Evaluating Artificial Intelligence-Based Industrial Wastewater Anaerobic Ammonium Oxidation Treatment Optimization and Its Environmental, Economic, and Social Benefits Using a Life Cycle Assessment–System Dynamics Model

This study integrates life cycle assessment (LCA) and system dynamics (SD) modeling to evaluate the potential of Artificial Intelligence (AI)-enhanced anaerobic ammonium oxidation (anammox) technology in industrial wastewater treatment. The research examines the environmental, economic, and social benefits of AI optimization, with a focus on its long-term implications for sustainable development. By constructing a detailed LCA model, the study analyzes the environmental impacts of wastewater treatment across its lifecycle, from raw material acquisition to final waste disposal. The integration of the SD model simulates dynamic feedback mechanisms, predicting the long-term effects of AI optimization on resource efficiency and environmental performance. Specifically, the AI system employs a convolutional neural network (CNN) to analyze real-time pollutant levels and a reinforcement learning algorithm to optimize operational parameters such as aeration rates, chemical dosing, and sludge retention time. This optimization achieves a 7.02% reduction in energy consumption, an 18% decrease in greenhouse gas emissions, and a 15% reduction in total nitrogen concentrations in treated water. Economically, AI predictive maintenance reduces operating costs by 10% and extends equipment lifespan by 20%, while socially, it enhances the public perception of corporate social responsibility, particularly in regions with stringent environmental regulations. This study underscores the effectiveness of combining LCA and SD models to evaluate sustainable wastewater treatment technologies, providing scientific evidence for policymakers and industry stakeholders to use to promote green technologies and social responsibility.