流量同时作为工艺优化变量和串级控制副变量时的工艺优化实现与分析

针对如何通过现存的"主被控变量-流量"这种典型的化工过程串级控制实现流量优化的在线优化问题,对在线优化的实现结构进行分析,给出一种流量优化变量通过一阶惯性环节改变主回路被控变量设定值的在线优化的理想方案,通过CSTR的仿真实例证明了优化实现方案的可行性。     

基于改进差分进化算法-径向基神经网络的电热水浴串级控制系统研究

针对电热水浴装置温度控制中被控对象存在的大惯性、非线性、大延迟等特点,设计了一种基于改进差分进化(improved differential evolution, IDE)算法的径向基（radial basis function, RBF）神经网络串级控制系统。采用IDE算法对RBF神经网络的初始参数进行优化,采用优化后的RBF神经网络辨识主控制回路被控对象的Jacobian信息,进而实现主控制回路PID(proportional integration differentiation)控制器参数的在线调整。针对主控制回路控制器包含输出噪声,导致控制性能下降的问题,引入Kalman 滤波器对串级控制的主回路进行重新设计,控制对象的输出值经过Kalman 滤波算法处理后再返回闭环控制系统。以微化工领域常用电热水浴装置为对象,对IDE-RBF-PID-PI串级控制系统进行仿真实验,结果表明,IDE-RBF-PID-PI串级控制系统相较于常规串级控制,大大提高了控制性能,主控制回路引入的Kalman滤波算法有效消减控制系统的输出噪声,控制效果接近于无噪声的理想状态。     

氯乙酸生产结晶过程的预测函数控制仿真研究

针对氯乙酸生产结晶过程被控对象大滞后、大惯性的特点,设计了基于PFC—PID的串级控制系统：该系统将串级控制结构和预测函数控制算法相结合,内回路采用常规PID控制器,外回路采用预测函数控制(PFC)。结合了2者的优点,仿真结果表明,该串级控制系统在控制品质、鲁棒性等方面明显优于常规PID控制系统。     

基于改进差分进化算法-径向基神经网络的电热水浴串级控制系统研究

针对电热水浴装置温度控制中被控对象存在的大惯性、非线性、大延迟等特点,设计了一种基于改进差分进化(improved differential evolution, IDE)算法的径向基(radial basis function, RBF)神经网络串级控制系统。采用IDE算法对RBF神经网络的初始参数进行优化,采用优化后的RBF神经网络辨识主控制回路被控对象的Jacobian信息,进而实现主控制回路PID(proportional integration differentiation)控制器参数的在线调整。针对主控制回路控制器包含输出噪声,导致控制性能下降的问题,引入Kalman滤波器对串级控制的主回路进行重新设计,控制对象的输出值经过Kalman滤波算法处理后再返回闭环控制系统。以微化工领域常用电热水浴装置为对象,对IDE-RBF-PID-PI串级控制系统进行仿真实验,结果表明,IDE-RBF-PID-PI串级控制系统相较于常规串级控制,大大提高了控制性能,主控制回路引入的Kalman滤波算法有效消减控制系统的输出噪声,控制效果接近于无噪声的理想状态。     

加热炉分数阶PID温度自适应控制系统设计

针对加热炉温度控制系统控制器参数不易选择的问题，提出一种分数阶自适应PID串级控制方法实现控制器参数自动调整。首先以分数阶PID控制器分别作为温度主控制器和燃气流量控制器，实现加热炉串级温度控制；其次应用自适应理论对控制器进行改进，设计了自适应分数阶PID控制器。最后通过数值仿真，验证了所设计控制系统对加热炉温度控制的有效性和可行性。     

重整装置中压汽包液位控制方案的改进策略

介绍了大连石化重整装置中压汽包液位控制方案,并对其缺陷进行改进:利用CS3000系统自带的前馈控制功能块解决了计算式过多的问题,实现了串级控制的无扰动切换;将两路给水流量和两路发汽流量与汽包液位组成的三冲量控制拆分为两个三冲量控制回路,取消了比值控制器,也能满足两路给水流量的比值关系。     

一种改进的锅炉主蒸汽温度多级智能控制系统的应用研究

针对电厂锅炉主蒸汽温度多级控制系统中减温水流量扰动对蒸汽温度的影响,且运行过程中控制系统超调量大、响应速度慢、系统具有非线性,对单回路常规控制系统进行了改进,提出了一种双单内环智能串级控制方法。主控制器采用内模控制方式,根据末级出口主蒸汽温度实测值与设定值之间的偏差经过智能PID在线修正控制参数。仿真结果表明:改进后的控制系统比原单回路控制系统具有更好的鲁棒性和动态特性,并能保持较好的调节性能。     

