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ORCIDJay H. Lee
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2020 – today
- 2025
[j74]Woohyun Jeong
, Namjin Jang, Jay H. Lee:
Bayesian optimization for quick determination of operating variables of simulated moving bed chromatography. Comput. Chem. Eng. 192: 108872 (2025)- 2024
- [j73]Prodromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, Leo Chiang, R. Bhushan Gopaluni, Artur M. Schweidtmann, Iiro Harjunkoski, Mehmet Mercangöz, Ali Mesbah, Fani Boukouvala, Fernando V. Lima, Ehecatl Antonio del Rio-Chanona, Christos Georgakis:
Machine learning in process systems engineering: Challenges and opportunities. Comput. Chem. Eng. 181: 108523 (2024) - [j72]Mujin Cheon, Ha-Eun Byun, Jay H. Lee:
Non-myopic Bayesian optimization using model-free reinforcement learning and its application to optimization in electrochemistry. Comput. Chem. Eng. 184: 108624 (2024) - [i5]Howoun Jung, Nohjin Park, Jay H. Lee:
Evaluating the Efficiency and Cost-effectiveness of RPB-based CO2 Capture: A Comprehensive Approach to Simultaneous Design and Operating Condition Optimization. CoRR abs/2403.08244 (2024) - [i4]Sunwoo Kim, Joungho Park, Jay H. Lee:
Multi-technology co-optimization approach for sustainable hydrogen and electricity supply chains considering variability and demand scale. CoRR abs/2406.00669 (2024) - [i3]Mujin Cheon, Jay H. Lee, Dong-Yeun Koh, Calvin Tsay:
EARL-BO: Reinforcement Learning for Multi-Step Lookahead, High-Dimensional Bayesian Optimization. CoRR abs/2411.00171 (2024) - 2023
- [j71]Ha-Eun Byun, Boeun Kim, Jay H. Lee:
Embedding active learning in batch-to-batch optimization using reinforcement learning. Autom. 157: 111260 (2023) - [j70]Wonsuk Chung, Woohyun Jeong, Jaeseo Lee, Junehyeok Kim, Kosan Roh, Jay H. Lee:
Electrification of CO2 conversion into chemicals and fuels: Gaps and opportunities in process systems engineering. Comput. Chem. Eng. 170: 108106 (2023) - [j69]Sunwoo Kim, Seongwhan Kang, Jay H. Lee:
Optimal design of offshore wind power farm in high resolution using geographical information system. Comput. Chem. Eng. 174: 108253 (2023) - [j68]Wonsuk Chung, Sunwoo Kim, Ali S. Al Hunaidy, Hasan Imran, Aqil Jamal, Jay H. Lee:
Identification of sustainable carbon capture and utilization (CCU) pathways using state-task network representation. Comput. Chem. Eng. 178: 108408 (2023) - [j67]Howoun Jung, Jay H. Lee:
Flexible operation of Post-combustion CO2 capture process enabled by NARX-MPC using neural network. Comput. Chem. Eng. 179: 108447 (2023) - [j66]Teemu J. Ikonen, Dongho Han, Jay H. Lee, Iiro Harjunkoski:
Stochastic programming of energy system operations considering terminal energy storage levels. Comput. Chem. Eng. 179: 108449 (2023) - [i2]Jaewook Lee, Seongmin Heo, Jay H. Lee:
Attention Mechanism for Lithium-Ion Battery Lifespan Prediction: Temporal and Cyclic Attention. CoRR abs/2311.10792 (2023) - 2022
- [j65]Juyeong Jung, Hyun-Kyu Choi, Sang Hwan Son, Joseph Sang-Il Kwon, Jay H. Lee:
Multiscale modeling of fiber deformation: Application to a batch pulp digester for model predictive control of fiber strength. Comput. Chem. Eng. 158: 107640 (2022) - [j64]Ashfaq Iftakher, David A. Liñán, Seyed Soheil Mansouri, Ahaduzzaman Nahid, M. M. Faruque Hasan, M. A. A. Shoukat Choudhury, Luis A. Ricardez-Sandoval, Jay H. Lee:
RD-toolbox: A computer aided toolbox for integrated design and control of reactive distillation processes. Comput. Chem. Eng. 164: 107869 (2022) - [j63]Hongbum Choi, Shin Hyuk Kim, Joongmyeon Bae, Sai P. R. Katikaneni, Aqil Jamal, Aadesh Harale, Stephen N. Paglieri, Jay H. Lee:
