Suitability of ANN and GP for Predicting Soak Pit Tank Efficiency under Limited Data Conditions

Abstract

Under Industry 4.0 scenario, smart sensors can be suitably integrated with control systems to monitor the treatment of wastewater systems. Such control systems need a sound modelling or forecasting tool for the real time monitoring. This work reports a pilot study to model the treatment efficiency of soak pit tanks for the treatment of grey water using Genetic Programming (GP) and Artificial Neural Network (ANN). Only the inlet total suspended solid is considered for modelling. The Root Mean Square of Errors for GP and ANN run models were found to be 1.8 and 12.5 respectively for Tank 1. The results indicate that GP is a more promising tool than ANN particularly when modelling under limited data conditions. The difference in performance of both the methods seem to depend on the type learning mode adopted in each case.