Patent Application
20230107739
The present specification generally relates to measuring dissolved oxygen in seawater. More particularly, the present specification relates to predicting a dissolved oxygen level in treated seawater using a neural network.
In oil and gas production, seawater is often injected into a hydrocarbon reservoir, such as an oil or gas reservoir, to help maintain pressure in the reservoir. Reservoir pressure declines as hydrocarbons are withdrawn from the reservoir and this decline leads to a reduction in hydrocarbon productivity. Reservoir pressure maintenance is a practice in the oil industry in which seawater is injected into the reservoir's aquifer zone below the reservoir's oil zone to support the reservoir pressure. This seawater is treated at a seawater treatment facility before injection into the oil and gas reservoir to remove dissolved oxygen from the seawater. Dissolved oxygen promotes corrosion and growth of bacteria in the reservoir and such bacterial growth in the reservoir can produce hydrogen sulfide, a source of production problems, and may block the pores in the subsurface rock. To maintain a low level of dissolved oxygen in treated seawater, it is important to measure dissolved oxygen. Conventionally, dissolved oxygen is measured using a physical dissolved oxygen analyzer. However, such physical analyzers are costly, require regular calibration, and often require extensive maintenance. Moreover, if the physical analyzer is not calibrated regularly, it will produce incorrect dissolved oxygen readings, allowing the seawater treatment facility to unknowingly provide high dissolved oxygen seawater to a hydrocarbon reservoir for injection.
Accordingly, a need exist for improved methods for measuring dissolved oxygen in a seawater treatment facility to provide consistent, low oxygen seawater for injection into a hydrocarbon reservoir.
According to a first aspect of the present disclosure, a method for predicting a dissolved oxygen level in treated seawater includes directing raw seawater from a fluid channel into a deaerator, directing an inert gas and an oxygen scavenger solution into the deaerator, thereby removing dissolved oxygen from the raw seawater such that treated seawater exits the deaerator, receiving information about a plurality of input parameters comprising a raw seawater flow rate, an inert gas flow rate, and an oxygen scavenger flow rate, implementing a neural network trained to predict the dissolved oxygen level of the treated seawater based on the plurality of input parameters and predicting, with the neural network, the dissolved oxygen level of the treated seawater based on the plurality of input parameters.
A second aspect of the present disclosure includes the method of the first aspect, wherein the plurality of input parameters are measured using one or more sensors.
A third aspect of the present disclosure includes the method of the second aspect, wherein the one or more sensors comprise a raw seawater flow sensor that is positioned along the fluid channel upstream the deaerator and is configured to measure the raw seawater flow rate.
A fourth aspect of the present disclosure includes the method of the third aspect, wherein the raw seawater flow sensor is positioned along the fluid channel such that the raw seawater flow sensor measures the raw seawater flow rate entering the deaerator.
A fifth aspect of the present disclosure includes the method of any of the second through fourth aspects, wherein the one or more sensors comprise an inert gas flow sensor and a solution flow sensor; the inert gas flow sensor is positioned along a first fluid pathway that fluidly couples an inert gas source and a gas inlet of the deaerator and is configured to measure the inert gas flow rate; and the solution flow sensor is positioned along a second fluid pathway that fluidly couples an oxygen scavenger source and a solution inlet of the deaerator and is configured to measure the oxygen scavenger flow rate.
A sixth aspect of the present disclosure includes the method of any of the second through fifth aspects, wherein the plurality of input parameters further comprise a volume of seawater in the deaerator and the one or more sensors comprise a volume sensor that is positioned in a fluid chamber of the deaerator and is configured to measure the volume of seawater in the deaerator.
A seventh aspect of the present disclosure includes the method of any of the second through sixth aspects, wherein the plurality of input parameters further comprise a temperature of seawater in the fluid channel upstream the deaerator, a temperature of seawater in the deaerator, or both, and the one or more sensors comprise a temperature senor that is positioned along the fluid channel upstream the deaerator, a temperature sensor positioned in the deaerator, or both and is configured to measure the temperature of seawater in the fluid channel upstream the deaerator, the temperature of seawater in the deaerator, or both.
An eighth aspect of the present disclosure includes the method of any of the first through seventh aspects, wherein the inert gas comprises nitrogen gas.
A ninth aspect of the present disclosure includes the method of any of the first through eighth aspects, wherein the oxygen scavenger solution comprises sulfurous acid.
