Patent Grant
10739798
The present disclosure is related to a method and system for managing the operation of a plant, such as a chemical plant or a petrochemical plant or a refinery, and more particularly to a method for improving on-stream reliability by preventing and mitigating temperature related emergency shutdowns of an operating plant. Typical plants may be those that provide hydrocarbon cracking, hydrotreating, isomerization, exothermic process plants, and/or other process units where potentially exothermic reactions take place. Other units may include units with depressure systems.
Industrial process control and automation systems are often used for large and complex industrial processes. Industrial processes are typically implemented using large numbers of devices, such as pumps, valves, compressors, or other industrial equipment used to implement various aspects of the industrial processes. With these large numbers of devices, improving detection and prevention of equipment or process malfunctions can increase efficiency and safety of an operation of a plant or refinery.
The following summary presents a simplified description of certain features. The summary is not an extensive overview and is not intended to identify key or critical elements.
One or more embodiments may include a system for preventing temperature excursion in a reactor. The system may include a reactor. The system may include a heater. The system may include a catalyst bed. The system may include a sensor configured to measure data associated with the catalyst bed. The system may include an analysis platform. The analysis platform may include one or more processors and memory. The memory may store executable instructions that, when executed, cause the analysis platform to: receive the data associated with the catalyst bed; analyze the data associated with the catalyst bed; determine, based on analyzing the data associated with the catalyst bed, whether a temperature of the reactor is above a threshold; and based on determining that the temperature of the reactor is above the threshold, send a message to a control system associated with the reactor, the message configured to cause an action to reduce the temperature of the reactor.
One or more embodiments may include non-transitory computer-readable media storing executable instructions that, when executed by one or more processors, cause a system including a reactor, a heater, a catalyst bed, and a sensor, to receive, from the sensor, data measured by the sensor and associated with the catalyst bed; analyze the data associated with the catalyst bed; determine, based on analyzing the data associated with the catalyst bed, whether a temperature of the reactor is above a threshold; and based on determining that the temperature of the reactor is above the threshold, send a message to a control system associated with the reactor, the message configured to cause an action to reduce the temperature of the reactor.
One or more embodiments may include a method including receiving, by a computing device and from a sensor configured to measure data associated with a catalyst bed associated with a reactor, data measured by the sensor and associated with the catalyst bed; analyzing, by the computing device, the data associated with the catalyst bed; determining, by the computing device, based on analyzing the data associated with the catalyst bed, whether a temperature of the reactor is above a threshold; and based on determining that the temperature of the reactor is above the threshold, sending, by the computing device, a message to a control system associated with the reactor, the message configured to cause an action to reduce the temperature of the reactor.
Other technical features may be readily apparent to one skilled in the art from the following figures, descriptions, and claims.
The present disclosure is illustrated by way of example and not limited in the accompanying figures in which like reference numerals indicate similar elements and in which:
In the following description of various illustrative embodiments, reference is made to the accompanying drawings, which form a part hereof, and in which is shown, by way of illustration, various embodiments in which aspects of the disclosure may be practiced. It is to be understood that other embodiments may be utilized, and structural and functional modifications may be made, without departing from the scope of the present disclosure.
It is noted that various connections between elements are discussed in the following description. It is noted that these connections are general and, unless specified otherwise, may be direct or indirect, wired or wireless, and that the specification is not intended to be limiting in this respect.
References herein to a “plant” or “system” are to be understood to refer to any of various types of chemical and petrochemical manufacturing or refining facilities. References herein to a plant “operators” are to be understood to refer to and/or include, without limitation, plant planners, managers, engineers, technicians, technical advisors, specialists (e.g., in instrumentation, pipe fitting, and welding), shift personnel, and others interested in, starting up, overseeing, monitoring operations of, and shutting down, the plant.
Refining processes produce desirable products, such as Lube oil base stock, Kerosene fuel, diesel fuel and other products known as middle distillates, as well as lower boiling hydrocarbon liquids, such as LPG, naphtha and gasoline. For example, heavy petroleum fractions may be catalytically hydrocracked into lighter, more valuable products.
In one example of a catalytic exothermic refining process where the present disclosure can be applied, feedstocks most often subjected to hydrocracking are the gas oils and heavy atmospheric and vacuum gas oils recovered from crude oil by distillation and catalytically or thermally cracked gas oils. These feedstocks are converted to lower molecular weight products. Simultaneously with the hydrocracking, sulfur, nitrogen, and oxygen compounds are almost completely removed and olefins are saturated, thereby giving products which are a mixture of essentially pure paraffins, naphthenes, and aromatics. The hydrocracking reactions proceed to a desired conversion as the feed is processed over several fixed beds of catalyst at elevated hydrogen pressure and temperature. Reactive feeds may include Light Cycle Oil (LCO) and Coker Gas Oil (CGO)—heavy (HCGO) or light (LCGO). Less reactive feeds may include straight run Vacuum Gas Oil (VGO).
The process makes use of two groups of reactions, hydrotreating and hydrocracking, to make clean, saturated, high value products. The hydrotreating reactions remove contaminants from the feed and product streams, while the hydrocracking reactions create usable lighter molecular weight products. The primary hydrotreating reactions are sulfur and nitrogen removal as well as olefin saturation. The products of these reactions are the corresponding contaminant-free hydrocarbon, along with H2S and NH3. Other treating reactions include oxygen, metals and halide removal, and aromatic saturation. The reactions are typically carried out at elevated pressures and temperatures in a hydrogen atmosphere.
There are many hydrotreating/hydrocracking systems including single stage and two stage systems.
Fresh feed (e.g., vacuum gas oil) 102 enters the system via feed surge drum 104. From the bottom of the surge drum the feed flows to the suction of the reactor charge pump 106. Hydrogen 114 may be added to the fresh feed stream before entering the heater 108. (Alternatively, the fresh feed may be combined with the hydrogen before the combined feed exchangers.)
The combined feed and hydrogen stream exchanges heat with the reactor effluent and is further heated in a combined feed heater 108. (Alternatively, feed and recycle gas are heated separately by exchange with reactor effluent, for example. The recycle gas may be further heated in a recycle gas heater and then joins with the feed at the reactor inlet.)
After heating, the feedstock enters a two-stage reactor system with catalytic reactor 110 and catalytic reactor 112. Reactors 110 and 112 each may be divided into individual catalyst beds supported on a beam and grid support system. The support system may be separated from the next bed of catalyst by a quench gas distributor, a reactant mixing chamber and a vapor/liquid distribution tray. The reactants flow downward through the catalyst beds.
After exiting the reactor 112, the product stream is separated from the reaction byproducts and excess recycle gas. A typical high conversion recycle operation unit incorporates a hot separator 116.
The hot separator vapor is normally cooled by exchange with the recycle gas stream. It may also be cooled by exchange with the liquid feed stream. The vapor is then further cooled in an air cooler 140 before entering the cold separator 118. The liquid hydrocarbon and water are allowed to settle in the separator. The water is sent to sour water treating facilities. The hydrocarbon liquid leaves the cold separator 118 and flows into the cold flash drum 122 where the liquid is depressured. As it flows across a level control valve, dissolved hydrogen, H2S, and light hydrocarbons are flashed off. Vapor is removed from the cold flash drum on pressure control. The hot separator liquid is routed to a hot flash drum 120 where dissolved hydrogen, H2S, and light hydrocarbon are flashed off. The flashed vapor from the hot flash drum is cooled via cooler 142 and then joins with the hydrocarbon liquid from the cold separator before entering the cold flash drum. Both hot and cold flash drum hydrocarbon liquids flow separately into the fractionation section.
The feed to the fractionation section will contain several species from hydrogen and hydrogen sulfide through the heaviest components, and this stream will be separated into the desired products such as light gases, LPG, gasoline, kerosene, diesel oil, and unconverted oil from the fractionator column bottoms.
After separation of the gas and liquid phases in the cold separator, the gas leaves from the top of the cold separator 118 and flows to the suction of the recycle gas compressor 126. In some cases the recycle gas will be first sent to an amine scrubber 124 to remove H2S. The recycle gas compressor 126 may be reciprocating or centrifugal.
After the recycle compressor discharge, some recycle gas will be split off the main stream for use as quench gas between catalyst beds of reactors 110 and 112. Separate quench gas streams are used to reduce reactor interbed temperatures before each catalyst bed. Quench flow is regulated by reactor bed inlet temperature, either by direct temperature control or by cascading through quench gas flow controller.
The bulk of the recycle gas is normally joined by the makeup gas 128. In some cases, the makeup gas joins the recycle gas before the recycle gas compressor. From this point, until it returns to the cold separator, the gas flows along with the liquid through the reactor circuit in the same manner previously described.
The makeup gas 128 for the unit is a hydrogen rich gas normally coming from a hydrogen plant. The makeup gas compressors will then compress the gas from supply pressure up to the reactor circuit pressure. From the discharge of the last stage of compression, the makeup gas typically joins the recycle gas at the discharge of the recycle gas compressor and flows to the reactors as described above. As hydrogen is consumed in the reactors, the pressure in the cold separator will start to decrease. This will in turn call for more makeup gas.
In each of the reactions described above, hydrogen is consumed and heat is released. All the major reactions are exothermic and result in a temperature rise across the reactors. The saturation of olefins generates the greatest amount of heat. Pressures generally range from 105-190 kg/cm2g (1500-2700 psig), and temperatures from 290-455° C. (550-850° F.).
Problems Encountered
Hydrocracking, hydrotreating, isomerization, or a number of different exothermic systems may be subject to reactor temperature excursions. As the reactants flow downward through the catalyst beds, chemical reactions occur. All the major chemical reactions which take place are exothermic; hence the temperature increases as the feed and recycle gas proceed through the catalyst beds. It is important that the temperature increase (delta T) be controlled carefully at all times. It is possible to generate more heat from the reactions than the flowing streams can remove from the reactors. If this happens, the temperature may increase very rapidly causing a temperature excursion or a temperature runaway. A temperature runaway is a very serious situation since extremely high temperatures can be generated within a short period of time. These high temperatures can cause damage to the catalyst, coking, and/or structural damage to the reactors or other equipment (e.g., reactor internals, reactor supports, piping, effluent exchangers), which result in expensive shutdowns and repairs of the reactor, the hydrocracking unit, or even other refinery units. In other instances, high temperatures may result in loss of containment (e.g., explosion, fire), environmental release of chemicals (e.g., hydrocarbons, sulfur compounds), and/or injury or death. It is important, therefore, to know if temperatures are rising above designed temperatures.
