分布式预测控制全局协调及稳定性分析

实际的化工过程系统维数都较高,对系统进行关联分解并使各子系统进行协调来实现整个大系统全局最优是必要的。对于关联作用存在反馈的化工过程大系统,分散式预测控制算法和基于串联过程推导的邻域优化分布式预测控制算法都不适用,因此在这两个算法的基础上推导出约束条件下基于全局协调的分布式预测控制算法。针对分解后得到的子系统,假设子系统间关联信息的传递存在一个采样时间的滞后,建立每个子系统的预测模型时考虑滞后的关联信息;建立子系统的目标函数时,综合考虑所有关联子系统的输入和输出对本子系统的关联作用;每个子系统滚动优化并行求解各自的最优控制作用。然后,在一定条件下分析了基于全局协调的分布式预测控制算法与集中预测控制算法的一致性,并说明了闭环系统的全局稳定性。最后,通过对Shell公司重油分馏塔和TE过程两个例子进行仿真并与其他算法进行比较,验证了本文提出算法的可行性和有效性。     

基于复杂网络的化工过程层次符号有向图模型建立及关键节点识别

针对定性符号有向图（signed directed graph,SDG）在化工过程系统中建模复杂度高、故障分辨率低、容易忽略部分变量等问题,提出一种基于复杂网络理论构建层次SDG网络模型并识别关键节点的方法。首先利用层次分析法对化工过程系统划分递阶层次结构,建立基于子系统的系统SDG网络模型,选取度中心性、接近中心性等多个节点重要性评价指标,采用主成分分析法确定各指标权重并利用逼近理想排序法（technique for order preference by similarity to an ideal solution,TOPSIS）多属性决策方法得到节点重要性的综合评价值,初步识别关键节点所在的子系统;然后建立子系统的SDG模型并细化为有向网络,采用LeaderRank算法对节点重要性进行排序,进而在子系统网络模型中确定关键节点的位置。案例计算结果表明该方法可以有效地降低建模的复杂性,提高关键节点识别的全面性和准确性,从而改善化工过程系统的安全稳定性。     

一类化工过程多变量系统的自适应非线性预测控制

针对化工过程的一类多变量非线性系统,提出了一种自适应非线性预测控制(ANMPC)算法。在采用递归最小二乘法进行预测模型参数在线辨识的基础上,将系统的静态非线性关系用一个反向传播(BP)神经网络稳态模型来表示,通过稳态模型求得的动态增益来进一步校正预测模型的参数。详述了ANMPC控制器设计步骤,通过在一个多变量pH中和过程中的仿真验证了本算法的可行性和有效性。     

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。     

基于时间差分和局部加权偏最小二乘算法的过程自适应软测量建模

工业过程软测量模型常常因为过程的变量漂移、非线性和时变等问题而使得预测性能下降。因此,时间差分已被应用于解决过程变量漂移问题。但是,时间差分框架下的全局模型往往不能很好地描述过程非线性和时变等特性。为此,提出了一种融合时间差分模型和局部加权偏最小二乘算法的自适应软测量建模方法。时间差分模型可以大大减少过程变量漂移的影响,而局部加权偏最小二乘算法作为一种即时学习方法,可以有效解决过程非线性和时变问题。该方法的有效性在数值例子和工业过程实例中得到了有效验证。     

基于最小路径覆盖算法的化工过程重要参数的识别

将复杂网络的目标控制理论应用到化工过程系统重要参数的识别中,以SDG(signed directed graph)模型和复杂网络理论为基础构建化工过程的网络模型,然后利用LeaderRank和节点相似度算法(SRank算法)对网络节点重要性进行排序并基于此对网络进行鲁棒性分析选取目标节点,通过最小路径覆盖算法对网络进行目标控制分析,确定驱动节点并对它们进行重点监控。案例分析结果表明,该方法可行,对化工过程系统中重要参数的监测和安全控制具有一定的指导意义。     

