具有预测校正的固定床反应器状态估计

本文建立了基于后集中方法的固定床反应器的非线性状态估计算法，并分别用正交配置法集中偏微分方程、Gear法积分常微分方程、Hybrid法求解非线性代数方程，实现状态估计器的在线估计．针对工业实际过程中浓度测量滞后较大、采样时间间隔较长的特点，提出了浓度估计的预测校正，进一步提高了状态估计器对模型误差的克服能力．同时，在实验和仿真两方面考察了固定床反应器状态估计器的性能，无论是收敛性还是鲁棒性，其品质都令人满意．     

具有预测校正的固定床反应器状态估计

本文建立了基于后集中方法的固定床反应器的非线性状态估计算法，并分别用正交配置法集中偏微分方程、Gear法积分常微分方程、Hybrid法求解非线性代数方程，实现状态估计器的在线估计．针对工业实际过程中浓度测量滞后较大、采样时间间隔较长的特点，提出了浓度估计的预测校正，进一步提高了状态估计器对模型误差的克服能力．同时，在实验和仿真两方面考察了固定床反应器状态估计器的性能，无论是收敛性还是鲁棒性，其品质都令人满意．     

反应器清理过程中火灾爆炸事故树分析

反应器是化工生产中的关键设备,是指进行化学反应的设备。工业上所使用的化学反应器可分为釜式反应器、固定床反应器、流化床反应器等。由于反应器长周期运行或由于原料纯度、温度或压力等未控制好,反应器发生粘壁或结块,     

基于振动信号分析法的催化剂积炭量测量模型

基于催化剂固体颗粒运动碰撞反应器壁面产生振动信号的机理,借助平滑消噪及偏最小二乘（PLS）回归等多元数据处理手段,结合振动信号的功率谱分析,建立了在一定表观气速下测量结焦催化剂积炭量的PLS回归模型。实验室冷模研究表明,在表观气速为0.06～0.12 m·s^-1的范围内,模型所得催化剂积炭量预测值与真实值的相关系数均在0.893以上,而预测均方根误差(RMSEP)均在0.51以下,具有较高的精度。由此,获得了一种便捷灵敏、安全环保的非侵入式结焦催化剂积炭量测量技术,能够实现反应器内结焦催化剂积炭量的实时在线检测与监控。     

PLS回归软测量方法在催化重整稳定油组分估计中的应用

提出扰动分类法和线性部分最小二乘（PLS）回归相结合的建立软测量模型的方法，并将它用于催化重整稳定油组分的估计中。仿真结果表明扰动分类法和PLS回归相结合建立的软测量模型简单、实用。     

膜控制氧化反应器中丁烯氧化脱氢的研究

在气体均布的无机膜控制氧化反应器上进行了丁烯氧化脱氢制丁二烯反应,并将其与固定床方式反应的实验结果进行了对比,结果表明在实验范围内膜反应器比传统的固定床反应更为有效。建立了描述控制氧化膜反应器操作性能的数学模型,并将模型求解值与实验值对比,吻合良好。     

非线性分布参数系统状态估计的最佳测量位置

研究了分布参数系统状态估计中特有的最佳测量位置问题．建立了基于后集中方法的分布参数系统的非线性状态估计器,包括状态估计偏微分方程和微分灵敏度矩阵偏微分方程,并用适当的数值计算方法实现状态估计器的求解;以一个最小化的空间域上积分函数表达最佳测量位置的目标函数,并相应地用非线性约束优化方法求解系统具有一个或多个测量时的最佳测量位置．还以壁冷式单管固定床反应器为例,讨论了各种因素对最佳测量位置的影响及其灵敏度,并得出了一些有普遍意义的结论．     

基于ISR-UKF的PET固相缩聚过程参数和状态估计

连续式PET固相缩聚移动床反应器具有显著的分布参数特性,由于建模简化或过程时变等原因使得所建模型参数不精确,导致反应器状态的估计失真。首先采用正交配置方法离散PET固相缩聚过程的偏微分方程模型,然后基于改进的平方根不敏Kalman滤波算法(ISR-UKF),设计自适应联合估计器,同时获得参数和状态估计值。实验结果表明,参数估计结果合理,状态估计精度较高且稳定性好;并获得了频率因子、活化能近似值和有效系数随反应温度动态变化的规律,表明所提出的联合估计器能获得较好的实际应用效果。     

