Prediction models to control final control elements for neutralization process in chemical industries

Author:

Dharshan Y1ORCID,Devasena D1,Srinivasan K1

Affiliation:

1. Department of Electronics and Instrumentation Engineering Sri Ramakrishna Engineering College Coimbatore India

Abstract

AbstractBACKGROUNDIndustrialization continues to increase in the 21st century, as industries start moving towards automation. Manufacturing of a component/item requires a lot of resources and energy. Usage of water in industries has increased in recent years, as it is used as both a resource and an energy generating source. Neutralization of a processed liquid is carried out by addition of solution through final control elements and most industries are utilizing pneumatic‐based control valves, which have a disadvantage in the regulation of additives.RESULTSA motorized valve system is introduced, which is compared with the pneumatic system in terms of their performance. The proposed system is implemented with different controllers such as proportional–integral controller tuned using linear matrix inequalities, modified internal model controller, model predictive controller and nonlinear autoregressive (NAR) model, where the final control elements are tested for pH neutralization. On implementation of the proposed system the NAR model has proven to be better under the motorized control valve system for the neutralization process, with a peak value of 7.08 (pH), settling time of 75.16 s, rise time of 5.96 s, maximum overshoot of 10.1%, slew rate of 0.21 per second, integral square error of 0.95, integral absolute error of 0.48 and integral time absolute error of 1.88.CONCLUSIONBased on the results obtained it is observed that the NAR controller utilized with motorized control valve has better functionality in neutralizing a solution. © 2024 Society of Chemical Industry (SCI).

Publisher

Wiley

Reference22 articles.

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5. Isolation of anticancer bioactive secondary metabolites from the sponge‐derived endophytic fungi Penicillium sp. and in‐silico computational docking approach;Kaliaperumal K;Front microbiol,2023

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