周利，副教授，chezli@scu.edu.cn

四川大学学士，浙江大学博士，剑桥大学新加坡智慧碳减排中心博士后。长期从事融合过程机理与数据驱动的化工过程系统多尺度（包括分子尺度、单元尺度、流程尺度、工厂尺度）高保真数学模型构建，以及基于各尺度数字化模型的系统耦合集成设计与优化理论研究与应用技术开发。在领域国内外高水平学术刊物发表SCI论文30余篇，主持国家青年基金1项、国家重点研发计划子课题1项、企业技术开发转化19项。

主要研究方向：

①材料基因工程技术开发与智慧实验室构建(AI for science)，针对传统材料研发范式（基于科学直觉与试错）周期长、成本高的问题，从事基于材料基因工程技术的智慧实验室技术研究，协同材料数据库、高通量材料计算与实验验证，开发智慧材料研发系统，加速新材料研发效率、降低成本。

②过程系统高保真建模与集成强化(AI for engineering)，针对化工能质传递过程集成强化受过程不确定因素干扰、模型规模与计算复杂度制约的问题，开展融合过程机理与数据驱动的智能建模研究，在单元、装置与流程等多个尺度构建高保真代理模型，助力实现化工过程系统的数字化、智能化、柔性化、低碳化。

讲授课程：

化工过程分析与合成、数据挖掘技术与智能化、化工数据库技术、Python语言与化工智能化、Python语言编程实践

研究成果:

1.Wang SH, Chen M, Luo L, Ji X, Liu C, Bi KX, Zhou L*, 2023. High-throughput screening of metal-organic frameworks for hydrogen purification. Chem. Eng. J. 451, 138436.

2.Chen M, Wang SH, Zhang ZY, Ji X, Liu C*, Dai YY, Dang YG, Zhou L*. 2023. High-Throughput Virtual Screening of Metal-Organic Frameworks for Xenon Recovery from Exhaled Anesthetic Gas Mixture. Chem. Eng. J. 451, 138218.

3.Zhang ZY, Cheng M, Xiao XY, Bi KX, Song T, Hu KQ, Dai YY, Zhou L*, Liu C*, Ji X, Shi WQ. 2022. Machine-learning-guided identification of coordination polymer ligands for crystallizing separation of Cs/Sr. ACS Appl. Mater. Interfaces. 14(29), 33076-33084.

4.Zhou L, Liao ZW*, Li HR, Ji X, Yang Y, Sun JY, Wang JD, Yang YR. 2022. Design of refinery hydrogen networks with pressure swing adsorption unit configuration under uncertainty: economy and flexibility aspects. Ind. & Eng. Chem. Res. 61, 7322-7334.

5.Xia ZP, Wang SH, Zhou L* , Dai YY, Ji X. 2021. Surrogate-assisted optimization of refinery hydrogen networks with hydrogen sulfide removal. J. Clean. Prod., 310(31), 127477.

6.Wang SH, Cheng M, Zhou L*, Dai YY, Ji X. 2021. QSPR modelling for intrinsic viscosity in polymer–solvent combinations based on density functional theory. SAR and QSAR in Environmental Research, 32(5): 379-353.

7.Wu JK, Wang SH, Zhou L*, Ji X, Dai YY, Dang YG, Kraft M. 2020. A deep learning architecture in QSPR modelling for the prediction of energy conversion efficiency of solar cells. Ind. & Eng. Chem. Res. 59(42), 18991–19000.

8.Chen C, Zhou L*, Ji X, He G, Dai YY, Dang YG. 2020. Adaptive Modeling Strategy Integrating Feature Selection and Random Forest for Fluid Catalytic Cracking Processes. Ind. & Eng. Chem. Res. 59, 11265−11274.

9.Zhao FR, Wu JK, Zhao YP, Ji X, Zhou L*, Sun ZP. 2020. A machine learning methodology for reliability evaluation of complex chemical production systems. RSC Adv., 10, 20374.

10.Wang SH, Zhou L*, Ji Xu, Karimi I.A., Dang YG, 2019. A Surrogate-Assisted Approach for the Optimal Synthesis of Refinery Hydrogen Networks. Ind. & Eng. Chem. Res. 58: 16798-16812.

11.Zhou L, Liao ZW*, Ji X, Wang JD, Yang YY, Dang YG, 2019. Simulation-Based Multiobjective Optimization of the Product Separation Process within an MTP Plant. Ind. & Eng. Chem. Res. 58: 12166-12178.