CFD analysis and scale up of a baffled membrane reactor for hydrogen production by steam methane reforming. Comput. Chem. Eng. 165: 107912 (2022) - [j62]Ha-Eun Byun, Boeun Kim, Jay H. Lee:
Multi-step lookahead Bayesian optimization with active learning using reinforcement learning and its application to data-driven batch-to-batch optimization. Comput. Chem. Eng. 167: 107987 (2022) - [j61]Yo Sung Yoon, Woohyun Jeong, Jaeyong Kim, Mingyun Seok, Junsoo Park, Jaemin Bae, Kyungmin Lee, Jay H. Lee:
Development of inferential sensor and real-time optimizer for a vacuum distillation unit by recurrent neural network modeling of time series data. Comput. Chem. Eng. 168: 108039 (2022) - [c14]Juyeong Jung, Hyun-Kyu Choi, Sang Hwan Son, Joseph Sang-Il Kwon, Jay H. Lee:
Model predictive control of fiber deformation in a batch pulp digester. ACC 2022: 2491-2496 - 2021
- [j60]Haeun Yoo, Ha-Eun Byun, Dongho Han, Jay H. Lee:
Reinforcement learning for batch process control: Review and perspectives. Annu. Rev. Control. 52: 108-119 (2021) - [j59]Haeun Yoo, Boeun Kim, Jong Woo Kim, Jay H. Lee:
Reinforcement learning based optimal control of batch processes using Monte-Carlo deep deterministic policy gradient with phase segmentation. Comput. Chem. Eng. 144: 107133 (2021) - [j58]Efstratios N. Pistikopoulos, Ana Barbosa-Póvoa, Jay H. Lee, Ruth Misener, Alexander Mitsos, Gintaras V. Reklaitis, Venkat Venkatasubramanian, Fengqi You, Rafiqul Gani:
Process systems engineering - The generation next?. Comput. Chem. Eng. 147: 107252 (2021) - [j57]Nikunja Mohan Modak, Jay H. Lee, José M. Merigó:
Erratum to "Forty years of computers & chemical engineering: A bibliometric analysis" [Computers & Chemical Engineering 141 (2020) 106978]. Comput. Chem. Eng. 152: 107377 (2021) - [j56]Shin Hyuk Kim, Jay H. Lee, Richard D. Braatz:
Multi-scale fluid dynamics simulation based on MP-PIC-PBE method for PMMA suspension polymerization. Comput. Chem. Eng. 152: 107391 (2021) - 2020
- [j55]Seongwoong Bae, Shin Hyuk Kim, Jay H. Lee:
An investigation into the hydrodynamics of a spinning cone column: CFD simulations by an Eulerian-Lagrangian approach. Comput. Chem. Eng. 132 (2020) - [j54]Shin Hyuk Kim, Jay H. Lee, Richard D. Braatz:
Multi-phase particle-in-cell coupled with population balance equation (MP-PIC-PBE) method for multiscale computational fluid dynamics simulation. Comput. Chem. Eng. 134: 106686 (2020) - [j53]Ha-Eun Byun, Boeun Kim, Jongkoo Lim, Jay H. Lee:
Multi-objective optimization of operation of lignocellulosic acetone-butanol-ethanol fermentation with ex situ butanol recovery (ESBR). Comput. Chem. Eng. 140: 106915 (2020) - [j52]Howoun Jung, Dasom Im, Seongmin Heo, Boeun Kim, Jay H. Lee:
Dynamic analysis and linear model predictive control for operational flexibility of post-combustion CO2 capture processes. Comput. Chem. Eng. 140: 106968 (2020) - [j51]Nikunja Mohan Modak, Valeria Lobos-Ossandón, José M. Merigó, Bogdan Gabrys, Jay H. Lee:
Forty years of computers & chemical engineering: A bibliometric analysis. Comput. Chem. Eng. 141: 106978 (2020) - [j50]Dasom Im, Howoun Jung, Jay H. Lee:
Modeling, simulation and optimization of the rotating packed bed (RPB) absorber and stripper for MEA-based carbon capture. Comput. Chem. Eng. 143: 107102 (2020) - [j49]Seongbin Ga, Sangwon Lee, Jihan Kim, Jay H. Lee:
Isotherm parameter library and evaluation software for CO2 capture adsorbents. Comput. Chem. Eng. 143: 107105 (2020) - [c13]Ha-Eun Byun, Boeun Kim, Jay H. Lee:
