Multi-objective genetic algorithm optimization of energy efficiency and biomass concentration of Synechococcus HS-9 cultivation for third-generation biodiesel feedstock
Microalgae is a sustainable biological resource since it can be used in biodiesel production, pharmaceutical development, nutritional supplements, carbon dioxide sequestration, and wastewater remediation. Due to its high lipid content and low carbon dioxide emissions, microalgae-derived biodiesel ca...
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| Veröffentlicht in: | Case studies in chemical and environmental engineering Jg. 9; S. 100614 |
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01.06.2024
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| Abstract | Microalgae is a sustainable biological resource since it can be used in biodiesel production, pharmaceutical development, nutritional supplements, carbon dioxide sequestration, and wastewater remediation. Due to its high lipid content and low carbon dioxide emissions, microalgae-derived biodiesel can replace fossil fuels. Microalgae biomass production involves cultivation, harvesting, and drying. To develop microalgae and produce high-quality biomass, culture procedures must be optimized. Microalgae cultivation is affected by a range of physical and chemical parameters, including light intensity (I), temperature (T), carbon dioxide (CO2), pH, dissolved oxygen (DO), and oxidation reduction potential (ORP). The objective of this study is to examine the impact of physico-chemical factors on the cultivation process of Synechococcus HS-9 in order to generate dry biomass. ANN and multi-objective genetic algorithms will also be used to optimize cultivation. The five main procedures of this study are: 1) preparation of the cultivation medium; 2) Synechococcus HS-9 inoculum and utilization of a photobioreactor; 3) inoculation; 4) cultivation and physical-chemical parameter data collection; and 5) prediction and optimization. The physico-chemical properties of Synechococcus HS-9 were measured during cultivation. The average values of these parameters were found to be 439 to 325 7 μmol m−2s−1 for light conditions and 190–200 μmol m−2s−1 for dark conditions. During cultivation, T ranged from 29 to 32 °C and CO2 from 82 to 84 ppm. Most pH levels are between 8 and 7.5. The dissolved oxygen (DO) concentration drops from 15 to 3 mg/l. The ORP also fluctuates, dropping from 170 mV to 155 mV and then rising to 180 mV. Based on multi-objective optimization, the optimal values for energy efficiency (ղ) and biomass concentration (C) are 0.043 % and 4.61 × 10−5 mg/ml, respectively. The optimal condition is achieved when the light intensity (I): 254.7 μmol m−2s−1, temperature (T): 29.7 °C, CO2 concentration: 83.4 ppm, pH: 8.6, dissolved oxygen (DO): 6.0 mg/l, and ORP: 149.1 mV. |
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| AbstractList | Microalgae is a sustainable biological resource since it can be used in biodiesel production, pharmaceutical development, nutritional supplements, carbon dioxide sequestration, and wastewater remediation. Due to its high lipid content and low carbon dioxide emissions, microalgae-derived biodiesel can replace fossil fuels. Microalgae biomass production involves cultivation, harvesting, and drying. To develop microalgae and produce high-quality biomass, culture procedures must be optimized. Microalgae cultivation is affected by a range of physical and chemical parameters, including light intensity (I), temperature (T), carbon dioxide (CO2), pH, dissolved oxygen (DO), and oxidation reduction potential (ORP). The objective of this study is to examine the impact of physico-chemical factors on the cultivation process of Synechococcus HS-9 in order to generate dry biomass. ANN and multi-objective genetic algorithms will also be used to optimize cultivation. The five main procedures of this study are: 1) preparation of the cultivation medium; 