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。     

聚氯乙烯聚合反应过程的串级预测函数控制

介绍并分析了预测函数控制方法的主要思想和特点,并针对聚氯乙烯聚合反应装置采用串级预测函数控制方法进行控制,取得了很好的控制效果和经济效益。     

串级控制系统在天津石化2#延迟焦化管式加热炉上的应用

本文介绍了串级控制系统的基本概念及管式加热炉的基本原理,并详细介绍了串级控制系统在2#延迟焦化管式加热炉出口温度控制上的应用。     

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。     

采用DCS控制系统实现串级调节

阐述了串级控制在DCS中的应用,并以加热炉温度与流量串级控制为例叙述了串级控制的实现,并从信号传输的角度描述了串级控制系统从DCS到现场的调节过程。     

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。     

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。     

串级控制在甲醇精馏系统中的应用

正1存在的问题河南心连心化肥有限公司二分公司精醇系统蒸汽经常出现时序性波动,其直接影响是造成各塔液位及回流槽液位不稳定,使各塔塔顶及塔温度变化,从而影响精醇的产品质量;其间接影响是造成蒸汽用量增加、能耗上升,从而增加精醇的生     

Distributed model predictive control based on dissipativity

A noncooperative approach to plant‐wide distributed model predictive control based on dissipativity conditions is developed. The plant‐wide process and distributed control system are represented as two interacting process and controller networks, with interaction effects captured by the dissipativity properties of subsystems and network topologies. The plant‐wide stability and performance conditions are developed based on global dissipativity conditions, which in turn are translated into the dissipative trajectory conditions that each local model predictive control MPC must satisfy. This approach is enabled by the use of dynamic supply rates in quadratic difference forms, which capture detailed dynamic system information. A case study is presented to illustrate the results. © 2012 American Institute of Chemical Engineers AIChE J, 59: 787–804, 2013     

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.     

从区间模型预测控制到双层结构模型预测控制

模型预测控制算法（MPC）存在设定点控制与区间控制两种策略,区间预测控制较之设定点控制在技术上具有先进性。目前,主流的预测控制软件技术均采用双层结构,即上层稳态优化计算最优设定点,下层动态控制负责动态跟踪最优设定点。从过程稳态的角度出发,分别对区间预测控制和双层结构预测控制进行了机理分析,从定性与定量两个方面比较了这两者的异同点,提出并证明了两者的一致性条件。论述了双层结构预测控制较之单层结构下的区间控制更具先进性。     

Distributed dissipative model predictive control for process networks with imperfect communication

Results are developed to ensure stability of a dissipative distributed model predictive controller in the case of structured or arbitrary failure of the controller communication network; bounded errors in the communication may similarly be handled. Stability and minimum performance of the process network is ensured by placing a dissipative trajectory constraint on each controller. This allows for the interaction effects between units to be captured in the dissipativity properties of each process, and thus, accounted for by choosing suitable dissipativity constraints for each controller. This approach is enabled by the use of quadratic difference forms as supply rates, which capture detailed dynamic system information. A case study is presented to illustrate the results. © 2014 American Institute of Chemical Engineers AIChE J, 60: 1682–1699, 2014     

Selection of control configurations for economic model predictive control systems

Economic model predictive control (EMPC) is a feedback control method that dictates a potentially dynamic (time‐varying) operating policy to optimize the process economics. The objective function used in the EMPC system may be a general nonlinear function that describes the process/system economics. As this function is not derived on the sole basis of classical control considerations (stabilization, tracking, and optimal control action calculation) but rather on the basis of economics, selecting the appropriate control configuration, and quantifying the influence of a given input on an economic cost is an important task for the proper design and computational efficiency of an EMPC scheme. Owing to these considerations, an input selection methodology for EMPC is proposed which utilizes the relative degree and the sensitivity of the economic cost with respect to an input to identify and select stabilizing manipulated inputs with the most dynamic and steady‐state influence on the economic cost function to be assigned to EMPC. Other considerations for input selection for EMPC are also discussed and integrated into a proposed input selection methodology for EMPC. The control configuration selection method for EMPC is demonstrated using a chemical process example. © 2014 American Institute of Chemical Engineers AIChE J, 60: 3230–3242, 2014     

Robust model predictive control and fault handling of batch processes

This work considers the control of batch processes subject to input constraints and model uncertainty with the objective of achieving a desired product quality. First, a computationally efficient nonlinear robust Model Predictive Control (MPC) is designed. The robust MPC scheme uses robust reverse‐time reachability regions (RTRRs), which we define as the set of process states that can be driven to a desired neighborhood of the target end‐point subject to input constraints and model uncertainty. A multilevel optimization‐based algorithm to generate robust RTRRs for specified uncertainty bounds is presented. We then consider the problem of uncertain batch processes subject to finite duration faults in the control actuators. Using the robust RTRR‐based MPC as the main tool, a robust safe‐steering framework is developed to address the problem of how to operate the functioning inputs during the fault repair period to ensure that the desired end‐point neighborhood can be reached upon recovery of the full control effort. The applicability of the proposed robust RTRR‐based controller and safe‐steering framework subject to limited availability of measurements and sensor noise are illustrated using a fed‐batch reactor system. © 2010 American Institute of Chemical Engineers AIChE J, 2011     

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     

Data‐driven model predictive quality control of batch processes

The problem of driving a batch process to a specified product quality using data‐driven model predictive control (MPC) is described. To address the problem of unavailability of online quality measurements, an inferential quality model, which relates the process conditions over the entire batch duration to the final quality, is required. The accuracy of this type of quality model, however, is sensitive to the prediction of the future batch behavior until batch termination. In this work, we handle this “missing data” problem by integrating a previously developed data‐driven modeling methodology, which combines multiple local linear models with an appropriate weighting function to describe nonlinearities, with the inferential model in a MPC framework. The key feature of this approach is that the causality and nonlinear relationships between the future inputs and outputs are accounted for in predicting the final quality and computing the manipulated input trajectory. The efficacy of the proposed predictive control design is illustrated via closed‐loop simulations of a nylon‐6,6 batch polymerization process with limited measurements. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2852–2861, 2013     

前馈变量对预测控制可行域的影响分析

实际生产过程中,预测控制因其解耦性能和强鲁棒性得以广泛使用。在预测控制的研究中大都忽略控制过程中的干扰作用。对于控制过程中存在的可测且变化规律已知的干扰作用,干扰对输出的影响具有一定的可预见性,可通过在预测控制器中引入前馈的方法加以利用。前馈变量的引入会对系统的控制效果产生影响,如果不先对其影响进行分析而直接求解优化,最终结果不能反映预测控制的实际效果。本文从可行域的角度出发,通过几何表现形式,直观分析前馈变量的引入造成的可行域变化;进一步使用了凸空间的思想,通过求解可行域的顶点集合来确定可行域的大小,进而得出前馈变量对系统可行域的影响效果,通过仿真验证了本文方法的有效性。