An artificial neural network approach to estimating the enzymatic hydrolysis of Chinese yam (Dioscorea opposita Thunb.) starch

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

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.