Revista de Chimie (Rev. Chim.), Year 2014, Volume 65, Issue 4,
WALID M. ALALAYAH, YAHIA ALHAMED, ABDULRAHIM AL-ZAHRANI, GABER EDRIS, HAMAD A. AL-TURAIF Benefits from Using an Artificial Neural Network as a Prediction Model for Bio-hydrogen Production
Abstract:
The performance of the glucose-based production of H2 in a batch reactor was predicted by an artificial neural network (ANN). The potential of utilizing an ANN modeling approach to simulate and predict the hydrogen production of Clostridium saccharoperbutylacetonicum N1-4 (ATCC 13564) was investigated. Sixty experimental data records have been utilized to develop the ANN model. In this paper, a unique architecture has been introduced to mimic the inter-relationship between three input parameters: initial substrate concentration, initial medium pH and temperature (10 g/l, 6.0±0.2, 37°C, respectively). A comparative analysis with a traditional Box-Wilson Design (BWD) statistical model proved that the ANN model output significantly outperformed the BWD model at similar experimental conditions. The results showed that the ANN model provides a higher level of accuracy for the H2 prediction and fewer errors and that it overcomes the limitation of the BWD approach with respect to the number of records, which merely considers a limited length of stochastic patterns for H2 prediction. Keywords: Hydrogen production, anaerobic fermentation, bioprocess modeling, artificial neural network model
