Browsing by Author "Koninckx, Jan"
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Item CONSOLE User's Manual.(1987) Fan, Michael K-H.; Koninckx, Jan; Wang, Li-Sheng; Tits, Andre L.; ISRThe CONSOLE tandem is a tool for optimization-based design of a large class of systems. The essential requirements are that a simulator be available for evaluating the performance of instances of the system under consideration and that the parameters to be optimally adjusted vary over a continuous (as opposed to discrete) set of values. Todate, CONSOLE has been used on problems as diverse as design of a controller for a flexible arm, an aircraft, or a copolymerization reactor. The manual is organized as follows. In Chapter 1, the ideas and principles upon which CONSOLE is constructed are outlined and the design methodology underlying CONSOLE is sketched. Chapter 2 introduces the novice user to CONSOLE by way of a simple tutorial example. This chapter is strongly recommended to new users as it leads them step by step through a CONSOLE session. Chapter 3 is entirely devoted to CONVERT. It includes a thorough description of the different data types, assignments and commands that form the CONVERT syntax. Chapter 4 discusses SOLVE. The essential features of the optimization algorithm are outlined and the operation of SOLVE SOLVE is discussed. Special attention is given to the interactive capabilities of SOLVE, in particuar the Pcomb display. In Chapter 5, the question of using an interface between SOLVE and simulators of the user's choice is discussed. A general structure is given. Finally, Chapter 6 presents two design examples. Appendices A and B consist in reference manuals, for CONVERT and SOLVE respectively.Item On-Line Optimization of Chemical Plants Using Steady State Models(1988) Koninckx, Jan; McAvoy, T.; ISRThe subject of this dissertation is the on-line optimization of continuous chemical plants that are operated under steady state. For these plants, transient periods are short compared to periods of steady operation, and a steady state of optimization (using a steady state nonlinear model) covers the major part of the potential gain that can be made through optimization. Cheaper computer technology and a more competitive market cause an increased industrial interest in this supervisory control technique. The existing literature on applications of on-line optimization using steady state models is discussed. Publications reflect the wide interest in the optimization based technique, but in general the reports are vague, and they do not answer many fundamental questions. Some are even contradictory on isssues such as partitioning of the optimization problem or choice of optimization variables. In this dissertation, a modular structure for an on-line optimizer is suggested. In this structure, existing algorithms in model updating, data reconciliation and optimization are combined with new applications. The application of sensitivity analysis is the most important new approach that is presented in this dissertation. Optimization sensitivity analysis is a computationally cheap tool that provides information about the status of an optimization result. That information can be used in an on-line optimizer for use in a significance test of setpoint changes, and for an on-line accuracy assessment of the on-line optimizer operation. Sensitivity information is therefore combined with statistical information from e.g. the model updating module. Results from a sensitivity analysis can also be used in short-cut feasibility studies. Also model execution frequency, data reconciliation techniques and particular problems with model updating are discussed, as well as the influence of noise on the performance of on-line optimized plants. Case study results are provided as illustration. The systems studied in these case studies are a distillation column (propane propylene splitter), a boiler network with common header and a simple heat exchanger network.Item On-Line Optimization of Chemical Plants Using Steady State Models.(1988) Koninckx, Jan; ISRThe subject of this dissertation is the on-line optimization of continuous chemical plants that are operated under steady state. For these plants, transient periods are short compared to periods of steady operation, and a steady state optimization (using a steady state nonlinear model) covers the major part of the potential gain that can be made through optimization. Cheaper computer technology and a more competitive market cause an increased industrial interest in this supervisory control technique. The existing literature on applications of on-line optimization using steady state models is discussed. Publications reflect the wide interest in the optimization based technique, but in general the reports are vague, and they do not answer many fundamental questions. Some are even contradictory on issues such as partitioning of the optimization problem or choice of optimization variables. In this dissertation, a modular structure for an on-line optimizer is suggested. In this structure, existing algorithms in model updating, data reconciliation and optimization are combined with new applications. The application of sensitivity analysis is the most important new approach that is presented in this dissertation. Optimization sensitivity analysis is a computationally cheap tool that provides information about the status of an optimization result. That information can be used in an on-line optimizer for use in a sign)ficance test of setpoint changes, and for an online accuracy assessment of the on-line optimizer operation. Sensitivity information is therefore combined with statistical information from e.g. the model updating module. Results from a sensitivity analysis can also be used in short-cut feasibility studies. Also model execution frequency, data reconciliation techniques and particular problems with model updating are discussed, as well as the influence of noise on the performance of on-line optimized plants. Case study results are provided as illustration. The systems studied in these case studies are a distillation column (propane propylene splitter), a boiler network with common header and a simple heat exchanger network.Item On-Line Optimization Using Steady State Models.(1988) Koninckx, Jan; McAvoy, Thomas J.; Marlin, T.E.; ISRMany sectors of the chemical industry suffer from a production overcapacity. Efficient production strategies are important. This situation combines with dropping computer costs to form an excellent environment for on-line optimization. If the transient periods of an operation are short relative to the steady intervals, a major part of the operation economics is determined by the steady state. Therefore, an optimization limited to the steady state will cover the main part of the attainable profit of the plant. A structure for an on-line optimizer is proposed. The optimization is conceived as a calculation of a set of optimal setpoints for the plant. The on-line optimizer is composed of a number of modules. The most important modules perform the optimization and identify the model. Although steady state models are much morre readily available and accurate than dynamic models, they still are approximate and contain parameters that have to be updated regularly to correct for the plant model mismatch and for slow changes in the plant. Sensitivity analysis of the optimization results and statistical analysis of the model identification results are combined in short-cut feasibility studies and on-line accuracy estimation. Data reconciliation improves the robustness of the application. Two examples serve as illustrations. The first example concerns a propane-propylene splitter. This example shows many of the interesting issues on a system of reduced size. The results are therefore easier to interpret. The second example is a boiler load allocation problem. This example is more involved and shows a realistic application.