Decentralized control and identified-model predictive control of divided wall columns

Distillation columns have proved to be the most reliable separation method for separating chemical mixtures to their pure components. As the number of components to be separated increases, the number of columns and the energy required to run these columns also increase. This results in huge capital costs that are unaffordable due to limited energy resources. A most "compact" column called the divided wall column appeared to solve this difficulty. This column is capable of separating mixtures of three or more components to high purity products with energy less than that of the conventional multicolumn process. However, the control of these columns is more complicated due to the coupling effect and increased number of variables in these columns. Our focus in this paper is limited to the divided wall columns separating ternary mixtures only. There are three main objectives of this paper. First, the paper presents a survey study about the different control aspects of the divided wall columns, based on the type of control used: composition control, temperature control, or cascaded composition−temperature control. An up-to-date overview of most of the current literature is presented. Second, conventional and adaptive proportional−integral− derivative (PID) controllers are proposed to control a divided wall distillation column separating a ternary mixture of ethanol, propanol, and n-butanol. Fuzzy logic control, neural network control, and adaptive neuro-fuzzy inference systems are suggested for the tuning of the PID controllers. Particle swarm optimization technique is also applied to improve the results obtained by the adaptive PID controllers. Finally, a multi-input−multi-output neural network model reference adaptive controller based on adaptive PID controller tuned by adaptive neuro-fuzzy inference systems based particle swarm optimization is suggested. The results indicate the superiority of the adaptive PID controllers over conventional PID controllers, especially in the case of disturbances.

Section: Design and Control Of Dwcmentioning

confidence: 99%

New Suggested Model Reference Adaptive Controller for the Divided Wall Distillation Column

El-Gendy

Saafan

Elksas

et al. 2019

“…For example, Kiss et al gave an overview of the available control strategies for DWC, which showed that multi-input and multioutput (MIMO) controllers usually performed better than multiloop PID controllers. Hernandez et al compared PID control and its equivalent model predictive control (MPC) schema for DWCs, which showed that the equivalent MPC schema was more convenient to use. In addition, Tututi-Avila et al optimized the gain of a fuzzy controller and confirmed that it was more suitable for DWC control compared with the PID controller.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Sliding Mode Control of Dividing-Wall Column

Wang

Zhou

et al. 2020

The unique construction of the dividing-wall column (DWC) has the potential for both energy and capital cost conservation. A sufficiently robust control strategy is needed to handle the DWC because it is a complex multivariable system with high process nonlinearity and time lag. In this paper, the single-factor, response surface methodology (RSM), and particle swarm optimization (PSO) optimizations are applied to the DWC, and the optimal operating parameters are obtained. Then, a sliding mode control (SMC) method is proposed for DWC. Specifically, the DWC is estimated by a first-order pure lag transfer function, and the SMC controller is developed with the aid of the Interpreted MATLAB Fcn of MATLAB/Simulink. Finally, the dynamic responses of both SMC controller and proportional-integral-derivative (PID) controller are analyzed using the disturbance in the feed flow rate (F). The results show that the settling time, oscillation, and steady-state deviation of the SMC controller are less than those of the PID controller. In this way, the SMC could present a better option for control of a complex distillation process, such as the DWC.

“…Also, effective control of RD for benzene hydrogenation was achieved, and Rodríguez et al extended this advanced control strategy to RDWC for methyl acetate hydrolysis to overcome the oscillatory response problem. In addition to RD and RDWC, MPC has been employed for other complex distillation systems such as the multiproduct dividing wall column. , Rangaiah et al applied temperature difference control based MPC to extractive dividing wall column (EDWC) for 2-methoxyethanol and toluene separation. Huang et al combined MPC with PI to formulate composition–temperature cascade control for a Kaibel column in their recent work. , Compared to decentralized PI control schemes, the MPC and cascade MPC–PI could realize smooth responses.…”

Section: Introductionmentioning

confidence: 99%

Optimal Design, Proportional–Integral Control, and Model Predictive Control of Intensified Process for Formic Acid Production. 1. Reactive Distillation and Reactive Dividing Wall Column

Yang

Han

et al. 2020

Formic acid production through methyl formate hydrolysis has been shown to be energy and capital cost intensive, and its performance could be significantly promoted by process intensification. However, integration of reaction and separation exhibits a complex nonlinear behavior, which requires corresponding optimization and control to be effectively addressed before further industrial implementation. In the present work, optimization was first performed for a conventional reactive distillation (RD) process and reactive dividing wall column (RDWC) by coupling genetic algorithm and rigorous simulations, in which a user-defined model was incorporated to take kinetics into account. Although the results demonstrate that RDWC is inferior to RD in terms of economic criteria, it provides the basis for proposing new easy-to-operate configurations in the subsequent part 2 of this series. Then multiloop proportional–integral (PI) control structures and linear model predictive control (MPC) schemes were designed for the conventional RD process and RDWC, respectively. The performances of two control structures were compared subject to feed disturbance, by using quantitative indexes such as oscillation, settling time, overshoot, and integral of the squared error (ISE). The dynamic response validates that MPC outperforms classical multiloop control schemes and could tackle the excessive overshoot deficiency in PI control for both the conventional RD process and RDWC.