A combined support vector regression with a firefly algorithm for prediction of energy consumption in wastewater treatment plants

Abstract

Wastewater treatment plants (WWTPs) comprise energy-intensive processes, serving as primary contributors to overall WWTP costs. This research study proposes a novel approach that integrates support vector regression (SVR) with the firefly algorithm (FFA) for the prediction of energy consumption in a WWTP in Chlef City, Algeria. The database comprises a comprehensive set of 1,653 samples, capturing diverse information categories. It includes chemical and physical characteristics, encompassing chemical oxygen demand, 5-day biochemical oxygen demand, potential of hydrogen, water temperature, total suspended sediment in water and basin, influent N-NH3 concentration, number of aerators, and operating time. Additionally, the hydraulic and energy-related parameters are represented by the flow entered at the station and the energy consumed by aerators, respectively. Finally, meteorological data, comprising rainfall, temperature, relative humidity, and the aridity index, are part of the dataset required for analysis. In this regard, 15 different models that correspond to 15 different combinations of input parameters are assessed in this study. The results show that the SVR–FFA-15 can render an improvement in the prediction accuracy of energy consumption in WWTPs. This study provides a useful tool for managing the energy consumption of wastewater treatment and makes insightful recommendations for future energy savings.