A tenth aspect of the present disclosure includes the method of any of the first through ninth aspects, wherein the dissolved oxygen level of the treated seawater is 10 ppb or less.
An eleventh aspect of the present disclosure includes the method of any of the first through tenth aspects, wherein the neural network is trained using a step test performed for each of the plurality of input parameters.
According to a twelfth aspect of the present disclosure, a seawater processing system includes a deaerator fluidly coupled to a fluid channel; one or more sensors configured to measure a plurality of input parameters comprising a raw seawater flow rate, an inert gas flow rate, and an oxygen scavenger flow rate; an electronic control unit communicatively coupled to the one or more sensors and configured to: receive, as an input to a neural network, information about the plurality of input parameters while raw seawater is directed into the deaerator, an oxygen scavenger solution is directed into the deaerator, and an inert gas is directed into the deaerator, such that dissolved oxygen is removed from the raw seawater in the deaerator and treated seawater is output by the deaerator; implement the neural network trained to predict a dissolved oxygen level of the treated seawater based on the plurality of input parameters; and predict, with the neural network, the dissolved oxygen level of the treated seawater based on the plurality of input parameters.
A thirteenth aspect of the present disclosure includes the seawater processing system of the twelfth aspect, wherein the deaerator comprises a seawater inlet for receiving raw seawater and a seawater outlet for outputting treated seawater, a gas inlet for receiving the inert gas, and a solution inlet for receiving the oxygen scavenger solution; and the gas inlet and the solution inlet are positioned between the seawater inlet and the seawater outlet and the seawater outlet is positioned nearer a lower end of the deaerator than the seawater inlet.
A fourteenth aspect of the present disclosure includes the seawater processing system of the twelfth aspect of the thirteenth aspect, wherein the deaerator further comprises a plurality of trays positioned in a fluid chamber of the deaerator.
A fifteenth aspect of the present disclosure includes the seawater processing system of any of the twelfth through fourteenth aspects, wherein the one or more sensors comprise a raw seawater flow sensor positioned along the fluid channel upstream the deaerator such that the raw seawater flow sensor measures the raw seawater flow rate entering the deaerator.
A sixteenth aspect of the present disclosure includes the seawater processing system of any of the twelfth through fifteenth aspects, wherein the one or more sensors comprise an inert gas flow sensor and a solution flow sensor; the inert gas flow sensor is positioned along a first fluid pathway that fluidly couples an inert gas source and a gas inlet of the deaerator and is configured to measure the inert gas flow rate; and the solution flow sensor is positioned along a second fluid pathway that fluidly couples an oxygen scavenger source and a solution inlet of the deaerator and is configured to measure the oxygen scavenger flow rate.
A seventeenth aspect of the present disclosure includes the seawater processing system of any of the twelfth through sixteenth aspects, wherein the plurality of input parameters further comprise a volume of seawater in the deaerator and the one or more sensors comprise a volume sensor that is positioned in a fluid chamber of the deaerator and is configured to measure the volume of seawater in the deaerator.
An eighteenth aspect of the present disclosure includes the seawater processing system of any of the twelfth through seventeenth aspects, wherein the plurality of input parameters further comprise a temperature of seawater in the fluid channel upstream the deaerator, a temperature of seawater in the deaerator, or both, and the one or more sensors comprise a temperature senor that is positioned along the fluid channel upstream the deaerator, a temperature sensor positioned in the deaerator, or both and is configured to measure the temperature of seawater in the fluid channel upstream the deaerator, the temperature of seawater in the deaerator, or both.
A nineteenth aspect of the present disclosure includes the seawater processing system of any of the twelfth through eighteenth aspects, further including a seawater filter fluidly coupled to the fluid channel upstream from the deaerator and the plurality of input parameters further comprise a flow rate of raw seawater through the seawater filter.
