L—天冬氨酸生物反应器的最优组合

采用本文作者提出的酶反应动力学方程，比较分析了ＣＳＴＲ，ＰＦＲ及ＣＳＴＲ串联ＰＦＲ反应器生产Ｌ－天冬氨酸的能力，结果表明，对于以单位重量大肠杆菌细胞为基础的Ｌ－天冬氨酸的生产能力，当底物富马酸的转化率低于８０％时，ＣＳＴＲ的生产能力最大；而当转化率高于９４％时，ＰＦＲ的生产能力高于ＣＳＴＲ的生产能力。．     

蛇管与夹套冷却CSTR温度双重控制

连续搅拌釜式化学反应器(CSTR)是重要的化工设备。对蛇管与夹套双冷却CSTR单回路温度控制方案只利用一种操纵变量,无法兼顾动态性能与静态性能的不足进行了深入分析后,提出了CSTR温度双重控制系统方案。通过双重系统的协调控制,在温度出现偏差时由蛇管冷却器快速消除温度偏差,使温度迅速返回设定值;然后由冷却效率高的夹套冷却器逐步取代蛇管冷却器所承担的冷却负荷变化——即在动态过程,由动态性能好的操作变量(蛇管冷却器)进行控制,过渡过程短,动态偏差小;在平稳生产过程,CSTR冷却负荷主要由静态性能好的操作变量(夹套冷却器)承担,冷却效率高,冷却水用量少。CSTR温度双重控制充分发挥了CSTR蛇管与夹套冷却器各自的优势,使CSTR温度控制的动态性能与静态性能都达到较为理想水平。最后,通过半实物仿真实验验证了CSTR温度双重控制在动态性能和节能降耗二方面的优越性。该双重控制方案可应用于具有类似结构特征的其他生产系统。     

Tuning an adaptive controller using a robust control approach

This article proposes a new technique for the tuning of a discrete adaptive controller that is designed based on Lyapunov stability concepts. The tuning is based on the minimisation of a performance index that can be calculated from a generalised eigenvalue problem (GEVP) using LMI's (Linear Matrix Inequalities). The proposed technique results in an adaptive controller with time-varying tuning gains. The solution is based on an approximation of the optimal dual adaptive control problem. The tuning technique was used to perform on-line control of a first-order system and an isothermal and a non-isothermal CSTR. The results show that the proposed approach provides better performance than an adaptive algorithm with the same structure, but with constant adaptation gains. Also, the proposed algorithm is shown to be superior to an adaptive controller based on a Recursive Least Squares (RLS) estimator during sudden changes in model parameters.     

反应器网络结构的研究

以目的产物量最大为目标 ,本文对单股进出料的等温恒容的反应器网络最优结构进行了研究。分别对 4种典型的最优结构 (CSTR ,PFR ,CSTR +PFR ,PFR +CSTR)与其可行的替代结构进行了模拟分析和对比研究。模拟计算结果表明 :由导数分析法获得的最优结构是合理的 ;对单股进出料问题 ,以目的产物量最大为目标的最优网络结构是CSTR ,PFR或由它们组成的串级结构 ,而采用向后分流或循环的结构是不可取的     

Critical evaluation of non-linear filter configurations for the state estimation of Continuous Stirred Tank Reactor

A systematic approach has been attempted to design a non-linear observer to estimate the states of a non-linear system. The neural network based state filtering algorithm proposed by A.G. Parlos et al. has been used to estimate the state variables, concentration and temperature in the Continuous Stirred Tank Reactor (CSTR) process. (CSTR) is a typical chemical reactor system with complex nonlinear dynamics characteristics. The variables which characterize the quality of the final product in CSTR are often difficult to measure in real-time and cannot be directly measured using the feedback configuration. In this work, the comparison of the performances of an extended Kalman filter (EKF), unscented Kalman filter (UKF) and neural network (NN) based state filter for CSTR that rely solely on concentration estimation of CSTR via measured reactor temperature has been done. The performances of these three filters are analyzed in simulation with Gaussian noise source under various operating conditions and model uncertainties.     

