CONTROL OF A CONTINUOUS POLYMERIZATION REACTOR BY WIENER INPUT/OUTPUT DATA-BASED PREDICTIVE CONTROLLER WITH DIRECT INVERSE IDENTIFICATION

This article reports an experimental study for the identification and predictive control of a continuous methyl methacrylate (MMA) solution polymerization reactor. The Wiener model was introduced to identify the polymerization reactor in a more efficient manner than the conventional methods of Wiener model identification. In particular, the method of subspace identification was employed and the inverse of the nonlinear part was directly identified. The input variables in this work were the jacket inlet temperature and the feed flow rate, while the monomer conversion and the weight average molecular weight were selected as the output variables. On the basis of the identified model a Wiener-type input/output data-based predictive controller was designed and applied to the property control of polymer product in the continuous MMA polymerization reactor by conducting an on-line digital control experiment with online densitometer and viscometer. Despite the complex and nonlinear characteristics of the polymerization reactor, the proposed controller was found to perform satisfactorily for property control in the multiple-input multiple-output system with input constraints for both set-point tracking and disturbance rejection. This was also confirmed by simulation results.     

CONTROL OF A CONTINUOUS POLYMERIZATION REACTOR BY WIENER INPUT/OUTPUT DATA-BASED PREDICTIVE CONTROLLER WITH DIRECT INVERSE IDENTIFICATION

This article reports an experimental study for the identification and predictive control of a continuous methyl methacrylate (MMA) solution polymerization reactor. The Wiener model was introduced to identify the polymerization reactor in a more efficient manner than the conventional methods of Wiener model identification. In particular, the method of subspace identification was employed and the inverse of the nonlinear part was directly identified. The input variables in this work were the jacket inlet temperature and the feed flow rate, while the monomer conversion and the weight average molecular weight were selected as the output variables. On the basis of the identified model a Wiener-type input/output data-based predictive controller was designed and applied to the property control of polymer product in the continuous MMA polymerization reactor by conducting an on-line digital control experiment with online densitometer and viscometer. Despite the complex and nonlinear characteristics of the polymerization reactor, the proposed controller was found to perform satisfactorily for property control in the multiple-input multiple-output system with input constraints for both set-point tracking and disturbance rejection. This was also confirmed by simulation results.     

基于最小二乘支持向量机的MIMO线性参数变化模型辨识及预测控制

将现有的面向单输入单输出系统的基于最小二乘支持向量机的参数变化模型辨识算法(SISO-LSSVM-LPV), 推广到多输入多输出系统, 实现了面向多输入多输出系统的基于最小二乘支持向量机的参数变化模型辨识算法(MIMO-LSSVM-LPV), 进一步结合基于遗传算法的预测控制算法(GA-MPC), 提出并实现了MIMO-LSSVM-LPV+ GA-MPC的建模控制一体化新架构。仿真结果表明, 该辨识算法可逼近复杂非线性MIMO系统, 辨识精度高, 并且保留了线性回归低计算量的优点, 结合了GA的MPC可实现最优控制量的在线实时寻优, 并取得了良好控制效果。     

Nonlinear model predictive control based on support vector machine and genetic algorithm

This paper presents a nonlinear model predictive control (NMPC) approach based on support vector machine (SVM) and genetic algorithm (GA) for multiple-input multiple-output (MIMO) nonlinear systems. Individual SVM is used to approximate each output of the controlled plant. Then the model is used in MPC control scheme to predict the outputs of the controlled plant. The optimal control sequence is calculated using GA with elite preserve strategy. Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.     

Model predictive control for the self-optimized operation in wastewater treatment plants: Analysis of dynamic issues

This paper describes a procedure to find the best controlled variables in an economic sense for the activated sludge process in a wastewater treatment plant, despite the large load disturbances. A novel dynamic analysis of the closed loop control of these variables has been performed, considering a nonlinear model predictive controller (NMPC) and a particular distributed NMPC-PI control structure where the PI is devoted to control the process active constraints and the NMPC the self-optimizing variables. The well-known self-optimizing control methodology has been applied, considering the most important measurements of the process. This methodology provides the optimum combination of measurements to keep constant with minimum economic loss. In order to avoid nonfeasible dynamic operation, a preselection of the measurements has been performed, based on the nonlinear model of the process and evaluating the possibility of keeping their values constant in the presence of typical disturbances.     

