Abstract

Artificial intelligence (AI) is critical in the development of more effective and efficient wastewater (WW) processes, in increased pollutant removal, and better ways of monitoring water quality. This paper aims to explain the submission of a decision support system as an effective tool toward nutrient cycling and heavy metal removal in the wastewater treatment plant (WWTP). The promotion of the key elements of AI, like machine learning (ML), predictive analysis, and real-time monitoring, facilitates exact regulation of operational parameters and forecast of treatment efficiencies, as well as compliance with environmental norms. For example, artificial neural networks (ANNs) have 0.989–0.997 https://www.w3.org/1998/Math/MathML" display="inline"> R 2 rates in nitrogen removal and over 90% efficiency in the incorporation of heavy metal remedial measures in conjunction with hybrid AI models. These technologies make the management adaptable; they cut energy use by 31.4%, such as aeration and operational costs, embracing the circular economy. Nitrogen and phosphorus removal through nutrient cycling, necessary for mitigating eutrophication, entails the use of AIs to periodically change aeration and chemical dosing to obtain the 80%–93% efficiency rates. Likewise, AI deals with the continuous presence of heavy metals like extract from industrial emissions like lead, cadmium, etc.; the entire yards are utilized with sensors and ML control algorithms; it reduces from 85% to 95% with the help of ANNs and support vector machines (SVMs). These help in increasing the buffer capacity of WWTPs against variations in the raw influent, which may be seasonal changes or increases in industrial effluents, to maintain constant effluent quality. The contribution of integrating AI still has many barriers. It was established that data quality is still a major issue because noisy or marginal data negatively affect model accuracy and inevitably result in overfitting. Several challenges related to AI deployment are as follows: variability of influent composition, weather, or equipment wear at operational conditions also harms the system performance and thus requires advanced resistance and adaptive algorithms. Moreover, these subsystems (e.g., ANNs, SVMs, genetic algorithms (GAs)) must be integrated and included into a single decision support system (DSS) in WWTPs, and this requires data standardization and computational platforms that are not always available in such facilities. Real-time analytics and hybrid modeling have answers, but these processes are complex and sophisticated, demanding a great deal of change and the use of personnel with certain levels of experience. This chapter looks at specific subsystems of AI, which include the ANN for predicting nutrients, the SVM for estimating heavy metals, and the hybrid model for a comprehensive process optimization plan. Effectiveness, such as Detroit’s reported 31.4% energy savings achieved through data mining and Spain’s pollutant removal using hybrid models, are some of the practical cases. But to take full advantage of it, there are technical and institutional challenges that need to be overcome. They are steps forward in the standardization of data acquisition, model integration, and improving stakeholder cooperation. Because of this, it can be said that AI-based DSS models have tremendous potential for enhancing sustainable WWTP management to benefit both environmental conservation and public health. AI offers the potential of revolutionizing WWTPs’ efficiency by focusing on data quality, variability in operations, and integration. They also call for more studies—an appeal to future researchers, engineers, and policymakers for research-based innovations in AI on the sustainable water provision.