Updated on 2026/04/14

写真a

 
SUZUKI Kensuke
 
Organization
Graduate School of Engineering Materials Process Engineering 1 Assistant Professor
Undergraduate School
School of Engineering Materials Science and Engineering
Title
Assistant Professor

Research Interests 4

  1. Process Systems Engineering

  2. Bayesian Statistics

  3. Preparative Chromatography

  4. Chemical Engineering

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Research Areas 1

  1. Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Chemical reaction and process system engineering

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Research History 4

  1. The University of Tokyo   The Graduate School of Engineering Department of Chemical System Engineering   Visiting Researcher

    2026.4

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    Country：Japan

  2. Nagoya University   Graduate School of Engineering Materials Process Engineering   Assistant Professor

    2026.4

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    Country：Japan

  3. The University of Tokyo   The Graduate School of Engineering Department of Chemical System Engineering   Project Researcher

    2025.4 - 2026.3

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    Country：Japan

  4. Faculty of Engineering (LTH), Lund University   Department of Process and Life Science Engineering   Guest Researcher

    2023.8 - 2024.8

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    Country：Sweden

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Education 3

  1. Nagoya University   Graduate School of Engineering   Materials Process Engineering Doctoral Course

    2021.4 - 2025.3

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    Country： Japan

  2. Nagoya University   Graduate School of Engineering   Materials Process Engineering Master Course

    2019.4 - 2021.3

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    Country： Japan

  3. Nagoya University   School of Engineering   Chemical and Biological Engineering

    2015.4 - 2019.3

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    Country： Japan

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Professional Memberships 2

  1. Society of Chemical Engineers, Japan

  2. International Adsorption Society

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Awards 2

  1. Research Encouragement Award

    2025.9   Division of SIS, SCEJ  

  2. Excellence Award (Master’s Midterm Presentation)

    2020.3   Nagoya University  

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Papers 7

  1. Quick robust design for simulated moving bed chromatography under comprehensive uncertainty via robust triangle theory Open Access

    Kensuke Suzuki, Tomoyuki Yajima, Yoshiaki Kawajiri

    Separation and Purification Technology   Vol. 391   page： 136979 - 136979   2026

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.seppur.2026.136979

    Open Access

  2. Process robustness evaluation for various operating configurations of multi-column chromatography processes with nonlinear isotherm Open Access

    Kensuke Suzuki, Tomoyuki Yajima, Yoshiaki Kawajiri

    Chemical Engineering Science     2025.5

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.ces.2025.121395

    Open Access

  3. Estimation and Uncertainty Quantification of Solvent Strength Parameters in Gradient Elution of Chromatography Using Sequential Monte Carlo Method Open Access

    Ziting Yuan, Kensuke Suzuki, Yota Yamamoto, Tomoyuki Yajima, Yoshiaki Kawajiri

    Processes     2025.1

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.3390/pr13010114

    Open Access

  4. Parameter estimation for reactive chromatography model by Bayesian inference and parallel sequential Monte Carlo Open Access

    Hikari Sugiyama, Yota Yamamoto, Kensuke Suzuki, Tomoyuki Yajima, Yoshiaki Kawajiri

    Chemical Engineering Research and Design     2024.3

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    Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.cherd.2024.01.056

  5. Using pretrained machine learning models to predict luminous and solar transmittance controllability of liquid crystal/polymer composites from microstructural images Open Access

    Hiroshi Kakiuchida, Kensuke Suzuki, Takuto Kojima

    Optics Express   Vol. 31 ( 18 ) page： 29954 - 29954   2023.8

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    Language：English   Publishing type：Research paper (scientific journal)   Publisher：Optica Publishing Group  

    <jats:p>Polarized optical microscopy (POM) images of polymer network liquid crystals (PNLCs) were first analyzed using a pretrained machine learning model for feature extraction and hierarchical clustering. The analyses worked well in predicting and improving the thermoresponsive changes individually in direct luminous and hemispheric solar transmittance, both of which are crucial properties of energy-saving smart windows. The features of a 1280 × 1920–pixel color POM image were extracted by the latest pretrained algorithm, EfficientNet-B7, as a 2560-dimensional vector and then reduced into a two-dimensional space for clustering and visualization using the uniform manifold approximation and projection (UMAP) algorithm while efficiently preserving the global structures of the distance relationship in a high-dimensional space. The feature vectors in the UMAP space were correlated with the thermoresponsive transmittance and classified using hierarchical clustering analysis. The extracted features belonging to some clusters were also correlated with the fabrication parameters. The PNLCs here were produced from various raw materials under different fabrication conditions. These analyses and predictability are extensively applied to different PNLCs for stimuli-responsive optical devices, such as solar- and privacy-control windows.</jats:p>

    DOI： 10.1364/oe.496460

    Open Access

  6. Utilization of operation data for parameter estimation of simulated moving bed chromatography

    Kensuke Suzuki, Hideki Harada, Kohei Sato, Kazuo Okada, Masaki Tsuruta, Tomoyuki Yajima, Yoshiaki Kawajiri

    Journal of Advanced Manufacturing and Processing   Vol. 4 ( 1 )   2022.1

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    Publishing type：Research paper (scientific journal)   Publisher：Wiley  

    DOI： 10.1002/amp2.10103

  7. Process development for advanced simulated moving bed (ASMB) chromatography by parameter refinement using pilot plant experimental data

    Hideki Harada, Kensuke Suzuki, Kohei Sato, Kazuo Okada, Masaki Tsuruta, Tomoyuki Yajima, Yoshiaki Kawajiri

    Separation and Purification Technology     2022.1

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    Language：English   Publishing type：Research paper (scientific journal)  

    DOI： 10.1016/j.seppur.2021.119932

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MISC 2

  1. Process Robustness Evaluation for Various Operating Configurations of Simulated Moving Bed Chromatography

    Computer Aided Chemical Engineering     page： 229 - 234   2024

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    Publisher：Elsevier  

    DOI： 10.1016/b978-0-443-28824-1.50039-9

  2. Comprehensive Quantification of Model Prediction Uncertainty for Simulated Moving Bed Chromatography

    Computer Aided Chemical Engineering     page： 943 - 948   2022

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    Publisher：Elsevier  

    DOI： 10.1016/b978-0-323-85159-6.50157-3

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Research Project for Joint Research, Competitive Funding, etc. 1

  1. モデル化誤差を考慮したタンパク質吸着分離プロセスのモデリング

    Grant number：202380015  2023.8 - 2024.8

    若⼿研究者海外挑戦プログラム 

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    Authorship：Principal investigator 

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KAKENHI (Grants-in-Aid for Scientific Research) 2

  1. モデル選択に依存しない関数空間に基づくプロセスモデリング手法の開発

    Grant number：26K17698  2026.4 - 2029.3

    日本学術振興会  科学研究費助成事業  若手研究

    鈴木 健介

  2. 工程間連携に着目した全体最適化アルゴリズムの開発と抗体産生プロセスへの応用

    Grant number：25K23521  2025.7 - 2027.3

    日本学術振興会  科学研究費助成事業  研究活動スタート支援

    鈴木 健介

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