Patent Application
20140020400
Embodiments of the subject matter disclosed herein generally relate to methods and systems and, more particularly, to mechanisms and techniques for automatically auto-tuning a gas turbine combustion system.
Gas turbines are used in many sectors of the industry, from military to power generation. They are used mainly to produce electrical energy. However, some gas turbines are used to propel various vehicles, airplanes, ships, etc. Depending on the application, turbines are required to operate under different settings and conditions. This has necessitated the development of control systems to maintain operation.
The control systems are designed to control the combustion system of the gas turbine. Unfortunately, many of these control systems use static analysis based on fixed values to perform turbine control. Also, many of these systems require at least some degree of manual intervention, which increases costs and increases the likelihood of errors. For example, a traditional combustion system of a gas turbine has to be adjusted a couple of times over the life of the gas turbine. Specialized technicians perform this step manually. These technicians have to be deployed to the site of each gas turbine, which is a slow and expensive process.
Notwithstanding these considerations, gas turbines are subject to regulations which require them to demonstrate at least a minimum level of emission control. These so-called “green” regulations serve as further impetus for controlling the operation of gas turbines. Accordingly, it would be desirable to provide systems and methods that avoid the afore-described problems and drawbacks.
According to an embodiment of the present invention, a method for auto-tuning a combustion system of a gas turbine is provided. The method includes selecting a first tuning curve from a set of tuning curves for the gas turbine; unbalancing a stable operating point of the gas turbine by modifying one or more operational parameters based on a predefined recipe; determining tuning parameters and storing them while a current operating point of the gas turbine is brought back on the first tuning curve; and generating a backup of tuning parameters to recover the stable operating point.
According to another embodiment of the present invention, a controller in a gas turbine for auto-tuning a combustion system of the gas turbine is provided. The controller includes a storage device configured to store tuning curves of the gas turbine; a processor connected to the storage device and configured to, select a first tuning curve from a set of tuning curves for the gas turbine; unbalance a stable operating point of the gas turbine by modifying one or more operational parameters based on a predefined recipe; determine tuning parameters and store them while a current operating point of the gas turbine is brought back on the first tuning curve; and generate a backup of tuning parameters to recover the stable operating point.
According to another embodiment of the present invention, a gas turbine is provided. The gas turbine includes a combustion system; a controller having a storage device configured to store tuning curves of the combustion system of the gas turbine; and a processor connected to the storage device. The processor is configured to select a first tuning curve from a set of tuning curves for the gas turbine; unbalance a stable operating point of the gas turbine by modifying one or more operational parameters based on a predefined recipe; determine tuning parameters and store them while a current operating point of the gas turbine is brought back on the first tuning curve; and generate a backup of tuning parameters to recover the stable operating point.
According to another embodiment of the present invention, computer readable medium is provided. The computer readable medium includes computer executable instructions, wherein the instructions, when executed, implement a method for auto-tuning a combustion system of a gas turbine, the method comprising: selecting a first tuning curve from a set of tuning curves for the gas turbine; unbalancing a stable operating point of the gas turbine by modifying one or more operational parameters based on a predefined recipe; determining tuning parameters and storing them while a current operating point of the gas turbine is brought back on the first tuning curve; and generating a backup of tuning parameters to recover the stable operating point.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate one or more embodiments and, together with the description, explain these embodiments. In the drawings:
The following description of the exemplary embodiments refers to the accompanying drawings. The same reference numbers in different drawings identify the same or similar elements. The following detailed description does not limit the invention. Instead, the scope of the invention is defined by the appended claims. The following embodiments are discussed, for simplicity, with regard to the terminology and structure of a combustion system of a gas turbine. However, the embodiments to be discussed next are not limited to the gas turbine, but may be applied to other turbo-machines.
Reference throughout the specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with an embodiment is included in at least one embodiment of the subject matter disclosed. Thus, the appearance of the phrases “in one embodiment” or “in an embodiment” in various places throughout the specification is not necessarily referring to the same embodiment. Further, the particular features, structures or characteristics may be combined in any suitable manner in one or more embodiments.
As shown in
The processor 10 monitors the turbine throughout various phases of operation and then automatically controls the combustion system of the gas turbine based on scheduling and control data and algorithms stored in an internal or external memory, to be discussed in greater detail below. Structurally, the processor may be a microcomputer, controller, or other type of processing chip or set of chips driven, for example, by the aforementioned scheduling and control programs. An example of an internal memory is shown by reference numeral 15.