According to a twentieth aspect of the present disclosure, a method for predicting a dissolved oxygen level in treated seawater includes training a neural network to predict the dissolved oxygen level of treated seawater using a step test performed for each of a plurality of input parameters, the plurality of input parameters comprising a raw seawater flow rate entering a deaerator, an inert gas flow rate, an oxygen scavenger flow rate, and a volume of seawater in the deaerator; directing raw seawater from a fluid channel into the deaerator; directing an inert gas and an oxygen scavenger solution into the deaerator, thereby removing dissolved oxygen from the raw seawater such that treated seawater exits the deaerator; receiving information about the plurality of input parameters from one or more sensors that include a raw seawater flow sensor that is positioned along the fluid channel upstream the deaerator and is configured to measure the raw seawater flow rate entering the deaerator, an inert gas flow sensor that is positioned along a first fluid pathway that fluidly couples an inert gas source and a gas inlet of the deaerator and is configured to measure the inert gas flow rate; a solution flow sensor that is positioned along a second fluid pathway that fluidly couples an oxygen scavenger source and a solution inlet of the deaerator and is configured to measure the oxygen scavenger flow rate; and a volume sensor that is positioned in a fluid chamber of the deaerator and is configured to measure the volume of seawater in the deaerator. The method further includes implementing the neural network trained to predict the dissolved oxygen level of the treated seawater based on the plurality of input parameters and predicting, with the neural network, the dissolved oxygen level of the treated seawater based on the plurality of input parameters.
Additional features and advantages of the processes and systems described herein will be set forth in the detailed description which follows, and in part will be readily apparent to those skilled in the art from that description or recognized by practicing the embodiments described herein, including the detailed description which follows, the claims, as well as the appended drawings.
It is to be understood that both the foregoing general description and the following detailed description describe various embodiments and are intended to provide an overview or framework for understanding the nature and character of the claimed subject matter. The accompanying drawings are included to provide a further understanding of the various embodiments, and are incorporated into and constitute a part of this specification. The drawings illustrate the various embodiments described herein, and together with the description serve to explain the principles and operations of the claimed subject matter.
The embodiments set forth in the drawings are illustrative and exemplary in nature and not intended to limit the subject matter defined by the claims. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals and in which:
Reference will now be made in detail to methods and systems of using a neural network to predict dissolved oxygen in seawater treated by a deaerator of a seawater processing system. Predicting dissolved oxygen levels allows the seawater processing system to output treated seawater with reliably low levels of dissolved oxygen, without the need for physical sensors which are costly and require regular calibration and maintenance. Treated seawater is often used in downstream processes such as hydrocarbon reservoir injection and dissolved oxygen increases corrosion of downstream pipelines and other downstream equipment. Thus, it is desirable to maintain low levels of dissolved oxygen in treated seawater. In the embodiments described herein, the measurement functionality of physical sensors is performed by a neural network to predict the level of dissolved oxygen in the treated seawater, using a variety of input parameters, such as seawater flow rate, seawater temperature, the flow rate of an inert gas injected into the deaerator, and the flow rate of an oxygen scavenger solution injected into the deaerator. Embodiments of a seawater processing system that uses a neural network for predicting dissolved oxygen in treated seawater will now be described and, whenever possible, the same reference numerals will be used throughout the drawings to refer to the same or like parts.
Referring now to
Referring now to
The deaerator 160 also comprises a gas inlet 170 and a solution inlet 172. The gas inlet 170 and the solution inlet 172 may each be positioned between the seawater inlet 162 and the seawater outlet 164, for example, between the upper end 161 and the lower end 163 of the deaerator 160. The gas inlet 170 is fluidly coupled to an inert gas source 174 by a first fluid pathway 171. The solution inlet 172 is fluidly coupled to an oxygen scavenger source 176 by a second fluid pathway 175. The inert gas may comprise nitrogen gas, argon gas, or any other inert gas. The oxygen scavenger solution may comprise a sulfurous acid (H2SO3) or other solution with a composition that reacts with dissolved oxygen such that a sulfur oxide entraps the dissolved oxygen that is present in the seawater. Other oxygen scavenger solutions may include carbohydrazide (CH6N4O), diethylhydroxylamine (C4H11NO), hydroquinone (C6H6O2), sodium sulfite (Na2SO3), and ammonium bisulfite (NH4HSO3). In operation, the inert gas may be directed into the fluid chamber 165 through the gas inlet 170 and the oxygen scavenger solution may be directed into the fluid chamber 165 through the solution inlet 172. The oxygen scavenger solution injected into the deaerator 160 chemically removes dissolved oxygen from the seawater. For example, when the oxygen scavenger solution comprises sulfurous acid, it chemically reacts with dissolved oxygen as shown by equations (1) and (2), below.
