关键词: P-GBDT算法, 催化裂化, 收率寻优模型, 人工智能算法

Abstract: Catalytic cracking unit is a highly nonlinear and strongly correlated system. Currently, the data mining techniques are powerful analytical methods for optimizing this process. Based on the industrial production data collected by the laboratory information management system and the distributed control system, 182 key indicators are selected and a gasoline yield prediction model is built based on gradient-growth decision tree (GBDT algorithm). Afterwards, a PGBDT model is constructed with reference to the GBDT framework, by introducing feature disturbances and feature weights, and increasing the weight of empirically controllable parameters. The results show that P-GBDT has significant higher accuracy with smaller R2 value and the root mean square error.

Key words: P-GBDT algorithm, fluidic catalytic cracking, product yield optimization model, artificial intelligence algorithms