Effluent composition prediction of a two-stage anaerobic digestion process: machine learning and stoichiometry techniques

Luz Alejo, John Atkinson, Víctor Guzmán-Fierro, Marlene Roeckel

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

41 Citas (Scopus)

Resumen

Computational self-adapting methods (Support Vector Machines, SVM) are compared with an analytical method in effluent composition prediction of a two-stage anaerobic digestion (AD) process. Experimental data for the AD of poultry manure were used. The analytical method considers the protein as the only source of ammonia production in AD after degradation. Total ammonia nitrogen (TAN), total solids (TS), chemical oxygen demand (COD), and total volatile solids (TVS) were measured in the influent and effluent of the process. The TAN concentration in the effluent was predicted, this being the most inhibiting and polluting compound in AD. Despite the limited data available, the SVM-based model outperformed the analytical method for the TAN prediction, achieving a relative average error of 15.2% against 43% for the analytical method. Moreover, SVM showed higher prediction accuracy in comparison with Artificial Neural Networks. This result reveals the future promise of SVM for prediction in non-linear and dynamic AD processes.

Idioma originalInglés
Páginas (desde-hasta)21149-21163
Número de páginas15
PublicaciónEnvironmental Science and Pollution Research
Volumen25
N.º21
DOI
EstadoPublicada - 1 jul. 2018
Publicado de forma externa

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