Publikationen von Teng Zhou

Chai, S.; Song, Z.; Zhou, T.; Zhang, L.; Qi, Z.: Computer-aided molecular design of solvents for chemical separation processes. Current Opinion in Chemical Engineering 35, 100732 (2022)

Qin, H.; Cheng, J.; Yu, H.; Zhou, T.; Song , Z.: Hierarchical Ionic Liquid Screening Integrating COSMO-RS and Aspen Plus for Selective Recovery of Hydrofluorocarbons and Hydrofluoroolefins from a Refrigerant Blend. Industrial & Engineering Chemistry Research 61 (11), S. 4083 - 4094 (2022)

Qin, H.; Wang, Z.; Song, Z.; Zhang, X.; Zhou, T.: High-Throughput Computational Screening of Ionic Liquids for Butadiene and Butene Separation. Processes 10 (1), 165 (2022)

Wang, Z.; Zhou, T.; Sundmacher, K.: Interpretable machine learning for accelerating the discovery of metal-organic frameworks for ethane/ethylene separation. Chemical Engineering Journal 444, 136651 (2022)

Wang, Z.; Zhou, Y.; Zhou, T.; Sundmacher, K.: Identification of Optimal Metal-Organic Frameworks by Machine Learning: Structure Decomposition, Feature Integration, and Predictive Modeling. Computers & Chemical Engineering 160, 107739 (2022)

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 16, S. 303 - 315 (2022)

Zhang, X.; Zhou, T.; Sundmacher, K.: Integrated metal–organic framework and pressure/vacuum swing adsorption process design: Descriptor optimization. AIChE Journal 68 (2), e17524 (2022)

Zhou, Y.; Zhang, X.; Zhou, T.; Sundmacher, K.: Computational Screening of Metal-Organic Frameworks for Ethylene Purification from Ethane/Ethylene/Acetylene Mixture. Nanomaterials 12 (5), 869 (2022)

Qin, H.; Wang, Z.; Zhou, T.; Song, Z.: Comprehensive Evaluation of COSMO-RS for Predicting Ternary and Binary Ionic Liquid-Containing Vapor–Liquid Equilibria. Industrial & Engineering Chemistry Research 60 (48), S. 17761 - 17777 (2021)

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), S. 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, S. 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)

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), S. 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 7 (9), S. 1231 - 1238 (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)