Modeling and Multiresponse Optimization for Anaerobic Codigestion of Oil Refinery Wastewater and Chicken Manure by Using Artificial Neural Network and the Taguchi Method

Esmaeil Mehryar; Weimin Ding; Abbas Hemmat; Muhammad Hassan; Zahir Talha; Jalal Kafashan; Hongying Huang

doi:10.1155/2017/2036737

Modeling and Multiresponse Optimization for Anaerobic Codigestion of Oil Refinery Wastewater and Chicken Manure by Using Artificial Neural Network and the Taguchi Method

Biomed Res Int. 2017:2017:2036737. doi: 10.1155/2017/2036737. Epub 2017 Dec 26.

Authors

Esmaeil Mehryar^{1

2}, Weimin Ding¹, Abbas Hemmat², Muhammad Hassan³, Zahir Talha¹, Jalal Kafashan^{4

5}, Hongying Huang⁶

Affiliations

¹ College of Engineering, Nanjing Agricultural University, Nanjing, Jiangsu 210031, China.
² Department of Biosystems Engineering, College of Agriculture, Isfahan University of Technology, Isfahan 84156-83111, Iran.
³ US-Pakistan Centre for Advanced Studies in Energy, National University of Science and Technology, Islamabad 44000, Pakistan.
⁴ Division of Mechatronics, Biostatistics and Sensors (MeBioS), KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium.
⁵ Department of Mechanical Engineering in Agromachinery & Mechanisation, Agricultural Engineering Research Institute, Agricultural Research, Education and Extension Organisation, P.O. Box 31585-845, Karaj, Iran.
⁶ Institute of Agricultural Resources and Environment, Jiangsu Academy of Agricultural Science, Nanjing, Jiangsu 210014, China.

Abstract

To study the optimum process conditions for pretreatments and anaerobic codigestion of oil refinery wastewater (ORWW) with chicken manure, L₉ (3⁴) Taguchi's orthogonal array was applied. The biogas production (BGP), biomethane content (BMP), and chemical oxygen demand solubilization (CODS) in stabilization rate were evaluated as the process outputs. The optimum conditions were obtained by using Design Expert software (Version 7.0.0). The results indicated that the optimum conditions could be achieved with 44% ORWW, 36°C temperature, 30 min sonication, and 6% TS in the digester. The optimum BGP, BMP, and CODS removal rates by using the optimum conditions were 294.76 mL/gVS, 151.95 mL/gVS, and 70.22%, respectively, as concluded by the experimental results. In addition, the artificial neural network (ANN) technique was implemented to develop an ANN model for predicting BGP yield and BMP content. The Levenberg-Marquardt algorithm was utilized to train ANN, and the architecture of 9-19-2 for the ANN model was obtained.

MeSH terms

Anaerobiosis
Animals
Biological Oxygen Demand Analysis
Chickens
Manure*
Neural Networks, Computer*
Wastewater*

Substances

Manure
Waste Water