One cause of temperature excursion is uneven flow distribution. The gas and liquid reactants must be evenly (homogenously) distributed across the cross-sectional area of the reactor as they enter the catalyst bed and should flow down through the catalyst bed contacting all catalyst completely. If the flow distribution is not homogenous, less catalyst is available to promote reaction and higher temperatures are required to reach the desired conversion. This can lead to shortened catalyst life, catalyst “hot spots”, catalyst temperatures which are in excess of the design temperature limit of the reactor vessel, damage to the vessel wall, and unstable temperature control. In some extreme cases, a local temperature runaway can develop which can go well over 540° C. (1000° F.). High localized temperatures can lead to increased coking and fusion of the catalyst.
Other causes of temperature excursions are feed composition changes causing increased cracking and heat release in catalyst beds, or changes in feed rate or recycle gas rate. To protect the reactors from damage due to extremely high temperatures, the unit should be depressured at the recommended high rate.
Depressuring the unit at the high rate is undesirable as it can cause extended downtime of the unit as well as associated refinery units, a loss in production, potential damage to the reactors, reactor internals, catalyst beds and the reactor effluent air coolers and environmental flaring of hydrocarbons and sulfur compounds.
A shutdown or a depressuring event typically results in approximately five days of loss of production, which can result in significant revenue losses.
Enhanced Control
Aspects of the disclosure provide an enhanced control system that provides early warnings of impending undesirable events, direct or indirect manipulation of certain process variables to reduce undesirable outcomes, and/or direct or indirect manipulation of certain process variables that may place the unit in a “safe park” state to avoid the high temperature trip, depressurization, and/or associated operating risks and losses.
An automated system may take control actions to restore normal operations or bring the process to a safe operating condition from which it can be easily restarted. The control actions may be triggered based on the detection of predefined patterns on key variables.
The control actions (e.g., increase quench to reduce temperature in the reactor beds) may be focused on reducing catalyst bed temperatures to prevent the runaway reaction from occurring. For example, the control actions may be focused on the bed where temperature elevation is detected and the beds immediately above and below as needed to direct available quench gas to where it is needed the most.
An automated system may eliminate operator hesitation and inconsistencies on executing recommended emergency procedures. The implementation of this system may allow for identifying and programmatically taking actions to mitigate the progression of a temperature excursion, thus preventing the emergency shutdown logic from triggering, which in turns avoids costly downtime and production losses. This system may be implemented on catalytic conversion units where exothermic reactions take place, for example, in potentially exothermic process units, such as hydrocracking units.
An illustrative diagram of an example system 900 for implementing early warnings of undesirable events is depicted in
The system 900 may be reliable, safe, and robust. The system 900 may include a fault-tolerant system with one or more processors, self diagnostics, redundant power supplies, and/or one or more certifications (e.g., TÜV AK 6 Certification).
Distributed control system 904 may be a local or remote control system that receives sensor data from one or more pieces of equipment in the process plant 902, and transmits control information to control operation of the one or more pieces of equipment in the process plant 902. Distributed control system 904 may be similar to, integrate, or be integrated in, for example, in a control platform (e.g., control platform 506, described herein). Distributed control system 904 may be a hydroprocessing unit distributed control system.
Advanced process control 906 may be associated with one or more closed loop optimization processes or services.
Process historian 908 may be associated with one or more open loop optimization processes or services 910 (e.g., connected performance services, process reliability advisor, process optimization advisor). The one or more open loop optimization processes or services 910 (e.g., connected performance services) may provide long term reliability and/or optimization (e.g., recommend changes) of one or more pieces of equipment in process plant 902.
Temperature excursion mitigation system 912 may be focused on closed loop risk mitigation. For example, temperature excursion mitigation system 912 may send electronic communication that changes or resets one or more process settings to mitigate temperature excursion (e.g., electronic communication that changes a digital controller set point). Temperature excursion mitigation system 912 may be similar to, integrate, or be integrated in, for example, in a data analysis platform (e.g., data analysis platform 504, described herein). Temperature excursion mitigation system 912 may be implemented on a Programmable Logic Controller (PLC) platform, or other suitable control solver platform, that communicates with distributed control system 904. Temperature excursion mitigation system 912 may implement at least one algorithm that monitors process variables measurements on a regular basis to detect the conditions that can potentially lead to a temperature excursion. Upon detection and verification of such condition(s), control actions may be sent to the distributed control system 904 in the form of single shot or repetitive commands via a communication protocol, and/or user notification alerts and/or alarms may be issued (e.g., via the distributed control system 904).
Emergency shutdown system 914 may perform or trigger emergency shutdown of process plant 902 or of one or more pieces of equipment in process plant 902.
Operator console human-machine interface 916 may include one or more devices (e.g., computer, terminal, tablet, laptop, smartphone) that include one or more graphical user interfaces (e.g., dashboard) for interacting with a human operator. The one or more graphical user interfaces may provide information on operations of process plant 902, such as operation information, warnings (e.g., warnings of potential temperature excursions, warnings of imminent temperature excursions, warnings of ongoing temperature excursions), alerts, or the like. The one or more graphical user interfaces may receive user input and cause one or more changes (e.g., via distributed control system 904) to the operation of process plant 902 or one or more pieces of equipment in process plant 902 (e.g., to stop or mitigate a temperature excursion). The temperature excursion mitigation system 912 may include a dedicated human-machine interface (e.g., computer, terminal, control panel, tablet, laptop, smartphone), that includes one or more graphical user interfaces for interacting with a human operator to change system configuration parameters and/or monitor the system performance.
In one or more embodiments, a multi-stage (e.g., two, three, four stages) approach may be utilized to mitigate a potential temperature excursion. One example of a multi-staged approach is depicted in
A stage may include use of a stability gauge 1002 (e.g., similar to stability gauge 800 depicted in
In one or more embodiments, one or more devices (e.g., temperature excursion mitigation system 912) may include an algorithm to determine unit stability and/or areas of concern. In one or more embodiments, the algorithm may determine unit stability and/or areas of concern using one or more process variables, such as, for example, reactor temperatures (e.g., radial temperature, axial temperature, rate of change, hot spots), LHSV (e.g., feed rate), feed stock type (e.g., reactive feeds), quench gas rates, spare quench capacity, makeup gas consumption, unit pressure, and/or the like. In one or more embodiments, the algorithm may apply a weighting to one or more of the process variables when determining unit stability and/or areas of concern. One or more of the process variables and/or a result of the algorithm may be displayed as a stability gauge (e.g., stability gauge 800).
Thus, one or more aspects of the present disclosure may include a stability gauge, based on a weighted algorithm, to alert when the unit is moving away from a stable state and is becoming more susceptible to a temperature runaway.
A stage may include an excursion mitigation level 1 stage (e.g., excursion mitigation level 1 stage 1004) to re-establish control. Excursion mitigation level 1 stage may have objectives, for example, such as the system recognizing a risk of an excursion, automating an action in response (e.g., to eliminate hesitation), and/or taking limited action to attempt to intervene and reestablish control. One or more secondary goals may include maintaining production.
In one or more embodiments, an automated action may include, for example, removing reactive feed stocks. Another automated action may include maintaining a constant feed rate. Another automated action may include reducing charge heater outlet temperature. For example, an automated action may include ramping down charge heater outlet temperature controller setpoint to decrease temperature by an amount (e.g., 5° F., 10° F., 15° F., 20° F., or the like) at a rate (e.g., 0.1° F., 0.2° F., 0.3° F., 0.4° F., 0.5° F., 0.6° F., 0.7° F., or the like per second) in one shot or multiple repetitions.
Another automated action may include reducing temperature in reactor bed N with elevated temperatures. For example, an automated action may include ramping down reactor cracking bed N inlet temperature controller setpoint to decrease temperature by an amount (e.g., 5° F., 10° F., 15° F., 20° F., or the like) at a rate (e.g., 0.1° F., 0.2° F., 0.3° F., 0.4° F., 0.5° F., 0.6° F., 0.7° F., or the like per second) in one shot or multiple repetitions.
Another automated action may include reducing the temperature in other reactor beds. For example, an automated action may include ramping down reactor cracking bed N+1 (e.g., the reactor bed immediately below) inlet temperature controller setpoint to decrease temperature by an amount (e.g., 5° F., 10° F., 15° F., 20° F., or the like) at a rate (e.g., 0.1° F., 0.2° F., 0.3° F., 0.4° F., 0.5° F., 0.6° F., 0.7° F., or the like per second) in one shot or multiple repetitions.
An automated action may include activating one or more alerts or alarms (e.g., alerting that temperature excursion control scheme Level 1 is activated). An automated action may include shedding a higher-level control scheme. An automated action may include resetting the setting of one or more of the more reactive feed flow controllers (e.g., a reactive feed flow controller, such as Light Cycle Oil (LCO) flow controller, Coker Gas Oil (CGO) flow controller) to reduce flow by a predefined percentage. An automated action may include compensating for feed reduction (e.g., LCO/CGO feed reduction) with less reactive feed, such as Straight Run Vacuum Gas Oil (VGO) and/or VGO from Storage to maintain level in the feed surge drum.
When all timers expire, temperature ramps have finished, and all steps are completed, an indicator and/or alert may indicate that the excursion mitigation level 1 stage is complete.
A stage may include an excursion mitigation level 2 stage (e.g., excursion mitigation level 2 stage 1006) to provide a safe park for a unit. A safe park may significantly improve startup/shutdown operations, especially after an excursion. Excursion mitigation level 2 stage may have objectives such as, for example, recognizing that an excursion is imminent, automating an action in response (e.g., to eliminate hesitation), placing the unit in a safe state, leaving the unit in a condition where restart can be fast and easy, and/or avoiding depressuring of the unit.
In one or more embodiments, an automated action may include, for example, activating one or more alerts or alarms (e.g., alerting that temperature excursion control scheme phase 2 is activated). An automated action may include ramping charge heater outlet temperature down to a predefined temperature (e.g., to 300° F., 400° F., 500° F., 600° F., or the like). An automated action may include checking operation at and/or near minimum burner pressure.
An automated action may include bypassing a combined feed exchanger, which may be done while avoiding a high temperature shutdown on a reactor effluent air cooler.
