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• W3183105877 abstract "This study investigated electrokinetic assisted anaerobic digestion of spent mushroom substrate supplemented with sugar mill wastewater for enhanced biogas production. Response surface method and artificial neural network tools were used to optimize the reactor performance. Findings showed that the best reactor performance was achieved at a temperature of 34.52 °C, a direct electric current of 1.61 V, and sugar mill wastewater loading of 59.61%, while the highest observed biogas yield and methane contents were 10344 mL and 63.05%, respectively. Moreover, amongst the different control parameters, sugar mill wastewater loading showed the most significant ( P < 0.001) effect on bioenergy recovery from spent mushroom substrate followed by temperature ( P < 0.0298) and electric current ( P < 0.1783). Besides this, the artificial neural network (feed-forward-backpropagation configuration with logistic function) simulated the biogas/methane production more efficiently as compared to the quadratic model of response surface method as revealed from R 2 (<0.9979 and 0.9987), root means-square error (>117.588 and 97.253), and model efficiency (<0.998) tools. Thus, supplementation of sugar mill wastewater along with low-level direct electric current can be useful for enhanced bioenergy recovery from the spent mushroom substrate." @default.
• W3183105877 created "2021-07-19" @default.
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• W3183105877 date "2021-12-01" @default.
• W3183105877 modified "2023-09-23" @default.
• W3183105877 title "Electrokinetic assisted anaerobic digestion of spent mushroom substrate supplemented with sugar mill wastewater for enhanced biogas production" @default.
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• W3183105877 doi "https://doi.org/10.1016/j.renene.2021.07.045" @default.
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