Multivariable control of ill-conditioned distillation columns utilizing process knowledge

In distillation, a reliable model of the column is generally considered as a prerequisite for the design of efficient two-product control by multivariable methods. However, such models are often very hard to obtain. In fact, even very small identification errors may introduce features which are in conflict with physical knowledge, and which make the model useless. Instead of focusing on the development of consistent models, this work is concerned with the utilization of physical process knowledge directly for multivariable control, even if a reliable input-output model is lacking. Such knowledge is, for example, the sign of the RGA-values and an estimate of the input-directionality. It is shown that such structural information of the process can form an entity of control-relevant knowledge that is sufficiently rich for the design of a multivariable SVD controller.     

Application of predictive control techniques to a distillation column

Model-based predictive control techniques are widely recognized as having useful application to control problems characterized by complex dynamics and severe time delay. The establishment of a representative process model is the key step in the procedure and for anything other than trivially simple systems is a major hurdle. This paper describes the application of predictive control techniques to a distillation problem which embodies a pure time delay of 2–3 h and time constants of 3–4 h. A sampled-data process model is identified from monitored input/output data and from this a predictive control algorithm is designed. The application of the controller has resulted in very effective closed-loop control of the base composition of the distillation column, where previously only manual supervision was possible.     

Multivariable weighted predictive control

A multivariable Weighted Predictive Control (MV WPC) algorithm is presented and properties of the multivariable closed-loop system are discussed. Simulation studies for industrial processes show that satisfactory closed-loop performance can be achieved by tuning the design parameters in the WPC algorithm. A series of tuning guidelines for a successful design are also proposed.     

Model predictive control with adaptive disturbance prediction and its application to fatty acid distillation column control

This paper describes the results of a joint university-industry study to control a fatty acid distillation sequence, which is plagued with severe disturbance problems. In order to solve the disturbance problem, a model predictive control algorithm is modified in terms of disturbance prediction. Assuming that the dynamics of the unmeasured disturbances is generated by an auto-regressive form, the dynamics of the disturbance can be adaptively identified by using time series data of prediction errors and inputs. Using an identified disturbance model with a process model, future outputs are predicted. Control actions are determined so that the predicted output is as close to the target value as possible. This modified model predictive control aglorithm is applied to a ratio control scheme for three distillation columns. The control system developed has been in use sucessfully for more than six years to produce commercial products.     

Adaptive dynamic matrix control of a distillation column with closed-loop online identification

This paper presents an online identification technique where a process is identified in terms of pseudo impulse response coefficients and subsequently used to update convolution type models to accommodate process-model mismatch. As an example, dynamic matrix control has been applied adaptively to control the top product composition of a distillation column for both servo and regulatory problems. The algorithm automatically detects a large step-like disturbance requiring fresh identification of the process and subsequently adapts the controller to the new model. Simulation studies using an analytical dynamic full order model of a distillation column demonstrated the usefulness of the adaptation scheme. Experimentation on a pilot scale distillation unit vindicated the simulation results.     

Constrained multivariable control of a distillation column using a simplified model predictive control algorithm

Distillation columns are important process units in petroleum refining and need to be maintained close to optimum operating conditions because of economic incentives. Model predictive control has been used for control of these units. However, the constrained optimization problem involved in the control has generally been solved in practice in a piece-meal fashion. To solve the problem without decomposition, the use of a linear programming (LP) formulation using a simplified model predictive control algorithm has been suggested in the literature. In this paper, the LP approach is applied for control of an industrial distillation column. The approach involved a very small size optimization problem and required very modest computational resources. The control algorithm eliminated the large cycling in the product composition that was present using SISO controllers. This resulted in a 2.5% increase in production rate, a 0.5% increase in product recovery, and a significant increase in profit.     

Self-tuning regulator applied to a binary distillation column

Utilization of a self-tuning regulator (STR) for control of top product composition of a binary distillation column has been investigated. Results from simulation studies and experimental evaluation of the STR on a pilot scale column are compared with the performance achieved using conventional proportional plus integral control. The STR resulted in significantly improved control for both servo and regulatory control.     

