隆建
隆建，男，1984年生，工学博士，副教授，博士生导师，硕士生导师。围绕数实融合赋能流程过程高质量发展，在检测与感知、过程模拟与优化等方面展开研究工作，研发了炼油生产过程多尺度特性表征与智能建模方法、多时间尺度资源优化决策方法以及集成知识和模型的多目标优化与性能评估方法，在油品特性表征、装置建模与优化方面形成了知识产权自主可控的智能制造系统，实现了大型石化企业核心过程智能协同优化。近年来主持/参与国家自然科学基金项目、省部级及企业委托项目等20余项。在Applied energy、Energy、IEEE TII、AEI、JPC、Fuel、CES、IECR、CCE等信息、能源、化工领域国际顶级/著名SCI期刊上发表学术论文50余篇；公开和申请国家发明专利50余项，已授权15项；申请国际专利5项，登记计算机软著作权50余项。获第24届中国专利优秀奖（2023年）、中国人工智能学会优秀科技成果奖（2020年）、上海市科技进步一等奖（2019年）、上海市技术发明一等奖（2019年）。IEEE TII、Fuel、JPC、IECR、Soft computing、Processes等国际著名期刊审稿人；2022-2023年校优秀研究生导师。
【基本信息】
所属部门：自动化系
办公电话：021-64253720
办公地址：华东理工大学实验十九楼1406室
邮箱：longjian@ecust.edu.cn
【研究方向】
(1) 机器学习、深度学习等人工智能方法及其工业应用；(2) 医学图像信息深度学习、多模态机器视觉；(3) 新能源过程多尺度智能混合建模与优化；(4) 过程工业智能制造：性能评估、状态检测及溯源诊断；(5) 鲁棒、博弈优化及复杂工业过程决策优化。
【科研项目】
近年来主持/参与国家自然科学基金、中国石化委托项目等20余项。
(1) 国家自然科学基金委员会，面上项目，新型变径流化床油转化催化反应过程多尺度耦合建模与多模态鲁棒优化，在研，主持；
(2) 科技部重点研发课题，石油基乙烯流程工艺仿真共性技术平台，在研，参与；
(3) 国家自然科学基金委员会，面上项目，油品近红外在线多模态智能检测和表征，结题，主持；
(4) 国家自然科学基金委员会，青年项目：基于预设重构并融合密度泛函理论和单位键指标-二次指数势法的催化裂化分子尺度动力学研究，结题，主持；
(5) 国家自然科学基金委员会，国际(地区)合作与交流项目，炼油装置短期最优操作运行研究，结题，技术骨干；
(6) 国家自然科学基金委员会，重大项目，炼油生产过程全局优化运行的基础理论与关键技术--课题1炼油生产过程全局优化运行的集成建模理论与技术，结题，技术骨干；
(7) 教育部，中央高校基本科研业务费专项资金-重点科研基地创新基金项目，原油快速评价研究，结题，主持。
【近年代表性论文】
[1] Jian Long, Yifan Chen, Liang Zhao*. Just-in-time learning method based on two kinds of local samples combined with two-stage training parallel learner for nonlinear chemical process soft sensing [J]. Measurement, 2024, 238: 115371.
[2] Jian Long, Cheng Huang, Kai Deng, Lei Wan, Guihua Hu*, Feng Zhang. Novel hybrid data-driven modeling integrating variational modal decomposition and dual-stage self-attention model: applied to industrial petrochemical process[J]. Energy, 2024, 304: 131895
[3] Tiantian Xu, Jian Long*, Liang Zhao, and Wenli Du. Material and energy coupling systems optimization for large-scale industrial refinery with sustainable energy penetration under multiple uncertainties using two-stage stochastic programming[J]. Applied Energy, 2024, 371: 123525
[4] Lei Wan, Kai Deng, Liang Zhao, Jian Long*. Multi-objective Optimization Strategy for Industrial Catalytic Cracking Units: Kinetic Model and Enhanced SPEA-2 Algorithm with Economic, CO2, and SO2 Emission Considerations[J]. Chemical Engineering Science, 2023, 282: 119331.
[5] Tiantian Xu, Tianyue Li, Jian Long*, Liang Zhao, Wenli Du. Data-driven multi-period modeling and optimization for the industrial steam system of large-scale refineries [J]. Chemical Engineering Science, 2023, 282: 119112.
[6] Yifan Chen, Anlan Li, Xiangyang Li, Dong Xue*, Jian Long*. Efficient JITL framework for nonlinear industrial chemical engineering soft sensing based on adaptive multi-branch variable scale integrated convolutional neural networks[J]. Advanced Engineering Informatics, 2023, 58: 102199.