苯酐生产中固定床反应器模型的研究

固定床气相反应器是苯酐生产中的主要设备,通过对比苯酐生产中不同固定床气相反应器操作法的优缺点,选择了低空气比率法作为固定床气相反应器研究对象。引入苯酐固定床气相反应器的反应动力学模型、转化率及温度分布模型、固定床反应器传热系数模型,并通过计算机对数学模型进行模拟计算得到数据,绘图得到相关曲线。通过对曲线进行分析,得出了与实际数据相符合的结论,该模型的建立不但可以优化固定床气相反应器的设计、生产,而且对苯酐生产中固定床气相反应器控制起到了指导作用。     

裂解炉裂解深度控制的应用

目前乙烯装置追求高附加值产率等最佳效益,然而由于乙烯裂解原料来源较复杂、各种油品变化较为频繁,仅实现裂解炉平均出口温度定值控制是不能充分实现乙烯裂解装置的最佳效益的,因此开发深度串级控制是各乙烯装置的目标。介绍了通过一种基于裂解深度神经网络预测模型的Smith预估控制方案建立的裂解深度软测量模型,以及该裂解深度软测量模型与APC控制相集成的方法,以实现对裂解炉裂解深度的平稳控制。     

Dynamic recurrent radial basis function network model predictive control of unstable nonlinear processes

A multistep model predictive control (MPC) strategy based on dynamically recurrent radial basis function networks (RBFNs) is proposed for single-input single-output (SISO) control of uncertain nonlinear processes. The control system consists of two automatically configured RBFNs, a trained network representing the plant model and a network with on-line learning to function as controller. The automatic configuration and learning of the networks is carried out by using a hierarchically self-organizing learning algorithm. This control strategy is structurally simple and computationally efficient since a single output node of each RBFN is configured to provide multistep predictions for plant output and controller. The performance of the proposed RBFNMPC strategy is evaluated by applying to two unstable nonlinear chemical processes, a chemical reactor and a biochemical reactor, and also a stable polymerization reactor. Further, the results of the RBFNMPC is compared with similar RBFN model based control strategies and also with well tuned PID/PI controller. The results show the better performance of the proposed RBFNMPC for the control of open-loop unstable nonlinear processes that exhibit multiple steady-state behavior.     

MODELING OF THE TRANSIENT AND STEADY STATE OPERATION OF A FLUIDIZED BED REACTOR WITH ADAPTIVE CONTROL OF TEMPERATURE

This paper concerns modeling of the transient and the steady state operation of a fluidized bed reactor for the catalytic ammoxidation of propylene to acrylonitrile. To maintain constant the temperature of the reaction in order to facilitate the phenomenological study as well as to avoid risks of destruction of the catalyst, a self tuning P.I.D. controller has been used. The controller derived from a discrete P.I.D. regulator is based on pole assignment. It uses a recursive parameter estimator based on the least square method. The reactor has been interfaced with an Apple II micro-computer. The results obtained illustrates the inherent capability of self adaptive control to adapt the change of the environment where conventional control fails

Modeling of the reactor is based on the Kato and Wen bubble assemblage model corrected by including the wake of the bubbles with their clouds, as proposed hy Stergiou. This modified model gives good predictions of the operation of the reactor for steady as well as transient operation.     

基于MLP网络的智能控制及应用

讨论了一种基于ＭＬＰ网络的智能控制系统。它利用一个多层感知器（ＭＬＰ）作为被控对象的在线辨识器，而用另一线性神经网络结构构成ＰＩＤ控制器。采用变步长ＢＰ加速算法求得多层感知器的各联接权重与阀值的学习规律与控制器参数的自整定算法。通过化肥厂变换炉控制的仿真研究与一列管换热器温度控制系统的仿真试验，说明该算法是有效的。     

Generalized delta rule (GDR) algorithm with generalized predictive control (GPC) for optimum temperature tracking of batch polymerization

The generalized delta rule (GDR) algorithm with generalized predictive control (GPC) control was implemented experimentally to track the temperature on a set point in a batch, jacketed polymerization reactor. An equation for optimal temperature was obtained by using co-state Hamiltonian and model equations. To track the calculated optimal temperature profiles, controller used should act smoothly and precisely as much as possible. Experimental application was achieved to obtain the desired comparison. In the design of this control system, the reactor filled with styrene-toluene mixture is considered as a heat exchanger. When the reactor is heated by means of an immersed heater, cooling water is passed through the reactor-cooling jacket. So the cooling water absorbs the heat given out by the heater. If this is taken into consideration, this reactor can be considered to be continuous in terms of energy. When such a mixing chamber was used as a polymer reactor with defined values of heat input and cooling flow rate, system can reach the steady-state condition. The heat released during the reaction was accepted as a disturbance for the heat exchanger. Heat input from the immersed heater is chosen as a manipulated variable. The neural network model based on the relation between the reactor temperature and heat input to the reactor is used. The performance results of GDR with GPC were compared with the results obtained by using nonlinear GPC with NARMAX model.The reactor temperature closely follows the optimal trajectory. And then molecular weight, experimental conversion and chain lengths are obtained for GDR with GPC.     

