【作者】 赵超；

【导师】 苏宏业；

【作者基本信息】 浙江大学 ， 控制科学与工程， 2009， 博士

【摘要】 控制系统经济性能评估是流程工业过程控制领域新兴的一个研究热点,它为控制工程师提供了一种从经济效益角度来评估当前控制系统控制品质的定量分析方法。通过估计控制系统在采用新的控制策略或对现有控制系统调节的情况下,生产企业可能获得的经济潜能(效益),确定提高系统经济性能的最佳途径,并为已有控制系统实施技术改造提供决策依据。此外,经济性能评估技术也可以在线监测系统控制性能的变化,结合反映经济性能的测评指标和结果,提供诊断性能下降的可靠信息,提出改善控制性能的策略,维护工业过程高品质运行。过程控制系统经济性能评估一般基于给定的方差估计基准,确定控制系统的经济性能函数和关键变量的概率密度函数,采用统计方法或优化方法估计系统的经济性能。经济性能评估涉及到参数估计与模型辨识、性能评估与监控以及数值优化等多个学科领域的知识和技术,是当今过程控制界最受关注的研究方向之一。本论文结合当前经济性能评估技术理论研究和国内工程应用现状,分别开展了以下几个方面的研究工作:(1)选择合适的控制性能基准对过程关键变量方差作出合理的评估,是提高控制系统经济性能评估结果可靠性的重要保证。针对已有经济性能评估算法大多采用最小方差控制(Minimum Variance Control,MVC)性能基准,存在对预测控制系统(Model Predictive Control,MPC)性能评估结果可靠性不高的问题,提出了基于线性二次高斯控制(Linear Quadratic Gaussian,LQG)性能基准的经济性能评估算法。通过数值计算方法确定LQG性能基准曲线,避免了复杂交互矩阵(Interactor Matrix)的计算。算法以基于模型的稳态经济优化技术为基础,将LQG基准和预测控制系统的经济性能估计相结合,并通过建立一系列稳态优化问题来描述控制系统在不同控制策略下的经济性能。与已有评估算法相比,本算法提供了一种更简单有效的评估方法,能够完成对包括预测控制在内的先进控制系统经济性能的评估。对Shell公司重油裂解装置的性能评估验证了该算法的有效性。(2)系统不确定性是大多数工业过程所固有的特性。过程存在不确定性动态经常造成控制器的实际工作点偏离稳态工作点,导致控制系统的性能严重下降或恶化。因此,针对过程/系统存在外扰,模型参数变动或模型失配等不确定性动态对控制性能的影响,提出一种基于随机优化的算法来估计控制系统在采取改进或调节控制策略所能产生的经济效益。算法在确定控制系统最优工作点以及相应的最优经济性能的过程中,采用了设置机会约束的方法来有效处理过程不确定性对系统性能的影响。评估算法引入随机优化思想有助于工程师在性能评估结果的经济性和可靠性之间做出一个合理的权衡,最终为企业实施控制系统技术改造提供科学决策的依据。精馏过程仿真算例验证了算法的可行性利有效性。(3)结合流程工业先进控制技术应用的需求,研究了提高控制系统经济性能的可能途径和相应的预测控制器调节方法。在经济性能评估结果的基础上,定义了系统在不同控制工况下的经济性能指标,并总结了性能评估流程的关键实施步骤。通过对控制系统经济效益的分析,给出了改善控制系统经济性能的策略和实施方案。对某工业二甲苯分馏过程先进控制系统,采用提出的性能评估算法进行了经济效益的分析,评估结果表明了该评估算法法是合理、有效的。(4)控制系统经济性能的提高取决于过程关键变量方差的减少,而减少关键变量的方差和波动幅度取决于控制性能的提高。因此,综合分析预测控制系统的经济性能不仅要关注经济性能优化目标,同时也要考虑控制系统自身控制性能的优劣。在预测控制策略中,某些被控输出量期望能被约束在一个特定的区域内,称之为区域控制(Regional Control,RC)问题。针对这类预测控制系统的性能评估问题,本文提出了基于加权偏离度统计的多变量预测控制区域控制性能评估方法。该方法易于工程实现,有效弥补了目前单纯基于方差性能评估策略在实现技术上存在困难的不足。并通过预测控制仿真和工业分馏过程应用实例证明了该评估算法的有效性。(5)过程模型是现代工业控制系统最基本和最重要的组成单元。无论是对控制系统进行经济性能评估还是控制性能评估,都需要高精度的过程数学模型。参数估计是过程控制领域建立数学模型的一个核心问题。本文研究了一种基于逆序贯算法的动态系统参数估计方法。该算法采用变量误差法的参数估计算法形式,可以同时求解数据校正问题和参数估计问题。在引入拟序贯优化方法的基础上,建立了一个求解参数估计问题的三层计算结构。通过采用正交配置离散化方法,将原动态系统参数估计问题转换为一个大规模非线性规划问题。通过分层结构和分解算法,使得参数估计问题的计算复杂度大大降低。针对连续搅拌反应器的仿真研究,计算结果验证了该参数估计算法的有效性。更多还原

