Table of Contents
Advances in Artificial Neural Systems
Volume 2013, Article ID 268064, 15 pages
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.

Citations to this Article [5 citations]

The following is the list of published articles that have cited the current article.

  • Wei-Bo Chen, and Wen-Cheng Liu, “Water Quality Modeling in Reservoirs Using Multivariate Linear Regression and Two Neural Network Models,” Advances in Artificial Neural Systems, vol. 2015, pp. 1–12, 2015. View at Publisher · View at Google Scholar
  • Mahesh R. Gadekar, and M. Mansoor Ahammed, “Coagulation/flocculation process for dye removal using water treatment residuals: modelling through artificial neural networks,” Desalination and Water Treatment, pp. 1–9, 2016. View at Publisher · View at Google Scholar
  • Saber Khodaei Ashan, Mohammad A. Behnajady, Nasim Ziaeifar, and Rana Khalilnezhad, “Artificial neural network modelling of Cr(VI) surface adsorption with NiO nanoparticles using the results obtained from optimization of response surface methodology,” Neural Computing and Applications, 2017. View at Publisher · View at Google Scholar
  • Manh-Ha Bui, Thanh-Luu Pham, and Thanh-Son Dao, “Prediction of cyanobacterial blooms in the Dau Tieng Reservoir using an artificial neural network,” Marine and Freshwater Research, vol. 68, no. 11, pp. 2070, 2017. View at Publisher · View at Google Scholar
  • Mingyi Fan, Jiwei Hu, Rensheng Cao, Wenqian Ruan, and Xionghui Wei, “A review on experimental design for pollutants removal in water treatment with the aid of artificial intelligence,” Chemosphere, vol. 200, pp. 330–343, 2018. View at Publisher · View at Google Scholar