Dynamic neural network modeling for hydrochloric acid recovery process

Kittisupakorn, Paisan; Tangteerasunun, Pantapong; Thitiyasook, Piyanuch

doi:10.1007/bf02705659

Cited by 6 publications

(3 citation statements)

References 26 publications

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“…This process is sometimes called 'coupled transport' or 'chemisorption'. There are many articles in practical and theoretical aspects of this separation method [10,[12][13][14].…”

Section: Theoretical Aspectsmentioning

confidence: 99%

A review on olefin/paraffin separation using reversible chemical complexation technology

Azhin

Kaghazchi

Rahmani

2008

Journal of Industrial and Engineering Chemistry

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“…This process is sometimes called 'coupled transport' or 'chemisorption'. There are many articles in practical and theoretical aspects of this separation method [10,[12][13][14].…”

Section: Theoretical Aspectsmentioning

confidence: 99%

A review on olefin/paraffin separation using reversible chemical complexation technology

Azhin

Kaghazchi

Rahmani

2008

Journal of Industrial and Engineering Chemistry

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“…An advantage of the implementation of the neural network is that it is easy to design and use and can provide the estimates of the relationship of input and output to explain the process. Kittisupakorn et al [10] has applied the neural network to predict the concentration of a hydrochloric acid in hydrochloric acid recovery process. Arpornwichanop and Shomchoam [11] has applied neural network to estimate the substrate concentration in a fed-batch bioreactor.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Modeling and Control for a Batch Heating/Cooling Evaporative Crystallization Process

Daosud

Thampasato

Kittisupakorn

2017

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Abstract. Crystallization processes have been widely used for separation in many fields to provide a high purity product. In this work, dynamic optimization and neural network (NN) have been applied to improve the quality of the product: citric acid. In the dynamic optimization, optimization problems maximizing both crystal yield and crystal size have been formulated. The neural networks have been developed to provide NN models to be used in the formulation of not only neural network inverse control (NNDIC) but also neural network model predictive control (NNMPC) strategies. The Levenberg Marquadt algorithm has been used to train the network and optimal neural network architectures have been determined by a mean squared error (MSE) minimization technique. In addition, a neural network model has been designed to provide estimates of the temperature and the concentration of the crystallizer. These estimates have been incorporated into the NNMPC controller. In the NNDIC controller, another neural network model has been applied to predict the set point of jacket temperature. The simulation results have shown that the obtained crystal size is increased by 19% and 30% compared to that by cooling and evaporation methods respectively and the obtained yield is increased more than 50%. The robustness of the proposed controller is investigated with respect to parameters mismatches. The results have shown that the NNMPC controller provides superior control performances in all case studies.

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“…Neural networks can be employed to be mathematical model, estimator and controller. Kittisupakorn et al [14] demonstrated dynamic neural network modeling for hydrochloric acid recovery acid process to predict the concentration profile of a hydrochloric acid recovery process consisting of double fixed-bed ion exchange columns. Rusinowski and Stanek [15] presented a method and example results of calculations of neural modeling of steam boilers.…”

Section: Introductionmentioning

confidence: 99%

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

Kittisupakorn

Charoenniyom

Daosud

2014

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Methyl methacrylate (MMA) production in an exothermic batch reactor provides a challenging problem for studying its dynamics behavior and temperature control. This work presents a neural network forward model (NN) to predict a concentration of methyl methacrylate, a jacket temperature and temperature profile in the reactor. An optimal NN model has been employed to predict state variables incorporating into a model predictive control (MPC) algorithm to determine optimal control actions. To control the temperature, neural network based control approaches: a neural network direct inverse control (NNDIC) and a neural network based model predictive control (NNMPC) have been formulated. In addition, a dynamic optimization approach has been applied to find out an optimal operating temperature to achieve maximizing the MMA product at specified final time. The robustness of the proposed control strategy is investigated with respect to changes in operating parameters: rate of reaction, heat of reaction and overall heat transfer coefficient. Simulation results have indicated that the NNMPC is robust and gives the best control results among the PID and NNDIC in all cases.

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Dynamic neural network modeling for hydrochloric acid recovery process

Cited by 6 publications

References 26 publications

A review on olefin/paraffin separation using reversible chemical complexation technology

A review on olefin/paraffin separation using reversible chemical complexation technology

Neural Network Based Modeling and Control for a Batch Heating/Cooling Evaporative Crystallization Process

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

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