Anaerobic digestion of food waste. Predicting of methane production by comparing kinetic models

(Eng) Anaerobic Digestion (AD) of food waste (FW) reduces risks to human health and environment, also increases the life of landfills, and mainly is an important strategy to produce energy renewable as methane. Kinetic models can determine the influence of the factors that affect the process of AD a...

Full description

Autores:
Parra Orobio, Brayan A.
Donoso Bravo, Andrés
Torres Lozada, Patricia
Tipo de recurso:
Article of journal
Fecha de publicación:
2017
Institución:
Universidad del Valle
Repositorio:
Repositorio Digital Univalle
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.univalle.edu.co:10893/18282
Acceso en línea:
https://hdl.handle.net/10893/18282
Palabra clave:
Digestión anaeróbica
Residuos de alimentos
Modelos cinéticos
Predicción de metano
Anaerobic digestion
Food waste
Kinetic models
Methane prediction
Rights
closedAccess
License
http://purl.org/coar/access_right/c_14cb
Description
Summary:(Eng) Anaerobic Digestion (AD) of food waste (FW) reduces risks to human health and environment, also increases the life of landfills, and mainly is an important strategy to produce energy renewable as methane. Kinetic models can determine the influence of the factors that affect the process of AD and predicts more precisely methane production in order to prevent overestimation or underestimation, which may lead to the definition of real criteria to implement the technology. This study evaluated by means of Biochemical Methane Potential (PBM) assays, the AD of FW from a university restaurant using as inoculum sludge from a UASB reactor in charge of treating municipal wastewater. The factor evaluated was the influence of Substrate-Inoculum (S/I: 0.5, 1, 2 and 4 gVSsubstrate·gVSinoculum -1) ratio. For the prediction of methane were applied the kinetic models: Transfer Function, Logistics Function and Modified Gompertz models. It was found that the S/I ratio affect both, the efficiency of AD process and prediction of methane production, presenting the better results for S/I ratio below one. Within the kinetic models evaluated, the Logistic Function presented the best settings for predicting methane production and lag phase (R2> 0.9).