |
| 7291257 | Ackerson et al. | Nov 2007 | B2 |
| 7332132 | Hedrick et al. | Feb 2008 | B2 |
| 7404411 | Welch et al. | Jul 2008 | B2 |
| 7419583 | Nieskens et al. | Sep 2008 | B2 |
| 7445936 | O'Connor et al. | Nov 2008 | B2 |
| 7459081 | Koenig | Dec 2008 | B2 |
| 7485801 | Pulter et al. | Feb 2009 | B1 |
| 7487955 | Buercklin | Feb 2009 | B1 |
| 7501285 | Triche et al. | Mar 2009 | B1 |
| 7551420 | Cerqueira et al. | Jun 2009 | B2 |
| 7571765 | Themig | Aug 2009 | B2 |
| 7637970 | Fox et al. | Dec 2009 | B1 |
| 7669653 | Craster et al. | Mar 2010 | B2 |
| 7682501 | Soni et al. | Mar 2010 | B2 |
| 7686280 | Lowery | Mar 2010 | B2 |
| 7857964 | Mashiko et al. | Dec 2010 | B2 |
| 7866346 | Walters | Jan 2011 | B1 |
| 7895011 | Youssefi et al. | Feb 2011 | B2 |
| 7914601 | Farr et al. | Mar 2011 | B2 |
| 7931803 | Buchanan | Apr 2011 | B2 |
| 7932424 | Fujimoto et al. | Apr 2011 | B2 |
| 7939335 | Triche et al. | May 2011 | B1 |
| 7981361 | Bacik | Jul 2011 | B2 |
| 7988753 | Fox et al. | Aug 2011 | B1 |
| 7993514 | Schlueter | Aug 2011 | B2 |
| 8007662 | Lomas et al. | Aug 2011 | B2 |
| 8017910 | Sharpe | Sep 2011 | B2 |
| 8029662 | Varma et al. | Oct 2011 | B2 |
| 8037938 | Jardim De Azevedo et al. | Oct 2011 | B2 |
| 8038774 | Peng | Oct 2011 | B2 |
| 8064052 | Feitisch et al. | Nov 2011 | B2 |
| 8066867 | Dziabala | Nov 2011 | B2 |
| 8080426 | Moore et al. | Dec 2011 | B1 |
| 8127845 | Assal | Mar 2012 | B2 |
| 8193401 | McGehee et al. | Jun 2012 | B2 |
| 8236566 | Carpenter et al. | Aug 2012 | B2 |
| 8286673 | Recker et al. | Oct 2012 | B1 |
| 8354065 | Sexton | Jan 2013 | B1 |
| 8360118 | Fleischer et al. | Jan 2013 | B2 |
| 8370082 | De Peinder et al. | Feb 2013 | B2 |
| 8388830 | Sohn et al. | Mar 2013 | B2 |
| 8389285 | Carpenter et al. | Mar 2013 | B2 |
| 8397803 | Crabb et al. | Mar 2013 | B2 |
| 8397820 | Fehr et al. | Mar 2013 | B2 |
| 8404103 | Dziabala | Mar 2013 | B2 |
| 8434800 | LeBlanc | May 2013 | B1 |
| 8481942 | Mertens | Jul 2013 | B2 |
| 8506656 | Turocy | Aug 2013 | B1 |
| 8518131 | Mattingly et al. | Aug 2013 | B2 |
| 8524180 | Canari et al. | Sep 2013 | B2 |
| 8569068 | Carpenter et al. | Oct 2013 | B2 |
| 8579139 | Sablak | Nov 2013 | B1 |
| 8591814 | Hodges | Nov 2013 | B2 |
| 8609048 | Beadle | Dec 2013 | B1 |
| 8647415 | De Haan et al. | Feb 2014 | B1 |
| 8670945 | van Schie | Mar 2014 | B2 |
| 8685232 | Mandal et al. | Apr 2014 | B2 |
| 8735820 | Mertens | May 2014 | B2 |
| 8753502 | Sexton et al. | Jun 2014 | B1 |
| 8764970 | Moore et al. | Jul 2014 | B1 |
| 8778823 | Oyekan et al. | Jul 2014 | B1 |
| 8781757 | Farquharson et al. | Jul 2014 | B2 |
| 8829258 | Gong et al. | Sep 2014 | B2 |
| 8916041 | Van Den Berg et al. | Dec 2014 | B2 |
| 8932458 | Gianzon et al. | Jan 2015 | B1 |
| 8986402 | Kelly | Mar 2015 | B2 |
| 8987537 | Droubi et al. | Mar 2015 | B1 |
| 8999011 | Stern et al. | Apr 2015 | B2 |
| 8999012 | Kelly et al. | Apr 2015 | B2 |
| 9011674 | Milam et al. | Apr 2015 | B2 |
| 9057035 | Kraus et al. | Jun 2015 | B1 |
| 9097423 | Kraus et al. | Aug 2015 | B2 |
| 9109176 | Stern et al. | Aug 2015 | B2 |
| 9109177 | Freel et al. | Aug 2015 | B2 |
| 9138738 | Glover et al. | Sep 2015 | B1 |
| 9216376 | Liu et al. | Dec 2015 | B2 |
| 9272241 | Königsson | Mar 2016 | B2 |
| 9273867 | Buzinski et al. | Mar 2016 | B2 |
| 9289715 | Høy-Petersen et al. | Mar 2016 | B2 |
| 9315403 | Laur et al. | Apr 2016 | B1 |
| 9371493 | Oyekan | Jun 2016 | B1 |
| 9371494 | Oyekan et al. | Jun 2016 | B2 |
| 9377340 | Hägg | Jun 2016 | B2 |
| 9393520 | Gomez | Jul 2016 | B2 |
| 9410102 | Eaton et al. | Aug 2016 | B2 |
| 9428695 | Narayanaswamy et al. | Aug 2016 | B2 |
| 9458396 | Weiss et al. | Oct 2016 | B2 |
| 9487718 | Kraus et al. | Nov 2016 | B2 |
| 9499758 | Droubi et al. | Nov 2016 | B2 |
| 9500300 | Daigle | Nov 2016 | B2 |
| 9506649 | Rennie et al. | Nov 2016 | B2 |
| 9580662 | Moore | Feb 2017 | B1 |
| 9624448 | Joo et al. | Apr 2017 | B2 |
| 9650580 | Merdrignac et al. | May 2017 | B2 |
| 9657241 | Craig et al. | May 2017 | B2 |
