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DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR INDUSTRIAL PROCESSES

Multi objective (MO) optimization is an emerging field which is increasingly being applied in many engineering-based industries globally. Whilst MO optimization problems have been actively researched for the past years, industrial-scale problems involving objectives more than two are rarely studi...

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Main Author: GANESAN, TIMOTHY
Format: Thesis
Language:English
Published: 2013
Subjects:
TP Chemical technology
Online Access:http://utpedia.utp.edu.my/22105/1/2013%20-CHEMICAL%20-%20DEVELOPMENT%20OF%20A%20MULTI-OBJECTIVE%20OPTIMIZATION%20FRAMEWORK%20FOR%20INDUSTRIAL%20PROCESSES%20-%20TIMOTHY%20GANESAN.pdf
http://utpedia.utp.edu.my/22105/
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spelling my-utp-utpedia.221052021-10-18T09:55:24Z http://utpedia.utp.edu.my/22105/ DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR INDUSTRIAL PROCESSES GANESAN, TIMOTHY TP Chemical technology Multi objective (MO) optimization is an emerging field which is increasingly being applied in many engineering-based industries globally. Whilst MO optimization problems have been actively researched for the past years, industrial-scale problems involving objectives more than two are rarely studied although frequently encountered. Thus, this work focuses on industrial MO problems encountered in process engineering with more than two objectives. This work investigates the performance and solution characteristics of metaheuristic algorithms when applied to such problems. In addition, the solution characteristics produced by these algorithms were also analysed and its influence on the algorithmic performance was ascertained. The knowledge obtained from the above explorations was then used to construct a novel framework for solving these problems efficiently. The proposed framework is introduced as the 'surgery framework (SF)' since its prime functionality is to dissect the algorithm and add certain mechanisms into it. The SF introduced here comprises of the measuring the solutions of multiple algorithms as well as modifying them by adding certain mechanisms into chosen algorithms. The SF was then used to solve four real-world industrial MO process optimization problems. The industrial problems are as follows; Application I: MO Optimization of Extraction Process of Bioactive Compounds from Gardenia, Application II: MO Optimization of Bioethanol Production during Cold Enzyme Starch Hydrolysis, Application Ill: MO Optimization of Synthesis Gas for Production Process and Application IV: MO Optimization of Pre-treatment Strategy for Bioethanol Production from Rice Husk. The SF applies four metaheuristic algorithms such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA) and particle swarm optimization (PSO) to solve each of the industrial MO process optimization problems. 2013-05 Thesis NonPeerReviewed application/pdf en http://utpedia.utp.edu.my/22105/1/2013%20-CHEMICAL%20-%20DEVELOPMENT%20OF%20A%20MULTI-OBJECTIVE%20OPTIMIZATION%20FRAMEWORK%20FOR%20INDUSTRIAL%20PROCESSES%20-%20TIMOTHY%20GANESAN.pdf GANESAN, TIMOTHY (2013) DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR INDUSTRIAL PROCESSES. PhD thesis, Universiti Teknologi PETRONAS.
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
language English
topic TP Chemical technology
spellingShingle TP Chemical technology
GANESAN, TIMOTHY
DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR INDUSTRIAL PROCESSES
description Multi objective (MO) optimization is an emerging field which is increasingly being applied in many engineering-based industries globally. Whilst MO optimization problems have been actively researched for the past years, industrial-scale problems involving objectives more than two are rarely studied although frequently encountered. Thus, this work focuses on industrial MO problems encountered in process engineering with more than two objectives. This work investigates the performance and solution characteristics of metaheuristic algorithms when applied to such problems. In addition, the solution characteristics produced by these algorithms were also analysed and its influence on the algorithmic performance was ascertained. The knowledge obtained from the above explorations was then used to construct a novel framework for solving these problems efficiently. The proposed framework is introduced as the 'surgery framework (SF)' since its prime functionality is to dissect the algorithm and add certain mechanisms into it. The SF introduced here comprises of the measuring the solutions of multiple algorithms as well as modifying them by adding certain mechanisms into chosen algorithms. The SF was then used to solve four real-world industrial MO process optimization problems. The industrial problems are as follows; Application I: MO Optimization of Extraction Process of Bioactive Compounds from Gardenia, Application II: MO Optimization of Bioethanol Production during Cold Enzyme Starch Hydrolysis, Application Ill: MO Optimization of Synthesis Gas for Production Process and Application IV: MO Optimization of Pre-treatment Strategy for Bioethanol Production from Rice Husk. The SF applies four metaheuristic algorithms such as differential evolution (DE), genetic algorithm (GA), gravitational search algorithm (GSA) and particle swarm optimization (PSO) to solve each of the industrial MO process optimization problems.
format Thesis
author GANESAN, TIMOTHY
author_facet GANESAN, TIMOTHY
author_sort GANESAN, TIMOTHY
title DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR INDUSTRIAL PROCESSES
title_short DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR INDUSTRIAL PROCESSES
title_full DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR INDUSTRIAL PROCESSES
title_fullStr DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR INDUSTRIAL PROCESSES
title_full_unstemmed DEVELOPMENT OF A MULTI-OBJECTIVE OPTIMIZATION FRAMEWORK FOR INDUSTRIAL PROCESSES
title_sort development of a multi-objective optimization framework for industrial processes
publishDate 2013
url http://utpedia.utp.edu.my/22105/1/2013%20-CHEMICAL%20-%20DEVELOPMENT%20OF%20A%20MULTI-OBJECTIVE%20OPTIMIZATION%20FRAMEWORK%20FOR%20INDUSTRIAL%20PROCESSES%20-%20TIMOTHY%20GANESAN.pdf
http://utpedia.utp.edu.my/22105/
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score 13.211869

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