基于多变量预测控制技术的丙烯精馏塔控制系统

精馏是化工中的重要过程,具有强耦合性、不确定性、非线性和大滞后等特征,且存在着苛刻的约束条件,控制难度大,长期以来都是各种先进控制与优化方案的实验对象。预测控制、内模控制、推断控制、模糊控制、神经元控制等多种控制策略都曾应用于精馏过程。选取了丙烯精馏塔作为研究对象,利用APC-Hiecon软件对其进行了基于多变量预测控制的研究。通过先进控制实施效果对比表明,方案实施后,过程变量运行平稳,有效保障了产品质量及精馏过程的稳定,达到了预期控制要求。     

先进控制技术及应用第四讲模型预测控制及其工业应用

在介绍模型预测控制的特点之后,系统地阐述了多变量协调预测控制的基本原理,仿真实例说明了该算法的有效性,最后以大型炼油企业ＦＣＣＵ反再系统为例介绍了其工程应用。     

多变量约束预估控制在FCC分馏塔上的应用研究

本文以某炼油厂FCC分馏塔为应用研究对象,介绍了多变量约束预估控制技术的思想和特点,重点论述了该技术在催化裂化装置分馏塔上应用的设计方案、数学模型的建立,并最终得出多变量约束预估控制系统具有优越性的结论。     

一种基于多模型切换的阶梯式广义预测控制算法

针对一类模型参数突变的系统,提出一种基于多模型切换的阶梯式广义预测控制算法。采用多个固定模型、一个常规自适应模型和一个可重新赋初值的自适应模型并行辨识系统的动态特性。多个固定模型可以提高系统的暂态性能,常规自适应模型可以保证系统的稳定性,可重新赋初值的自适应模型可以进一步提高系统的暂态性能。在每个采样时刻基于性能指标切换到最优的局部模型作为当前模型,设计阶梯式广义预测控制器,从而实现系统全局的控制。最后的仿真结果表明,其控制效果明显优于单一模型的控制器。     

多变量约束控制在大型FCCU装置中的应用（下）：在线优化控制

“大型催化裂化装置多变量约束控制与优化”是国家“九．五”重点科技攻关项目中的一个课题。介绍了多变量的约束控制软件包的整体框架,并详细阐述多变量约束控制的控制策略和优化功能。     

多变量预测控制技术在重芳烃分离塔控制中的应用

针对扬子石化公司某二甲苯分离装置中重芳烃分离塔进料波动大、操作变量多、关联性强、容量滞后大等特点,使用多变量预测控制软件APC-Hiecon,实现对重芳烃分离塔的平稳控制并提高分离效果,取得了很好的控制效果和显著的经济效益.     

非线性多变量系统的多模型广义预测解耦控制

针对实际工业过程中多变量系统存在着非线性、工况范围广、耦合强的特点,提出基于设定值观测器的非线性多模型广义预测解耦控制算法。该方法由线性广义预测控制器、一种新的设定值观测器和切换机构组成。理论分析和仿真结果表明,该控制策略不但可以保证闭环系统B IBO稳定和渐近收敛,而且能够得到很好的控制效果。     

汽油产品质量先进控制与工程应用

针对催化裂化装置主分馏塔汽油产品质量先进控制系统，从实时控制应用出发，建立了塔顶塔段汽油产品质量先进控制的简化收态空间数学模型。使用实测状态变量反馈和可测干扰变量前馈的状态反馈预测控制方法，提高控制系统的性能及抗干扰能力，使模型的适应范围大大增强，并且不需要对实际运行装置进行测试建模，减少对生产过程的干扰影响。     

自适应正余弦搜索樽海鞘群优化算法

针对基本樽海鞘群智能优化算法的收敛速度慢、搜索精度低、容易陷入局部最优的缺点,提出了一种自适应正余弦搜索樽海鞘群优化算法.该算法引入正余弦搜索,以加强领导者位置更新速度,提升算法寻优速率;在跟随者位置更新公式中引入自适应权重因子,提高算法跳出局部最优的能力,并且提高了算法的收敛精度.使用所提出的算法对12个典型寻优测试...     

