Optimisation and control of fed-batch yeast production using q-learning

In this work, the optimal production of yeast with minimal production of ethanol in fed-batch yeast fermentation is investigated. Q-learning (QL) is a heuristic approach suggested for the process dynamic handling to achieve the multiobjective optimisation. The QL agent interacts with the fermentatio...

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Main Author:	Helen, Chuo Sin Ee
Format:	Thesis
Language:	English English
Published:	2013
Subjects:	TP1-1185 Chemical technology
Online Access:	https://eprints.ums.edu.my/id/eprint/41805/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/41805/2/FULLTEXT.pdf
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spelling	my-ums-ep.418052024-12-04T07:14:36Z Optimisation and control of fed-batch yeast production using q-learning 2013 Helen, Chuo Sin Ee TP1-1185 Chemical technology In this work, the optimal production of yeast with minimal production of ethanol in fed-batch yeast fermentation is investigated. Q-learning (QL) is a heuristic approach suggested for the process dynamic handling to achieve the multiobjective optimisation. The QL agent interacts with the fermentation environment will gain experience on the state transitions, which are represented by the change of substrate, yeast, oxygen and ethanol concentration and the system volume. In the present study, multistep action (MSA) has been implemented in consideration of the inborn process delay for the substrate feeding to take effect on the yeast growth. Parameter deviated model has been implemented in the QL to test the robustness of the algorithm besides to identify the process disturbance. From the result, QL was able to perform multiobjective decision making for the optimal substrate feeding profile. The final yeast production using QL-optimised feeding profile is 20.86% higher compare to the nominal exponential feeding (EF), and 19.59% higher compare to EF with process disturbance. To cater for the process disturbance, Q-learning with exploration (QLE) has been included in this work for online optimisation. QLE signifies the importance of exploration from time to time based on the developed “past experience” in Q-table to optimise the process. The performance of QLE in both nominal and disturbance cases yielded 51.00% and 46.87% higher yeast production than EF respectively, while maintaining low ethanol production. In a nutshell, QL is an alternative that can be considered to perform multiobjective optimisation in a frequently changing bioenvironment and suggest a substrate feeding profile that satisfied the process goal. The QLE can cope better with the process disturbance. 2013 Thesis https://eprints.ums.edu.my/id/eprint/41805/ https://eprints.ums.edu.my/id/eprint/41805/1/24%20PAGES.pdf text en public https://eprints.ums.edu.my/id/eprint/41805/2/FULLTEXT.pdf text en validuser masters Universiti Malaysia Sabah School of Engineering and Information Technology
institution	Universiti Malaysia Sabah
collection	UMS Institutional Repository
language	English English
topic	TP1-1185 Chemical technology
spellingShingle	TP1-1185 Chemical technology Helen, Chuo Sin Ee Optimisation and control of fed-batch yeast production using q-learning
description	In this work, the optimal production of yeast with minimal production of ethanol in fed-batch yeast fermentation is investigated. Q-learning (QL) is a heuristic approach suggested for the process dynamic handling to achieve the multiobjective optimisation. The QL agent interacts with the fermentation environment will gain experience on the state transitions, which are represented by the change of substrate, yeast, oxygen and ethanol concentration and the system volume. In the present study, multistep action (MSA) has been implemented in consideration of the inborn process delay for the substrate feeding to take effect on the yeast growth. Parameter deviated model has been implemented in the QL to test the robustness of the algorithm besides to identify the process disturbance. From the result, QL was able to perform multiobjective decision making for the optimal substrate feeding profile. The final yeast production using QL-optimised feeding profile is 20.86% higher compare to the nominal exponential feeding (EF), and 19.59% higher compare to EF with process disturbance. To cater for the process disturbance, Q-learning with exploration (QLE) has been included in this work for online optimisation. QLE signifies the importance of exploration from time to time based on the developed “past experience” in Q-table to optimise the process. The performance of QLE in both nominal and disturbance cases yielded 51.00% and 46.87% higher yeast production than EF respectively, while maintaining low ethanol production. In a nutshell, QL is an alternative that can be considered to perform multiobjective optimisation in a frequently changing bioenvironment and suggest a substrate feeding profile that satisfied the process goal. The QLE can cope better with the process disturbance.
format	Thesis
qualification_level	Master's degree
author	Helen, Chuo Sin Ee
author_facet	Helen, Chuo Sin Ee
author_sort	Helen, Chuo Sin Ee
title	Optimisation and control of fed-batch yeast production using q-learning
title_short	Optimisation and control of fed-batch yeast production using q-learning
title_full	Optimisation and control of fed-batch yeast production using q-learning
title_fullStr	Optimisation and control of fed-batch yeast production using q-learning
title_full_unstemmed	Optimisation and control of fed-batch yeast production using q-learning
title_sort	optimisation and control of fed-batch yeast production using q-learning
granting_institution	Universiti Malaysia Sabah
granting_department	School of Engineering and Information Technology
publishDate	2013
url	https://eprints.ums.edu.my/id/eprint/41805/1/24%20PAGES.pdf https://eprints.ums.edu.my/id/eprint/41805/2/FULLTEXT.pdf
_version_	1818611431110606848

Optimisation and control of fed-batch yeast production using q-learning

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