A Mini Review on Fundamentals and Practical Applications of Machine Learning in Algae-Based Wastewater Treatment

With the growing demand for sustainable wastewater treatment, algae-based technologies have gained increasing attention as a promising solution, owing to their capacity to efficiently remove pollutants and recover valuable nutrients from various wastewater sources. Microalgae offer a cost-effective and environmentally friendly approach by combining biological treatment with resource recovery. Despite their potential, large-scale deployment is often constrained by environmental variability and the physiological complexity of microalgal systems. Machine learning (ML), a key branch of artificial intelligence (AI), has emerged as a powerful tool for predicting pollutant levels and water quality parameters, due to its ability to model complex, non-linear relationships between input variables and system responses. Recent advancements in ML present new opportunities to enhance process control, operational stability, and treatment efficiency. This review explores the application of ML techniques, including neural networks, support vector machines, decision trees, and genetic algorithms in the modelling, prediction, and optimisation of microalgae-based wastewater treatment processes. It further discusses the potential of intelligent algorithms to manage large, complex datasets and address operational uncertainties, while also identifying current limitations and future directions for integrating AI in algae-based treatment systems.