复杂非线性系统的子系统分解方法

利用社区发现算法研究了一种复杂非线性化工系统的子系统分解方法,并进行了分布式模型预测控制设计。引入信息图论的节点表示系统的状态、输入和输出变量,构建非线性过程系统的加权有向图,节点通过加权边连接,加权反映了节点间连接的强度,因而能够同时反映系统内部的连通性和连接强度。利用社区结构发现算法将所有变量分成子系统的群组,使得每个组内的关联比不同组间的相互作用强,从而得到复杂化工过程系统的子系统分解。针对连续搅拌反应釜过程,实施子系统分解,并设计分布式模型预测控制算法,结果表明,所提出的子系统分解方法更能考虑子系统之间的连接权重,得到更有利于分布式模型预测控制的子系统划分,提升系统控制的性能。     

基于大规模化工过程的快速分布式MPC算法研究

针对目前化工过程复杂,在分布式模型预测控制(MPC)的实施中会面临强耦合以及慢速收敛的问题,提出了改进全局性能指标的快速分布式MPC算法。首先在每个采样时刻分别求解子系统自身的局部优化问题,同时考虑关联子系统之间的相互作用,然后在协调过程中对全局最优性能指标进行改进以减少迭代次数。该方法降低了控制问题的复杂度,减少了迭代时间,有效地改善了收敛速度。最后分别对二元精馏塔过程和苯乙烯聚合反应过程进行了仿真,验证了所提算法的有效性。     

基于子系统关联变量轮换的大系统在线协调优化

化工过程复杂大系统的在线优化过程中,存在流程前后的关联导致优化时间过长或得不到优化解的问题,需要按子系统优化并协调的优化方法来解决,而协调优化方法存在关联变量寻优方向不一致的问题。提出了一种基于子系统间关联变量轮换思想的分解协调优化方法,对大系统分解得到的子系统以轮换的方式进行优化,子系统中包含的多个优化问题分别在固定关联变量优化独立变量和固定独立变量优化关联变量的条件下求解,此轮换过程迭代进行,直至满足优化终止条件。将提出的方法应用在催化裂化装置仿真实例中与整体优化方法的结果作比较,证明了方法的有效性。最后,将基于关联变量轮换的协调优化方法应用在化工过程的在线优化中,结果表明本方法在在线优化应用中是可行的。     

化工过程集成智能生产调度系统开发

本文叙述了化工过程集成智能生产调度系统的目的，作用，给出了该系统的组成结构和设计思想，重点介绍了优化排产子系统和线故障诊断子系统，提出了智能化分解协调优化算法和人工智能与人工神经网络相结合的在线故障诊断系统的组成结构，并给出了某厂优化排产和在线故障诊断子系统的实例。     

化工过程运行系统的集成

提出了基于智能体方法的化工过程运行系统的集成策略.依据过程操作响应的时间尺度,把过程运行系统分成故障检测与诊断、模拟优化和调度3个子系统,分别建立相应的智能体模型.通过信息集成和任务集成实现化工过程运行系统的整体集成.报道了该集成方法对TE过程的案例应用.结果表明：基于智能体的方法能实现多个运行决策的全局协调和集成,达到过程运行系统整体优化的目标.     

一类非线性动态系统的一致性分散可靠性预测

针对一类带有隐含退化过程的非线性动态系统,提出了一种基于粒子滤波的一致性分散可靠性预测方法。该方法将原系统重叠分解为若干个相互关联的子系统,每个子系统利用其局部测量信息以及子系统间通信得到的信息,估计出原系统的状态和参数,进而预测原系统的可靠性。同时,该方法通过一致性策略使得所有子系统的估计和预测结果保持一致。五容水箱模型的仿真实验结果表明,该方法能够很好地预测系统的可靠性,同时该方法能够使得所有子系统的估计和预测结果保持一致。     

过程系统的能量综合和优化

本文论述了从能量变化的角度分析过程系统以及按照Ｙｏｎｇ经济学关系建立系统优化的目标函数和约束条件的必要性，概述了按照过程系统能量结构特点所建立的三环节Ｙｏｎｇ经济分析模型，及其在四类子系统－单元过程设备两级能量综合优化中的应用。然后讨论了全局综合优化问题，介绍了以Ｙｏｎｇ经济学为基础提出的３个全局综合优化策略：子系统分解协调优化、复合措施和全局改进方案的Ｙｏｎｇ经济评价调优，及这一系统方法的研究、     

基于局部建模的预测控制及其在芳烃异构化过程的应用

针对一类非线性系统提出了基于局部建模的预测控制算法,对于局部模型的训练数据选取,同时考虑了样本数据之间的欧氏距离与向量的夹角,使得训练数据的选取更合理,并对芳烃异构化过程进行了实验仿真。首先通过最小二乘支持向量机进行局部建模,并将局部模型作为算法的预测模型。然后利用粒子群优化算法进行滚动优化,寻找到最优控制量。最终仿真实验验证了该算法的有效性和鲁棒性。     

一类面向多级过程系统的实时进化方法

针对传统的实时优化执行前需要达到稳态、系统整体优化时间长、难以实时调整控制参数等问题提出一类面向多级过程系统的实时进化方法。把多级过程系统分成多个相互关联的子系统，当发生扰动时将系统等待稳态的过程分为若干个“拟稳态”。在每个拟稳态区间内，应用粒子群算法依次优化每一个子系统，其他各子系统设为静态，对子系统间的共享变量和内部变量进行协调，最终得到系统的最优解，把优化操作解送入控制器，连续改变系统设定值。给出了算法的详细步骤以两级串联反应器为对象进行了实例研究，验证了方法的有效性。     

