Method and apparatus for coordinating the utilization of an operating area to achieve production goals

Abstract

A system, method, and apparatus for a production process are provided to coordinate the utilization of operating regions of process variables and automatically perform target setting to maximize production goals without manual intervention.

Description

Background Art

[0001] The present disclosure generally relates to production process control, and more particularly to improving the control of a production process by coordinating the target settings of variables used in the production process to achieve a desired production goal.

[0002] Typically, there are multiple input variables as well as a process output variable, such as production throughput, that are important for a process for producing products with various characteristics. For example, a paper machine may include input variables such as different refined fibers, chemical additives, dyes, water, steam, electricity, various flows, pressures, temperatures, and speed settings to produce paper with different output variables such as basis weight, moisture content, thickness, strength, color, and other characteristics. The relationship between these input variables and output variables of the process is complex. Even experienced and skilled operators cannot always find the correct settings for the input variables and output variables to produce a product that meets various production goals, such as maximizing production throughput while minimizing the use of fibers, chemicals, and energy and achieving the required quality specifications of the product.

[0003] Advanced process control, such as multivariable model predictive control (MPC), has been applied in a wide range of industries to obtain better product quality and more stable operation while producing products. However, the targets of the variables used under advanced process control may still be determined by human operators based on their experience and skills. Therefore, it is difficult to achieve the preferred variable targets through the target settings established by human operators. Accordingly, there is a need to coordinate the target settings of the variables used in an advanced control scheme to follow and / or maximize the economic goals of the process by effectively utilizing the operating regions of the variables. The present disclosure aims to achieve systems, methods, and apparatuses for these purposes and the like. Summary of the Invention

[0004] According to the present disclosure, there is provided a system, method, and apparatus for a production process to coordinate the target settings of process variables and automatically perform the target settings to maximize production goals without manual intervention. Accordingly, various input variables such as raw materials, resources, and energy consumption can be saved while achieving output variables that provide desired product specifications. Additionally, less expensive materials and resources can also be employed in the production process while improving operations and reducing manpower and errors. According to the present disclosure, there is also provided a computer control system that is operable to perform the above operations.

[0005] The present invention content is provided to introduce a selection of concepts further described in the illustrative embodiments below. The present invention content is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in delimiting the scope of the claimed subject matter. Other embodiments, forms, objects, features, advantages, aspects, and benefits will become apparent from the following description and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0006] The features, aspects, and advantages of the present invention will be better understood with reference to the following description, the appended claims, and the accompanying drawings, in which

[0007] Figure 1 a schematic diagram of an exemplary system utilizing an operating region coordinator together with a controller for a production process is shown;

[0008] Figure 2 a graph of an exemplary process variable having upper and lower limits, associated tolerances, an operating region, and the relationship between the process variable and the current and future targets of the process variable is shown; and

[0009] Figure 3 is a flowchart of a process by which the operating region coordinator coordinates the target setting of production process variables. DETAILED DESCRIPTION

[0010] To facilitate understanding of the principles of the present invention, reference is now made to the embodiments shown in the accompanying drawings and described in specific language. However, it should be understood that the scope of the present invention is not intended to be limited thereby, and any alterations and other modifications of the shown embodiments as well as any other applications of the principles of the present invention as would normally occur to one of ordinary skill in the art to which the present invention pertains are contemplated herein.

[0011] For any production process 10, as Figure 1 shown, a set of input variables u and a set of output variables y can be identified. Figure 1 A computer control system 20 is also included, which receives the output variables y from the process 10 and creates the input variables u for the process 10. The computer control system 20 can include, but is not limited to, an operating region coordinator 12 and a process controller 14 that can utilize an internal response model 16 of the process 10. The computer control system 20 is connected to the process 10 having one or more machines operable to produce a product using the target settings established by the operating region coordinator 12, which utilizes the operating region of the input variables u and the output variables y according to the present disclosure.