| 9663729 | Baird et al. | May 2017 | B2 |
| 9665693 | Saeger et al. | May 2017 | B2 |
| 9709545 | Mertens | Jul 2017 | B2 |
| 9757686 | Peng | Sep 2017 | B2 |
| 9789290 | Forsell | Oct 2017 | B2 |
| 9803152 | Kar et al. | Oct 2017 | B2 |
| 9834731 | Weiss et al. | Dec 2017 | B2 |
| 9840674 | Weiss et al. | Dec 2017 | B2 |
| 9873080 | Richardson | Jan 2018 | B2 |
| 9878300 | Norling | Jan 2018 | B2 |
| 9890907 | Highfield et al. | Feb 2018 | B1 |
| 9891198 | Sutan | Feb 2018 | B2 |
| 9895649 | Brown et al. | Feb 2018 | B2 |
| 9896630 | Weiss et al. | Feb 2018 | B2 |
| 9914094 | Jenkins et al. | Mar 2018 | B2 |
| 9920270 | Robinson et al. | Mar 2018 | B2 |
| 9925486 | Botti | Mar 2018 | B1 |
| 9982788 | Maron | May 2018 | B1 |
| 10047299 | Rubin-Pitel et al. | Aug 2018 | B2 |
| 10087397 | Phillips et al. | Oct 2018 | B2 |
| 10099175 | Takashashi et al. | Oct 2018 | B2 |
| 10150078 | Komatsu et al. | Dec 2018 | B2 |
| 10228708 | Lambert et al. | Mar 2019 | B2 |
| 10239034 | Sexton | Mar 2019 | B1 |
| 10253269 | Cantley et al. | Apr 2019 | B2 |
| 10266779 | Weiss et al. | Apr 2019 | B2 |
| 10295521 | Mertens | May 2019 | B2 |
| 10308884 | Klussman | Jun 2019 | B2 |
| 10316263 | Rubin-Pitel et al. | Jun 2019 | B2 |
| 10384157 | Balcik | Aug 2019 | B2 |
| 10435339 | Larsen et al. | Oct 2019 | B2 |
| 10443006 | Fruchey et al. | Oct 2019 | B1 |
| 10457881 | Droubi et al. | Oct 2019 | B2 |
| 10479943 | Liu et al. | Nov 2019 | B1 |
| 10494579 | Wrigley et al. | Dec 2019 | B2 |
| 10495570 | Owen et al. | Dec 2019 | B2 |
| 10501699 | Robinson et al. | Dec 2019 | B2 |
| 10526547 | Larsen et al. | Jan 2020 | B2 |
| 10533141 | Moore et al. | Jan 2020 | B2 |
| 10563130 | Narayanaswamy et al. | Feb 2020 | B2 |
| 10563132 | Moore et al. | Feb 2020 | B2 |
| 10563133 | Moore et al. | Feb 2020 | B2 |
| 10570078 | Larsen et al. | Feb 2020 | B2 |
| 10577551 | Kraus et al. | Mar 2020 | B2 |
| 10584287 | Klussman et al. | Mar 2020 | B2 |
| 10604709 | Moore et al. | Mar 2020 | B2 |
| 10640719 | Freel et al. | May 2020 | B2 |
| 10655074 | Moore et al. | May 2020 | B2 |
| 10696906 | Cantley et al. | Jun 2020 | B2 |
| 10808184 | Moore | Oct 2020 | B1 |
| 10836966 | Moore et al. | Nov 2020 | B2 |
| 10876053 | Klussman et al. | Dec 2020 | B2 |
| 10954456 | Moore et al. | Mar 2021 | B2 |
| 10961468 | Moore et al. | Mar 2021 | B2 |
| 10962259 | Shah et al. | Mar 2021 | B2 |
| 10968403 | Moore | Apr 2021 | B2 |
| 11021662 | Moore et al. | Jun 2021 | B2 |
| 11098255 | Cooper et al. | Aug 2021 | B2 |
| 11124714 | Eller et al. | Sep 2021 | B2 |
| 11136513 | Moore et al. | Oct 2021 | B2 |
| 11164406 | Meroux et al. | Nov 2021 | B2 |
| 11168270 | Moore | Nov 2021 | B1 |
| 11175039 | Lochschmied et al. | Nov 2021 | B2 |
| 11203719 | Cantley et al. | Dec 2021 | B2 |
| 11203722 | Moore et al. | Dec 2021 | B2 |
| 11214741 | Davdov et al. | Jan 2022 | B2 |
| 11306253 | Timken et al. | Apr 2022 | B2 |
| 11319262 | Wu et al. | May 2022 | B2 |
| 11352577 | Woodchick et al. | Jun 2022 | B2 |
| 11352578 | Eller et al. | Jun 2022 | B2 |
| 11384301 | Eller et al. | Jul 2022 | B2 |
| 11421162 | Pradeep et al. | Aug 2022 | B2 |
| 11467172 | Mitzel et al. | Oct 2022 | B1 |
| 11542441 | Larsen et al. | Jan 2023 | B2 |
| 11578638 | Thobe | Feb 2023 | B2 |
| 11667858 | Eller et al. | Jun 2023 | B2 |
| 11692141 | Larsen et al. | Jul 2023 | B2 |
| 11702600 | Sexton et al. | Jul 2023 | B2 |
| 11715950 | Miller et al. | Aug 2023 | B2 |
| 11720526 | Miller et al. | Aug 2023 | B2 |
| 11802257 | Short et al. | Oct 2023 | B2 |
| 11835450 | Bledsoe, Jr. et al. | Dec 2023 | B2 |
| 20020014068 | Mittricker et al. | Feb 2002 | A1 |
| 20020061633 | Marsh | May 2002 | A1 |
| 20020170431 | Chang et al. | Nov 2002 | A1 |
| 20030041518 | Wallace et al. | Mar 2003 | A1 |
| 20030113598 | Chow et al. | Jun 2003 | A1 |
| 20030188536 | Mittricker | Oct 2003 | A1 |
| 20030194322 | Brandl et al. | Oct 2003 | A1 |
| 20040010170 | Vickers | Jan 2004 | A1 |
| 20040033617 | Sonbul | Feb 2004 | A1 |
