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State of the art process systems engineering articles from Na Research Group

Ultrasound separation is economically advantageous and sustainable (Cell Reports Physical Science)

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A paper by Ji Woo Ha from our lab has been featured in Cell Reports Physical Science. Ha, J. W., Liu, J., Feng, H., Sahinidis,…

Our Carbon Capture and Utilization (CCU) research is highlighted on the front cover of Green Chemistry

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Suhyun Lee (이수현)'s first paper, "Techno-economic analysis and life-cycle assessment of the electrochemical conversion process with captured CO2 in an amine-based solvent", was published in…

Our optimization study published in International Journal of Energy Research

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The study Comparison of derivative-free optimization: Energy optimization of steam methane reforming process, co-first authored by student Areum Han from our lab and Dr. Minsu…

The group’s AI-based energy systems is discussed in an issue of Energy

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Our research "Optimal planning of hybrid energy storage systems using curtailed renewable energy through deep reinforcement learning" is published in Energy. Our alumni Doeun Kang…
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Featured and Cover Articles

Journal Articles (Refereed)

†Equally contributed, *Corresponding author

 


2023

  1. Shin, D.†, Karasu, H.†, Jang, J.†, Kim, C., Kim, C., Kim, D., Sa, Y. J., Lee, K. B., Moon, I.*, Won, D. H.*, Na, J.*, Lee, U.* (2023). Uncovering the origin of catalyst degradation for electrochemical CO2 reduction through explainable artificial intelligence, Nature Catalysis, (under review).
  2. Kim, H.†, Choi, H.†, Kang, D.†, Lee, W. B. *, & Na, J.* (2023). Materials Discovery with Extreme Properties via AI-Driven Combinatorial Chemistry, Chemical Science, under review (1st round).
  3. Ha, J.W., Seo, H., Liu, J., Siirola, J.J., Feng, H., Sahinidis, N.*, & Na, J.* (2023). Ultrasound-based separation of ethanol-water mixtures is economically advantageous and sustainable. Cell Reports Physical Science, under review (2nd round).
  4. Lee, C., Mou, M., Kim, S., Jiang, M., Na, J.* (2023). Computational fluid dynamics simulation for controllable residence time distribution in slug flow crystallizer. Chemical Engineering Science, under review (1st round).
  5. Lee, S.†, Choi, W.†, Kim, J. H., Park, S., Hwang, Y. J.*, & Na, J.* (2023). Techno-economic analysis and life-cycle assessment of the electrochemical conversion process with captured CO2 in amine-based solvent, Green Chemistry, accepted. [Link]
    [Highlights on Front Cover (inside) Article]
    [2023 Green Chemistry Hot Articles]
  6. Kang, D.†, Kang, D.†, Hwangbo, S., Niaz, H., Lee, W.B., Liu, J., & Na, J.* (2023). Optimal Planning of Hybrid Energy Storage Systems using Curtailed Renewable Energy through Deep Reinforcement Learning. Energy, 284, 128623. [Link]
  7. Kim, M.†, Han, A.†, Lee, J., Cho, S., Moon, I.*, & Na, J.* (2023). Comparison of derivative-free optimization: Energy optimization of steam methane reforming process. International Journal of Energy Research, 2023. [Link]
  8. Mok, D. H.†, Shin, D.†, Na, J.*, & Back, S.* (2023). Chemically Inspired Convolutional Neural Network using Electronic Structure Representation. Journal of Materials Chemistry A, 11, 10184-10194. [Link]
    [Highlights on Back Cover Article]
  9. Akhtar, M. S., Khan, H., Liu, J. J.*, & Na, J. (2023). Green hydrogen and sustainable development–A social LCA perspective highlighting social hotspots and geopolitical implications of the future hydrogen economy. Journal of Cleaner Production, 395, 136438. [Link]
  10. Jang, K., Hong, S., Kim, M., Moon, I.*, Na, J.* (2023) . Explainable Fault Detection using Adversarial Autoencoder and SHAP value. IEEE Transactions on Industrial Informatics, Early Access. [Link]
  11. Cho, S.†, Kim, M.†, Lee, J., Han, A., Na, J.*, & Moon I.* (2023). Multi-objective Optimization of Explosive Waste Treatment Process Considering Environment and Process Cost via Bayesian Active Learning. Engineering Applications of Artificial Intelligence, 117, 105463. [Link]


