化工过程多回路PID控制系统模式切换参数自整定

化工过程一般为多变量系统,但其主要控制方案为分散多回路PID常规控制。由于多变量系统内部存在不同程度的耦合作用,各控制回路之间存在相互影响,当其他回路进行手动/自动模式切换时,本回路等效被控对象将会发生突变,导致本回路的原有控制参数不能适应等效被控对象的变化,造成控制性能下降,甚至闭环系统不稳定。为避免这种情况的发生,从整个系统的角度研究控制回路模式切换时的稳定性,采用多变量频域Nyquist阵列设计法。基于对角优势下正Nyquist稳定性判据,从Gershgorin圆边界点的角度定量分析各个控制回路在模式切换前后的稳定性变化程度,从而确定各回路控制器增益的调整方向及程度,实现各回路的控制器参数在控制回路模式切换瞬间的自动整定,尽可能抵消控制回路模式切换对整个系统的扰动,保证整个系统的闭环稳定性。以Shell公司重油分馏塔的多回路PID控制系统为例,将3个PID控制回路依次投用时,根据Gershgorin圆边界点进行控制参数的自整定,闭环系统仍能保持一定的控制性能,否则闭环系统将不稳定。     

一种基于模型的多变量协调控制技术

给出的基于模型的多变量协调控制技术将被控变量的动态控制问题与操作变量的静态优化问题有机地结合起来.对于操作变量多于被控变量的“胖”系统,本控制技术在保证对关键变量实施定点控制的同时,综合考虑操作变量的控制速度、操作成本等经济因素,尽可能将一些操作变量协调至理想操作区内,达到优化(节能、降耗)的目的.对于操作变量少于被控变量的“瘦”系统,则据被控变量的重要程度对其施加不同的控制.给出了控制器的唯一解条件、稳态无差和稳定性性质.针对催化裂化主分馏塔给出的仿真结果说明本协调控制有良好的控制性能,适合生产过程的需要.     

Use of nonlinear transformations and a self-tuning regulator to develop an algorithm for catalytic reactor temperature control

Prior to developing a multivariable control scheme for conversion and production rates in a packed bed tubular reactor carrying out highly exothermic reactions, it was important to stabilize the reactor temperature. In this paper we illustrate the use of a self-tuning regulator to develop an inner loop controller that satisfies some necessary conditions on temperature response, and that is capable of controlling the reactor over a wide range of operating conditions. The importance of using a nonlinear transformation of the reactor hot-spot temperature is demonstrated. Varying the input constraining parameter in the “one-step optimal” self-tuning controller is shown to be a very effective way of achieving a controller with the desired properties for response smoothness.     

Multivariable model predictive control of a novel rapid pressure swing adsorption system

A multivariable model predictive control (MPC) algorithm is developed for the control and operation of a rapid pressure swing adsorption (RPSA)‐based medical oxygen concentrator. The novelty of the approach is the use of all four step durations in the RPSA cycle as independent manipulated variables in a truly multivariable context. The RPSA has a complex, cyclic, nonlinear multivariable operation that requires feedback control, and MPC provides a suitable framework for controlling such a multivariable system. The multivariable MPC presented here uses a quadratic optimization program with integral action and a linear model identified using subspace system identification techniques. The controller was designed and tested in simulation using a complex, highly coupled, nonlinear RPSA process model. The model was developed with the least restrictive assumptions compared to those reported in the literature, thereby providing a more realistic representation of the underlying physical phenomena. The resulting MPC effectively tracks set points, rejects realistic process disturbances and is shown to outperform conventional PID control. © 2017 American Institute of Chemical Engineers AIChE J, 64: 1234–1245, 2018     

Sub-optimal control of a multivariable non-linear chemical reactor using quasilinearization

The performance of a multivariable non-linear stirred tank chemical reactor has been used to compare the practicability of an optimal controller and two types of suboptimal controller. The optimal controller, which by its nature required lengthy calculations, was suitable for open-loop control only and therefore sensitive to modelling errors. By contrast a suboptimal controller, which used the method of quasilinearization to solve the two point boundary value problem, needed only a small amount of computation time and was capable of online implementation. This controller was much less sensitive to modelling errors, and also had the advantage of decoupling the control variables. An extension of this controller incorporated a feedback feature which compensated further for modelling errors and, in addition, unmeasured disturbances. This controller, which is expected to be of interest to the process industries facing inaccurate-model problems, produced responses close to the true optimal response even when the actual model used was in gross error.     