| 20040040201 | Roos et al. | Mar 2004 | A1 |
| 20040079431 | Kissell | Apr 2004 | A1 |
| 20040121472 | Nemana et al. | Jun 2004 | A1 |
| 20040129605 | Goldstein et al. | Jul 2004 | A1 |
| 20040139858 | Entezarian | Jul 2004 | A1 |
| 20040154610 | Hopp et al. | Aug 2004 | A1 |
| 20040232050 | Martin et al. | Nov 2004 | A1 |
| 20040251170 | Chiyoda et al. | Dec 2004 | A1 |
| 20050042151 | Alward et al. | Feb 2005 | A1 |
| 20050088653 | Coates et al. | Apr 2005 | A1 |
| 20050123466 | Sullivan | Jun 2005 | A1 |
| 20050139516 | Nieskens et al. | Jun 2005 | A1 |
| 20050143609 | Wolf et al. | Jun 2005 | A1 |
| 20050150820 | Guo | Jul 2005 | A1 |
| 20050229777 | Brown | Oct 2005 | A1 |
| 20060037237 | Copeland et al. | Feb 2006 | A1 |
| 20060042701 | Jansen | Mar 2006 | A1 |
| 20060049082 | Niccum et al. | Mar 2006 | A1 |
| 20060162243 | Wolf | Jul 2006 | A1 |
| 20060169305 | Jansen et al. | Aug 2006 | A1 |
| 20060210456 | Bruggendick | Sep 2006 | A1 |
| 20060169064 | Anschutz et al. | Oct 2006 | A1 |
| 20060220383 | Erickson | Oct 2006 | A1 |
| 20070003450 | Burdett et al. | Jan 2007 | A1 |
| 20070082407 | Little, III | Apr 2007 | A1 |
| 20070112258 | Soyemi et al. | May 2007 | A1 |
| 20070202027 | Walker et al. | Aug 2007 | A1 |
| 20070212271 | Kennedy et al. | Sep 2007 | A1 |
| 20070212790 | Welch | Sep 2007 | A1 |
| 20070215521 | Havlik et al. | Sep 2007 | A1 |
| 20070243556 | Wachs | Oct 2007 | A1 |
| 20070283812 | Liu et al. | Dec 2007 | A1 |
| 20080078693 | Sexton et al. | Apr 2008 | A1 |
| 20080078694 | Sexton et al. | Apr 2008 | A1 |
| 20080078695 | Sexton et al. | Apr 2008 | A1 |
| 20080081844 | Shires et al. | Apr 2008 | A1 |
| 20080087592 | Buchanan | Apr 2008 | A1 |
| 20080092436 | Seames et al. | Apr 2008 | A1 |
| 20080109107 | Stefani et al. | May 2008 | A1 |
| 20080149486 | Greaney et al. | Jun 2008 | A1 |
| 20080156696 | Niccum et al. | Jul 2008 | A1 |
| 20080207974 | McCoy et al. | Aug 2008 | A1 |
| 20080211505 | Trygstad et al. | Sep 2008 | A1 |
| 20080247942 | Kandziora et al. | Oct 2008 | A1 |
| 20080253936 | Abhari | Oct 2008 | A1 |
| 20090151250 | Agrawal | Jun 2009 | A1 |
| 20090152454 | Nelson et al. | Jun 2009 | A1 |
| 20090158824 | Brown et al. | Jun 2009 | A1 |
| 20100127217 | Lightowlers et al. | May 2010 | A1 |
| 20100131247 | Carpenter et al. | May 2010 | A1 |
| 20100166602 | Bacik | Jul 2010 | A1 |
| 20100243235 | Caldwell et al. | Sep 2010 | A1 |
| 20100301044 | Sprecher | Dec 2010 | A1 |
| 20100318118 | Forsell | Dec 2010 | A1 |
| 20110147267 | Kaul et al. | Jun 2011 | A1 |
| 20110155646 | Karas et al. | Jun 2011 | A1 |
| 20110175032 | Günther | Jul 2011 | A1 |
| 20110186307 | Derby | Aug 2011 | A1 |
| 20110237856 | Mak | Sep 2011 | A1 |
| 20110247835 | Crabb | Oct 2011 | A1 |
| 20110277377 | Novak et al. | Nov 2011 | A1 |
| 20110299076 | Feitisch et al. | Dec 2011 | A1 |
| 20110319698 | Sohn et al. | Dec 2011 | A1 |
| 20120012342 | Wilkin et al. | Jan 2012 | A1 |
| 20120125813 | Bridges et al. | May 2012 | A1 |
| 20120125814 | Sanchez et al. | May 2012 | A1 |
| 20120131853 | Thacker et al. | May 2012 | A1 |
| 20130014431 | Jin et al. | Jan 2013 | A1 |
| 20130109895 | Novak et al. | May 2013 | A1 |
| 20130112313 | Donnelly et al. | May 2013 | A1 |
| 20130125619 | Wang | May 2013 | A1 |
| 20130186739 | Trompiz | Jul 2013 | A1 |
| 20130225897 | Candelon et al. | Aug 2013 | A1 |
| 20130288355 | DeWitte et al. | Oct 2013 | A1 |
| 20130334027 | Winter et al. | Dec 2013 | A1 |
| 20130342203 | Trygstad et al. | Dec 2013 | A1 |
| 20140019052 | Zaeper et al. | Jan 2014 | A1 |
| 20140024873 | De Haan et al. | Jan 2014 | A1 |
| 20140041150 | Sjoberg | Feb 2014 | A1 |
| 20140121428 | Wang et al. | May 2014 | A1 |
| 20140229010 | Farquharson et al. | Aug 2014 | A1 |
| 20140296057 | Ho et al. | Oct 2014 | A1 |
| 20140299515 | Weiss et al. | Oct 2014 | A1 |
| 20140311953 | Chimenti et al. | Oct 2014 | A1 |
| 20140316176 | Fjare et al. | Oct 2014 | A1 |
| 20140332444 | Weiss et al. | Nov 2014 | A1 |
| 20140353138 | Amale et al. | Dec 2014 | A1 |