H2SO3→H++HSO3− (1)
2HSO3−+O2→2SO42−+2H+ (2)
As shown in equations (1) and (2), hydrogen sulfite (HSO3−) reacts with dissolved oxygen (O2) to form sulfate (SO42−) and hydrogen (2H+). The amount of dissolved oxygen removed by this reaction is time dependent and dependent on the concentration of the oxygen scavenger solution (i.e., the concentration of sulfurous acid). The reaction time is dependent of the geometry of the deaerator 160, the seawater volume in the deaerator 160, and the flow rate of seawater entering the deaerator 160. In addition, the concentration of the oxygen scavenger solution is dependent on the flow rate of the oxygen scavenger solution entering the deaerator 160. In operation, seawater flows from the upper end 161 to the lower end 163 of the deaerator 160 in the flow direction 10 and the injected inert gas flows from the gas inlet 170 toward the upper end 161, in a flow direction 12 opposite the flow direction 10 of the seawater in the deaerator 160. Thus, the inert gas comes into close contact with the seawater and removes dissolved oxygen from the seawater by mechanical stripping. The combination of the inert gas and the oxygen scavenger solution removes dissolved oxygen from seawater in the deaerator 160 such that treated seawater exiting the deaerator comprises a dissolved oxygen level of 100 ppb or less, such as 75 ppb or less, 50 ppb or less, 25 ppb or less, 10 ppb or less, 5 ppb or less, 1 ppb or less, or any range having any two of these values as endpoints. This treated seawater may be directed from the seawater processing system 100 to a hydrocarbon production reservoir for seawater injection.
Referring now to
Referring still to
In operation, the level of seawater in the fluid chamber 165, which may be determined by a level sensor, and the diameter of the fluid chamber 165 along the depth of the seawater in the fluid chamber 165 may be used to determine the total volume of seawater in the fluid chamber. Moreover, the ratio of seawater volume to the seawater flow rate at the seawater outlet 164 may be used to determine the resident time of the seawater in the deaerator 160, 160′ (i.e., the average time seawater remains in the deaerator 160, 160′). This is the time available for the oxygen scavenger solution to react with dissolved oxygen present in the seawater. The shape (e.g., diameter) of the deaerator is a fixed constant and the seawater level and the discharge flow rate are dynamic constants which are measured to help determine the amount of dissolved oxygen present in the treated seawater, for example, using a plurality of sensors 120, described in more detail below.
Referring now to
Referring still to
The plurality of sensors 120 also comprise a volume sensor 125 positioned in the fluid chamber 165 of the deaerator 160, 160′ and configured to measure the volume of seawater in the deaerator 160, 160′. For example, the volume sensor 125 may comprise a level sensor, such as a level transmitter. The level transmitted may measure the level of seawater in the deaerator 160, 160′, for example, in feet, and based on this level measurement the volume of seawater in the deaerator 160, 160′ may be calculated, for example, by an electronic control unit 130. In addition, the plurality of sensors 120 also comprise an inert gas flow sensor 126 positioned along the first fluid pathway 171, positioned at the gas inlet 170, or a combination thereof, a scavenger solution flow sensor 128 positioned along the second fluid pathway 175, positioned at the solution inlet 172, or a combination thereof. The inert gas flow sensor 126 is configured to measure the flow rate of the inert gas entering the deaerator 160, 160′ and the scavenger solution flow sensor 128 is configured to measure the flow rate of the oxygen scavenger solution into the deaerator 160, 160′. While not depicted, the plurality of sensors 120 may further comprise one or more pressure sensors positioned along the fluid channel 112 to measure the pressure of seawater flowing in the fluid channel 112. For example, pressure sensors may be used to measure differential pressure across each of the seawater filters.
Referring now to
The communications pathway 131 may be formed from any medium that is capable of transmitting a signal such as, for example, conductive wires, conductive traces, optical waveguides, or the like. The communications pathway 131 may also refer to the expanse in which electromagnetic radiation and their corresponding electromagnetic waves traverses. Moreover, the communications pathway 131 may be formed from a combination of mediums capable of transmitting signals. In one embodiment, the communications pathway 131 comprises a combination of conductive traces, conductive wires, connectors, and buses that cooperate to permit the transmission of electrical data signals to components such as processors 132, memories, sensors, input devices, output devices, and communication devices. Additionally, it is noted that the term “signal” means a waveform (e.g., electrical, optical, magnetic, mechanical or electromagnetic), such as DC, AC, sinusoidal-wave, triangular-wave, square-wave, vibration, and the like, capable of traveling through a medium. As used herein, the term “communicatively coupled” means that coupled components are capable of exchanging signals with one another such as, for example, electrical signals via conductive medium, electromagnetic signals via air, optical signals via optical waveguides, and the like.