【Abstract】 With the widespread implementation of advanced process control strategies in chemical and petrochemical plants in the last two decades, there have been an increasing need for a tool to reliably assess the economic performance of advanced process control applications. Economic performance assessment of process control has been recognized to be one of the best ways to identify potential benefits resulting from control upgrade projects. It can provide control engineers with a quantitative metric to justify the potential benefits due to control system improvement and can priorize the best control upgrading opportunity. It also provides useful information on performance monitoring or better management of abnormal situations in order to maintain or improve the economic performance of modern process control systems.Quantifying the economic benefit resulting from improved control is often based on the reduction of variability. Once the probability density function of key process variables and the economic performance function are both identified, the statistical-based or optimization-based method is utilized to calculated the potential benefits. Economic performance assessment of process control has been an area of active research in process control community since it involves the technology and knowledge of system identification and parameter estimation, performance assessment and monitoring, numerical analysis and optimization. Taking the new trends of the technology into accounts, this work will investigate the following issues from the theoretic development and engineering applications.(1) An approach to economic performance assessment of advanced control system is presented. The method builds on steady-state economic optimization techniques and uses the linear quadratic gaussian (LQG) benchmark other than conventional minimum variance control (MVC) to estimate the potential of reduction in variance. The LQG control is a more practical performance benchmark compared to MVC for performance assessment since it considers input variance and output variance, and it thus provides a desired basis for determining the theoretical maximum economic benefit potential arising from variability reduction. Combining the LQG benchmark directly with benefit potential of MPC control system, both the economic benefit and the optimal operation condition can be obtained by solving the economic optimization problem. The proposed algorithm is illustrated by a simulated example of Shell standard problem.(2) Uncertainty is an inherent characteristic in most industrial processes. Process uncertainties may lead to significant disturbances to the processes, thereby degrading the operation performance. It is therefore necessary and desirable to incorporate the effects of uncertainty dynamics into the assessment problem to make sure that the estimated process performance is relevant and practically realizable. An optimization-based approach for economic performance assessment of the constrained process control is integrated with the LQG benchmark as the variance benchmark. By explicitly incorporating uncertainty into the performance assessment problem, the performance evaluation can be formulated as a stochastic optimization problem, which helps to identify the opportunity to improve the profitability of the process by taking appropriate risk levels. Using the LQG benchmark to estimate the achievable variability reduction through the control system upgrades, the proposed method provides an estimate of both the performance that can be expected from the control system and the operating condition that delivers the improved performance. The results obtained can serve as a tool for control engineers to make decisions on control system tuning and/or upgrading. The proposed algorithm is illustrated via a simulation example of a model predictive control system for distillation process model.(3) Based on the results of estimated economic performance calculation, the economic performance indices under different control upgrade strategies are defined. Then a decision is made on whether a control system upgrade can improve the process performance, and which proposed control system should be implemented. The proposed approach is illustrated by the application to economic performance assessment of an industrial model predictive control system for xylene distillation unit.(4) Economic performance of advanced process control should pay attention to the control performance since the improvement of control performance does improve economic performance of process control. Performance assessment and monitoring of model predictive control (MPC) systems has been a great interest for both academia and industry. The presence of constraints renders MPC controller nonlinear and, thus, makes the use of traditional linear techniques problematic. When in consideration of the constraint and optimization prosperities of MPC, the controlled variables (CVs) are expected to be constrained within a certain region but not at a certain set point. Performance assessment and monitoring of such type of problem is meaningful and practical. In this study, a new approach based on the weighted points statistics is developed for the performance assessment of the above problem. The important advantage of the proposed approach is that just the routine closed-loop operation data of the system and constrained region of each CV are required, which is convenient for the industrial applications. Simulation example and industrial case study illustrate the applicability of the proposed approach.(5) Parameter estimation is a key problem in the development of process models, and thus is an important issue in both economic performance assessment and control performance monitoring of process control. A novel three stage computation framework is developed, which is based on a quasi-sequential dynamic optimization method. By dividing the variables space into a dependent and an independent space, only independent variables are treated by " the SQP solver. The lower stage is also named simulation layer, where the process model in terms of DAEs are discretized with orthogonal collocation and solved using Newton method to compute the dependent variables. Since the degree of freedom is limited to the number of parameters in the upper stage and the number of independent variables in the middle stage, any standard NLP solver can be used to solve the problem. Thus, the computation expensive is significantly reduced. An example of parameter estimation for a CSTR model is employed to demonstrate the effectiveness of the proposed approach.更多还原