    2022

  12. Choi, W.†, Choi, Y.†, Choi, E., Yun, H., Jung, W., Lee, W. H., Oh, H.-S., Won, D.H., Na, J.*, & Hwang, Y. J.* (2022). Microenvironments of Cu catalysts in zero-gap membrane electrode assembly for efficient CO2 electrolysis to C2+ products. Journal of Materials Chemistry A, 10, 10363. [Link]
    [Highlights on Front Cover Article]
  13. Choi, W.†, Park, S.†, Jung, W.†, Won, D. H., Na, J.*, & Hwang, Y. J.* (2022). Origin of Hydrogen Incorporated into Ethylene during Electrochemical CO2 Reduction in Membrane Electrode Assembly. ACS Energy Letters, 7, 939-945. [Link]
    [Highlights on Front Cover Article]
  14. Seo, S. K., Yoon, Y. G., Lee, J. S., Na, J.*, & Lee, C. J.* (2022). Deep Neural Network-based Optimization Framework for Safety Evacuation Route during Toxic Gas Leak Incidents. Reliability Engineering & System Safety, 218, 108102. [Link]
  15. Choi, S., Jung, I., Kim, H., Na, J.*, & Lee, J. M.* (2022). Physics-informed deep learning for data-driven solutions of computational fluid dynamics. Korean Journal of Chemical Engineering, 39, 515-528. [Link]
  16. Kim, M., Cho, S., Jang, K., Hong, S., Na, J.*, Moon, I.*, Data-driven Robust Optimization for Minimum Nitrogen Oxide Emission Under Process Uncertainty. Chemical Engineering Journal. 2022, 428, 130971. [Link]
  17. Shin, S., Choi, S., Na, J., Jung, I., Kim, M.-K, Park, M.-Y.*, Lee, W.B.*, CFD modeling for the prediction of molecular weight distribution in the LDPE autoclave reactor: Effects of non-ideal mixing. Chemical Engineering Journal. 2022, 427, 131829. [Link]