12.Zhou L., Zhang C., Karimi I.A., Kraft M., 2018. An ontology framework towards decentralized information management for eco-industrial parks. Computers & Chemical Engineering, 118:49-63.

13.Zhou L, Pan M, Sikorski J, Garud S, Aditya LK, Kleinelanghorst MJ, Karimi IA, Kraft M. 2017. Towards an infrastructure for chemical process simulation and optimization in the context of eco-industrial parks. Appl. Energ. 204:1284-1298.

14.Zhou L, Pan M, Sikorski J, Garud S, Kleinelanghorst MJ, Karimi IA, Kraft M. 2017. System Development for Eco-industrial Parks Using Ontological Innovation. Energy Procedia. 105: 2239-2244.

15.Zhou L, Zhang C, Karimi IA, Kraft M. 2017. J-Park Simulator, an intelligent system for information management of eco-industrial parks. Energy Procedia. 17:2953-2958.

16.Zhou L, Liao ZW, Wang JD, Jiang BB, Yang YR, Du WL. 2015. Energy configuration and operation optimization of refinery fuel gas networks. Appl. Energ. 139:365-375.

17.Zhou L, Liao ZW, Wang JD, Jiang BB, Yang YR, Yu HJ. 2015. Simultaneous optimization of heat-integrated water allocation networks using MPEC strategy. Ind. & Eng. Chem. Res. 54(13): 3355-3366.

18.Zhou L, Liao ZW, Wang JD, Jiang BB, Yang YR. 2014. MPEC strategies for efficient and stable scheduling of hydrogen pipeline network operation. Appl. Energ. 119: 296-305.

19.Zhou L, Liao ZW, Wang JD, Jiang BB, Yang YR., Davide Hui. 2013. Optimal design of sustainable hydrogen networks. Int J Hydrogen Energy. 38: 2937-2950.

20.Zhou L, Liao ZW, Wang JD, Jiang BB, Yang YR. 2012. Hydrogen sulfide removal process embedded optimization of hydrogen network. Int J Hydrogen Energ. 37: 18163-18174.

21.Zhou L, Tokos H, Krajnc D, Yang YR. 2012. Sustainability performance evaluation in industry by composite sustainability index. Clean Technol. Envir. 14: 789-803.

22.Zhou L, Liao ZW, Wang JD, Jiang BB, Yang YR. 2012. Simultaneously optimization of hydrogen network with desulfurization processes embedded. Proceedings of the 11th International Symposium on Process System Engineering. 215-219.

23.Zhou L, Liao ZW, Tokos Hella, Wang JD, Yang YR. 2013. Multi-contaminant H2 network optimization considering H2S remove. Acta Petrolei Sinica. 29(2): 304-311.

24.于程远, 吴金奎, 周利*, 吉旭, 戴一阳, 党亚固. 基于深度学习预测有机光伏电池能量转换效率[J]. 化工学报, 2021, 72(3): 1487-1495

25.张淑君, 王诗慧, 张欣, 吉旭, 戴一阳, 党亚固, 周利*. 集成轻烃回收单元代理模型的氢气网络多目标优化[J]. 化工学报, 2022, 73(4):1658-1672

26.张欣, 周利*, 王诗慧, 吉旭. 考虑原油性质波动的炼厂氢气网络集成优化[J]. 化工学报, 2022, 73(4): 1631-1646

27.陈琳，周利*，吉旭. 基于深度学习的催化裂化过程建模方法[J]. 西安石油大学学报(自然科学版), 2023, 38

28.叶诗洋, 程敏, 吉旭, 戴一阳, 党亚固, 赵志伟, 周利*. 高性能COF材料的高通量筛选策略：己烷异构体分离. 化工学报, 2023

29.陈少臣，程敏，王诗慧，吴金奎，罗磊，薛小雨，赵志伟，吉旭，周利*. 预测金属有机骨架的甲烷和氢气输送能力的迁移学习建模, 高等学校化学学报, 2023

30.党雨萌，周利*，党亚固，吉旭，戴一阳，李好．一种新型换热网络多级超结构及其应用[J]．华东理工大学学报(自然科学版). 2023

31.冯夏源, 戴一阳, 吉旭, 周利*. 机器学习与分子模拟协同的 CH4/H2分离金属有机框架高通量计算筛选[J]. 化学学报, 2022, 80.