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Conversion of lignocellulosic biomass into monomeric carbohydrates is economically beneficial and suitable for sustainable production of biofuels. Hydrolysis of lignocellulosic biomass using high acid concentration results in decomposition of sugars into fermentative inhibitors. Thus, the main aim of this work was to investigate the optimum hydrolysis conditions for sorghum brown midrib IS11861 biomass to maximize the pentose sugars yield with minimized levels of fermentative inhibitors at low acid concentrations. Process parameters investigated include sulfuric acid concentration (0.2-1 M), reaction time (30-120 min) and temperature (80-121°C). At the optimum condition (0.2 M sulfuric acid, 121°C and 120 min), 97.6% of hemicellulose was converted into xylobiose (18.02 mg/g), xylose (225.2 mg/g), arabinose (20.2 mg/g) with low concentration of furfural (4.6 mg/g). Furthermore, the process parameters were statistically optimized using response surface methodology based on central composite design. Due to the presence of low concentration of fermentative inhibitors, 78.6 and 82.8% of theoretical ethanol yield were attained during the fermentation of non-detoxified and detoxified hydrolyzates, respectively, using Pichia stipitis 3498 wild strain, in a techno-economical way.

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In the current investigation, we have adapted response surface methodology (RSM) and artificial neural network-genetic algorithm (ANN-GA) based optimization to develop a defined medium for maximizing human interferon gamma production from recombinant Kluyveromyces lactis (K. lactis).In the initial screening studies, sorbitol and glycine emerged as a carbon and nitrogen source respectively having higher influence on hIFN-g production. Substrate inhibition studies were performed by varying the initial substrate concentration, and we found maximum hIFN-g concentration at 50 g L À1 of sorbitol. Inhibition kinetics studies were carried out using 3 and 4-parametric models. Among the estimated models, the Moser model was observed as the best fitted model followed by the Luong model with R 2 values of 0.882 and 0.75, respectively. The model acceptability test was carried out using the extra sum of squares F-test and Akaike information criterion (AIC). The Plackett-Burman multifactorial design identified sorbitol, glycine, Na 2 HPO 4 , and MgSO 4 .7H 2 O as the parameters significantly influencing the hIFN-g production. Further, the Box-Behnken design (BBD) followed by the artificial neural network coupled with genetic algorithm (ANN-GA) was employed for the precise optimization of medium components. With ANN-GA a maximum hIFN-g yield of 2.1 AE0.3 mg L À1 in shake flask level and 3.5 AE0.1 mg L À1 in reactor level was achieved. The findings of this study serve as a model for a process development strategy (bench scale to reactor scale) to achieve a high productivity of the desired protein from a microbial cell factory.