Characterization of co-digestion of industrial sludges for biogas production by artificial neural network and statistical regression models

Environmental Technology
Biswanath MahantyHung-Suck Park

Abstract

The characteristics and impact of industrial sludges of paper, chemical, petrochemical, automobile, and food industries situated in the Ulsan Industrial Complex, Ulsan, Republic of Korea in co-digestion for biogas production were assessed by artificial neural network (ANN) and statistical regression models. The regression model was based on a simplex-centroid mixture design and the ANN was based on a resilient back-propagation algorithm (topology 5-7-1). Using connection weights and bias of the trained ANN model, the impact of each sludge of co-digestion was assessed using Garsons' algorithm. Results suggested that the modelling and predictability of ANN were superior to the regression model with accuracy (A(f)) 1.01, bias (B(f)) 1.00, root mean square error 3.56, and standard error of prediction 2.51%. Sludge from the chemical industry showed the highest impact on specific methane yield (SMY(VS)) with a relative importance of 28.59% followed by sludges from paper (20.07%), food (19.59%), petrochemical (15.92%), and automobile (15.82%) industries. The interactions between diverse industrial sludges were successfully modelled and partitioned into various synergistic and antagonistic effects on SMY(VS). Synergistic interactions b...Continue Reading

References

Aug 2, 2005·International Journal of Food Microbiology·R M García-GimenoG Zurera-Cosano
Mar 17, 2010·Waste Management·Shishir Kumar BeheraHung-Suck Park
Sep 20, 2012·Environmental Technology·Elen Aquino PerpetuoClaudio Augusto Oller Nascimento

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