Can artificial neural network and response surface methodology reliably predict hydrogen production and COD removal in an UASB bioreactor?-Reference-Cited by-同舟云学术

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Can artificial neural network and response surface methodology reliably predict hydrogen production and COD removal in an UASB bioreactor?

Published:2017-07 Issue:30 Volume:42 Page:18875-18883
ISSN:0360-3199
Container-title:International Journal of Hydrogen Energy
language:en
Short-container-title:International Journal of Hydrogen Energy

Author:

Jha Priyanka,Kana E.B.G.,Schmidt Stefan

Funder

Umgeni Water

NRF (SS)

University of KwaZulu-Natal

National Research Foundation (NRF)

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Condensed Matter Physics,Fuel Technology,Renewable Energy, Sustainability and the Environment

Reference32 articles.

1. Global energy assessment: toward a sustainable future;GEA,2012

2. The relative effectiveness of pH control and heat treatment for enhancing biohydrogen gas production;Oh;Environ Sci Technol,2003

3. A comparative overview of hydrogen production processes;Nikolaidis;Ren Sustain Energy Rev,2017

4. Bioreactor design for continuous dark fermentative hydrogen production;Jung;Bioresour Technol,2011

5. Enhanced hydrogen production in a UASB reactor by retaining microbial consortium onto carbon nanotubes (CNTs);Liu;Int J Hydrogen Energ,2012

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