[7] Haifei Peng, Jian Long*, Cheng Huang, Shibo Wei, Zhencheng Ye*. Multi-modal hybrid modeling strategy based on Gaussian mixture variational autoencoder and spatial–temporal attention: Application to industrial process prediction[J]. Chemometrics and Intelligent Laboratory Systems, 2024, 244: 105029.
[8] LuYao Wang, Jian Long*, XiangYang Li, Haifei Peng, ZhenCheng Ye*. Industrial units modeling using self-attention network based on feature selection and pattern classification[J]. Chemical Engineering Research and Design, 2023, 200: 176-185.
[9] Jian Long, Kai Deng, Renchu He*. Closed-loop scheduling optimization strategy based on particle swarm optimization with niche technology and soft sensor method of attributes-applied to gasoline blending process[J]. Chinese Journal of Chemical Engineering, 2023, (61): 43–57.
[10] Chen Fan, Tianbo Liu, Guihua Hu, Minglei Yang & Jian Long*. Online Determination on the Properties of Naphtha as the Ethylene Feedstock Using Near-Infrared Spectroscopy[J]. Petroleum Chemistry, 2023, 63(9): 1069-1079
[11] Jian Long, Tiantian Xu, Chen Fan. Practical Online Characterization of the Properties of Hydrocracking Bottom Oil via Near-Infrared Spectroscopy [J]. Processes, 2023, 11： 829.
[12] Renchu He, Keshuai, Liang Zhao, Jian Long*. Minglei Yang*. Data-driven worst case model predictive control algorithm for propylene distillation column under uncertainty of top composition [J]. Journal of Process Control, 2023, 124: 199-213.
[13] Jian Long, Yifan Chen, Dengke Cao, et al. Yield and properties prediction based on the multicondition lstm model for the solvent deasphalting process[J]. ACS omega, 2023, 8(6): 5437-50.
[14] Renchu He, Keshuai Ju, Linlin Li, Jian Long*. Multi-Objective Collaborative Optimization of Distillation Column Group Based on System Identification[J]. Processes. 2023, 11: 436
[15] Jian Long, Siyi Jiang, Wei Wang, et al. Modeling and optimization of a fractionation, absorption, and stabilization system in an industrial fluid catalytic cracking process[J]. China Petroleum Processing & Petrochemical Technology, 2022, 24(3): 117-27.
[16] Jian Long, Siyi Jiang, Tianbo Liu, Kai Wang, Renchu* He, and Liang Zhao. Modified Hybrid Strategy Integrating Online Adjustable Oil Property Characterization and Data-Driven Robust Optimization under Uncertainty: Application in Gasoline Blending[J]. Energy &fuels, 2022, 36, 6581−6596.
[17] Tianyue Li, Jian Long*, Liang Zhao, Wenli Du, Feng Qian*. A bilevel data-driven framework for robust optimization under uncertainty – applied to fluid catalytic cracking unit[J]. Computers and Chemical Engineering, 166 (2022) 107989.
[18] Xinglong Qin, Lei Ye, Alqubati Murad, Jichang Liu*, Qiang Ying, Jian Long*, Wenxin Yu, Jinquan Xie, Lixin Hou, Xin Pu, Xin Han, Jigang Zhao, Hui Sun, Hao Ling. Reaction network and molecular distribution of sulfides in gasoline and diesel of FCC process[J]. Fuel, 2022, 319, 123567
[19] Yue Lou, Yuxiang Chen, Yang Zhao, Cheng Qian, Cheng Niu, Hao Jiang, Chuanlei Liu, Kongguo Wu, Benxian Shen, Jian Long*, Yiming Wang*, Hui Sun*, Jigang Zhao, Jichang Liu, Hao Ling, Di Wu, Yujun Tong. Hosting AlCl3 on ternary metal oxide composites for catalytic oligomerization of 1-decene: Revealing the role of supports via performance evaluation and DFT calculation[J]. Microporous and Mesoporous Materials. 2022, 333:111665
[20] Jian Long, Siyi Jiang, Renchu He*, Liang Zhao*. Diesel blending under property uncertainty: A data-driven robust optimization approach[J]. Fuel, 2021, 306: 121647.
【近三年培养研究生情况】
9人次获国家奖学金、4人上海市优秀毕业生、2人获校优秀毕业生、3人获校优秀毕业论文，21人次获一等奖学金；毕业研究生任职于顶级央企头部设计院、顶级军工企业、互联网大厂、中国电信、国资委新兴数字科技公司、杭州知名上市企业等。