Commentary
Artificial Intelligence for Electrocoagulation Treatment of Olive Mill Wastewater
| Mahmoud Nasr1* and Abeer EL Shahawy2 | |
| 1Department of Sanitary Engineering, Faculty of Engineering, Alexandria University, PO Box 21544, Alexandria, Egypt | |
| 2Department of Civil Engineering, Faculty of Engineering, Suez Canal University, PO Box 41522, Ismailia, Egypt | |
| Corresponding Author : | Mahmoud Nasr Sanitary Engineering Department Faculty of Engineering, Alexandria University PO Box 21544, Alexandria, Egypt Tel: +201006390400 E-mail: mahmmoudsaid@gmail.com |
| Received March 31, 2016; Accepted April 08, 2016; Published April 15, 2016 | |
| Citation: Nasr M, EL Shahawy A (2016) Artificial Intelligence for Electrocoagulation Treatment of Olive Mill Wastewater. J Bioremed Biodeg 7: 345. doi:10.4172/2155-6199.1000345 | |
| Copyright: © 2016 Nasr M, et al. This is an open-a ccess article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | |
Abstract
An electrocoagulation system using bipolar aluminium electrodes was studied for the treatment of olive mill wastewater (OMW). Response surface methodology and adaptive neuro-fuzzy inference system (ANFIS) were employed to study the effects of operating parameters on the removal of chemical oxygen demand (COD). At the optimum condition of initial pH 4, current density 83 mA cm-2 and 20 min-electrolysis time, the estimated COD removal efficiency of 40.4% was close to the experimental result (42.7%) with a coefficient of determination r2=0.92. Results from ANFIS indicated that the order of operating parameters affecting the COD removal efficiency was pH>current density>electrolysis time. Additionally, the optimal combination of two inputs influencing the COD removal efficiency was current density × pH, since it recorded the least training root mean square error of 5.04. This study demonstrated that ANFIS could be used as a tool to describe the factors influencing electrocoagulation process.
