| Summary: | The most effective method for producing fermentable sugars (FSs) from biomass is thermochemical pretreatment assisted by enzymatic hydrolysis. However, the enzymatic hydrolysis-assisted thermochemical pretreatment method is limited by the formation of fermentation inhibitors, and it is time-consuming. There is growing interest in using a microwave (MW) pretreatment due to its uniform and rapid heating. This study aimed to determine, perform data-driven modeling, and optimize the effect of MW combined with dilute acid pretreatment (MW-DA) on the production of FSs from hazelnut shells. An artificial neural networks (ANNs) model based on Box-Behnken Design (BBD) was the best model described for fermentable sugar extraction (FSE). Optimization via BBD-based ANNs model was carried out for an acid concentration of 0.5 to 2% (w/w), a pretreatment time of 5 to 25 min, a pressure of 5 to 15 bar, and a temperature of 120 to 160 °C. The optimized FSE was estimated at 374 mg/g (81.4% conversion efficiency), with a severity factor of 3.61 under 1.58% H2SO4 for 13 min at 160 °C and 8.5 bar. Using the MW-DA pretreatment process lowered the costs significantly due to the decreases in acid concentration and pretreatment time.
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