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Chonghyo Joo
Chonghyo Joo
Ph.D. Candidate, Yonsei University | Korea Institute of Industrial Technology
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Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters
C Joo, H Park, J Lim, H Cho, J Kim
International Journal of Intelligent Systems 37 (6), 3625-3653, 2022
302022
Design of multistage fixed bed reactors for SMR hydrogen production based on the intrinsic kinetics of Ru-based catalysts
J Lee, C Joo, H Cho, Y Kim, S Ga, J Kim
Energy Conversion and Management 268, 115981, 2022
142022
Machine learning approach to predict physical properties of polypropylene composites: Application of MLR, DNN, and random forest to industrial data
C Joo, H Park, H Kwon, J Lim, E Shin, H Cho, J Kim
Polymers 14 (17), 3500, 2022
102022
Multiobjective Optimization of Plastic Waste Sorting and Recycling Processes Considering Economic Profit and CO2 Emissions Using Nondominated Sorting ¡¦
J Lee, J Lim, C Joo, Y Ahn, H Cho, J Kim
ACS Sustainable Chemistry & Engineering 10 (40), 13325-13334, 2022
92022
Multi-objective robust optimization of profit for a naphtha cracking furnace considering uncertainties in the feed composition
J Kim, C Joo, M Kim, N An, H Cho, I Moon, J Kim
Expert Systems with Applications 216, 119464, 2023
72023
Novel carbon-neutral hydrogen production process of steam methane reforming integrated with desalination wastewater-based CO2 utilization
J Lim, C Joo, J Lee, H Cho, J Kim
Desalination 548, 116284, 2023
62023
Interpretable machine learning framework for catalyst performance prediction and validation with dry reforming of methane
J Roh, H Park, H Kwon, C Joo, I Moon, H Cho, I Ro, J Kim
Applied Catalysis B: Environmental 343, 123454, 2024
32024
Machine learning-based heat deflection temperature prediction and effect analysis in polypropylene composites using catboost and shapley additive explanations
C Joo, H Park, J Lim, H Cho, J Kim
Engineering Applications of Artificial Intelligence 126, 106873, 2023
32023
A novel graph-based missing values imputation method for industrial lubricant data
S Jeong, C Joo, J Lim, H Cho, S Lim, J Kim
Computers in Industry 150, 103937, 2023
32023
Data-driven modeling for physical property prediction of polypropylene composites using artificial neural network and principal component analysis
J Chonghyo, P Hyundo, H Seokyoung, L Jongkoo, C Hyungtae, M Il, ...
Computer Aided Chemical Engineering 51, 1369-1374, 2022
32022
A novel on-site SMR process integrated with a hollow fiber membrane module for efficient blue hydrogen production: Modeling, validation, and techno-economic analysis
C Joo, J Lee, Y Kim, H Cho, B Gu, J Kim
Applied Energy 354, 122227, 2024
22024
Prediction for heat deflection temperature of polypropylene composite with Catboost
J Chonghyo, P Hyundo, H Scokyoung, L Jongkoo, H Insu, C Hyungtae, ...
Computer Aided Chemical Engineering 49, 1801-1806, 2022
22022
pyAPEP: An all-in-one software package for the automated preparation of adsorption process simulations
S Ga, N An, C Joo, J Kim
Computer Physics Communications 291, 108830, 2023
12023
Multidisciplinary high-throughput screening of metal–organic framework for ammonia-based green hydrogen production
S Ga, N An, GY Lee, C Joo, J Kim
Renewable and Sustainable Energy Reviews 192, 114275, 2024
2024
Hybrid Quantum Neural Network Model with Catalyst Experimental Validation: Application for the Dry Reforming of Methane
J Roh, S Oh, D Lee, C Joo, J Park, I Moon, I Ro, J Kim
ACS Sustainable Chemistry & Engineering, 2024
2024
Multiobjective Optimization of CO2 Emission and Net Profit for a Naphtha Cracking Furnace Using a Deep Neural Network with a Nondominated Sorting Genetic ¡¦
C Joo, H Kwon, J Lim, J Lee, J Kim
ACS Sustainable Chemistry & Engineering, 2024
2024
Economic analysis with multiscale high-throughput screening for covalent organic framework adsorbents in ammonia-based green hydrogen separation
S Ga, N An, GY Lee, C Joo, J Kim
Renewable and Sustainable Energy Reviews 189, 113989, 2024
2024
Quantum Computing Assisted Data-Driven Modeling for Yield Prediction of Naphtha Cracking Process
C Joo, S Oh, D Lee, S Ray, H Cho, I Moon, J Kim
2023 AIChE Annual Meeting, 2023
2023
Chemical Property-Guided Neural Networks for Naphtha Composition Prediction
C Joo, J Kim, H Cho, J Lee, S Suh, J Kim
2023 IEEE 21st International Conference on Industrial Informatics (INDIN), 1-6, 2023
2023
Machine-learning-based optimization of operating conditions of naphtha cracking furnace to maximize plant profit
C Joo, H Kwon, J Kim, H Cho, J Lee
Computer Aided Chemical Engineering 52, 1397-1402, 2023
2023
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