Comments on self-tuning regulator applied to a binary distillation column

Some comments are made on a paper describing an application of self-tuning control in the presence of step disturbances. It is demonstrated that the behaviour of the self-tuning regulator observed in the paper is a consequence of the slow convergence of the regulator parameters. By an increase of the convergence rate quite different results can be obtained. For example, no steady state error in the output is observed, contrary to the results in the paper in question.     

Nonlinear parametric predictive temperature control of a distillation column

The integration of a nonlinear reduced process model with Parametric Predictive Control (PPC) is discussed for the bottom temperature control of a stabilizer distillation column. One of the main objectives is ensure the quality of the bottom product despite disturbances and complex dynamics. The purpose is to balance nonlinear control with simplicity, facilitating implementation in a DCS. The controllers developed were first tested in a simulated environment and then in the field, showing good performance under a wide range of operating conditions. The use of an estimator to compensate for modeling errors and unmeasured disturbances is also discussed.     

Experimental evaluation of a multivariable adaptive nonlinear predictive controller

A new multivariable adaptive nonlinear predictive controller is designed using a general nonlinear input-output model and variable transformations. The controller is similar in form to typical linear predictive controllers can be tuned analogously or by specifying a single parameters for each controlled variable. In addition, the design procedure is computationally efficient. The new controller is compared to a multi-loop proportional-integral (PI) controller with one-way static decoupling and to an adaptive linear predictive controller through tests on a simulated nonlinear distillation column. The new controller performed well in an experimental application to a multicomponent distillation column.     

Robust discrete-time set-based adaptive predictive control for nonlinear systems

The problem of robust adaptive predictive control for a class of discrete-time nonlinear systems is considered. First, a parameter estimation technique, based on an uncertainty set estimation, is formulated. This technique is able to provide robust performance for nonlinear systems subject to exogenous variables. Second, an adaptive MPC is developed to use the uncertainty estimation in a framework of min–max robust control. A Lipschitz-based approach, which provides a conservative approximation for the min–max problem, is used to solve the control problem, retaining the computational complexity of nominal MPC formulations and the robustness of the min–max approach. Finally, the set-based estimation algorithm and the robust predictive controller are successfully applied in two case studies. The first one is the control of anonisothermal CSTR governed by the van de Vusse reaction. Concentration and temperature regulation is considered with the simultaneous estimation of the frequency (or pre-exponential) factors of the Arrhenius equation. In the second example, a biomedical model for chemotherapy control is simulated using control actions provided by the proposed algorithm. The methods for estimation and control were tested using different disturbances scenarios.     

Optimizing model predictive control of an industrial distillation column

The main scope of this work is the implementation of an MPC that integrates the control and the economic optimization of the system. The two problems are solved simultaneously through the modification of the control cost function that includes an additional term related to the economic objective. The optimizing MPC is based on a quadratic program (QP) as the conventional MPC and can be solved with the available QP solvers. The method was implemented in an industrial distillation system, and the results show that the approach is efficient and can be used, in several practical cases.     

一类串联生产过程的分布式解耦预测控制

研究一类由多个子过程串联而成的生产过程的预测控制，提出了在分布式通信模式下的预测控制算法．该算法由两部分组成：一部分利用其他子过程在上一时刻的控制策略，对子过程间的弱耦合进行前馈补偿；另一部分通过顺序求解对强耦合进行解耦控制．在线性无约束情况下，得到了分布式解耦动态矩阵控制律．最后通过仿真验证了算法的有效性．     

Robust control of a distillation column by the method of inequalities

The method of inequalities is applied to design a robust PI controller for the control of distillate composition using the reflux flow as the manipulated variable. The controller design method takes into account wide variations in k,τ and τ_D of the first order plus the delay transfer function of the process. The performance of the controlled system is evaluated for different levels and changes in direction of set point changes on the linear and also on the original non-linear model equations. The performance of the proposed controller is compared with that of a controller with Zieglar-Nichols (Z-N) settings based on a nominal operating point. The closed loop system becomes unstable at other operating points for the Z-N method whereas the present controller gives good response for wide operating points.     

