Ethylene cracking severity modeling method based on expert knowledge and neutral network

The invention relates to a modeling method for soft sensing the cracking severity of an ethylene cracking furnace based on expert knowledge and a neutral network. A model established by using the method has high prediction precision, high reliability and high extrapolation performance. The model can reflect the actual operation characteristics of the cracking furnace accurately in real time so as to instruct an operator to adjust the operation variables of the cracking process in time, and thus, the economical benefits are increased. In the invention, the expert knowledge about the ethylene cracking process is added to the network training process of ethylene cracking severity neutral network modeling, and a training sample set is formed by acquiring and preprocessing onsite production data. Meanwhile, the neutral network is optimized by using an intelligent evolutionary algorithm, and the neutral network soft sensing model of ethylene cracking severity is established.