面向过程控制的两段提升管重油催化裂解动态建模

两段提升管重油催化裂解多产丙烯技术具有原料适应性强、丙烯和高品质汽油产率高等优点,工业应用前景广阔。开展动态建模、非线性分析与控制等方面的研究对于揭示装置运行规律、提高能量/质量转化效率具有重要意义。从过程控制的角度出发,基于TMP工艺流程,通过合理假设,分别建立两段提升管、汽提段以及再生系统的数学模型并由循环催化剂连接形成一个整体动态数学模型。数值模拟结果表明,所建模型可以准确描述过程关键变量的动态变化趋势以及两段提升管-再生器之间的耦合关系,从而为后续非线性分析和控制系统设计创造有利条件。     

Online machine learning modeling and predictive control of nonlinear systems with scheduled mode transitions

This work develops a model predictive control (MPC) scheme using online learning of recurrent neural network (RNN) models for nonlinear systems switched between multiple operating regions following a prescribed switching schedule. Specifically, an RNN model is initially developed offline to model process dynamics using the historical operational data collected in a small region around a certain steady-state. After the system is switched to another operating region under a Lyapunov-based MPC with suitable constraints to ensure satisfaction of the prescribed switching schedule policy, RNN models are updated using real-time process data to improve closed-loop performance. A generalization error bound is derived for the updated RNN models using the notion of regret, and closed-loop stability results are established for the switched nonlinear system under RNN-based MPC. Finally, a chemical process example with the operation schedule that requires switching between two steady-states is used to demonstrate the effectiveness of the proposed RNN-MPC scheme.     

On identification of well‐conditioned nonlinear systems: Application to economic model predictive control of nonlinear processes

The focus of this work is on economic model predictive control (EMPC) that utilizes well‐conditioned polynomial nonlinear state‐space (PNLSS) models for processes with nonlinear dynamics. Specifically, the article initially addresses the development of a nonlinear system identification technique for a broad class of nonlinear processes which leads to the construction of PNLSS dynamic models which are well‐conditioned over a broad region of process operation in the sense that they can be correctly integrated in real‐time using explicit numerical integration methods via time steps that are significantly larger than the ones required by nonlinear state‐space models identified via existing techniques. Working within the framework of PNLSS models, additional constraints are imposed in the identification procedure to ensure well‐conditioning of the identified nonlinear dynamic models. This development is key because it enables the design of Lyapunov‐based EMPC (LEMPC) systems for nonlinear processes using the well‐conditioned nonlinear models that can be readily implemented in real‐time as the computational burden required to compute the control actions within the process sampling period is reduced. A stability analysis for this LEMPC design is provided that guarantees closed‐loop stability of a process under certain conditions when an LEMPC based on a nonlinear empirical model is used. Finally, a classical chemical reactor example demonstrates both the system identification and LEMPC design techniques, and the significant advantages in terms of computation time reduction in LEMPC calculations when using the nonlinear empirical model. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3353–3373, 2015     

Modeling and predictive control using fuzzy logic: Application for a polymerization system

In this study, a predictive control system based on type Takagi‐Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010     

Model-based control of titer and glycosylation in fed-batch mAb production: Modeling and control system development

Therapeutic monoclonal antibodies (mAbs) are typically manufactured using mammalian cell cultures in fed-batch bioreactors, with increasing emphasis on meeting productivity and product quality attribute targets that depend strongly on such process variables as nutrient feed rates and bioreactor operating conditions. In this article, we identify, categorize, and address the challenges of achieving both productivity and product quality goals simultaneously, by developing a multivariable, model-based control system that can satisfy multiple production objectives in a fed-batch cell culture process. Here, we discuss model development and present theoretical concepts of observability and controllability that are essential to understanding and handling effectively these intrinsic challenges. Subsequently, we evaluate via simulation the performance of the outer-loop model predictive control and demonstrate the overall capability to satisfy complex production objectives in a laboratory scale bioreactor, as a first step toward the ultimate goal of creating an advanced control system for fed-batch mAb manufacturing processes.     