In accordance with one application, the processor may be or include one or more of a rules-based engine, a neural network, or a virtual or state machine that perform cognitive modeling and dynamic control. Storage device 20 may store the scheduling and control data to assist in this modeling. This storage device may also serve as the archive for storing raw sensor and other data from which turbine performance, efficiency, and emission information may be derived.
If the processor is or includes a rules-based (or inference) engine, storage device 20 may serve as a knowledge base that stores information including, for example, an initial set of settings, rules, constraints, safety margins and/or other control data to be used in mapping and modeling operation of the combustion system of the gas turbine. The storage device may also store various control algorithms to govern the turbine, in lieu of or in addition to the algorithms stored in memory 15. Also, performance, efficiency, emissions-related data including or derived from sensor data, and combustion dynamics may be archived in storage device 20 as the turbine continues to operate.
Based on this archived data, the initial settings, rules, constraints, and/or other control data may be automatically adjusted to achieve an intended level of performance. For example, the initial control data in the storage device may not be optimal for purposes of achieving a predetermined level of performance. Alternatively, the combustion system may need to be brought back to desired operation conditions. Over time, status information and sensor data may be archived and analyzed to determine, using the rules-based engine, how the control data may be adjusted to improve performance. The rules of the engine may govern these adjustments, and the adjustments may then be stored as new control data for the turbine.
Through these “learning” techniques, the processor may dynamically and adaptively build a backup curve to be used for maintaining the unit in operation in case of combustion issues. The backup curve or a new curve is able to minimize combustion dynamics that will reduce the risk of a trip and also will reduce the needs of manual tuning of the curve.
If the processor is or includes a neural network (or other techniques), the network may include a plurality of neurons (e.g., programming constructs) that correspond to different parameters, settings, constraints, and/or conditions to model operation of the turbine. The neurons may be interconnected to define how the turbine is to be controlled under different operating and/or load conditions. The interconnections and/or the neurons themselves may be adjusted, deleted, and/or replaced based on information fed back from the sensors and archived information, to cause the turbine to operate within one or more intended ranges.
According to one implementation, the neural network may be based on a finite state machine having states and transitions which correspond to predetermined conditions or operating parameters. The machine may be dynamically modified from its initial configuration in order to control the turbine based, for example, on one or more stored algorithms. Different mathematical modeling techniques may be used in other embodiments to perform the functions of the processor in the system of
Moreover, while artificial intelligence may prove beneficial for some applications, different processing architectures may be used in other embodiments. For example, the processor may be a microprocessor-driven circuit with preloaded control programs that perform adaptive control of the turbine based on archived sensor, performance, and/or emissions data. In other embodiments, the processor may be a notebook or other type of computer which performs the type of adaptive control described herein.
Irrespective of the processing architecture, in an exemplary application storage device 20 may store initial sets of control data for the combustion system of the gas turbine under various conditions and phases of operation. As indicated, this control data may include operating parameters, constraints, and/or scheduling data For a DLE gas turbine, the parameters may include but are not limited to firing temperature, compressor ratio, compressor inlet pressure and temperature, compressor exit pressure and temperature, turbine exhaust temperature, inlet guide vane settings, combustion reference temperature and/or other combustion parameters, and inlet bleed heat flow as well as others.
The constraints may include emission levels, combustion dynamic pressure, lean-blow-out, compressor surge limits, compressor icing, fuel flow limits, combustor fuel distribution levels (or splits), efficiency limits, compressor clearances as well as other operational and/or environmental settings or restrictions.
The scheduling data may include exhaust temperature vs. compressor pressure ratio, fuel splits vs. combustion reference temperature, inlet bleed heat vs. inlet guide vane settings, and compressor operating limit line vs. corrected speed and inlet guide vane settings as well as others. Different or additional parameters may be used for other types of turbines and their applications.
Taken together, the information in storage device 20 allows operation of the combustion system of the gas turbine to be modeled under various conditions. Put differently, this information may “map” the operating space of the gas turbine and its associated load, which map may be used as a basis for controlling combustion and/or other operational aspects in order to maintain the gas turbine operating in a stable, optimum, or other intended range. In terms of structure, the storage device may be one or more of a memory, database, rules or knowledge base, or a combination thereof.
