Abstract:
Mixed vegetable and food wastes left from consumption and processing were important uncontrolled sources of greenhouse gas emissions. Biofuel production from mixed vegetable waste is ineffective due to the recalcitrant structure of lignocellulose. In this work, an artificial neural network (ANN) and response surface methodology (RSM) have been used to optimize the process parameters for the pretreatment of mixed vegetable waste by using organic acid (acetic, citric, oxalic acid) and inorganic acid (hydrochloric acid). The study revealed that the maximum reducing sugars yield of 314.1 mg g−1 biomass was obtained from acetic acid-pretreated biomass at a time of 68.32 min, a temperature of 140 °C, and an acid concentration of 10% w/v, which was 1.66 fold higher than the HCl-pretreated sample. The analysis of biomass composition and FTIR suggested that both inorganic acid and organic acid pretreatment removed hemicellulose and lignin from biomass, and subsequently enhanced the cellulase accessibility to biomass during enzymatic saccharification. It was observed that organic acid pretreatment is more effective to remove lignin compared to inorganic acid. The pretreated samples were converted to ethanol, and the maximum yield of 7.60 g L−1 was obtained for the oxalic acid-pretreated sample, which was 2.82 fold higher than the HCl-pretreated biomass. Also, the maximum biogas yield of 110.72 mL g-VS−1 was obtained for citric acid-pretreated biomass, which was fivefold higher than the untreated biomass. Hence, organic acid pretreatment is highly recommended for bioethanol and biogas production from mixed vegetable wastes. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.