[0012] The process controller 14 includes one or more controllers and / or one or more computers. The computer control system 20 may also include one or more other computers for performing offline tasks associated with the production process and / or the process controller 14. At least one computer of the computer control system 20 can access a user interface device (UI) 18 including one or more display devices, such as a monitor (with or without a touch screen) or a handheld device (such as a smart phone, a tablet computer, a laptop computer, or other device for displaying graphics) and one or more input devices (such as a keyboard, a mouse, a trackball, a joystick, a handheld device, and / or a voice control device).

[0013] Figure 1 The process input variable u in is also identified herein as one or more manipulated variables (MV). Figure 1 The process output variable y in is also identified herein as one or more controlled variables (CV). The input variable MV can affect the output variable CV in many different ways. The MV typically also exhibits a static effect and a dynamic effect on the CV, and these static and dynamic effects can be identified as a response model 16 used in the process controller 14. The response model 16 can be implemented in many different mathematical representations (such as transfer functions, state space equations, neural networks, and other mathematical functions).

[0014] Regardless of which response model representation is used, the goal of the production process is typically set to produce the maximum amount of product with the desired quality in the most efficient manner using the least amount of input materials, resources, and time. These production goals are common to many production processes.

[0015] For any given production process, even though achieving the production goals as described above is the primary priority, the production process may be constrained by its physical limits. For example, the input variable MV of the process typically has a physical range between an upper limit and a lower limit, as well as rate limits, tolerances, and / or target settings. The output variable CV typically must also meet certain high specifications, low specifications, or target specifications. Figure 2 is an example chart illustrating typical process variables MV or CV. The illustrated process variables include an upper limit (L h ) 22, an upper tolerance (t h ) 24, a lower limit (L l ) 26, and a lower tolerance (t l ) 28. The operating upper limit z h is the upper limit 22 minus the high tolerance 24, i.e., z h = L h - t h . The operating lower limit z l is the lower limit 26 plus the lower tolerance 28, i.e., z l = L l+t l Upper operating limit z h and the lower operating limit z l The region between them is the operating region 30. The process variable 32 and its long-term trend 34 are typical within this operating region around its target 36. The operating region coordinator 12 generates a new target 38 according to the present invention for moving the future process variable 40 towards the new target 38, for example, closer to the lower end of the operating region 30. As discussed herein, other new targets 38 are also contemplated within the operating region 30.

[0016] The tolerances 24, 28 allow the process variable (MV or CV) sufficient freedom to vary and also achieve the economic objective of minimizing production costs or keeping the product within its quality specifications. The tolerances 24 and 28 can be specified by a human user and / or dynamically derived and updated based on the short-term variability of the difference between the process variable 32 and the long-term trend 34 of the process variable 32. If the short-term variability is high, the tolerance is set larger.

[0017] Human operators require a high degree of skill and training to manually operate the production process to achieve the specified production goals while attempting to meet various constraints. The present disclosure provides a system, method, and apparatus to automatically maximize production goals while coordinating the utilization of input and output variables in the process operating region. For example, for a process with a static gain of G, its steady-state manipulated variable MV(u) and controlled variable CV(y) can be expressed as follows:

[0018] y - y k = G(u - u k ) Equation 1

[0019] where MV and CV can be combined into a common process variable x:

[0020]

[0021] Reference Figure 3 shows a process 42 of a method operable by the operating region coordinator 12 in a computer control system 20 according to the present disclosure. The process 42 includes an operation 44 of classifying the preferences of each element of the common process variable x. Within the operating region of each element of the common process variable x, different positions may be associated with different preferences for achieving the operating goal. For example, each element of the common process variable x can be designated as one of the following four preference categories, regardless of whether the variable is an input variable or an output variable.

[0022] - Setpoint (SP): Preference for the common process variable x having a clearly defined goal to be achieved or maintained. For example, the basis weight goal of paper or the temperature goal in lime kiln operation.

[0023] - Near High (NH): Prefers a common process variable x that has no explicit target but needs to be maintained as close as possible to the high operating region limit without exceeding its upper bound.

