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Dynamic Neural Network Based Sensing and Controlling a Reactive Distillation Column Having Inverse Response

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Abstract

The inverse response is one of the obstacles in control studies which leads to instability and causes difficulty in a control system. In this paper, a dynamic neural network based estimator and controller are studied for a reversible butyl acetate esterification reaction in a reactive distillation column showing inverse response. The product composition in the bottoms of the column has been estimated using a recurrent neural network (RNN) based soft sensor and controlled using a model predictive controller (MPC) containing a dynamic neural network based model. To study the closed loop response of the model, disturbances in the form of pseudo random binary sequence have been used for the regulatory response and step disturbances are taken for the servo response. The closed loop results of the MPC are then compared with those of the PI controlled closed loop using the performance index of integral errors. It is observed that the MPC performs better than the PI controller for the process with high nonlinearity and inverse characteristics.

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Authors and Affiliations

  1. Department of Chemical Engineering, Manipal University Jaipur, Jaipur, India

    Gaurav Kataria

  2. Department of Chemical Engineering, Malaviya National Institute of Technology, Jaipur, India

    Kailash Singh

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  1. Gaurav Kataria
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Gaurav Kataria, Kailash Singh Dynamic Neural Network Based Sensing and Controlling a Reactive Distillation Column Having Inverse Response. Theor Found Chem Eng 55, 167–179 (2021). https://doi.org/10.1134/S0040579521010085

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