| 20140374322 | Venkatesh | Dec 2014 | A1 |
| 20150005547 | Freel et al. | Jan 2015 | A1 |
| 20150005548 | Freel et al. | Jan 2015 | A1 |
| 20150034599 | Hunger et al. | Feb 2015 | A1 |
| 20150057477 | Ellig et al. | Feb 2015 | A1 |
| 20150071028 | Glanville | Mar 2015 | A1 |
| 20150122704 | Kumar et al. | May 2015 | A1 |
| 20150166426 | Wegerer et al. | Jun 2015 | A1 |
| 20150240167 | Kulprathipanja et al. | Aug 2015 | A1 |
| 20150240174 | Bru et al. | Aug 2015 | A1 |
| 20150337207 | Chen et al. | Nov 2015 | A1 |
| 20150337225 | Droubi et al. | Nov 2015 | A1 |
| 20150337226 | Tardif et al. | Nov 2015 | A1 |
| 20150353851 | Buchanan | Dec 2015 | A1 |
| 20160090539 | Frey et al. | Mar 2016 | A1 |
| 20160122662 | Weiss et al. | May 2016 | A1 |
| 20160122666 | Weiss et al. | May 2016 | A1 |
| 20160160139 | Dawe et al. | Jun 2016 | A1 |
| 20160168481 | Ray et al. | Jun 2016 | A1 |
| 20160244677 | Froehle | Aug 2016 | A1 |
| 20160298851 | Brickwood et al. | Oct 2016 | A1 |
| 20160312127 | Frey et al. | Oct 2016 | A1 |
| 20160312130 | Majcher et al. | Oct 2016 | A1 |
| 20170009163 | Kraus et al. | Jan 2017 | A1 |
| 20170115190 | Hall et al. | Apr 2017 | A1 |
| 20170131728 | Lambert et al. | May 2017 | A1 |
| 20170151526 | Cole | Jun 2017 | A1 |
| 20170183575 | Rubin-Pitel et al. | Jun 2017 | A1 |
| 20170198910 | Garg | Jul 2017 | A1 |
| 20170226434 | Zimmerman | Aug 2017 | A1 |
| 20170233670 | Feustel et al. | Aug 2017 | A1 |
| 20180017469 | English et al. | Jan 2018 | A1 |
| 20180037308 | Lee et al. | Feb 2018 | A1 |
| 20180080958 | Marchese et al. | Mar 2018 | A1 |
| 20180119039 | Tanaka et al. | May 2018 | A1 |
| 20180134974 | Weiss et al. | May 2018 | A1 |
| 20180163144 | Weiss et al. | Jun 2018 | A1 |
| 20180179457 | Mukherjee et al. | Jun 2018 | A1 |
| 20180202607 | McBride | Jul 2018 | A1 |
| 20180230389 | Moore et al. | Aug 2018 | A1 |
| 20180246142 | Glover | Aug 2018 | A1 |
| 20180355263 | Moore et al. | Dec 2018 | A1 |
| 20180361312 | Dutra e Mello et al. | Dec 2018 | A1 |
| 20180371325 | Streiff et al. | Dec 2018 | A1 |
| 20190002772 | Moore et al. | Jan 2019 | A1 |
| 20190010405 | Moore et al. | Jan 2019 | A1 |
| 20190010408 | Moore et al. | Jan 2019 | A1 |
| 20190016980 | Kar et al. | Jan 2019 | A1 |
| 20190093026 | Wohaibi et al. | Mar 2019 | A1 |
| 20190099706 | Sampath | Apr 2019 | A1 |
| 20190100702 | Cantley et al. | Apr 2019 | A1 |
| 20190127651 | Kar et al. | May 2019 | A1 |
| 20190128160 | Peng | May 2019 | A1 |
| 20190136144 | Wohaibi et al. | May 2019 | A1 |
| 20190153340 | Weiss et al. | May 2019 | A1 |
| 20190153942 | Wohaibi et al. | May 2019 | A1 |
| 20190169509 | Cantley et al. | Jun 2019 | A1 |
| 20190185772 | Berkhous et al. | Jun 2019 | A1 |
| 20190201841 | McClelland | Jul 2019 | A1 |
| 20190203130 | Mukherjee | Jul 2019 | A1 |
| 20190218466 | Slade et al. | Jul 2019 | A1 |
| 20190233741 | Moore et al. | Aug 2019 | A1 |
| 20190292465 | McBride | Sep 2019 | A1 |
| 20190338205 | Ackerson et al. | Nov 2019 | A1 |
| 20190382668 | Klussman et al. | Dec 2019 | A1 |
| 20190382672 | Sorensen | Dec 2019 | A1 |
| 20200049675 | Ramirez | Feb 2020 | A1 |
| 20200080881 | Langlois et al. | Mar 2020 | A1 |
| 20200095509 | Moore et al. | Mar 2020 | A1 |
| 20200123458 | Moore et al. | Apr 2020 | A1 |
| 20200181502 | Paasikallio et al. | Jun 2020 | A1 |
| 20200199462 | Klussman et al. | Jun 2020 | A1 |
| 20200208068 | Hossain et al. | Jul 2020 | A1 |
| 20200291316 | Robbins et al. | Sep 2020 | A1 |
| 20200312470 | Craig et al. | Oct 2020 | A1 |
| 20200316513 | Zhao | Oct 2020 | A1 |
| 20200332198 | Yang | Oct 2020 | A1 |
| 20200353456 | Zalewski et al. | Nov 2020 | A1 |
| 20200378600 | Craig et al. | Dec 2020 | A1 |
| 20200385644 | Rogel et al. | Dec 2020 | A1 |
| 20210002559 | Larsen et al. | Jan 2021 | A1 |
| 20210003502 | Kirchmann et al. | Jan 2021 | A1 |
| 20210033631 | Field et al. | Feb 2021 | A1 |
| 20210103304 | Fogarty et al. | Apr 2021 | A1 |
| 20210115344 | Perkins et al. | Apr 2021 | A1 |
