Researcher Database
KIM Sanghong
| ■Faculty | Institute of Engineering Division of Applied Chemistry | |||
| ■Position | Associate Professor | |||
Last Updated :2026/03/02
Activity information
Name and contact details
Name
キム サンホン, 金 尚弘, KIM SanghongYear of birth
1986E-mail
sanghong
go.tuat.ac.jp
Affiliation / Position
Institute of Engineering Division of Applied Chemistry, Associate Professor
Other affiliation
- 准教授
Faculty of Engineering Department of Chemical Engineering - 准教授
Graduate School of Engineering Department of Applied Chemistry
Research History
- Tokyo University of Agriculture and Technology
Department of Chemical Engineering
Associate professor
From 01 Mar. 2021 - Kyoto University
Department of Chemical Engineering
Assistant professor
From 01 Apr. 2014, To 28 Feb. 2021
Education
- Kyoto University
工学研究科
化学工学専攻
To 24 Mar. 2014, graduated, master course - Kyoto University
工学研究科
化学工学専攻
To 31 Mar. 2011, graduated, master course
Degree
Current state of research and teaching activities
Subject of research
- Process systems engineering
From 20140401 - Process modeling
From 20140401 - Process control
From 20140401 - Process data analytics
From 20140401
Grants-in-Aid for Scientific Research
- 基盤研究（B）
医薬品連続生産プロセスの革新的管理戦略構築
From 2023, To 2023 - 基盤研究(C)
ダイナミックモデルの構築・管理・更新のための方法論の開発と応用
From 2022, To 2024 - 基盤研究(B)
医薬品連続生産プロセスの革新的管理戦略構築
From 2022, To 2022 - 基盤研究(B)
医薬品連続生産プロセスの革新的管理戦略構築
From 2021, To 2021
Papers
- Highly Precise Anomaly Detection Using Multivariate Statistical Process Control with Appropriate Scaling of Input Variables in Pharmaceutical Continuous Manufacturing
28 Mar. 2025, Research paper (scientific journal), joint, DOI(公開)(r-map) - Targeted excitation and re-identification methods for multivariate process and model predictive control
Masanori Oshima, Sanghong Kim, Yuri A.W. Shardt, Ken-Ichiro Sotowa
Journal of Process Control
ELSEVIER SCI LTD
A process controlled using model predictive control is required to be re -identified when significant plantmodel mismatch (PMM) occurs. During data acquisition for re -identification, the process is excited to enable accurate re -identification. However, the process excitation worsens the control performance. To prevent this problem, a new model -update framework that consists of targeted excitation (TE) and targeted re -identification (TR) is proposed. In TE, only the manipulated variables corresponding to problematic transfer functions that have significant PMM are excited during data acquisition. On the other hand, the other manipulated variables are optimized to suppress the variations of the controlled variables. After data is acquired using TE, the TR method re -identifies only the problematic transfer functions by using the other transfer -function models without large PMM. The validity of the proposed framework is examined by theoretical analysis and numerical case studies. In the theoretical analysis, the stability during data acquisition using TE and the asymptotic bias of the parameters re -identified using TR were considered. In the numerical case studies, the applicability of the proposed framework to several processes including a fluid catalytic cracking (FCC) process was examined. As a result, it was shown that, for all the processes, the proposed framework can improve both the control performance during data acquisition and the model accuracy after re -identification, compared to an existing method that excites all the inputs during data acquisition.
Apr. 2024, Research paper (scientific journal), joint, 136, 0959-1524, DOI(公開)(r-map), 103190 - Feed factor profile prediction model for two-component mixed powder in the twin-screw feeder
Yuki Kobayashi, Sanghong Kim , Takuya Nagato, Takuya Oishi, Manabu Kano
International Journal of Pharmaceutics: X
31 Mar. 2024, Research paper (scientific journal), joint, DOI(公開)(r-map) - Greedy design space construction based on regression and latent space extraction for pharmaceutical development
Tanabe, Shuichi; Muraki, Tatsuya; Yaginuma, Keita; Kim, Sanghong; Kano, Manabu
INTERNATIONAL JOURNAL OF PHARMACEUTICS
ELSEVIER
Implementation of the design space (DS) is a scientific concept for ensuring quality to be submitted as a part of the regulatory filing of a drug product for approval to market. An empirical approach is constructing the DS based on the regression model whose inputs are process parameters and material attributes over the different unit operations, i.e., a high-dimensional statistical model. While the high-dimensional model assures quality and process flexibility through a comprehensive process understanding, it has difficulty visualizing the feasible range of input parameters, i.e., DS. Therefore, this study proposes a greedy approach to constructing the extensive and flexible low-dimensional DS based on the high-dimensional statistical model and the observed internal repre-sentations that satisfies both comprehensive process understanding and the DS visualization capability. Intro-ducing the observed correlation structure enabled the dimensionality reduction of the DS. The non-critical controllable parameters were fixed to the target values in visualizing the low-dimensional DS as a function of critical parameters. The expected variation of non-critical non-controllable parameters was considered the source of variation in prediction. The case study demonstrated the proposed approach's usefulness for developing the pharmaceutical manufacturing process.