A computer-aided design tool for robustness analysis and control-relevant identification of Horizon Predictive Control with application to a binary distillation column

This paper describes a MATLAB-based computer-aided design tool, IRA-HPC, which accomplishes integrated system identification and robustness analysis for Horizon Predictive Control (HPC), a model predictive control algorithm implemented on the Application Module of the Honeywell TDC 3000 distributed control system. The tool addresses lifecycle as well as functional aspects of the technology, with the goal of making advanced control principles more accessible to the practising control engineer. IRA-HPC systematically performs the various stages of system identification in a control-relevant framework (addressing input design, parameter estimation, and model validation from the standpoint of the final purpose of the model, which is control system design), followed by robust HPC controller tuning using the Structured Singular Value (μ) paradigm as a basis. The benefits of the tool are shown experimentally in the modelling and control of a methanol/isopropanol pilot-scale distillation column, interfaced to an industrial-scale real-time computing testbed. The example demonstrates the practical feasibility of this tool and its benefits in terms of simplifying the choices of design variables in integrated identification and control design.     

矩阵分解的多变量模型参考自适应控制

研究了基于高频增益矩阵分解的多变量模型参考自适应控制 .为此首先基于矩阵分解构造出新的参数化方程 ,选取必然等价自适应控制律 .然后 ,引入规范化信号和类Lyapunov函数 ,获得了规范化自适应律 .并进一步给出了虚拟规范化信号的有用性质 .最后 ,严格地给出了闭环系统的稳定性和收敛性分析     

大型炼油常减压装置多变量解耦模糊控制系统

一、引言一套印处理量为250万吨的炼油常减压装置由初馏塔、常压塔、减压塔和加热炉组成。它有八个侧线,产品为汽油、柴油和润滑油。优化控制目标为提高收率和保证产品质量(干点,凝固点,350℃馏出,粘度)。为此,本文提出了“卡边温度”概念:使产品质量控制在靠近工艺上限的指定区间内(叫质量卡边域)的侧线平均馏出温度叫“卡边温度”。由蒸馏机理知质量卡边有利于提高收率。于是问题转化为对各侧线按其卡边温度控制问题。本文提出一种基于模式识别原理在线计算各侧线的卡边温度的方法。由蒸馏机理知,在采样时刻τ,侧线产品质量化验值Z(τ)主要由塔压力P(τ-k)和侧线馏出温度T(τ-k)决定,k是采样滞后时间,称(Z(τ),P(τ-k),T(τ-k))为样本,称所有样本集合Q为样本空间。设在目前时刻t塔压力为P(t),则视Q中使Z(τ_i)落在指     

1000MW超超临界机组的多变量约束预测控制的研究

针对1000MW超超临界机组,分析了机组协调控制的3输入3输出系统,研究了机组系统的双层结构多变量约束预测控制(multivariable constrained predictive control,MCPC),给出了基于阶跃响应的多变量约束预测控制方法和具体算法,并给出了仿真效果.最后与传统协调控制进行了对比,仿真结果表明了算法的有效性.     

Multivariable adaptive predictive control of an aerated lagoon for a wastewater treatment process

The application of an adaptive multivariable predictive model-based control scheme for the control of biotechnological processes is reported. Control design consists of regulating the residual concentrations of two main variables of a multistage wastewater treatment process. Unavailability of measurements leads to the development of an identification technique derived to estimate simultaneously unavailable state variables and time-varying parameters of a nonlinear process. Convergence of the estimation scheme is demonstrated via a theorem and its proof using Lyapunov's method. The estimated variables are used in the explicit design of the control algorithm. Good simulation results have been obtained in regulation, tracking, disturbance rejection and transient behaviour, showing the efficiency of this adaptive control strategy.     

Model predictive control of reactive distillation for benzene hydrogenation

Benzene hydrogenation via reactive distillation is a process that has been widely adopted in the process industry. However, studies in the open literature on control of this process are rare and seem to indicate that conventional decentralized PI control results in sluggish responses when the reactive distillation column is subjected to disturbances in the feed concentration. In order to overcome this performance limitation, this work investigates model predictive control (MPC) strategies of a reactive distillation column model, which has been implemented in gPROMS. Several MPCs based upon different sets of manipulated and controlled variables are investigated where the remaining variables remain under regular feedback control. Further, MPC controllers with output disturbance correction and, separately, with input disturbance correction have been investigated. The results show that the settling time of the column can be reduced and the closed loop dynamics significantly improved for the system under MPC control compared to a decentralized PI control structure.