Prediction of Sludge Volume Index in a Wastewater Treatment Plant Using Recurrent Neural Network

Sludge Volume Index (SVI) is one of the most important operational parameters in an activated sludge process. It difficult to predict SVI because nonlinearity data and variability operation conditions. With complex time-series from Wastewater Treatment Plants (WWTPs), Recurrent Neural Network (RNN) with Explainable Artificial Intelligence was applied interpret prediction result. RNN architecture has been proven efficiently handle non-uniformity data. Moreover, due complexity model, newly concept used Data were collected Nine Springs Plant, Madison, Wisconsin, analyzed cleaned using Python program analytics approaches. An model predicted accurately after training historical big at Spring WWTP. The (AI) analysis able determine which input affected higher most. will benefit WWTPs establish corrective measures maintaining stable SVI. method help wastewater treatment sector improve performance, system management, process reliability.