主持国家重点研发计划子课题2项、国家自然科学基金项目3项、北京市自然科学基金项目1项、荣获首届北京市科协青年人才托举计划、2013北京高等学校“青年英才计划”、2013年度北京市优秀人才培养资助，同时主持软件开发环境国家重点实验室开放课题1项、测绘遥感信息工程国家重点实验室开放研究基金1项、中石油吉林石化分公司研究开发项目1项、联合主持国家自然科学基金项目1项、等，

[1]Yuan Xu, Cuihuan Fan, Qun-Xiong Zhu, Abbas Rajabifard, Nengcheng Chen, Yiqun Chen, and Yan-Lin He*. Novel pattern matching integrated KCVA with adaptive rank-order morphological filter and its application to gault diagnosis. Industrial & Engineering Chemistry Research, 2019, DOI: 10.1021/acs.iecr.9b05403 (TOP SCI, JCRQ1, IF= 3.375) [2]Qun-Xiong Zhu, Zhong-Sheng Chen, Xiao-Han Zhang, Abbas Rajabifard, Yuan Xu*, Yiqun Chen, Dealing with small sample size problems in process industry using virtual sample generation: a Kriging-based approach, Soft Computing, 2019, DOI: 10.1007/s00500-019-04326-3 (JCRQ3, IF= 2.784) [3] Meng, Q. Q. , Zhu, Q. X. , Gao, H. H. , He, Y. L. , Xu, Y. *. A novel scoring function based on family transfer entropy for bayesian networks learning and its application to industrial alarm systems. Journal of Process Control, 2019, 76: 122-132 (JCRQ3, IF= 3.316) [4] Yuan Xu, Sheng-Qi Shen, Yan-Lin He, Qun-Xiong Zhu*. A novel hybrid method integrating CA-PCA with relevant vector machine for multivariate process monitoring. IEEE Transactions on Control Systems Technology, 2018,99 :1-8 (JCRQ2, IF= 5.371) [5]Zhu, Qun-Xiong; Zhang, Chen; He, Yan-Lin; Xu, Yuan*. Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry. Applied Energy, 2018, 213: 322-333 (TOP SCI, JCRQ1, IF= 8.426) [6]Yuan Xu, Mingqing Zhang, Liangliang Ye, Qun-Xiong Zhu, Zhiqiang Geng, Yanlin He, Yongming Han*. A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction. Energy, 2018, 164: 137-146 (TOP SCI, JCRQ1, IF= 5.537) [7]Yan-Lin He, Ping-Jiang Wang, Ming-Qing Zhang, Qun-Xiong Zhu, Yuan Xu*. A novel and effective nonlinear interpolation virtual sample generation method for enhancing energy prediction and analysis on small data problem: A case study of Ethylene industry. Energy, 2018, 147 :418-427 (TOP SCI, JCRQ1, IF= 5.537) [8]Zhang, Xiao-Han ; Zhu, Qun-Xiong ; He, Yan-Lin; Xu, Yuan*. Energy modeling using an effective latent variable based functional link learning machine. Energy, 2018, 162: 883-891 (TOP SCI, JCRQ1, IF= 5.537) [9] Zhang, Xiao-Han ; Zhu, Qun-Xiong; He, Yan-Lin; Xu, Yuan*. A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry. Energy, 2018, 162: 593-602 (TOP SCI, JCRQ1, IF= 5.537) [10]Qun-Xiong Zhu, Xiao Wang, Yan-Lin He, Yuan Xu*. An improved extreme learning machine integrated with nonlinear principal components and its application to monitoring complex chemical processes. Applied Thermal Engineering, 2018, 130: 745-753 (TOP SCI, JCRQ2, IF=4.026) [11]Yongming Han, Qunxiong Zhu, ZhiqiangGeng, Yuan Xu*.Energy and carbon emissions analysis and prediction of complex petrochemical systems based on an improved extreme learning machine integrated interpretative structural model, Applied Thermal Engineering, 2017, 115: 280-291 (TOP SCI, JCRQ2, IF=3.771) [12]Yuan Xu, Mingqing Zhang, Qunxiong Zhu, Yanlin He*. An improved multi-kernel RVM integrated with CEEMD for high-quality intervals prediction construction and its intelligent modeling application. Chemometrics and Intelligent Laboratory Systems, 2017, 171: 151-160 (JCRQ3, IF=2.701) [13]Zi-Qian Zhou, Qun-Xiong Zhu, Yuan Xu*. Time series extended finite-state machine-based relevance vector machine multi-fault prediction. Chemical Engineering & Technology, 2017, 40 (4): 639-647 (JCRQ3, IF=1.588) [14]Han Liu, Gao Huihui, Xu Yuan*, Zhu Qunxiong. Combining FAP, MAP and correlation analysis for multivariatealarm thresholds optimization in industrial process. Journal of Loss Prevention in the Process Industries. 2016, 40: 471-478 (JCRQ3, IF=1.818) [15]XU Yuan, YE Liangliang, ZHU Qunxiong*. A new DROS extreme learning machine with differential vector KPCA approach for real-time fault recognition of nonlinear processes. Journal of Dynamic Systems. Measurement and Control, 2015. 137(5): 1101-1110 (JCRQ4, IF=0.975) [16]XU Yuan, ZHOU Ziqian, ZHU Qunxiong*. A new feedback DE-ELM with time delay-based EFSM approach for fault prediction of non-linear process. Canadian Journal of Chemical Engineering, 2015, 93: 1603-1612 (JCRQ4, IF=1.066)