多层次结构模型预测控制系统的层次间模型切换方法

引言实际的工业过程对象,大部分都呈现出很强的非线性特性,其控制过程十分复杂。虽然近年来,对非线性技术的研究已经取得了很多的成果。但是非线性系统精确建模困难[1]、非线性微分方程求解     

Model based robust control approach for batch crystallization product design

The paper presents a novel control approach for crystallization processes, which can be used for designing the shape of the crystal size distribution to robustly achieve desired product properties. The approach is based on a robust optimal control scheme, which takes parametric uncertainties into account to provide decreased batch-to-batch variability of the shape of the crystal size distribution. Both open-loop and closed-loop robust control schemes are evaluated. The open-loop approach is based on a robust end-point nonlinear model predictive control (NMPC) scheme which is implemented in a hierarchical structure. On the lower level a supersaturation control approach is used that drives the system in the phase diagram according to a concentration versus temperature trajectory. On the higher level a robust model-based optimization algorithm adapts the setpoint of the supersaturation controller to counteract the effects of changing operating conditions. The process is modelled using the population balance equation (PBE), which is solved using a novel efficient approach that combines the quadrature method of moment (QMOM) and method of characteristics (MOC). The proposed robust model based control approach is corroborated for the case of various desired shapes of the target distribution.     

Optimization-based control of a reactive simulated moving bed process for glucose isomerization

In this paper, we investigate the continuous production of high-fructose corn syrup in a reactive simulated moving bed (RSMB) process which combines a quasi-continuous chromatographic separation with the enzymatic biochemical conversion of glucose to fructose. Such an integration of reaction and separation in one unit operation is advantageous for the equilibrium limited glucose isomerization. However, it complicates process design and process control. The continuous operating parameters and the discrete distribution of the columns over the different zones of the RSMB process are determined using a rigorous model-based optimization strategy. In order to maintain the product purity in the presence of disturbances while injecting a minimal additional amount of eluent, a nonlinear model predictive controller was developed which can deal with the complex hybrid (continuous/discrete) dynamics of the RSMB plant and takes hard process constraints (e.g. the maximal allowable pressure drop) into account. The efficiency of the control concept is proven in experimental studies using a 6-column RSMB plant of pharmaceutical scale.     

Robust model predictive control of an industrial partial combustion fluidized-bed catalytic cracking converter

It is presented here in the study of the application of a robust model predictive control to an industrial partial combustion fluidized-bed catalytic cracking (FCC) converter. This particular type of FCC converter shows an interesting dynamics in which most of the system outputs are integrating with respect to the manipulated inputs. Time delays are also present and the model parameters can change depending on the operating point. Then, the system model should be represented by a set of possible plants, which can stand for different operating conditions of this process system. Moreover, one needs to include a comprehensive model formulation in order to accommodate time-delays for both stable and integrating outputs. The proposed control strategy was tested through simulation for the disturbances commonly found in the FCC converter unit, taking into consideration the plant/model mismatch. Results obtained from the simulated scenarios point out a fine prospective method. The robust controller shows a good potential to be implemented in the real process.     

Actuator stiction compensation via model predictive control for nonlinear processes

The problem of valve stiction is addressed, which is a nonlinear friction phenomenon that causes poor performance of control loops in the process industries. A model predictive control (MPC) stiction compensation formulation is developed including detailed dynamics for a sticky valve and additional constraints on the input rate of change and actuation magnitude to reduce control loop performance degradation and to prevent the MPC from requesting physically unrealistic control actions due to stiction. Although developed with a focus on stiction, the MPC‐based compensation method presented is general and has potential to compensate for other nonlinear valve dynamics which have some similarities to those caused by stiction. Feasibility and closed‐loop stability of the proposed MPC formulation are proven for a sufficiently small sampling period when Lyapunov‐based constraints are incorporated. Using a chemical process example with an economic model predictive controller (EMPC), the selection of appropriate constraints for the proposed method is demonstrated. The example verified the incorporation of the stiction dynamics and actuation magnitude constraints in the EMPC causes it to select set‐points that the valve output can reach and causes the operating constraints to be met. © 2016 American Institute of Chemical Engineers AIChE J, 62: 2004–2023, 2016     

