Authors : Prashant Dhorabe, Sulabh Laswante, Shubham Khobragade, Nayan kumar Dhargawe, Vedant Nimbarte, Aman Wasnik
Page Nos : 48-55
Description :
This paper aim to prediction of Waste Water Treatment Plant(WWTP) efficiency by using Artificial Neural Network (ANN) models. It provides as an assessment tool for design and modeling performance for WWTP to controlling the operation of the processes. This paper focuses on approach with a Feed Forward Back Propagation to predict the performance on the terms of Chemical Oxygen Demand (COD), Biochemical Oxygen Demand (BOD), Total Suspended Solid (TSS), pH, Temperature (T) and other parameters data gathered during research. ANN process to assess the stability of environmental balance as well as these would minimize of operation cost performance of ANN models are Technique Forecasting data i.e. Statistical Analysis to compared via the parameters of Root Mean Squared Error(RMSE), Mean Absolute Error (MAE) and Correlation Coefficient(R) between the observed and predicted output variables reached upto the ranges 0.01 to 0.99which implies that the model is viable as a soft sensor for control and management systems for WWTPs. Overall, ANN models provides a simple approach for the data analyzing purposes used to MATLAB software most of the times in neural network analysis.
