Transforming Wastewater Treatment with AI for Sustainability and Circular Economy Goals

The paper shows how artificial intelligence can transform wastewater treatment by making plants smarter, more energy-efficient, and better able to handle emerging pollutants such as PFAS, microplastics, heavy metals, and antibiotics. By enabling real-time monitoring, predictive control, and resource recovery, AI supports a shift toward sustainable, circular, and climate-resilient water systems.

A study by researchers from Graphic Era (Deemed to be University), Pondicherry University, Shoolini University, the ICAR–Central Institute of Agricultural Engineering in India, Peoples’ Friendship University of Russia (RUDN University), the Joint Institute for High Temperatures of the Russian Academy of Sciences, and Swami Rama Himalayan University brings together wide-ranging insights into the future of wastewater treatment. The work addresses a growing global concern that conventional treatment systems are no longer sufficient in the face of rapid urbanization, industrial expansion, climate stress, and increasingly complex pollution. It presents wastewater treatment as a fundamental pillar of public health, ecosystem protection, and long-term water security, closely aligned with global sustainability goals, while underscoring that nearly one-third of the world’s wastewater is still discharged untreated and that many existing plants struggle to manage modern contaminants and rising operational pressures.

Why conventional systems are falling short

Traditional wastewater treatment plants were designed primarily to remove organic matter, nutrients, and suspended solids. While effective for earlier pollution profiles, these systems were never built to manage today’s “emerging contaminants,” such as PFAS, antibiotics, microplastics, and heavy metals. These pollutants are persistent, resist degradation, and can accumulate in ecosystems and human bodies. At the same time, treatment plants face internal inefficiencies: energy-intensive aeration can consume up to 70 percent of total electricity use, fixed control systems struggle with fluctuating inflows, and extreme weather events increasingly overwhelm infrastructure. Together, these factors make conventional treatment costly, rigid, and environmentally vulnerable.

How artificial intelligence changes the picture

The paper presents artificial intelligence as a powerful tool to overcome these limitations. Based on a review of around 150 recent studies, the authors show that AI systems, especially machine learning and deep learning models, can learn directly from large volumes of real-time and historical data. Unlike traditional models that rely on fixed assumptions, AI captures complex, non-linear behavior in wastewater processes. In practice, models such as artificial neural networks, long short-term memory networks, random forests, and gradient boosting machines achieve very high accuracy in predicting influent loads, effluent quality, and energy use. This predictive strength allows treatment plants to move from reactive operation to proactive and adaptive control.

Smarter operations and better pollution control

AI is already demonstrating clear operational benefits. So-called “soft sensors” use AI to estimate water quality indicators like BOD, COD, nitrogen, and phosphorus in near real time, reducing reliance on slow and costly laboratory tests. AI-driven aeration control can cut energy consumption by 20–30 percent while maintaining regulatory compliance. Predictive models help detect membrane fouling, equipment faults, and abnormal operating conditions before they cause major failures. Importantly, AI also strengthens the treatment of emerging contaminants. Machine learning models help predict PFAS occurrence and behavior, guide heavy metal adsorption design, automate microplastic detection using imaging and spectroscopy, and enable faster identification of antibiotic resistance genes. These advances improve both environmental protection and public health surveillance.

Supporting the circular economy and future water systems

Beyond treatment efficiency, the review highlights AI’s role in advancing the circular economy. Wastewater plants are increasingly seen as resource recovery centers rather than waste disposal facilities. AI helps optimize the recovery of water, energy, and nutrients such as nitrogen and phosphorus, reducing waste and creating added value. A key innovation is the use of digital twins, virtual replicas of treatment plants that combine AI models, physical process simulations, and real-time sensor data. Digital twins allow operators to test scenarios, train staff, and improve decision-making without disrupting actual operations. Case studies show measurable reductions in energy use and better system resilience.

The authors conclude that while AI’s potential is clear, challenges remain, including data quality issues, high implementation costs, cybersecurity risks, and limited regulatory acceptance of opaque models. They argue that future progress depends on standardized data systems, explainable and physics-informed AI, large-scale validation, and skills development. If these hurdles are addressed, AI-driven wastewater treatment could play a decisive role in delivering cleaner water, lower emissions, and more resilient circular water systems for the future.