Immersion and invariance adaptive control of a class of continuous stirred tank reactors

An immersion and invariance(I&I) manifold based adaptive control algorithm is presented for a class of continuous stirred tank reactors(CSTR) to realize performance-oriented control in this paper. The nonlinear contraction method is combined into the control law design to render the closed-loop CSTR system globally asymptotically stable, firstly. Then, the I&I method is used to form the adaptation law such that the off-the-manifold coordinate(the parameter estimation error) converges to zero using P-monotone property enforced by selecting tuning function in manifold. As a result, the state of the closed-loop CSTR converges to its desired value asymptotically. The simulation is given to illustrate the effectiveness of the presented algorithm.     

Adaptive neural network control for a class of continuous stirred tank reactor systems

In this paper, the control problem of continuous stirred tank reactors （CSTR） is studied. The considered CSTR are required to contain unknown functions and unknown dead zone input. An adaptive controller that uses the neural networks （NNs） is provided to solve the unknown terms. The proposed approach overcomes the effect of the dead zone input. The dead zone input in the systems is compensated for by introducing a new Lyapunov form and Young＇s inequality. The backstepping procedure is exploited to implement controller design with adaptation laws. The stability is analyzed using Lyapunov method. The performance is examined for CSTR to confirm the effectiveness of the proposed approach based on computer simulation.     

Nonlinear CSTR control system design using an artificial bee colony algorithm

This paper proposes a novel controller design method based on using artificial bee colony (ABC) algorithms for an unstable nonlinear continuously stirred tank reactor (CSTR) chemical system. Such CSTR process is highly nonlinear and its dynamic is significantly dominated by system parameters. It is a good challenge to access the controller design performance when the controller is applied in the CSTR control system. The commonly used proportional–integral-derivative (PID) controller is taken into account in this study, and tuning three PID control gains is carried out by the artificial bee colony algorithm. With the use of the optimal ABC algorithm, PID controller gains can be derived suitably by means of minimizing the cost function given in advance. Finally, several control operations are provided to confirm the feasibility and effectiveness of the proposed method. We also discuss the influence of algorithm initial conditions on the control performance with many different tests.     

基于EMPC的CSTR过程优化控制仿真

连续搅拌反应釜CSTR广泛应用于化工、燃料、制药等生产过程,由于其机理复杂、多变量耦合、非线性强,CSTR过程的常规控制难于取得理想效果。本文在一类通用的CSTR非线性机理模型基础上应用EMPC控制策略,针对不同生产场景定义了相应的优化指标。在考虑质量、安全等多种约束的条件下,直接基于非线性模型进行预测和优化,解决了CSTR过程的优化控制问题,并在多种控制模式下验证了控制算法的有效性。     

基于集成神经网络的CSTR状态预测

针对连续搅拌反应釜(CSTR)具有的多重稳态性质,提出使用多个相同拓扑结构的神经网络模块组成的集成神经网络对CSTR的状态进行预测的方法。对集成神经网络的所有网络模块使用多目标粒子群优化算法进行同步训练,使训练结果收敛于参数空间内最优的Pareto面。避免了单一神经网络训练收敛到某一最优点可能产生的过拟和的问题;解决了使用传统训练方法对集成神经网络的子网络进行独立训练时增加学习算法复杂度的问题。对CSTR浓度预测的测试结果证明集成神经网络比同等规模的单一神经网络更适用于CSTR的状态参数预测。     

连续搅拌釜式反应器的自耦比例–积分–微分控制

针对一类参数不确定的连续搅拌釜式反应器,使用了一种与被控对象无关的自耦比例–积分–微分(autocoupling proportional-integral-differential, ACPID)控制方法.该方法将系统内部所有不确定因素及外部扰动定义为一个总扰动,建立了以总扰动为激励的受控误差系统,并根据ACPID镇定规则建立了连续搅拌釜式反应器(continuous stirred tank reactor, CSTR)的ACPID控制系统.理论分析了ACPID控制系统的鲁棒稳定性和抗扰动鲁棒性.仿真结果表明了ACPID控制系统的有效性,在CSTR稳态调节控制领域具有重要的应用价值.     

