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

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

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

Carbon Letters
Abbr : Carbon Lett.
2025, 35(2), pp.861~880
DOI : 10.1007/s42823-024-00837-8
Publisher : Korean Carbon Society
Research Area : Natural Science > Natural Science General > Other Natural Sciences General
Received : June 15, 2024
Accepted : December 23, 2024
Published : June 5, 2025

Arwa Al‑Huqail ¹, Khidhair Jasim Mohammed ², Meldi Suhatril ³, Hamad Almujibah ⁴, Sana Toghroli ⁵, Sultan Saleh Alnahdi ⁶, Joffin Jose Ponnore ⁷

¹Princess Nourah bint Abdulrahman University
²Al-Mustaqbal University College
³Universiti Malaya
⁴Taif University
⁵Saveetha Institute of Medical and Technical Sciences
⁶University of Business and Technology
⁷Prince Sattam Bin Abdulaziz University

Accredited

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.

KEYWORDS

Microalgae, Reinforcement learning (RL), Water treatment, Activated carbon, Renewable energy technologies, Environmental management, Machine learning-enhanced microalgae CO2, capture

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Carbon Letters 2025 KCI Impact Factor : 0.73 KCI-WoS Impact Factor : 6.26

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

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Citation status

This is the result of checking the information with the same ISSN, publication year, volume, and start page between the WoS and the KCI journals. (as of 2026-07-09)

Total Citation Counts(KCI+WOS) (5) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

* References for papers published after 2025 are currently being built.

Carbon Letters 2025 KCI Impact Factor : 0.73 KCI-WoS Impact Factor : 6.26

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

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Total Citation Counts(KCI+WOS) (5) This is the number of times that the duplicate count has been removed by comparing the citation list of WoS and KCI.

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