    2021

  18. Kim, H., Na, J.*, & Lee, W. B.* (2021). Generative Chemical Transformer: Neural Machine Learning of Molecular Geometric Structures from Chemical Language via Attention. Journal of Chemical Information and Modeling, 61(12), 5804-5814. [Link]
    [Highlights on Back Cover Article]
  19. Jung, B.†, Park, S.†, Lim, C., Lee, W.H., Lim, Y., Na, J., Lee, C.-J., Oh, H.-S.*, Lee, U.*, Design methodology for mass transfer-enhanced large-scale electrochemical reactor for CO2 Chemical Engineering Journal, 2021, 424, 130265. [Link]
  20. Kim, D., Choi, W., Lee, H.W., Lee, S.Y., Choi, Y., Lee, D.K., Kim, W., Na, J., Lee, U.*, Hwang, Y.J.*, Won, D.H.*, Electrocatalytic Reduction of Low Concentrations of CO2 Gas in a Membrane Electrode Assembly Electrolyzer. ACS Energy Letters. 2021, 6(10), 3488-3495. [Link]
  21. Shams, M.H., Niaz, H., Na, J., Anvari-Moghaddam, A.*, Liu J., Machine learning-based utilization of renewable power curtailments under uncertainty by planning of hydrogen systems and battery storages. Journal of Energy Storage. 2021, 41, 103010. [Link]
  22. Na, J., Bak, J.H., Sahinidis, N.*, Efficient Bayesian inference using adversarial machine learning and low-complexity surrogate models. Computers & Chemical Engineering, 2021, 151, 107322. [Link]
  23. Lee, D., Na, J., Park, D., Lee, J.M.*, Bayesian Optimization of Semicontinuous Carbonation Process Operation Recipe. Industrial & Engineering Chemistry Research. 2021, 60(27), 9871-9884. [Link]
  24. Park, D., Na, J., Lee, J.M.*, Clustered Manifold Approximation and Projection for Semisupervised Fault Diagnosis and Process Monitoring. Industrial & Engineering Chemistry Research. 2021, 60(26), 9521-9531. [Link]
  25. Park, S., Atwair, M., Kim, K., Lee, U., Na, J., Zahid, U.*, Lee, C.-J.*, Bayesian optimization of industrial-scale toluene diisocyanate liquid-phase jet reactor with 3-D computational fluid dynamics model. Journal of Industrial and Engineering Chemistry, 2021, 98, 327-339. [Link]
  26. Jang, K., Hong, S., Kim, M., Na, J.*, & Moon, I.* (2021). Adversarial Autoencoder Based Feature Learning for Fault Detection in Industrial Processes. IEEE Transactions on Industrial Informatics, 18(2), 827-834. [Link]
  27. Back, S.†, Na, J.†, & Ulissi, Z.*, Efficient Discovery of Active, Selective, and Stable Catalysts for Electrochemical H2O2 Synthesis through Active Motif Screening. ACS Catalysis, 2021, 11(5), 2483-2491. [Link]
  28. Park, S.†, Wijaya, D. T.†, Na, J.*, & Lee, C. W.* Towards the Large-Scale Electrochemical Reduction of Carbon Dioxide. Catalysts, 2021, 11(2). [Link]
  29. Kim, J.†, Na, J.†, Kim, K., Bak, J.H., Lee, H., & Lee, U.* Learning the properties of a water-lean amine solvent from carbon capture pilot experiments. Applied Energy, 2021, 283, 116213. [Link]


    2020

  30. Back, S., Na, J., Tran, K. & Ulissi, Z. W. In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation. Physical Chemistry Chemical Physics 22, 19454-19458, (2020). [Link]
  31. Sa, Y. J., Lee, C. W., Lee, S. Y., Na, J., Lee, U.*, & Hwang, Y. J.* Catalyst–electrolyte interface chemistry for electrochemical CO2 reduction. Chemical Society Reviews. 2020, 49, 6632-6665. [Link]
  32. Kim, K., Lee, W. H., Na, J., Hwang, Y. J., Oh, H.-S., & Lee, U.*, Data driven pilot optimization for electrochemical CO mass production. Journal of Materials Chemistry A. 2020, 448781– 8798. [Link]
  33. Lee, H. W., Kim, K., An, J., Na, J., Kim, H., Lee, U.* Toward the practical application of direct CO2 hydrogenation technology for methanol production. International Journal of Energy Research. 2020, 1– 18. [Link]
  34. Nguyen, D. L. T.†, Lee, C. W.†, Na, J., Kim, M.-C. Tu, N. D. K., Lee, S. Y., Sa, Y. J., Won, D. H., Oh, H.-S., Kim, H., Han, S. S., Min, B. K., Lee, U.*, Hwang, Y. J.*, Mass transport control by surface graphene oxide for selective CO production from electrochemical CO2 reduction. ACS Catalysis. 2020, 10 (5), 3222-3231. [Link]


    -2020 (At CMU, KIST, SNU)