[0024] - Near Low (NL): Prefers a common process variable x that has no explicit target but needs to be maintained as close as possible to the low operating region limit without exceeding its lower bound.

[0025] - Within Range (WR): Prefers a common process variable x that has no explicit target but needs to be maintained at any level between the high operating region limit and the low operating region limit.

[0026] Process 42 continues at operation 46 to determine the operating region for each common process variable x. For each common process variable in x, the operating region can be specified based on the upper and lower bounds and their corresponding tolerances. As Figure 2 shown, for each common process variable x, the upper bound 22 is L h , and the lower bound 26 is L l . High tolerance 24 and low tolerance 28 (t h and t l ) can also be specified. The operating region 30 is the region between the high region limit z h = L h - t h and the low region limit z l = L l + t l . The operating region 30 is specified using z h and z l .

[0027] Process 42 continues at operation 48 to determine the reference target for each common process variable x, such as the reference target (x r ). The reference target x of the setpoint (SP) common process variable r can be set to the same as the explicit manual target of the common process variable. The reference target x of the Near High (NH) common process variable r can be set to the high operating region limit of the common process variable (x r = z h ). The reference target x of the Near Low (NL) common process variable r can be set to the lower limit of the operating region of the common process variable (x r = z l ). The reference target x of the Within Range (WR) common process variable r can be set to the current long-term steady-state value of the common process variable (x r = x ss ).

[0028] Process 42 continues at operation 50 with the designed cost function and constraints. The cost function can be designed as a weighted sum of the variance of the deviation of the common process variables in x from their reference targets, as specified above.

[0029] J = (x - x r ) T W(x - x r ) Equation 3

[0030] where x r is the reference target for the common process variable x defined in operation 48 above, and W is a diagonal matrix where each diagonal element specifies the weight factor for each element of the common process variable x.

[0031] Both the operating region limits and the response model 16 of the process can be combined into a complete set of constraints. The region upper limit and the region lower limit can be expressed as follows:

[0032] z l < x < z h Equation 4

[0033] where z l and z h are the region lower limit and the region upper limit, respectively.

[0034] The static response model 16 in Equation 1 can be expressed as follows:

[0035] Ax = b Equation 5

[0036] where A = [I -G], b = y k -Gu k , I is the identity matrix, and y k and u k are the current long-term trends of the CV and the current long-term trend of the MV, which are derived as moving averages of their most recent past history or rigorously filtered trend values.

[0037] Process 42 continues at operation 52 to perform constraint optimization. The designed cost function can be subjected to constraint optimization according to the combined constraints on the common process variable x.

[0038] x new = quadprog(W, -Wx r , A, b, z l , z h ) Equation 6

[0039] where quadprog is an example of a quadratic programming operation that optimizes the cost function (Equation 3) with respect to the common process variable x while also satisfying the constraints specified in Equations 4 and 5.

[0040] At operation 54 of process 42, the optimization result (x new ) is then set as the new target for the input and output variables by which controller 14 controls production process 10. The slow transition 38 between the current target and the new target x new is typically achieved by passing the difference between the new target and the current target through a low-pass filter. The controller 14 that uses the new target to control the production process is typically a multivariable feedback controller, such as an internal model controller or a model predictive controller.

[0041] The above steps of process 42 can be performed while the dynamic feedback control of the production process reaches its steady state, or the new target from process 42 is slowly applied to the dynamic feedback control. This can make full use of the available operating range of the variables and effectively achieve the production target.

[0042] According to the present disclosure, many applications for coordinating the utilization of the operating range for process control are envisioned. One application includes operating a paper machine to control the weight and humidity of the finished paper. For a paper machine, instead of the conventional control of maintaining output variables such as the weight and moisture of the paper at their specified targets, process 42 will control the process for operating the paper machine to produce paper such that the paper weight is as close as possible to its specified lower limit without exceeding the lower limit. At the same time, process 42 can be used to control the process for producing paper to make the moisture content of the paper as close as possible to its specified upper limit without exceeding its upper limit. As a result, the paper machine will produce qualified paper products that utilize lower material and energy costs.

