GOALI: Optimization Based Design and Plantwide Control Systems

摘要-McAbstract-0071320全厂范围控制涉及应测量、控制和操纵哪些变量以及应如何将这些变量联系在一起的问题。 对于某些工厂，分散的比例积分微分（PID）方法产生可接受的结果，而在其他情况下，它不。 目前，还没有系统的方法，允许设计人员确定如何以及一个特定的工厂范围内的控制架构可以预期执行。 在这项研究中解决的基本问题是：给定一个动态过程模型和预期的设定点变化/干扰，是一个分散的架构足够的可接受的性能，或者应该一个相互作用的模型预测控制（MPC）的架构被用于所有或部分工厂？PI开发了两种基于优化的方法来回答这个问题。 一个涉及最优控制（OC），这是重点的研究计划在这里，而另一个利用混合整数线性规划（MILP）制定。 要研究的方法假设有一个动态过程模型，因为这种模型在实践中越来越普遍。 该方法的目的是从过程模型中提取信息，需要什么类型的架构来控制该模型所代表的工厂。 PI的OC研究使用初始状态强制，全状态反馈，并且它需要为每个候选分散架构调整PID反馈和P前馈控制器。 这种OC方法成功地应用于两个杜邦公司的例子。 在此资助下进行的研究使用输出反馈，设定点或负载的步进强制，并且在架构筛选过程中不需要PID调整。 PI计划将新方法应用于杜邦的两个例子，以及其他几个更大的工厂范围的例子。 这些其它实例包括田纳西伊士曼工艺、乙酸乙烯酯工艺和可能的HAD工艺。 本研究采用杜邦公司和白杨公司提供的动态模拟软件。 杜邦公司的合作者将为所研究的过程开发模型预测控制器提供帮助。

Abstract - McAvoy - 0071320Plantwide control involves the issue of which variables should be measured, controlled, and manipulated, and how these variables should be linked together. For some plants a decentralized proportional-integral-derivative (PID) approach produces acceptable results, while in others it does not. At present there is no systematic methodology that allows a designer to determine how well a particular plantwide control architecture can be expected to perform. The basic question addressed in this research is: given a dynamic process model and expected set point changes/disturbances, is a decentralized architecture sufficient for acceptable performance, or should an interacting model predictive control (MPC) architecture be used for all or part of the plant?The PI has developed two optimization-based approaches to answering this question. One involves optimal control (OC) and it is the focus of the research planned here, while the other makes use of a mixed integer linear programming (MILP) formulation. The approach to be studied assumes that a dynamic process model is available, since such models are becoming more and more common in practice. The objective of the approach is to extract information from the process model about what type of architecture is required to control the plant that the model represents. The PI's OC research used initial state forcing, full state feedback, and it required tuning PID feedback and P feedforward controllers for each candidate decentralized architecture. This OC approach was applied successfully to two DuPont examples. The research to be done under this grant uses output feedback, step forcing for either set points or loads, and no PID tuning is required during the architecture screening process. The PI plans to apply the new methodology to the two DuPont examples, as well as to several other, larger plantwide examples. These other examples include the Tennessee Eastman process, a vinyl acetate process, and possibly an HAD process. Dynamic simulation software has been provided by DuPont and by Aspen Technology and will be used in this research. The DuPont collaborators will provide assistance in developing model predictive controllers for the processes studied.