25 Jul. 2023, Research paper (scientific journal), joint, 642, 0378-5173, DOI(公開)(r-map) - Gray-Box Model-Based Predictive Control of Czochralski Process with Successive Model Update
Kato, Shota; Kim, Sanghong; Mizuta, Masahiko; Kano, Manabu
JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
SOC CHEMICAL ENG JAPAN
The Czochralski (CZ) process, a well-established monocrystalline silicon ingot production process, is a nonlinear, timevarying batch process. In the semiconductor industry, it is desirable to improve the control method and manufacture higher-quality 300-mm-diameter silicon ingots at a lower cost. The authors developed a nonlinear model predictive control method based on the gray-box (GB) model of the CZ process and successive linearization. This study proposes a method for updating the prediction model, to handle a plant-model mismatch. The proposed method constructs several GB models beforehand and selects a proper model based on the moving horizon estimation. This method was applied to the GB model-based predictive control, and its disturbance rejection performance was compared with that of the conventional control method without a model update. The obtained control simulation results demonstrated that the sums of the integral absolute errors of the controlled variables using the proposed method were smaller than those using the conventional method in 128/180 simulations.
Mar. 2022, Research paper (scientific journal), joint, 55, 3, 0021-9592, DOI(公開)(r-map), 154, 161 - Gray-box model-based predictive control of Czochralski process
Kato, Shota; Kim, Sanghong; Mizuta, Masahiko; Oshima, Masanori; Kano, Manabu
JOURNAL OF CRYSTAL GROWTH
ELSEVIER
The present study proposes a gray-box (GB) model-based predictive control method to produce high-quality 300 mm silicon ingots in the commercial Czochralski (CZ) process. The GB model consists of an energy transfer, hydrodynamic, and geometrical model and a statistical model, predicts three controlled variables, i.e., crystal radius, growth rate, and melt position, and represents the time-varying and nonlinear characteristics of the CZ process. Solving an optimization problem with the GB model requires heavy computational load; therefore, the proposed method derives the prediction model by successive linearization of the GB model to compute optimal manipulated variables in several seconds. The proposed method was compared with the conventional method using PID controllers in disturbance rejection performance through control simulations. The results have demonstrated that the integral absolute error (IAE) of the proposed method was reduced by 60% on average and 89% at maximum even when a plant-model mismatch exists.
01 Nov. 2021, Research paper (scientific journal), joint, 573, 0022-0248, DOI(公開)(r-map) - Inferential Control of a Distillation Column through the Successive Update of the Soft-sensor and Control Algorithm
Oshima, Masanori; Kim, Sanghong; Sotowa, Ken-Ichiro
JOURNAL OF CHEMICAL ENGINEERING OF JAPAN
SOC CHEMICAL ENG JAPAN
Inferential control facilitates the direct feedback control of a variable that is difficult to measure in real time and achieves more efficient plant operation. However, when an inferential control system is introduced into the existing plant, the difficult-to-measure variable cannot be estimated accurately because operating conditions during data acquisition differ from those during inferential control operation. Thus, the control performance of the difficult-to-measure variable is poor. The contribution of this research is to propose a method to obtain an inferential control system that has high control performance and robustness against estimation error. In the proposed method, the degree of change in the operating conditions is limited by setting constraints on the inferential control system. Restrictions are relaxed in a step-bystep manner when the model is updated with the newly acquired data under the inferential control operation. The usefulness of the proposed method was evaluated through simulation case studies. In the case studies, control simulations of a vinyl acetate monomer (VAM) plant were performed. The bottom water concentration of the distillation column of the VAM plant was controlled. Four control methods including the proposed method were compared. The results of the case study showed that using the proposed method enhances both control performance and robustness against estimation error.
20 Jul. 2021, Research paper (scientific journal), joint, 54, 7, 0021-9592, DOI(公開)(r-map), 395, 405 - Robust parameter tuning method of LW-PL S and verification of its effectiveness by twelve industrial processes
Matsuyama, Yukio; Kim, Sanghong; Hasebe, Shinji
COMPUTERS & CHEMICAL ENGINEERING
PERGAMON-ELSEVIER SCIENCE LTD
Soft-sensors have been used to estimate variables that are difficult to measure in real time. Many soft sensor design methods have been proposed, however, the time-consuming trial and error such as parameter tuning is still necessary. Main reason is that the usefulness of each method have been often evaluated by a single dataset, and the robustness to other dataset and/or other process data was not evaluated. To solve this problem, this research proposes a robust parameter tuning method of locally weighted PL S (LW-PL S) model, based on the comparative study in twelve industrial processes such as the chemical, the pharmaceutical, and the food industries. The effectiveness of the proposed method is also validated by twelve industrial process data, and the results show that the proposed method can perform stable estimation considering nonlinearity while preventing over-fitting than the conventional methods. (c) 2021 Elsevier Ltd. All rights reserved.
