Performance evaluation of microbial fuel cell by artificial intelligence methods

Vijayaraghavan, V.; Tai, K.; Wong, C.H.; Garg, A.; Mahapatra, S.S.; Wong, Chee How

doi:10.1016/j.eswa.2013.08.038

Record: found
Abstract: not found
Article: not found

Performance evaluation of microbial fuel cell by artificial intelligence methods

Author(s): A. Garg , V. Vijayaraghavan , S.S. Mahapatra , K. Tai , C.H. Wong

Publication date Created: March 2014

Publication date (Print): March 2014

Journal: Expert Systems with Applications

Publisher: Elsevier BV

Read this article at

ScienceOpenPublisher

Bookmark

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Related collections

Most cited references 37

Record: found
Abstract: not found
Article: not found

Overview on the developments of microbial fuel cells

L.F. Melo, V.B. OLIVEIRA, A.M.F.R. Pinto … (2013)

0 comments Cited 51 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

A two-population bio-electrochemical model of a microbial fuel cell.

R.P. Pinto, B. Srinivasan, M-F Manuel … (2010)

This work presents a two-population model describing the competition of anodophilic and methanogenic microbial populations for a common substrate in a microbial fuel cell (MFC). Fast numerical solution of the model is provided by using ordinary differential equations to describe biomass growth and retention in the anodic compartment. The model parameters are estimated and validated using experimental results obtained in four continuous-flow air-cathode MFCs operated at various external resistances and organic loads. Model analysis demonstrates the influence of operating conditions on MFC performance and suggests ways to maximize MFC power output. The model is suitable both for process optimization and on-line control applications.