A new constrained self-tuning pid controller

A new constrained self-tuning PID controller is developed based on Clarke and Gawthrop's (1975) generalized minimum variance (GMV) controller. Convergence properties for the new algorithm are derived and the effect of the constraint parameter is evaluated. Extension of the controller to include more than the usual three PID parameters occurs naturally. No process model information is required for our algorithms except the dead time. This is an improvement over other algorithms which impose severe constraints on the process structure. The controller algorithm is tested on a variety of models using simulation as well as under experimental conditions. The algorithm is easy to implement and compares favourably with the more complex GMV controller.     

Optimization‐Based Nonlinear Centralized Controller Tuning of Liquid‐Liquid Extraction Processes

Abstract

The control problem of an agitated contactor is considered in this work. A Scheibel extraction column is modeled using the non‐equilibrium backflow mixing cell model. Model dynamic analysis shows that this process is highly nonlinear, thus the control problem solution of such a system needs to tackle the process nonlinearity efficiently. The control problem of this process is solved by developing a multivariable nonlinear control system implemented in MATLAB?. In this control methodology, a new controller tuning method is adopted, in which the time‐domain control parameter‐tuning problem is solved as a constrained optimization problem. A MIMO (multi‐input multi‐output) PI controller structure is used in this strategy. The centralized controller uses a 2×2 transfer function and accounts for loops interaction. The controller parameters are tuned using an optimization‐based algorithm with constraints imposed on the process variables reference trajectories. Incremental tuning procedure is performed until the extractor output variables transient response satisfies a preset uncertainty which bounds around the reference trajectory. A decentralized model‐based IMC (internal model control) control strategy is compared with the newly developed centralized MIMO PI control one. Stability and robustness tests are applied to the two algorithms. The performance of the MIMO PI controller is found to be superior to that of the conventional IMC controller in terms of stability, robustness, loops interaction handling, and step‐change tracking characteristics.     

Self-tuning PID controllers based on the Lyapunov approach

In this paper, a self-tuning PID controller using just-in-time learning (JITL) technique is proposed. A set of linear models obtained by the JITL provides the information required to adjust the parameters of PID controller. The self-tuning algorithm for the PID parameters is derived by a rigorous analysis based on the Lyapunov method such that the JITL's predicted tracking error converges asymptotically. Simulation results are presented to illustrate the proposed self-tuning PID controller and a comparison with its conventional counterparts is made.     

Infrared Drying: From Process Modeling to Advanced Process Control

The article surveys the drying of solids materials and polymer solutions when infrared radiation (IR) is employed as the main heating source. The study reviews the current research trends of IR drying of specific applications. A case study similar to an industrial setting is presented to illustrate a model development and control scheme of an IR drying unit. The design and online implementation of an internal model controller (IMC) is discussed. The study demonstrates the controller capabilities to suppress random variations of the moisture content in the material entering the dryer. Simulation results also showed the success of model predictive control (MPC) multivariable controller ability, while handling process interactions and process constraints, to track setpoint changes in the humidity and temperature of the material exiting the dryer and to reject unmeasured stochastic disturbances in the inlet humidity stream.     

基于DRNN神经网络的石油钻机自动送钻系统智能控制研究

针对钻井过程的非线性、不确定性和实时性要求,采用基于对角神经网络(DRNN)的控制方案,能在线自适应的调整P ID控制器的3项参数。以自动送钻实验系统模型为被控系统进行仿真实验,仿真结果表明可以达到较为理想的控制结果,基于DRNN神经网络控制器结构简单,易于实现,效果明显优于传统的P ID控制器。     

Infrared Drying: From Process Modeling to Advanced Process Control

The article surveys the drying of solids materials and polymer solutions when infrared radiation (IR) is employed as the main heating source. The study reviews the current research trends of IR drying of specific applications. A case study similar to an industrial setting is presented to illustrate a model development and control scheme of an IR drying unit. The design and online implementation of an internal model controller (IMC) is discussed. The study demonstrates the controller capabilities to suppress random variations of the moisture content in the material entering the dryer. Simulation results also showed the success of model predictive control (MPC) multivariable controller ability, while handling process interactions and process constraints, to track setpoint changes in the humidity and temperature of the material exiting the dryer and to reject unmeasured stochastic disturbances in the inlet humidity stream.     

Model predictive control for the self-optimized operation in wastewater treatment plants: Analysis of dynamic issues

This paper describes a procedure to find the best controlled variables in an economic sense for the activated sludge process in a wastewater treatment plant, despite the large load disturbances. A novel dynamic analysis of the closed loop control of these variables has been performed, considering a nonlinear model predictive controller (NMPC) and a particular distributed NMPC-PI control structure where the PI is devoted to control the process active constraints and the NMPC the self-optimizing variables. The well-known self-optimizing control methodology has been applied, considering the most important measurements of the process. This methodology provides the optimum combination of measurements to keep constant with minimum economic loss. In order to avoid nonfeasible dynamic operation, a preselection of the measurements has been performed, based on the nonlinear model of the process and evaluating the possibility of keeping their values constant in the presence of typical disturbances.     

