Predicting effluent BOD5 in wastewater treatment using machine learning
Accurate and quick prediction of effluent Biochemical Oxygen Demand (BOD5) is important for maintaining regulatory standards and improving the operation of wastewater treatment (WWT) plants. Traditional laboratory tests for BOD5 take several days, causing delays in assessing water quality and adjusting plant performance. To overcome this issue, this study develops a machine learning model to estimate effluent BOD5 using easily available plant data. A dataset from a large-scale wastewater treatment plant was used, comprising 12 variables from both influent and effluent data, such as pH, Chemical Oxygen Demand (COD), conductivity, Total Suspended Solids (TSS), BOD5, and temperature, with effluent BOD5 as the target variable.