间歇式反应釜温度串级预测函数控制

采用先进预测函数控制(PFC)技术实现实验室规模釜式反应器温度的控制。借助于被控对象的参数模型,对在线不可能测量或很难得到的工艺参数进行了估算,设计并在可编程逻辑控制器(PLC)中实现了温度串级控制。通过修正内嵌参数模型,抵消了模型的不确定性和各种扰动对系统稳定性的影响。结果表明:PFC在控制过程中既能很好地兼顾被控系统中子单元的动态性能,又具有很理想的控制响应品质。     

混沌优化反馈校正串级控制

针对串级控制系统控制器参数不好选取的问题,提出一种混沌优化反馈校正方法。该方法利用混沌遍历性首先对内环被控对象进行模型识别,然后根据识别出的模型进行离线控制器参数优化,再对整体进行模型识别得到控制器参数,最后设计反馈校正主控制器。以数学模型为被控对象进行了仿真,仿真结果表明:该控制方法具有较强的跟踪能力和鲁棒性。     

串级控制及串级比值控制在硝酸等装置的应用

结合具体案例对串级控制系统及串级比值控制系统做了介绍,分别以硝酸铵装置中反应器闪蒸槽液位与进管式反应器与参反应的液氨流量之间的串级控制系统及硝酸装置氨氧化反应中氧化炉温度与氨空比的串级比值控制系统为例进行了分析。通过以上分析可以看出采用串级控制更有利于被控变量的稳定。     

A model predictive formulation for control of open-loop unstable cascade systems

Cascade control is commonly used in the operation of chemical processes to reject disturbances that have a rapid effect on a secondary measured state, before the primary measured variable is affected. In this paper, we develop a state estimation-based model predictive control approach that has the same general philosophy of cascade control (taking advantage of secondary measurements to aid disturbance rejection), with the additional advantage of the constraint handling capability of model predictive control (MPC). State estimation is achieved by using a Kalman filter and appending modeled disturbances as augmented states to the original system model. The example application is an open-loop unstable jacketed exothermic chemical reactor, where the jacket temperature is used as a secondary measurement in order to infer disturbances in jacket feed temperature and/or reactor feed flow rate. The MPC-based cascade strategy yields significantly better performance than classical cascade control when operating close to constraints on the jacket flow rate.     

Application of model predictive control to the BSM1 benchmark of wastewater treatment process

Wastewater treatment processes are difficult to be controlled because of their complex and nonlinear behavior. This paper applied model predictive control (MPC) to the Benchmark Simulation Model 1 (BSM1) wastewater treatment process to maintain the effluent quality within regulations-specified limits. Good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. In presence of influent disturbances, two approaches have been studied: the addition of a feedforward action based on the measurement of the influent flow rate; the use of nonlinear model predictive controller by addition of a penalty function. The effects of two approaches were visible on the decrease of ammonium and nitrogen concentration which were considered as being of major importance. The results show that MPC can be effectively used for control in wastewater treatment process. By comparing performances, the nonlinear model predictive control strategy with penalty function demonstrates best with small effluent quality index and acceptable aeration and pumping energy consumption.     

Model predictive control for wastewater treatment process with feedforward compensation

Being an optimizing technology, model predictive control (MPC) can now be found in a wide variety of application fields. The main and most obvious control goal to be achieved in a wastewater treatment plant is to fulfill the effluent quality standards, while minimizing the operational costs. In order to maintain the effluent quality within regulation-specified limits, the MPC strategy has been applied to the Benchmark Simulation Model 1 (BSM1) simulation benchmark of wastewater treatment process. After the discussion of open loop responses of outputs to manipulated inputs and measured influent disturbances, the strategies of feedback by linear dynamic matrix control (DMC), quadratic dynamic matrix control (QDMC) and nonlinear model predictive control (NLMPC), and improvement by feedforward based on influent flow rate or ammonium concentration have been investigated. The simulation results indicate that good performance was achieved under steady influent characteristics, especially concerning the nitrogen-related species. Compared to DMC and QDMC, NLMPC with penalty function brings little improvement. Two measured disturbances have been used for feedforward control, the influent flow rate and ammonium concentration. It is shown that the performance of feedforward with respect to the influent ammonium concentration is much higher than for the feedforward with respect to the influent flow rate. However, this latter is slightly better than the DMC feedback. The best performance is obtained by combining both feedforward controllers with respect to the influent ammonium concentration and flow rate. In all cases, the improvement of performance is correlated with more aeration energy consumption.     

Performance assessment of cascade controllers for nitrate control in a wastewater treatment process

A cascade control strategy is proposed to the benchmark simulation model 1 (BSM1) to enhance the treatment performance of nitrogen removal in a biological wastewater treatment plant. The proposed control approach consists of two control loops, a primary outer loop and a secondary inner loop. The method has two controllers of which the primary loop has a model predictive control (MPC) controller and the secondary loop has a proportional-integralderivative (PID) controller, which is a cascade MPC-PID controller. The primary MPC controller is to control the nitrate concentration in the effluent, and the secondary PID controller is to control the nitrate concentration in the final anoxic compartment. The proposed method controls the nitrate concentrations in the effluent as well as in the final anoxic reactor simultaneously to strictly satisfy the quality of the effluent as well as to remove the effects of disturbances more quickly by manipulating the external carbon dosage rate. Because the control performance assessment (CPA) technique has the features of determining the capability of the current controller and locating the best achievable performance, the other novelty of this paper is to suggest a relative closed-loop potential index (RCPI) which updates the CPA technology into a closed-loop cascade controller. The proposed method is compared with a cascade PID-PID control strategy and the original PID controller in BSM1 and an improved performance of the suggested cascade MPC-PID controller is obtained by using the CPA approach.     