基于滑模的多变量广义预测解耦控制

针对多变量控制系统的耦合问题,将广义预测控制和滑模控制结合起来,提出一种基于滑模的多变量广义预测解耦控制方法.首先把m个输入n个输出的多变量耦合系统分解成m个输入单个输出子系统,再通过对子系统输出预测得到滑模切换函数值,求解开环优化求得控制律,最后通过仿真实验表明,该控制方法对多变量耦合系统的控制是正确有效的.     

SYSTEMATIC DERIVATIONS OF MODEL PREDICTIVE CONTROL BASED ON ARTIFICIAL NEURAL NETWORK

This article presents systematic derivations of setting up a nonlinear model predictive control based on the artifical neural network. Unlike most research in the past, the control law is mathematically developed in detail so that the performance of the ANN-based controller can be improved. In this paper, a three-layer feedforward neural network with hyperbolic tangent functions in the hidden layer and with a linear function in the output layer is used. The two-stage scheme including pseudo Gauss-Newton and least squares is proposed for training ANN. This training method is better than the traditional algorithm in terms of training speed. The Levenberg-Marquardt approximation is also utilized for the minimum of the predictive control criterion. Two typical chemical processes are simulated and the ANN model predictive control applications can reach fairly good results.     

ADAPTIVE FUZZY MODEL BASED PREDICTIVE CONTROL OF AN EXOTHERMIC BATCH CHEMICAL REACTOR*

An adaptive fuzzy model based predictive control (AFMBPC) approach is presented to track the desired temperature trajectories in an exothermic batch chemical reactor. The AFMBPC incorporates an adaptive fuzzy modeling framework into a model based predictive control scheme to derive analytical controller output. This approach has the flexibility to cope with different fuzzy model structures whose choice also lead to improve the controller performance. In this approach, adaptation of fuzzy models using dynamic process information is carried out to build a predictive controller, thus eliminating the determination of a predefined fixed fuzzy model based on various sets of known input-output relations. The performance of the AFMBPC is evaluated by comparing to a fixed fuzzy model based predictive controller (FFMBPC) and a conventional PID controller. The results show the better suitability of AFMBPC for the control of highly nonlinear and time varying batch chemical reactors.     

MULTIVARIABLE PROCESS IDENTIFICATION AND CONTROL OF CONTINUOUS FLUIDIZED BED DRYERS

ABSTRACT

Dynamic models that rigorously describe fluidized bed dryers based on the fundamental principles of the process are usually so complex to be employed in control system design. To obtain simple reduced-order models for such systems, a sequence of step changes in the manipulated and load variables is introduced into the rigorous model. The obtained input–output dynamic response data are used for off-line model identification. Different types of linear models are generated, which are shown to be adequately representing the fluidized bed drying dynamics. The derived models are useful to develop model-based control algorithms such as Internal Model Control (IMC) and Model Predictive Control (MPC). Performance and robustness properties of these controllers are analyzed. Simulation results demonstrate a good performance in terms of tracking and load rejection capabilities.     

MULTIVARIABLE PROCESS IDENTIFICATION AND CONTROL OF CONTINUOUS FLUIDIZED BED DRYERS

Dynamic models that rigorously describe fluidized bed dryers based on the fundamental principles of the process are usually so complex to be employed in control system design. To obtain simple reduced-order models for such systems, a sequence of step changes in the manipulated and load variables is introduced into the rigorous model. The obtained input-output dynamic response data are used for off-line model identification. Different types of linear models are generated, which are shown to be adequately representing the fluidized bed drying dynamics. The derived models are useful to develop model-based control algorithms such as Internal Model Control (IMC) and Model Predictive Control (MPC). Performance and robustness properties of these controllers are analyzed. Simulation results demonstrate a good performance in terms of tracking and load rejection capabilities.     

