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Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions

A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the dyna...

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Detalles Bibliográficos
Autores principales: Minzu, Viorel, Ifrim, George, Arama, Iulian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659673/
https://www.ncbi.nlm.nih.gov/pubmed/34884070
http://dx.doi.org/10.3390/s21238065
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author Minzu, Viorel
Ifrim, George
Arama, Iulian
author_facet Minzu, Viorel
Ifrim, George
Arama, Iulian
author_sort Minzu, Viorel
collection PubMed
description A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the dynamic model are very well known and have been validated in previous papers. The control solution is a closed-loop structure whose controller generates predicted control sequences. An efficient way to make optimal predictions is to use a metaheuristic algorithm, the particle swarm optimization algorithm. Even if this metaheuristic is efficient in treating predictions with a very large prediction horizon, the main objective of this paper is to find a tool to reduce the controller’s computational complexity. We propose a soft sensor that gives information used to reduce the interval where the control input’s values are placed in each sampling period. The sensor is based on measurement of the biomass concentration and numerical integration of the process model. The returned information concerns the specific growth rate of microalgae and the biomass yield on light energy. Algorithms, which can be used in real-time implementation, are proposed for all modules involved in the simulation series. Details concerning the implementation of the closed loop, controller, and soft sensor are presented. The simulation results prove that the soft sensor leads to a significant decrease in computational complexity.
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spelling pubmed-86596732021-12-10 Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions Minzu, Viorel Ifrim, George Arama, Iulian Sensors (Basel) Article A metaheuristic algorithm can be a realistic solution when optimal control problems require a significant computational effort. The problem stated in this work concerns the optimal control of microalgae growth in an artificially lighted photobioreactor working in batch mode. The process and the dynamic model are very well known and have been validated in previous papers. The control solution is a closed-loop structure whose controller generates predicted control sequences. An efficient way to make optimal predictions is to use a metaheuristic algorithm, the particle swarm optimization algorithm. Even if this metaheuristic is efficient in treating predictions with a very large prediction horizon, the main objective of this paper is to find a tool to reduce the controller’s computational complexity. We propose a soft sensor that gives information used to reduce the interval where the control input’s values are placed in each sampling period. The sensor is based on measurement of the biomass concentration and numerical integration of the process model. The returned information concerns the specific growth rate of microalgae and the biomass yield on light energy. Algorithms, which can be used in real-time implementation, are proposed for all modules involved in the simulation series. Details concerning the implementation of the closed loop, controller, and soft sensor are presented. The simulation results prove that the soft sensor leads to a significant decrease in computational complexity. MDPI 2021-12-02 /pmc/articles/PMC8659673/ /pubmed/34884070 http://dx.doi.org/10.3390/s21238065 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Minzu, Viorel
Ifrim, George
Arama, Iulian
Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions
title Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions
title_full Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions
title_fullStr Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions
title_full_unstemmed Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions
title_short Control of Microalgae Growth in Artificially Lighted Photobioreactors Using Metaheuristic-Based Predictions
title_sort control of microalgae growth in artificially lighted photobioreactors using metaheuristic-based predictions
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8659673/
https://www.ncbi.nlm.nih.gov/pubmed/34884070
http://dx.doi.org/10.3390/s21238065
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