Arwa Al-Huqail, Khidhair Jasim Mohammed, Meldi Suhatril, Hamad Almujibah, Sana Toghroli, Sultan Saleh Alnahdi, Joffin Jose Ponnore
{"title":"Optimizing microalgal biomass conversion into carbon materials and their application in water treatment: a machine learning approach","authors":"Arwa Al-Huqail, Khidhair Jasim Mohammed, Meldi Suhatril, Hamad Almujibah, Sana Toghroli, Sultan Saleh Alnahdi, Joffin Jose Ponnore","doi":"10.1007/s42823-024-00837-8","DOIUrl":null,"url":null,"abstract":"<div><p>Microalgae, such as <i>Chlorella vulgaris</i> and <i>Scenedesmus obliquus</i>, are highly efficient at capturing carbon dioxide through photosynthesis, converting it into valuable biomass. This biomass can be further processed into carbon materials with applications in various fields, including water treatment. The reinforcement learning (RL) method was used to dynamically optimize environmental conditions for microalgae growth, improving the efficiency of biodiesel production. The contributions of this study include demonstrating the effectiveness of RL in optimizing biological systems, highlighting the potential of microalgae-derived materials in various industrial applications, and showcasing the integration of renewable energy technologies to enhance sustainability. The study demonstrated that <i>Chlorella vulgaris</i> and <i>Scenedesmus obliquus</i>, cultivated under controlled conditions, significantly improved absorption rates by 50% and 80%, respectively, showcasing their potential in residential heating systems. Post-cultivation, the extracted lipids were effectively utilized for biodiesel production. The RL models achieved high predictive accuracy, with <i>R</i><sup>2</sup> values of 0.98 for temperature and 0.95 for oxygen levels, confirming their effectiveness in system regulation. The development of activated carbon from microalgae biomass also highlighted its utility in removing heavy metals and dyes from water, proving its efficacy and stability, thus enhancing the sustainability of environmental management. This study underscores the successful integration of advanced machine learning with biological processes to optimize microalgae cultivation and develop practical byproducts for ecological applications.</p><h3>Graphical abstract</h3>\n<div><figure><div><div><picture><source><img></source></picture></div></div></figure></div></div>","PeriodicalId":506,"journal":{"name":"Carbon Letters","volume":"35 2","pages":"861 - 880"},"PeriodicalIF":5.5000,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Carbon Letters","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s42823-024-00837-8","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Microalgae, such as Chlorella vulgaris and Scenedesmus obliquus, are highly efficient at capturing carbon dioxide through photosynthesis, converting it into valuable biomass. This biomass can be further processed into carbon materials with applications in various fields, including water treatment. The reinforcement learning (RL) method was used to dynamically optimize environmental conditions for microalgae growth, improving the efficiency of biodiesel production. The contributions of this study include demonstrating the effectiveness of RL in optimizing biological systems, highlighting the potential of microalgae-derived materials in various industrial applications, and showcasing the integration of renewable energy technologies to enhance sustainability. The study demonstrated that Chlorella vulgaris and Scenedesmus obliquus, cultivated under controlled conditions, significantly improved absorption rates by 50% and 80%, respectively, showcasing their potential in residential heating systems. Post-cultivation, the extracted lipids were effectively utilized for biodiesel production. The RL models achieved high predictive accuracy, with R2 values of 0.98 for temperature and 0.95 for oxygen levels, confirming their effectiveness in system regulation. The development of activated carbon from microalgae biomass also highlighted its utility in removing heavy metals and dyes from water, proving its efficacy and stability, thus enhancing the sustainability of environmental management. This study underscores the successful integration of advanced machine learning with biological processes to optimize microalgae cultivation and develop practical byproducts for ecological applications.
期刊介绍:
Carbon Letters aims to be a comprehensive journal with complete coverage of carbon materials and carbon-rich molecules. These materials range from, but are not limited to, diamond and graphite through chars, semicokes, mesophase substances, carbon fibers, carbon nanotubes, graphenes, carbon blacks, activated carbons, pyrolytic carbons, glass-like carbons, etc. Papers on the secondary production of new carbon and composite materials from the above mentioned various carbons are within the scope of the journal. Papers on organic substances, including coals, will be considered only if the research has close relation to the resulting carbon materials. Carbon Letters also seeks to keep abreast of new developments in their specialist fields and to unite in finding alternative energy solutions to current issues such as the greenhouse effect and the depletion of the ozone layer. The renewable energy basics, energy storage and conversion, solar energy, wind energy, water energy, nuclear energy, biomass energy, hydrogen production technology, and other clean energy technologies are also within the scope of the journal. Carbon Letters invites original reports of fundamental research in all branches of the theory and practice of carbon science and technology.