[0043] Another application includes coordinating the utilization of the operating range of the production throughput of a paper machine. The production throughput of a paper machine is typically directly related to the operating speed at which the paper machine outputs paper. However, there are design limits for each paper machine that limit its production throughput. For example, variables such as machine speed, stock flow, dry section steam pressure, and many other variables associated with the production throughput have upper and lower limits. Although the production target is to set the machine speed as high as possible, paper that meets the target settings of the output variables, such as target weight, moisture, thickness, etc., must be produced. Process 42 controls the processes for: operating the paper machine to operate the paper machine at the highest possible speed while keeping variables such as pulp flow, steam pressure, etc. within their ranges, and further achieving other variables that meet its target settings such as paper weight, moisture, etc. Thus, the production throughput of the paper machine is maximized while producing paper of the desired quality.

[0044] Another application involves coordinating the utilization of operating zones for papermachine chemical addition. At the wet end of a papermachine, various retention aids can be added to enhance the retention capacity of the wet end process. These retention chemicals are expensive. Process 42 can be used to control the operation of the papermachine to reduce the amount of chemical retention aids added, such that the amount of added retention aid chemicals is as close as possible to its lower limit, while keeping the white water consistency as close as possible to, but not exceeding, its upper limit. At the same time, Process 42 can be used to maintain variables such as the basis weight and ash content of the paper at their target settings. In this way, Process 42 will produce paper of the required quality using the minimum amount of chemical additives.

[0045] Another application involves coordinating the utilization of operating zones of a papermachine to produce paper with appropriate paper strength. Paper strength is affected by various process variables such as fiber furnish, fiber refining, chemical addition, jet-to-wire ratio, basis weight, water content of the paper, paper thickness, and fiber orientation. The production goal of reducing production costs requires reducing the use of high-cost fibers, minimizing fiber refining, selecting appropriate jet settings for the headbox, and controlling the chemical dosage, while achieving the required minimum paper strength within its product specifications. Process 42 allows the operation of the papermachine to achieve the production goal of reducing the total cost by coordinating multiple process variables within its operating zones, while producing paper strength that meets the minimum specification requirements.

[0046] Another application for coordinating the utilization of operating zones for process control is during the operation of a lime kiln. A lime kiln uses fuel combustion to heat lime mud to produce quicklime. The lime quality is closely related to the temperature achieved in the lime kiln. To achieve cost-effective lime production, the lime kiln should be operated at the highest possible temperature without exceeding the upper limit, and with as little excess oxygen as possible in the combustion chamber of the kiln without falling below the lower limit. At the same time, the fuel usage is kept as low as possible relative to its lower limit. Process 42 allows the control of lime kiln operation to achieve such production goals while achieving the desired output of lime quality at a lower cost.

[0047] Another application involves coordinating the utilization of operating zones for the thick stock washing process in a pulp mill. Thick stock washing, which removes dissolved impurities from the pulp, is a critical step in the pulp production line. The thick stock washing process can directly affect the recovery rates of organic and inorganic chemicals. The operation of thick stock washing is an action that is difficult to balance among the following requirements: (a) maintaining the liquid level of the filter tank within a critical range, (b) maintaining the conductivity below the operating upper limit, (c) maximizing the outlet consistency and solids content without exceeding the upper limit, and (d) using as little fresh water as possible. Process 42 allows thick stock washing to achieve its production goals while minimizing the operating cost and improving the washing efficiency.

[0048] Another application involves coordinating the utilization of operating zones for chemical feeds in a multi-stage bleaching process in a pulp mill. The bleaching process is an important step in ensuring that the quality of the final produced pulp meets the specifications, i.e., both the main quality requirements (i.e., kappa number and whiteness) meet their targets. In this process, various expensive chemicals are added at various stages of operation, and it is desirable to minimize the chemical dosage without compromising the pulp properties. Process 42 will ensure that the pulp production targets are achieved without impairing the pulp quality, while prioritizing the various chemicals to minimize the total production cost as well as the waste pulp.

