Multivariable Distillation Column Research Articles

Decoupled Fractional Order Filter with PID Controller Design for Multivariable Distillation Column System

The main objective of this research work is the design of decentralized PID controller with fractional order filter for strongly connected multivariable distillation column system. Wherein, the major contribution of this paper is the proposition of compensators for the decoupling process, in addition to extending the application of the proposed approach from Single-Input Single-Output (SISO) systems into Multi-Input Multi-Output (MIMO) systems. More precisely, the proposed idea is based on eliminating the strongly interactive terms between the inputs and outputs of the multivariable distillation column systems so that, the separated multi-loop parts can be treated as a set of SISO subsystems, which have an equivalent dynamic of the whole original system. Thereafter, a PID controller with a fractional filter has been introduced to the decoupled subsystems in decentralized scheme in order to improve the control system robustness. Two multivariable systems have been carefully considered in this paper; the first is a theoretical Two-Input Two-Output (TITO) system while the second is the Wood and Berry distillation column. The obtained results have confirmed the superiority of the proposed approach over the two degrees of freedom technique, which is based on Internal Model Control method. Indeed, the proposed approach has reduced the dynamic error, the settling time, and the rise time. Furthermore, the designed decentralized PID controller with fractional filter has demonstrated rejection to structured perturbation from one loop to another.

Tuning of Multivariable Model Predictive Control for Industrial Tasks

This work is concerned with the tuning of the parameters of Model Predictive Control (MPC) algorithms when used for industrial tasks, i.e., compensation of disturbances that affect the process (process uncontrolled inputs and measurement noises). The discussed simulation optimisation tuning procedure is quite computationally simple since the consecutive parameters are optimised separately, and it requires only a very limited number of simulations. It makes it possible to perform a multicriteria control assessment as a few control quality measures may be taken into account. The effectiveness of the tuning method is demonstrated for a multivariable distillation column. Two cases are considered: a perfect model case and a more practical case in which the model is characterised by some error. It is shown that the discussed tuning approach makes it possible to obtain very good control quality, much better than in the most common case in which all tuning parameters are constant.

Dynamic Control of Sensor and Actuator failures in Multivariable Distillation Column

This paper examines the impact of sensor and actuator failures in the operation of a multivariable distillation column. Several failure scenarios are evaluated including failures of sensors and actuators in various scales of magnitudes and durations. The results obtained illustrate the ability of process controllers in suppressing the impact of these unwanted events. Closed-loop dynamic responses of the process revealed capabilities of these controllers in dealing with upsets that are small in magnitude and duration. In the case of larger and longer process upsets, process controllers are not adequate in providing the necessary corrective measures. This leaves the necessary interventions to be taken by the plant operators, following alarms that would have been triggered in typical plant operation scenario.

Robust Control Approach for Volterra Models

Recently, different algorithms for identification of several uncertain nonlinear models, referred to as Volterra-type models, were introduced in the literature. This work deals with the development of a suitable robust model predictive control (MPC) scheme able to cope with the uncertain characterization of those types of models. A discrete-time multivariable algorithm with efficient computational performance is developed. A simulation example based on a multivariable distillation column is introduced to illustrate the behavior of this methodology under the presence of uncertainties and constraints on the manipulated and controlled variables.

Control and Optimization of Aromatic Compounds in Multivariable Distillation Column

Product separations in petroleum refineries depend significantly on distillation process, which is known to be challenging to be optimally managed, especially when multiple products with variety of purity requirements are involved due to nonlinear dynamics and high degree of process interactions. In this paper, control and optimization aspects of a multivariable distillation process are discussed. A mathematical model of the system is simulated in MATLAB programming environment, and analyses of process behavior and control performances are carried out. The controllers are tuned using conventional Ziegler-Nichols method and L-V control configuration was adopted. The results on disturbance rejection and set point tracking capabilities, in order to maintain the purity of benzene in the distillate above 98.5 % are discussed. Based on these insights, the optimum operating conditions were determined, which serves as a good starting point for further works in addressing variety of problems related to process operations.

Relay feedback‐based time domain modelling of off‐diagonal elements of linear 2‐by‐2 MIMO systems

AbstractA novel approach to model the off‐diagonal closed‐loop transfer function of 2‐by‐2 multi input multi output (MIMO) system in time domain using ideal as well as biased relay feedback tests are presented. Analytical expressions are derived, for transfer function models of 2‐by‐2 MIMO system having individual transfer function elements of first‐order plus dead time (FOPDT) nature, using ideal and biased relay feedback tests in time domain and have been validated on 2‐by‐2 multivariable distillation column examples from the literature as well as on coupled tanks liquid level experimental set‐up. The results show that the derived expression matches with the original undesired relay response even without approximation of time delay in the denominator of closed‐loop transfer function of 2‐by‐2 MIMO system. These analytical expressions are useful to identify unknown system parameters. © 2012 Canadian Society for Chemical Engineering

Observer-based control algorithms for a distillation column

This paper studies the synthesis of nonlinear observer-based globally linearizing control (GLC) algorithms for a multivariable distillation column. Two closed-loop observers/estimators, namely extended Kalman filter (EKF) and adaptive state observer (ASO), have been designed within the GLC framework to estimate the state variables along with the poorly known parameters. Exactly same basic model structure was used for developing the observers. The model structure is so simple that the estimator design was performed based on only two component balance equations around the condenser-reflux drum and the reboiler-column base systems of the distillation column. To construct these observers, the poorly known parameters, namely component vapor flow rate leaving top tray, component liquid flow rate leaving bottom tray and distribution coefficient in the reboiler, were considered as extra states with no dynamics. The comparative study has been carried out between the proposed GLC in conjunction with ASO (GLC-ASO) and that coupled with EKF (GLC-EKF). The GLC-ASO control scheme showed comparatively better performance in terms of set point tracking and disturbance as well as noise rejections. The control performance of GLC-ASO and a dual-loop proportional integral derivative (PID) controller was also compared under set point step changes and modeling uncertainty. The proposed GLC-ASO structure provided better closed-loop response than the PID controller.

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.

Inclusion of model uncertainty in a computational framework for dynamic operability assessment

This paper deals with the treatment of model uncertainty in aQ-parametrization framework for dynamic operability assessment. Structured nonlinear and/or time-varying model uncertainty is considered using the ℓ 1 robust control theory of Khammash and Pearson (1991). In the case of unstructured model uncertainty, dynamic operability assessment is posed as a convex quadratic programming problem and solved efficiently using sparse matrix techniques. If the uncertainty is structured, the resultant problem is nonconvex and is solved at present using a hybrid approach, with more sophisticated global optimization methods being investigated. The approach is applied to a multivariable distillation column containing all four performance limiting factors and the results are discussed.