教育背景与工作经历
教育背景
1992/09-1996/06，河北工程大学，供热、通风及空调工程专业，学士
1996/09-1999/03，同济大学，供热、供燃气、通风及空调工程专业，硕士
2001/09-2002/09，香港理工大学，屋宇设备工程系，助理研究员
2006/03-2012/04，上海交通大学，制冷及低温工程专业，博士
工作经历
2020/07 至今，上海理工大学，能源与动力工程学院，副教授
2012/09-2020/06，上海理工大学，能源与动力工程学院，讲师
2012/06-2012/09，上海理工大学，能源与动力工程学院 教师
2004/05-2005/10，三丰医疗国际企业集团，工程设计部，经理、总裁助理
2002/10-2003/11，上海信宁科技有限公司，翻译、国际事业部副经理
1999/05-2000/09，上海新晃空调设备有限公司，技术支持、翻译
代表性研究成果
科研项目
l 国家自然科学基金委员会，青年基金项目，51506125，基于敏感特征的制冷系统故障扩散机理研究及早期预测，2016-01至2018-12，24万元，项目负责人
l 重庆美的通用制冷设备有限公司，冷水机组故障诊断研究开发计划，2017-09至2018-12，34万，项目负责人
l 国家自然科学基金委员会，面上项目，51076104，混合工质振荡热管的传热传质特性研究，2011-01至2013-12，35万元，参与
l 国家自然科学基金委员会，面上项目，50876059，制冷系统故障诊断关键问题定量研究，2009-01-2011-12，30万，主要完成人
代表性论文
专 著：
[1] 《Heat Pipes---Design, Applications and Technology》（参编），NOVA, USA, 2018.
[2] 《直面矛盾》（参编），同济大学出版社，1999.
期刊论文：
[1] H. Han*; B. Gu; T. Wang; Z. Li, Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning, International Journal of Refrigeration, 2011, 34(2): 586-599. (ESI高被引论文)
[2] 梁晴晴, 韩华, 崔晓钰*, 谷波. 基于主元分析-概率神经网络的制冷系统故障诊断[J]. 化工学报. 2016(67): 1022-1031. (入选领跑者F5000计划)
[3] H. Han*, L. Xu, X. Cui, Y. Fan, Novel Chiller Fault Diagnosis Using Deep Neural Network (DNN) with Simulated Annealing (SA). International Journal of Refrigeration, 2021, 121: 269-278.
[4] H. Han, X. Cui*, Y. Zhu, T. Xu, Y. Sui, S. Sun. Experimental study on a closed-loop pulsating heat pipe (CLPHP) charged with water-based binary zeotropes and the corresponding pure fluids. Energy, 2016, 109: 724-736.
[5] H. Han*, Z. Zhang, X. Cui, Q. Meng, Ensemble Learning with Member Optimization for Fault Diagnosis of a Building Energy System. Energy and Buildings, 2020, 226: 110351.
[6] H. Han*, X. Cui, Y. Fan, H. Qing, Least squares support vector machine (LS-SVM)-based chiller fault diagnosis using fault indicative features. Applied Thermal Engineering, 2019, 154:540-547.
[7] H. Han, X. Cui*, Y. Zhu, S. Sun, A comparative study of the behavior of working fluids and their properties on the performance of pulsating heat pipes (PHP). International Journal of Thermal Sciences, 2014, 82: 138-147.
[8] Y. Gao, H. Han*, Z. Ren, J. Gao, S. Jiang, Y. Yang, Comprehensive study on sensitive parameters for chiller fault diagnosis. Energy and Buildings. 2021, 251: 111318.
[9] Z. Zhang, H. Han*, X. Cui, Y. Fan, Novel application of multi-model ensemble learning for fault diagnosis in refrigeration systems [J]. Applied Thermal Engineering, 2020, 164(1): 1-11.
[10] J. Gao, H. Han*, Z. Ren, Y. Fan, Fault diagnosis for building chillers based on data self-production and deep convolutional neural network[J]. Journal of Building Engineering, 2021, 34: 102043.
[11] Z. Ren, H. Han*, X. Cui, Hong Qing, Huiyun Ye. Application of PSO-LSSVM and hybrid programming to fault diagnosis of refrigeration systems[J]. Science and Technology for the Built Environment, 2020: 1-21.
[12] Q. Liang, H. Han, X. Cui*, et al. Comparative study of probabilistic neural network and back propagation network for fault diagnosis of refrigeration systems [J]. Science and Technology for the Built Environment, 2018,24: 448-457.
[13] H. Han*, Z. Cao, B. Gu, N. Ren, PCA-SVM-Based Automated Fault Detection and Diagnosis (AFDD) for Vapor-Compression Refrigeration Systems. International Journal of HVAC&R Research. 2010, 16(3): 295-313.
[14] H. Han*, B. Gu, J. Kang, Z.R. Li. Study on a Hybrid SVM Model for Chiller FDD Applications, Applied Thermal Engineering. 2011, 31: 582-592.
[15] H. Han*, B. Gu, Y. Hong, J. Kan, Automated FDD of multiple- simultaneous faults (MSF) and the application to building chillers, Energy and Buildings. 2011, 43: 2524–2532.
[16] H. Han, S.M. Deng*, A Study on Residential Clothes Drying Using Waste Heat Rejected from a Split-Type Room Air Conditioner (RAC). Drying Technology. 2003, 21(8): 1471- 1490.
[17] Y. Fan, X. Cui*, H. Han*, H. Lu. Feasibility and Improvement of Fault Detection and Diagnosis Based on Factory-Installed Sensors for Chillers [J]. Applied Thermal Engineering, 2020, 164(1):1-10.
[18] Y. Fan, X. Cui*, H. Han*, H. Lu, Chiller fault diagnosis with field sensors using the technology of imbalanced data [J]. Applied Thermal Engineering, 2019, 159(8): 1-12.
[19] Y. Fan, X. Cui*, H. Han*, H. Lu. Chiller Fault Detection and Diagnosis by Knowledge Transfer Method using Adaptive Imbalanced Processing Technology [J], Science and Technology for the Built Environment, 2020, 26(8):1-23.
[20] Y. Zhu, X. Cui*, H. Han, S. Sun. The study on the difference of the start-up and heat-transfer performance of the pulsating heat pipe with water-acetone mixtures [J]. International Journal of Heat and Mass Transfer, 2014, 77: 834–842.
[21] S. Shi, X. Cui*, H. Han, J. Weng, Z. Li. A study of the heat transfer performance of a pulsating heat pipe with ethanol-based mixtures [J]. Applied Thermal Engineering, 2016, 102: 1219–1227.
[22] 范雨强, 崔晓钰, 韩华*, 陆海龙. 不平衡数据技术在冷水机组故障诊断中的应用[J]. 工程热物理学报, 2019(6):1219-1228.
[23] 武浩, 韩华*, 崔晓钰, 范雨强, 徐玲. 基于概念漂移检测的制冷系统故障诊断模型自适应[J]. 制冷学报, 2019(4):121-128.
[24] 徐玲, 韩华*, 崔晓钰, 范雨强, 武浩. 基于PSO优化BP的冷水机组故障诊断研究[J]. 制冷学报, 2019(3):115-123+131.
专利
基于粒子群算法优化BP神经网络模型故障诊断方法，2021年1月22日，中国，CN108334059B。（第二发明人）
荣誉与奖励
1998年同济大学“三八”红旗手
2011年上海交通大学优秀毕业生
主讲课程
【本科生课程】自动控制原理、动力工程测控技术A、制冷装置自动化
【研究生课程】换热器设计制造原理