[0049] The schematic diagrams and processes described above are generally elaborated herein. Thus, the depicted order and the marked steps indicate representative embodiments. It should be contemplated that other steps, orderings, combinations of steps, and methods that are equivalent in function, logic, or effect to one or more steps or portions thereof of the methods illustrated in the schematic diagrams.

[0050] Additionally, the formats and symbols employed are provided to explain the logical steps of the schematic diagrams and are understood not to limit the scope of the systems, devices, and methods illustrated in the figures. Additionally, the order in which a particular method occurs may or may not strictly adhere to the order of the corresponding steps shown. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented by a dedicated system based on hardware that performs the specified functions or actions, or a combination of dedicated hardware and program code.

[0051] Many of the functional units described in this specification have been labeled to more particularly emphasize their implementation independence. For example, one or more aspects of the computer control system 20 and / or the operating zone coordinator 12 can be implemented as a hardware circuit, which includes custom VLSI circuits or gate arrays, off-the-shelf semiconductors, such as logic chips, transistors, or other discrete components. The computer control system 20 and / or the operating zone coordinator 12 can also be implemented in programmable hardware devices, such as field programmable gate arrays, programmable array logic, programmable logic devices, etc.

[0052] One or more aspects of the computer control system 20 and / or the operation area coordinator 12 may also be implemented in a machine-readable medium for execution by various types of processors. In some instances, the machine-readable medium for execution by various types of processors may be implemented in the hardware circuits described above. For example, the identified executable code modules may include one or more physical or logical blocks of computer instructions, which may be organized, for example, as objects, procedures, or functions. However, the executable files of the identified circuits need not be physically located together, but may include different instructions stored in different locations, which, when logically combined, constitute the circuits and achieve the stated purpose of the computer control system 20 and / or the operation area coordinator 12.

[0053] For example, the computer-readable program code may be a single instruction or many instructions, and may even be distributed over several different code segments, among different programs, and across multiple memory devices. Similarly, the operation data may be identified and described herein within a module, monitor, or circuit, and may be embodied in any suitable form and organized within any suitable type of data structure. The operation data may be collected as a single data set, or may be distributed over different locations, including on different storage devices; and may exist at least in part only as electronic signals on a system or network. Where a module, monitor, or circuit or a part thereof is implemented in a machine-readable medium (or computer-readable medium), the computer-readable program code may be stored and / or propagated on one or more computer-readable media.

[0054] The computer-readable medium may be a tangible computer-readable storage medium storing the computer-readable program code. The computer-readable storage medium may be, for example but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, holographic, micro-mechanical, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.

[0055] More specific examples of the computer-readable medium may include but are not limited to a portable computer floppy disk, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD), optical storage device, magnetic storage device, holographic storage medium, micro-mechanical storage device, or any suitable combination of the foregoing. In the context of this document, the computer-readable storage medium may be any tangible medium that can contain and / or store the computer-readable program code for use by and / or in connection with an instruction execution system, apparatus, or device.

[0056] A computer-readable medium can also be a computer-readable signal medium. A computer-readable signal medium can include, for example, a propagated data signal that contains computer-readable program code in a baseband or as part of a carrier wave. Such a propagated signal can take any of a variety of forms, including but not limited to electrical, electromagnetic, magnetic, optical, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium that is not a computer-readable storage medium and that can communicate, propagate, or transport computer-readable program code for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable program code contained on the computer-readable signal medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, fiber optic cable, radio frequency (RF), etc., or any suitable combination of the foregoing.

[0057] In one embodiment, a computer-readable medium can include a combination of one or more computer-readable storage media and one or more computer-readable signal media. For example, computer-readable program code can be propagated as an electromagnetic signal through a fiber optic cable for execution by a processor or can be stored on a RAM storage device for execution by a processor.

