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Multilinear Regression Model for Biogas Production Prediction from Dry Anaerobic Digestion of OFMSW

Author

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  • Elena Rossi

    (Department of Energy, Systems Territory and Construction Engineering, University of Pisa, Via C.F. Gabba 22, 56122 Pisa, Tuscany, Italy)

  • Isabella Pecorini

    (Department of Energy, Systems Territory and Construction Engineering, University of Pisa, Via C.F. Gabba 22, 56122 Pisa, Tuscany, Italy)

  • Renato Iannelli

    (Department of Energy, Systems Territory and Construction Engineering, University of Pisa, Via C.F. Gabba 22, 56122 Pisa, Tuscany, Italy)

Abstract

The aim of this study was to develop a multiple linear regression (MLR) model to predict the specific methane production (SMP) from dry anaerobic digestion (AD) of the organic fraction of municipal solid waste (OFMSW). A data set from an experimental test on a pilot-scale plug-flow reactor (PFR) including 332 observations was used to build the model. Pearson′s correlation matrix and principal component analysis (PCA) examined the relationships between variables. Six parameters, namely total volatile solid (TVSin), organic loading rate (OLR), hydraulic retention time (HRT), C/N ratio, lignin content and total volatile fatty acids (VFAs), had a significant correlation with SMP. Based on these outcomes, a simple and three multiple linear regression models (MLRs) were developed and validated. The simple linear regression model did not properly describe the data (R 2 = 0.3). In turn, the MLR including all factors showed the optimal fitting ability (R 2 = 0.91). Finally, the MLR including four uncorrelated explanatory variables of feedstock characteristics and operating parameters (e.g., TVSin, OLR, C/N ratio, and lignin content), resulted in the best compromise in terms of number of explanatory variables, model fitting and predictive ability (R 2 = 0.87).

Suggested Citation

  • Elena Rossi & Isabella Pecorini & Renato Iannelli, 2022. "Multilinear Regression Model for Biogas Production Prediction from Dry Anaerobic Digestion of OFMSW," Sustainability, MDPI, vol. 14(8), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:8:p:4393-:d:788718
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    References listed on IDEAS

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