Publications of Teng Zhou

Shi, H.; Zhang, X.; Sundmacher, K.; Zhou, T.: Model-Based Optimal Design of Phase Change Ionic Liquids for Efficient Thermal Energy Storage. Green Energy & Environment 6 (3), pp. 392 - 404 (2021)

Shi, H.; Zhou, T.: Computational design of heterogeneous catalysts and gas separation materials for advanced chemical processing. Frontiers of Chemical Science and Engineering 15, pp. 49 - 59 (2021)

Wang, Z.; Song, Z.; Zhou, T.: Machine Learning for Ionic Liquid Toxicity Prediction. Processes 9 (1), 65 (2021)

Xu, J.; Peng, Z.; Rong, S.; Jin, H.; Guo, L.; Zhang, X.; Zhou, T.: Model-based thermodynamic analysis of supercritical water gasification of oil-containing wastewater. Fuel 306, 121767 (2021)

Yang, A.; Su, Y.; Shi, T.; Ren, J.; Shen, W.; Zhou, T.: Energy-efficient recovery of tetrahydrofuran and ethyl acetate by triple-column extractive distillation: entrainer design and process optimization. Frontiers of Chemical Science and Engineering (2021)

Zhang, X.; Ding, X.; Song, Z.; Zhou, T.; Sundmacher, K.: Integrated Ionic Liquid and Rate‐Based Absorption Process Design for Gas Separation: Global Optimization Using Hybrid Models. AIChE Journal 67 (10), 17340 (2021)

Zhang, X.; Wang, J.; Song, Z.; Zhou, T.: Data-Driven Ionic Liquid Design for CO2 Capture: Molecular Structure Optimization and DFT Verification. Industrial & Engineering Chemistry Research 60 (27), pp. 9992 - 10000 (2021)

Zhang, X.; Zhou, T.; Ng, K. M.: Optimization‐based cosmetic formulation: Integration of mechanistic model, surrogate model, and heuristics. AIChE Journal 67 (1), e17064 (2021)

Zhou, T.; Gani, R.; Sundmacher, K.: Hybrid data-driven and mechanistic modeling approaches for multiscale material and process design. Engineering (2021)

Zhou, T.; Shi, H.; Ding, X.; Zhou, Y.: Thermodynamic modeling and rational design of ionic liquids for pre-combustion carbon capture. Chemical Engineering Science 229, 116076 (2021)

Song, Z.; Shi, H.; Zhang, X.; Zhou, T.: Prediction of CO2 solubility in ionic liquids using machine learning methods. Chemical Engineering Science 223, 115752 (2020)

Song, Z.; Zhou, T.; Qi, Z.; Sundmacher, K.: Extending the UNIFAC model for ionic liquid‐solute systems by combining experimental and computational databases. AIChE-Journal 66 (2), e16821 (2020)

Yang, A.; Su, Y.; Teng, L.; Jin, S.; Zhou, T.; Shen, W.: Investigation of energy-efficient and sustainable reactive/pressure-swing distillation processes to recover tetrahydrofuran and ethanol from the industrial effluent. Separation and Purification Technology 250, 117210 (2020)

Zhang, X.; Song, Z.; Gani, R.; Zhou, T.: Comparative Economic Analysis of Physical, Chemical, and Hybrid Absorption Processes for Carbon Capture. Industrial & Engineering Chemistry Research 59, pp. 2005 - 2012 (2020)

Zhang, C.; Song, Z.; Jin, C.; Nijhuis, J.; Zhou, T.; Noël, T.; Gröger, H.; Sundmacher, K.; van Hest, J.; Hessel, V.: Screening of functional solvent system for automatic aldehyde and ketone separation in aldol reaction: A combined COSMO-RS and experimental approach. Chemical Engineering Journal 385, 123399 (2020)

Zhou, T.; McBride, K.; Linke, S.; Song, Z.; Sundmacher, K.: Computer-aided solvent selection and design for efficient chemical processes. Current Opinion in Chemical Engineering 27, pp. 35 - 44 (2020)

Song, Z.; Zhou, Y.; Zhou, T.; Qi, Z.; Sundmacher, K.: Rational design of double salt ionic liquids as extraction solvents: Separation of thiophene/n‐octane as example. AIChE-Journal 65 (8), e16625 (2019)

Zhang, X.; Zhou, T.; Zhang, L.; Fung, K. Y.; Ng, K. M.: Food Product Design: A Hybrid Machine Learning and Mechanistic Modeling Approach. Industrial & Engineering Chemistry Research 58, pp. 16743 - 16752 (2019)

Zhou, T.; Song, Z.; Sundmacher, K.: Big Data Creates New Opportunities for Materials Research: A Review on Methods and Applications of Machine Learning for Materials Design. Engineering 5 (6), pp. 1017 - 1026 (2019)

Zhou, T.; Song, Z.; Zheng, X.; Gani, R.; Sundmacher, K.: Optimal Solvent Design for Extractive Distillation Processes: A Multiobjective Optimization-Based Hierarchical Framework. Industrial and Engineering Chemistry Research 58 (15), pp. 5777 - 5786 (2019)