| 20210181164 | Shirkhan et al. | Jun 2021 | A1 |
| 20210213382 | Cole | Jul 2021 | A1 |
| 20210238487 | Moore et al. | Aug 2021 | A1 |
| 20210253964 | Eller et al. | Aug 2021 | A1 |
| 20210253965 | Woodchick et al. | Aug 2021 | A1 |
| 20210261874 | Eller et al. | Aug 2021 | A1 |
| 20210284919 | Moore et al. | Sep 2021 | A1 |
| 20210292661 | Klussman et al. | Sep 2021 | A1 |
| 20210301210 | Timken et al. | Sep 2021 | A1 |
| 20210396660 | Zarrabian | Dec 2021 | A1 |
| 20210403819 | Moore et al. | Dec 2021 | A1 |
| 20220040629 | Edmoundson et al. | Feb 2022 | A1 |
| 20220041940 | Pradeep et al. | Feb 2022 | A1 |
| 20220048019 | Zalewski et al. | Feb 2022 | A1 |
| 20220268694 | Bledsoe et al. | Aug 2022 | A1 |
| 20220298440 | Woodchick et al. | Sep 2022 | A1 |
| 20220343229 | Gruber et al. | Oct 2022 | A1 |
| 20230015077 | Kim | Jan 2023 | A1 |
| 20230078852 | Campbell et al. | Mar 2023 | A1 |
| 20230080192 | Bledsoe et al. | Mar 2023 | A1 |
| 20230082189 | Bledsoe et al. | Mar 2023 | A1 |
| 20230084329 | Bledsoe et al. | Mar 2023 | A1 |
| 20230087063 | Mitzel et al. | Mar 2023 | A1 |
| 20230089935 | Bledsoe et al. | Mar 2023 | A1 |
| 20230093452 | Sexton et al. | Mar 2023 | A1 |
| 20230111609 | Sexton et al. | Apr 2023 | A1 |
| 20230113140 | Larsen et al. | Apr 2023 | A1 |
| 20230220286 | Cantley et al. | Jul 2023 | A1 |
| 20230241548 | Holland et al. | Aug 2023 | A1 |
| 20230242837 | Short et al. | Aug 2023 | A1 |
| 20230259080 | Whikehart et al. | Aug 2023 | A1 |
| 20230259088 | Borup et al. | Aug 2023 | A1 |
| 20230272290 | Larsen et al. | Aug 2023 | A1 |
| 20230295528 | Eller et al. | Sep 2023 | A1 |
| 20230332056 | Larsen et al. | Oct 2023 | A1 |
| 20230332058 | Larsen et al. | Oct 2023 | A1 |
| 20230357649 | Sexton et al. | Nov 2023 | A1 |
| Number | Date | Country |
|---|---|---|
| 11772 | Apr 2011 | AT |
| PI0701518 | Nov 2008 | BR |
| 2949201 | Nov 2015 | CA |
| 2822742 | Dec 2016 | CA |
| 3009808 | Jul 2017 | CA |
| 2904903 | Aug 2020 | CA |
| 3077045 | Sep 2020 | CA |
| 2947431 | Mar 2021 | CA |
| 3004712 | Jun 2021 | CA |
| 2980055 | Dec 2021 | CA |
| 2879783 | Jan 2022 | CA |
| 2991614 | Jan 2022 | CA |
| 2980069 | Nov 2022 | CA |
| 3109606 | Dec 2022 | CA |
| 432129 | Mar 1967 | CH |
| 2128346 | Mar 1993 | CN |
| 201306736 | Sep 2009 | CN |
| 201940168 | Aug 2011 | CN |
| 102120138 | Dec 2012 | CN |
| 203453713 | Feb 2014 | CN |
| 203629938 | Jun 2014 | CN |
| 203816490 | Sep 2014 | CN |
| 104353357 | Feb 2015 | CN |
| 204170623 | Feb 2015 | CN |
| 103331093 | Apr 2015 | CN |
| 204253221 | Apr 2015 | CN |
| 204265565 | Apr 2015 | CN |
| 105148728 | Dec 2015 | CN |
| 204824775 | Dec 2015 | CN |
| 103933845 | Jan 2016 | CN |
| 105289241 | Feb 2016 | CN |
| 105536486 | May 2016 | CN |
| 105804900 | Jul 2016 | CN |
| 103573430 | Aug 2016 | CN |
| 205655095 | Oct 2016 | CN |
| 104326604 | Nov 2016 | CN |
| 104358627 | Nov 2016 | CN |
| 106237802 | Dec 2016 | CN |
| 205779365 | Dec 2016 | CN |
| 106407648 | Feb 2017 | CN |
| 105778987 | Aug 2017 | CN |
| 207179722 | Apr 2018 | CN |
| 207395575 | May 2018 | CN |
| 108179022 | Jun 2018 | CN |
| 108704478 | Oct 2018 | CN |
| 109126458 | Jan 2019 | CN |
| 109423345 | Mar 2019 | CN |
| 109499365 | Mar 2019 | CN |
| 109705939 | May 2019 | CN |
| 109722303 | May 2019 | CN |
| 110129103 | Aug 2019 | CN |
| 110229686 | Sep 2019 | CN |
| 209451617 | Oct 2019 | CN |
| 110987862 | Apr 2020 | CN |
| 215288592 | Dec 2021 | CN |
| 113963818 | Jan 2022 | CN |
| 114001278 | Feb 2022 | CN |
| 217431673 | Sep 2022 | CN |
| 218565442 | Mar 2023 | CN |
| 10179 | Jun 1912 | DE |
| 3721725 | Jan 1989 | DE |
| 19619722 | Nov 1997 | DE |
| 102010017563 | Dec 2011 | DE |
| 102014009231 | Jan 2016 | DE |
| 0142352 | May 1985 | EP |
| 0527000 | Feb 1993 | EP |
| 0783910 | Jul 1997 | EP |
| 0949318 | Oct 1999 | EP |
| 0783910 | Dec 2000 | EP |
| 0801299 | Mar 2004 | EP |
| 1413712 | Apr 2004 | EP |
| 1600491 | Nov 2005 | EP |
| 1870153 | Dec 2007 | EP |
| 2047905 | Apr 2009 | EP |
| 2903955 | Aug 2015 | EP |
| 2955345 | Dec 2015 | EP |
