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.     

Multi-objective constraint optimizing IOL control of distillation column with nonlinear observer

Performance of input–output linearizing (IOL) controllers suffers due to constraints on input and output variables. This problem is successfully tackled by augmenting IOL controllers with quadratic dynamic matrix controller (QDMC). However, this has created a constraint-mapping problem for coupled MIMO systems like distillation column. A multi-objective optimization problem needs to be solved to map the constraints on inputs. A suitable transformation technique is proposed to convert this multi-objective optimization problem to a single objective one. This makes the controller less computationally intensive and easy to implement. This controller (IOL-QDMC) along with nonlinear observer is implemented on a binary distillation column for dual composition control. Its performance is evaluated against a quadratic dynamic matrix controller (QDMC) and input–output linearization with PI controller (IOL-PI).     

Nonlinear control of a reactive distillation column

Control of reactive distillation columns is a challenging task due to the complex dynamics arising from the coupling of reaction and separation. In this paper, asymptotically exact input/output-linearization is applied in simulation studies to an industrial reactive distillation column which is operated by Bayer AG. The resulting control law is rather general and can be easily adopted for other reactive distillation columns. This control scheme requires knowledge of the complete state of the process and therefore an observer is designed. Asymptotically exact input/output-linearization inherits robust stability from a robust observer. It is intuitively argued that the proposed observer is robust w.r.t. both model structure and parameter errors. In order to compensate for steady state observer offsets an outer control loop with simple PI-controllers is implemented. Simulation studies evidence that in comparison with a well-tuned linear controller the nonlinear controller shows a superior performance with respect to setpoint-changes and disturbances, even in the presence of unknown input delays.     

Identification and bilinear control of a binary distillation column

A reduced bilinear model of a binary distillation column is presented. This model has as control inputs the reflux rate and the heat flow of the reboiler, as perturbations the feed stream and its concentration, and as outputs the distillate and residual concentrations. A bilinear model is simpler than a non-linear one, and more exact than a linear model. Furthermore, its structure allows the design of a control structure via optimal control theory. In this paper, two optimal control schemes designed by the reduced bilinear model have been applied to a column simulated by an analytical model, obtaining a robust control of the process even in the presence of disturbances.     

Nonlinear model predictive control of a reactive distillation column

This paper considers the application of nonlinear model predictive control (NLMPC) to a highly nonlinear reactive distillation column. NLMPC was applied as a nonlinear programming problem using orthogonal collocation on finite elements to approximate the ODEs that constitute the model equations for the reactive distillation column. Diagonal PI controls were used to identify that the [L/D,V] and the [L/D,V/B] configurations performed best. NLMPC was applied using the [L/D,V] configuration and found to provide a factor of 2–3 better performance than the corresponding PI controller. The effect of process/model mismatch on the performance of the NLMPC controller was also evaluated.     

Nonlinear model-based control of a batch reactive distillation column

The inherent trade off between model accuracy and computational tractability for model-based control applications is addressed in this article by the development of reduced order nonlinear models. Traveling wave phenomena is used to develop low order models for multicomponent reactive distillation columns. A motivational example of batch esterification column is used to demonstrate the synthesis procedure. Tight control of the column is obtained with the use of reduced model in a model predictive control algorithm.     

Multivariable adaptive predictive control of a binary distillation column

This paper presents a tutorial review of an adaptive predictive control system (APCS). Special emphasis is given to the key issues involved in the practical application of APCS to real processes. These practical issues are illustrated by actual application of SISO and MIMO control of a pilot scale binary distillation column. The experimental evaluation of this method reveals the simplicity of the adaptive algorithm and its excellent performance in an industrial type environment. The experimental results easily outperformed well-tuned classical PID controllers. A brief review of other applications of adaptive control to chemical processes is also included in this paper.     

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.     

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.     

Robust model predictive control of a pilot plant distillation column

In this paper, the development and application of a robust MPC to a pilot plant ethanol–water distillation column is described. It is shown through experimental tests in the pilot plant, how the linear model can change depending on the operating point. The obtained model has time delays and repeated poles. For this kind of system, the development of an MPC, based on the step response model that is robust to multi-model uncertainty, is presented. The experimental results that confirm the good performance of the proposed controller are also shown.     

Linearizing control of a binary distillation column based on a neuro-estimator

In this work, the LV-control problem in binary distillation columns is addressed. With least prior knowledge, a linear reference model with unknown terms is obtained. The time variations of the unknown terms are estimated using two on-line trained perceptrons. These estimates are subsequently used to design a feedback linearizing-like controller. The closed-loop behavior is analyzed through numerical examples. The resulting controller shows robustness against external disturbances and set-point changes.     

Triangular decoupling for a binary distillation column under pressure control

The possibility of attaining complete decoupling of a distillation column under pressure control is studied using the state space model. It is shown that under the situation where the distillation column is under pressure control by the manipulation of the condenser duty, the complete decoupling using constant state feedback may not be attained, while the triangular decoupling can be attained with eight measurement variables.     