  35. Na, J., Seo, B., Kim, J., Lee, C. W., Lee, H., Hwang, Y. J., Min, B. K., Lee, D. K., Oh, H.-S.*, Lee, U*, General technoeconomic analysis for electrochemical coproduction coupling carbon dioxide reduction with organic oxidation. Nature Communications. 2019, 10, 5193. [Link]
    [Editor’s Highlights in Nature Communications]
    Also appeared in Nature Research Device and Materials Engineering Community, 2019 [Link]
  36. Lee, Y., Jeon, K., Cho, J., Na, J, Park J., Jung, I., Park, J., Park, M.J., Lee, W.B.*, Multicompartment Model of an Ethylene–Vinyl Acetate Autoclave Reactor: A Combined Computational Fluid Dynamics and Polymerization Kinetics Model. Industrial & Engineering Chemistry Research. 2019, 58, 16459-1641.
  37. Hwang, J., Kim, J., Lee, H. W., Na, J., Ahn, B. S., Lee, S. D., Kim, H.S., Lee, H., Lee, U. *, An experimental based optimization of a novel water lean amine solvent for post combustion CO2 capture process. Applied Energy. 2019, 248, 174-184.
  38. Na, J.†, Park, S.†, Bak, J. H., Lee, D., Yoo, Y., Kim, I., Park, J., Lee, U., Lee, J.M.*, Bayesian Parameter Estimation of Aqueous Mineral Carbonation Kinetics. Industrial & Engineering Chemistry Research. 2019, 58(19), 8246-8259.
  39. Jeon, K., Yang, S., Kang, D., Na, J., Lee, W.B.*, Development of surrogate model using CFD and deep neural networks to optimize gas detector layout. Korean Journal of Chemical Engineering. 2019, 36(3), 325-332.
  40. Lee, Y.†, Na, J.†, Lee, W.B.*, Robust design of ambient-air vaporaizer based on time-series clustering. Computers & Chemical Engineering. 2018, 118, 236-247
  41. Park, S., Na, J., Kim, M., Lee, J.M.*, Multi-objective Bayesian optimization of chemical reactor design using computational fluid dynamics. Computers & Chemical Engineering. 2018, 119, 25-37.
  42. Lee, W.-J.†, Na, J., Kim, K., Lee, C., Lee, Y., Lee, J.M.*, NARX Modeling for Real-Time Optimization of Air and Gas Compression Systems in Chemical Processes. Computers & Chemical Engineering. 2018, 115, 262-274.
  43. Na, J., Kshetrimayum, K. S.†, Jung, I., Park, S., Lee, Y., Kwon, O., Mo, Y., Chung, J., Yi, J., Lee, U., Han, C.*, Pilot Scale Compact Gas-to-Liquid Process: Design and Operation of Microchannel Reactor for Fischer-Tropsch Synthesis. Chemical Engineering and Processing: Process Intensification. 2018, 128, 63-76.
  44. Na, J., Jeon, K.†, Lee, W.-B.*, Toxic gas release modeling for real-time analysis using variational autoencoder with convolutional neural networks. Chemical Engineering Science, 2018. 181, 68-78.
  45. Kim, M., Na, J., Park, S., Park, J.-H., Han, C.*, Modeling and validation of a pilot-scale aqueous mineral carbonation reactor for carbon capture using computational fluid dynamics. Chemical Engineering Science, 2018, 177, 301-312.
  46. An, J.†, Na, J., Lee, U.*, Han, C.*, Design of carbon dioxide dehydration process using derivative-free superstructure optimization. Chemical Engineering Research and Design 2018, 129, 344-355.
  47. Shin, S., Lee, G., Ahmed, U., Lee, Y., Na, J., Han, C.*, Risk-based underground pipeline safety management considering corrosion effect. Journal of Hazardous Materials 2018, 342 (Supplement C), 279-289.
  48. Kshetrimayum, K., Na, J., Park, S., Jung, I., Lee, Y., Han, C., Intensified Low-Temperature Fischer-Tropsch Synthesis Using Microchannel Reactor Block: A Computational Fluid Dynamics Simulation Study. Journal of the Korean Institute of Gas 2017, 21 (4), 92-102.
  49. Na, J., Lim, Y., Han, C.*, A modified DIRECT algorithm for hidden constraints in an LNG process optimization. Energy 2017, 126, 488-500.
  50. Na, J., Kshetrimayum, K. S., Lee, U.*, Han, C.*, Multi-objective optimization of microchannel reactor for Fischer-Tropsch synthesis using computational fluid dynamics and genetic algorithm. Chemical Engineering Journal 2017, 313, 1521-1534.