| 3130773 | Feb 2017 | EP |
| 3139009 | Mar 2017 | EP |
| 3239483 | Nov 2017 | EP |
| 3085910 | Aug 2018 | EP |
| 3355056 | Aug 2018 | EP |
| 2998529 | Feb 2019 | EP |
| 3441442 | Feb 2019 | EP |
| 3569988 | Nov 2019 | EP |
| 3878926 | Sep 2021 | EP |
| 2357630 | Feb 1978 | FR |
| 3004722 | Mar 2016 | FR |
| 3027909 | May 2016 | FR |
| 3067036 | Dec 2018 | FR |
| 3067037 | Dec 2018 | FR |
| 3072684 | Apr 2019 | FR |
| 3075808 | Jun 2019 | FR |
| 775273 | May 1957 | GB |
| 933618 | Aug 1963 | GB |
| 1207719 | Oct 1970 | GB |
| 2144526 | Mar 1985 | GB |
| 202111016535 | Jul 2021 | IN |
| 59220609 | Dec 1984 | JP |
| 2003129067 | May 2003 | JP |
| 3160405 | Jun 2010 | JP |
| 2015059220 | Mar 2015 | JP |
| 2019014275 | Jan 2019 | JP |
| 101751923 | Jul 2017 | KR |
| 101823897 | Mar 2018 | KR |
| 20180095303 | Aug 2018 | KR |
| 20190004474 | Jan 2019 | KR |
| 20190004475 | Jan 2019 | KR |
| 2673558 | Nov 2018 | RU |
| 2700705 | Sep 2019 | RU |
| 2760879 | Dec 2021 | RU |
| 320682 | Nov 1997 | TW |
| 9408225 | Apr 1994 | WO |
| 199640436 | Dec 1996 | WO |
| 1997033678 | Sep 1997 | WO |
| 199803249 | Jan 1998 | WO |
| 1999041591 | Aug 1999 | WO |
| 2001051588 | Jul 2001 | WO |
| 2006126978 | Nov 2006 | WO |
| 2008088294 | Jul 2008 | WO |
| 2010144191 | Dec 2010 | WO |
| 2012026302 | Mar 2012 | WO |
| 2012062924 | May 2012 | WO |
| 2012089776 | Jul 2012 | WO |
| 2012108584 | Aug 2012 | WO |
| 2014096703 | Jun 2014 | WO |
| 2014096704 | Jun 2014 | WO |
| 2014191004 | Jul 2014 | WO |
| 2014177424 | Nov 2014 | WO |
| 2014202815 | Dec 2014 | WO |
| 2016167708 | Oct 2016 | WO |
| 2017067088 | Apr 2017 | WO |
| 2017207976 | Dec 2017 | WO |
| 2018017664 | Jan 2018 | WO |
| 2018073018 | Apr 2018 | WO |
| 2018122274 | Jul 2018 | WO |
| 2018148675 | Aug 2018 | WO |
| 2018148681 | Aug 2018 | WO |
| 2018231105 | Dec 2018 | WO |
| 2019053323 | Mar 2019 | WO |
| 2019104243 | May 2019 | WO |
| 2019155183 | Aug 2019 | WO |
| 2019178701 | Sep 2019 | WO |
| 2020160004 | Aug 2020 | WO |
| 2021058289 | Apr 2021 | WO |
| 2022133359 | Jun 2022 | WO |
| 2022144495 | Jul 2022 | WO |
| 2022149501 | Jul 2022 | WO |
| 2022219234 | Oct 2022 | WO |
| 2022220991 | Oct 2022 | WO |
| 2023038579 | Mar 2023 | WO |
| 2023137304 | Jul 2023 | WO |
| 2023164683 | Aug 2023 | WO |
| Entry |
|---|
| Platvoet et al., Process Burners 101, American Institute of Chemical Engineers, Aug. 2013. |
| Luyben, W. L., Process Modeling, Simulation, and Control for Chemical Engineers, Feedforward Control, pp. 431-433. |
| Lerh et al., Feature: IMO 2020 draws more participants into Singapore's bunkering pool., S&P Global Platts, www.spglobal.com, Sep. 3, 2019. |
| Cremer et al., Model Based Assessment of the Novel Use of Sour Water Stripper Vapor for NOx Control in CO Boilers, Industrial Combustion Symposium, American Flame Research Committee 2021, Nov. 19, 2021. |
| Frederick et al., Alternative Technology for Sour Water Stripping, University of Pennsylvania, Penn Libraries, Scholarly Commons, Apr. 20, 2018. |
| Da Vinci Laboratory Solutions B. V., DVLS Liquefied Gas Injector, Sampling and analysis of liquefied gases, https://www.davinci-ls.com/en/products/dvls-products/dvls-liquefied-gas-injector. |
| Wasson ECE Instrumentation, LPG Pressurization Station, https://wasson-ece.com/products/small-devices/lpg-pressurization-station. |
| Mechatest B. V., Gas & Liquefied Gas Sampling Systems, https://www.mechatest.com/products/gas-sampling-system/. |
| La Rivista dei Combustibili, The Fuel Magazine, vol. 66, File 2, 2012. |
| Rodriguez, Elena et al., Coke deposition and product distribution in the co-cracking of waste polyolefin derived streams and vacuum gas oil under FCC unit conditions, Fuel Processing Technology 192 (2019), 130-139. |
| Passamonti, Francisco J. et al., Recycling of waste plastics into fuels, PDPE conversion in FCC, Applied Catalysis B: Environmental 125 (2012), 499-506. |
| De Rezende Pinho, Andrea et al., Fast pyrolysis oil from pinewood chips co-processing with vacuum gas oil in an FCC unit for second generation fuel production, Fuel 188 (2017), 462-473. |
