Table of Contents
Advances in Artificial Neural Systems
Volume 2013, Article ID 268064, 15 pages
http://dx.doi.org/10.1155/2013/268064
Research Article

Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor

Environmental Engineering Division, Civil Engineering Department, Jadavpur University, Kolkata 700032, India

Received 30 May 2013; Accepted 4 October 2013

Academic Editor: Manwai Mak

Copyright © 2013 Pradyut Kundu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen ( -N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and -N level of 2000 ± 100 mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models “A,” “B,” and “C”) using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models “A,” “B,” and “C”) were trained and tested reasonably well to predict COD and -N removal efficiently with 3.33% experimental error.