GOALI: Performance Monitoring Principles for Nonlinear and Linear Model Predictive Control

目前对模型预测控制(MPC)在过程工业中的经济影响的估计是每年数十亿美元。但是流程工业目前还没有公认的方法来评估目前在模型预测控制下运行的数千个回路的性能。本项目的目标是建立对已安装的MPC技术进行此类评估的原则。确立这些原则将使软件工具的开发能够自动量化所研究的每个MPC控制系统的关键性能指数(KPI)。通过对不同循环的KPI进行排名排序，可以进行比较、确定优先顺序，并确定需要进一步关注的系统。将每个循环的实际KPI与其他三个计算的KPI进行比较，可以进一步进行诊断，包括：工艺限制的影响、糟糕的工艺模型、不适当的噪声过滤和大干扰。纵向分析允许用户选择适当的时间段来对性能进行基准测试，并发现季节性趋势和变化。这一评估分析为控制系统技术供应商提供了反馈，激励并指出了控制技术进一步改进的前景。基于非线性模型的预测控制性能评估的既定理论很少，因此这一部分的研究进入了一个全新的领域。这项目标计划中的工业合作伙伴埃克森美孚将提供相关的工业数据集，并在埃克森美孚指导受支持的博士生两个夏天。智力优势：这项研究要解决的问题是根本性的和深远的。所需的理论包括：最优化；概率、统计和统计推断；随机变量和马尔可夫链；非线性控制理论；数值模拟和确定统计特性的数值模拟的时间平均(蒙特卡罗方法)。该理论借鉴了在这些基础学科中开发的当前技术，并将暴露出这些领域提供的当前工具无法解决的新问题。广泛影响：先进技术已经获得并实施，但用户没有客观数据和信息的信息交换所，告诉他们这些系统在哪里工作得很好，或许更重要的是，他们在哪里工作得不好。开发提供这种信息的新系统将使：(I)用户能够系统地管理较大的安装，(Ii)确定系统中的故障部件并确定其优先顺序，以及(Iii)促进新控制理论的发展，以解决已确定的、未得到满足的实际需求。负责实施和维护先进技术的人员需要管理工具来评估技术。本研究开发的系统理论和算法是完全通用的，可以应用于任何一类可以开发出合理预测模型的动态制造过程。

Current estimates of the economic impact in the process industries of model predictive control (MPC) are in the billions of dollars per year. But the process industries currently have no accepted methods tailored to the problem of assessing the performance of the thousands of loops currently running under model predictive control.The goal of this project is to establish principles for performing such assessment for installed MPC technology. Establishing these principles will enable the development of software tools to automatically quantify the key performance index (KPI) for each MPC control system under study. Rank ordering of the different loops' KPIs enables comparison, prioritization, and pinpoints those systems requiring further attention. Comparisons of each loop's actual KPI to three other calculated KPIs allow further diagnosis including: impact of process constraints, poor process models, inappropriate noise filtering, and large disturbances. Longitudinal analysis allows the user to select appropriate time periods for benchmarking performance and spotting seasonal trends and variations. Feedback from this assessment analysis to the control system technology vendors creates incentives and points to promising areas for further improving the control technology.There is little established theory for assessing performance of MPC based on nonlinear models, so this part of the research ventures into a completely new area. The industrial partner in this GOALI proposal, ExxonMobil, will provide the relevant industrial data sets and supervise the supported Ph.D. student for two summers at ExxonMobil.Intellectual Merit:The problems to be addressed in this research are fundamental and far reaching. The required theory draws upon: optimization; probability, statistics and statistical inference; random variables and Markov chains; nonlinear control theory; numerical simulation and time-averaging of numerical simulations to determine statistical properties (Monte Carlo methods). The theory draws from current techniques developed in these fundamental disciplines and will expose new problems that cannot be addressed with the currenttools provided by these fields.Broader Impact:Advance technology has been acquired and implemented, but users have no clearinghouse of objective data and information telling them where these systems are working well, and perhaps more importantly, where they are not working well. Developing new systems to provide this information will enable: (i) users to systematically manage larger installations, (ii) identify and prioritize the troublesome parts of their systems, and (iii) spur development of new control theory tailored to addressing the identified, unmet practical needs. The people responsible for implementing and maintaining advanced technology require management tools to assess the technology. This is true of almost any advanced technology.The systems theory and algorithms developed in this research are completely general and can be applied to any class of dynamic manufacturing processes for which a reasonable predictive model can be developed.