Use este identificador para citar o ir al link de este elemento: http://hdl.handle.net/1843/57141
Tipo: Artigo de Periódico
Título: Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion
Autor(es): Gabriela Rodrigues Niquini
Suzimara Reis da Silva
Esly Ferreira da Costa Junior
Andréa Oliveira Souza da Costa
Resumen: In this study, several linear regression models were tested to predict the cumulative 30-day methane yield produced in mesophilic solid-state anaerobic digestion, employing diverse lignocellulosic biomass sources. Data collected from 13 studies were utilized, totalizing 86 experimental points, divided into regression and validation. Models containing higher order terms, the inverse of variables and interactions among all eleven input variables were tested. Simple linear models utilizing a single variable were unable to describe the methane production, giving an R² lower than 0.37. However, combinations of multiple variables and its inverses as only independent variable permitted an increase in simple linear models predictive capacity up to 63% of experimental variability. Higher order models presented an improvement in predictive quality: for a fourth-order multiple linear model, a validation R² of 0.8329 was achieved. In view of the obtained results, the proposed linear regression models consist in an attractive tool to propose experimental routines and to investigate new biomass sources for methane production using solid-state anaerobic digestion, significantly reducing time and cost requirements to experiments’ execution.
Asunto: Biotecnologia
Biomaterial
Energia - Fontes alternativas
Metano
Idioma: eng
País: Brasil
Editor: Universidade Federal de Minas Gerais
Sigla da Institución: UFMG
Departamento: ENG - DEPARTAMENTO DE ENGENHARIA QUÍMICA
ENGENHARIA - ESCOLA DE ENGENHARIA
Tipo de acceso: Acesso Aberto
Identificador DOI: https://doi.org/10.1590/0001-3765201920181181
URI: http://hdl.handle.net/1843/57141
Fecha del documento: 13-may-2019
metadata.dc.url.externa: https://www.scielo.br/j/aabc/a/ZZP7srQp3xvCJL6c8bfSmTp/?lang=en
metadata.dc.relation.ispartof: Anais da Academia Brasileira de Ciências
Aparece en las colecciones:Artigo de Periódico



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