| Seo et al., Methanol absorption characteristics for the removal of H2S (hydrogen sulfide), COS (carbonyl sulfide) and CO2 (carbon dioxide) in a pilot-scale biomass-to-liquid process, Energy 66, pp. 56-62, 2014. |
| Zulkefi et al., Overview of H2S Removal Technologies from Biogas Production, International Journal of Applied Engineering Research ISSN 0973-4562, vol. 11, No. 20, pp. 10060-10066, © Research India Publications, 2016. |
| Niaei et al., Computational Study of Pyrolysis Reactions and Coke Deposition in Industrial Naphtha Cracking, P.M.A. Sloot et al., Eds.: ICCS 2002, LNCS 2329, pp. 723-732, 2002. |
| Hanson et al., An atmospheric crude tower revamp, Digital Refining, Article, Jul. 2005. |
| Lopiccolo, Philip, Coke trap reduces FCC slurry exchanger fouling for Texas refiner, Oil & Gas Journal, Sep. 8, 2003. |
| Martino, Germain, Catalytic Reforming, Petroleum Refining Conversion Processes, vol. 3, Chapter 4, pp. 101-168, 2001. |
| Baukal et al., Natural-Draft Burners, Industrial Burners Handbook, CRC Press 2003. |
| Spekuljak et al., Fluid Distributors for Structured Packing Colums, AICHE, Nov. 1998. |
| Hemler et al., UOP Fluid Catalytic Cracking Process, Handbook of Petroleum Refining Processes, 3rd ed., McGraw Hill, 2004. |
| United States Department of Agriculture, NIR helps Turn Vegetable Oil into High-Quality Biofuel, Agricultural Research Service, Jun. 15, 1999. |
| NPRA, 2006 Cat Cracker Seminar Transcript, National Petrochemical & Refiners Association, Aug. 1-2, 2006. |
| Niccum, Phillip K. et al. KBR, CatCracking.com, More Production—Less Risk!, Twenty Questions: Identify Probably Cuase of High FCC Catalyst Loss, May 3-6, 2011. |
| NPRA, Cat-10-105 Troubleshooting FCC Catalyst Losses, National Petrochemical & Refiners Association, Aug. 24-25, 2010. |
| Fraser, Stuart, Distillation in Refining, Distillation Operation and Applications (2014), pp. 155-190 (Year: 2014). |
| Yasin et al., Quality and chemistry of crude oils, Journal of Petroleum Technology and Alternative Fuels, vol. 4(3), pp. 53-63, Mar. 2013. |
| Penn State, Cut Points, https://www.e-education.psu.edu/fsc432/content/cut-points, 2018. |
| The American Petroleum Institute, Petroleum HPV Testing Group, Heavy Fuel Oils Category Analysis and Hazard Characterization, Dec. 7, 2012. |
| Increase Gasoline Octane and Light Olefin Yeilds with ZSM-5, vol. 5, Issue 5, http://www.refiningonline.com/engelhardkb/crep/TCR4_35.htm. |
| Fluid Catalytic Cracking and Light Olefins Production, Hydrocarbon Publishing Company, 2011, http://www.hydrocarbonpublishing.com/store10/product.php?productid+b21104. |
| Zhang et al., Multifunctional two-stage riser fluid catalytic cracking process, Springer Applied Petrocchemical Research, Sep. 3, 2014. |
| Reid, William, Recent trends in fluid catalytic cracking patents, part V: reactor section, Dilworth IP, Sep. 3, 2014. |
| Akah et al., Maximizing propylene production via FCC technology, SpringerLink, Mar. 22, 2015. |
| Vogt et al., Fluid Catalytic Cracking: Recent Developments on the Grand Old Lady of Zeolite Catalysis, Royal Society of Chemistry, Sep. 18, 2015. |
| Zhou et al., Study on the Integration of Flue Gas Waste He Desulfuization and Dust Removal in Civilian Coalfired Heating Furnance, 2020 IOP Conf. Ser.: Earth Environ. Sci. 603 012018. |
| Vivek et al., Assessment of crude oil blends, refiner's assessment of the compatibility of opportunity crudes in blends aims to avoid the processing problems introduced by lower-quality feedstocks, www.digitalrefining.com/article/10000381, 2011. |
| International Standard, ISO 8217, Petroleum products—Fuels (class F)—Specifications of marine fuels, Sixth Edition, 2017. |
| International Standard, ISO 10307-1, Petroleum products—Total sediment in residual fuel oils—, Part 1: Determination by hot filtration, Second Edition, 2009. |
| International Standard, ISO 10307-2, Petroleum products—Total sediment in residual fuel oils—, Part 2: Determination using standard procedures for aging, Second Edition, 2009. |
