Identification of Lubricating Oil Additives Using XGBoost and Ant Colony Optimization Algorithms

作     者：Xia Yanqiu Cui Jinwei Xie Peiyuan Zou Shaode Feng Xin Xia Yanqiu;Cui Jinwei;Xie Peiyuan;Zou Shaode;Feng Xin

作者机构：School of Energy Power and Mechanical EngineeringNorth China Electric Power UniversityBeijing 102206China 

出 版 物：《China Petroleum Processing & Petrochemical Technology》 (中国炼油与石油化工（英文版）)

年 卷 期：2024年第26卷第2期

页      面：158-167页

核心收录：

学科分类：0820[工学-石油与天然气工程] 12[管理学] 081702[工学-化学工艺] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0817[工学-化学工程与技术] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：the Beijing Natural Science Foundation(Grant No.2232066) the Open Project Foundation of State Key Laboratory of Solid Lubrication(Grant LSL-2212) 

主　　题：lubricant oil additives fourier transform infrared spectroscopy type identification ACO-XGBoost combinatorial algorithm 

摘      要：To address the problem of identifying multiple types of additives in lubricating oil,a method based on midinfrared spectral band selection using the eXtreme Gradient Boosting(XGBoost)algorithm combined with the ant colony optimization(ACO)algorithm is *** XGBoost algorithm was used to train and test three additives,T534(alkyl diphenylamine),T308(isooctyl acid thiophospholipid octadecylamine),and T306(trimethylphenol phosphate),separately,in order to screen for the optimal combination of spectral bands for each *** ACO algorithm was used to optimize the parameters of the XGBoost algorithm to improve the identification *** this process,the support vector machine(SVM)and hybrid bat algorithms(HBA)were included as a comparison,generating four models:ACO-XGBoost,ACO-SVM,HBA-XGboost,and *** results showed that all four models could identify the three additives efficiently,with the ACO-XGBoost model achieving 100%recognition of all three *** addition,the generalizability of the ACO-XGBoost model was further demonstrated by predicting a lubricating oil containing the three additives prepared in our laboratory and a collected sample of commercial oil currently in use。