Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700865 |
Resumo: | Abstract: 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. |
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Anais da Academia Brasileira de Ciências (Online) |
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Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestionlignocellulosic biomasslinear regressionmethanepolynomial modelssolid-state anaerobic digestionAbstract: 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.Academia Brasileira de Ciências2019-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700865Anais da Academia Brasileira de Ciências v.91 n.4 2019reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765201920181181info:eu-repo/semantics/openAccessNIQUINI,GABRIELA R.SILVA,SUZIMARA R.COSTA JUNIOR,ESLY F.COSTA,ANDRÉA O.S.eng2019-11-26T00:00:00Zoai:scielo:S0001-37652019000700865Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2019-11-26T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion |
title |
Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion |
spellingShingle |
Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion NIQUINI,GABRIELA R. lignocellulosic biomass linear regression methane polynomial models solid-state anaerobic digestion |
title_short |
Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion |
title_full |
Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion |
title_fullStr |
Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion |
title_full_unstemmed |
Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion |
title_sort |
Feedstock and inoculum characteristics and process parameters as predictors for methane yield in mesophilic solid-state anaerobic digestion |
author |
NIQUINI,GABRIELA R. |
author_facet |
NIQUINI,GABRIELA R. SILVA,SUZIMARA R. COSTA JUNIOR,ESLY F. COSTA,ANDRÉA O.S. |
author_role |
author |
author2 |
SILVA,SUZIMARA R. COSTA JUNIOR,ESLY F. COSTA,ANDRÉA O.S. |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
NIQUINI,GABRIELA R. SILVA,SUZIMARA R. COSTA JUNIOR,ESLY F. COSTA,ANDRÉA O.S. |
dc.subject.por.fl_str_mv |
lignocellulosic biomass linear regression methane polynomial models solid-state anaerobic digestion |
topic |
lignocellulosic biomass linear regression methane polynomial models solid-state anaerobic digestion |
description |
Abstract: 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. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700865 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652019000700865 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765201920181181 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.91 n.4 2019 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
instacron_str |
ABC |
institution |
ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
_version_ |
1754302868166279168 |