ETDs @PUC-Rio
| Título: | MODELING AND OPTIMIZATION STRATEGIES IN SUGARCANE BAGASSE DELIGNIFICATION PROCESS | ||||||||||||
| Autor: |
ISABELLE CUNHA VALIM |
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| Colaborador(es): |
BRUNNO FERREIRA DOS SANTOS - Orientador CECILIA VILANI - Coorientador |
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| Catalogação: | 07/JAN/2019 | Língua(s): | PORTUGUESE - BRAZIL |
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| Tipo: | TEXT | Subtipo: | THESIS | ||||||||||
| Notas: |
[pt] Todos os dados constantes dos documentos são de inteira responsabilidade de seus autores. Os dados utilizados nas descrições dos documentos estão em conformidade com os sistemas da administração da PUC-Rio. [en] All data contained in the documents are the sole responsibility of the authors. The data used in the descriptions of the documents are in conformity with the systems of the administration of PUC-Rio. |
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| Referência(s): |
[pt] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=35985&idi=1 [en] https://www.maxwell.vrac.puc-rio.br/projetosEspeciais/ETDs/consultas/conteudo.php?strSecao=resultado&nrSeq=35985&idi=2 |
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| DOI: | https://doi.org/10.17771/PUCRio.acad.35985 | ||||||||||||
| Resumo: | |||||||||||||
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Sugarcane bagasse is a plant biomass that has a great potential for use due
to its three structural elements: cellulose, hemicellulose and lignin. To serve
as raw material in the production of other products, sugarcane bagasse needs
to undergo a pre-treatment process. In this study, two methodologies for the
sugarcane bagasse pretreatment process were used: delignification via hydrogen
peroxide (H2O2) and via supercritical carbon dioxide (ScCO2). The
models for study the process with H2O2 were developed from experimental
planning, Genetic Algorithms (GA), Artificial Neural Networks (ANN) and
Neuro-Fuzzy (ANFIS). The independent variables were: temperature (25-
60 degrees Celsius), H2O2 concentration (2 - 15 percent m/v) and pH (10-13). The residual
and oxidized lignin contents in the process were evaluated from FT-IR and
Klason method analysis. The models for study the process with ScCO2 were
developed from RNA and ANFIS. The variables studied in the process were:
temperature (35-100 degrees Celsius), pressure (75-300 bar) and ethanol content in the
aqueous solution of co-solvent (0-100 percent). In general, for the two processes,
the developed models consider the independent variables to be neurons in
the input layer and the dependent variables to be neurons in the output
layer. All the neural and ANFIS models developed in this study were evaluated
by the correlation coefficient and error indexes (SSE, MSE and
RMSE), as well as the number of parameters. From the stipulated indices
of performance, among the results obtained by the different strategies, the
neural models were the most satisfactory for the prediction of pretreatment
responses with H2O2. The same occurred in the neural model for prediction
of the residual lignin content in the pre-treatment with ScCO2. Response
surfaces and the contour curves were investigated for each polynomial and
neural model developed. With this resource, it was possible to identify the
best operational points for the processes, pointing at minimizing the residual
and oxidized lignin contents in the biomass.
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