Boiler modeling and controller performance optimization using genetic algorithm for palm oil industry

This research developed control algorithms and Graphical User Interfaces (GUI) using Genetic Algorithm (GA) optimization analysis for the boiler control system. The trade-off optimized PI controller tunings visualized by Adaptive GA Optimization Contol Toolboxes provided the best control performance...

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Main Author:	Chew Ing Ming
Format:	Thesis
Language:	English English
Published:	2020
Subjects:	QA76.75-76.765 Computer software
Online Access:	https://eprints.ums.edu.my/id/eprint/40726/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/40726/2/FULLTEXT.pdf
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id	my-ums-ep.40726
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spelling	my-ums-ep.407262024-10-10T03:58:21Z Boiler modeling and controller performance optimization using genetic algorithm for palm oil industry 2020 Chew Ing Ming QA76.75-76.765 Computer software This research developed control algorithms and Graphical User Interfaces (GUI) using Genetic Algorithm (GA) optimization analysis for the boiler control system. The trade-off optimized PI controller tunings visualized by Adaptive GA Optimization Contol Toolboxes provided the best control performance in terms of settling time and integral error values for both servo and regulatory controls. Routh-Hurwitz necessity criterion analysis has been applied to determine the stability margins. This criterion has restricted the search region of GA to ensure proper searching of chromosomes to minimize the period for optimization analysis and avoiding the optimum PI tunings values from missing out on any search region. The conducted simulation and validation tests have shown that Adaptive GA Optimization Analysis Toolboxes has provided better PI tunings to improve the control performance indexes up to 84.61% for the simulation analysis and 93.25% improvement on the validation tests. In addition, the settling time of the control responses have improved up to 80.18% for simulation analysis and 83.49% for validation test. The reason is due to Adaptive GA Optimization has applied stochastic optimization technique, which is repetitively proposed individuals or chromosomes to be tested using objective function in the computation analysis and then, will choose the controller tunings with least integral error values for both servo and regulatory controls. It offers better tuning opportunity without relying on the fixed tuning formulas as performed by manually calculated controller tunings. 2020 Thesis https://eprints.ums.edu.my/id/eprint/40726/ https://eprints.ums.edu.my/id/eprint/40726/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/40726/2/FULLTEXT.pdf text en validuser dphil doctoral Universiti Malaysia Sabah Faculty Of Engineering
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	QA76.75-76.765 Computer software
spellingShingle	QA76.75-76.765 Computer software Chew Ing Ming Boiler modeling and controller performance optimization using genetic algorithm for palm oil industry
description	This research developed control algorithms and Graphical User Interfaces (GUI) using Genetic Algorithm (GA) optimization analysis for the boiler control system. The trade-off optimized PI controller tunings visualized by Adaptive GA Optimization Contol Toolboxes provided the best control performance in terms of settling time and integral error values for both servo and regulatory controls. Routh-Hurwitz necessity criterion analysis has been applied to determine the stability margins. This criterion has restricted the search region of GA to ensure proper searching of chromosomes to minimize the period for optimization analysis and avoiding the optimum PI tunings values from missing out on any search region. The conducted simulation and validation tests have shown that Adaptive GA Optimization Analysis Toolboxes has provided better PI tunings to improve the control performance indexes up to 84.61% for the simulation analysis and 93.25% improvement on the validation tests. In addition, the settling time of the control responses have improved up to 80.18% for simulation analysis and 83.49% for validation test. The reason is due to Adaptive GA Optimization has applied stochastic optimization technique, which is repetitively proposed individuals or chromosomes to be tested using objective function in the computation analysis and then, will choose the controller tunings with least integral error values for both servo and regulatory controls. It offers better tuning opportunity without relying on the fixed tuning formulas as performed by manually calculated controller tunings.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Chew Ing Ming
author_facet	Chew Ing Ming
author_sort	Chew Ing Ming
title	Boiler modeling and controller performance optimization using genetic algorithm for palm oil industry
title_short	Boiler modeling and controller performance optimization using genetic algorithm for palm oil industry
title_full	Boiler modeling and controller performance optimization using genetic algorithm for palm oil industry
title_fullStr	Boiler modeling and controller performance optimization using genetic algorithm for palm oil industry
title_full_unstemmed	Boiler modeling and controller performance optimization using genetic algorithm for palm oil industry
title_sort	boiler modeling and controller performance optimization using genetic algorithm for palm oil industry
granting_institution	Universiti Malaysia Sabah
granting_department	Faculty Of Engineering
publishDate	2020
url	https://eprints.ums.edu.my/id/eprint/40726/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/40726/2/FULLTEXT.pdf
_version_	1818611372914638848

Boiler modeling and controller performance optimization using genetic algorithm for palm oil industry

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