Analysis of energy consumption of tobacco drying process based on industrial big data

To promote green upgrading, energy conservation, and consumption reduction in the tobacco manufacturing process, the process relationship between parameters of drying and the thermal energy consumption is qualitatively and quantitatively studied by using statistical analysis methods such as interpretable machine learning (RF, Extra-Trees, XGBoost, and LightGBM) and regression analysis. At the same time, the key indicators of energy saving and consumption reduction are concerned with various mathematical models. The thermal energy consumption during drying is mainly affected by the main steam temperature generated by the power workshop. 145 similar to 275 MJ/h energy (for every 2 degrees C reduction in mainstream temperature) are saved in production based on main steam temperature regulation. Therefore, stable high-temperature steam and control systems are essential for green upgrades. By regulating the process parameters of other equipment, an energy-saving effect of 2.1 similar to 2.2% (main steam temperature is 184 similar to 188 degrees C) can be further achieved, which is about 81.9 similar to 90.2 MJ/h. This research provides an improved guidance for green, energy-saving, and intelligent processing of tobacco manufacturing.