Comparative evaluation of bioethanol fermentation process parameters using <scp>RSM</scp>, <scp>ANN</scp> and <scp>ANFIS</scp>

The drive for renewable energy as an alternative to fossil fuel is on the increase globally. Modeling a process crucial increasing monitoring and control of plant efficiency ultimate objective optimal biorefinery operations. This study aims develop compare models that best predict fermentation parameters bioethanol production using corn-steep liquor (CSL) media supplement. response surface method (RSM), artificial neural networks (ANNs) adaptive neuro-fuzzy inference system (ANFIS) were tested in modeling processes. Box–Behnken design was used investigate considering effect CSL (0.5–5.5 w/v), pH (4, 5), time (12–36 h), temperature (25–35°C) inoculum size v/v). results from kinetics show formulation with corn steep comparable yield substituted yeast extract. shows that, CSL, maximum ethanol concentration 17.40 g L−1 obtained after 48 h while 21.53 attained 6 Based model evaluation statistical error indices, ANN predictability better at R2 = 0.90; R 0.95; SEP 1.73. described than ANFIS RSM. intelligent predictive ability could be useful scale-up industry.