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。     

基于复杂网络理论的符号有向图(SDG)化工故障诊断

化工过程系统的大型化和复杂性,仅通过常规方式来描述故障机理越来越受到限制。本文以流程图建模法构建的符号有向图(signed directed graph,SDG)故障模型为基础,将化工过程系统抽象为网络拓扑结构,通过对网络模型的统计特征描述,判断网络的复杂性、小世界性和无标度性,进而以复杂网络中心性理论定量计算网络中各个节点的重要性,分析比较各指标来确定网络中的核心节点,并通过Capocci算法对网络进行社团结构的定量划分,最后以网络中的核心节点确定化工过程中易引起安全事故的关键变量,并用社团划分的结果绘制出化工故障诊断模型的关键路径,确定重点监测部位。案例应用结果表明:该方法可行,为化工过程系统中故障节点和监测提供了新的解决思路,丰富了化工过程故障诊断和预防控制的相关理论。     

一种非线性系统在线辨识的选择性递推方法

针对非线性系统的在线辨识, 提出了一种选择性递推岭参数极限学习机方法。首先, 推导了岭参数极限学习机模型节点增加的递推算法, 以有效地更新在线模型。其次, 结合训练模型的相对误差, 提出模型节点递推增加的选择性策略, 以限制模型的复杂度, 获得更简单的递推辨识模型。通过一个典型非线性化工过程的在线辨识, 从多方面比较验证了所提出方法的简单有效, 更适合非线性过程的在线辨识。     

基于聚类多模型建模的多模态预测控制

多模型预测控制(MMPC)是解决非线性控制问题的重要手段,本文针对多模态控制器设计中模态匹配准则的选取问题,利用当前样本状态与各聚类建模子空间距离差异,提出了一种基于距离匹配的多模型控制器加权算法。然后,基于模态融合思想,提出了模态加权构建实时预测模型的控制策略。通过对pH中和过程进行仿真,结果表明：两种方法都提高了非线性系统的暂态响应,跟踪特性优良,体现了它们对非线性系统大范围控制的有效性。     

Subsystem decomposition of process networks for simultaneous distributed state estimation and control

An appropriate subsystem configuration is a prerequisite for a successful distributed control/state estimation design. Existing subsystem decomposition methods are not designed to handle simultaneous distributed estimation and control. In this article, we address the problem of subsystem decomposition of general nonlinear process networks for simultaneous distributed state estimation and distributed control based on community structure detection. A systematic procedure based on modularity is proposed. A fast folding algorithm that approximately maximizes the modularity is used in the proposed procedure to find candidate subsystem configurations. Two chemical process examples of different complexities are used to illustrate the effectiveness and applicability of the proposed approach. © 2018 American Institute of Chemical Engineers AIChE J, 65: 904–914, 2019     

Distributed model predictive control-oriented network decomposition based on full dynamic response

This article addresses network decomposition for distributed model predictive control (DMPC), which includes two improvements. First, in the weighted input–output bipartite graph construction of a process network, a new measure called frequency affinity is proposed to characterize the input–output interaction considering the full dynamic response and structural information of a process. Then, in community detection, which is used to decompose the process network, the gap metric is added to quantify stability and the loss of control performance of each subsystem. Through the proposed decomposition, the obtained subsystems can be dynamically well-decoupled since both transient and steady-state responses are measured by the frequency affinity. As structural information is considered, the decomposition is consistent with the process physical topology. Furthermore, the utilization of gap metric can facilitate controller design for DMPC. Case studies on a reactor separator process and an air separation process demonstrate the effectiveness of the proposed decomposition method.     

Subsystem decomposition and distributed state estimation of nonlinear processes with implicit time-scale multiplicity

In this work, we propose a subsystem decomposition approach and a distributed estimation scheme for a class of implicit two-time-scale nonlinear systems. Taking the advantage of the time scale separation, these processes are decomposed into fast subsystem and slow subsystem according to the dynamics. In the proposed method, an approach that combines the approximate solutions obtained from both the fast and slow subsystems to form a composite solution of the original system is proposed. Also, based on the fast and slow subsystems, a distributed state estimation scheme is proposed to handle the implicit time-scale multiplicity. In the proposed design, an extended Kalman filter (EKF) is designed for the fast subsystem and a moving horizon estimator (MHE) is designed for the slow subsystem. In the design, the slow subsystem is only required to send information to the fast subsystem one-directionally. The fast subsystem estimator does not send out any information. The estimators use different sampling times, that is, fast sampling of the fast state variables is considered in the fast EKF and slow sampling of the slow state variables is considered in the slow MHE. Extensive simulations based on a chemical process are performed to illustrate the effectiveness and applicability of the proposed subsystem decomposition and composite estimation architecture.     

