The energy consumption in wastewater treatment plants (WWTPs) is steadily increasing, particularly due to diverse applications and the demand for higher levels of treatment. This rise necessitates a thorough examination of energy efficiency in WWTPs, given the negative environmental impacts associated with excessive energy use and the reliance on fossil fuels for energy production. Optimizing energy consumption must be prioritized, achievable through energy benchmarking, which involves comparing the energy use of a treatment facility against a reference plant. This process serves as an effective tool for identifying weaknesses and opportunities for improvement in wastewater treatment systems. This research focuses on the energy benchmarking of 15 industrial wastewater treatment plants employing activated sludge processes in Isfahan province. The treatment methods utilized in these facilities primarily involve various activated sludge techniques, including extended aeration and fixed and moving bed bioreactors. Energy consumption data, influent flow rates, and chemical oxygen demand (COD) concentrations in both incoming and outgoing wastewater were analyzed using several benchmarking methods. The study categorizes energy benchmarking methods into three main types based on complexity and accuracy. The first type includes normalization methods that utilize key performance indicators (KPIs) for eva‎luating energy efficiency and are widely adopted due to their simplicity. The second type encompasses statistical methods that involve linear regression techniques such as Ordinary Least Squares (OLS). The third type consists of linear programming methods, with advanced tools like Data Envelopment Analysis (DEA) being used for assessing system efficiency under complex conditions. The results from the performance eva‎luations indicate that smaller treatment plants tend to consume more energy across all analytical methods employed. Inefficient facilities were identified according to KPI analysis, with plants 7, 8, and 11 being the least efficient; OLS analysis highlighted plants 1, 3, and 4; while DEA identified plants 3, 5, and 8 as underperformers. A direct correlation was observed between plant efficiency and the ratio of food to microorganisms (F/M) in the effluent, alongside the operational percentage relative to design capacity. After normalizing the results using a weighted average approach, a comprehensive overview of energy benchmarking outcomes was achieved. The prioritization for optimization based on inefficiency was determined to be plants 3, 1, and 4. The integration of results from multiple analytical methods is crucial in scientific research and engineering as it provides a more holistic understanding of the subject matter. By amalgamating data from various approaches, patterns and overarching trends that may be overlooked in isolated analyses can be discerned. Each analytical method has its strengths and weaknesses; thus, merging results allows for leveraging each method's advantages while mitigating potential errors or uncertainties. Effective energy management in wastewater treatment facilities is essential not only for maintaining water quality but also for minimizing environmental harm. The findings underscore the need for serious attention to energy consumption management in WWTPs to enhance operational efficiency while safeguarding community health and environmental integrity.

مسعود طاهريون

حميدرضا صفوي , علي دهنوي