Optimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations

Due to low oxygen solubility and mechanical stirring limitations of a bioreactor, ensuring an adequate oxygen supply during a recombinant <i>Escherichia coli</i> cultivation is a major challenge in process control. Under the light of this fact, a fuzzy dissolved oxygen controller was dev...

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Autores principales:	Rafael Akira Akisue, Matheus Lopes Harth, Antonio Carlos Luperni Horta, Ruy de Sousa Junior
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	adaptive network-based fuzzy inference system dissolved oxygen fuzzy control recombinant <i>Escherichia coli</i> SUPERSYS_HCDC Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/25fa5b6e3aa64eaabdecb72fcd6487bb
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spelling	oai:doaj.org-article:25fa5b6e3aa64eaabdecb72fcd6487bb2021-11-25T16:13:14ZOptimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations10.3390/a141103261999-4893https://doaj.org/article/25fa5b6e3aa64eaabdecb72fcd6487bb2021-11-01T00:00:00Zhttps://www.mdpi.com/1999-4893/14/11/326https://doaj.org/toc/1999-4893Due to low oxygen solubility and mechanical stirring limitations of a bioreactor, ensuring an adequate oxygen supply during a recombinant <i>Escherichia coli</i> cultivation is a major challenge in process control. Under the light of this fact, a fuzzy dissolved oxygen controller was developed, taking into account a decision tree algorithm presented in the literature, and implemented in the supervision software SUPERSYS_HCDC. The algorithm was coded in MATLAB with its membership function parameters determined using an Adaptive Network-Based Fuzzy Inference System tool. The controller was composed of three independent fuzzy inference systems: Princ1 and Princ2 assessed whether there would be an increment or a reduction in air and oxygen flow rates (respectively), whilst Delta estimated the size of these variations. To test the controller, simulations with a neural network model and <i>E. coli</i> cultivations were conducted. The fuzzification of the decision tree was successful, resulting in smoothing of air and oxygen flow rates and, hence, in an attenuation of dissolved oxygen oscillations. Statistically, the average standard deviation of the fuzzy controller was 2.45 times lower than the decision tree (9.48%). Results point toward an increase in the flow meter lifespan and a possible reduction of the metabolic stress suffered by <i>E. coli</i> during the cultivation.Rafael Akira AkisueMatheus Lopes HarthAntonio Carlos Luperni HortaRuy de Sousa JuniorMDPI AGarticleadaptive network-based fuzzy inference systemdissolved oxygenfuzzy controlrecombinant <i>Escherichia coli</i>SUPERSYS_HCDCIndustrial engineering. Management engineeringT55.4-60.8Electronic computers. Computer scienceQA75.5-76.95ENAlgorithms, Vol 14, Iss 326, p 326 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	adaptive network-based fuzzy inference system dissolved oxygen fuzzy control recombinant <i>Escherichia coli</i> SUPERSYS_HCDC Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95
spellingShingle	adaptive network-based fuzzy inference system dissolved oxygen fuzzy control recombinant <i>Escherichia coli</i> SUPERSYS_HCDC Industrial engineering. Management engineering T55.4-60.8 Electronic computers. Computer science QA75.5-76.95 Rafael Akira Akisue Matheus Lopes Harth Antonio Carlos Luperni Horta Ruy de Sousa Junior Optimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations
description	Due to low oxygen solubility and mechanical stirring limitations of a bioreactor, ensuring an adequate oxygen supply during a recombinant <i>Escherichia coli</i> cultivation is a major challenge in process control. Under the light of this fact, a fuzzy dissolved oxygen controller was developed, taking into account a decision tree algorithm presented in the literature, and implemented in the supervision software SUPERSYS_HCDC. The algorithm was coded in MATLAB with its membership function parameters determined using an Adaptive Network-Based Fuzzy Inference System tool. The controller was composed of three independent fuzzy inference systems: Princ1 and Princ2 assessed whether there would be an increment or a reduction in air and oxygen flow rates (respectively), whilst Delta estimated the size of these variations. To test the controller, simulations with a neural network model and <i>E. coli</i> cultivations were conducted. The fuzzification of the decision tree was successful, resulting in smoothing of air and oxygen flow rates and, hence, in an attenuation of dissolved oxygen oscillations. Statistically, the average standard deviation of the fuzzy controller was 2.45 times lower than the decision tree (9.48%). Results point toward an increase in the flow meter lifespan and a possible reduction of the metabolic stress suffered by <i>E. coli</i> during the cultivation.
format	article
author	Rafael Akira Akisue Matheus Lopes Harth Antonio Carlos Luperni Horta Ruy de Sousa Junior
author_facet	Rafael Akira Akisue Matheus Lopes Harth Antonio Carlos Luperni Horta Ruy de Sousa Junior
author_sort	Rafael Akira Akisue
title	Optimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations
title_short	Optimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations
title_full	Optimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations
title_fullStr	Optimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations
title_full_unstemmed	Optimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations
title_sort	optimized dissolved oxygen fuzzy control for recombinant <i>escherichia coli</i> cultivations
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/25fa5b6e3aa64eaabdecb72fcd6487bb
work_keys_str_mv	AT rafaelakiraakisue optimizeddissolvedoxygenfuzzycontrolforrecombinantiescherichiacoliicultivations AT matheuslopesharth optimizeddissolvedoxygenfuzzycontrolforrecombinantiescherichiacoliicultivations AT antoniocarloslupernihorta optimizeddissolvedoxygenfuzzycontrolforrecombinantiescherichiacoliicultivations AT ruydesousajunior optimizeddissolvedoxygenfuzzycontrolforrecombinantiescherichiacoliicultivations
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Optimized Dissolved Oxygen Fuzzy Control for Recombinant <i>Escherichia coli</i> Cultivations

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