Neural Network Modeling for Prediction of Oxidized Lignin Content by Delignification of Sugarcane Bagasse Through Hydrogen Peroxide with RAMAN Spectroscopy Data

Abstract

Sugarcane bagasse has great capacity for conversion into new products, resulting from a more noble application of the residue. Hydrogen peroxide delignification is an alternative to conventional processes that provide energy and environmental demands for a cleaner process in terms of product generation. In this study, the experiments, performed in duplicate, consist of batchings with combinations of temperature values (25, 35 and 45 ° C) and hydrogen peroxide concentration values (1,5, 3,0, 4,5 and 7 , 5% v / v). The performance of the delignification process was evaluated by the RAMAN analytical spectroscopy technique, by verifying the oxidized lignin content. It was possible to collect a large amount of information on the samples submitted to each established condition. Thus, it was advantageous to use the artificial neural networks (ANN) as a method of predicting information for the lignin / cellulose ratio, since the ANNs are fast implementation and high performance learning methodologies, presenting high recognition and association of patterns. Pretreatment models with hydrogen peroxide were proposed for the prediction of oxidized lignin content. An ANN topology was selected and the performance was evaluated by the correlation coefficient (R2) and error indexes (MSE and SSE). The model developed from the neural network was satisfactory, since the R² valuewas 0.92 and the error index values were 0.277 for SSE and 0.0046 for MSE.