Community detection based process decomposition and distributed monitoring for large-scale processes

Distributed architectures wherein multiple decision-making units are employed to coordinate their decision-making/actions based on real-time communication have become increasingly important for monitoring processes that have large scales and complex structures. Typically, the development of a distributed monitoring scheme involves two key steps, that is, the decomposition of the process into subsystems, and the design of local monitors based on the configured subsystem models. In this article, we propose a distributed process monitoring approach that tackles both steps for large-scale processes. A data-driven process decomposition approach is proposed by leveraging community structure detection to divide variables into subsystems optimally via finding a maximal value of the metric of modularity. A two-layer distributed monitoring scheme is developed where local monitors are designed based on the configured subsystems of variables using canonical correlation analysis. Inner-subsystem interactions and inter-subsystem interactions are tackled by the two layers separately, such that the sensitivity of this monitoring scheme to certain types of faults is improved. We utilize a numerical example to illustrate the effectiveness and superiority of the proposed method. It is then applied to a simulated wastewater treatment process.     

Distributed lyapunov‐based model predictive control with neighbor‐to‐neighbor communication

This work considers distributed predictive control of large‐scale nonlinear systems with neighbor‐to‐neighbor communication. It fulfills the gap between the existing centralized Lyapunov‐based model predictive control (LMPC) and the cooperative distributed LMPC and provides a balanced solution in terms of implementation complexity and achievable performance. This work focuses on a class of nonlinear systems with subsystems interacting with each other via their states. For each subsystem, an LMPC is designed based on the subsystem model and the LMPC only communicates with its neighbors. At a sampling time, a subsystem LMPC optimizes its future control input trajectory assuming that the states of its upstream neighbors remain the same as (or close to) their predicted state trajectories obtained at the previous sampling time. Both noniterative and iterative implementation algorithms are considered. The performance of the proposed designs is illustrated via a chemical process example. © 2014 American Institute of Chemical Engineers AIChE J 60: 4124–4133, 2014     

Subsystem decomposition and configuration for distributed state estimation

Distributed state estimation plays a very important role in process control. Improper subsystem decomposition for distributed state estimation may increase the computational burdens, degrade the estimation performance, or even deteriorate the observability of the entire system. The subsystem decomposition problem for distributed state estimation of nonlinear systems is investigated. A systematic procedure for subsystem decomposition for distributed state estimation is proposed. Key steps in the procedure include observability test of the entire system, observable states identification for each output measurement, relative degree analysis and sensitivity analysis between measured outputs and states. Considerations with respect to time‐scale multiplicity and direct graph are discussed. A few examples are used to illustrate the applicability of the methods used in different steps. The effectiveness of the entire distributed state estimation configuration procedure is also demonstrated via an application to a chemical process example used in coal handling and preparation plants. © 2016 American Institute of Chemical Engineers AIChE J, 62: 1995–2003, 2016     

Partition-based distributed extended Kalman filter for large-scale nonlinear processes with application to chemical and wastewater treatment processes

In this article, we address a partition-based distributed state estimation problem for large-scale general nonlinear processes by proposing a Kalman-based approach. First, we formulate a linear full-information estimation design within a distributed framework as the basis for developing our approach. Second, the analytical solution to the local optimization problems associated with the formulated distributed full-information design is established, in the form of a recursive distributed Kalman filter algorithm. Then, the linear distributed Kalman filter is extended to the nonlinear context by incorporating successive linearization of nonlinear subsystem models, and the proposed distributed extended Kalman filter approach is formulated. We conduct rigorous analysis and prove the stability of the estimation error dynamics provided by the proposed method for general nonlinear processes consisting of interconnected subsystems. A chemical process example is used to illustrate the effectiveness of the proposed method and to justify the validity of the theoretical findings. In addition, the proposed method is applied to a wastewater treatment process for estimating the full-state of the process with 145 state variables.