An automated action may include ramping down combined feed heat exchangers outlet temperature controller setpoint to decrease temperature at a rate (e.g., 100° F., 200° F., 300° F., 400° F., 500° F., 600° F., or the like per hour) until reaching a threshold temperature (e.g., 300° F., 400° F., 500° F., 600° F., or the like). If the temperature reaches a reactor effluent air cooler inlet pretrip point, an alert and/or alarm may be triggered and/or sent. Alternatively or additionally, the ramp down of combined feed from combined feed exchangers outlet temperature controller setpoint may be paused and/or the bypass valves may be directly manipulated by the excursion mitigation system through an internal control algorithm to prevent the temperature from reaching the trip point. High reactor effluent air coolers 140 and/or 142 inlet temperatures alarm may be generated while the ramp down is paused. After the alarm is reset, ramp may resume. For example, the ramp may resume and alarm be reset by reactor effluent air cooler inlet pretrip reset.
Another automated action may include ramping all reactor temperatures (e.g., in a particular unit or in an entire plant) down to a particular temperature (e.g., to 300° F., 400° F., 500° F., 600° F., or the like). For example, an automated action may include ramping down all the reactor beds inlet temperature controllers setpoints to decrease the temperature at a rate e.g., 100° F., 200° F., 300° F., 400° F., 500° F., 600° F., or the like per hour) until reaching a threshold temperature (e.g., 300° F., 400° F., 500° F., 600° F., or the like). In one or more embodiments, if the recycle gas flow goes below a pretrip point (which may be set above the recycle gas low flow shutdown trip point), then the ramp down of all reactors beds inlet temperature controllers may be paused. An alarm (e.g., low recycle gas flow alarm) may be generated while the ramp down is paused. The ramp may resume and the alarm be reset by recycle gas flow pretrip reset.
Another automated action may include reducing a pressure of the unit by a particular percentage (e.g., 5%, 6%, 7%, 8%, 9%, 10%, or the like) of operating pressure.
Another automated action may include reducing temperature in one or more other reactor catalyst beds. For example, an automated action may include ramping down reactor cracking bed N and N+1 inlet temperature controller setpoint to decrease temperature first by an amount (e.g., 30° F., 40° F., 50° F., 60° F., or the like) at a rate (e.g., 0.1° F., 0.2° F., 0.3° F., 0.4° F., 0.5° F., 0.6° F., 0.7° F., or the like per second), and the continue to decrease the temperature until reaching a temperature (e.g., 300° F., 400° F., 500° F., 600° F., or the like) at a rate (e.g., 100° F., 200° F., 300° F., 400° F., 500° F., 600° F., or the like per hour).
Another automated action may include ramping down a reactor's all other catalyst beds inlet temperature controller setpoint to decrease temperature until reaching a temperature (e.g., 300° F., 400° F., 500° F., 600° F., or the like) at a rate (e.g., 100° F., 200° F., 300° F., 400° F., 500° F., 600° F., or the like per hour).
When all timers expire, temperature ramps have finished, conditions have normalized (e.g., no longer in danger of a runaway), and all steps are completed, an indicator and/or alert may indicate that the excursion mitigation level 2 stage is complete.
Another automated action may include reducing the temperature of other reactor beds. For example, an automated action may include ramping down reactor cracking bed N−1 inlet temperature controller setpoint to decrease temperature by an amount (e.g., 5° F., 10° F., 15° F., 20° F., or the like) at a rate (e.g., 0.1° F., 0.2° F., 0.3° F., 0.4° F., 0.5° F., 0.6° F., 0.7° F., or the like per second).
A final stage may include an emergency shutdown stage (e.g., emergency shutdown stage 1008). A system may include an emergency interlock system (e.g., emergency shutdown system 914). The emergency shutdown stage may allow for an automated or manual shutdown of a unit, plant, or process to avoid or mitigate damage or a disaster. The emergency shutdown stage may be simple, easy to maintain, and/or avoid spurious shutdowns. The emergency shutdown stage may include depressuring to flare.
In conjunction with or in addition to the multi-stage approach described herein, some systems may use reactor models to adjust process control signals in a more predictive vs. reactive way, or to control ramping rates (e.g., for startup). In one or more embodiments, the present system may analyze operating data and apply experience-based control algorithms focused on excursion prevention and mitigation. Aspects of the present system may reduce unplanned shutdowns, eliminate associated flaring, eliminate associated equipment damage, eliminate associated production losses, and/or shorten time needed to regain full production.
The proper operation of the reactor unit may depend on the careful selection and control of the processing conditions. There are many process variables that may affect catalytic conversion process units performance including operating severity, product yields and quality, and catalyst life. By careful monitoring and control of these process variables, the unit can operate to its full potential. Monitoring also helps to collect data that can be correlated and used to predict behavior or problems in systems used in the same plant or in other plants and/or processes.
The amount of conversion which takes place in the reactors may be determined by several variables; the type of feedstock, the amount of time the feed is in the presence of catalyst, the partial pressure of hydrogen in the catalyst bed, and, the temperature of the catalyst and reactants. Generally, the higher the temperature, the faster the rate of reaction and therefore, the higher the conversion.
In one or more embodiments, a system may include a heater minimum firing mode. This feature may avoid burner flameout, which could lead to hazardous conditions in the heater fire box.
In one or more embodiments, a system may include an auto quench. An auto quench may be programmed to quench the beds requiring additional quench while staying within the constraints set to keep the required flow to the other beds.
In one or more embodiments, a system may include bed temperature control (e.g., bed outlet control, WABT control). For example, a refiner may set a temperature controller's setpoint based on the highest temperature in the bed.
In one or more embodiments, a system may include conversion control. This may be used (e.g., for two-stage units) to determine how to set conversion per pass. Balancing the conversion between the stages may improve operations.
Sensor Data Collection and Processing
The system may include one or more computing devices or platforms for collecting, storing, processing, and analyzing data from one or more sensors.
Although the computing system environment 500 of
In yet another example, the data collection platform 502 and data analysis platform 504 may reside on a single server computer and be depicted as a single, combined logical box on a system diagram. Moreover, a data store may be illustrated in
Referring to
Other sensors may transmit signals to a processor or a hub that collects the data and sends to a processor. For example, temperature and pressure measurements may be sent to a hub (e.g., data collection platform 502). In one example, temperature sensors may include thermocouples, fiber optic temperature measurement, thermal cameras, and/or infrared cameras. Skin thermocouples may be applied to supports, walls, or other locations inside of or near a catalytic reactor unit. Alternatively, thermal (infrared) cameras may be used to detect temperature (e.g., hot spots) in all aspects of the equipment. A shielded (insulated) tube skin thermocouple assembly may be used to obtain accurate measurements. For example, a magnetic skin thermocouple may allow for installation without welding onto the reactor. Alternatively or additionally, clips and/or pads may be utilized for ease of replacement. As another example, Daily Thermetrics CatTracker or Gayesco Flex-R multipoint thermocouples may be used for special catalyst bed temperature measurement.
Sensors may be also used throughout a plant to detect and monitor various issues such as maldistribution, thermal stresses, and temperature excursion.
Furthermore, flow sensors 531 may be used in flow paths such as the inlet to the path, outlet from the path, or within the path. If multiple feed pipes are utilized, the flow sensors 531 may be placed in corresponding positions in each of the pipes. Flow may be determined by pressure-drop across a known resistance, such as by using pressure taps. Other types of flow sensors 531 include, but are not limited to, ultrasonic sensors 525, turbine meter, hot wire anemometer, vane meter, Kármán™, vortex sensor, membrane sensor (membrane has a thin film temperature sensor printed on the upstream side, and one on the downstream side), tracer, radiographic imaging (e.g., identify two-phase vs. single-phase region of channels), an orifice plate in front of or integral to each tube or channel, pitot tube, thermal conductivity flow meter, anemometer, internal pressure flow profile, and/or measure cross tracer (measuring when the flow crosses one plate and when the flow crosses another plate).
Sensor data, process measurements, and/or calculations made using the sensor data or process measurements may be used to monitor and/or improve the performance and reliability of the process unit or the performance of the equipment and parts making up the equipment, as discussed in further detail below. For example, sensor data may be used to detect that a desirable or an undesirable chemical reaction (e.g., a temperature excursion) is taking place within a particular piece of equipment, and one or more actions may be taken to encourage or inhibit the chemical reaction (e.g., to slow, stop, or mitigate the temperature excursion). Chemical sensors may be used to detect the presence of one or more chemicals or components in the streams, such as corrosive species, oxygen, hydrogen, sulfur, and/or water (moisture). Chemical sensors may utilize gas chromatographs, liquid chromatographs, distillation measurements, density measurements, and/or octane measurements.
Monitoring the equipment and processes includes collecting data that can be correlated and used to predict behavior or problems in other plants and/or processes. Data collected from the various sensors (e.g., measurements such as temperature, pressure, flow, pressure drop, thermal performance, vessel skin temperature) may be correlated with external data, such as environmental or weather data. Process changes or operating conditions may be able to be altered to preserve the equipment or the catalyst until the next scheduled maintenance period. At a high level, sensor data collected (e.g., by the data collection platform 502) and data analysis (e.g., by the data analysis platform 504) may be used together, for example, for process simulation, equipment simulation, and/or other tasks. For example, sensor data may be used for process simulation and reconciliation of sensor data. The resulting, improved process simulation may provide a stream of physical properties that are used to calculate heat flow, etc. These calculations may lead to thermal and/or pressure-drop performance prediction calculations for specific equipment or the process, and comparisons of equipment or process predictions to observations from the operating data (e.g., predicted/expected outlet temperature and pressure vs. measured outlet temperature and pressure). This may be used for identification of conditions leading to temperature excursion, and/or other issues that eventually lead to a potential control changes and/or recommendation etc.
Corrective action may be taken based on determining this process and/or equipment information. One or more inputs or controls relating to a process may be adjusted as part of the corrective action. These and other details about the equipment, sensors, processing of sensor data, and actions taken based on sensor data are described in further detail below.
In addition, computing system environment 500 may include transmitters and deviation alarms. These may be programmed to set off an alarm, which may be audible and/or visual. In one or more embodiments, an alert may be transmitted to one or more devices (e.g., remote device 518, 520, client portal 508, dashboard 510, 512).
