PLANTWIDE CONTROL STUDY OF A VINYL ACETATE MONOMER PROCESS DESIGN

A simulation of a vinyl acetate monomer (VAM) process design was developed and compared with the work of Luyben and Tyreus (1998). Two incremental changes were made to the two main control substructures. Specifically, the two schemes focus on improving the liquid inventory system control and controllability of the azeotropic distillation column. The level control strategy was tested and found to produce a faster response with less oscillatory behavior. Two alternative control techniques for the azeotropic distillation column were tested, a feed-forward model predictive controller and a static feed-forward ratio controller. The model predictive controller results illustrated the large difference between the water composition analyzer sample time and the controller step size. The static feed-forward ratio controller showed excellent disturbance rejection of large feed flow variations to the azeotropic distillation column. Simulation results are presented to illustrate the effectiveness of the new control strategies.     

工业过程多变量系统的辅助常规控制设计

利用PID控制器对多变量系统进行控制,主要采取分散常规控制或解耦控制的策略。对于存在耦合的系统,分散常规控制无法消除各变量间的相互扰动,而直接设计动态解耦矩阵有时候会存在困难,静态解耦的效果却并不明显。针对已设计好分散常规控制器的方系统,以解耦为目标和原则,设计多变量系统中分散常规控制方案剩余配对的控制器作为辅助控制器,以频域近似的方法整定其PID参数,实现多输入多输出的控制结构,降低了各变量的相互耦合作用,提高了控制性能。通过对Shell公司的典型控制问题的辅助控制器设计与仿真实验,取得了比原有分散常规控制与静态解耦控制更好的控制效果,验证了控制策略的可行性与有效性。     

基于小波神经网络的通用模型控制

针对通用模型控制要求被控对象有显式解的局限性,应用小波神经网络来建立非线性被控对象的逆模型.再结合通用模型控制算法,将非线性过程模型直接嵌入到控制器中,来实现对被控对象的逆控制.其参考轨迹是一条典型的二阶曲线,控制器参数具有明显的物理意义,且易于整定.仿真结果验证了该控制策略的有效性.     

MULTIVARIABLE CONTROL OF CATALYTIC CRACKING PROCESSES

Simple, explicit and physically intuitive Feedforward and Feedback control policies are designed for Fluidized Catalytic Cracking Processes. The Feedforward (FF) control algorithm compensates for changes in the feed rate and feed coking tendency by the use of the air flow and catalyst circulation rates as control variables to maintain the conversion and the reactor temperature at fixed levels. Through steady state and dynamic simulations the FF controller is shown to be very effective. To improve the dynamic response of the process and to account for the process/model mismatch a feedback (FB) controller is also designed to complement the FF action. The FB action is designed by use of the transformation related to the physical modes which correspond to the extensive variables of the process. It is shown that the required control structure consists of two loops. One uses the air flow rate to control the total sensible heat content of the reactor and regenerator solid phases. The other loop controls the regenerator enthalpy by changes in the catalyst circulation rate. The air flow rate controller includes an integral action to avoid reactor temperature offsets, while the catalyst circulation rate controller requires a nonlinear static observer to predict the coke concentration on the regenerated catalyst from dense bed and flue gas regenerator temperatures. The performance of the controller for changes on the oil feed rate, caking tendency of the feed, as well as for reactor temperature set point changes is faster and smoother than Kurihara's scheme.     

非线性Hammerstein模型预测控制策略及其在pH中和过程中的应用

所有实际工业过程都包含一定程度的非线性,如pH中和过程由于其本身的强非线性是工业过程控制中具有挑战性的难题,但至今为止仍缺乏有效的非线性控制方法。将基于差分方程模型的模型预测控制策略(model predictive control,MPC)推广到包含一个静态非线性多项式函数和一个线性差分方程动态环节的非线性Hammerstein系统,详细描述了基于静态非线性多项式函数的最优控制作用求解方法,提出了一套新的非线性Hammerstein MPC 控制策略(nonlinear Hammerstein predictive control,NLHPC)。pH中和过程控制仿真和控制实验表明,NLHPC的控制结果好于工业上常用的非线性 PID(nonlinear PID,NL-PID)控制器。     

Saturated PI control of continuous bioreactors with Haldane kinetics

The problem of designing a saturated Output-Feedback (OF) controller for a continuous bioreactor with Haldane kinetics is addressed. The reactor must be operated at maximum biomass production rate by manipulating the feed rate on the basis of a (biomass or substrate) measurement. The consideration of the problem as an interlaced observer-control design leads to a saturated PI controller that recovers (up to observer convergence) the behavior of a detailed model-based robust nonlinear state-feedback (SF) globally stabilizing saturated controller. The saturated PI control scheme has: (i) closed-loop stability conditions in terms of control gains and limits, and (ii) a simple construction-tuning procedure. The proposed approach is illustrated and tested with a representative case example through numerical simulations.     

