Welcome to AIPSE

We are team of process systems engineers making world with AI

Get Started

Few Words About Us

Up to date, either mechanistic model (i.e., white box) or data-driven model (i.e., black box) has been playing a primary role in Process Systems Engineering. Now, we plan to integrate both of them via hybrid modeling in order to achieve the next generation Process Systems Engineering world based on AI.

Mechanistic Model

This is also known as first-principles or mathematical model, depending on specific equations and constraints.

Data-driven Model

Machine learning and deep-learning can be employed because we have data available.

Hybrid Model

In combination of white box and black box, the resulting gray box will be able to give us a better understanding.

Associate Prof. Soonho Hwangbo


Ph.D. POSTECH (2017) (Supervsior: Prof. In-Beum Lee)
B.S. POSTECH (2009)
Daeil Foreign Language High School (2005)

Work experience

Associate Prof. @ Gyeongsang National University, Korea (Early promotion, 2024~)
Assistant Prof. @ Gyeongsang National University, Korea (2020~2023)
Postdoc. @ Technical Univerisity of Denmark, Denmark (2018~2020)
Postdoc. @ Kyunghee University, Korea (2017~2018)
Visiting researcher @ École Nationale Supérieure des Ingénieurs en Arts Chimiques et Technologiques, France (2016~2017)
Visiting researcher @ RWTH Aachen University, Germany (2011)



Peer-reviewed articles






Vacant positions



30. Jaewon Byun1, Byeongmin Ha, Hoyoung Park, Soonho Hwangbo*, and Jeehoon Han*. Techno-economic assessment of renewable electricity generation networks based on food waste-to-energy conversion technologies and variational auto-encoder, under review

29. Byeongmin Ha1, Seolji Nam1, Jaewon Byun, Jeehoon Han, and Soonho Hwangbo*. Stochastic techno-economic assessment of future renewable energy networks based on integrated deep-learning framework: A case study of South Korea, Chemical Engineering Journal

28. Taehyun Kim1, Dongmin Lee, and Soonho Hwangbo*. A deep-learning framework for forecasting renewable electricity demands with variational auto-encoder and bidirectional long short-term memory, Sustainable Energy, Grids and Networks

27. SungKu Heo1, Jaewon Byun1, Pouya Ifaei, Jaerak Ko, Byeongmin Ha, Soonho Hwangbo*, and ChangKyoo Yoo*. Towards mega-scale decarbonized industrial park (Mega-DIP): AI-driven techno-economic and environmental assessment of renewable and sustainable energy utilization in petrochemical industry, Renewable & Sustainable Energy Reviews


26. Taehyun Kim1, Byeongmin Ha1, and Soonho Hwangbo*. Online machine learning approach for system marginal price forecasting using multiple economic indicators: A novel model for real-time decision making, Machine Learning with Applications

25. Junghee Joo1, Jiwon Kim1, Jaewon Byun 1, Yoonjae Lee, Taehyun Kim, Soonho Hwangbo*, Jeehoon Han*, Sung-Kon Kim*, and Jechan Lee*. Environmentally-viable utilization of chicken litter as energy recovery and electrode production: A machine learning approach, Applied Energy

24. Junho Cha1, Sujin Eom, Subin Lee, Changwon Lee, and Soonho Hwangbo*. Evaluation on large-scale biowaste process: Spent coffee ground along with real option approach, Clean Technology


23. SungKu Heo1, Jaerak Ko, SangYoun Kim, Soonho Hwangbo*, and ChangKyoo Yoo*. Explainable AI-driven net-zero carbon roadmap for petrochemical industry considering stochastic scenarios of remotely sensed offshore wind energy, Journal of Cleaner Production

22. Yoonjae Lee1, Byeongmin Ha1, and Soonho Hwangbo*. Generative model-based hybrid forecasting model for renewable electricity supply using long short-term memory networks: A case study of South Korea's energy transition policy, Renewable Energy

21. Min Beom Kim1, Soonho Hwangbo, Sungho Jang, and Yun Kee Jo*. Bioengineered Co-culture of Organoids to Recapitulate Host-Microbe Interactions, Materials Today Bio

20. Taehyun Kim1, Yoonjae Lee1, and Soonho Hwangbo*. Research on forecasting framework for system marginal price based on deep recurrent neural networks and statistical analysis models, Clean Technology

19. Soonho Hwangbo1, SungKu Heo1, and ChangKyoo Yoo*. Development of deterministic-stochastic model to integrate variable renewable energy-driven electricity and large-scale utility networks: Towards decarbonization petrochemical industry, Energy


18. Gahee Rhee1, Juin Yau Lim1, Soonho Hwangbo*, and ChangKyoo Yoo*. Evaluation of an integrated microalgae-based biorefinery process and energy-recovery system from livestock manure using a superstructure model, Journal of Cleaner Production

17. Soonho Hwangbo1,*, Resul Al, Xueming Chen, and Gürkan Sin. Integrated model for understanding N2O emissions from wastewater treatment plants: A deep learning approach, Environmental Science & Technology


16. Soonho Hwangbo1,*, Resul Al, and Gürkan Sin. An integrated framework for plant data-driven process modeling using deep-learning with Monte-Carlo simulations, Computers & Chemical Engineering