Systems Facilitating Sensor Data Collection
Sensor data may be collected by a data collection platform 502. The sensors may interface with the data collection platform 502 via wired or wireless transmissions. Sensor data (e.g., temperature data) may be collected continuously or at periodic intervals (e.g., every second, every five seconds, every ten seconds, every minute, every five minutes, every ten minutes, every hour, every two hours, or another interval). Data may be collected at different locations at different intervals. For example, data at a known hot spot may be collected at a first interval, and data at a spot that is not a known hot spot may be collected at a second interval. In another example, data for a catalyst bed with a history of temperature excursions may be collected at a different rate than data for a catalyst bed without history of temperature excursions. The data collection platform 502 may continuously or periodically (e.g., every second, every minute, every hour, every day, once a week, once a month, etc.) transmit collected sensor data to a data analysis platform 504, which may be nearby (e.g. local to) or remote from the data collection platform 502.
The computing system environment 500 of
In addition, the platform and/or devices in
Furthermore, the platform and/or devices in
In some examples, one or more sensor devices in
Referring to
Data collection platform 502 may include or be in communication with one or more data historians. The data historian may be implemented as one or more software modules, one or more virtual machines, or one or more hardware elements (e.g., servers). The data historian may collect data at regular intervals (e.g., every minute, every two minutes, every ten minutes, every thirty minutes). The data historian may include or be in communication with an instance of remote data collection hardware and/or software, such as, for example, Honeywell Uniformance Scout Express. The remote data collection may be implemented as one or more software modules, one or more virtual machines, or one or more hardware elements (e.g., servers). In one or more embodiments, the Uniformance Scout Express may work with or in place of the data collection module and/or the data historian to handle one or more aspects of data replication.
In addition, the data collection module 566 may assist the processor 560 in the data collection platform 502 in communicating with, via the communications interface 568, and processing data received from other sources, such as data feeds from third-party servers and manual entry at the field site from a dashboard graphical user interface.
Referring to
The data analysis platform 504 may include a data service. In some embodiments, the data service may comprise computer-executable instructions that, when executed by the processor, cause the data analysis platform to perform one or more of the steps disclosed herein. In other embodiments, the data service may be a virtual machine. In some embodiments, the data service may be a hybrid of software-based and/or hardware-based instructions to perform one or more of the steps disclosed herein.
Also, the data analysis platform 504 may include a data historian. In some embodiments, the data historian may comprise computer-executable instructions that, when executed by the processor, cause the data analysis platform to perform one or more of the steps disclosed herein. In other embodiments, the data historian may be a virtual machine. In some embodiments, the data historian may be a hybrid of software-based and/or hardware-based instructions to perform one or more of the steps disclosed herein. The data historian may collect data at regular intervals (e.g., every minute, every two minutes, every ten minutes, every thirty minutes).
Additionally, the data analysis platform 504 may include a data lake. In some embodiments, the data lake may comprise computer-executable instructions that, when executed by the processor, cause the data analysis platform to perform one or more of the steps disclosed herein. In other embodiments, the data lake may be a virtual machine. In some embodiments, the data lake may be a hybrid of software-based and/or hardware-based instructions to perform one or more of the steps disclosed herein. The data lake may perform relational data storage. The data lake may provide data in a format that may be useful for processing data and/or performing data analytics.
Moreover, the data analysis platform 504 may include a calculations service. In some embodiments, the calculations service may comprise computer-executable instructions that, when executed by the processor, cause the data analysis platform to perform one or more of the steps disclosed herein. In other embodiments, the calculations service may be a virtual machine. In some embodiments, the calculations service may be a hybrid of software-based and/or hardware-based instructions to perform one or more of the steps disclosed herein. The calculations service may collect data, perform calculations, and/or provide key performance indicators. The calculations service may implement, for example, process dynamic modeling software or tools (e.g., UniSim).
Furthermore, the data analysis platform 504 may include a utility service. In some embodiments, the utility service may comprise computer-executable instructions that, when executed by the processor, cause the data analysis platform to perform one or more of the steps disclosed herein. In other embodiments, the utility service may be a virtual machine. In some embodiments, the utility service may be a hybrid of software-based and/or hardware-based instructions to perform one or more of the steps disclosed herein. The utility service may take information from the calculations service and put the information into the data lake. The utility service may provide data aggregation service, such as taking all data for a particular range, normalizing the data (e.g., determining an average), and combining the normalized data into a file to send to another system or module.
One or more components of the data analysis platform 504 may assist the processor in the data analysis platform in processing and analyzing the data values stored in the database. In some embodiments, the data analysis platform may perform statistical analysis, predictive analytics, and/or machine learning on the data values in the database to generate predictions and models. For example, the data analysis platform may analyze sensor data to monitor for, predict, and prevent determine temperature excursion in the equipment of a plant. The data analysis platform 504 may compare temperature data from different times and dates to determine if changes are occurring. Such comparisons may be made on a monthly, weekly, daily, hourly, real-time, or some other basis.
The analysis unit may be partially or fully automated. In one embodiment, the system is performed by a computer system, such as a third-party computer system, local to or remote from the plant and/or the plant planning center. The system may receive signals and parameters via the communication network, and display in real time related performance information on an interactive display device accessible to an operator or user. The platform allows two or more users to work with the same information, thereby creating a collaborative environment for sharing best practices or for troubleshooting. The method may provide accurate prediction and optimization results due to fully configured models.
Referring to
Although the elements of
Referring to
In a plant environment such as illustrated in
Referring to
Although
Although the elements of
As shown in
Dashboard of Stability Gauge
The graphical user interface 700 may include one or more visual representations of data (e.g., chart, graph, etc.) that shows information about a plant, a particular piece of equipment (e.g., one or more reactor units) in a plant, or a process performed by a plant or a particular piece or combination of equipment in the plant. For example, a graph may show information about an operating condition, an efficiency, a production level, or the like. The graphical user interface may include a description of the equipment, the combination of equipment, or the plant to which the visual display of information pertains.
The graphical user interface 700 may display the information for a particular time or period of time (e.g., the last five minutes, the last ten minutes, the last hour, the last two hours, the last 12 hours, the last 24 hours, etc.). The graphical user interface may be adjustable to show different ranges of time, automatically or based on user input.
The graphical user interface 700 may include one or more buttons that allow one or more actions. For example, the graphical user interface may include a button (e.g., an “Actions” button) that, when pressed, shows one or more available actions. The graphical user interface may include a button (e.g., a “Change View” button) that, when pressed, changes one or more views of one or more elements of the graphical user interface. The graphical user interface may include a button (e.g., a “Settings” button) that, when pressed, shows one or more settings of the application of which the graphical user interface is a part. The graphical user interface may include a button (e.g., a “Refresh Data” button) that, when pressed, refreshes data displayed by the graphical user interface. In some aspects, data displayed by the graphical user interface may be refreshed in real time, according to a preset schedule (e.g., every five seconds, every ten seconds, every minute, etc.), and/or in response to a received refresh request. The graphical user interface may include a button (e.g., a “Send Data” button) that, when pressed, allows sending data to one or more other devices. For example, the data may be sent via email, SMS, text message, iMessage, FTP, cloud sharing, AirDrop, or via some other method. The interface may receive a selection of one or more pieces of data, graphics, charts, graphs, elements of the display, or the like, to share or send. The graphical user interface may include a button (e.g., an “Analyze Data” button) that, when pressed, causes one or more data analysis functions to be performed. In some aspects, the interface may receive additional input about the desired data analysis, such as desired input, desired output, desired granularity, desired time to complete the data analysis, desired time of input data, or the like.
Detecting and Preventing Temperature Excursion
Aspects of the disclosure are directed to a system that predicts, detects, and/or adjusts process conditions before a temperature excursion can occur forcing a shutdown of the process unit. Referring to
The advisory stability monitor (e.g., Stability Gauge 1002) functions with process indicators to determine the stability of the process unit. The advisory stability monitor uses a weighted algorithm to determine the stability of the unit (weighted based on how much operations deviate from a stable state and how quickly the rate of change is occurring).
The advisory stability monitor (e.g., Stability Gauge 1002) may provide a stability gauge 404 to allow an instant assessment of the process conditions and the risk of a temperature excursion. The stability gauge may be weighted in value from 0 to 100 with predefined regions, for example, from 0 to 33 may be the green, stable area, 33 to 66 may be the yellow, concern area and 66 to 99 may be the red, danger area. As operating conditions change, the stability gauge may change to indicate whether there are any issues or areas of concern. The advisory stability monitor may provide a continuous assessment of risk, and may indicate whether the risk is decreasing or still increasing when operations parameters are changed. The stability monitor may also provide one or more options to trend and graph certain variables used in the algorithm, as shown in
The advisory stability monitor may use an algorithm based on operating conditions (variables) in the plant including, for example, Feed flowrate (F); Makeup Gas Consumption (M); Quench flowrate (Q1, Q2, Q3, etc.); Quench Capacity (C); Pressure (P); Reactive Feed Ratio (A); Recycle Gas Purity (H); Recycle Gas flowrate (R); Gas to oil ratio (G); Axial Temperature (X1, X2, X3, X4 etc.); Radial Temperature (D1, D2, D3, D4, etc.); WABT (W1, W2 etc.); Reactor Bed Temperatures (T); Reactor Bed Inlet Temperatures (I); Light Ends Make (L); Conversion (S).
Exemplary positions of process indicators are shown in
In order to properly monitor the reactions as the reactants pass through the catalyst bed, it is not sufficient to just measure the temperature of the flowing stream at the inlet and outlet of the reactor. It is necessary to observe the temperature at the inlet, outlet, and radially throughout the catalyst bed. A bed thermocouple measures the temperature at one point in a large cross-sectional area. The reactors may be equipped with many bed thermocouples at regular intervals in the reactors in order to monitor the reactions. The exact location and distance between bed thermocouples will depend upon the depth and diameter of the catalyst bed.
There will be a maximum temperature at which the catalyst and reactors can safely operate. This maximum temperature depends on the type of catalyst system employed. The maximum allowable operating temperature will also be determined by the metallurgical limit of the reactors and should never be exceeded.
An advisory stability monitor (e.g., Stability Gauge 410) may constantly and/or periodically receive and assess one or more process indicators 420. Normal operating conditions may be indicated 402. Block 410 advises that the reactor temperature has increased from normal and into a cautionary state and action should be taken per established procedures. For example, a stability gauge (e.g., stability gauge 800, stability gauge 1002) may provide a first level of messaging to encourage operator action to resolve an issue. In one or more embodiments, the stability gauge might not work based on temperature threshold values, but rather may trend away from historical average (stable states).
