Artificial intelligence techniques applied as estimator in chemical process systems - A literature survey

Ali, J.M. and Hussain, Mohd Azlan and Tade, M.O. and Zhang, J. (2015) Artificial intelligence techniques applied as estimator in chemical process systems - A literature survey. Expert Systems with Applications, 42 (14). pp. 5915-5931. ISSN 0957-4174,

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Abstract

The versatility of Artificial Intelligence (AI) in process systems is not restricted to modelling and control,only, but also as estimators to estimate the unmeasured parameters as an alternative to the conventional observers and hardware sensors. These estimators, also known as software sensors have been successfully applied in many chemical process systems such as reactors, distillation columns, and heat exchanger due to their robustness, simple formulation, adaptation capabilities and minimum modelling requirements for the design. However, the various types of AI methods available make it difficult to decide on the most suitable algorithm to be applied for any particular system. Hence, in this paper, we provide a broad literature survey of several AI algorithms implemented as estimators in chemical systems together with their advantages, limitations, practical implications and comparisons between one another to guide researchers in selecting and designing the AI-based estimators. Future research suggestions and directions in improvising and extending the usage of these estimators in various chemical operating units are also presented. (C) 2015 Elsevier Ltd. All rights reserved

Item Type:	Article
Funders:	University of Malaya UM.C/HIR/MOHE/ENG/25, Ministry of Higher Education in Malaysia UM.C/HIR/MOHE/ENG/25
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Zhang, Jie Engineering, Faculty /I-7935-2015 Engineering, Faculty /0000-0002-4848-7052 University of Malaya UM.C/HIR/MOHE/ENG/25; Ministry of Higher Education in Malaysia UM.C/HIR/MOHE/ENG/25 The authors are grateful to the University of Malaya and the Ministry of Higher Education in Malaysia for supporting this collaborative work under the high impact research grant UM.C/HIR/MOHE/ENG/25. 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD EXPERT SYST APPL
Uncontrolled Keywords:	Artificial intelligence, estimator, soft-sensor, chemical process systems, hybrid neural-network, batch distillation column, bioreactor state estimation, ann-based estimator, genetic algorithm, soft sensors, polymer quality, expert-system, fuzzy-logic, inferential estimation,
Subjects:	T Technology > T Technology (General) T Technology > TP Chemical technology
Divisions:	Faculty of Engineering
Depositing User:	Mr Jenal S
Date Deposited:	04 Apr 2016 01:02
Last Modified:	10 Feb 2021 03:58
URI:	http://eprints.um.edu.my/id/eprint/15723

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