Adaptive Generic Model Control of Multivariable Processes: Application to Semi-Batch Reactors with Different Relative Degrees

In this study, a multivariable Generic Model Control (GMC) approach is proposed based on input-output linear-in-parameters time series data-driven models. Adaptation of the model parameters is carried out at every sampling instant. For higher relative degree systems, two different definitions are used for output derivatives, yielding two versions of adaptive GMC for multivariable processes. The performance of the proposed control algorithms is illustrated by application to multivariable semi-batch reactors without and with coolant dynamics for control of temperature and one of the reactant concentrations. The study indicated that the adaptive GMC (AGMC) algorithms for higher relative degree multiple-input and multiple-output (MIMO) systems with a different relative degree have exhibited performance comparable to or better than the phenomenological model-based GMC with respect to both set point tracking and smooth input profiles, and also that the predictive version of AGMC (AGMC-II) has exhibited slightly lower integral square error (ISE) values compared to AGMC-I in case of multivariable semi-batch reactor with coolant dynamics.     

A simple adaptive control strategy for temperature trajectory tracking in batch processes

In this paper, a simple adaptive control strategy is suggested for temperature tracking control of batch processes. A nonlinear controller, which is in structure very simple and consists of a single parameter, is proposed. To enable this controller to control a batch process adaptively, a simple parameter tuning algorithm is derived based on the Lyapunov stability theorem. The proposed adaptive control scheme is directly operational, which does not depend on process model and the only a priori process information required is the system response direction. To demonstrate the effectiveness and applicability of the proposed scheme, illustrative examples are provided. Extensive simulation results reveal that the proposed adaptive control strategy appears to be a simple and effective approach to batch process control, which provides robust control despite the wide range of operating conditions and nonlinear dynamics of the system.     

Detection and management of actuator faults in controlled particulate processes using population balance models

This paper presents a methodology for the design of an integrated fault detection and fault-tolerant control (FD-FTC) architecture for particulate processes described by population balance models (PBMs) with control constraints, actuator faults and a limited number of process measurements. The architecture integrates model-based fault detection, state estimation, nonlinear feedback and supervisory control on the basis of an appropriate reduced-order model that captures the dominant dynamics of the process and is obtained through application of the method of weighted residuals. The architecture comprises a family of control configurations together with a fault detection filter and a supervisor. For each configuration, a stabilizing output feedback controller with well-characterized stability properties is designed through the combination of a state feedback controller and a state observer that uses the available measurements of principal moments of the particle size distribution (PSD) and the continuous-phase variables to provide appropriate state estimates. A fault detection filter that simulates the behavior of the fault-free, reduced-order model is designed, and its discrepancy from the behavior of the actual process state estimates is used as a residual for fault detection. Finally, a switching law based on the stability regions of the constituent control configurations is derived to reconfigure the control system in a way that preserves closed-loop stability in the event of fault detection. Appropriate fault detection thresholds and control reconfiguration criteria that account for model reduction and state estimation errors are derived for the implementation of the FD-FTC architecture on the particulate process. Finally, the methodology is applied to the problem of constrained, actuator fault-tolerant stabilization of an unstable steady-state of a continuous crystallizer.     

Discrete-time contraction constrained nonlinear model predictive control using graph-based geodesic computation

Modern chemical processes need to operate around time-varying operating conditions to optimize plant economy, in response to dynamic supply chains (e.g., time-varying specifications of product and energy costs). As such, the process control system needs to handle a wide range of operating conditions whilst optimizing system performance and ensuring stability during transitions. This article presents a reference-flexible nonlinear model predictive control approach using contraction based constraints. Firstly, a contraction condition that ensures convergence to any feasible state trajectories or setpoints is constructed. This condition is then imposed as a constraint on the optimization problem for model predictive control with a general (typically economic) cost function, utilizing Riemannian weighted graphs and shortest path techniques. The result is a reference flexible and fast optimal controller that can trade-off between the rate of target trajectory convergence and economic benefit (away from the desired process objective). The proposed approach is illustrated by a simulation study on a CSTR control problem.