[0058] The computer-readable program code for performing the operations of the aspects of the present disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, Smalltalk, C++, etc., and conventional procedural programming languages such as the "C" programming language, Python, Matlab, R, or similar programming languages. The computer-readable program code can execute entirely on the user's computer, partly on the user's computer, as a stand-alone computer-readable package, partly on the user's computer and partly on a remote computer, or entirely on a remote computer or server. In the latter case, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0059] The program code can also be stored in a computer-readable medium that can direct a controller, computer, other programmable data processing apparatus, or other device to operate in a particular manner, such that the instructions stored in the computer-readable medium produce an article of manufacture that includes instructions for performing the functions / actions specified herein.

[0060] Aspects of the present disclosure are envisioned. For example, according to one aspect, a method for coordinating operating regions of common process variables during a production process is provided. The method includes classifying preferences for each common process variable according to the following aspects: a) the target, if the common process variable has a target setting, or b) one or more preferences, if the common process variable does not have a target setting; determining an operating region for each common process variable between a high operating region limit and a low operating region limit for each common process variable; determining a reference target for each common process variable; designing a cost function as a weighted sum of the variance of the deviation of each common process variable from the reference target of each common process variable; combining the operating regions of the common process variables and the response models of the common process variables into a set of combined constraints for the common process variables; performing constrained optimization on the designed cost function according to the combined constraints to determine an optimized result for the common process variables; and setting the optimized result as a new target setting for a multivariable feedback controller that controls the production process.

[0061] Any combination of one or more of the following may be incorporated into the method. In one embodiment, the multivariable feedback controller is one of an internal model controller or a model predictive controller. In one embodiment, preferences for setting target categories are provided for classifying each common process variable having a target setting.

[0062] In one embodiment, the preferences for the categories for classifying each common process variable among the common process variables without a target setting include: a preference for maintaining a near-high category of the common process variable close to the high operating region limit, which is a tolerance amount below the upper limit; a preference for maintaining a near-low category of the common process variable close to the low operating region limit, which is a tolerance amount above the lower limit; and a preference for keeping the in-range category of the common process variable at any level between the upper and lower limits of the operating region. In one embodiment, the tolerance may be specified manually or calculated dynamically based on the short-term variability of the difference between the current measurement and the long-term trend of each common process variable.

[0063] In one embodiment, the operating region of each common process variable is an area between an upper limit adjusted for high tolerance and a lower limit adjusted for low tolerance.

[0064] In one embodiment, determining a reference target for each common process variable includes: setting the reference target of each common process variable in the set target category to the target setting of each common process variable; setting the reference target of each common process variable in the near high category to the high operating region limit, which is the upper limit minus its tolerance amount; setting the reference target of each common process variable in the near low category to the low operating region limit, which is the lower limit plus its tolerance amount; and setting the reference target of each common process variable in the in range category to the current steady state value of each common process variable.

[0065] In one embodiment, the cost function is Equation 3 above. In one embodiment, the response model is Equation 5 above. In one embodiment, the constrained optimization of the designed cost function is Equation 6 above.

[0066] According to another aspect, a computer system is provided that is operable to coordinate the utilization of operating regions of a production process. The computer system is operable to: classify preferences for each common process variable with respect to: a) a target if the common process variable has a target setting; or b) one or more preferences if the common process variable does not have a target setting; determine an operating region between the upper and lower limits of the operating region of each common process variable for the production process; determine a reference target for each common process variable; design a cost function as a weighted sum of the variances of the deviations of each common process variable from the reference target of the common process variable; combine the operating region limits and the response model of the process into a set of combined constraints for the common process variable; perform constrained optimization of the designed cost function according to the set of combined constraints to determine an optimized result for the common process variable; and set the optimized result as a new target setting for a multivariable feedback controller that controls the production process.

[0067] In one embodiment, the computer system is operable to classify each common process variable in the preferences of the set target category that has a target setting. In one embodiment, the operating region of each common process variable is the region between its upper and lower limits.

