Determinants of efficiency in an industrial-scale anaerobic digestion food waste-to-biogas project in an Asian megacity based on data envelopment analysis and exploratory multivariate statistics

• Published in: Journal of cleaner production Vol. 168; pp. 983 - 996
• Main Authors: De Clercq, Djavan, Wen, Zongguo, Lu, Xi, Caicedo, Luis, Cao, Xin, Fan, Fei
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-12-2017
• Subjects: Anaerobic digestion; Asia; DEA; Food waste; PCA; Food waste; DEA; Asia; Anaerobic digestion; PCA
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2017.09.062

This paper aims to extract determinants of efficiency in an industrial-scale biogas project treating food waste in a major Asian megacity. The research involved a 4-step methodology combining statistical and operations research tools. The findings were as follows: (1) the project suffered from high variability and low performance across several important process parameters; (2) Principal component analysis (PCA) showed that approximately 40.51% of variability in the data could be explained by two principal components; (3) Data envelopment analysis (DEA) revealed that 47% of the decision making units (DMUs) were inefficient, and 73% of DMUs exhibited increasing returns to scale; (4) regression results showed that adjusted R2 values ranged between 0.913 and 0.996 for the models of DEA efficiency, and significant explanatory variables were extracted based on type III sum of squares. This research is significant in the biowaste-to-energy literature in that it provides a robust method for identifying process bottlenecks in industrial-scale anaerobic digestion of food waste.
•Determinants of efficiency in an industrial-scale biogas project treating food waste are identified.•The project suffers from high variability and low performance across important process parameters.•PCA results show 40.51% of variability in the data can be explained by two principal components.•DEA results show 47% of DMUs are inefficient and 73% of DMUs have increasing returns to scale.•Multiple linear regression results identified variables explaining variability in efficiency scores.