快速增量约束预测控制及在GLCC液位控制中的应用

针对气-液柱状旋流式(GLCC)多相流量计的液位控制问题,提出一种增量多变量模型预测控制(MPC)算法。采用控制增量状态空间模型和阶梯式控制策略,建立约束多变量MPC优化控制问题。为在线计算约束优化问题,引入坐标轮换法和黄金分割法,在线计算控制变量增量值,进而得到预测控制量。最后,以GLCC多相流量计的两输入单输出液位控制模型为例,仿真验证本文算法的有效性。     

Model predictive control of an industrial pyrolysis gasoline hydrogenation reactor

This study focuses on the implementation of a nonlinear model predictive control (MPC) algorithm for controlling an industrial fixed-bed reactor where hydrogenations of raw pyrolysis gasoline occur. An orthogonal collocation method is employed to approximate the original reactor model consisting of a set of partial differential equations. The approximate model obtained is used in the synthesis of a MPC controller to control the temperature rising across a catalyst bed within the reactor. In the MPC algorithm, a sequential optimization approach is used to solve an open-loop optimal control problem. Feedback information is incorporated in the MPC to compensate for modeling error and unmeasured disturbances. The control studies are demonstrated in cases of set point tracking and disturbance rejection.     

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     

Sequential and iterative architectures for distributed model predictive control of nonlinear process systems

In this work, we focus on distributed model predictive control of large scale nonlinear process systems in which several distinct sets of manipulated inputs are used to regulate the process. For each set of manipulated inputs, a different model predictive controller is used to compute the control actions, which is able to communicate with the rest of the controllers in making its decisions. Under the assumption that feedback of the state of the process is available to all the distributed controllers at each sampling time and a model of the plant is available, we propose two different distributed model predictive control architectures. In the first architecture, the distributed controllers use a one‐directional communication strategy, are evaluated in sequence and each controller is evaluated only once at each sampling time; in the second architecture, the distributed controllers utilize a bi‐directional communication strategy, are evaluated in parallel and iterate to improve closed‐loop performance. In the design of the distributed model predictive controllers, Lyapunov‐based model predictive control techniques are used. To ensure the stability of the closed‐loop system, each model predictive controller in both architectures incorporates a stability constraint which is based on a suitable Lyapunov‐based controller. We prove that the proposed distributed model predictive control architectures enforce practical stability in the closed‐loop system and optimal performance. The theoretical results are illustrated through a catalytic alkylation of benzene process example. © 2010 American Institute of Chemical Engineers AIChE J, 2010     

先进控制技术在纯碱生产碳化过程中的应用

纯碱生产装置中的碳化塔既是多相反应器又是多步结晶器,其中包含了吸收、反应、多相流、传热和传质等多种复杂的物理、化学过程。本文以某纯碱厂碳化系统为背景,将模型预测控制软件应用于5组共25座碳化塔中,开发了一个包含上百个被控变量、操纵变量和干扰变量的多变量预测控制器来解决该碳化系统的约束多变量控制问题。工业应用结果表明：模型预测控制软件能够确保碳化系统长期在最佳状态运行,进而降低物料消耗、改善产品质量和节省操作费用,使生产效益得到优化。     

流化床反应器中气相丙烯聚合反应的动力学和预测控制(英文)

A two-phase dynamic model, describing gas phase propylene polymerization in a fluidized bed reactor, was used to explore the dynamic behavior and process control of the polypropylene production rate and reactor temperature. The open loop analysis revealed the nonlinear behavior of the polypropylene fluidized bed reactor, jus- tifying the use of an advanced control algorithm for efficient control of the process variables. In this case, a central- ized model predictive control （MPC） technique was implemented to control the polypropylene production rate and reactor temperature by manipulating the catalyst feed rate and cooling water flow rate respectively. The corre- sponding MPC controller was able to track changes in the setpoint smoothly for the reactor temperature and pro- duction rate while the setpoint tracking of the conventional proportional-integral （PI） controller was oscillatory with overshoots and obvious interaction between the reactor temperature and production rate loops. The MPC was able to produce controller moves which not only were well within the specified input constraints for both control vari- ables, but also non-aggressive and sufficiently smooth for practical implementations. Furthermore, the closed loop dynamic simulations indicated that the speed of rejecting the process disturbances for the MPC controller were also acceotable for both controlled variables.