Robust Dual Control of Batch Processes with Parametric Uncertainty using Proximal Policy Optimization. CDC 2020: 3016-3021 - [i1]Haeun Yoo, Victor M. Zavala, Jay H. Lee:
Dynamic penalty function approach for constraints handling in reinforcement learning. CoRR abs/2012.11790 (2020)
2010 – 2019
- 2019
- [j48]Kyung Hwan Ryu, Boeun Kim, Jay H. Lee:
A model-based optimization of microalgal cultivation strategies for lipid production under photoautotrophic condition. Comput. Chem. Eng. 121: 57-66 (2019) - [j47]Joohyun Shin, Jay H. Lee:
Multi-timescale, multi-period decision-making model development by combining reinforcement learning and mathematical programming. Comput. Chem. Eng. 121: 556-573 (2019) - [j46]Haeun Yoo, Kosan Roh, Ali S. Al Hunaidy, Hasan Imran, Jay H. Lee:
Optimal design of heat and water recovery system utilizing waste flue gases for refinery CO2 reduction. Comput. Chem. Eng. 124: 140-152 (2019) - [j45]Heungjo An, Sangdo (Sam) Choi, Jay H. Lee:
Integrated scheduling of vessel dispatching and port operations in the closed-loop shipping system for transporting petrochemicals. Comput. Chem. Eng. 126: 485-498 (2019) - [j44]Seongmin Heo, Jay H. Lee:
Parallel neural networks for improved nonlinear principal component analysis. Comput. Chem. Eng. 127: 1-10 (2019) - [j43]Joohyun Shin, Thomas A. Badgwell, Kuang-Hung Liu, Jay H. Lee:
Reinforcement Learning - Overview of recent progress and implications for process control. Comput. Chem. Eng. 127: 282-294 (2019) - 2018
- [j42]Jay H. Lee, Joohyun Shin, Matthew J. Realff:
Machine learning: Overview of the recent progresses and implications for the process systems engineering field. Comput. Chem. Eng. 114: 111-121 (2018) - [j41]Prodromos Daoutidis, Jay H. Lee, Iiro Harjunkoski, Sigurd Skogestad, Michael Baldea, Christos Georgakis:
Integrating operations and control: A perspective and roadmap for future research. Comput. Chem. Eng. 115: 179-184 (2018) - [j40]Kyungseok Noh, Joohyun Shin, Jay H. Lee:
An optimization based strategy for crude selection in a refinery with lube hydro-processing. Comput. Chem. Eng. 116: 91-111 (2018) - [c12]Boeun Kim, Jay H. Lee:
Improved Parameter Estimation of Ill-Conditioned Problems. CDC 2018: 3044-3049 - 2017
- [j39]Seongbin Ga, Hong Jang, Jay H. Lee:
New performance indicators for adsorbent evaluation derived from a reduced order model of an idealized PSA process for CO2 capture. Comput. Chem. Eng. 102: 188-212 (2017) - 2016
- [j38]Muhammad Zaman, Hong Jang, Muhammad Rizwan, Jay H. Lee:
Optimal design for flexible operation of the post-combustion CO2 capture plant with uncertain economic factors. Comput. Chem. Eng. 84: 199-207 (2016) - [j37]Hong Jang, Kwang-Ki K. Kim, Richard D. Braatz, R. Bhushan Gopaluni, Jay H. Lee:
Regularized maximum likelihood estimation of sparse stochastic monomolecular biochemical reaction networks. Comput. Chem. Eng. 90: 111-120 (2016) - [j36]Joohyun Shin, Jay H. Lee:
Multi-time scale procurement planning considering multiple suppliers and uncertainty in supply and demand. Comput. Chem. Eng. 91: 114-126 (2016) - [j35]Kosan Roh, Rebecca Frauzem, Tuan B. H. Nguyen, Rafiqul Gani, Jay H. Lee:
A methodology for the sustainable design and implementation strategy of CO2 utilization processes. Comput. Chem. Eng. 91: 407-421 (2016) - [c11]Joohyun Shin, Jay H. Lee, Matthew J. Realff:
Stochastic optimization with value function approximation for micro-grid operation. CDC 2016: 7099-7104 - 2015
