Hybrid Neural Network Controller Design for a Batch Reactor to Produce Methyl Methacrylate

Kittisupakorn, Paisan; Charoenniyom, Thanutchaporn; Daosud, Wachira

doi:10.4186/ej.2014.18.1.145

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…Mean square error (MSE) [15] given in Eq. (16) is used to examine the accuracy of training output with validated data sets and examine the number of nodes to achieve an optimal architecture.…”

Section: Neural Network Based Modelingmentioning

confidence: 99%

“…Most MPC algorithms are based on a linear model of a process and therefore the disadvantage associated with the linear controller is that it does not perform well over the wide range of operating conditions and with large disturbances [14]. As a result, a number of nonlinear model based control strategies have been developed recently [15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

Daosud¹,

Jariyaboon²,

Kittisupakorn³

et al. 2016

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Abstract. In a pharmaceutical industry, batch extractive distillation (BED), a combination process between extraction and distillation processes, has been widely implemented to separate waste solvent mixture of acetone-methanol because of minimum-boiling azeotrope properties. Normally, water is used as solvent and semi-continues mode is proposed to improve purity of acetone. The solvent is charged into the BED column with total reflux start-up until the purity of a desired product is achieved. After the total reflux start-up period is ended, a dynamic optimization strategy is applied to determine an acetone distillate composition profile maximizing the weight of the distillate product (acetone). The acetone distillate composition profile is used as the set point of neural network model-based controllers: the neural network direct inverse model control (NNDIC) and neural network based model predictive control (NNMPC) in order to achieve the acetone composition with the purity of 94.0% by mole within 9.5 hours. It has been found that although both NNDIC and proportional integral derivative (PID) control can maintain the distillate purity on its specification for the set point tracking and in presence of plant uncertainties, the NNMPC provides much more satisfactory control performance and gives the smoothest controller action without any fluctuation when compared to the NNDIC and PID.Keywords: Batch extractive distillation, dynamic optimization, neural network based model predictive control, neural network direct inverse model control.

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Section: Neural Network Based Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

Daosud¹,

Jariyaboon²,

Kittisupakorn³

et al. 2016

Self Cite

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“… 15 The applications of hybrid models and corresponding simulation have been studied in the chemical domain. 16 Methyl methacrylate production using a hybrid NN approach has been presented by Kittisupakorn et al 17 A GA-RBP neural network and improved gradient descent method has been presented as MPC for nonlinear application. 18 Correspondingly, 19 the control of polystyrene batch reactors using NN-based model predictive control in an experimental approach has been shown.…”

Section: Introductionmentioning

confidence: 99%

Wiener-Neural-Network-Based Modeling and Validation of Generalized Predictive Control on a Laboratory-Scale Batch Reactor

Kumbhare

Yadav

et al. 2022

ACS Omega

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Batch reactors are large vessels in which chemical reactions take place. They are mostly found to be used in process control industries for processes such as reactant mixing, waste treatment of leather byproducts, and liquid extraction. Modeling and controlling of these systems are complex due to their highly nonlinear nature. The Wiener neural network (WNN) is employed in this work to predict and track the temperature profile of a batch reactor successfully. WNN is different from artificial neural networks in various aspects, mainly its structure. The brief methodology that was deployed to complete this work consisted of two parts. The first part is modeling the WNN-based batch reactor using the provided input–output data set. The input is feed given to the reactor, and the reactor temperature needs to be maintained in line with the optimal profile. The objective in this part is to train the neural network to efficiently track the nonlinear temperature profile that is provided from the data set. The second part is designing a generalized predictive controller (GPC) using the data obtained from modeling the reactor to successfully track any arbitrary temperature profile. Therefore, this work presents the experimental modeling of a batch reactor and validation of a WNN-based GPC for temperature profile tracking.

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“…In this way, neural networks offer alternative nonlinear models for implementing MPC in such as systems [10][11][12][13]. The applications of neural networks for chemical process modeling and MPC have also been investigated for SISO systems and iterative multistep neural network predictions in MPC based control for MIMO chemical processes [14][15][16][17][18][19]. Production of a uniform and reproducible CSD is a main difficulty in batch crystallization [20,21].…”

Section: Introductionmentioning

confidence: 99%

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

Kittisupakorn¹,

Somsong²,

Hussain³

et al. 2017

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Abstract. A main difficult task in batch crystallization is to control the size distribution of crystal products. Complexity and highly nonlinear dynamic behavior directly affect to model-based control strategies which heavily depend on the rigorous knowledge of crystallization. In this work, neural network-based model predictive control and inverse neural network control strategies are proposed and integrated with an optimization based on neural network-based hybrid model to control temperatures of a purified terephthalic acid batch crystallizer. A neural network-based hybrid model of the batch crystallizer is developed to provide nonlinear dynamic responses used in optimization algorithm for finding an optimal temperature profile related to the quality of a crystal product. Then, the obtained optimal profile is used as set points of the proposed control strategies for improving the crystal product quality. The performances and robustness of the proposed controllers are evaluated in several cases such as for set point tracking and plant/model mismatches. Simulation results show that the neural network-based model predictive control gives the best control performance among the inverse neural network control and a conventional PID controller in all cases.

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Hybrid Neural Network Controller Design for a Batch Reactor to Produce Methyl Methacrylate

Cited by 4 publications

References 22 publications

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

Neural Network Based Model Predictive Control of Batch Extractive Distillation Process for Improving Purity of Acetone

Wiener-Neural-Network-Based Modeling and Validation of Generalized Predictive Control on a Laboratory-Scale Batch Reactor

Improving of Crystal Size Distribution Control Based on Neural Network-Based Hybrid Model for Purified Terephthalic Acid Batch Crystallizer

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