Neural network modeling to support an experimental study of the delignification process of sugarcane bagasse after alkaline hydrogen peroxide pre-treatment

• Published in: Bioresource technology Vol. 243; pp. 760 - 770
• Main Authors: Valim, Isabelle C., Fidalgo, Juliana L.G., Rego, Artur S.C., Vilani, Cecília, Martins, Ana Rosa F.A., Santos, Brunno F.
• Format: Journal Article
• Language: English
• Published: England Elsevier Ltd 01-11-2017
• Subjects: Cellulose; Failure prediction; Hydrogen Peroxide; Lignin; Neural networks; Saccharum; Spectroscopy, Fourier Transform Infrared; Sugarcane bagasse; Lignin; Failure prediction; Hydrogen peroxide; Neural networks; Sugarcane bagasse
• ISSN: 0960-8524; 1873-2976
• DOI: 10.1016/j.biortech.2017.06.029

•Sugarcane bagasse was chemical pretreated using hydrogen peroxide (H2O2).•The FT-IR efficacy is specified by the lignin quantity after pretreatment.•Development of three ANN models for delignification process.•ANN models can predict important features with more than 90% accuracy.
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.