文献类型：期刊文献

英文题名：Hybrid Modeling of Catalytic Cracking Processes Based on the 17-Lump Kinetic Model and Light Gradient Boosting Machine

作者：Chen, Xiangming[1];Luo, Kai[1];Huang, Cheng[1];Gong, Jiahao[1];Long, Jian[1,2];Guo, Wenze[1,2,3]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Engn Res Ctr Proc Syst Engn, Minist Educ, Shanghai 200237, Peoples R China;[3]Qingyuan Innovat Lab, Quanzhou 362801, Peoples R China

年份：2025

卷号：39

期号：14

起止页码：6942

外文期刊名：ENERGY & FUELS

收录：;EI(收录号：20251318113176);WOS:【SCI-EXPANDED(收录号:WOS:001450945400001)】；

基金：This work was supported by National Key Research and Development Program of China (2023YFB3307800), National Natural Science Foundation of China (Key Program: 62136003, 62373155), Major Program of Qingyuan Innovation Laboratory (Grant No. 00122002), the Programme of Introducing Talents of Discipline to Universities (the 111 Project) under Grant B17017 and the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Adaptive boosting - Fluid catalytic cracking - Prediction models

摘要：Fluid catalytic cracking is a crucial procedure in the petrochemical industry, but understanding and predicting its process remains challenging due to its high degree of complexity and nonlinearities. This paper proposes a hybrid model incorporating a 17-lump kinetic model (SLKM) and a light gradient boosting machine (LightGBM). First, as the difficulty of parameter determination of SLKM, this paper improves the dung beetle optimization algorithm for solving the parameters in SLKM. The algorithm simulates the search behavior of insects and uses a fitness function to optimize the parameters, thereby addressing the inaccuracy and time-consuming nature of parameter solving in the mechanistic model. Second, the challenge of hyperparameter optimization in LightGBM, which greatly influences the precision of model predictions, is addressed in this paper by applying Bayesian optimization with Hyperband. The prediction performance of the LightGBM model improves significantly following this hyperparameter optimization. SLKM, backed by existing theoretical knowledge, produces outputs consistent with physicochemical laws and requires less data, though its prediction performance is limited. To enhance performance, we construct three hybrid models by combining SLKM and LightGBM: the series model, the parallel model, and the hybrid model with adaptive weights, realizing the complementarity of both models. The industrial data validation results demonstrate that the adaptive weighted hybrid model achieves the best prediction performance, with the mean relative error, mean squared error, and mean absolute error reaching 1.36%, 0.0169, and 0.077 on the industrial plant data set, respectively.