Publications of 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), pp. 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)

Xu, J.; Rong, S.; Sun, J.; Peng, Z.; Jin, H.; Guo, L.; Zhang, X.; Zhou, T.: Optimal design of non-isothermal supercritical water gasification reactor: From biomass to hydrogen. Energy 244, 123163 (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, pp. 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)

Zhang, X.; Zhou, T.; Sundmacher, K.: Integrated metal‐organic framework (MOF) and pressure/vacuum swing adsorption process design:MOFmatching. AIChE Journal 68 (9), e17788 (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), pp. 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), 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)

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 7 (9), pp. 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)

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, 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)

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)

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)

Liu, X.; Zhou, T.; Zhang, X.; Zhang, S.; Liang, X.; Gani, R.; Kontogeorgis, G. M.: Application of COSMO-RS and UNIFAC for ionic liquids based gas separation. Chemical Engineering Science 192, pp. 816 - 828 (2018)

Song, Z.; Zhang, C.; Qi , Z.; Zhou, T.; Sundmacher, K.: Computer-aided design of ionic liquids as solvents for extractive desulfurization. AIChE-Journal 64 (3), pp. 1013 - 1025 (2018)

Zhang, X.; Song, Z.; Zhou, T.: Rigorous design of reaction-separation processes using disjunctive programming models. Computers and Chemical Engineering 111, pp. 16 - 26 (2018)

Zhou, T.; Jhamb, S.; Liang, X.; Sundmacher, K.; Gani, R.: Prediction of acid dissociation constants of organic compounds using group contribution methods. Chemical Engineering Science 183, pp. 95 - 105 (2018)

Song, Z.; Zhou, T.; Qi, Z.; Sundmacher, K.: Systematic Method for Screening Ionic Liquids as Extraction Solvents Exemplified by an Extractive Desulfurization Process. ACS Sustainable Chemistry & Engineering 5 (4), pp. 3382 - 3389 (2017)

Zhang, J.; Peng, D.; Song, Z.; Zhou, T.; Cheng, H.; Chen, L.; Qi, Z.: COSMO-descriptor based computer-aided ionic liquid design for separation processes. Part I: Modified group contribution methodology for predicting surface charge density profile of ionic liquids. Chemical Engineering Science 162, pp. 355 - 363 (2017)

Zhang, J.; Qin, L.; Peng, D.; Zhou, T.; Cheng, H.; Chen, L.; Qi, Z.: COSMO-descriptor based computer-aided ionic liquid design for separation processes: Part II: Task-specific design for extraction processes. Chemical Engineering Science 162, pp. 364 - 374 (2017)

Zhou, T.; Zhou, Y.; Sundmacher, K.: A hybrid stochastic-deterministic optimization approach for integrated solvent and process design. Chemical Engineering Science 159, pp. 207 - 2016 (2017)

Zhou, T.; Wang, J.; McBride, K.; Sundmacher, K.: Optimal design of solvents for extractive reaction processes. AIChE-Journal 62 (9), pp. 3238 - 3249 (2016)

Song, Z.; Zhou, T.; Zhang, J.; Cheng , H.; Chen , L.; Qi , Z.: Screening of ionic liquids for solvent-sensitive extraction –with deep desulfurization as an example. Chemical Engineering Science 129, pp. 69 - 77 (2015)

Zhou, T.; Lyu, Z.; Qi, Z.; Sundmacher, K.: Robust design of optimal solvents for chemical reactions − A combined experimental and computational strategy. Chemical Engineering Science 137, pp. 613 - 625 (2015)

Zhou, T.; McBride, K.; Zhang, X.; Qi, Z.; Sundmacher, K.: Integrated solvent and process design exemplified for a Diels–Alder reaction. AIChE Journal 61 (1), pp. 147 - 158 (2015)

Lyu, Z.; Zhou, T.; Chen , L.; Ye , Y.; Sundmacher, K.; Qi, Z.: Reprint of: Simulation based ionic liquid screening for benzene–cyclohexane extractive separation. Chemical Engineering Science 115, pp. 186 - 194 (2014)

Zhou, T.; Qi, Z.; Sundmacher, K.: Model-based method for the screening of solvents for chemical reactions. Chemical Engineering Science 115, pp. 177 - 185 (2014)

Wang, Z.; Zhou, T.; Sundmacher, K.: A Novel Machine Learning-Based Optimization Approach for the Molecular Design of Solvents. In: 32nd European Symposium on Computer Aided Process Engineering, pp. 1477 - 1482. 32nd European Symposium on Computer Aided Process Engineering : ESCAPE 32, Toulouse, France, June 12, 2022 - June 15, 2022. Elsevier (2022)

Zhang, X.; Zhou, T.; Sundmacher, K.: Metal-Organic Framework Targeting for Optimal Pressure Swing Adsorption Processes. In: 14th International Symposium on Process Systems Engineering:, pp. 295 - 300 (Eds. Yamashita, Y.; Kano, M.). 14th International Symposium on Process Systems Engineering - PSE 2021 +, Kyoto, Japan, June 19, 2022 - June 23, 2022. Elsevier (2022)

Zhou, T.; Wang, Z.; Sundmacher, K.: A New Machine Learning Framework for Efficient MOF Discovery: Application to Hydrogen Storage. In: 14th International Symposium on Process Systems Engineering:, pp. 1807 - 1812 (Eds. Yamashita, Y.; Kano, M.). 14th International Symposium on Process Systems Engineering - PSE 2021+, Kyoto, Japan, June 19, 2022 - June 23, 2022. Elsevier (2022)

Song, Z.; Zhou, T.; Qi, Z.; Sundmacher, K.: Computer-Aided Screening of Deep Eutectic Solvent Systems for the Associative Extraction of α-Tocopherol from Deodorizer Distillate. In: 31st European Symposium on Computer Aided Process Engineering, pp. 341 - 346 (Eds. Türkay, M.; Gani, R.). 31st European Symposium on Computer Aided Process Engineering, Istanbul, Turkey/virtual, June 06, 2021 - June 09, 2021. Elsevier, Amsterdam, Netherlands (2021)

Zhou, T.; Shi, H.; Sundmacher, K.: Rational Design of Ionic Liquid Phase-Change Material for Efficient Thermal Energy Storage. In: 31st European Symposium on Computer Aided Process Engineering, pp. 191 - 196 (Eds. Türkay, M.; Gani, R.). 31st European Symposium on Computer Aided Process Engineering, Istanbul, Turkey/virtual, June 06, 2021 - June 09, 2021. Elsevier, Amsterdam, Netherlands (2021)

Zhou, Y.; Zhou, T.; Sundmacher, K.: In silico Screening of Metal-organic Frameworks for Acetylene/ethylene Separation. In: 30th European Symposium on Computer Aided Process Engineering, pp. 895 - 900. ESCAPE 30 , Virtual Symposium, August 31, 2020 - September 02, 2020. Elsevier B.V. (2020)