周利，副教授，chezli@scu.edu.cn
四川大學學士，浙江大學博士，劍橋大學新加坡智慧碳減排中心博士后。長期從事融合過程機理與數(shù)據(jù)驅(qū)動的化工過程系統(tǒng)多尺度（包括分子尺度、單元尺度、流程尺度、工廠尺度）高保真數(shù)學模型構(gòu)建，以及基于各尺度數(shù)字化模型的系統(tǒng)耦合集成設計與優(yōu)化理論研究與應用技術(shù)開發(fā)。在領(lǐng)域國內(nèi)外高水平學術(shù)刊物發(fā)表SCI論文30余篇，主持國家青年基金1項、國家重點研發(fā)計劃子課題1項、企業(yè)技術(shù)開發(fā)轉(zhuǎn)化19項。
主要研究方向：
①材料基因工程技術(shù)開發(fā)與智慧實驗室構(gòu)建(AI for science)，針對傳統(tǒng)材料研發(fā)范式（基于科學直覺與試錯）周期長、成本高的問題，從事基于材料基因工程技術(shù)的智慧實驗室技術(shù)研究，協(xié)同材料數(shù)據(jù)庫、高通量材料計算與實驗驗證，開發(fā)智慧材料研發(fā)系統(tǒng)，加速新材料研發(fā)效率、降低成本。
②過程系統(tǒng)高保真建模與集成強化(AI for engineering)，針對化工能質(zhì)傳遞過程集成強化受過程不確定因素干擾、模型規(guī)模與計算復雜度制約的問題，開展融合過程機理與數(shù)據(jù)驅(qū)動的智能建模研究，在單元、裝置與流程等多個尺度構(gòu)建高保真代理模型，助力實現(xiàn)化工過程系統(tǒng)的數(shù)字化、智能化、柔性化、低碳化。
講授課程：
化工過程分析與合成、數(shù)據(jù)挖掘技術(shù)與智能化、化工數(shù)據(jù)庫技術(shù)、Python語言與化工智能化、Python語言編程實踐
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.于程遠, 吳金奎, 周利*, 吉旭, 戴一陽, 黨亞固. 基于深度學習預測有機光伏電池能量轉(zhuǎn)換效率[J]. 化工學報, 2021, 72(3): 1487-1495
25.張淑君, 王詩慧, 張欣, 吉旭, 戴一陽, 黨亞固, 周利*. 集成輕烴回收單元代理模型的氫氣網(wǎng)絡多目標優(yōu)化[J]. 化工學報, 2022, 73(4):1658-1672
26.張欣, 周利*, 王詩慧, 吉旭. 考慮原油性質(zhì)波動的煉廠氫氣網(wǎng)絡集成優(yōu)化[J]. 化工學報, 2022, 73(4): 1631-1646
27.陳琳，周利*，吉旭. 基于深度學習的催化裂化過程建模方法[J]. 西安石油大學學報(自然科學版), 2023, 38
28.葉詩洋, 程敏, 吉旭, 戴一陽, 黨亞固, 趙志偉, 周利*. 高性能COF材料的高通量篩選策略：己烷異構(gòu)體分離. 化工學報, 2023
29.陳少臣，程敏，王詩慧，吳金奎，羅磊，薛小雨，趙志偉，吉旭，周利*. 預測金屬有機骨架的甲烷和氫氣輸送能力的遷移學習建模, 高等學?；瘜W學報, 2023
30.黨雨萌，周利*，黨亞固，吉旭，戴一陽，李好．一種新型換熱網(wǎng)絡多級超結(jié)構(gòu)及其應用[J]．華東理工大學學報(自然科學版). 2023
31.馮夏源, 戴一陽, 吉旭, 周利*. 機器學習與分子模擬協(xié)同的 CH4/H2分離金屬有機框架高通量計算篩選[J]. 化學學報, 2022, 80.
