Use of regression models for development of a simple and effective biogas decision-support tool

Abstract

Anaerobic digestion (AD) is an alternative way to treat manure while producing biogas as a renewable fuel. To increase the efficiency of AD performance, accurate prediction of biogas yield in different working conditions is necessary. In this study, regression models were developed to estimate biogas production from co-digesting swine manure (SM) and waste kitchen oil (WKO) at mesophilic temperatures. A dataset was collected from the semi-continuous AD studies across nine treatments of SM and WKO, evaluated at 30, 35 and 40 °C. Application of polynomial regression models and variable interactions with the selected data resulted in an adjusted R2 value of 0.9656, much higher than the simple linear regression model (R2 = 0.7167). The significance of the model was observed with the mean absolute percentage error score of 4.16%. Biogas estimation using the final model resulted in a difference between predicted and actual values from 0.2 to 6.7%, except for one treatment which was 9.8% different than observed. A spreadsheet was created to estimate biogas production and other operational factors using substrate loading rates and temperature settings. This user-friendly program could be used as a decision-support tool to provide recommendations for some working conditions and estimation of the biogas yield under different scenarios.