AI-based wastewater treatment for a circular economy and sustainable management of PFAS, heavy metals, microplastics, and antibiotics

Wastewater treatment plays a vital role for safeguarding public health, protecting ecosystems, and ensuring long-term water security. However, rapid urbanization, industrial growth, and rising water demand are exposing the limitations of conventional treatment systems, which often require high operational costs and struggle to maintain efficiency. The integration of artificial intelligence (AI), supported by practical case studies, offers a transformative pathway for addressing these challenges. Advanced AI algorithms, including machine learning (ML), deep learning (DL) models such as artificial neural networks (ANNs), recurrent neural networks (RNNs), fuzzy neural networks (FNNs), and hybrid frameworks demonstrate high predictive accuracy (R² >0.99) in anomaly detection, process modelling, optimization, and automated control, enabling efficient management of the complex and non-linear behaviour of wastewater systems. These capabilities are especially valuable for tackling emerging contaminants such as per- and polyfluoroalkyl substances (PFAS), microplastics, heavy metals, and antibiotics, contributing to reduced operational costs, enhanced treatment performance. This manuscript aims to critically evaluate the transformative potential of artificial intelligence (AI) in addressing the limitations of conventional wastewater treatment systems. • Machine learning and deep learning optimize treatment with high predictive accuracy. • AI addresses PFAS, microplastics, heavy metals, and antibiotics in wastewater. • Integration of AI reduces costs, improves efficiency, and boosts sustainability. • AI advances circular economy by enhancing water, nutrient, and energy recovery.