Simulation of biological hydrogen production in a UASB reactor using neural network and genetic algorithm

Mu, Yang and Yu, Han-Qing (2007) Simulation of biological hydrogen production in a UASB reactor using neural network and genetic algorithm. International journal of hydrogen energy, 32 1: 3308-3314. doi:10.1016/j.ijhydene.2007.05.021


Author Mu, Yang
Yu, Han-Qing
Title Simulation of biological hydrogen production in a UASB reactor using neural network and genetic algorithm
Journal name International journal of hydrogen energy   Check publisher's open access policy
ISSN 0360-3199
Publication date 2007-10
Sub-type Article (original research)
DOI 10.1016/j.ijhydene.2007.05.021
Volume 32
Issue 1
Start page 3308
End page 3314
Total pages 7
Editor T. Nejat Veziroğlu
Place of publication New York; Oxford
Publisher Pergamon Press
Language eng
Subject 090703 Environmental Technologies
090608 Renewable Power and Energy Systems Engineering (excl. Solar Cells)
090702 Environmental Engineering Modelling
Abstract In this study the performance of a granule-based H2-producing upflow anaerobic sludge blanket (UASB) reactor was simulated using neural network and genetic algorithm. A model was designed, trained and validated to predict the steady-state performance of the reactor. Organic loading rate, hydraulic retention time (HRT), and influent bicarbonate alkalinity were the inputs of the model, whereas the output variables were one of the following: H2 concentration, H2 production rate, H2 yield, effluent total organic carbon, and effluent aqueous products including acetate, propionate, butyrate, valerate, and caporate. Training of the model was achieved using a large amount of experimental data obtained from the H2-producing UASB reactor, whereas it was validated using independent sets of performance data obtained from another H2-producing UASB reactor. Subsequently, predictions were performed using the validated model to determine the effects of substrate concentration and HRT on the reactor performance. The simulation results demonstrate that the model was able to effectively describe the daily variations of the UASB reactor performance, and to predict the steady-state reactor performance at various substrate concentrations and HRTs.
Keyword Granules
Genetic algorithm
Hydrogen
Model
Neural Network
Upflow anaerobic sludge blanket reactor
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
Collection: Advanced Water Management Centre Publications
 
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