[0068] In one embodiment, a common process variable classified according to one or more limits is placed in one of the following categories: a preference in the near high category of the common process variable is within a tolerance amount below the upper limit; a preference in the near low category of the common process variable is maintained within a tolerance range above the lower limit; and a preference within the in range category of the common process variable is maintained at any level between the upper and lower limits of the operating region.

[0069] In one embodiment, the reference target is to set the target setting of each common process variable in the target category; the upper limit of the operating region, i.e., the upper limit minus the tolerance amount of each common process variable in the near upper category; the lower limit of the operating region, i.e., the lower limit plus the tolerance amount of each common process variable in the near lower category; and the current steady-state value of each common process variable in the in-range category.

[0070] In one embodiment, the cost function is Equation 3 above. In one embodiment, the response model is Equation 5 above. In one embodiment, the constrained optimization of the designed cost function is Equation 6 above.

[0071] In one embodiment, the computer system is operable to set the optimization result as the new targets of the input variables and output variables of the multivariable feedback controller. In another embodiment, the machine is connected to the computer control system for running the production process.

[0072] References throughout this specification to "one embodiment", "an embodiment", or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the phrases "in one embodiment", "in an embodiment", and similar language that appear throughout this specification may, but do not necessarily, all refer to the same embodiment.

[0073] Therefore, the present disclosure may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. Thus, the scope of the present disclosure is indicated by the appended claims rather than by the foregoing description. All changes that come within the meaning and range of equivalency of the claims are to be embraced within their scope.

[0074] Although the present invention has been described in detail in the drawings and the foregoing description, it should be considered illustrative rather than restrictive, and it should be understood that only certain exemplary embodiments have been shown and described. Those skilled in the art should appreciate that many modifications can be made in the exemplary embodiments without materially departing from the present invention. Therefore, all such modifications are intended to be included within the scope of the present disclosure as defined in the following claims.

[0075] In reading the claims, it is intended that when words such as "a", "an", "at least one", or "at least a portion" are used, the claim is not to be construed as limited to only one unless there is a clear contrary indication in the claim. When the language "at least a portion" and / or "a portion" is used, the item can include a portion and / or the whole item unless there is a clear contrary indication.

Claims

1. A method for coordinating operating regions of common process variables during a production process, the method comprising: classifying the preference for each common process variable relative to: a) if the common process variable has a target setting, classifying relative to the target; or b) if the common process variable has no target setting, classifying relative to one or more preferences; wherein the preference for the class used to classify each common process variable among the common process variables without the target setting includes: a preference for a near-high class for maintaining the common process variable close to a high operating region limit, the high operating region limit being a tolerance amount below an upper limit; a preference for a near-low class for maintaining the common process variable close to a low operating region limit, the low operating region limit being a tolerance amount above a lower limit; and a preference for a within-range class for keeping the common process variable at any level between an operating region upper limit and an operating region lower limit; for each common process variable, determining an operating region for each common process variable between a high operating region limit and a low operating region limit; determining a reference target for each common process variable; designing a cost function as a weighted sum of the variance of the deviation of each common process variable from the reference target of each common process variable, wherein the cost function is: J = (x - x r ) T W(x - x r ), where x is the common process variable, including input variables and output variables, and x r is the reference target for each common process variable, and W is a diagonal matrix, where each diagonal element specifies a weight factor for each component of x; combining the operating region of the common process variable and the response model of the common process variable into a set of combined constraints for the common process variable; performing constrained optimization on the designed cost function according to the set of combined constraints to determine an optimized result for the common process variable; and setting the optimized result as a new target setting for a multivariable feedback controller that controls the production process.

2. The method according to claim 1, wherein the multivariable feedback controller is one of an internal model controller or a model predictive controller.

3. The method according to claim 1, wherein a preference for a set target class is provided for classifying each common process variable among the common process variables having the target setting.

4. The method according to claim 1, wherein the tolerance amount can be manually specified or dynamically calculated according to the short-term variability of the difference between the current measurement and the long-term trend of each common process variable.