Robust discrete control of nonlinear processes: Application to chemical reactors

Trajectory tracking or rejecting persistent disturbances with digital controllers in nonlinear processes is a class of problems where classical control methods breakdown since it is very difficult to describe the dynamic behavior over the entire trajectory. In this paper, a model-based robust control scheme is proposed as a potential solution approach for these systems. The proposed control algorithm is a robust error feedback controller that allows us to track predetermined operation profiles while attenuating the disturbances and maintaining the stability conditions of the nonlinear processes. Various numerical simulation examples demonstrate the effectiveness of this robust scheme. Two examples deal with effective trajectory tracking in chemical reactors over a wide range of operating conditions. The third example analyses the attenuation of periodic load in a biological reactor. All examples illustrate the ability of the robust control scheme to provide good control in the face of parameter uncertainties and load disturbances.     

Controlling of Proton Exchange Membrane Fuel Cell by Model Predictive Controller Based on ANFIS Model

Proton exchange fuel cell is one of the most promising new technologies in electrical energy production. Due to slow dynamic, nonlinearity and dependency of time changing variables of proton exchange membrane fuel cell (PEMFC), its control issue is a challenging problem. In this paper, model predictive controller (MPC) based on the adaptive neuro‐fuzzy interface model of the PEMFC is proposed to control the output voltage. First the adaptive neuro‐fuzzy interference system (ANFIS) model is identified to approximate the dynamic behavior of the PEMFC system with a set of data which are taken from a physical model of a 5 kW PEMFC setup plant. Then the branch‐and‐bound method and the greedy algorithm are used to solve the constrained optimization function of the predictive control problem. The results reveal that the ANFIS model can effectively approximate the dynamic behavior of the PEMFC and the predictive controller based on this model can successfully control the output and satisfy the constraints.     

催化剂窑炉温度的启发式动态规划控制

针对催化剂生产过程中焙烧窑炉温度控制问题,提出了一种启发式动态规划（heuristic dynamical programming,HDP）控制方法。该方法通过策略评价及策略提升的重复进行逐渐逼近最优的控制策略。采用人工神经网络建立了被控系统和评价指标模型,基于梯度下降原理阐明了控制器各模块的在线学习方法。对某催化剂公司的窑炉温度控制实验表明,与常规PID控制相比,HDP控制方案具有较强的工况适应能力,其控制精度较常规控制提高约70%,加热电流均值减小约5%。     

Optimization‐Based Nonlinear Centralized Controller Tuning of Liquid‐Liquid Extraction Processes

Abstract

The control problem of an agitated contactor is considered in this work. A Scheibel extraction column is modeled using the non‐equilibrium backflow mixing cell model. Model dynamic analysis shows that this process is highly nonlinear, thus the control problem solution of such a system needs to tackle the process nonlinearity efficiently. The control problem of this process is solved by developing a multivariable nonlinear control system implemented in MATLAB?. In this control methodology, a new controller tuning method is adopted, in which the time‐domain control parameter‐tuning problem is solved as a constrained optimization problem. A MIMO (multi‐input multi‐output) PI controller structure is used in this strategy. The centralized controller uses a 2×2 transfer function and accounts for loops interaction. The controller parameters are tuned using an optimization‐based algorithm with constraints imposed on the process variables reference trajectories. Incremental tuning procedure is performed until the extractor output variables transient response satisfies a preset uncertainty which bounds around the reference trajectory. A decentralized model‐based IMC (internal model control) control strategy is compared with the newly developed centralized MIMO PI control one. Stability and robustness tests are applied to the two algorithms. The performance of the MIMO PI controller is found to be superior to that of the conventional IMC controller in terms of stability, robustness, loops interaction handling, and step‐change tracking characteristics.     

Distributed model predictive control based on adaptive sampling mechanism

In this work, an adaptive sampling control strategy for distributed predictive control is proposed. According to the proposed method, the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function. Then, the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller, and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the nextcontrolperiod, the adaptive sampling mechanism recalculates the sampling rate of each subsystem's measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system, and this process is repeated. Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object. It can also accurately capture dynamic changes, meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment, significantly improving the performance of distributed model predictive control (DMPC). A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.     

