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。     

基于自适应模糊神经网络的非线性系统模型预测控制

针对非线性动态系统的控制问题,提出了一种基于自适应模糊神经网络(adaptive fuzzy neural network,AFNN)的模型预测控制(model predictive control, MPC)方法。首先,在离线建模阶段,AFNN采用规则自分裂技术产生初始模糊规则,采用改进的自适应LM学习算法优化网络参数;然后,在实时控制过程,AFNN根据系统输出和预测输出之间的误差调整网络参数,从而为MPC提供一个精确的预测模型;进一步,AFNN-MPC利用带有自适应学习率的梯度下降寻优算法求解优化问题,在线获取非线性控制量,并将其作用到动态系统实施控制。此外,给出了AFNN-MPC的收敛性和稳定性证明,以保证其在实际工程中的成功应用。最后,利用数值仿真和双CSTR过程进行实验验证。结果表明,AFNN-MPC能够取得优越的控制性能。     

预分解窑风量控制方法探讨

新型干法窑生产过程中，风、煤、料和窑速是公认的四大操作要素，其中用风问题是最重要，也是最为复杂的控制参数。系统用风变数较大，某个部位结构尺寸的细微变化，或外在因素（如积料、结皮和漏风等）的影响，都会使用风状况发生变化，给窑系统的产质量、电热耗产生负面影响，实际生产过程中调控操作难度较大。本文结合我公司采用的RSP炉预分解系统、高原型第三代充气梁篦冷机和1000t／Ф＋3．3m×50m窑，探讨窑系统的风量控制方法，分析各子系统用风的要理及其相互间的影响，供同仁参考。     

稀疏方程系统分解的结构矩阵法

建议用结构矩阵法进行系统分解以提高稀疏方程组的求解效率。对结构矩阵及其重排、子系统的划分和方程的求解顺序进行了讨论。     

复杂大系统的数据校正

由于复杂大系统包含设备较多、物料品种多、工艺流程复杂,所需测量的数据繁多、解题规模庞大.传统的数据校正算法耗费了计算机大量的存储单元,而且计算时间较长.而新的分解协调算法无需协调参数和迭代计算,故计算速度加快,很好地解决了这个问题.     

模式切换类经济预测控制切换时间在线估计

针对一类非线性仿射系统,提出一种在线估计切换时间的经济模型预测控制算法,并将其拓展到长周期控制过程中。有限时间内,将切换时间作为变量实时更新估计,确定最优的切换操作点,以保证每一时刻都可以在控制目标可达的前提下经济性能最优,避免了传统切换经济预测控制策略可能出现的控制目标不可达或经济性能较差的情况。进一步,将该策略作为单周期应用到长周期优化控制过程中,当系统受到扰动时,开始一个新的优化控制周期,实现优化模式与控制模式的灵活切换,同时可以及时应对扰动的出现。该策略保证系统的综合性能最优,仿真结果证明了方法的有效性。     

模糊预测函数控制在水泥回转窑分解炉温控系统中的应用研究

通过对水泥回转窑系统中预分解炉温度控制回路的特性分析,提出一种基于模糊模型的增量型预测函数控制算法,根据生料流量的波动来修正相应的预测函数控制律,并以Hoenywell公司的Plantscape集散控制系统为平台,开发了实时控制软件,实际应用表明对存在大迟后和参数时变的水泥回转窑系统,该算法能达到良好的控制效果。     

预分解窑的预分解系统堵塞浅析

预分解窑是继悬浮预热器窑之后的又一次重大革新,是水泥工业发展的方向。它不仅可以大幅度提高回转窑的生产能力、节能降耗,而且可以减轻窑内煅烧带的热负荷,有利于缩小窑的规格及生产大型化。但预分解窑生产工艺存在一个难题,那就是预分解系统的堵塞。堵塞的发生不仅扰乱了整个系统的热工制度,影响窑的产质量,而且处理费时费力,甚至会造成人员烧伤。为了保证预分解窑长期安全运行,必须攻克这一难题。     

一种分解炉出口温度控制的专家优化控制方法

某水泥厂分解炉出口温度主要采用手动或PID控制。由于延时现象严重,且存在负荷变化及各种未知扰动,导致分解炉出口温度变化剧烈难以控制。采用南京凯盛专家优化控制以后,实现了分解炉出口温度的自动控制。     

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     

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.     

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     

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.     

Real‐time economic model predictive control of nonlinear process systems

Closed‐loop stability of nonlinear systems under real‐time Lyapunov‐based economic model predictive control (LEMPC) with potentially unknown and time‐varying computational delay is considered. To address guaranteed closed‐loop stability (in the sense of boundedness of the closed‐loop state in a compact state‐space set), an implementation strategy is proposed which features a triggered evaluation of the LEMPC optimization problem to compute an input trajectory over a finite‐time prediction horizon in advance. At each sampling period, stability conditions must be satisfied for the precomputed LEMPC control action to be applied to the closed‐loop system. If the stability conditions are not satisfied, a backup explicit stabilizing controller is applied over the sampling period. Closed‐loop stability under the real‐time LEMPC strategy is analyzed and specific stability conditions are derived. The real‐time LEMPC scheme is applied to a chemical process network example to demonstrate closed‐loop stability and closed‐loop economic performance improvement over that achieved for operation at the economically optimal steady state. © 2014 American Institute of Chemical Engineers AIChE J, 61: 555–571, 2015     

Control‐relevant decomposition of process networks via optimization‐based hierarchical clustering

A systematic method is proposed for control‐relevant decomposition of complex process networks. Specifically, hierarchical clustering methods are adopted to identify constituent subnetworks such that the components of each subnetwork are strongly interacting while different subnetworks are loosely coupled. Optimal clustering is determined through the solution of integer optimization problems. The concept of relative degree is used to measure distance between subnetworks and compactness of subnetworks. The application of the proposed method is illustrated using an example process network. © 2016 American Institute of Chemical Engineers AIChE J, 62: 3177–3188, 2016     

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.     

Sequential and iterative architectures for distributed model predictive control of nonlinear process systems

In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one‐directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi‐directional communication strategy, are evaluated in parallel and iterate to improve closed‐loop performance. In the design of the distributed model predictive controllers, Lyapunov‐based model predictive control techniques are used. To ensure the stability of the closed‐loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov‐based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed‐loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

Off-line model reduction for on-line linear MPC of nonlinear large-scale distributed systems

Model predictive control (MPC) is an efficient method for the controller design of a large number of processes. However, linear MPC is often inappropriate for controlling nonlinear large-scale systems, while non-linear MPC can be computationally costly. The resulting optimization-based procedure can lead to local minima due to the, non-convexities that non-linear systems can exhibit. To overcome the excessive computational cost of MPC application for large-scale nonlinear systems, model reduction methodology in conjunction with efficient system linearizations have been exploited to enable the efficient application of linear MPC for nonlinear distributed parameter systems (DPS). An off-line model reduction technique, the proper orthogonal decomposition (POD) method, combined with a finite element Galerkin projection is first used to extract accurate non-linear low-order models from the large-scale ones. Trajectory Piecewise-Linear (TPWL) methodologies are subsequently developed to construct a piecewise linear representation of the reduced nonlinear model, both in a static and in a dynamic fashion. Linear MPC, based on quadratic programming, can then be efficiently performed on the resulting low-order, piece-wise affine system. Our combined methodology is readily applicable in combination with advanced MPC methodologies such as multi-parametric MPC (MP-MPC) (Pistikopoulos, 2009). The stabilisation of the oscillatory behaviour of a tubular reactor with recycle is used as an illustrative example to demonstrate our methodology.