The electronic control unit 130 may be any device or combination of components comprising a processor 132 and the memory component 134. The processor 132 may be any device capable of executing the machine-readable instruction set stored in the memory component 134. Accordingly, the processor 132 may be an electric controller, an integrated circuit, a microchip, a field programmable gate array, a computer, or any other computing device. The processor 132 is communicatively coupled to other components of the seawater processing system 100 by the communications pathway 131. Accordingly, the communications pathway 131 may communicatively couple any number of processors 132 with one another, and allow the components coupled to the communications pathway 131 to operate in a distributed computing environment. Specifically, each of the components may operate as a node that may send and/or receive data.
The memory component 134 is coupled to the communications pathway 131 and communicatively coupled to the processor 132. The memory component 134 may be a non-transitory computer readable memory and may comprise RAM, ROM, flash memories, hard drives, or any non-transitory memory device capable of storing machine-readable instructions such that the machine-readable instructions can be accessed and executed by the processor 132. The machine-readable instruction set may comprise logic or algorithm(s) written in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL) such as machine language that may be directly executed by the processor 132, or assembly language, object-oriented programming (OOP), scripting languages, microcode, etc., that may be compiled or assembled into machine readable instructions and stored in the memory component 134. Alternatively, the machine-readable instruction set may be written in a hardware description language (HDL), such as logic implemented via either a field-programmable gate array (FPGA) configuration or an application-specific integrated circuit (ASIC), or their equivalents. Accordingly, the functionality described herein may be implemented in any conventional computer programming language, as pre-programmed hardware elements, or as a combination of hardware and software components.
The data storage component 140 may be a volatile and/or nonvolatile digital storage component and, as such, may include random access memory (including SRAM, DRAM, and/or other types of random access memory), flash memory, registers, compact discs (CD), digital versatile discs (DVD), and/or other types of storage components. The data storage component 140 may reside local to and/or remote from the seawater processing system 100 and may be configured to store one or more pieces of data (e.g., seawater information 240, treatment fluid information 242, deaerator information 244, and/or past data 246) for access by the electronic control unit 130 and/or other components. As illustrated in
Referring now to
This raw information includes a plurality of input parameters, such as, seawater information 240, which includes measurement data from the one or more seawater temperature sensors 122a-122c and the one or more seawater flow sensors 124a-124c, treatment fluid information 242, which includes measurement data from the inert gas flow sensor 126 and the scavenger solution flow sensor 128, and/or deaerator information 244, which includes measurement data from the seawater temperature sensor 122d and the volume sensor 125. The seawater information 240 may comprise the flow rate of seawater through the coarse filter 150, flow rate of seawater through the fine filter 152, the flow rate of seawater entering the deaerator 160, 160′, and the temperature of seawater entering the deaerator 160, 160′. The seawater information 240 may further comprise information regarding the pressure of seawater flowing through the seawater processing system 100. Treatment fluid information 242 may comprise the flow rate of inert gas entering the deaerator 160, 160′, the flow rate of oxygen scavenger solution entering the deaerator 160, 160′, and the concentration and composition of the oxygen scavenger solution. The deaerator information 244 may comprise the temperature of seawater in the deaerator 160, 160′ the volume of seawater in the deaerator 160, 160′, and geometric properties of deaerator 160, 160′, such as the volume of the fluid chamber 165 and the design, position, and number of trays 180.
The plurality of input parameters may be input into the neural network 200 at the input layer 205. The neural network 200 processes the raw information received at the input layer 205 through nodes 201 and node connections 202. The one or more hidden layers 210, 215, depending on the inputs from the input layer 205 and the weights on the node connections 202, carry out computational activities. In other words, the hidden layers 210, 215 perform computations and transfer information from the input layer 205 to the output layer 220 through their associated nodes 201 and node connections 202.
In general, when the neural network 200 is learning, the neural network 200 is identifying and determining patterns within the raw information received at the input layer 205. In response, one or more parameters, for example, weights associated to node connections 202 between nodes 201, may be adjusted through a process known as back-propagation. It should be understood that there are various processes in which learning may occur, however, two general learning processes include associative mapping and regularity detection. Associative mapping refers to a learning process where a neural network 200 learns to produce a particular pattern on the set of inputs whenever another particular pattern is applied on the set of inputs. Regularity detection refers to a learning process where the neural network 200 learns to respond to particular properties of the input patterns. Whereas in associative mapping the neural network 200 stores the relationships among patterns, in regularity detection the response of each unit has a particular ‘meaning’. This type of learning mechanism may be used for feature discovery and knowledge representation.