Machine learning-based predictive control of nonlinear processes. Part I: Theory

This article focuses on the design of model predictive control (MPC) systems for nonlinear processes that utilize an ensemble of recurrent neural network (RNN) models to predict nonlinear dynamics. Specifically, RNN models are initially developed based on a data set generated from extensive open-loop simulations within a desired process operation region to capture process dynamics with a sufficiently small modeling error between the RNN model and the actual nonlinear process model. Subsequently, Lyapunov-based MPC (LMPC) that utilizes RNN models as the prediction model is developed to achieve closed-loop state boundedness and convergence to the origin. Additionally, machine learning ensemble regression modeling tools are employed in the formulation of LMPC to improve prediction accuracy of RNN models and overall closed-loop performance while parallel computing is utilized to reduce computation time. Computational implementation of the method and application to a chemical reactor example is discussed in the second article of this series.     

EXPERIMENTAL APPLICATION OF GENERALIZED PREDICTIVE CONTROL OF THE TEMPERATURE IN A POLYSTRENE POLYMERIZATION REACTOR

The performance of generalized predictive control (GPC) was examined and compared with conventional control applied to the temperature of as free radical solution polymerization of styrene in a jacketed batch reactor. Optimal conditions were obtained at different initiator concentrations by applying Lagrange's multiplier to the relevant polymerization reactor. The use of the polynomial ARIMAX model related with reactor temperature and heat input was emphasized. Model parameters were determined using the Kalman algorithm. A pseudo random binary sequence (PRBS) signal was employed in order to operate the system. The GPC control method was based on the ARIMAX model. The performance criteria of GPC in evaluating the temperature control results were the required monomer conversion and molecular weight.     

基于ELM的一类不确定性纯反馈非线性系统的Backstepping自适应控制

针对一类不确定性纯反馈非线性动力学系统,在中值定理、Backstepping控制的基础上,提出一种基于极限学习机（ELM）的自适应神经控制方法。ELM随机确定单隐层前馈网络（SLFNs）的隐含层参数,仅需调整网络的输出权值,能以极快的学习速度获得良好的推广性。在每一步的Backstepping设计中,应用ELM网络对子系统的未知非线性项进行在线逼近,通过Lyapunov稳定性分析设计的权值参数自适应调节律,可以保证闭环非线性系统所有信号半全局最终一致有界,系统的输出收敛于期望轨迹的很小邻域内。将所设计的控制方法应用于化工过程中的连续搅拌反应釜（CSTR）非线性系统实例中,仿真结果表明了控制方法的有效性。     

AUTOMATICALLY CONFIGURING RADIAL BASIS FUNCTION NEURAL NETWORKS FOR NONLINEAR INTERNAL MODEL CONTROL

A nonlinear internal model control (NIMC) strategy based on automatically configuring radial basis function networks (RBFN) is proposed for single-input single-output (SISO) systems of relative degree greater than unity. The automatic configuration and training of the RBFN is carried out employing hierarchically-self-organizing-learning algorithm, which eliminates a predefined network structure, with closed-loop input-output data generated for a series of setpoint changes using PI controller. Simulation studies with automatically configuring RBFN for isothermal polymerization reactor control demonstrate the superior performance of the proposed control strategy with automatically configuring RBFN over PI control for setpoint tracking as well as disturbance rejection.     

ROBUST NONLINEAR PREDICTIVE CONTROL USING A DISTURBANCE ESTIMATOR

A robust nonlinear predictive control strategy using a disturbance estimator is presented. The disturbance estimator is comprised of two parts: one is the disturbance model parameter adaptation and the other is future disturbance prediction. A linear discrete model is proposed as a disturbance model which is formulated by using process inputs and available process measurements. The recursive least square (RLS) method with exponential forgetting is used to determine the uncertain disturbance model parameters and for the future disturbance prediction, future disturbances projected by the future process inputs are used. Two illustrative examples: a jacketed CSTR as a SISO system: an adiabatic CSTR as a MIMO system, and experimental results of the distillation column control are presented. The results indicate that a substantial improvement in nonlinear predictive control performance is possible using the disturbance estimator.