01 Mar. 2021, Research paper (scientific journal), joint, 146, 0098-1354, DOI(公開)(r-map), 107224 - Gray-box modeling of 300 mm diameter Czochralski single-crystal Si production process
Kato, Shota; Kim, Sanghong; Kano, Manabu; Fujiwara, Toshiyuki; Mizuta, Masahiko
JOURNAL OF CRYSTAL GROWTH
ELSEVIER
More than 95% of 300 mm diameter single-crystal silicon ingots, the raw material for semiconductors, are produced by the Czochralski process. The demand for improving yield, throughput, and control performance has been increasing. The present study developed a gray-box model that can predict controlled variables from manipulated variables with higher accuracy than the conventional first-principle model (Zheng et al., 2018), aiming at realizing model predictive control of the Czochralski process. The proposed gray-box model used a statistical model to predict the temperature gradient of the crystal at the solid-liquid interface Gcry, which was constant in the first-principle model. The crystal length and the melt temperature are used as the input variables to predict Gcry. The prediction accuracy of the proposed gray-box model was compared with that of the first principle model using real process data obtained during the production of four silicon ingots. The results demonstrated that the proposed model reduced the root mean square errors of the crystal radius, the crystal growth rate, and the heater temperature by 94.1%, 62.7%, and 70.6% on average, respectively.
01 Jan. 2021, Research paper (scientific journal), joint, 553, 0022-0248, DOI(公開)(r-map), 125929
Presentations
- 燃料電池システムの先端研究と製品開発の加速を目指した統合システムシミュレーターFC-DynaMoの開発
26 Nov. 2024, Oral presentation(invited, special) - 晶折と燃料電池システムの材料開発，制御系設計，ハードウェア設計
08 Dec. 2024, Oral presentation(invited, special) - 包括的な機能を有するソフトセンサー設計ツールの開発
13 Mar. 2025, Oral presentation(invited, special) - An Integrated Fuel Cell System Simulator ‘FC-DynaMo’for the Multi-Purpose Applications
30 Jun. 2024, Oral presentation(keynote) - モデリングの方法論と感情論
ISPE日本本部年次大会
19 May 2023, Oral presentation(invited, special) - データに基づく固形製剤連続生産の管理戦略
CCPMJ第4回国際連携講演会
23 Mar. 2023, Oral presentation(invited, special) - Roles of Process Systems Engineering in Fuel Cell Research and Development
10th Asian Symposium on Process Systems Engineering
13 Dec. 2022, Oral presentation(invited, special) - 統計的プロセスモデリングによる堅牢な医薬品品質予測手法の開発
一般財団法人 新製剤技術とエンジニアリング振興基金
06 Jul. 2022, Oral presentation(general) - モデリングにあるべきは方法論か感情論か
日本学術振興会プロセスシステム工学第143委員会研究会
30 Jul. 2021, Oral presentation(invited, special)
Committee Memberships
- 化学工学会
広報委員
From 20230401, To 20250331 - 化学工学会
PSE分科会
From 20220401, To 20250331 - 化学工学会
SIS部会
From 20170401, To 20250331
Media Coverage
- 学会賞
計測と制御
From Aug. 2024, To Aug. 2024 - 堀場雅夫賞に大阪府立大・飯田教授ら3氏
堀場製作所が開催した「2021年度堀場雅夫賞授賞式」で東京農工大学の金尚弘准教授が、特別賞を受賞したことが紹介される。
日本経済新聞
From 19 Oct. 2021, To 19 Oct. 2021 - 「堀場雅夫賞」飯田氏ら３氏特別賞に金氏
東京農工大学の金尚弘准教授が特別賞を受賞したことが紹介される。
日刊工業新聞
From 30 Jul. 2021, To 30 Jul. 2021
Professional Memberships
- The Institute of Systems, Control and Information Engineers
- American Institute of Chemical Engineers
- The Society of Instrument and Control Engineers (SICE)
- The Electrochemical Society of Japan
- The Society of Chemical Engineers, Japan (SCEJ)
Awards
- 第57回市村賞 市村地球環境学術賞 貢献賞
18 Apr. 2025 - 公益社団法人 化学工学会
化学工学会技術賞
「包括的な機能を有するソフトセンサー設計ツールの開発」
化学工学会の会員および技術者の中から、化学工学に関する技術または化学関連産業の技術またはシステム開発に関して特に顕著な功績のあった個人もしくは5名以内の共同研究・開発者に対して授与される。
12 Mar. 2025 - 公益社団法人計測自動制御学会
2024年 計測自動制御学会技術賞
"Development of Comprehensive Soft- Sensor Design Tools”
計測自動制御学会が関与する技術および産業の分野において、新しい方式、デバイス、製品、設備等を創案、または実施することによって顕著な効果をもたらした技術的業績に対して、それぞれを実現した個人または団体に対し授与される。
29 Aug. 2024 - 新製剤技術とエンジニアリング振興基金
第9回一般財団法人 新製剤技術とエンジニアリング振興基金 パーティクルデザイン賞
06 Jul. 2022 - Masao Horiba Awards
分光データを利用した医薬品生産プロセスのリアルタイムモニタリングと制御
30 Jul. 2021