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  Dynamic constrained optimization of chemical manufacturingUSPTO Application #: 20070059838Title: Dynamic constrained optimization of chemical manufacturing Abstract: System and method for chemical manufacture utilizing a dynamic optimizer for a chemical process including upstream and downstream processes. The dynamic optimizer includes a maximum feed calculator, operable to receive one or more local constraints on the downstream processes and one or more model offsets, and execute steady state models for the downstream processes in accordance with the local constraints and the offsets to determine maximum feed capacities of the downstream processes; and a feed coordinator, operable to receive the maximum feed capacities, and execute steady state models for the upstream processes in accordance with the maximum feed capacities and a specified objective function, subject to global constraints, to determine upstream production parameters for the upstream processes, which are usable to control the upstream processes to provide feeds to the downstream processes in accordance with the determined maximum feeds and the objective function subject to the global constraints. (end of abstract) Agent: Meyertons, Hood, Kivlin, Kowert & Goetzel, P.C. - Austin, TX, US Inventors: Timothy Morrison, Michael Sugars USPTO Applicaton #: 20070059838 - Class: 436055000 (USPTO) Related Patent Categories: Chemistry: Analytical And Immunological Testing, Condition Responsive Control The Patent Description & Claims data below is from USPTO Patent Application 20070059838. Brief Patent Description - Full Patent Description - Patent Application Claims
FIELD OF THE INVENTION [0001] The present invention generally relates to the field of chemical production. More particularly, the present invention relates to systems and methods for optimizing chemical production in a manufacturing process with downstream and/or upstream constraints using predictive control methodologies. DESCRIPTION OF THE RELATED ART [0002] Like any other commercial enterprise, those in the business of producing chemical products desire to maximize efficiencies and profitability, while meeting various constraints, such as, for example, raw material and energy costs, plant equipment limitations, product prices, and so forth. The ability to produce chemicals in such a manner may be further complicated for chemical plants producing more than one grade or type of chemical product. [0003] As shown in prior art FIG. 1, a chemical plant 104 may produce chemicals, including, for example, olefin, gasoline, and fuel oil, among others, of varying grades, from feedstock, e.g., naphtha, heavy oil, liquefied petroleum gas (LPG), and ethane, among others. Typically, a chemical plant 104 includes reactors (e.g., furnaces) that make the product followed by separation equipment such as distillation columns to recover the product. The reactors/furnaces are generally referred to as the "hot side" or "hot section" of the plant or plant unit, while the separation portion is referred to as the "cold side" or "cold section" of the plant or unit. It should be noted, however, that these terms are not intended to limit the application of the techniques disclosed herein to any particular chemical process. Rather, the various techniques described are contemplated as being broadly applicable to any process that includes an upstream process, e.g., a reactor process, and a downstream process, e.g., a separation process. [0004] A chemical plant 104 may employ one or more processing lines that are capable of transforming raw materials 101 into chemical products 103, e.g., olefin, gasoline, fuel oil, etc. One processing line may be capable of producing two or more different grades of chemicals, or even two or more different types of chemicals. For example, production of a first product, e.g., olefin, may also result in production of a second product, e.g., ethane, as a byproduct or impurity. This secondary product may itself be valuable, e.g., as a saleable product, or as feedstock for further processing. [0005] Such systems and processes, especially those that utilize multiple feed/product streams, are characterized by the fact that many different inter-related parameters contribute to the behavior of the system or process. It is often desirable to determine values or ranges of values for some or all of these parameters that correspond to beneficial behavior patterns of the system or process, such as safety, profitability, efficiency, etc. However, the complexity of most real world systems generally precludes the possibility of arriving at such solutions analytically, i.e., in closed form. Therefore, many analysts have turned to predictive models and optimization techniques to characterize and derive solutions for these complex systems or processes. [0006] Predictive models generally refer to any representation of a system or process that receives input data or parameters related to system or model attributes and/or external circumstances/environment and generates outputs indicating the behavior of the system or process under those parameters. In other words, the model or models may be used to predict behavior or trends based upon previously acquired data. There are many types of predictive models, including linear, non-linear, analytic, and empirical models, among others, several types of which are described in more detail below. [0007] Optimization generally refers to a process whereby past (or synthesized) data related to a system or process are analyzed or used to select or determine optimal parameter sets for operation of the system or process. For example, the predictive models mentioned above may be used in an optimization process to test or characterize the behavior of the system or process under a wide variety of parameter values. The results of each test may be compared, and the parameter set or sets corresponding to the most beneficial outcomes or results may be selected for implementation in the actual system or process. [0008] FIG. 2A illustrates a general optimization process as applied to an industrial process 104, such as a manufacturing plant, according to the prior art. It may be noted that the optimization techniques described with respect to the manufacturing plant are generally applicable to all manner of systems and processes. [0009] As FIG. 2A shows, the operation of the process 104 generates information or data 106 that is typically analyzed and/or transformed into useful knowledge 108 regarding the system or process. For example, the information 106 produced by the