期刊
BIORESOURCE TECHNOLOGY
卷 267, 期 -, 页码 634-641出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2018.07.087
关键词
Sugarcane bagasse; Lignin; Hydrogen peroxide; Neural networks; Neuro-fuzzy; Optimization
资金
- Coordination for the Improvement of Higher Education Personnel (CAPES)
- SENAI Institute of Technology
The present study compares the optimization using Artificial Neural Networks (ANN) and Adaptive Network-based Fuzzy Inference System (ANFIS) in the sugarcane bagasse delignification process using Alkaline Hydrogen Peroxide (AHP). Two variables were assessed experimentally: temperature (25-45 degrees C) and hydrogen peroxide concentration (1.5-7.5%(w/v)). The Klason Method was used to measure the amount of insoluble lignin, the High Performance Liquid Chromatography (HPLC) was used to determine the glucose and xylose concentrations and the Fourier Transform Infrared Spectroscopy (FT-IR) was applied to identify oxidized lignin structure in the samples. The analytical results were used for training and testing of ANN and ANFIS models. The statistical quality of the models was significant due to the low values of the errors indices (RMSE) and determination coefficient R-2 between experimental and calculated values.
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