The gas turbine 30 may be any type of turbine including a dry low-emission (DLE) gas turbine or dry-low nitrous (NOx) turbine, as well as any one of a number of other turbines used for electrical power, vehicle, or industrial applications. Examples of DLE turbines include ones used for turbojet, turbofan, and turboprop-based engines.
The gas turbine also includes a number of ducts. For example, an inlet duct 120 feeds ambient air to compressor 112 through a set of inlet guide vanes 121, and an exhaust duct 122 directs combustion gases from the turbine 116 to or through, for example, an electrical generator 124. One or more emission control and noise abatement devices may also be included to comply with regulations.
The gas turbine also includes a fuel control system 128 for regulating the flow of fuel from a fuel supply to the combustor 114 (the combustion system) and through one or more splits between fuel flowing in different groups of nozzles. For example, a combustor may include primary and secondary nozzles while other combustors may include three different burner groups with many possible combinations of the split. The fuel control system may also select the type of fuel for the combustor. The fuel control system 128 may be a separate unit or may be a component of the main controller 118.
The controller 118 (which may correspond to processor 10 in
The commands generated by the controller may cause actuators on the combustion system of the gas turbine to, for example, adjust valves (e.g., actuator 127) between the fuel supply and combustors that regulate the flow, fuel splits and type of fuel flowing to the combustors; adjust inlet guide vanes 121 (actuator 129) on the compressor; adjust inlet bleed heat; as well as activate other control settings on the gas turbine.
When used herein, the term “gas turbine” may not only refer to the turbine itself but also all of its attendant features including but not limited to the inlet duct, guide vanes, compressor, fuel controller, combustor, and outlet duct. Sensor data for these elements may be fed back to the processor to be used to adjust the initial modeling data as well as to perform other aspects relating to adaptive, dynamic control.
Returning to
The sensors may also include groups of redundant temperature sensors to monitor, for example, ambient temperature surrounding the gas turbine, compressor discharge temperature, turbine exhaust gas temperature, and other temperature measurements of the gas stream through the turbine.
Other sensors may include ones that monitor ambient pressure, and static and dynamic pressure levels at the compressor inlet and outlet turbine exhaust, as well as at other locations in the gas stream. Additional examples include wet and dry bulb thermometers, humidity sensors in the compressor inlet duct, flow sensors, speed sensors, flame detector sensors, valve position sensors, guide vane angle sensors.
The load 50 differs depending on the application. For example, the load may be an electrical generator or an engine/throttle-based load.
In an initial step, information relating to the combustion system of the gas turbine is stored in the storage device. (Block 210). As previously indicated, this information may be an initial set of control data including but not limited to settings, parameters, constraints, and/or scheduling data for initially controlling operation of the turbine. This information may be loaded into the storage device by a system manufacturer and therefore may be considered to be an initial mapping or modeling of the turbine operating points and conditions. This initial modeling may not control the turbine to operate within optimum or predetermined ranges and therefore adjustment in accordance with subsequent steps described herein is required.
A companion set of algorithms may be stored for use with the information in the storage device. The algorithms may control the timing an operation of the various parts of the turbine based on the stored control data. In accordance with one embodiment, the algorithms themselves may not be subject to adjustment. In this case, only the settings, constraints, and other stored control data may be adjusted to affect performance. In other embodiments, adjustments may be automatically made to the algorithms themselves in addition to the control data.
After the information and algorithms have been stored, the turbine may be operated based on the initial mapping and modeling data. During operation, status information, sensor data, and performance statistics are received from the sensors on a real- or near real-time basis by the processor. (Block 220). This information is archived in storage device 20 or another storage device, and/or may be sent through a network to a remote location for storage and analysis.
After a predetermined operational time has elapsed, an historical record of the performance, emissions, and/or other aspects of combustion system of the turbine operation is developed. This information may now be analyzed by the processor. (Block 230). The predetermined elapse time may correspond, for example, to a certain time of operation and/or may correspond to certain schedules used to control the turbine during various phases of operation. The analysis may involve, for example, comparing various performance statistics to predetermined standards or constraints, and/or comparing emissions levels to government or other intended limits. Based on the outcome of these comparisons, the processor may determine whether the gas turbine is operating at acceptable or optimum levels.