process 104 may comprise raw production numbers for the plant that are used to generate knowledge 108, such as profit, revenue flow, inventory depth, etc. This knowledge 108 may then be analyzed in the light of various goals and objectives 112 and used to generate decisions 110 related to the operation of the system or process 104 subject to various goals and objectives 112 specified by the analyst. As used herein, an "objective" may include a goal or desired outcome of an optimization process. Example goals and objectives 112 may include or involve profitability, schedules, energy use, inventory levels, cash flow, production, or any other attribute that the user may wish to minimize or maximize. These goals and objectives 112 may be used to select from among the possible decisions 110, where the decisions may comprise various parameter values over which the user may exercise control. The selected decision(s) may then determine one or more actions 114 to be applied to the operation of the system or process 104. The subsequent operation of the system or process 104 then generates more information 106, from which further knowledge 108 may be generated, and so on in an iterative fashion. In this way, the operation of the process 104 may be "tuned" to perform in a manner that most closely meets the goals and objectives of the business or enterprise. [0010] FIG. 2B illustrates an optimization system where a computer based optimization system 102 operates in conjunction with a process 104 to optimize the process, according to the prior art. In other words, the computer system 102 executes software programs (including computer based predictive models) that receive process data 106 from the process 104 and generate optimized decisions and/or actions that may then be applied to the process 104 to improve operations based on the goals and objectives. [0011] Thus, many predictive systems may be characterized by the use of an internal model that represents a process or system 104 for which predictions are made. FIG. 3A illustrates a number of predictive model types usable in optimization systems, according to the prior art. As mentioned above, predictive model types may be linear, non-linear, stochastic, or analytical, among others. However, for complex phenomena non-linear models may generally be preferred due to their ability to capture non-linear dependencies among various attributes of the phenomena. Examples of non-linear models may include neural networks and support vector machines (SVMs). [0012] As FIG. 3A shows, the types of models used in optimization systems include fundamental or analytic models 302 that use known information about the process 104 to predict desired unknown information, such as product conditions and product properties. A fundamental model may be based on scientific and engineering principles. Such principles may include the conservation of material and energy, the equality of forces, and so on. These basic scientific and engineering principles may be expressed as equations that are solved mathematically or numerically, usually using a computer program. Once solved, these equations may give the desired prediction of unknown information. [0013] Conventional computer fundamental models have significant limitations, such as: [0014] (1) They may be difficult to create since the process may be described at the level of scientific understanding, which is usually very detailed; [0015] (2) Not all processes are understood in basic engineering and scientific principles in a way that may be computer modeled; [0016] (3) Some product properties may not be adequately described by the results of the computer fundamental models; and [0017] (4) The number of skilled computer model builders is limited, and the cost associated with building such models is thus quite high. [0018] These problems result in computer fundamental models being practical only in some cases where measurement is difficult or impossible to achieve. [0019] As also shown in FIG. 3A, empirical models 304, also referred to as computer-based statistical models, may be used to model the system or process 104 in an optimization system. Such models typically use known information about process to determine desired information that may not be easily or effectively measured. A statistical empirical model may be based on the correlation of measurable process conditions or product properties of the process. Examples of computer-based empirical or statistical models include neural networks and support vector machines (SVMs). [0020] For one example of a use of a computer-based statistical model, assume that it is desired to be able to predict the color of a plastic product. This is very difficult to measure directly, and takes considerable time to perform. In order to build a computer-based statistical model that may produce this desired product property information, the model builder would need to have a base of experience, including known information and actual measurements of desired unknown information. For example, known information may include the temperature at which the plastic is processed. Actual measurements of desired unknown information may be the actual measurements of the color of the plastic. [0021] A mathematical relationship (i.e., an equation) between the known information and the desired unknown information may be created by the developer of the empirical statistical model. The relationship may contain one or more constants (which may be assigned numerical values) that affect the value of the predicted information from any given known information. A computer program may use many different measurements of known information, with their corresponding actual measurements of desired unknown information, to adjust these constants so that the best possible prediction results may be achieved by the empirical statistical model. Such a computer program, for example, may use non-linear regression. [0022] Computer-based statistical models may sometimes predict product properties that may not be well described by computer fundamental models. However, there may be significant problems associated with computer statistical models, which include the following: Continue reading... Full patent description for Dynamic constrained optimization of chemical manufacturing Brief Patent Description - Full Patent Description - Patent Application Claims Click on the above for other options relating to this Dynamic constrained optimization of chemical manufacturing patent application. ###  How KEYWORD MONITOR works... a FREE service from FreshPatents1. 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