The advisory stability monitor (e.g., Stability Gauge 410) continues to monitor and assess the process indicators. As the reactor temperatures rise, the stability monitor may alert to take appropriate action before the automated sequences disrupt production. If the reactor temperatures continue to rise and reach a preset temperature Level 1 trip point, then the Temperature Excursion Mitigation Control System may advise that Temperature Excursion Level 1 (block 412) has been reached and automatic actions are being taken. Such actions may include one or more of the following: remove/reduce more reactive feedstock; increase less reactive feed; lower charge heater outlet temperature; and lower the controlled temperature in the reactor bed with an elevated temperature and in the following reactor bed. The objective of Level 1 is to attempt to return operations to safe parameters with minimal or no loss of production.
The advisory stability monitor (e.g., Stability Gauge 410) continues to monitor and assess the process indicators. As the reactor temperatures rise, the stability monitor may alert to take appropriate action before the automated sequences disrupt production. If the reactor temperatures reach a preset temperature Level 2 trip point, then the Temperature Excursion Mitigation Control System may advise that Temperature Excursion Level 2 (block 414) has been reached and automatic actions are being taken. This phase places the unit in a safe state, reducing temperatures and pressures, to allow for an easy and expedient restart of the unit. Such actions may include one or more of the following: ramp the charge heater outlet temperature down to a lower temperature (e.g., 300° F., 400° F., 500° F., 600° F., or the like); ramp the combined feed exchanger outlet temperature down to a lower temperature (e.g., 300° F., 400° F., 500° F., 600° F., or the like); ramp all reactor bed temperatures down to a lower temperature (e.g., 300° F., 400° F., 500° F., 600° F., or the like); reduce a pressure of the unit by a particular percentage (e.g., 5%, 6%, 7%, 8%, 9%, 10%, or the like) of operating pressure. The objective of Level 2 may be to attempt to prevent a depressuring event that may result in extended downtime of the unit as well as associated refinery units, a loss in production, potential damage to the reactors, reactor internals, catalyst beds and the reactor effluent air coolers and environmental flaring of hydrocarbons and sulfur compounds. Another objective of Level 2 may be to place the unit in a safe state that will also allow for an easy and expedient restart of the unit.
If the reactor temperatures reach the temperature shutdown trip point, then the Emergency Interlock Shutdown (block 416) System may take automatic actions. Such actions may include high rate depressure of the reactor and/or a complete shutdown of the process unit. The Emergency Interlock Shutdown System may already be part of typical design and will remain as the fail safe action to protect the equipment, environment, and prevent loss of life.
In the various steps, temperature runaways (excursions) can be avoided by
The examples described herein, including the examples in the preceding paragraph, may be implemented in a graphical user interface, such as illustrated in
In some aspects, if the advisory stability monitor (e.g., Stability Gauge 410, Stability Gauge 1002) determines one or more conditions that may indicate a problem, an alarm (e.g., a visual and/or audible alarm) may be triggered. The alarm could be an alarm at a plant, an alarm that is sent to one or more devices, an alarm that shows on a web page or dashboard, or the like.
In some aspects, if a problem is detected, the control platform may take one or more actions, which may be triggered, requested, or recommended by data analysis platform. Alternatively or additionally, the data analysis platform may trigger an alert to one or more remote devices (e.g., remote device 1, remote device 2). The alert may include information about the problem. The alert may provide information about one or more determined correlations between the problem and a particular operating condition or combination of operating conditions. The alert may include one or more recommendations for and/or commands causing adjustments to operating conditions, such as adjustments to flows, pressures, temperatures, valves, nozzles, drains, or the like.
In some aspects, a remote device may send a command for a particular action (e.g., a corrective action) to be taken, which may or may not be based on the alert. In some aspects, the data analysis platform may send a command for a particular action to be taken, whether or not an alert was sent to or a command was sent by the remote device. The command may cause one or more actions to be taken, which may prevent equipment (e.g., reactor) damage, avoid failure, or the like.
Stability Monitor Algorithm
An algorithm for weighting the changes in the process variables to determine the stability of the unit is described below. This is a simplified version to exemplify how and why variables may be used to measure the stability of the unit in order to provide a gauge. The plant process variables used in the algorithm may include, but are not limited to, for example, Feed flowrate (F); Makeup Gas Consumption (M); Quench flowrate (Q1, Q2, Q3, etc.); Quench Capacity (C); Pressure (P); Reactive Feed Ratio (A); Recycle Gas Purity (H); Recycle Gas flowrate (R); Gas to oil ratio (G); Axial Temperature (X1, X2, X3, X4 etc.); Radial Temperature (D1, D2, D3, D4, etc.); WABT (W1, W2 etc.); Reactor Bed Temperatures (T); Reactor Bed Inlet Temperatures (I); Light Ends Make (L); Conversion (S).
Stability={[(ΔF−Bfeed)*Kfeed]−[(ΔIcrackingx−BcrackingIx)*KcrackingIx]}+[(ΔM−Bmakeup)*Kmakeup]+[(ΔQ−Bquench)*Kquench)*Kquench]+[C*Kqcapacity]+[(ΔP−Bpressure)*Kpressure]+[(ΔA−Breactratio)*Kreactratio]+U*Ku+[(ΔHincrease−Bhpurityincrease)*Khpurityincrease]+[(ΔHdecrease−Bhpuritydecrease)*Khpuritydecrease]+[(ΔR−Brecycle)*Krecycle]+J*KJ+[(ΔG−BGOratio)*KGOratio]+[(ΔT−Btempx)*Ktemp]+Z*Kz+YKY+[(ΔWx−BWABTx)*KWABTx]}+[(ΔL−BLightends)*KLightends]+[(ΔS−Bconversion)*KConversion]
Gain (K) Constants and % Biases (B)
All the variables will have Gain (K) constants and % Biases (B) so the system may be tuned and/or weighting adjusted. Initial numbers generally will be based on design recommendations (e.g., Feed change of more than 2%, 3%, 4,%, 5%, 6%, 10%, 15%, etc.; Quench increase by more than 5%, 7%, 10%, 12%, 15%, 20%, 25%, etc.; Cracked feedstock ratio increase by X %), which can be adjusted to avoid nuisance alarms and customize to each unique unit.
Factors in the Equation, i.e. [(ΔX−BX)*KX]
In certain aspects, if the factors in the equation, i.e. [(ΔX−BX)*KX], are negative, then those factors will go to zero and the system will not take credit in the stability measurement for variables moving to a safer state, unless defined. Only variables moving towards instability will be included in the stability measurement.
Examples of Various Stability Algorithms
A method for weighting the changes in the process variables and defining the stability of the unit is described below in a simplified version of the algorithm that exemplifies how and why variables will be used to measure the stability of the unit in order to provide a gauge.
(F) Feed Flowrate Decreased
Decreases to the feed rate may be preceded by a change in reactor temperatures such that temperatures are lowered before feed rate is changed. An equation for the change in feed rate may be: ΔF={[(F60min−Factual))/F60min]*100} if ΔF is >B % then the unit stability, unless the reactor bed inlet temperatures in the cracking beds, variable I, have also decreased, may increase in trending toward caution or danger. So an algorithm for decreasing the feed rate may be:
ΔF may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
Another factor may be used to measure instability due to increases in feed rate. The algorithm for determining and weighting feed rate increases will be similar to the algorithm above.
(M) Makeup Gas Consumption Increase
Changes in Makeup Gas Rate indicate that more makeup gas is being consumed and reactions may be increasing. The stability factor for Makeup Gas consumption may be calculated based on a change in makeup gas rate over a period of time, ΔM={[(Mactual−M60min)/M60min]*100}, such that if ΔM is >Bmakeup% , then the stability gauge will increase in trending toward caution or danger.
ΔM may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
Another factor may be used to measure instability due to a decrease in Makeup Gas Rate. The algorithm for determining and weighting Makeup Gas Rate decreases will be similar to the algorithm above.
(Q) Quench Rates Increase
Increases in the Quench Gas Rate indicate that there is a higher temperature rise in the bed above and reactions in that bed may be increasing. The stability factor for Quench Gas Rates may be calculated based on a change in Quench Gas Rate for each reactor bed over a period of time, ΔQ={[(Qactual−Q60min)/Q60min]*100}, such that if ΔQ is >Bquench%, then the stability gauge will increase in trending toward caution or danger. As any of the several catalyst beds may become independently unstable, the stability gauge may weight the instability by using only the quench with the largest increase.
Quench Rate increases (ΔQ) may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average. A further display may graph and trend all the various ΔQ1, ΔQ2, . . . ΔQn such that the specific catalyst beds with higher instability can be identified.
(C) Quench Capacity
It is important to monitor the quench control valve opening to determine the reserve quench available, in case of an upset. In some embodiments, the valve may be open all the way. Alternatively, in some embodiments, the valve may be open less than all the way (e.g., 95%, 90%, 85%, 80%, 75%, 70%, 65%, 60%, 55%, 50%, 45%, 40%, 35%, 30%, 25%, 20%, 15%, 10%, 5%, 1%). In one or more embodiments, a quench valve position may be determined from a quench valve position sensor, which may read and send a position of a particular quench valve of a reactor. In one or more embodiments, a quench valve might not have a quench valve position sensor. Even if a quench valve does not have a quench valve position sensor, the quench valve might be set by sending a command indicating a position that the quench valve should be set to. A quench valve position may be determined from the command sent to the quench valve. Quench capacity may be defined as the available valve opening remaining, such that if the valve opening is at a first percentage, the available remaining capacity is a second percentage (e.g., 100%—first percentage). Thus, for example, if the valve opening is at 75%, then the available remaining capacity is 25%. Similarly, if the valve opening is at 65%, then the available remaining capacity is 35%. If the valve opening is at 45%, then the available remaining capacity is 55%. Decreasing Quench Capacity indicates that there is a higher temperature rise in the bed above and reactions in that bed may be increasing. The stability factor for Quench capacity may be calculated based on a percent of quench control valve opening. Different benchmarks (e.g., normal operation, caution, danger) may be set. For example, if the quench control valve opening moves above 50%, the stability gauge may indicate caution, and if the quench control valve opening moves above 65%, the stability gauge may indicate danger. In another embodiment, if the quench control valve opening moves above 60%, the stability gauge may indicate caution, and if the quench control valve opening moves above 75%, the stability gauge may indicate danger. In another embodiment, if the quench control valve opening moves above 30%, the stability gauge may indicate caution, and if the quench control valve opening moves above 50%, the stability gauge may indicate danger. The stability gauge may weight the instability by using only the valve with the least capacity.
