Prediction of entire thermal degradation process of polymethyl methacrylate infiltrated with kerosene by a modified artificial neural network

Abstract

Predicting the entire thermal degradation process of solid combustibles infiltrated with flammable liquids is a challenge at present. In the current study, a novel artificial neural network (ANN) framework containing data preprocessing, data normalization and data transformation is proposed to predict the entire thermal degradation process of polymethyl methacrylate infiltrated with kerosene at three scenarios: (1) fixed kerosene mass fraction with various heating rates, (2) fixed heating rate with various kerosene mass fractions, and (3) various kerosene mass fractions with various heating rates. The entire thermal degradation process of scenario (1) can be accurately predicted using the ANN with 2-4-2-1 topology. Using the data transformation formula exp(x₁(1 + x₂) to generate a new input variable based on temperature and kerosene mass fraction, a new ANN with 3-4-2-1 topology can accurately predict the entire thermal degradation process of scenario (2). Two new input variables are generated using the data transformation formula 1/(1 + log((1 + x₁)(1 + x₂))) based on two data sets: (1) kerosene mass fraction and temperature, and (2) heating rate and temperature. The new ANN with 5-4-2-1 topology can accurately predict the entire thermal degradation process at all three scenarios. The new ANN with Levenberg–Marquardt training function and Tanh activation function possesses the best prediction performance.

Highlights

• Data preprocessing can significantly improve the prediction accuracy of ANN.
• ANN with a suitable hidden layer structure has high prediction accuracy.
• The new ANN can predict the entire pyrolysis process in various scenarios.
• ANN with LM and Tanh has the highest prediction accuracy.