Neural Based pH System in Effluent Treatment Process

B. Meenakhsipriya, K. Saravanan, S. Sathiyavathi


This investigation considers the application of Artificial Neural Network (ANN) techniques to estimate the pH value for effluent treatment process. ANN has the ability to identify the non-linear dynamical systems from the input-output data. An important requirement of the application is robustness of the system against erroneous sensor measurements. The simulation model of the pH system for common effluent treatment plant (CETP) is developed using MATLAB 7.5, GUI tool box. A novel off-line and on-line training scheme for the neural network is developed by error back propagation training algorithm to model the pH system for CETP, accurately. For this purpose, a simple feed forward, back propagation neural network, with only one hidden layer, and sigmoidal activation functions is used. The training of such network is based on Input-Output data which is collected from Perundurai common effluent treatment plant (PCETP). Experimentation and simulation results on this neuro identifier of pH system for common effluent treatment plant are shown.

Full Text:



Copyright (c)

Modern Applied Science   ISSN 1913-1844 (Print)   ISSN 1913-1852 (Online)  Email:

Copyright © Canadian Center of Science and Education

To make sure that you can receive messages from us, please add the '' domain to your e-mail 'safe list'. If you do not receive e-mail in your 'inbox', check your 'bulk mail' or 'junk mail' folders.