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.     

莱文伯格-马夸特算法的精馏动态模拟

本文研究了以神经网络理论为核心的精馏塔的动态模拟。结合牛顿全局优化的思想,在多层前馈BP神经网络的基础上,提出一种Levenberg-Marquardt优化神经网络算法,应用于甲醇精馏塔的动态模拟。模拟结果表明,进料组成影响着塔顶的气相组成和塔底的液相组成;稳态因组分变化而破坏后,塔顶的气相流率和塔底的液相流率都发生变化,最后达到新的平衡;当进料组成、回流比发生很小变化时,甲醇精馏塔的平衡状态影响较大。     

Globally linearized control system design of a constrained multivariable distillation column

The goal of this paper is to develop a discrete-time multivariable globally linearized control (GLC) algorithm, which provides low computational requirements with constraint handling ability. The control strategy is constructed with four elements: a transformer that accounts for process nonlinearities; an estimator, which observes the required unmeasured states; a variable constraint mapping optimizer that transforms the input constraints of the nonlinear process into constraints on the manipulated inputs of the globally linearized system and a quadratic dynamic matrix controller (QDMC) that provides constraints handling ability. The effectiveness of the designed controller has been tested on a multi-input multi-output (MIMO) nonlinear distillation column through extensive numerical simulations. The control law showed a high quality performance for set point tracking and disturbance rejection in presence of parametric uncertainty. The effect of unmeasured disturbance also has been studied through the simulation experiment. In the comparative study, the proposed GLC-QDMC control technique outperformed the GLC-DMC control law.     

Two-point control of a reactive distillation column for composition and conversion

Reactive distillation is a hybrid process with dual process objectives: reactant conversion and product composition. Control schemes for reactive distillation frequently neglect the effect of the principal operating parameters on the reactant conversion, and this has a detrimental effect on the overall process profitability. An ETBE reactive distillation column has been used as a case study to show how a two-point control configuration, which recognises the importance of both composition and conversion, can be developed and implemented for a reactive distillation process. The combined composition and conversion control configuration was tested using SpeedUp dynamic simulations and proved to be effective in maintaining a high isobutylene conversion despite process disturbances. The two-point control scheme also had superior disturbance rejection capability, especially for feed rate changes, and composition set-point sensitivity compared with a one-point control scheme.     

Online tuning of a steady state crude distillation unit model for real time applications

The steady state simulators, used for on-line performance prediction and for on-line optimization in crude distillation units are often sensitive to small variations in the feed composition, which is specified in terms of a True Boiling Point (TBP) vs volume percent distilled curve. The exact feed TBP is often not available during the plant operation. Also stratification of raw crude oil into layers in the large tank farm sections cause severe operating problems in terms of the stability of the column. If feed TBP can be predicted online, necessary feedforward action can considerably reduce the operating problems. A model has been developed for backcalculation of feed TBP using measured plant parameters. A heat balance is performed around an envelope encompassing the rectifying section of the fractionator and is followed by the calculation of Equilibrium Flash Vaporization (EFV) temperatures at six different locations of the column which are correlated with corresponding feed TBP temperatures. The second part of model tuning consists of calculating model parameters in the form of point efficiencies so as to minimize the discrepancy between the simulator predicted and measured column parameters which arises out of modelling approximations such as assumption of phase equilibria at each stage and use of imperfect thermodynamics correlations. The simulator results, after tuning, were found to match the plant measurements within two percent in all the cases investigated. The simulator output was used to predict various product properties using a Property Prediction package and these were also found to match well with those of laboratory measurements. Both the backcalculation of feed TBP and the efficiency tuning need to be implemented on-line for inferential control and supervisory optimization.     

稳态在线数据校正在炼油厂气体分离装置上的应用

文章系统地研究了稳态过程在线数据校正技术,在具体实现中采用均值法进行稳态检测,修正系数法进行误差的侦破、识别,通过两层次变换进行数据分类。开发了稳态过程在线数据校正软件,并将校正后的数据作为输入值用于某炼油厂气体分离系统产品质量的在线预测,应用结果表明,采用校正后的数据作为输入值进行产品质量在线预测比直接用原始数据更稳定、更符合实际情况。     

State estimation and inferential control for a reactive batch distillation column

An optimal reflux ratio profile is obtained for a reactive batch distillation system utilizing the capacity factor as the objective function in a nonlinear optimization problem. Then, an Artificial Neural Network (ANN) estimator system, which utilizes the use of several ANN estimators, is designed to predict the product composition values of the distillation column from temperature measurements inferentially. The network used is an Elman network with two hidden layers. The designed estimator system is used in the feedback inferential control algorithm, where the estimated compositions and the reflux ratio information are given as inputs to the controller to see the performance of the ANN. In the control law, a scheduling policy is used and the optimal reflux ratio profile is considered as pre-defined set-points. It is found that, it is possible to control the compositions in this dynamically complex system by using the designed ANN estimator system with error refinement whenever necessary.