  51. Jung, I., Na, J., Park, S., Jeon, J., Mo, Y.-G., Yi, J.-Y., Chung, J.-T., Han, C.*, Optimal design of a large scale Fischer-Tropsch microchannel reactor module using a cell-coupling method. Fuel Processing Technology 2017, 159, 448-459.
  52. Park, S., Na, J., Kim, M., An, J., Lee, C., Han, C., CO2 Mineral Carbonation Reactor Analysis using Computational Fluid Dynamics: Internal Reactor Design Study for the Efficient Mixing of Solid Reactants in the Solution. Korean Chemical Engineering Research 2016, 54 (5), 612-620.
  53. Shin, S., Lee, Y., Song, K., Na, J., Park, S., Lee, Y., Lee, C.-J., Han, C.*, Design and economic analysis of natural gas hydrate regasification process combined with LNG receiving terminal. Chemical Engineering Research and Design 2016, 112, 64-77.
  54. Park, S., Jung, I., Lee, Y., Kshetrimayum, K. S., Na, J., Park, S., Shin, S., Ha, D., Lee, Y., Chung, J., Lee, C.-J.*, Han, C.*, Design of microchannel Fischer–Tropsch reactor using cell-coupling method: Effect of flow configurations and distribution. Chemical Engineering Science 2016, 143, 63-75.
  55. Kshetrimayum, K. S., Jung, I., Na, J., Park, S., Lee, Y., Park, S., Lee, C.-J.*, Han, C.*, CFD Simulation of Microchannel Reactor Block for Fischer–Tropsch Synthesis: Effect of Coolant Type and Wall Boiling Condition on Reactor Temperature. Industrial & Engineering Chemistry Research 2016, 55 (3), 543-554.
  56. Jung, I., Kshetrimayum, K. S., Park, S., Na, J., Lee, Y., An, J., Park, S., Lee, C.-J.*, Han, C.*, Computational Fluid Dynamics Based Optimal Design of Guiding Channel Geometry in U-Type Coolant Layer Manifold of Large-Scale Microchannel Fischer–Tropsch Reactor. Industrial & Engineering Chemistry Research 2016, 55 (2), 505-515.
  57. Lee, Y., Jung, I., Na, J., Park, S., Kshetrimayum, K. S., Han, C., Analysis on Thermal Effects of Process Channel Geometry for Microchannel Fischer-Tropsch Reactor Using Computational Fluid Dynamics. Korean Chemical Engineering Research 2015, 53 (6), 818-823.
  58. Park, S., Jung, I., Lee, U., Na, J., Kshetrimayum, K. S., Lee, Y., Lee, C.-J.*, Han, C.*, Design and modeling of large-scale cross-current multichannel Fischer–Tropsch reactor using channel decomposition and cell-coupling method. Chemical Engineering Science 2015, 134, 448-456.
  59. Na, J., Jung, J., Park, C., Han, C.*, Simultaneous synthesis of a heat exchanger network with multiple utilities using utility substages. Computers & Chemical Engineering 2015, 79, 70-79.
  60. Na, J., Jung, I., Kshetrimayum, K. S., Park, S., Park, C., Han, C., Computational Fluid Dynamics Study of Channel Geometric Effect for Fischer-Tropsch Microchannel Reactor. Korean Chemical Engineering Research 2014, 52 (6), 826-833.
  61. Park, C., Jung, I., Park, S., Na, J., Kshetrimayum, K., Han, C., Lee, J. Y., Jung, J., Modeling of Liquid Hold-up in Fixed-bed Reactor for Fischer-Tropsch Synthesis. Journal of the Korean Institute of Gas 2014, 18 (4), 63-67.
  62. Park, S., Park, C., Lee, U., Jung, I., Na, J., Kshetrimayum, K. S., Han, C.*, Comparative Study of Process Integration and Retrofit Design of a Liquefied Natural Gas (LNG) Regasification Process Based on Exergy Analyses: A Case Study of an LNG Regasification Process in Republic of Korea. Industrial & Engineering Chemistry Research 2014, 53 (37), 14366-14376.
  63. Jung, I., Park, C., Park, S., Na, J., Han, C.*, A Comparative Study of Various Fuel for Newly Optimized Onboard Fuel Processor System under the Simple Heat Exchanger Network. Korean Chemical Engineering Research 2014, 52 (6), 720-726.
  64. Jung, J., Song, K., Park, S., Na, J., Han, C.*, Optimal operation strategy of batch vacuum distillation for sulfuric acid recycling process. Computers & Chemical Engineering 2014, 71, 104-115.

