Neural network modeling to support an experimental study of the delignification process of sugarcane bagasse after alkaline hydrogen peroxide pre-treatment
| Autor: | Isabelle C. Valim, Juliana L.G. Fidalgo, Artur S.C. Rego, Cecília Vilani, Brunno F. Santos, Ana Rosa Fonseca de Aguiar Martins |
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| Rok vydání: | 2017 |
| Předmět: |
Pre treatment
Environmental Engineering Correlation coefficient 020209 energy Bioengineering 02 engineering and technology 010501 environmental sciences 01 natural sciences Lignin chemistry.chemical_compound symbols.namesake Oxidizing agent Spectroscopy Fourier Transform Infrared 0202 electrical engineering electronic engineering information engineering Hydrogen peroxide Cellulose Waste Management and Disposal 0105 earth and related environmental sciences Mathematics Waste management Artificial neural network Renewable Energy Sustainability and the Environment General Medicine Hydrogen Peroxide Pulp and paper industry Saccharum Fourier transform chemistry Scientific method symbols Bagasse |
| Zdroj: | Bioresource technology. 243 |
| ISSN: | 1873-2976 |
| Popis: | The present study examines the use of Artificial Neural Networks (ANN) as prediction and fault detection tools for the delignification process of sugarcane bagasse via hydrogen peroxide (H2O2). Experimental conditions varied from 25 to 45 °C for temperature and from 1.5% to 7.5% (v/v) for H2O2 concentrations. Analytical results for the delignification were obtained by Fourier Transform Infrared (FT-IR) analysis and used for the ANN training and testing steps, allowing for the development of ANN models. The condition experimentally identified as the most suitable for the delignification process was of 25 °C with 4.5% (v/v) H2O2, oxidizing 54% of total lignin. An ANN topology was selected for each proposed model, whose performance was evaluated by the correlation coefficient (R2) and error indices (MSE and SSE). The values obtained for R2 and the error indices indicated good agreements of the theoretical and actual data, of close to 1 and close to 0, respectively. |
| Databáze: | OpenAIRE |
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