Abstract
The increasing population and its associated wastewater production stresses the freshwater resources, necessitating a synergistic approach for remediation and simultaneous resource recovery. Conventional wastewater treatment systems, including activated sludge processes, trickling filters, oxidation ponds, and physicochemical treatments, are widely employed; however, they are energy-intensive, generate large volumes of sludge, require extensive chemical inputs, and offer limited nutrient recovery. Microalgae-based wastewater treatment offers the potential to convert nutrients into valuable products such as proteins, carbohydrates, and lipids. However, the complexity and non-linear nature of microalgal cultivation parameters pose significant challenges to cost-effective wastewater treatment operations. Artificial intelligence (AI) and machine learning (ML) tools offer pattern recognition capabilities for managing non-linear data. Despite various studies addressing advanced microalgae-related problems, the real-time application of ML in wastewater industries remains largely untapped. This study critically examines a range of Commonly used ML models (including artificial neural networks (ANN), support vector machines (SVM), random forest (RF), k-nearest neighbors (k-NN), decision trees (DT), gradient boosting models, and deep learning architectures), discusses solutions to key problems, and highlights the advantages and challenges of real-time microalgal technologies.
•This review evaluates AI/ML frameworks for microalgal wastewater treatment systems.•It integrates predictive modeling, optimization, and hybrid mechanistic-data-driven methods.•Pilot-scale algal systems operate at TRL 5–7, enabling scalable AI-driven wastewater treatment.•Microalgae achieve 90–99% nutrient removal, with AI models reaching R2 up to 0.99.
[Display omitted]