Control of a distillation column using self-tuning regulators

Self-tuning regulators were applied to the multivariable control of a pilot distillation unit. Both the top and the bottom product compositions were simultaneously controlled. The multivariable interactions were compensated for by different strategies. It was shown that a proper choice of the control variables made it possible to use a regulator with a smaller number of parameters. The evaluation of the results shows that the self-tuning regulator is a valuable tool in multivariable control of distillation columns.     

Control of a distillation column using self-tuning regulators

Self-tuning regulators were applied to the multivariable control of a pilot distillation unit. Both the top and the bottom product compositions were simultaneously controlled. The multivariable interactions were compensated for by different strategies. It was shown that a proper choice of the control variables made it possible to use a regulator with a smaller number of parameters. The evaluation of the results shows that the self-tuning regulator is a valuable tool in multivariable control of distillation columns.     

基于模糊自整定PID燃气锅炉仿真研究

针对燃气热水锅炉具有非线性、大惯性及多变量等特点,借助经验公式分析并用MATLAB系统辨识工具箱完成了系统数学模型的辨识,确定了燃气锅炉温度控制系统的模型结构可等效为一阶惯性加滞后环节,在此基础上确立了基于MATLAB的系统SIMULINK仿真框图,设计了模糊自整定PID控制器.通过模糊自整定PID与常规PID算法的仿真对比分析,得出模糊自整定PID不仅在调节时间、超调量等动态性能上的控制效果明显优于常规PID控制效果,而且增强了系统的鲁棒性和适应性,具有良好的动、静态性能.     

A study on the adaptive predictive control method for multivariable bilinear processes

A predictive control method for multivariable bilinear processes is derived based on ARMA model. To identify bilinear process models, we use simple equation error method extended to multivariable system. We can obtain the adaptive predictive controller for multivariable bilinear processes by incorporation of the identification algorithm. Offset compensator is provided to correct for the effects of unmeasured disturbances and model inaccuracies. A filter with a singled parameter is used to correct for the effects of an incorrect model. Results of simulation on multivariable bilinear processes show that the proposed control method has satisfactory performance.     

Multivariable computer control of a butane hydrogenolysis reactor: Part II: Design and on-line implementation of a stochastic controller using an identified multivariate noise model

A multivariable stochastic controller is implemented on a pilot scale, plug flow, butane hydrogenolysis reactor. In the synthesis of the controller, a multivariate time series model of the process disturbances is used. The success of this controller is compared to a previous controller where the process disturbances are not directly modeled.     

复合控制器及其在连消中的应用

由于常规PID控制方式对非线性、大滞后对象难以进行有效的控制,模糊控制具有很好的动态特性,所以结合常规PID和模糊控制的优势设计了参数自调整Fuzzy-PID复合控制器。通过模糊推理实现参数自调整,以使控制器能够适应不同对象和对象的不同状态。采用模糊推理的方法完成两种控制方式的平稳过渡。对某制药厂连消温度的控制表明,该控制器可以大幅度提高控制精度和缩短系统响应时间,从而避免了染菌事故的发生,提高了发酵单位。     

Oversizing analysis in plant-wide control design for industrial processes

In this work, an alternative plant-wide control design approach based on oversizing analysis is presented. The overall strategy can be divided in two main sequential tasks: 1 – defining the optimal decentralized control structure, and 2 – setting the controller interaction degree and its implementation. Both problems represent combinatorial optimizations based on multi-objective functional costs and were solved efficiently by genetic algorithms. The first task defines the optimal selection of controlled and manipulated variables simultaneously, the input–output pairing, and the overall controller dimension in a sum of square deviations context. The second task analyzes the potential improvements by defining the controller interaction degree via the net load evaluation approach. In addition, some insights are given about the feasibility (implementation load) of these control structures for a decentralized or centralized framework. The well-known Tennessee Eastman (TE) process is selected here for sake of comparison with other multivariable control designs.     

自校正P ID算法在再热器中的应用

针对再热器的温度控制问题,设计了一种极点配置PID控制器。给出了CARMA模型,引入带遗忘因子的最小二乘实时参数估计算法和带数字滤波器的增量式PID控制算法,同时给出了极点配置自校正PID的整定方法过程,建立PID参数与系统参数及控制性能指标之间的关系式,并进行了MATLAB仿真。