一类非线性系统的变负荷预测控制

生产过程的变负荷运行使得其非线性动态特性的影响凸显。针对变负荷生产过程中机理模型为常微分方程或半显式Heisenberg微分-代数方程的一类非线性动态系统,采用非线性预测控制算法,构造出稳态优化与动态优化的两层控制结构,并采用联立法进行优化数值求解。最后对化工过程的夹套CSTR进行仿真验证,表明该算法的有效性。     

基于神经网络的pH中和过程非线性预测控制

针对pH中和过程这一化工过程系统中的典型非线性对象特点，应用神经网络建模思想和模型预测控制方法，并结合Hammerstein模型特点，研究pH中和过程非线性系统的两种新型模型预测控制手段，分别建立基于神经网络的非线性预测控制系统整体求解策略和基于Hammerstein模型的两步法预测控制策略，并用MATLAB对其进行仿真。控制仿真结果表明，建立的神经网络预测控制策略和非线性Hammerstein模型预测控制均优于传统PID控制方法，具有良好的设定值跟踪效果和抗干扰控制响应，说明这两种控制策略是非线性过程的有效控制方法。     

Improving scenario decomposition algorithms for robust nonlinear model predictive control

This paper deals with the efficient computation of solutions of robust nonlinear model predictive control problems that are formulated using multi-stage stochastic programming via the generation of a scenario tree. Such a formulation makes it possible to consider explicitly the concept of recourse, which is inherent to any receding horizon approach, but it results in large-scale optimization problems. One possibility to solve these problems in an efficient manner is to decompose the large-scale optimization problem into several subproblems that are iteratively modified and repeatedly solved until a solution to the original problem is achieved. In this paper we review the most common methods used for such decomposition and apply them to solve robust nonlinear model predictive control problems in a distributed fashion. We also propose a novel method to reduce the number of iterations of the coordination algorithm needed for the decomposition methods to converge. The performance of the different approaches is evaluated in extensive simulation studies of two nonlinear case studies.     

Explicit distributed model predictive control design for chemical processes under constraints and uncertainty

In multivariable industrial processes, the common distributed model predictive control strategy is usually unable to deal with complex large-scale systems efficiently, especially under system constraints and high control performance requirements. Based on this situation, we use the distributed idea to divide the large-scale system into multiple subsystems and transform them into the state space form. Combined with the output tracking error term, we build an extended non-minimal state space model that includes output error and measured output and input. When dealing with system constraints, the new constraint matrix is divided into range and kernel space by using the explicit model predictive control algorithm, which reduces the difficulty of solving constraints in the extended system and further improves the overall control performance of the system. Finally, taking the coke furnace pressure control system as an example, the proposed algorithm is compared with the conventional distributed model predictive control algorithm using non-minimal state space, and the simulation results show the feasibility and superiority of this method.     

Fast distributed MPC based on active set method

Modern chemical plants are characterized by their large-scale, strong interactions and the presence of constraints. With its ability to systematically handle these issues, distributed model predictive control (DMPC) is a promising approach for the control of such systems. However, the problem of how to efficiently solve the resulting distributed optimization problem is still an open question. This paper develops a novel fast DMPC approach based on a distributed active set method and offline inversion of the Hessian matrix to efficiently solve a constrained distributed quadratic program. A dual-mode optimization strategy based on the value of unconstrained optimal solution is developed to accelerate the computation of control action. The proposed method allows for the optimization to be terminated before convergence to cope with the fast sampling periods. Furthermore, a warm-start strategy based on the solution of the previous sampling instant is integrated with the approach to further improve convergence speed. The approach is highly parallelized as constraints can be checked in parallel. The approach is demonstrated using an academic example as well as a chemical process network control.     

基于模型预测控制的反应再生工艺控制实现

介绍了先进控制系统中的模型预测控制技术。具体阐述了模型预测控制中的动态矩阵控制算法（DMC）,包括预测模型、滚动优化、反馈校正。以炼油厂催化裂化装氍的反应再生工艺为例,分析了反应再生工艺控制的多变量、多耦合特性,指出了控制的难点。最后,采用动态矩阵控制算法对反应再生工艺的一再、二再温度进行了优化控制,最终实现了控制目标。     

化工过程系统优化的分布式并行计算

针对大规模化工过程系统优化计算能力不够的情况 ,讨论用机群系统建构成并行优化计算环境 .在分析并行计算的原理和现状后 ,对优化算法的并行化进行探讨 ,并且深入讨论了大规模优化算法SQP的并行化和如何提高机群系统效率的问题 .通过精馏塔优化算例 ,证明利用机群系统开展并行优化是行之有效的     

Distributed economic model predictive control for operational safety of nonlinear processes

Achieving operational safety of chemical processes while operating them in an economically‐optimal manner is a matter of great importance. Our recent work integrated process safety with process control by incorporating safety‐based constraints within model predictive control (MPC) design; however, the safety‐based MPC was developed with a centralized architecture, with the result that computation time limitations within a sampling period may reduce the effectiveness of such a controller design for promoting process safety. To address this potential practical limitation of the safety‐based control design, in this work, we propose the integration of a distributed model predictive control architecture with Lyapunov‐based economic model predictive control (LEMPC) formulated with safety‐based constraints. We consider both iterative and sequential distributed control architectures, and the partitioning of inputs between the various optimization problems in the distributed structure based on their impact on process operational safety. Moreover, sufficient conditions that ensure feasibility and closed‐loop stability of the iterative and sequential safety distributed LEMPC designs are given. A comparison between the proposed safety distributed EMPC controllers and the safety centralized EMPC is demonstrated via a chemical process example. © 2017 American Institute of Chemical Engineers AIChE J, 63: 3404–3418, 2017