Journal
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY
Volume 38, Issue 8, Pages 3189-3195Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijhydene.2012.12.109
Keywords
Hydrogen; Dark fermentation; Batch; Artificial neural network; Back propagation neural network
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In this study, an artificial neural network (ANN) model was developed to estimate the hydrogen production profile with time in batch studies. A back propagation artificial neural network ANN configuration of 5-6-4-1 layers was developed. The ANN inputs were the initial pH, initial substrate and biomass concentrations, temperature, and time. The model training was done using 313 data points from 26 published experiments. The correlation coefficient between the experimental and estimated hydrogen production was 0.989 for training, validating, and testing the model. Results showed that the trained ANN successfully predicted the hydrogen production profile with time for new data with a correlation coefficient of 0.976. Copyright (C) 2013, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
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