Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm

Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response... Bioresource Technology 214 (2016) 386–395 Contents lists available at ScienceDirect Bioresource Technology journal homepage: www.elsevier.com/locate/biortech Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm Samuel Jacob, Rintu Banerjee Microbial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India highlights graphical a bstract Utilization of PS as a cosubstrate with PW for efficient biomethanation. Application of mixed anaerobic consortia (MAC) as an inoculum. Optimization of biomethanation process based on CCD-RSM and ANN- GA. ar ti c l e i nf o ab stra ct Article history: A novel approach to overcome the acidification problem has been attempted in the present study by codi- Received 8 March 2016 gesting industrial potato waste (PW) with Pistia stratiotes (PS, an aquatic weed). The effectiveness of codi- Received in revised form 14 April 2016 gestion of the weed and PW was tested in an equal (1:1) proportion by weight with substrate Accepted 15 April 2016 concentration of 5 g total solid (TS)/L (2.5 g PW + 2.5 g PS) which resulted in enhancement of methane Available online 19 April 2016 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Bioresource Technology Elsevier

Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm

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Publisher
Elsevier
Copyright
Copyright © 2016 Elsevier Ltd
ISSN
0960-8524
D.O.I.
10.1016/j.biortech.2016.04.068
Publisher site
See Article on Publisher Site

Abstract

Bioresource Technology 214 (2016) 386–395 Contents lists available at ScienceDirect Bioresource Technology journal homepage: www.elsevier.com/locate/biortech Modeling and optimization of anaerobic codigestion of potato waste and aquatic weed by response surface methodology and artificial neural network coupled genetic algorithm Samuel Jacob, Rintu Banerjee Microbial Biotechnology and Downstream Processing Laboratory, Agricultural and Food Engineering Department, Indian Institute of Technology, Kharagpur 721302, West Bengal, India highlights graphical a bstract Utilization of PS as a cosubstrate with PW for efficient biomethanation. Application of mixed anaerobic consortia (MAC) as an inoculum. Optimization of biomethanation process based on CCD-RSM and ANN- GA. ar ti c l e i nf o ab stra ct Article history: A novel approach to overcome the acidification problem has been attempted in the present study by codi- Received 8 March 2016 gesting industrial potato waste (PW) with Pistia stratiotes (PS, an aquatic weed). The effectiveness of codi- Received in revised form 14 April 2016 gestion of the weed and PW was tested in an equal (1:1) proportion by weight with substrate Accepted 15 April 2016 concentration of 5 g total solid (TS)/L (2.5 g PW + 2.5 g PS) which resulted in enhancement of methane Available online 19 April 2016

Journal

Bioresource TechnologyElsevier

Published: Aug 1, 2016

References

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