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 °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 R² between experimental and calculated values.

本研究比较了在碱性过氧化氢（AHP）对甘蔗蔗渣脱木质素过程中使用人工神经网络（ANN）和基于自适应网络的模糊推理系统（ANFIS）进行的优化。实验评估了两个变量：温度（25–45°C）和过氧化氢浓度（1.5–7.5％（w / v））。Klason方法用于测量不溶性木质素的含量，高效液相色谱（HPLC）用于确定葡萄糖和木糖的浓度，傅立叶变换红外光谱（FT-IR）用于鉴定乙醇中氧化的木质素结构。样品。分析结果用于训练和测试ANN和ANFIS模型。²在实验值和计算值之间。