NONLINEAR MODEL PREDICTIVE CONTROL

Nonlinear Model Predictive Control (NMPC), a strategy for constrained, feedback control of nonlinear processes, has been developed. The algorithm uses a simultaneous solution and optimization approach to determine the open-loop optimal manipulated variable trajectory at each sampling instant. Feedback is incorporated via an estimator, which uses process measurements to infer unmeasured state and disturbance values. These are used by the controller to determine the future optimal control policy. This scheme can be used to control processes described by different kinds of models, such as nonlinear ordinary differential/algebraic equations, partial differential/algebraic equations, integra-differential equations and delay equations. The advantages of the proposed NMPC scheme are demonstrated with the start-up of a non-isothermal, non-adiabatic CSTR with an irreversible, first-order reaction. The set-point corresponds to an open-loop unstable steady state. Comparisons have been made with controllers designed using (1) nonlinear variable transformations, (2) a linear controller tuned using the internal model control approach, and (3) open-loop optimal control. NMPC was able to bring the controlled variable to its set-point quickly and smoothly from a wide variety of initial conditions. Unlike the other controllers, NMPC dealt with constraints in an explicit manner without any degradation in the quality of control. NMPC also demonstrated superior performance in the presence of a moderate amount of error in the model parameters, and the process was brought to its set-point without steady-state offset.     

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.     

EFFECTIVE IMPLEMENTATION OF CONSERVATIVE MODEL BASED CONTROL ALGORITHM

Recently a digital control algorithm, known as Conservative Model Based Controller (CMBC) with superior performance compared to many other currently popular controllers was discussed in the literature. It provides offset-free performance; however a large number of terms will have to be included in the algorithm to achieve this. This paper describes two modifications to ensure zero offset with limited number of terms in the algorithm. These modifications are evaluated through simulation by considering a few typical first order with dead time (FODT) processes. Both modifications provide offset-free performance, and either could be employed depending on the nature of the process response data on hand.     

APPLICATION OF MULTIVARIABLE GENERALIZED PREDICTIVE CONTROL TO A PACKED DISTILLATION COLUMN

The present work deals with the application of Multivariate Generalized Predictive Control (MGPC) systems to a packed distillation column. The steady-state and dynamic behaviour of the system have been simulated using two film plug flow model. The model solutions have been obtained employing orthogonal collocation on finite element. The Jacobi polynomials within the finite element procedure was tested to determine the phase flow rates, the liquid and vapour composition profiles and the temperature profiles. All the theoretical results were compared with experimental data obtained from a pilot-plant packed distillation column distilling methanol-water mixture. Decoupling and MGPC control of overhead and/or bottom compositions were examined. Perturbation in feed composition and, reflux ratio and the reboiler heat duty were utilized as the disturbance and the manipulated variables respectively. Performance of these systems was tested by using an integral square of error (ISE and IAE) criterion.     

APPLICATION OF MULTIVARIABLE NONLINEAR ADAPTIVE GENERIC MODEL CONTROL TO A PACKED DISTILLATION COLUMN

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.     

APPLICATION OF MULTIVARIABLE NONLINEAR ADAPTIVE GENERIC MODEL CONTROL TO A PACKED DISTILLATION COLUMN

Nonlinear adaptive generic model control and self-tuning PID control systems were applied to control the top and bottom product temperature of a packed distillation column separating methanol-water mixture. In the first control algorithm, an adaptive generic model control (AGMC) structure was proposed for dual temperature control of the system. In the second control algorithm, nonlinear self tuning PID (NLSTPID) control based on pole-placement technique was used to control the same system. For NLSTPID control purposes pseudo random binary sequence (PRBS) signal and recursive identification algorithm were used to estimate the relevant parameters of a polynomial NARMAX model. In this work, real-time application has been carried out. In both dynamic and control studies, perturbations in feed composition were utilized as the disturbance, and the reboiler heat duty and the reflux ratio were selected as the manipulated variables. The control performances have been obtained by using ISE and, in general, AGMC results were better than those of the STPID control algorithm.