Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization

The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artif...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Olivieri, Alejandro César, Goicoechea, Héctor Casimiro, Beccaria, Alejandro José, Giordano, Pablo César
Otros Autores: Simonetta, Arturo: for sharing his milling equipment.
Lenguaje:Inglés
Publicado: Elsevier 2018
Materias:
Glucose
Hydrolysis
Biomass
Lignocellulose
Neural Networks
Acceso en línea:http://hdl.handle.net/2133/11441
http://hdl.handle.net/2133/11441
Aporte de:
Repositorio Hipermedial de la Universidad Nacional de Rosario (UNR) de Universidad Nacional de Rosario
id I15-R121-2133-11441
record_format dspace
institution Universidad Nacional de Rosario
institution_str I-15
repository_str R-121
collection Repositorio Hipermedial de la Universidad Nacional de Rosario (UNR)
language Inglés
orig_language_str_mv eng
topic Glucose
Hydrolysis
Biomass
Lignocellulose
Neural Networks
spellingShingle Glucose
Hydrolysis
Biomass
Lignocellulose
Neural Networks
Olivieri, Alejandro César
Goicoechea, Héctor Casimiro
Beccaria, Alejandro José
Giordano, Pablo César
Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
topic_facet Glucose
Hydrolysis
Biomass
Lignocellulose
Neural Networks
description The concentrations of glucose and total reducing sugars obtained by chemical hydrolysis of three different lignocellulosic feedstocks were maximized. Two response surface methodologies were applied to model the amount of sugars produced: (1) classical quadratic least-squares fit (QLS), and (2) artificial neural networks based on radial basis functions (RBF). The results obtained by applying RBF were more reliable and better statistical parameters were obtained. Depending on the type of biomass, different results were obtained. Improvements in fit between 35% and 55% were obtained when comparing the coefficients of determination (R²) computed for both QLS and RBF methods. Coupling the obtained RBF models with particle swarm optimization to calculate the global desirability function, allowed to perform multiple response optimization. The predicted optimal conditions were confirmed by carrying out independent experiments.
author2 Simonetta, Arturo: for sharing his milling equipment.
author_facet Simonetta, Arturo: for sharing his milling equipment.
Olivieri, Alejandro César
Goicoechea, Héctor Casimiro
Beccaria, Alejandro José
Giordano, Pablo César
author Olivieri, Alejandro César
Goicoechea, Héctor Casimiro
Beccaria, Alejandro José
Giordano, Pablo César
author_sort Olivieri, Alejandro César
title Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title_short Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title_full Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title_fullStr Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title_full_unstemmed Optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
title_sort optimization of the hydrolysis of lignocellulosic residues by using radial basis functions modeling and particle swarm optimization
publisher Elsevier
publishDate 2018
url http://hdl.handle.net/2133/11441
http://hdl.handle.net/2133/11441
work_keys_str_mv AT olivierialejandrocesar optimizationofthehydrolysisoflignocellulosicresiduesbyusingradialbasisfunctionsmodelingandparticleswarmoptimization
AT goicoecheahectorcasimiro optimizationofthehydrolysisoflignocellulosicresiduesbyusingradialbasisfunctionsmodelingandparticleswarmoptimization
AT beccariaalejandrojose optimizationofthehydrolysisoflignocellulosicresiduesbyusingradialbasisfunctionsmodelingandparticleswarmoptimization
AT giordanopablocesar optimizationofthehydrolysisoflignocellulosicresiduesbyusingradialbasisfunctionsmodelingandparticleswarmoptimization
bdutipo_str Repositorios
_version_ 1764820410015154177

Ejemplares similares