When the gas turbine is determined to operate outside of an intended level or range, based, for example, on the archived information and/or sensor data, the processor may automatically “tune” one or more of the initial control data (e.g., stored settings, constraints, or other information) stored for mapping or modeling the gas turbine. (Block 240). This tuning process may be performed in a variety of ways.
For example, if an efficiency or performance parameter or a protection parameter (e.g., a combustion dynamic pressure is a measure used to protect the engine by excessive vibrations) is determined to lie outside a certain range, the firing temperature may be automatically adjusted by a predetermined positive or negative increment. The performance of the turbine may then be measured to determine whether any improvement has been made. If not, additional incremental adjustments may be iteratively performed until sensor and/or other performance data indicate that the turbine is operating within the intended range. The incremented data may then be stored as part of a modified set of control data for subsequent use when, for example, the same or similar set of conditions or circumstances present themselves. (Block 250). In this way, the modeling data controlling operation of the combustion system of the gas turbine may be adaptively adjusted over time, thereby providing automatic dynamic tuning of the turbine. Also the fuel split is a parameter that can be adjusted in the same way to minimize combustion dynamics.
According to another example, the firing temperature may be set to one or more predetermined fixed values when the turbine is determined to be operating out of range. In still other embodiments, a different adjustment technique may be used.
Other parameters may also be adjusted to achieve combustion dynamics control. These parameters may include compressor ratio, compressor inlet pressure and temperature, compressor exit pressure and temperature, turbine exhaust temperature, inlet guide vane settings, combustion reference temperature and/or other combustion parameters, and inlet bleed heat flow, all of which relate to turbine efficiency. Of course, load status and conditions may also be taken into consideration when performing adjustments.
Based on the comparisons performed by the processor, various constraints may also be automatically adjusted. For example, if an emission level of the turbine is determined to exceed certain limits during one or more phases of operation, then corresponding settings of the combustion system of the gas turbine may be controlled to being those emissions under the limit. Also, when existing regulations change or new regulations are imposed, the control data corresponding to these constraints may be updated in the storage device to cause the turbine to operate in a compliant manner.
Other constraints include but are not limited to gas-fuel composition, lean-blow-out, compressor surge limits, compressor icing, fuel flow limits, combustor fuel distribution levels (or splits), and compressor clearances.
Based on the comparisons performed by the processor, various scheduling data may also be automatically adjusted. This data may include exhaust temperature vs. compressor pressure ratio, fuel splits vs. combustion reference temperature, inlet bleed heat vs. inlet guide vane settings, and compressor operating limit line vs. corrected speed and inlet guide vane settings.
The result of these steps is to form an improved set of control data for modeling and controlling operation of the turbine. The adjusted control data may be stored in storage device 20 in
In an initial step, an initial tuning curve is selected for use in controlling the combustion system of the gas turbine. (Block 310). A given gas turbine has a plurality of tuning curves that are stored and used for operating the combustion system of the gas turbine. The tuning curve may designate one or more settings, parameters, or constraints of different elements of the turbine, including but not limited to any of the ones previously discussed in other embodiments. The tuning curve, therefore, may be considered to provide an initial mapping of the gas turbine for modeling operation in its various phases of operation at steady-state.
According to one example, the tuning curve may be one relating turbine temperature exhaust (ttx) to turbine pressure ratio (tpr). The turbine temperature exhaust may correspond to the temperature at an outlet of the turbine and the turbine pressure ratio may correspond to a ratio between discharge pressure of the compressor and exhaust pressure of the turbine. Alternatively, the pressure readings may be taken at other locations of the gas turbine. According to another example relevant to the combustion system, the tuning curve may be one relating a flame temperature of burners groups to a combustor inlet temperature.
Other tuning curves may relate a combination of the following parameters: firing temperature, speed, inlet gate vane angle, humidity, and bleed conditions, just to name a few. In addition, different curves may be used under different environmental conditions. For example, one curve may be used when the humidity is at a relatively high level and another curve may be used when humidity is at a lower level. The same may be true based on ambient temperature. Still other curves may relate to gas-fuel ratios or other combustion-related parameters in order to allow for adjustments in efficiency, emissions and (acceptable) combustion dynamics.