- [j34]Kevin Yeh, Craig Whittaker, Matthew J. Realff, Jay H. Lee:
Two stage stochastic bilevel programming model of a pre-established timberlands supply chain with biorefinery investment interests. Comput. Chem. Eng. 73: 141-153 (2015) - [j33]Muhammad Zaman, Jay H. Lee:
Optimization of the various modes of flexible operation for post-combustion CO2 capture plant. Comput. Chem. Eng. 75: 14-27 (2015) - [j32]Muhammad Rizwan, Muhammad Zaman, Jay H. Lee, Rafiqul Gani:
Optimal processing pathway selection for microalgae-based biorefinery under uncertainty. Comput. Chem. Eng. 82: 362-373 (2015) - 2014
- [j31]Hong Jang, Jay H. Lee, Richard D. Braatz, Kwang Ki Kevin Kim:
Fast moving horizon estimation for a two-dimensional distributed parameter system. Comput. Chem. Eng. 63: 159-172 (2014) - [j30]Kevin Yeh, Matthew J. Realff, Jay H. Lee, Craig Whittaker:
Analysis and comparison of single period single level and bilevel programming representations of a pre-existing timberlands supply chain with a new biorefinery facility. Comput. Chem. Eng. 68: 242-254 (2014) - [j29]Richard D. Braatz, Jay H. Lee:
Special issue in Honor of Manfred Morari's 60th Birthday. Comput. Chem. Eng. 70: 1-2 (2014) - [j28]Jay H. Lee:
From robust model predictive control to stochastic optimal control and approximate dynamic programming: A perspective gained from a personal journey. Comput. Chem. Eng. 70: 114-121 (2014) - [c10]Kwang Ki Kevin Kim, Hong Jang, R. Bhushan Gopaluni, Jay H. Lee, Richard D. Braatz:
Sparse identification in chemical master equations for monomolecular reaction networks. ACC 2014: 3698-3703 - 2013
- [j27]Rakshita Agrawal, Matthew J. Realff, Jay H. Lee:
MILP based value backups in partially observed Markov decision processes (POMDPs) with very large or continuous action and observation spaces. Comput. Chem. Eng. 56: 101-113 (2013) - [j26]Muhammad Rizwan, Jay H. Lee, Rafiqul Gani:
Optimal processing pathway for the production of biodiesel from microalgal biomass: A superstructure based approach. Comput. Chem. Eng. 58: 305-314 (2013) - 2012
- [j25]Seung Sohn, Tae Kim, Jay H. Lee, Sang Lee:
Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth. BMC Syst. Biol. 6: 49 (2012) - 2011
- [j24]Jinkyung Kim, Matthew J. Realff, Jay H. Lee:
Optimal design and global sensitivity analysis of biomass supply chain networks for biofuels under uncertainty. Comput. Chem. Eng. 35(9): 1738-1751 (2011)
2000 – 2009
- 2009
- [j23]Rafiqul Gani, Andy Hrymak, Jay H. Lee, Venkat Venkatasubramanian:
Editorial note. Comput. Chem. Eng. 33(1): 1 (2009) - [j22]Nikolaos E. Pratikakis, Matthew J. Realff, Jay H. Lee:
Controlled exploration of state space in off-line ADP and its application to stochastic shortest path problems. Comput. Chem. Eng. 33(12): 2111-2122 (2009) - 2008
- [c9]Wee Chin Wong, Jay H. Lee:
A reinforcement learning-based scheme for adaptive optimal control of linear stochastic systems. ACC 2008: 57-62 - 2006
- [j21]Anshul Dubey, Matthew J. Realff, Jay H. Lee, Andreas S. Bommarius:
Identifying the interacting positions of a protein using Boolean learning and support vector machines. Comput. Biol. Chem. 30(4): 268-279 (2006) - [j20]Jay H. Lee, Jong Min Lee:
Approximate dynamic programming based approach to process control and scheduling. Comput. Chem. Eng. 30(10-12): 1603-1618 (2006) - 2005
- [j19]Jong Min Lee, Jay H. Lee:
Approximate dynamic programming-based approaches for input-output data-driven control of nonlinear processes. Autom. 41(7): 1281-1288 (2005) - [j18]Catalina Valencia Peroni, Niket S. Kaisare, Jay H. Lee:
