Curriculum Vitae
Education
Since 2020 Doctor of Engineering in Process Systems Engineering
Max Planck Institute for Dynamics of Complex Technical Systems, Germany
Advisors: Prof. Kai Sundmacher, Prof. Teng Zhou
2017-2020 Master of Science in Chemical Engineering and Technology
Chongqing University, China
Advisor: Prof. Weifeng Shen
2013-2017 Bachelor of Engineering in Chemical Engineering and Technology
Chongqing University, China
Advisor: Prof. Shun’an Wei
Teaching Activity
Teaching Assistant Process Systems Engineering
Otto-von-Guericke University Magdeburg, Germany
- Winter Semester 2023
Teaching Assistant Analysis and Design of Experiments
Otto-von-Guericke University Magdeburg, Germany
- Summer Semester 2023
- Summer Semester 2022
- Summer Semester 2021
Journal Article
- Z. Wang, T. Zhou, K. Sundmacher. Data-driven integrated design of solvents and extractive distillation processes. AIChE Journal, 2023, 69(12), e18236. (Full text and GitHub repository)
- T. Zhou, C. Gui, L. Sun, Y. Hu, H. Lyu, Z. Wang, Z. Song, G. Yu. Energy applications of ionic liquids: Recent developments and future prospects. Chemical Reviews, 2023, 123(21), 12170–12253. (Full text)
- Z. Wang, T. Zhou, K. Sundmacher. Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation. Chemical Engineering Journal, 2022, 444, 136651. (Full text and GitHub repository)
- Z. Wang, Y. Zhou, T. Zhou, K. Sundmacher. Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling. Computers & Chemical Engineering, 2022, 160, 107739. (Full text and GitHub repository)
- Z. Wang, H. Wen, Y. Su, W. Shen, J. Ren, Y. Ma, J. Li. Insights into ensemble learning-based data-driven model for safety-related property of chemical substances. Chemical Engineering Science, 2022, 248, 117219. (Full text)
- H. Qin, Z. Wang, Z. Song, X. Zhang, T. Zhou. High-throughput computational screening of ionic liquids for butadiene and butene separation. Processes, 2022, 10(1), 165. (Full text)
- H. Wen, Y. Su, Z. Wang, S. Jin, J. Ren, W. Shen, M. Eden. A systematic modeling methodology of deep neural network-based structure-property relationship for rapid and reliable prediction on flashpoints. AIChE Journal, 2022, 68(1), e17402. (Full text)
- X. Zhang, S. Sethi, Z. Wang, T. Zhou, Z. Qi, K. Sundmacher. A neural recommender system for efficient adsorbent screening. Chemical Engineering Science, 2022, 259, 117801. (Full text)
- Z. Wang, Song, Z., Zhou, T. Machine learning for ionic liquid toxicity prediction. Processes, 2021, 9(1), 65. (Full text and GitHub repository)
- H. Qin, Z. Wang, T. Zhou, Z. Song. Comprehensive evaluation of COSMO-RS for predicting ternary and binary ionic liquid-containing vapor–liquid equilibria. Industrial & Engineering Chemistry Research, 2021, 60(48), 17761–17777. (Full text)
- A. Yang, Y. Su, Z. Wang, S. Jin, J. Ren, X. Zhang, W. Shen, J.H. Clark. A multi-task deep learning neural network for predicting flammability-related properties from molecular structures. Green Chemistry, 2021, 23(12), 4451–4465. (Full text)
- Z. Wang, Y. Su, S. Jin, W. Shen, J. Ren, X. Zhang, J.H. Clark. A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties. Green Chemistry, 2020, 22(12), 3867–3876. (Full text)
- Z. Wang, Y. Su, W. Shen, S. Jin, J.H. Clark, J. Ren, X. Zhang. Predictive deep learning models for environmental properties: the direct calculation of octanol-water partition coefficients from molecular graphs. Green Chemistry, 2019, 21(16), 4555–4565. (Full text)
- Y. Su, Z. Wang, S. Jin, W. Shen, J. Ren, M.R. Eden. An architecture of deep learning in QSPR modeling for the prediction of critical properties using molecular signatures. AIChE Journal, 2019, 65(9), e16678. (Full text)
- S. Zhao, M. Zhang, Z. Wang, X. Xian. Enhanced high-rate performance of Li4Ti5O12 microspheres/multiwalled carbon nanotubes composites prepared by electrostatic self-assembly. Electrochimica Acta, 2018, 276, 73–80. (Full text)
- S. Zhao, M. Zhang, X. Xian, O. Ka, Z. Wang, J. Wang. Insight into the formation mechanism of Li4Ti5O12 microspheres obtained by a CTAB-assisted synthetic method and their electrochemical performances. Journal of Materials Chemistry A, 2017, 5(26), 13740–13747. (Full text)
Conference Paper
- Z. Wang, T. Zhou, K. Sundmacher. Molecular property targeting for optimal solvent design in extractive distillation processes. Computer Aided Chemical Engineering, 2023, 1247–1252. (Full text and GitHub repository)
- Z. Wang, T. Zhou, K. Sundmacher. A novel machine learning-based optimization approach for the molecular design of solvents. Computer Aided Chemical Engineering, 2022, 1477–1482. (Full text and GitHub repository)
- T. Zhou, Z. Wang, K. Sundmacher. A new machine learning framework for efficient MOF discovery: Application to hydrogen storage. Computer Aided Chemical Engineering, 2022, 1807–1812. (Full text)
Conference Talk
- Z. Wang, T. Zhou, K. Sundmacher. Data-driven integrated design of solvents and extractive distillation processes. Presented at: 2023 AIChE Annual Meeting. Orlando, USA, November 08, 2023.
- Z. Wang, T. Zhou, K. Sundmacher. A novel machine learning-based optimization approach for the molecular design of solvents. Keynote presented at: 32nd European Symposium on Computer-Aided Process Engineering (ESCAPE-32). Toulouse, France, June 13, 2022.
Conference Poster
- Z. Wang, T. Zhou, K. Sundmacher. Molecular property targeting for optimal solvent design in extractive distillation processes. Presented at: 33rd European Symposium on Computer-Aided Process Engineering (ESCAPE-33). Athens, Greece, June 19, 2023.
- T. Zhou, Z. Wang, K. Sundmacher. A new machine learning framework for efficient MOF discovery: application to hydrogen storage. Presented at: 14th International Symposium on Process Systems Engineering (PSE 2021+). Kyoto, Japan, June 21, 2022.
- Z. Ayaz, Z. Wang, A. Lieb, K. Sundmacher, F. Scheffler. Synthesis and characterisation of CALF-20/GO nanocomposites for microwave assisted adsorbate regeneration. Presented at: 8th International Conference on Metal-Organic Frameworks and Open Framework Compounds. Dresden, Germany, September 5, 2022
Master’s Thesis Supervision
- S.D. Sreedhar. Surrogate-based optimization of extractive distillation processes using active learning. Otto-von-Guericke University Magdeburg, Germany, 2023.