遗传算法在反应器网络综合中的应用

反应器网络综合问题一般都是复杂的非线性规划问题,遗传算法作为一种启发式全局优化方法,具有计算简单、功能性强的特点。本文将全局优化遗传算法应用到反应器网络综合中以避免传统的优化方法很难求得其全局最优解的缺点。本文首先分析了几种反应器的替代结构,以及在此基础上建立的通用的全混流反应器(Continuous Stirred Tank Reactor,CSTR)网络结构模型;然后采用全局优化遗传算法以及传统的数学优化工具GAMS(General Algorithm Model System)分别对该模型进行求解。实例研究表明,遗传算法可有效地求解此类反应器网络综合问题,且其计算结果优于传统优化方法的结果。     

连续搅拌反应釜系统的自抗扰控制

针对连续搅拌反应釜系统(CSTR)大滞后,大惯性以及动态特性随工况不确定性变化的特点,通过设计参数可调的跟踪微分器和扩张状态观测器,改进经典PID算法的固有缺陷,提出不依赖于精确CSTR模型的离散自抗扰控制方案,采用西门子新一代工业可编程控制器-SIMATIC PCS7在多功能过程与控制系统的CSTR实验仿真对象上实现...     

基于Takagi-Sugeno模糊双线性模型的连续搅拌反应釜H∞控制

本文研究了一类连续搅拌反应釜(CSTR)系统的H1控制问题. 系统中的非线性动态特性可采用Takagi-Sugeno(T-S)模糊双线性模型进行描述. 通过引入两个自由矩阵, 给出一个新的保证闭环模糊双线性系统在H1性能指标下全局渐近稳定的充分条件和控制器设计方法, 并且该条件最终可归结为求解一组线性矩阵不等式的可行性问题. CSTR系统的仿真结果表明设计方法的有效性.     

化学反应器的非线性最优控制设计方法

本文针对具有非线性动态特性的连续搅拌釜式反应器，提出了一种基于分步变换的控制器设计方法，通过引入一种简单的中间非线性模型和相应的非线性状态变换，综合得到了一种新的非线性系统的非线性控制器设计方法，最后的控制仿真结果表明这种设计方法是有十分有效的。     

MPCE实验装置连续反应过程控制策略研究与实现

以多功能过程和实验系统（NIPCE）中的连续反应过程（CSTR）为对象,对整个CSTR系统进行了控制策略研究。系统融合了PID控制、模糊控制、串级控制、分程控制等多种控制算法,实现了对系统的开车顺序控制、反应过程控制和安全控制。利用S7—300PLC对系统进行设计与实现。最终的运行结果表明系统对CSTR有较好的控制效果,同时控制系统具有较强的鲁棒性、实用性及可行性。     

Thermodynamics and chemical systems stability: The CSTR case study revisited

This paper is dedicated to the stability analysis of the Continuous Stirred Tank Reactor (CSTR) model by considering thermodynamics based arguments. Different Lyapunov function candidates related to the thermodynamics, more specifically based on the entropy, the entropy production and the internal energy, are considered. These provide new insight and physical interpretation in the stability/instability of the equilibrium points of the CSTR. This includes in particular extension of the results of Georgakis [C. Georgakis, On the use of extensive variables in process dynamics and control, Chemical Engineering Science 41(6) (1986) 1471–1484] for less restrictive conditions on the system dynamics and thermodynamics, and invariants sets for the stable equilibrium points of the CSTR by considering an internal energy based Lyapunov function.     

一种基于逆系统方法的化学反应器改进控制方案

对具有非线性动态特性的连续搅拌釜式化学反应器，按逆系统方法设计了控制器，并与分步变换设计法进行比较。仿真结果表明，采用逆系统方法可以使闭环系统取得更优的品质，这对于化工工业过程的控制具有明显的工程实践意义。     

Monitoring of a biodiesel production process via reset observer

This paper is concerned with the monitoring of a biodiesel production process, more specifically with the monitoring of the esterification of grease trap wastes, a low quality feedstock for biodiesel production typically characterized by its high content of Free Fatty Acids (FFAs). The esterification takes place in a jacketed Continuous Stirred Tank Reactor (CSTR). A reset observer is designed and applied in order to provide on-line estimation of the concentration of FFAs from temperature measurements within the CSTR. In addition, the proposed reset observer is compared to two other observers (classical fuzzy observer and extended Kalman filter). According to a multiple range test conducted for analyzing the estimation error, the monitoring task for the process under study has been better fulfilled by the reset observer which is able to update the estimation results every instant when the measurements were available.