Ethylene cracker generally utilizes petroleum products as raw materials

and turn the long-chain hydrocarbon molecules into a variety of short chain hydrocarbons like gaseous hydrocarbons and a small amount of liquid hydrocarbons at high temperature

thereby obtaining ethylene

propylene and other products. Establishing the yield model for main products is of great significance to advanced control

operation optimization of the pyrolysis furnace in theory and practice. Due to the semi-continuous and periodic characteristics of cracking furnace

establishing a yield model for each condition is time-consuming. Considering the temperature

residence time and pressure have common effect on product yields

so this article uses these similarities exist in the new and old processes

supplemented with transfer learning to accomplish quick modeling for different conditions. Compared to previous studies

this method can be able to fully exploit the information contained in the historical data from a small amount of new data. Meanwhile

building a model of ethylene in the process of cracking furnace

case studies demonstrate the efficacy of the developed methodology.