隆建   副教授/硕导

【联系方式】

地址：7003全讯入口实验十九楼1406室

电话：021-64253720

Email：longjian@ecust.edu.cn

【个人简介】

2019.09 -至今       7003全讯入口，信息科学与工程学院，副教授

2015.12-2019.08  7003全讯入口，信息科学与工程学院，讲师

2013.09-2015.11  7003全讯入口，团队博士后，合作导师：钱锋院士

2012.07-2013.08  中国石油化工股份有限公司石油化工科学研究院，工程师

2007.09-2012.06  7003全讯入口，化学工艺，博士，导师：沈本贤教授

2003.09-2007.06  7003全讯入口，油气储运工程，学士

【研究方向】

基于人工智能，大数据、深度学习等新一代信息技术与传统石化产业深度融合，重点研究石化生产过程数字孪生场景、模型构建及优化技术、方法、软件和系统，赋能石化企业数字化转型。

【承担项目】

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)  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，在研，主持

(3)  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

(4)  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

(5)  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

(6)  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

【主要学术业绩】

基于人工智能、机器学习，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、催化裂化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。相关成果在国内外核心学术期刊，如Advanced Engineering Informatics、Fuel、Chemical Engineering Science、Industrial & Engineering Chemistry Research、Computers & Chemical Engineering、Energy & Fuels等，发表学术论文40余篇。公开和申请国家发明专利40余项，已授权13项；申请国际PCT专利5项，登记计算机软著作权40余项。获得了2023年/第24届中国专利优秀奖、2020年中国人工智能学会优秀科技成果奖、2019年上海市科技进步一等奖以及2019年上海市技术发明一等奖。

【近三年代表性论文】

[1]  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, 244 (2024) 105029.

[2]  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，Chemical Engineering Research and Design 200 (2023) 176-185.

[3]  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, 2023，102199.

[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 282 (2023) 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 (2023) 119112.

[6]  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.

[7]  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.

[8]   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.

[9]  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.

[10] 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

[11] 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.

[12] 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.

[13] 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.

[14] 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

[15] 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. 333 (2022) 111665.

[16] Jian Long, Siyi Jiang, Renchu He*, Liang Zhao*. Diesel blending under property uncertainty: A data-driven robust optimization approach[J]. Fuel, 2021. 306: p. 121647.