An integrated machine learning framework for effluent quality prediction in Sewage Treatment Units

Abstract

ABSTRACT The influent and effluent data from wastewater treatment plants being highly correlated with multi-variable coupling and time-varying features may degrade the performance of conventional soft sensors over time. Adaptive strategies based on just-in-time learning (JIT), moving windows (MW), and time difference (TD) are used in this work to develop an adaptive soft sensor. Multi-output Gaussian-process regression (MGPR) is selected and hybrid methods such as TD JIT, MW TD, and JIT MW TD along with TD and MGPR methods are implemented. Data from the benchmark simulation model No.1, closed-loop architecture after applying PI controller, and real-time data from the Rithala Plant of Delhi are obtained. The improved error percentage is 15.03% for total phosphorus (open-loop) using the JIT TD method when compared with the MW TD method. Fair results are observed with JIT TD on real time data with a strong correlation between predicted and observed values, above 0.8 for any variable being estimated.