5. The method according to claim 1, wherein the operating region of each common process variable is a region between an upper limit adjusted for high tolerance and a lower limit adjusted for low tolerance.

6. The method according to claim 1, wherein determining the reference target for each common process variable includes: setting the reference target of each common process variable in the set target class as the target setting of each common process variable; setting the reference target of each common process variable in the near-high class as the high operating region limit, the high operating region limit being the upper limit minus the tolerance amount of each common process variable; Set the reference target of each common process variable in the near - low category to the low operating region limit, which is the lower limit plus the tolerance amount for each common process variable; And Set the reference target of each common process variable in the in - range category to the current steady - state value of each common process variable.

7. The method according to claim 1, wherein the response model is: Ax = b, where A = [I - G] b = y k -Gu k , where x is the common process variable; G is the static gain of the process; I is the identity matrix; y and u are the input variable and output variable respectively; and y k and u k are the current long-term trends of the input variable and the output variable respectively.

8. The method according to claim 1, wherein the constrained optimization of the designed cost function is: x new = quadprog(W, -Wx r , A, b, z l , z h ) where x new is the optimized result, z l and z h are the corresponding lower and upper limits of the operation area, and quadprog is the quadratic optimization operation.

9. A computer system capable of operating to coordinate the utilization of the operating regions of a production process, the computer system capable of operating to: Classify the preferences for each common process variable relative to: a) if the common process variable has a target setting, relative to the target; or b) if the common process variable has no target setting, relative to one or more preferences; Wherein the common process variables classified relative to one or more limits are placed in one of the following categories: Preferences for the near - high category, for maintaining the common process variable within a tolerance amount below the upper limit; Preferences for the near - low category, for maintaining the common process variable within a tolerance amount above the lower limit; and Preferences for the in - range category, for keeping the common process variable at any level between the upper and lower limits of the operating region; Determine an operating region between a high operating region limit and a low operating region limit for each common process variable going to and coming from the production process; Determine a reference target for each common process variable; Design a cost function as a weighted sum of the variance of the deviation of each common process variable from its reference target, Wherein the cost function is: J=(x - x r ) T W(x - x r ), where x is the common process variable for the input variable and the output variable, x r is the reference target for each common process variable, W is a diagonal matrix, where each diagonal element specifies a weight factor for each component of x; Combine the operating region limits and the response model of the process into a set of combined constraints for the common process variables; Perform constrained optimization on the designed cost function according to the set of combined constraints to determine an optimized result for the common process variables; And Set the optimized result as a new target setting for a multivariable feedback controller that controls the production process.

10. The computer system according to claim 9, wherein the computer system is capable of operating to classify each common process variable having the target setting among the preferences of the set target category.

11. The computer system according to claim 9, wherein the operating region of each common process variable is the region between the upper limit and the lower limit of the operating region.

12. The computer system according to claim 9, wherein the reference target is: The target setting of each common process variable in the set target category; The high operating region limit, which is the upper limit minus the tolerance amount for each common process variable in the near - high category; The low operating region limit, which is the lower limit plus the tolerance amount for each common process variable in the near - low category; and The current steady - state value of each common process variable in the in - range category.

13. The computer system according to claim 9, wherein the response model is: Ax = b, where A = [I - G], and b = y k -Gu k , where x is the common process variable; G is the static gain of the process; I is the unit gain matrix; y and u are the input variable and the output variable respectively; and y k and u k are the current long-term trends of the input variable and the output variable respectively.

14. The computer system according to claim 9, wherein the constrained optimization of the designed cost function is: x new = quadprog(W, -Wx r , A, b, z l , z h ), where x new is the optimized result, z l and z h are the corresponding lower and upper limits of the operation area, and quadprog is the quadratic optimization operation.

15. The computer system according to claim 14, wherein the computer system is operable to set the optimized result as a new target for the input variables and output variables of the multivariable feedback controller.

16. The computer system according to claim 9, further comprising a machine, the machine being connected to the computer control system to run the production process.

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