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Prediction model of biogas production from co-digestion of swine manure and waste kitchen oil
Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org
Citation: 2022 ASABE Annual International Meeting 2200133.(doi:10.13031/aim.202200133)
Authors: Cuong Manh Duong, Teng Teeh Lim
Keywords: Biogas, Regression model, Biogas prediction, Statistical learning, Decision-support tool
Abstract. Anaerobic digestion (AD) has provided an alternative to treat manure while producing biogas as renewable fuel. For more efficient biogas production and to avoid costly construction and design mistake, accurate prediction of biogas production is necessary before new AD plants, and even applied to improve current ADs. In this study, regression model was developed as a tool to estimate biogas production of co-digesting swine manure (SM) and waste kitchen oil (WKO), while considering the digester operating temperature. Dataset was collected from the semi-continuous lab study with hydraulic retention time (HRT) of 21 days, in which nice treatments of SM and WKO were conducted at 30, 35 and 40 °C for 3.5 – 4 HRTs. Application of polynomial regression models and variable interaction resulted in the model with adjusted R-squared value of 0.9655. Stepwise procedure reduced numbers of predictors from 10 to eight in the quadric model, including three key variables (SM loading, SM/WKO ratio, temperature) and their interactions, while not altering the adjusted R-squared value. Prediction of biogas production using the final model resulted in the difference between predicted and actual values from 0.2% to 8.6%, except in one case of which 15.9% difference was observed. An Excel-based program was developed to apply the model estimate of biogas production, digester volume and other operation factors based on SM and WKO loading and temperature setting. The Excel tool is user-friendly and can be used as a decision support tool to provide recommendations of digester volume, oil loading, dilution water, and biogas estimation for new AD systems or improve existing ADs.
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