Modeling and optimization of biomethane production from solid-state anaerobic co-digestion of organic fraction municipal solid waste and other co-substrates

ABSTRACT

One of the simplest methods for increasing productivity in biogas production is co-digestion. Co-digestion leads to more biogas yield as well as more nutrient bioavailability. This study reports the modeling and optimization of biomethane potential tests from different combinations of organic fraction of municipal solid wastes (OFMSW), cow manure (CM), and municipal sewage sludge (MSS) in solid-state. The simplex-centroid mixture design (SCMD) and artificial neural network (ANN) models were validated with a high correlation to the real data (R² = 0.99). Experimental results indicated that the maximum amount of CH₄ production of 436 mL CH₄/g VS occurs at the identical weight percent of the three substances (33.33%). The maximum methane yield was found to be 445.9 mL CH₄/g VS as obtained by the genetic algorithm (GA) optimization process, while 448.5 mL CH₄/g VS (448.5 ± 3.05) of methane was produced experimentally. Therefore, integration of the SCMD and ANN model with the GA optimization is useful in the prediction of biomethane production. This research is the first in providing the best combination of co-treating and co-utilizing OFMSW with CM and MSS in the solid-state for biowaste management.

KEYWORDS:

• Anaerobic digestion
• artificial neural network modeling
• genetic algorithm optimization
• methane production
• statistical regression model

Nomenclature

AAD	=	Absolute average deviation MLR Multiple linear regression
AD	=	Anaerobic digestion MSE Mean square error
AI	=	Artificial intelligence MSS Municipal sewage sludge
ANN	=	Artificial neural network OFMSW The organic fraction of municipal solid wastes
ANOVA	=	Analysis of variance RBF Radial basis function
BOD	=	Biochemical oxygen demand RMSE Root mean square error
BPNN	=	Back-propagation neural network RSM Response surface methodology
CCD	=	Central composite design RTMO Recycling and transformation of materials organization
CM	=	Cow manure SCMD Simplex-centroid mixture design
CSTR	=	Continuous stirred-tank reactor SSAD Solid-state anaerobic digestion
FNN	=	Fuzzy neural networks trainbr Bayesian regularization back-propagation
GA	=	Genetic algorithm trainlm Levenberg-Marquardt back-propagation
IF	=	Interaction factor TC Total carbon
LOF	=	Lack-of-fit TN Total nitrogen
MAPE	=	Mean absolute percentage error TS Total solids
MC	=	Moisture content VS Volatile solids
MLP	=	Multi-layer perceptron R2 Coefficient of determination

Acknowledgments

The authors would like to appreciate the financial supports provided by the University of Tabriz. We also thank the kindly cooperation of the biogas lab in the Department of Biosystems Engineering, Ferdowsi University of Mashhad.

Additional information

Funding

This work was supported by the University of Tabriz [1974].