Affiliations: Department of Civil Engineering, Maulana Azad National
Institute of Technology, Bhopal, India | Department of Chemical Engineering, Maulana Azad
National Institute of Technology, Bhopal, India
Abstract: In developing countries like India, common effluent treatment
plants (CETPs) are often suggested as cost-effective options for centralized
treatment of effluents emerging from separate waste-streams. The
treatment/operation cost of CETPs is met by individual waste generators in
proportion to the strength of the waste generated by them. This necessitates
the regular determination of parameters such as BOD, which often is cost
intensive, requires specialized lab personnel, and is time consuming (3-5 days
minimum). Approximation of BOD, therefore, presents a relevant strategy which
can save upon cost, effort and time. This paper presents a framework that
employs Artificial Neural Network (ANN) technique to approximate influent and
effluent BOD for common effluent treatment process. The framework is applied to
the case of a CETP at Bhopal city, India. In the present work, a three-layered
feed forward ANN that compares two different learning algorithms has been
applied, and suitable architecture of the neural network models has been
ascertained after several steps of training and testing of the models. The
results indicate accuracy above 90%, thereby ANN proves to be a promising tool
in the field of modelling.
Keywords: BOD, common effluent treatment plants, ANN