2) Synechococcus HS-9 inoculum and utilization of a photobioreactor; 3) inoculation; 4) cultivation and physical-chemical parameter data collection; and 5) prediction and optimization. The physico-chemical properties of Synechococcus HS-9 were measured during cultivation. The average values of these parameters were found to be 439 to 325 7 μmol m−2s−1 for light conditions and 190–200 μmol m−2s−1 for dark conditions. During cultivation, T ranged from 29 to 32 °C and CO2 from 82 to 84 ppm. Most pH levels are between 8 and 7.5. The dissolved oxygen (DO) concentration drops from 15 to 3 mg/l. The ORP also fluctuates, dropping from 170 mV to 155 mV and then rising to 180 mV. Based on multi-objective optimization, the optimal values for energy efficiency (ղ) and biomass concentration (C) are 0.043 % and 4.61 × 10−5 mg/ml, respectively. The optimal condition is achieved when the light intensity (I): 254.7 μmol m−2s−1, temperature (T): 29.7 °C, CO2 concentration: 83.4 ppm, pH: 8.6, dissolved oxygen (DO): 6.0 mg/l, and ORP: 149.1 mV. |
| ArticleNumber | 100614 |
| Author | Oktaufik, M.A.M. Irwansyah, Ridho Nastiti, Kania Dyah Prihantini, Nining Betawati Indrijarso, Surat Levison, Alfred Kampira Nasruddin, N. Rahman, Arif |
| Author_xml | – sequence: 1 givenname: Arif surname: Rahman fullname: Rahman, Arif organization: Research Center for Sustainable Production System and Life Cycle Assessment, National Research and Innovation Agency – BRIN, Indonesia – sequence: 2 givenname: Kania Dyah surname: Nastiti fullname: Nastiti, Kania Dyah organization: Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, Indonesia – sequence: 3 givenname: Nining Betawati surname: Prihantini fullname: Prihantini, Nining Betawati organization: Department of Biology, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, 16424, Indonesia – sequence: 4 givenname: M.A.M. surname: Oktaufik fullname: Oktaufik, M.A.M. organization: Research Center for Sustainable Production System and Life Cycle Assessment, National Research and Innovation Agency – BRIN, Indonesia – sequence: 5 givenname: Surat surname: Indrijarso fullname: Indrijarso, Surat organization: Principal Expert of The Steering Board, National Research and Innovation Agency - BRIN, Indonesia – sequence: 6 givenname: Alfred Kampira surname: Levison fullname: Levison, Alfred Kampira organization: Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, Indonesia – sequence: 7 givenname: Ridho surname: Irwansyah fullname: Irwansyah, Ridho email: ridho.irwansyah04@ui.ac.id organization: Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, Indonesia – sequence: 8 givenname: N. orcidid: 0000-0002-5289-0039 surname: Nasruddin fullname: Nasruddin, N. email: nasruddin@eng.ui.ac.id organization: Department of Mechanical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, Indonesia |
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| CitedBy_id | crossref_primary_10_1007_s10904_024_03374_z crossref_primary_10_1016_j_algal_2025_103911 crossref_primary_10_1186_s12896_025_00955_9 crossref_primary_10_1016_j_tsep_2025_103716 crossref_primary_10_1007_s42823_024_00837_8 crossref_primary_10_1038_s41598_025_13711_y crossref_primary_10_1016_j_cherd_2024_09_004 |
| Cites_doi | 10.1016/S0168-1656(01)00353-4 10.1080/15435075.2017.1402772 10.1002/bit.27128 10.1016/j.cscee.2023.100348 10.1016/j.biortech.2012.12.082 10.1016/j.biosystemseng.2018.10.002 10.1007/s00521-018-03989-7 10.1016/j.ibiod.2013.01.016 10.1007/s10750-006-0429-0 10.1016/j.jbiotec.2020.02.010 10.1016/j.rser.2009.06.021 10.1111/pce.14074 10.1021/acs.iecr.7b03008 10.1016/j.csite.2021.101264 10.1016/j.cscee.2020.100046 10.1016/j.bej.2021.108072 10.1016/j.algal.2021.102364 10.1007/s42452-020-2097-5 |
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| Keywords | Multi – Objective optimization Synechococcus HS-9 Microalgae Artificial neural network Biomass production Genetic algorithm |
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