纯滞后系统变积分数字控制研究

提出一种基于动态性能指标的纯滞后系统变积分数字控制方法,目的是消除延迟对系统过度过程的影响,改善系统的性能。该算法的基本思想是将系统的阶跃响应划分为6个区域,在不同的区域实时地调整控制器的控制量,从而实现控制效果的整体最优,给出了控制策略和仿真控制结果。该算法只需要整定两个控制参数,他们有明确的物理意义。仿真实验表明该控制方法具有参数整定简单、容易使用、具有一定的自适应性等特点,可获得满意的控制效果。     

APPLICATION OF FUZZY ADAPTIVE CONTROLLER IN NONLINEAR PROCESS CONTROL

In general, physical processes are usually nonlinear and control system design based on the linearization technique cannot control the process well for a wide range of operation. Use of the variable transformation method may not always solve the problem. In this paper, a fuzzy adaptive controller is proposed to control the nonlinear process. The CSTR control problem has also been considered. The results are compared with the method of nonlinear model predictive control (NMPC) with constrained and unconstrained control variables. A fuzzy model-following control system scheme is also proposed. The results show that the proposed controller is a feasible control structure for a nonlinear or parameter-variations process control.     

pH Process Control with Fictitious Smoothing Tanks

This paper is concerned with pH process control based on sensitivity analysis, applied to a strong acid‐strong base system. The models were developed, using the concept of chemical reaction invariants. Derived from GMC synthesis, a non‐linear PI control law was presented and the results compared to the classical PI controller. The presence of uncertainties or a shift in the system's behavior has been considered, either on steady state or from the dynamic point of view. The analysis showed that sensitivity presents the same behavior in both cases, thus demonstrating the feasibility of the application with the non‐linear PI controller. The results of sensitivity analysis indicate a strategy for improving control structure performance, using the concept of fictitious smoothing tanks, inserted into computer codes for process control, yet without incurring appreciable computational load or additional costs.     

模糊PID-Smith串级控制在加热炉温度控制系统中的应用

提出一种基于模糊PID和Smith预估控制器的串级控制器,克服了工业加热炉温度控制系统中加热炉出口温度的时变性、非线性及滞后性等缺点。仿真结果表明,模糊PID-Smith串级控制器具有较强的鲁棒性,提高了控制系统的动、静态性能指标。     

Nonlinear adaptive temperature control of multi-product, semi-batch polymerization reactors

This paper considers the temperature control of semi-batch polymerization reactors in which some of the following issues must be considered: (i) production of multiple products in the same reactor; (ii) changing heat transfer characteristics, during a batch and from batch to batch; (iii) time varying and nonlinear reaction rate due to changing monomer concentration and diffusion controlled termination reactions (gel effect); (iv) the absence of detailed kinetic models for the reactors. The industrial challenge problem published by Chylla and Haase [Chylla, R. W. and Haase, D. R. (1993) Temperature control of semi-batch polymerization reactors (with corrected updates). Comput. Chem. Eng. 17, 257–264) is used as the simulation basis for evaluating these problems.

A nonlinear adaptive controller consisting of a nonlinear controller (based on differential geometric concepts) coupled with an extended Kalman filter (which uses only readily available data and knowledge) is shown to provide excellent control in all the above situations. In particular, the on-line estimation is critical for the strong performance of the nonlinear controller over a broad range of conditions. PID controllers with feedforward terms can perform well at one set of conditions, however, they require retuning as conditions and products change.     

LMI-based robust model predictive control for a continuous MMA polymerization reactor

A linear matrix inequality (LMI)-based robust model predictive control (MPC) is applied to a continuous stirred-tank reactor for the polymerization of methyl methacrylate (MMA). The polytopic model is constructed to predict the responses to various control input sequences by using Jacobians of uncertain nonlinear model at several operating points and the controller design is characterized as the problem of minimizing an upper bound on the ‘worst-case’ infinite horizon objective function subject to constraints on the control input and plant output. Simulation studies under different conditions are conducted to validate the feasibility of the optimization problem and evaluate the applicability of such a control scheme. Simulation results show that, despite the model uncertainty, the LMI-based robust model predictive controller performs quite satisfactorily for the property control of the continuous polymerization reactor and guarantees the robust stability.     

PID参数的意义与整定方法

用人工控制温度的策略，介绍PID控制器主要参数的意义，和整定PID参数的方法。用仿真实验的结果验证了整定方法的有效性。     

NONLINEAR PI CONTROLLER FOR pH PROCESS

The control of pH is one of the most difficult challenges in the process industry because of the severe nonlinearities and high precision required in manipulating the flow rate. The Wiener model, which consists of a linear dynamic element followed by a nonlinear static element, is used for representing such nonlinear processes. Piecewise continuous polynomials are used for mapping the nonlinear static gain accurately. A nonlinear PI controller was designed based on the Wiener model. Simulation results on the nonlinear mathematical model are presented to highlight the superior performance of the Wiener model based nonlinear PI controller in comparison to that of the local linear PI controller. The performance of the nonlinear PI controller was further improved upon by using the method of inequalities to obtain a single set of PI controller settings that takes into account the parametric variations in the linear dynamic element at different operating points. Simulation and experimental results are presented to support the work.     

草甘膦生产过程中温度的模糊PID复合控制

考虑草甘膦生产过程中最重要的温度控制问题,给出总体温度监控方案,其中恒温段应用一种带自动修正因子的模糊-PID控制器,应用JX-300进行现场实现,得到较好的应用效果,提高了产品收率。