Comparative evaluation of bioethanol fermentation process parameters using RSM, ANN and ANFIS

The drive for renewable energy as an alternative to fossil fuel is on the increase globally. Modeling a renewable energy process is crucial for increasing process monitoring and control of plant efficiency for the ultimate objective of optimal biorefinery operations. This study aims to develop and compare models that best predict the fermentation process parameters of bioethanol production using corn-steep liquor (CSL) as a media supplement. The response surface method (RSM), artificial neural networks (ANNs) and adaptive neuro-fuzzy inference system (ANFIS) were tested in the modeling of bioethanol fermentation processes. Box-Behnken design was used to investigate fermentation process parameters considering the effect of CSL (0.5-5.5 w/v), pH (4, 5), time (12-36 h), temperature (25-35 degrees C) and inoculum size (0.5-5.5 v/v). The results from the kinetics study show media formulation with corn steep liquor results in a comparable yield with that substituted with yeast extract. The study shows that, for CSL, a maximum ethanol concentration of 17.40 g L-1 was obtained after 48 h of fermentation while 21.53 g L-1 was attained after 6 h for the media formulation with yeast extract. Based on the model evaluation using statistical error indices, ANN predictability was better at R-2 = 0.90; R = 0.95; SEP = 1.73. The ANN models described the process better than ANFIS and RSM. This study shows the intelligent predictive ability of ANN that could be useful for the scale-up process of ethanol production in industry.

Automated summary

This study aims to develop and compare models that best predict the fermentation process parameters of bioethanol production using corn-steep liquor (CSL) as a media supplement. The results show that the artificial neural network (ANN) model has better predictability with statistical error indices of R-2 = 0.90; R = 0.95; SEP = 1.73.