研究者データベース
金 尚弘KIM Sanghongキム サンホン
| ■所属部署名 | 工学研究院 応用化学部門 | |||
| ■職名 | 准教授 | |||
Last Updated :2025/01/22
業績情報
氏名・連絡先
氏名
キム サンホン, 金 尚弘, KIM Sanghong生年
1986eメールアドレス
sanghong
go.tuat.ac.jp個人ホームページ
https://web.tuat.ac.jp/~sanghong/
主たる所属・職名
工学研究院 応用化学部門, 准教授
その他の所属
経歴
学歴
教育・研究活動状況
研究テーマ
科学研究費助成事業
- 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
自 2023年, 至 2023年 - 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
自 2022年, 至 2024年 - 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
自 2022年, 至 2022年 - 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
自 2021年, 至 2021年
論文
- 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
2024年03月31日, 研究論文（学術雑誌）, 共同, 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
2024年03月10日, 研究論文（学術雑誌）, 共同, 136, DOI(公開)(r-map), 103190 - 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.
2023年07月25日, 研究論文（学術雑誌）, 共同, 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.
2022年03月, 研究論文（学術雑誌）, 共同, 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.
2021年11月01日, 研究論文（学術雑誌）, 共同, 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.
2021年07月20日, 研究論文（学術雑誌）, 共同, 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.
2021年03月01日, 研究論文（学術雑誌）, 共同, 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.
2021年01月01日, 研究論文（学術雑誌）, 共同, 553, 0022-0248, DOI(公開)(r-map), 125929
研究発表、招待講演等
- モデリングの方法論と感情論
ISPE日本本部年次大会
2023年05月19日, 口頭発表（招待・特別） - 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
2023年03月23日, 口頭発表（招待・特別） - Roles of Process Systems Engineering in Fuel Cell Research and Development
10th Asian Symposium on Process Systems Engineering
2022年12月13日, 口頭発表（招待・特別） - 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
2022年07月06日, 口頭発表（一般） - 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
2021年07月30日, 口頭発表（招待・特別）
委員歴
メディア報道
- 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
自 2021年10月19日, 至 2021年10月19日 - 「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
自 2021年07月30日, 至 2021年07月30日