Neural networks possess knowledge that is contained in the values of the node connection weights. Modifying the knowledge stored in the network as a function of experience implies a learning rule for changing the values of the weights. Information is stored in a weight matrix W of a neural network. Learning is the determination of the weights. Following the way learning is performed, two major categories of neural networks can be distinguished: 1) fixed networks in which the weights cannot be changed (i.e., dW/dt=0) and 2) adaptive networks that are able to change their weights (i.e., dW/dt not=0). In fixed networks, the weights are fixed a priori according to the problem to solve.
In order to train a neural network to perform some task, such as predicting dissolved oxygen levels in treated seawater, adjustments to the weights are made in such a way that the error between the desired output and the actual output is reduced. This process may require that the neural network compute the error derivative of the weights (EW). In other words, it must calculate how the error changes as each weight is increased or decreased slightly. A back propagation algorithm is one method that is used for determining the EW.
The algorithm computes each EW by first computing the error derivative (EA), the rate at which the error changes as the activity level of a unit is changed. For output units, the EA is simply the difference between the actual and the desired output. To compute the EA for a hidden unit in the layer just before the output layer, first all the weights between that hidden unit and the output units to which it is connected are identified. Then, those weights are multiplied by the EAs of those output units and the products are added. This sum equals the EA for the chosen hidden unit. After calculating all the EAs in the hidden layer just before the output layer, in like fashion, the EAs for other layers may be computed, moving from layer to layer in a direction opposite to the way activities propagate through the neural network, hence “back propagation”. Once the EA has been computed for a unit, it is straightforward to compute the EW for each incoming connection of the unit. The EW is the product of the EA and the activity through the incoming connection. It should be understood that this is only one method in which a neural network is trained to perform a task.
Referring still to
To develop training data for the neural network 200, a step test is conducted. During the step test, one of the plurality of input parameters is changed in incremental steps in a range of 5% to 10% and the impact of this change on dissolved oxygen in treated seawater is measured, for example, using a physical analyzer which may be used when training of the neural network 200. The step test may be performed for each of the plurality of input parameters and data is collected and stored in the data storage component 140 and may be repeated as needed to hone the stored data. The step tests may be continuously monitored such that inconsistent data points and outliers may be rejected. The training data from the step tests may be used to develop the neural network 200 such that the neural network may be used to accurately predict dissolved oxygen levels in seawater treated by the seawater processing system 100. Furthermore, the training data used to develop the neural network 200 provides information regarding the optimal flow rate and concentration of the oxygen scavenger solution to achieve desired levels of dissolved oxygen in the treated seawater.
Referring again to
As used herein, the term “about” means that amounts, sizes, formulations, parameters, and other quantities and characteristics are not and need not be exact, but may be approximate and/or larger or smaller, as desired, reflecting tolerances, conversion factors, rounding off, measurement error and the like, and other factors known to those of skill in the art. When the term “about” is used in describing a value or an end-point of a range, the specific value or end-point referred to is included. Whether or not a numerical value or end-point of a range in the specification recites “about,” two embodiments are described: one modified by “about,” and one not modified by “about.” It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.
Directional terms as used herein—for example up, down, right, left, front, back, top, bottom—are made only with reference to the figures as drawn and are not intended to imply absolute orientation.
Unless otherwise expressly stated, it is in no way intended that any method set forth herein be construed as requiring that its steps be performed in a specific order, nor that with any apparatus specific orientations be required. Accordingly, where a method claim does not actually recite an order to be followed by its steps, or that any apparatus claim does not actually recite an order or orientation to individual components, or it is not otherwise specifically stated in the claims or description that the steps are to be limited to a specific order, or that a specific order or orientation to components of an apparatus is not recited, it is in no way intended that an order or orientation be inferred, in any respect. This holds for any possible non-express basis for interpretation, including: matters of logic with respect to arrangement of steps, operational flow, order of components, or orientation of components; plain meaning derived from grammatical organization or punctuation, and; the number or type of embodiments described in the specification.
As used herein, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a” component includes aspects having two or more such components, unless the context clearly indicates otherwise.
It will be apparent to those skilled in the art that various modifications and variations can be made to the embodiments described herein without departing from the spirit and scope of the claimed subject matter. Thus, it is intended that the specification cover the modifications and variations of the various embodiments described herein provided such modification and variations come within the scope of the appended claims and their equivalents.
