Optimizing microalgal biomass conversion into carbon materials and their application in water treatment: a machine learning approach

IF 5.5 3区 材料科学 Q2 CHEMISTRY, MULTIDISCIPLINARY
Arwa Al-Huqail, Khidhair Jasim Mohammed, Meldi Suhatril, Hamad Almujibah, Sana Toghroli, Sultan Saleh Alnahdi, Joffin Jose Ponnore
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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.

Graphical abstract

Abstract Image

优化微藻生物质转化为碳材料及其在水处理中的应用:机器学习方法
微藻,如小球藻(Chlorella vulgaris)和斜小球藻(Scenedesmus obliquus),在通过光合作用捕获二氧化碳并将其转化为有价值的生物质方面效率很高。这种生物质可以进一步加工成碳材料,应用于包括水处理在内的各个领域。采用强化学习(RL)方法动态优化微藻生长环境条件,提高生物柴油生产效率。本研究的贡献包括展示了RL在优化生物系统方面的有效性,突出了微藻衍生材料在各种工业应用中的潜力,并展示了可再生能源技术的整合以提高可持续性。研究表明,在控制条件下培养的小球藻(Chlorella vulgaris)和斜小球藻(Scenedesmus obliquus)的吸收率分别提高了50%和80%,显示了它们在住宅供暖系统中的潜力。培养后,提取的油脂被有效地用于生物柴油的生产。RL模型获得了较高的预测精度,温度和氧气水平的R2值分别为0.98和0.95,证实了其在系统调节中的有效性。微藻生物质活性炭的开发也突出了其在去除水中重金属和染料方面的应用,证明了其有效性和稳定性,从而提高了环境管理的可持续性。这项研究强调了先进的机器学习与生物过程的成功结合,以优化微藻培养并开发用于生态应用的实用副产品。图形抽象
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来源期刊
Carbon Letters
Carbon Letters CHEMISTRY, MULTIDISCIPLINARY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
7.30
自引率
20.00%
发文量
118
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.
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