Prediction of typical gas components in cigarette smoke based on transformer

Abstract Harm reduction of cigarette products has become one of the primary objectives of the entire tobacco industry. The prediction of typical gas components (TGC) and dynamic adjustment of process parameters are crucial for cigarette coke reduction and harm reduction. In this study, a deep learning (DL)-assisted TGC prediction framework is proposed to predict CO and NI simultaneously. Firstly, a large number of experimental and simulation parameters of cigarette process are collected as the data base for subsequent model construction. Then, the feature importance analysis of the process parameters was carried out by combining the mechanism of the combustion process through the mutual information method. Finally, DL models based on Multilayer Perceptron, One-Dimension Convolutional Neural Network and Transformer (TF) was developed as data-driven surrogate models to establish the mapping relationship between process parameters and TGC. The results show that TF model generalizes best and can predict TGC quickly and accurately with R 2 over 0.99. This work will provide a valuable predictive and decision-making tool for cigarette harm reduction.