姓名： 赵天逸
邮箱：tianyi_z@u.nus.edu
ty_zhao_3326@126.com
ORCID：0009-0000-4898-7412
教育/工作经历
2021.09-2025.11 新加坡国立大学，工学博士
2020.06-2021.09 大连理工大学，科研助理
2016.09-2020.06 大连理工大学，工学学士
研究方向
化工过程系统工程；化工过程建模与控制；智能化工；信号处理；人工智能；机器学习；化学计量学
代表性学术成果
1.论文
[1] Zhao, T.; Zheng, Y.; Gong, J. and Wu, Z.* Machine Learning-Based Reduced-Order Modeling and Predictive Control of Nonlinear Processes. Chem. Eng. Res. & Des. 2022, 179, 435-451
[2] Zhao, T.; Zheng, Y. and Wu, Z.* Improving Computational Efficiency of Machine Learning Modeling of Nonlinear Processes Using Sensitivity Analysis and Active Learning. Digit. Chem. Eng. 2022, 3, 100027
[3] Zhao, T.; Zheng, Y. and Wu, Z.* Feature Selection-Based Machine Learning Modeling for Distributed Model Predictive Control of Nonlinear Processes. Comp. & Chem. Eng. 2023, 169, 108074
[4] Chen, Y.; Zhao, T.; Liu, R.; Lu, Z.; Pei, C.* and Gong, J.* CFD-DEM Analysis of the Influence of Heat Storage Materials on Propane Dehydrogenation Process. Chem. Eng. Sci. 2023, 276, 118816
[5] Zheng, Y.; Zhao T.; Wang, X. and Wu, Z.* Online Learning-Based Predictive Control of Crystallization Processes under Batch-to-Batch Parametric Drift. AIChE J. 2022, 68, e17815
[6] Han, P.*; Li, Z.; Tu, Y.; Zhao, T.; Wei, L.* and Ye, S. Recovery of Valuable Metals from Iron-Rich Pyrite Cinder by Chlorination-Volatilization Method. Mining, Metall Explor 2024, 41, 345–352
2.专利
1. A Combined Process for the Recovery of Valuable Metals from Waste Ternary Cathode Materials based on Chlorination Roasting at Medium Temperature and Water Leaching. Zhao Tianyi1. Patent number: AU 2020100054 A4.
2. Synthesis of Prussion Blue Nanozymes with Peroxidase-like Activity for the Colorimetric Detection of Fe2+ Ions. Zhao Tianyi1, Yan Aonan1. Patent number: AU 2019101464 A4.
3.会议报告
[1] Zhao, T., Y. Zheng, C. Hu and Z. Wu, “Improving Computational Efficiency of Machine Learning-Based Distributed Predictive Control of Nonlinear Processes Using Feature Selection,” AIChE Annual Meeting, Phoenix, Arizona, 2022.
[2] Zhao, T., Y. Zheng, and Z. Wu, “Reduced-Order Modeling and Predictive Control of Nonlinear Processes Using Machine Learning,” AIChE Annual Meeting, Phoenix, Arizona, 2022.
[3] Zheng, Y., T. Zhao, X. Wang and Z. Wu, “Machine Learning-Based Modeling and Predictive Control of Crystallization Processes Under Batch-to-Batch Parametric Drift,” AIChE Annual Meeting, Phoenix, Arizona, 2022.
