Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion

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dc.creatorGabriela Rodrigues Niquini
dc.creatorSuzimara Reis da Silva
dc.creatorEsly Ferreira da Costa Junior
dc.creatorAndréa Oliveira Souza da Costa
dc.date.accessioned2023-07-28T18:00:50Z
dc.date.accessioned2025-09-08T23:11:56Z
dc.date.available2023-07-28T18:00:50Z
dc.date.issued2019-05-13
dc.description.sponsorshipFAPEMIG - Fundação de Amparo à Pesquisa do Estado de Minas Gerais
dc.description.sponsorshipCAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
dc.format.mimetypepdf
dc.identifier.doihttps://doi.org/10.1590/0001-3765201920181181
dc.identifier.issn1678-2690
dc.identifier.urihttps://hdl.handle.net/1843/57141
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofAnais da Academia Brasileira de Ciências
dc.rightsAcesso Aberto
dc.subjectBiotecnologia
dc.subjectBiomaterial
dc.subjectEnergia - Fontes alternativas
dc.subjectMetano
dc.subject.otherLignocellulosic biomass
dc.subject.otherLinear regression
dc.subject.otherMethane
dc.subject.otherPolynomial models
dc.subject.otherSolid-state anaerobic digestion
dc.titleFeedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion
dc.typeArtigo de periódico
local.citation.epage9
local.citation.issue4
local.citation.spage1
local.citation.volume91
local.description.resumoIn 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.
local.identifier.orcidhttps://orcid.org/0000-0001-9075-0814
local.identifier.orcidhttps://orcid.org/0000-0002-6763-9752
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA QUÍMICA
local.publisher.departmentENGENHARIA - ESCOLA DE ENGENHARIA
local.publisher.initialsUFMG
local.url.externahttps://www.scielo.br/j/aabc/a/ZZP7srQp3xvCJL6c8bfSmTp/?lang=en

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