The Synergy of Artificial Intelligence and Algal Systems for Sustainable Wastewater Treatment and Carbon Sequestration
Abstract
In environmental biotechnology, the integration of artificial intelligence [AI] into algal systems is transforming strategies for wastewater treatment and carbon sequestration. This comprehensive review examines AI's transformative role of AI in algal biotechnology and addresses traditional bottlenecks such as high harvesting costs, environmental sensitivity and scalability in algal cultivation. Using predictive analytics and real-time system optimization, AI increases the efficiency and robustness of algae processes and makes them more adaptable to fluctuating environmental conditions. This paper focuses on an in-depth analysis of AI-driven control mechanisms in photobioreactors, where machine learning algorithms continuously monitor and fine-tune critical parameters such as light incidence, nutrient levels and temperature. These optimizations promote ideal growth conditions, which are essential for scaling up algae systems to meet industrial requirements. The report also highlights the integration of intelligent control systems and advanced engineering design, showing how AI can streamline operations, improve decision making and boost overall system performance in large-scale applications. Beyond immediate applications, this interdisciplinary study emphasizes the need for continued innovation in AI-algal systems to achieve sustainable environmental outcomes. By envisioning future advances, this work lays the foundation for further research into AI-enabled algal solutions and positions these hybrid systems as an important tool for addressing global environmental challenges such as waste management and carbon reduction.
Recommended Citation
P. O. Obidi and D. J. Bayless, "The Synergy of Artificial Intelligence and Algal Systems for Sustainable Wastewater Treatment and Carbon Sequestration," Environmental Technology Reviews, vol. 14, no. 1, pp. 1040 - 1067, Taylor and Francis Group; Taylor and Francis, Jan 2025.
The definitive version is available at https://doi.org/10.1080/21622515.2025.2581330
Department(s)
Mechanical and Aerospace Engineering
Keywords and Phrases
algal systems; artificial intelligence; carbon capture; sustainable biotechnology; wastewater remediation
International Standard Serial Number (ISSN)
2162-2523; 2162-2515
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Taylor and Francis Group; Taylor and Francis, All rights reserved.
Publication Date
01 Jan 2025
