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Development of short chain fatty acid-based artificial neuron network tools applied to biohydrogen production
توسعه ابزارهای شبکه عصبی مصنوعی مبتنی بر اسیدهای چرب با زنجیره کوتاه استفاده شده برای تولید بیوهیدروژن-2020 The biological production of biohydrogen through dark fermentation is a very complex
system where the use of an artificial neuron network (ANN) for prediction, controlling and
monitoring has a great potential. In this study three ANN models based on volatile fatty
acids (VFA) production and speciation were evaluated for their capacity to predict (i)
accumulated H2 production, (ii) hydrogen production rate and (iii) H2 yield. Lab-scale biohydrogen
and VFA production kinetics from a previous study were used for training and
validation of the models. The input parameters studied were: time and acetate and butyrate
concentrations (model 1), time and lactate, acetate, propionate and butyrate concentrations
(model 2), time and the sum of all VFA (model 3) and time and butyrate/acetate
(model 4). All models could predict biohydrogen accumulated production, hydrogen production
rate and H2 yield with high accuracy (R2 > 0.987). VFAT is the input parameter
indicated for processes using pure cultures, while for complex/mixed cultures a model
based on acetate and butyrate is recommended. Keywords: Volatile fatty acids | Artificial intelligence | Biofuel | Dark fermentation |
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