 

Proceedings, Reports, and Expositions

  1. Na, J., Lee, U. Research trends of process optimization using machine learning and its applications. NICE (News & Information for Chemical Engineers) 2018, 1, 4-8.
  2. Lee, Y., Na, J., Lee, W.-B. Optimal Design of an Ambient Air Vaporizer using Numerical Model and DIRECT Algorithm. Computer Aided Chemical Engineering 2018, 44 (eds Mario R. Eden, Marianthi G. Ierapetritou, & Gavin P. Towler), 1795-1800.
  3. Na, J., Jung, J., Park, C., Han, C., Simultaneous Optimization Models for Heat Exchanger Network Synthesis with Multiple Utilities: A New Strategy by Using Utility Sub-stage. Computer Aided Chemical Engineering 2015, 33 (eds Jiří Jaromír Klemeš, Petar Sabev Varbanov, & Peng Yen Liew), 1675-1680.
  4. Kshetrimayum, K. S., Park, S., Jung, I., Na, J., Han, C. Simulation Study of Heat Transfer Enhancement due to Wall Boiling Condition in a Microchannel Reactor Block for Fischer-Tropsch Synthesis. Computer Aided Chemical Engineering 2015, 37 (eds Krist V. Gernaey, Jakob K. Huusom, & Rafiqul Gani), 1355-1360.
Main News

Ultrasound separation is economically advantageous and sustainable (Cell Reports Physical Science)

A paper by Ji Woo Ha from our lab has been featured in Cell Reports Physical Science. Ha, J. W., Liu, J., Feng, H., Sahinidis,…
Main News
Our Carbon Capture and Utilization (CCU) research is highlighted on the front cover of Green Chemistry
News
Our optimization study published in International Journal of Energy Research
Main News
The group’s AI-based energy systems is discussed in an issue of Energy
Main News
The cover of JMCA highlights the group’s Autonomous Materials Discovery research
Main News
Our XAI for Process Monitoring research is published in IEEE TII (IF: 11.6, <3%)
Main News
Featured Content: Front cover for JMCA
Main News
The front cover of ACS Energy Letters highlights the group’s research in multiphysics simulation of electrochemical CO2 reduction
Main News
Our autonomous product design research is discussed in an issue of Journal of Chemical Information and Modeling
Main News
The group’s ML-PSE research is discussed in an issue of Computers & Chemical Engineering
News
Our ML-based catalyst design research is discussed in an issue of ACS Catalysis (IF: 12.350, JCR: 7.233%)
News
Subin Park’s Review Paper has been published in a special issue of Catalysts
Main News
The group’s Machine Learning research is discussed in an issue of Applied Energy (IF: 8.848, JCR: 3.846%)
News
Collaborative work with Prof. Back (Sogang Univ.) “In silico discovery of active, stable, CO-tolerant and cost-effective electrocatalysts for hydrogen evolution and oxidation” is published in Physical Chemistry Chemical Physics
Main News
The group’s electrochemical CO2 reduction research is discussed in an issue of Chemical Society Reviews (IF: 42.846, JCR: 0.847%)
News
Data driven pilot optimization for electrochemical CO mass production published in Journal of Materials Chemistry A (IF: 11.301)
News
Toward the practical application of direct carbon dioxide hydrogenation technology for methanol production published in International Journal of Energy Research (June 2020, JCR 1.471%)
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