After operation has begun based on the initial tuning curve, one of two events may occur. The first event corresponds to the case where an alert is received (Block 320). The alert may be generated by an internal control algorithm when a detected combustion parameter falls into or out of a predetermined range and/or when a detected emissions parameter violates a predetermined constraint. When this occurs, the alert information is send to the controller 118 to implement an unbalancing step (Block 340), which is discussed later. The ranges, constraints, or thresholds used to generate the alert may be programmed into the system, for example, by the manufacturer or by a technician on site. It is noted that the emission parameters may be directly measured at the gas turbine or evaluated by a dedicated module based on operating parameters of the gas turbine.
The second event that triggers the unbalancing step 340 corresponds to the case where a predetermined scheduling algorithm is executed. (Block 330). This scheduling algorithm (auto-tuning algorithm) may cause the controller to receive and analyze sensor data at regular or predetermined intervals throughout the period of operation, in order to determine performance efficiency and/or emissions. This may happen a couple of times per day, for example, during an initial learning period. It is noted that when the step 330 is taking place, the gas turbine operates under a steady state.
When the unbalance step is performed, the controller determines one or more parameters of the gas turbine. The gas turbine operates just before this step under a steady state. The controller modifies the one or more parameters of the gas turbine so that the gas turbine moves away from the steady state. In other words, after the unbalance step 340, the operating point of the gas turbine moves away from the specific tuning curve selected in step 310. At this stage, action needs to be taken so that the gas turbine moves back on the tuning curve and operates under a steady state.
However, if the unbalancing step 340 is triggered by the step 320, there are two alternatives. Either the event that generated the alerts are considered to have unbalanced the system and no more unbalancing is necessary or the unbalancing step 340 further unbalances the gas turbine. Examples of parameters that may be used to unbalance the gas turbine are illustrated in step 350 and they include but are not limited to fuel split in various rings of the gas turbine, split of fuel between burners, fuel/air ratio, bias, etc. However, the margins of these parameters are defined during this period to prevent the gas turbine to be unbalanced beyond a critical state from which the gas turbine cannot be brought back to the steady state.
The unbalancing may involve deviating from the initial tuning curve, for example, by automatically incrementing or otherwise adjusting iteratively or on a one-time basis one or more related parameters as previously described. These parameters may be related to the combustion system of the gas turbine.
After the unbalancing step (which may happen a couple of times a day for a learning period, e.g., 3 to 12 months), the controller monitors changes in performance and/or emissions of the gas turbine. (Block 360). The tuning parameters are recorded to map an impact of the unbalance on the system and how the system responds to the imbalance.
In step 370, the controller adjusts appropriate parameters (depending on which parameters have been unbalanced in step 350) in order to bring the gas turbine to operate under a steady state. These processes of unbalancing the gas turbine with various combinations of parameters and then bringing back the operation point of the gas turbine in a desired curve constitute the learning step 370. During this step a backup for the specific tuning curve under study is generated. This backup is improved or extended during the “learning period.” Optionally, the backup may be proposed to the operator, by the controller, when in a real case situation the gas turbine becomes unbalanced.
The tuning curve new acceptance criteria are formulated/stored in step 380 after which the controller returns to step 310 to select another tuning curve. The steps described above are repeated until all turning curves have been studied. This process is iteratively performed, each time making further and further adjustments until an adaptively generated tuning curve is automatically generated to optimize gas turbine performance or to otherwise cause the turning to operate within one or more intended ranges or levels.
A number of optional steps may be included. For example, after the tuning curve has been modified, backup information corresponding to the modified tuning curve may be stored in an external storage device in order to allow the curve to be recovered if a malfunction occurs. The backup information may be transferred to the external storage device, for example, through the internet or back-channel communications link. Also, storing this curve will allow an operator to make further adjustments, if desired.
The above discussed method may be implemented in a controller as illustrated in
Thus, the gas turbine having the auto-tuning module 408 will use the operating parameters and gas turbine daily records to define a surface of operating points. The module verifies the distance between the operating point and the critical conditions in order to define a backup of tuning constants (e.g., the identified tuning constants may be tables linking a firing temperature Tfire with a T3 in different combustion modes and for different combustion rings) to recover stable operations and maintain the gas turbine in production. Using this map and analyzing the current operating point the module will propose and correct new sets of combustion parameters enabling reliable operations and system self adjustment. Thus, such a novel module to be provided to the gas turbine advantageously learns from the history of the gas turbine to define a map of safe conditions, collects data from the gas turbine to define safety margins for operability, helps the operators to manage gas turbines reducing the need for tuning of the combustion system, provides a set of diagnostic indicators to understand potential issues in the combustion section, and provides a system that reduces emissions by updating combustion tuning constants based on previous optimized states.
