Application of artificial neural networks for modeling of biohydrogen production

被引：79
|
作者：
Nasr, Noha [1 ]
Hafez, Hisham [2 ]
El Naggar, M. Hesham [1 ]
Nakhla, George [1 ,3 ]
机构：
[1] Univ Western Ontario, Dept Civil Engn, London, ON N6A 5B9, Canada
[2] GreenField Ethanol Inc, Chatham, ON N7M 5J4, Canada
[3] Univ Western Ontario, Dept Chem & Biochem Engn, London, ON N6A 5B9, Canada
来源：
INTERNATIONAL JOURNAL OF HYDROGEN ENERGY | 2013年 / 38卷 / 08期
关键词：
Hydrogen; Dark fermentation; Batch; Artificial neural network; Back propagation neural network; BIOLOGICAL HYDROGEN-PRODUCTION;
D O I：
10.1016/j.ijhydene.2012.12.109
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
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.
引用
收藏
页码：3189 / 3195
页数：7
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