Please use this identifier to cite or link to this item: http://hdl.handle.net/11189/7190
Title: Optimizing methane production from co-digestion of cassava biomass and winery solid waste using response surface methodology
Authors: Mkruqulwa, U. L. 
Okudoh, Vincent Ifeanyi 
Oyekola, O. O. 
Keywords: Methane;Wineries -- Waste disposal;Cassava -- Industrial applications;Waste products as fuel;Wine and wine making -- By-products;Renewable energy sources
Issue Date: 2019
Publisher: Springer
Source: Mkruqulwa, U.L., Okudoh, V.I., & Oyekola, O.O. 2019. Optimizing methane production from co-digestion of cassava biomass and winery solid waste using response surface methodology. Waste and Biomass Valorization [https://doi.org/10.1007/s12649-019-00801-y]
Journal: Waste and Biomass Valorization 
Abstract: Renewable energy security for the future and better use of natural resources are key challenges that can be concurrently managed by a practical anaerobic co-digestion approach where substrates with a high carbon/nitrogen (C/N) ratio are combined with lower ones to create a balance of the nutrients during the production of methane. This study determined the optimal conditions for methane production from anaerobic co-digestion of cassava biomass (CB) and winery solid waste (WSW) using response surface methodology (RSM). A three-factor central composite design was used to set-up the anaerobic co-digestion experiments. The individual and interactive effects of temperature (25–45 °C), pH 6–8 and a range of substrate (CB/WSW) ratios (0–100) on the methane yield were explored. Optimisation using RSM showed a close fit between the predicted and experimental data as indicated by the coefficient of determination (R2) value of 0.9521. The RSM model predicted a maximum methane yield of 346.28 mL CH4/g VSadded for the optimal conditions of pH 7, temperature of 35 °C ± 0.5 and 70/30 ratio of CB/WSW. The verification experiment produced 396 mL CH4/g VSadded, 12.6% higher than the predicted value at the same conditions. Although, there was a gap between the predicted and actual yield, the significance of the variables in the analysis of variance (ANOVA) shows the model could be relevant to similar research. The substrate ratio at 70 CB:30 WSW was the most significant factor during methane production. The RSM model proved successful in the optimisation process of methane yield.
URI: http://hdl.handle.net/11189/7190
ISSN: 1877-2641
DOI: https://doi.org/10.1007/s12649-019-00801-y
Appears in Collections:Appsc - Journal Articles (DHET subsidised)

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