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Home > Introduction of our tenure-track faculties > Kim Sanghong

Introduction of our tenure-track faculties

Kim Sanghong

Affiliation Institute of Engineering
Division Division of Applied Chemistry
Research field Process systems engineering
Keyword(S) Process data analytics, process modeling, process control
Research experience

・Apr.2014-Feb.2021:Assistant Professor Dept. of Chemical Engineering, Kyoto University
・Mar.2021-present:Associate Professor Tokyo University of Agriculture and Technology

Educational background

・Mar.2009:B.S.Dept.of Chemical Engineering, Kyoto University, Japan
・Mar.2011:M.S.Dept.of Chemical Engineering, Kyoto University, Japan
・Mar.2014:Ph.D.Dept.of Chemical Engineering, Kyoto University, Japan


2012:Research Award for Young Investigators of the Institute of Systems, Control and Information Engineers
2014:Technology Award of the 13th Annual Conference on Control Systems
2014:Technology Award of Society of Instrument and Control Engineers
2015:Outstanding reviewer, Chemometrics and Intelligent Laboratory Systems
2016:Young Investigator Researcher Award of the Division of SIS, SCEJ
2016:JCEJ Best Reviewer Award
2017:Outstanding reviewer, Computers and Chemical Engineering, 2017  JCEJ Best Reviewer Award
2019:【Student】Best Oral Presentation Award,PSE Asia 2019
2019:【Student】 Student poster award,SCI19
2019:JCEJ Outstanding Paper Award of 2019

Selected papers and publications

・Sanghong Kim, Manabu Kano, Hiroshi Nakagawa, Shinji Hasebe: "Estimation of active pharmaceutical ingredients content using locally weighted partial least squares and statistical wavelength selection", International Journal of Pharmaceutics, Vol. 421, No.2, pp. 269–274 (2011)
・Hiroshi Nakagawa, Takahiro Tajima, Manabu Kano, Sanghong Kim, Shinji Hasebe, Tatsuya Suzuki, Hiroaki Nakagami: "Evaluation of infrared-reflection absorption spectroscopy measurement and locally weighted partial least-squares for rapid analysis of residual drug substances in cleaning processes", Analytical Chemistry, Vol. 84, No. 8, pp. 3820–3826 (2012)
・Sanghong Kim, Ryota Okajima, Manabu Kano, Shinji Hasebe: "Development of soft-sensor using locally weighted PLS with adaptive similarity measure", Chemometrics and Intelligent Laboratory Systems, Vol. 124, pp. 43–49 (2013)
・Sanghong Kim, Manabu Kano, Shinji Hasebe, Akitoshi Takinami, Takeshi ・Seki: "Long-term industrial applications of inferential control based on just-in-time soft-sensors: economical impact and challenges", Industrial & Engineering Chemistry Research, Vol.52, No. 35, pp. 12346–12356 (2013)
・Sanghong Kim, Manabu Kano, Hiroshi Nakagawa, Shinji Hasebe, "Input variable scaling for statistical modeling", Computers & Chemical Engineering, Vol. 74, No. 4, pp. 59-65 (2015)
・Zhongchao Zheng, Tatsuru Seto, Sanghong Kim, Manabu Kano, Toshiyuki ・Fujiwara, Masahiko Mizuta, Shinji Hasebe, "A first-principle model of 300 mm Czochralski single-crystal Si production process for predicting crystal radius and crystal growth rate", Journal of Crystal Growth, Vol. 492, No. 15, pp. 105-113 (2018)
・Sanghong Kim, Kazuki Mishima, Manabu Kano, Shinji Hasebe, "Database management method based on strength of nonlinearity for locally weighted linear regression", Vol. 52, No. 6, pp. 554-561 (2019)
・Shota Kato, Sanghong Kim, Manabu Kano, Toshiyuki Fujiwara, Masahiko Mizuta, Gray-box modeling of 300 mm diameter Czochralski single-crystal Si production process, Journal of Crystal Growth,Vol. 553, 125929 (2021)
・Yukio Matsuyama Sanghong Kim ShinjiHasebe, “Robust parameter tuning method of LW-PLS and verification of its effectiveness by twelve industrial processes”, Computers & Chemical Engineering, Vol. 146, 107224 (2021)

Research Description

Research in process systems engineering can be classified as follows.

Modeling: To describe the behavior of a target process with mathematical equations.
State monitoring: Understanding the current state of the process based on the model.
Simulation: To predict the future of a process based on a model.
Control/optimization: To make the future situation desirable.

An example of modeling is soft-sensor design based on statistical data analysis of process data. In the process industry, it is required to produce products that meet quality requirements. However, it is not always easy to measure the quality in real time. This is mainly due to the high cost of measurement equipment and the time required for the measurement itself. In some cases, the frequency of measurement is not sufficient. Soft-sensors are used to estimate variables that are difficult to measure from those that can be measured in real time. The name “soft-sensor" comes from the contrast with sensors that measure directly by hardware. However, it is difficult to strictly distinguish between soft sensors and hard sensors. In this paper, we refer to regression models based on process data. A typical application of soft sensors is in distillation columns, where they are used to estimate product composition based on temperature, flow rate, and pressure. Soft sensors have also been used in a wide range of other industries such as pharmaceuticals, steel, and semiconductors. In this research, we are developing and applying a method for designing highly accurate soft sensors.

About TUAT's tenure-track program

I feel that the support in terms of funding, space, and administrative procedures is generous. There is a mentor system, and I appreciate that I can get advice from mentors from various viewpoints. It is also attractive that there is an exchange meeting among tenure-track faculty members.

Future aspirations

I want to enjoy all of my activities, including research, teaching, and other activities.