15. Soonho Hwangbo1,* and Gürkan Sin*. Design of control framework based on deep reinforcement learning and Monte-Carlo sampling in downstream separation, Computers & Chemical Engineering

14. Paulina Vilela1, SungKu Heo1, Soonho Hwangbo*, and ChangKyoo Yoo*. Optimal utility supply network under demand uncertainty for operational risk assessment on a petrochemical industrial park, Korean Journal of Chemical Engineering

13. Juin Yau Lim1, Bing Shen How, Gahee Rhee, Soonho Hwangbo*, and ChangKyoo Yoo*. Debottlenecking of localized renewable energy system towards sustainable hydrogen development planning: P-graph approach, Applied Energy

12. KiJeon Nam1, Soonho Hwangbo1, and ChangKyoo Yoo*. A deep learning-based forecasting model for renewable energy scenarios to guide renewable energy policy on Jeju Island, Renewable & Sustainable Energy Reviews

11. Soonho Hwangbo1, Gürkan Sin, Gahee Rhee, and ChangKyoo Yoo*. Development of integrated waste-to-energy and multi-site utility network considering air pollutant emissions pinch analysis, Journal of Cleaner Production


10. Qian Li1, Jorege Loy-Benitez1, Soonho Hwangbo*, Jouan Rashidi, and ChangKyoo Yoo*. Sustainable and reliable design of reverse osmosis desalination with hybrid renewable energy systems through supply chain forecasting using recurrent neural networks, Energy

9. Soonho Hwangbo1, KiJeon Nam, SungKu Heo, and ChangKyoo Yoo*. Hydrogen-based self-sustaining integrated renewable electricity network (HySIREN) using a supply-demand forecasting model and deep-learning algorithms, Energy Conversion and Management

8. Tuan-Viet Hoang1, Pouya Ifaei, Kijeon Nam, Jouan Rashidi, Soonho Hwangbo*, and ChangKyoo Yoo*. Optimal management of a hybrid renewable energy system coupled with a membrane bioreactor using enviro-economic and power pinch analyses for sustainable climate change adaption, Sustainability


7. Gahee Rhee1, Soonho Hwangbo, and ChangKyoo Yoo*. Fate Analysis and Impact Assessment fof Vehicle Polycyclic Aromatic Hydrocarbons (PAHs) Emitted from Metropolitan City Using Multimedia Fugacity Model, Korean Chemical Engineering Research

6. Soonho Hwangbo1, KiJeon Nam, Jeehoon Han, In-Beum Lee, and ChangKyoo Yoo*. Integrated hydrogen supply networks for waste biogas upgrading and hybrid carbon-hydrogen pinch analysis under hydrogen demand uncertainty, Applied Thermal Engineering

5. Paulina Vilela1, Hongbin Liu, SeungChul Lee, Soonho Hwangbo, KiJeon Nam, and ChangKyoo Yoo*. A systematic approach of removal mechanisms, control and optimization of silver nanoparticle in wastewater treatment plants, Science of the Total Environment


4. Soonho Hwangbo1, Seungchul Lee, and ChangKyoo Yoo*. Optimal network design of hydrogen production by integrated utility and biogas supply networks, Applied Energy

3. Seungchul Lee1, Soonho Hwangbo, and ChangKyoo Yoo*. Gain scheduling based ventilation control with varying periodic indoor air quality (IAQ) dynamics for healthy IAQ and energy savings, Energy and Buildings

2. Soonho Hwangbo1, In-Beum Lee, and Jeehoon Han*. Mathematical model to optimize design of integrated utility supply network and future global hydrogen supply network under demand uncertainty, Applied Energy


1. Soonho Hwangbo1, In-Beum Lee, and Jeehoon Han*. Multi-period stochastic mathematical model for the optimal design of integrated utility and hydrogen supply network under uncertainty in raw material prices, Energy

Team (current)

We are looking for Ph.D. students who are in charge of the next generation Process Systems Engineering

Byeongmin Ha

Ph.D. program
Email: habmin@naver.com

Jongseo Kwon

Ph.D. program
Email: kwjs321@naver.com

Seolji Nam

Master's program
Email: seolji99@naver.com

Jaerak Ko

Master's program
Email: jr_ko98@gnu.ac.kr

Hyeonjeong Lee

Master's program
Email: 2018011668@gnu.ac.kr

Sumin Jeong

Master's program
Email: wjdtnals37@gmail.com

Yeongeun Joo

Master's program
Email: wnduddms15@naver.com

Nahyeon Kim

Master's program
Email: ehdfl1@gnu.ac.kr

Team (alumni)

Taehyun Kim

Master's Program
Email: alwpfs501@daum.net

Yoonjae Lee

Master's Program
Email: dkdlwks123@naver.com

Byeongmin Ha

Master's program
Email: habmin@naver.com

Soomin Kang

Bachelor's program
Email: soomin2177@naver.com

Dongmin Lee

Bachelor's program
Email: dlehde@naver.com

Kwangil Kim

Bachelor's program
Email: akim12333@naver.com

Hyeongyu Min

Bachelor's program
Email: 654321min@naver.com


Feel free to contact us

Gyeongsang National University, Jinju-daero 501
Jinju-si, Gyeongsangnam-do, 52828, South Korea


+82 55 772 1783