Quench Capacity (C) may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average. A further display may graph and trend all the various C1, C2, . . . Cn such that the specific valves with least capacity can be identified.
(P) Pressure Decreases
Decreases in pressure indicate that more makeup gas is being consumed than can be replenished to maintain pressure in the unit. This is an indication that reactions may be increasing. The stability factor for Pressure decreasing may be calculated based on a change in pressure over a period of time. So the equation for the change in pressure may be as follows: ΔP={[(P60min−Pactual))/P60min]*100} if ΔP is >Bpressure % then the unit stability may increase in trending toward caution or danger:
Pressure decreases (ΔP) may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
Other factors may be calculated to measure instability due to an increase in pressure, as an increment in the hydrogen partial pressure in the unit will change reaction rates. The algorithm for determining and weighting pressure increases will be similar to the algorithm above.
(A) Ratio of Reactive Feed Increases
Increases to the Reactive Feed Ratio indicate that reactions in the beds may be increasing. The stability factor for the Reactive Feed Ratio may be calculated based on a change in the ratio over a period of time, ΔA={[(Aactual−A60min)/A60min]*100}, such that if ΔA is >Breactratio%, then the stability gauge will increase in trending toward caution and danger. Also, included in the algorithm is a parameter, U, which considers the unit design Reactive Feed Ratio. As the operating Reactive Feed Ratio nears the design ratio, the stability gauge will increase in trending toward danger.
Reactive Feed Ratio (ΔA) and Design Reactive Feed Ratio (U) may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
(H) Recycle Gas Purity
Increases or decreases in Recycle Gas Purity indicates that the unit may be moving into areas of instability. Decreases in Recycle Gas Purity indicates that reactions in the beds may be increasing and more light ends are being produced. Increasing Recycle Gas Purity increases the partial pressure of the unit and may result in changes to the conversion or increased hydrotreating reactions. The stability factor for Recycle Gas Purity may be calculated based on a change in purity over a period of time, ΔH={[(H60min−Hactual)/H60min]*100}, such that if ΔH is >Bhpurity%, then stability gauge will increase in trending toward caution and danger.
Recycle Gas Purity ΔHincrease and ΔHdecrease may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
(R) Recycle Gas Rate
A decrease in Recycle Gas rate can cause instability. The stability factor for the Recycle Gas rate may be calculated based on a change in flow over a period of time, ΔR={[(R60min−Ractual)/R60min]*100}, such that if ΔR is >Brecycle%, then the stability gauge will increase in trending toward caution. Also, included in the algorithm is a parameter, J, which considers the unit design Recycle Gas rate. As the operating Recycle Gas rate nears the design rate, the stability gauge will increase in trending toward danger.
Recycle Gas rate (ΔR) and Design Recycle Gas rate (J) may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
(G) Gas to Oil Ratio
A decrease in Gas to Oil Ratio can indicate instability. The stability factor for Gas to Oil Ratio may be calculated based on a change in the ratio over a period of time, ΔG={[(G60min−Gactual)/G60min]*100}, such that if ΔG is >BGOratio%, then stability gauge will increase in trending toward caution and danger.
Gas to Oil Ratio (ΔG) may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
(T) Reactor Bed Temperatures
Increases to the Reactor Bed Temperatures indicate that reactions in the beds may be increasing. The stability factor for Reactor Bed Temperatures may be calculated based on a change in temperature over a period of time, ΔT={(Tactual−T60min)}, such that if ΔT is >Btemps, then the stability gauge will increase in trending toward caution and danger. Btempsx, may be a unique, adjustable bias for each reactor bed, so hydrotreating and cracking beds can be tuned and customized as required. The stability gauge may weight the instability by using only the temperature with the largest increase.
Reactor Bed Temperatures may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
(X) Axial Bed Temperatures Increasing
Increases to the Axial Reactor Bed Temperatures indicate that reactions in the beds may be increasing. The stability factor for Axial Reactor Bed Temperatures may be calculated based on the recommended average and Maximum Bed Temperature Rise, ΔX=(Xoutlet−Xinlet), such that if ΔX is >Baverage_xtemps, then stability gauge will start to increase in trending toward yellow, caution, and if ΔX is >Bmaximum_xtemps, then the stability gauge will start to increase in trending toward red, danger. The stability gauge may weight the instability by using only the axial bed with the largest increase.
The axial bed temperature and/or the average of the axial bed temperatures in a bed may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average. A further display may graph and trend all the various Z1, Z2, . . . Zn such that the axial bed rises with the largest increase can be identified.
(D) Radial Bed Temperatures Increasing
Increases to the Radial Reactor Bed Temperatures, D, indicate that reactions in the beds may be increasing. The stability factor for Radial Reactor Bed Temperatures may be calculated based on the recommended Radial Temperatures Spread, ΔD=(Dhighest−Dlowest), such that if ΔD is >Bradialspread, then stability gauge will start to increase in trending toward caution and then toward danger. The stability gauge may weight the instability by using only the radial bed with the largest increase.
D and/or Y may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average. A further display may graph and trend all the various ΔD1, ΔD2, . . . ΔDn such that the axial bed rises with the largest increase can be identified.
(W) Weighted Average Bed Temperature (WABT)
The WABT is measurement of the weighted average bed temperatures and will be used to indicate instability in individual reactor beds. The equation for the change in WABT may be as follows: ΔW={[(Wactual−W60min)/W60min]*100} if ΔWWABTx is >B %, then the unit stability may increase in trending toward caution or danger. The algorithm for increasing the WABT may be:
ΔW may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
(L) Light Ends Make Increases
Changes in Light Ends Make (L) is visible in the Separator off gas flow rate, the Stripper Off gas flow rate and the Receiver Liquid level and Receiver Liquid flow rate. Increasing Light Ends Make indicates that reactions may be increasing, conversion is higher and temperatures may be increasing. The stability factor for Light Ends Make may be calculated based on a change in Light Ends Make over a period of time, ΔL={[(Lactual−L60min)/L60min]*100}, such that if ΔL is >BLightends%, then the stability gauge will increase in trending toward caution or danger.
Light Ends Make (ΔL) may be indicated on a summary display and may be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
(S) Conversion Increases
Increasing conversion (S) is visible in a decreasing Product Fractionator Bottoms Level or the Product Fractionator Bottoms flow rate, depending on the flow scheme. Increasing conversion is a lagging indicator that increasing instability. The stability factor for conversion may be calculated based on a change in conversion over a period of time, ΔS={[(S60min−Sactual)/S60min]*100}, such that if ΔS is >Bconversion%, then the stability gauge will increase in trending toward caution or danger.
Conversion Increase (ΔS) may also be graphed and trended on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average.
Individual Reactor Bed Stability Gauge
Individual bed stability may be calculated, trended and indicated. The variables applicable to each reactor bed stability gauge include:
The individual bed stability gauge may use the same equations as defined above for each parameter but may combine only the factors listed above that are applicable to each bed. The individual bed stability gauge may be based on an algorithm to weight each individual bed temperature, as well as the axial and radial temperature rise, and combine those factors with weighted amounts for the quench flowrate and available quench capacity for the bed. The individual bed stability gauge may be graphed and trended on the variable display. The display may depict one or more individual bed stabilities, and/or whether the stability of each individual bed is increasing or decreasing over the hourly average.
Illustrative Embodiments of a Stability Gauge Interface
For example, building off the examples provided above, the stability gauge may display an indication of a green zone when, inter alia, the reactor bed temperature is within the desired limits. But if the reactor bed temperature (denoted as T) measurements increase, this may be an indication that the reactions in the beds may be increasing. T may be calculated based on a change in temperature over a period of time, ΔT={(Tactual−T60min)}, such that if ΔT is >Btemps, then the stability gauge will increase in trending toward caution and danger. When this occurs, the visual indication on the stability gauge may transition from a green zone to an amber/yellow zone, or from an amber/yellow zone to a red zone. Notably, Btempsx, may be a unique, adjustable bias for each reactor unit/bed, and as such, may be tuned and customized as desired. Examples of illustrative stability algorithm with appropriate weighting and factors are described herein.
Moreover, the stability gauge may consider other temperature measurements in its final display of an indication zone. For example, measurements of one or more of an axial bed temperature (X) and radial bed temperature (D) may be used in adjusting the stability gauge indicator from one zone to another zone, or in micro-adjusting the stability gauge indicator within the same zone. In the case of axial bed temperature changes in measurement, the Z variable may be indicated on a summary display and may be graphed on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average. In the case of radial bed temperature changes in measurement, the Y variable may be indicated on a summary display and may be graphed on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average. In the case of a weighted average bed temperature change, ΔW may be indicated on a summary display and may be graphed on the variable display that may assist in assessing which variable is leading to instability and whether the change in that variable is increasing or decreasing over the hourly average. In each instance, the stability gauge illustrated in
The stability gauge may monitor process conditions indicative of the beginning stages of a temperature excursion and automate a response to the situation, thus limiting the progression and severity of the excursion. In particular, in some examples, when the indicator of the stability gauge is outside of the green zone, the Temperature Excursion Mitigation System may take automated actions to alleviate possible excursions. Some examples of automatic actions/response that may be taken, include, but are not limited to one or more of the following: remove or reduce reactive feedstock; increase less reactive feed; lower temperature in the charge heater; and/or lower temperature in the reactor bed in alarm and the following bed. To implement these automated responses, the plant may be equipped with the numerous wired and wireless communication and control capabilities described herein. For example, the flow rate of various feeds may be increased or decreased through automated valve controls that open and/or close based on commands sent from a control platform illustrated in
Sensor Systems—Detection and Analysis
Sensor information may be gathered by one or more sensors and transmitted to data collection platform. Data collection platform may transmit the collected sensor data to data analysis platform, which may be at a plant or remote from a plant (e.g., in the cloud).
One or more calculations may be performed for reactor unit remote monitoring service. These calculations may assist in alerting and helping diagnose the status of one or more reactor units and/or other components used in a plant. A data processing platform may receive (e.g., from one or more sensors) one or more operational parameters, which may be used alone or in combination for determining the efficiency of the reactor unit. The data processing platform may use one or more design parameters, alone or in combination, for determining the status of the reactor unit. A design parameter may be a level at which the reactor unit was designed to operate at, below, or above. For example, a reactor unit may be designed to operate within a particular temperature range (e.g., the operating temperature should never exceed a metallurgical limit of the reactor).
