The industrial sector in the United States is responsible for utilizing over 47% of the annual freshwater supply to facilitate a variety of essential processes. This sector commonly is responsible for on-site management, treatment, and disposal of their wastewater. Recently, many of these industries are also committed to mitigating the environmental and economic implications associated with their operational activities. Despite the presence of current methods for treating wastewater, a critical need exists to adapt and enhance the effectiveness of these treatment processes to achieve optimal efficiency that aligns with their compliance and sustainability goals. This study aims to develop a digital twin of an aerated stabilization basin, which is commonly used by industrial wastewater facilities. By integrating process treatment models, a digital twin is created in Python that enables users to modify key operation conditions for real-time exploration and decision-making to meet effluent requirements. To optimize and inform the process models, a laboratory-scale aerated stabilization basin is used to assess performance and model sensitivity under various conditions. To aid in decision making, life cycle assessment and techno-economic analysis are layered onto process models to evaluate the environmental and economic sustainability, respectively. The environmental sustainability is evaluated by estimating greenhouse gas emissions from operation and maintenance, electricity, and direct emissions. The economic sustainability is evaluated by estimating costs from operation and maintenance as well as energy requirements. By leveraging this digital twin, industries can optimize performance to meet effluent requirements, while charting pathways towards sustainable practices.