| Ebner et al., Deactivatin and durability of the catalyst for Hotspot™ natural gas processing, OSTI, 2000, https://www.osti/gov/etdeweb/servlets/purl/20064378, (Year: 2000). |
| Morozov et al., Best Practices When Operating a Unit for Removing Hydrogen Sulfide from Residual Fuel Oil, Chemistry and Technology of Fuels and Oils, vol. 57, No. 4, Sep. 2001. |
| Calbry-Muzyka et al., Deep removal of sulfur and trace organic compounds from biogas to protect a catalytic methananation reactor, Chemical Engineering Joural 360, pp. 577-590, 2019. |
| Cheah et al., Review of Mid- to High-Tempearture Sulfur Sorbents for Desulfurization of Biomass- and Coal-derived Syngas, Energy Fuels 2009, 23, pp. 5291-5307, Oct. 16, 2019. |
| Mandal et al., Simultaneous absorption of carbon dioxide of hydrogen sulfide into aqueous blends of 2-amino-2-methyl-1 propanol and diethanolamine, Chemical Engineering Science 60, pp. 6438-6451, 2005. |
| Meng et al., In bed and downstream hot gas desulphurization during solid fuel gasification: A review, Fuel Processing Technology 91, pp. 964-981, 2010. |
| Okonkwo et al., Role of Amine Structure on Hydrogen Sulfide Capture from Dilute Gas Streams Using Solid Adsorbents, Energy Fuels, 32, pp. 6926-6933, 2018. |
| Okonkwo et al., Selective removal of hydrogen sulfide from simulated biogas streams using sterically hindered amine adsorbents, Chemical Engineering Journal 379, pp. 122-349, 2020. |
| Lloyd's Register, Using technology to trace the carbon intensity of sustainable marine fuels, Feb. 15, 2023. |
| Cooper et al., Calibration transfer of near-IR partial least squares property models of fuels using virtual standards, Wiley Online Library, Jul. 19, 2011. |
| ABB Measurement & Analytics, Using FT-NIR as a Multi-Stream Method for CDU Optimization, Nov. 8, 2018. |
| Modcon Systems LTD., On-Line NIR Analysis of Crude Distillation Unit, Jun. 2008. |
| ABB Measurement & Analytics, Crude distillation unit (CDU) optimization, 2017. |
| Guided Wave Inc., The Role of NIR Process Analyzers in Refineries to Process Crude Oil into Useable Petrochemical Products, 2021. |
| “Optimizing Refinery Catalytic Reforming Units with the Use of Simple Robust On-Line Analyzer Technology”, ABB Measurement & Analytics—Analytical Measurement Products, Nov. 27, 2017, found at: https://www.azom.com/article.aspx?ArticleID=14840. |
| Bueno, Alexis et al., “Characterization of Catalytic Reforming Streams by NIR Spectroscopy”, Energy & Fuels 2009, 23, 3172-3177, Apr. 29, 2009. |
| Caricato, Enrico et al., “Catalytic Naphtha Reforming—a Novel Control System for the Bench-Scale Evaluation of Commercial Continuous Catalytic Regeneration Catalysts”, Industrial & Engineering Chemistry Research, ACS Publications, May 18, 2017. |
| Alves, J.C.L. et al., “Dielsel Oil Quality Parameter Determinations Using Support Vector Regression and Near Infrared Spectroscopy for Hydrotreating Feedstock Monitoring”, Journal of Near Infrared Spectroscopy, 20, 419-425 (2012), Jul. 23, 2012. |
| Bollas et al., “Modeling Small-Diameter FCC Riser Reactors. A Hydrodynamic and Kinetic Approach”, Industrial and Engineering Chemistry Research, 41(22), 5410-5419, 2002. |
| Voutetakis et al., “Computer Application and Software Development for the Automation of a Fluid Catalytic Cracking Pilot Plant—Experimental Results”, Computers & Chemical Engineering, vol. 20 Suppl., S1601-S1606, 1996. |
| “Development of Model Equations for Predicting Gasoline Blending Properties”, Odula et al., American Journal of Chemical Engineering, vol. 3, No. 2-1, 2015, pp. 9-17. |
| Number | Date | Country | |
|---|---|---|---|
| 20230118319 A1 | Apr 2023 | US |
| Number | Date | Country | |
|---|---|---|---|
| 63268875 | Mar 2022 | US | |
| 63268827 | Mar 2022 | US | |
| 63268456 | Feb 2022 | US | |
| 63153452 | Feb 2021 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17652431 | Feb 2022 | US |
| Child | 18052773 | US |