In some instances, the timestamp of a calculated attribute may match the timestamp of the raw data used for the calculation. In some instances, a calculated attribute may use one or more results of one or more other calculated attributes; therefore, the order in which the attributes are calculated may be relevant. Meanwhile, in some embodiments, raw values may be checked for bad values. If bad values are detected, the data processing platform may either skip calculation or replace the bad value with NULL, as appropriate for subsequent calculations. For averages, a provision may be made to skip bad/null values and/or timestamps. Moreover, some units of measurement for variables may be specified. Some variables may be dimensionless, and therefore might not have a defined unit of measurement.
Aspects of the disclosure have been described in terms of illustrative embodiments thereof. Numerous other embodiments, modifications, and variations within the scope and spirit of the appended claims will occur to persons of ordinary skill in the art from a review of this disclosure. For example, one or more of the steps illustrated in the illustrative figures may be performed in other than the recited order, and one or more depicted steps may be optional in accordance with aspects of the disclosure.
This application claims the benefit under 35 U.S.C. 119(e) of U.S. Provisional Patent Application No. 62/522,612, filed Jun. 20, 2017, which is incorporated by reference herein.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4159239 | Schwartz | Jun 1979 | A |
| 4267458 | Uram | May 1981 | A |
| 4284494 | Bartholic | Aug 1981 | A |
| 4362614 | Asdigian | Dec 1982 | A |
| 4380146 | Yannone | Apr 1983 | A |
| 4385985 | Gross | May 1983 | A |
| 4411773 | Gross | Oct 1983 | A |
| 4709546 | Weiler | Dec 1987 | A |
| 4775460 | Reno | Oct 1988 | A |
| 4795545 | Schmidt | Jan 1989 | A |
| 4902469 | Watson | Feb 1990 | A |
| 5077252 | Owen et al. | Dec 1991 | A |
| 5227121 | Scarola | Jul 1993 | A |
| 5582684 | Holmqvist et al. | Dec 1996 | A |
| 5605435 | Haugen | Feb 1997 | A |
| 5616214 | Leclerc | Apr 1997 | A |
| 5642296 | Saxena | Jun 1997 | A |
| 5666297 | Britt et al. | Sep 1997 | A |
| 5817517 | Perry et al. | Oct 1998 | A |
| 6038540 | Krist et al. | Mar 2000 | A |
| 6081230 | Hoshino | Jun 2000 | A |
| 6230486 | Yasui | May 2001 | B1 |
| 6266605 | Yasui | Jul 2001 | B1 |
| 6271845 | Richardson | Aug 2001 | B1 |
| 6392114 | Shields et al. | May 2002 | B1 |
| 6760716 | Ganesamoorthi et al. | Jul 2004 | B1 |
| 6772044 | Mathur et al. | Aug 2004 | B1 |
| 6795798 | Eryurek et al. | Sep 2004 | B2 |
| 6982032 | Shaffer et al. | Jan 2006 | B2 |
| 6983227 | Thalhammer-Reyero | Jan 2006 | B1 |
| 7006889 | Mathur et al. | Feb 2006 | B2 |
| 7067333 | Pasadyn et al. | Jun 2006 | B1 |
| 7133807 | Karasawa | Nov 2006 | B2 |
| 7151966 | Baier et al. | Dec 2006 | B1 |
| 7246039 | Moorhouse | Jul 2007 | B2 |
| 7313447 | Hsuing et al. | Dec 2007 | B2 |
| 7415357 | Stluka et al. | Aug 2008 | B1 |
| 7567887 | Emigholz et al. | Jul 2009 | B2 |
| 7742833 | Herbst et al. | Jun 2010 | B1 |
| 7836941 | Song et al. | Nov 2010 | B2 |
| 7877596 | Foo Kune et al. | Jan 2011 | B2 |
| 7925979 | Forney et al. | Apr 2011 | B2 |
| 7936878 | Kune et al. | May 2011 | B2 |
| 7979192 | Morrison et al. | Jul 2011 | B2 |
| 7995526 | Liu et al. | Aug 2011 | B2 |
| 8050889 | Fluegge et al. | Nov 2011 | B2 |
| 8055371 | Sanford et al. | Nov 2011 | B2 |
| 8111619 | Liu et al. | Feb 2012 | B2 |
| 8128808 | Hassan et al. | Mar 2012 | B2 |
| 8204717 | McLaughlin et al. | Jun 2012 | B2 |
| 8244384 | Pachner et al. | Aug 2012 | B2 |
| 8280057 | Budampati et al. | Oct 2012 | B2 |
| 8352049 | Hsiung et al. | Jan 2013 | B2 |
| 8354081 | Wheat et al. | Jan 2013 | B2 |
| 8385436 | Holm et al. | Feb 2013 | B2 |
| 8428067 | Budampati et al. | Apr 2013 | B2 |
| 8458778 | Budampati et al. | Jun 2013 | B2 |
| 8571064 | Kore et al. | Oct 2013 | B2 |
| 8630962 | Maeda | Jan 2014 | B2 |
| 8644192 | Budampati et al. | Feb 2014 | B2 |
| 8811231 | Budampati et al. | Aug 2014 | B2 |
| 8815152 | Burgess et al. | Aug 2014 | B2 |
| 8923882 | Gandhi et al. | Dec 2014 | B2 |
| 8926737 | Chatterjee et al. | Jan 2015 | B2 |
| 9053260 | Romatier et al. | Jun 2015 | B2 |
| 9134717 | Trnka | Sep 2015 | B2 |
| 9166667 | Thanikachalam | Oct 2015 | B2 |
| 9176498 | Baramov | Nov 2015 | B2 |
| 9354631 | Mohideen et al. | May 2016 | B2 |
| 9580341 | Brown et al. | Feb 2017 | B1 |
| 9751817 | Jani et al. | Sep 2017 | B2 |
| 9864823 | Horn et al. | Jan 2018 | B2 |
| 9968899 | Gellaboina et al. | May 2018 | B1 |
| 10095200 | Horn et al. | Oct 2018 | B2 |
| 10107295 | Brecheisen | Oct 2018 | B1 |
| 10180680 | Horn et al. | Jan 2019 | B2 |
| 10183266 | Victor et al. | Jan 2019 | B2 |
| 10222787 | Romatier et al. | Mar 2019 | B2 |
| 10328408 | Victor et al. | Jun 2019 | B2 |
| 20020123864 | Eryurek et al. | Sep 2002 | A1 |
| 20020179495 | Heyse et al. | Dec 2002 | A1 |
| 20030036052 | Delwiche et al. | Feb 2003 | A1 |
| 20030105775 | Shimada | Jun 2003 | A1 |
| 20030147351 | Greenlee | Aug 2003 | A1 |
| 20030223918 | Cammy | Dec 2003 | A1 |
| 20040079392 | Kuechler | Apr 2004 | A1 |
| 20040099572 | Evans | May 2004 | A1 |
| 20040109788 | Li et al. | Jun 2004 | A1 |
| 20040122273 | Kabin | Jun 2004 | A1 |
| 20040122936 | Mizelle et al. | Jun 2004 | A1 |
| 20040147036 | Krenn et al. | Jul 2004 | A1 |
| 20040148144 | Martin | Jul 2004 | A1 |
| 20040204775 | Keyes et al. | Oct 2004 | A1 |
| 20040204913 | Mueller et al. | Oct 2004 | A1 |
| 20040220689 | Mathur et al. | Nov 2004 | A1 |
| 20040220778 | Imai et al. | Nov 2004 | A1 |
| 20050027721 | Saenz | Feb 2005 | A1 |
| 20050029163 | Letzsch | Feb 2005 | A1 |
| 20050098033 | Mallavarapu et al. | May 2005 | A1 |
| 20050133211 | Osborn et al. | Jun 2005 | A1 |
| 20050216209 | Evans | Sep 2005 | A1 |
| 20060020423 | Sharpe, Jr. | Jan 2006 | A1 |
| 20060133412 | Callaghan | Jun 2006 | A1 |
| 20060252642 | Kanazirev | Nov 2006 | A1 |
| 20060259163 | Hsiung et al. | Nov 2006 | A1 |
| 20070020154 | Evans | Jan 2007 | A1 |
| 20070059159 | Hjerpe | Mar 2007 | A1 |
| 20070059838 | Morrison et al. | Mar 2007 | A1 |
| 20070091824 | Budampati et al. | Apr 2007 | A1 |
| 20070091825 | Budampati et al. | Apr 2007 | A1 |
| 20070185664 | Tanaka | Aug 2007 | A1 |
| 20070192078 | Nasle et al. | Aug 2007 | A1 |
| 20070212790 | Welch et al. | Sep 2007 | A1 |
| 20070250292 | Alagappan et al. | Oct 2007 | A1 |
| 20070260656 | Wiig | Nov 2007 | A1 |
| 20070271452 | Foo Kune et al. | Nov 2007 | A1 |
| 20080086322 | Wallace | Apr 2008 | A1 |
| 20080130902 | Foo Kune et al. | Jun 2008 | A1 |
| 20080154434 | Galloway et al. | Jun 2008 | A1 |
| 20080217005 | Stluka et al. | Sep 2008 | A1 |
| 20080282606 | Plaza et al. | Nov 2008 | A1 |
| 20090059786 | Budampati et al. | Mar 2009 | A1 |
| 20090060192 | Budampati et al. | Mar 2009 | A1 |
| 20090064295 | Budampati et al. | Mar 2009 | A1 |
| 20090201899 | Liu et al. | Aug 2009 | A1 |
| 20090204245 | Sustaeta | Aug 2009 | A1 |
| 20090245286 | Kore et al. | Oct 2009 | A1 |
| 20090268674 | Liu et al. | Oct 2009 | A1 |
| 20090281677 | Botich | Nov 2009 | A1 |
| 20100014599 | Holm et al. | Jan 2010 | A1 |
| 20100108567 | Medoff | May 2010 | A1 |
| 20100125347 | Martin et al. | May 2010 | A1 |
| 20100152900 | Gurciullo et al. | Jun 2010 | A1 |
| 20100158764 | Hedrick | Jun 2010 | A1 |
