| Sumario: | This study proposes an integrated approach by combining a pattern recognition technique and a process simulation model, to assess the impact of various climatic conditions on influent characteristics of the largest Italian wastewater treatment plant (WWTP) at Castiglione Torinese. Eight years (viz. 2009–2016) of historical influent data namely influent flow rate (Q in), chemical oxygen demand (COD), ammonium (N-NH 4) and total suspended solids (TSS), in addition to two climatic attributes, average temperature and daily mean precipitation rates (P I) from the plant catchment area, are evaluated in this study. Following the outlier removal and missing-data imputation, five influent climate-based scenarios are identified by K -means clustering approach. Statistical characteristics of clustered observations are further investigated. Finally, to demonstrate that the proposed approach could improve the process control and efficiency, a process simulation model was developed and calibrated. Steady-state simulations were conducted, and the performance of the plant was studied under five influent scenarios. Further, an optimization scenario-based method was conducted to improve the energy consumption of the plant while meeting effluent requirements. The results indicate that with the adaptation of suitable aeration strategies for each of the influent scenarios, 10–40% energy saving can be achieved while meeting effluent requirements. • An integrated approach is proposed to investigate the impact of climatic variations on the performance of a WWTP. • Data pre-processing and clustering methods are performed on eight years of historical influent and climatic data. • Five influent climate-based scenarios are identified by K-means clustering approach. • The process simulation model is developed and calibrated for steady state condition. • Aeration energy optimization measures are proposed for each of the obtained influent climate-based scenarios.
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