基于BP神经网络的重油催化裂解模型

基于BP神经网络，利用重油催化裂解反应过程的试验数据，以涉及原料性质、催化剂活性、操作条件等的11个参数作为输入变量，以乙烯、丙烯和轻芳烃 BTX（苯、甲苯、二甲苯）的产率作为输出变量，构建了结构为11-12-3、以贝叶斯算法为学习算法的BP神经网络重油催化裂解模型，并进行了验证。结果表明，该模型对乙烯、丙烯和BTX产率的预测平均相对误差分别为4.59%，3.92%，2.28%，说明所建模型对重油催化裂解反应产物产率的预测效果较好。

DEEP CATALYTIC CRACKING MODEL OF HEAVY OIL BASED ON BP NEURAL NETWORK

A deep catalytic cracking model of heavy oil based on BP neural network with structure 11-12-3 and Bayesian algorithm as learning algorithm was constructed and verified by using the experiment data of the heavy oil catalytic cracking reaction process and selecting 11 parameters such as raw material properties, catalyst activity, operation technology as input variables, and the yield of ethylene, propylene and light aromatics (BTX) as output variables. The results showed that the average relative errors of the model for the forecasts of the yields of ethylene, propylene and BTX were 4.59%, 3.92% and 2.28%, respectively. The established model has a good prediction effect on the yield of heavy oil catalytic cracking reaction products.