| 20100230324 | Al-Alloush et al. | Sep 2010 | A1 |
| 20100262900 | Romatier et al. | Oct 2010 | A1 |
| 20110112659 | Pachner et al. | May 2011 | A1 |
| 20110152590 | Sadler et al. | Jun 2011 | A1 |
| 20110152591 | Sadler et al. | Jun 2011 | A1 |
| 20110311014 | Hottovy et al. | Dec 2011 | A1 |
| 20120029966 | Cheewakriengkrai et al. | Feb 2012 | A1 |
| 20120083933 | Subbu et al. | Apr 2012 | A1 |
| 20120095808 | Kattapuram et al. | Apr 2012 | A1 |
| 20120104295 | Do et al. | May 2012 | A1 |
| 20120121376 | Huis in Het Veld | May 2012 | A1 |
| 20120123583 | Hazen et al. | May 2012 | A1 |
| 20120197616 | Trnka | Aug 2012 | A1 |
| 20120259583 | Noboa et al. | Oct 2012 | A1 |
| 20130029587 | Gandhi et al. | Jan 2013 | A1 |
| 20130031960 | Delrahim | Feb 2013 | A1 |
| 20130079899 | Baramov | Mar 2013 | A1 |
| 20130090088 | Chevsky et al. | Apr 2013 | A1 |
| 20130094422 | Thanikachalam | Apr 2013 | A1 |
| 20130172643 | Pradeep | Jul 2013 | A1 |
| 20130253898 | Meagher et al. | Sep 2013 | A1 |
| 20130270157 | Ferrara | Oct 2013 | A1 |
| 20130311437 | Stluka et al. | Nov 2013 | A1 |
| 20130327052 | O'Neill | Dec 2013 | A1 |
| 20140008035 | Patankar et al. | Jan 2014 | A1 |
| 20140026598 | Trawicki | Jan 2014 | A1 |
| 20140074273 | Mohideen et al. | Mar 2014 | A1 |
| 20140114039 | Benham et al. | Apr 2014 | A1 |
| 20140131027 | Chir | May 2014 | A1 |
| 20140163275 | Yanagawa et al. | Jun 2014 | A1 |
| 20140179968 | Yanagawa et al. | Jun 2014 | A1 |
| 20140212978 | Sharpe, Jr. et al. | Jul 2014 | A1 |
| 20140262949 | Kem | Sep 2014 | A1 |
| 20140294683 | Siedler | Oct 2014 | A1 |
| 20140294684 | Siedler | Oct 2014 | A1 |
| 20140296058 | Sechrist et al. | Oct 2014 | A1 |
| 20140309756 | Trygstad | Oct 2014 | A1 |
| 20140337256 | Varadi et al. | Nov 2014 | A1 |
| 20140337277 | Asenjo et al. | Nov 2014 | A1 |
| 20150059714 | Bernards | Mar 2015 | A1 |
| 20150060331 | Sechrist et al. | Mar 2015 | A1 |
| 20150077263 | Ali et al. | Mar 2015 | A1 |
| 20150078970 | Iddir et al. | Mar 2015 | A1 |
| 20150098862 | Lok et al. | Apr 2015 | A1 |
| 20150158789 | Keusenkothen | Jun 2015 | A1 |
| 20150185716 | Wichmann et al. | Jul 2015 | A1 |
| 20150276208 | Maturana et al. | Oct 2015 | A1 |
| 20150284641 | Shi | Oct 2015 | A1 |
| 20150330571 | Beuneken | Nov 2015 | A1 |
| 20160033941 | T et al. | Feb 2016 | A1 |
| 20160048119 | Wojsznis | Feb 2016 | A1 |
| 20160098037 | Zornio et al. | Apr 2016 | A1 |
| 20160098234 | Weaver | Apr 2016 | A1 |
| 20160122663 | Pintart et al. | May 2016 | A1 |
| 20160147204 | Wichmann et al. | May 2016 | A1 |
| 20160237910 | Saito | Aug 2016 | A1 |
| 20160260041 | Horn et al. | Sep 2016 | A1 |
| 20160291584 | Horn et al. | Oct 2016 | A1 |
| 20160292188 | Horn et al. | Oct 2016 | A1 |
| 20160292325 | Horn et al. | Oct 2016 | A1 |
| 20160313653 | Mink | Oct 2016 | A1 |
| 20160363315 | Colannino et al. | Dec 2016 | A1 |
| 20170009932 | Oh | Jan 2017 | A1 |
| 20170058213 | Oprins et al. | Mar 2017 | A1 |
| 20170082320 | Wang | Mar 2017 | A1 |
| 20170107188 | Kawaguchi | Apr 2017 | A1 |
| 20170284410 | Sharpe, Jr. | Oct 2017 | A1 |
| 20170315543 | Horn et al. | Nov 2017 | A1 |
| 20170323038 | Horn et al. | Nov 2017 | A1 |
| 20170352899 | Asai | Dec 2017 | A1 |
| 20180046155 | Horn et al. | Feb 2018 | A1 |
| 20180081344 | Romatier et al. | Mar 2018 | A1 |
| 20180082569 | Horn et al. | Mar 2018 | A1 |
| 20180121581 | Horn et al. | May 2018 | A1 |
| 20180122021 | Horn et al. | May 2018 | A1 |
| 20180155638 | Al-Ghamdi | Jun 2018 | A1 |
| 20180155642 | Al-Ghamdi et al. | Jun 2018 | A1 |
| 20180197350 | Kim | Jul 2018 | A1 |
| 20180275690 | Lattanzio et al. | Sep 2018 | A1 |
| 20180275691 | Lattanzio et al. | Sep 2018 | A1 |
| 20180275692 | Lattanzio et al. | Sep 2018 | A1 |
| 20180280914 | Victor et al. | Oct 2018 | A1 |
| 20180280917 | Victor et al. | Oct 2018 | A1 |
| 20180282633 | Van de Cotte et al. | Oct 2018 | A1 |
| 20180282634 | Van de Cotte et al. | Oct 2018 | A1 |
| 20180282635 | Van de Cotte et al. | Oct 2018 | A1 |
| 20180283368 | Van de Cotte et al. | Oct 2018 | A1 |
| 20180283392 | Van de Cotte et al. | Oct 2018 | A1 |
| 20180283404 | Van de Cotte et al. | Oct 2018 | A1 |
| 20180283811 | Victor et al. | Oct 2018 | A1 |
| 20180283812 | Victor et al. | Oct 2018 | A1 |
| 20180283813 | Victor et al. | Oct 2018 | A1 |
| 20180283815 | Victor et al. | Oct 2018 | A1 |
| 20180283816 | Victor et al. | Oct 2018 | A1 |
| 20180283818 | Victor et al. | Oct 2018 | A1 |
| 20180284705 | Van de Cotte et al. | Oct 2018 | A1 |
| 20180286141 | Van de Cotte et al. | Oct 2018 | A1 |
| 20180311609 | McCool et al. | Nov 2018 | A1 |
| 20180362862 | Gellaboina et al. | Dec 2018 | A1 |
| 20180363914 | Faiella et al. | Dec 2018 | A1 |
| 20180364747 | Charr et al. | Dec 2018 | A1 |
| 20190002318 | Thakkar et al. | Jan 2019 | A1 |
| 20190003978 | Shi et al. | Jan 2019 | A1 |
| 20190015806 | Gellaboina et al. | Jan 2019 | A1 |
| 20190041813 | Horn et al. | Feb 2019 | A1 |
| 20190083920 | Bjorklund et al. | Mar 2019 | A1 |
| 20190101336 | Victor et al. | Apr 2019 | A1 |
| 20190101342 | Victor et al. | Apr 2019 | A1 |
| 20190101907 | Charr et al. | Apr 2019 | A1 |
| 20190102966 | Lorenz | Apr 2019 | A1 |
| 20190108454 | Banerjee et al. | Apr 2019 | A1 |
| 20190120810 | Kumar KN et al. | Apr 2019 | A1 |
| 20190151814 | Victor et al. | May 2019 | A1 |
| 20190155259 | Romatier et al. | May 2019 | A1 |
| Number | Date | Country |
|---|---|---|
| 0181744 | May 1986 | EP |
| 2746884 | Jun 2014 | EP |
| 2801937 | Nov 2014 | EP |
| 1134439 | Nov 1968 | GB |
| 2008031940 | Feb 2008 | JP |
| WO 1990010083 | Sep 1990 | WO |
| WO 2001060951 | Aug 2001 | WO |
| WO 2006044408 | Apr 2006 | WO |
| WO 2007095585 | Aug 2007 | WO |
| WO 2009046095 | Apr 2009 | WO |
| WO 2014042508 | Mar 2014 | WO |
| WO 2014123993 | Aug 2014 | WO |
| WO 2016141128 | Sep 2016 | WO |
| WO 2017079058 | May 2017 | WO |
| Entry |
|---|
| Oct. 4, 2018—PCT/U.S. Notification of Receipt of Search Copy—App 2018/038286. |
| Dec. 27, 2018—PCT/U.S. Notice of Publication—App 2018/038286. |
| Nov. 29, 2018—PCT/U.S. International Search Report—App 2018/038286. |
| 2001 Control systems of chemical and technological processes, Bespalov A. V. et al, pp. 508-509. |
| Nov. 29, 2018—PCT/U.S. Notification of Transmittal of the International Search Report—App 2018/038286. |
| Bespalov A. V. et al., Control systems of chemical and technological processes, pp. 508-509 (2001) (Russian). |
| Daniel Goebel, Dry Gas Seal Contamination During Operation and Pressurization Hold, [online], Feb. 2016, [retrieved on Jun. 19, 2019]. Retrieved from <https ://core.ac.uk/download/pdf/84815277. pdf> (Year: 2016). |
| EnergyMEDOR®, Product brochure (Nov. 2014). |
| Chistof Huber, Density and Concentration Measurement Application for Novel MEMS-based Micro Densitometer for Gas, [online], 2016, [retrieved on Jun. 19, 2019]. Retrieved from <https://www.ama-science.org/proceedings/getFile/ZwZ1 BD==> (Year: 2016). |
| Lotters, Real-time Composition Determination of Gas Mixtures, [online], 2015, [retrieved on Jun. 19, 2019]. Retrieved from <https:// www .ama-science.org/proceedings/getFile/ZwNOZj==> (Year: 2015). |
| Maybeck, Peter S., “Stochastic models, estimation, and control,” vol. 1, Academic Press (1979), 19 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20180364747 A1 | Dec 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62522612 | Jun 2017 | US |