According to an exemplary embodiment illustrated in
Optionally, the method includes a step of learning a behavior of the gas turbine by selecting a second tuning curve and repeating the above steps for the second tuning curve, or a step of storing daily operating parameters of the gas turbine and a step of generating the backup of tuning parameters based on. the stored daily operating parameters and current operating parameters. Further, the method may include a step of checking a distance between critical conditions of the gas turbine and the backup of tuning parameters, a step of receiving alerts related to combustion dynamic and gas turbine emissions, and a step of generating the backup of tuning parameters based on the stored daily operating parameters, current operating parameters, and gas turbine emissions.
In accordance with another embodiment, a computer-readable medium for storing computer instructions and code may be provided to execute all or a portion of the steps of the embodiments of the control methods previously described. The computer-readable medium may, for example, correspond to memory 15 in
An example of a representative controller and/or module capable of carrying out operations in accordance with the exemplary embodiments discussed above is illustrated in
The exemplary structure 800 suitable for performing the activities described in the exemplary embodiments may include server 801, which may correspond to any of the controllers shown in
The server 801 may also include one or more data storage devices, including hard and floppy disk drives 812, CD-ROM drives 814, and other hardware capable of reading and/or storing information such as DVD, etc. In one embodiment, software for carrying out the above discussed steps may be stored and distributed on a CD-ROM 816, diskette 818 or other form of media capable of portably storing information. These storage media may be inserted into, and read by, devices such as the CD-ROM drive 814, the disk drive 812, etc. The server 801 may be coupled to a display 820, which may be any type of known display or presentation screen, such as LCD displays, plasma display, cathode ray tubes (CRT), etc. A user input interface 822 is provided, including one or more user interface mechanisms such as a mouse, keyboard, microphone, touch pad, touch screen, voice-recognition system, etc.
The server 801 may be coupled to other computing devices, such as components of the gas turbine. The server may be part of a larger network configuration as in a global area network (GAN) such as the Internet 828, which allows ultimate connection to the various landline and/or mobile client/watcher devices.
Such a structure 800 (controller) may be configured to perform one or more of the following steps: learn a behavior of the gas turbine by selecting a second tuning curve and repeating the above steps for the second tuning curve; store daily operating parameters of the gas turbine; generate the backup of tuning parameters based on the stored daily operating parameters and current operating parameters; check a distance between critical conditions of the gas turbine and the backup of tuning parameters; receive alerts related to combustion dynamic and gas turbine emissions; and generate the backup of tuning parameters based on the stored daily operating parameters, current operating parameters, and gas turbine emissions.
As also will be appreciated by one skilled in the art, the exemplary embodiments may be embodied in a wireless communication device, a telecommunication network, as a method or in a computer program product. Accordingly, the exemplary embodiments may take the form of an entirely hardware embodiment or an embodiment combining hardware and software aspects. Further, the exemplary embodiments may take the form of a computer program product stored on a computer-readable storage medium having computer-readable instructions embodied in the medium. Any suitable computer readable medium may be utilized including hard disks, CD-ROMs, digital versatile disc (DVD), optical storage devices, or magnetic storage devices such a floppy disk or magnetic tape. Other non-limiting examples of computer readable media include flash-type memories or other known memories.
The disclosed exemplary embodiments provide a controller, a method, and computer software for auto-tuning a gas turbine. It should be understood that this description. is not intended to limit the invention. On the contrary, the exemplary embodiments are intended to cover alternatives, modifications and equivalents, which are included in the spirit and scope of the invention as defined by the appended claims. Further, in the detailed description of the exemplary embodiments, numerous specific details are set forth in order to provide a comprehensive understanding of the claimed invention. However, one skilled in the art would understand that various embodiments may be practiced without such specific details.
Although the features and elements of the present exemplary embodiments are described in the embodiments in particular combinations, each feature or element can be used alone without the other features and elements of the embodiments or in various combinations with or without other features and elements disclosed herein.
This written description uses examples of the subject matter disclosed to enable any person skilled in the art to practice the same, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the subject matter is defined by the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims.
