Simulation-Based Artificial Neural Network Predictive Control of BTX Dividing Wall Column

Dohare, Rajeev Kumar; Singh, Kanhaiya; Kumar, Rajesh; Upadhyaya, Sushant

doi:10.1007/s13369-015-1846-z

Cited by 5 publications

(6 citation statements)

References 27 publications

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“…All the state estimation schemes for hybrid systems in the literature involve in an analytical or statistical linearization (Lefebvre et al, 2002) and preclude their use from systems which require more accurate state estimates. Introduction of ANN in state estimation and control of different systems considerably improves the performance, which is very clear from the work reported in Kumar et al (2011); Zhang et al (2016); Dohrae et al (2015); and Parlos et al (2001). But, in the case of Nandola and Bharatiya (2009), if a nonlinear scheme is used in correction of a priori estimates, more accurate state estimates can be obtained in hybrid systems also and this has been implemented in this work.…”

Section: Introductionmentioning

confidence: 87%

Performance comparison of estimation-based control schemes for hybrid systems

Mottaiyandi¹,

Vellayikot²,

Kaliappan³

2021

Transactions of the Institute of Measurement and Control

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In this paper, the authors have proposed an estimation-based control scheme. Artificial neural network and unscented Kalman filter are used for state estimation and inverse dynamics controller is implemented that utilizes the estimation of unmeasured state variables. The benchmark example taken for the analysis and implementation is nonlinear autonomous three-tank hybrid system and non-autonomous switched mode non-isothermal continuous stirred tank reactor, which are subjected to state and measurement noise. The performance comparison of both proposed estimation-based control scheme has been carried out and results are presented. Further, the proposed scheme is validated with three-tank hybrid system experimental setup.

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Section: Introductionmentioning

confidence: 87%

Performance comparison of estimation-based control schemes for hybrid systems

Mottaiyandi¹,

Vellayikot²,

Kaliappan³

2021

Transactions of the Institute of Measurement and Control

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“…In the composition control, the compositions of the three products are the controlled variables. This method has been widely used in the literature, but it needs expensive composition analyzers due to the time delay in the process. , In the temperature control, specific trays are selected as the controlled variables such that controlling the temperature of these trays can maintain product purity. These sensitive trays are chosen according to their sensitivity to the manipulated variables.…”

Section: Controllability Studymentioning

confidence: 99%

“…However, R V is based on the cross-sectional area of each side of the bottom of the DWC, which is fixed by the location of the wall at the design stage. Hence, R V cannot be adjusted during operation for control purposes . However, different papers studied theoretically the effect of changing R V on the operation of the DWC.…”

Section: Controllability Studymentioning

confidence: 99%

“…Their results showed that the performance of the MPC is much better than the performance of the PI controller as compared with ref . Dohare et al also used artificial neural network predictive control (ANNPC) to control the same DWC in ref , and the same disturbances were also applied. Different performance criteria parameters such as IAE, ITAE, ISE, integral time square error (ITSE), rise time, and settling time were used to compare the performance of their controller to ref .…”

Section: Design and Control Of Dwcmentioning

confidence: 99%

“…Hence, R V cannot be adjusted during operation for control purposes. 20 However, different papers studied theoretically the effect of changing R V on the operation of the DWC. Six degrees of freedom are available to control the purities of the three products, i.e., regulatory control, and also the level of the tanks of the reboiler (bottom base) and condenser (reflux drum), i.e., inventory control.…”

Section: Controllability Studymentioning

confidence: 99%

See 2 more Smart Citations

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

El-Gendy

Saafan

Elksas

et al. 2019

Ind. Eng. Chem. Res.

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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.

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