Optimal control of a fed-batch bioreactor using simulation-based approximate dynamic programming. IEEE Trans. Control. Syst. Technol. 13(5): 786-790 (2005) - 2004
- [j17]Jaein Choi, Matthew J. Realff, Jay H. Lee:
Dynamic programming in a heuristically confined state space: a stochastic resource-constrained project scheduling application. Comput. Chem. Eng. 28(6-7): 1039-1058 (2004) - [j16]Jaein Choi, Matthew J. Realff, Jay H. Lee:
An algorithmic framework for improving heuristic solutions: Part I. A deterministic discount coupon traveling salesman problem. Comput. Chem. Eng. 28(8): 1285-1296 (2004) - [j15]Jaein Choi, Jay H. Lee, Matthew J. Realff:
An algorithmic framework for improving heuristic solutions: Part II. A new version of the stochastic traveling salesman problem. Comput. Chem. Eng. 28(8): 1297-1307 (2004) - [j14]Thidarat Tosukhowong, Jong Min Lee, Jay H. Lee, Joseph Lu:
An introduction to a dynamic plant-wide optimization strategy for an integrated plant. Comput. Chem. Eng. 29(1): 199-208 (2004) - [c8]Thidarat Tosukhowong, Jay H. Lee:
Real-time economic optimization for an integrated plant via a dynamic optimization scheme. ACC 2004: 233-238 - [c7]Jong Min Lee, Jay H. Lee:
Empirical model based control of nonlinear processes using approximate dynamic programming. ACC 2004: 3041-3046 - 2003
- [c6]Dae Ryook Yang, Jay H. Lee:
Java applet modules for undergraduate process control education. ACC 2003: 694-699 - [c5]Swathy Ramaswamy, Jay H. Lee:
Robust forecasts and run-to-run control for processes with linear drifts. ACC 2003: 3986-3991 - 2002
- [j13]Douglas G. Robertson, Jay H. Lee:
On the use of constraints in least squares estimation and control. Autom. 38(7): 1113-1123 (2002) - [c4]Kangwook Lee, Jay H. Lee, Mitsuko Fujiwara, David L. Ma, Richard D. Braatz:
Run-to-run control of multidimensional crystal size distribution in a batch crystallizer. ACC 2002: 1013-1018 - [c3]Yangdong Pan, ChangKyoo Yoo, Jay H. Lee, In-Beum Lee:
Process monitoring of continuous processes with periodic operation patterns. ACC 2002: 3882-3887 - 2001
- [j12]Brian L. Cooley, Jay H. Lee:
Control-relevant experiment design for multivariable systems described by expansions in orthonormal bases. Autom. 37(2): 273-281 (2001) - [j11]Christopher V. Rao, James B. Rawlings, Jay H. Lee:
Constrained linear state estimation - a moving horizon approach. Autom. 37(10): 1619-1628 (2001) - 2000
- [j10]Jay H. Lee, Brian L. Cooley:
Min-max predictive control techniques for a linear state-space system with a bounded set of input matrices. Autom. 36(3): 463-473 (2000) - [j9]Jay H. Lee, Kwang S. Lee, Won C. Kim:
Model-based iterative learning control with a quadratic criterion for time-varying linear systems. Autom. 36(5): 641-657 (2000) - [j8]Kwang S. Lee, Jay H. Lee:
Convergence of constrained model-based predictive control for batch processes. IEEE Trans. Autom. Control. 45(10): 1928-1932 (2000) - [c2]Yangdong Pan, Jay H. Lee:
Recursive data-based prediction and control of product quality for a batch PMMA reactor. ACC 2000: 1747-1751 - [c1]Jay H. Lee, Yudi Samyudia:
LMI-based optimal design of test signals for control-relevant system identification. ACC 2000: 3224-3228
1990 – 1999
- 1998
- [j7]Douglas G. Robertson, Jay H. Lee:
A method for the estimation of infrequent abrupt changes in nonlinear systems. Autom. 34(2): 261-270 (1998) - [j6]Jay H. Lee, Brian L. Cooley:
Optimal feedback control strategies for state-space systems with stochastic parameters. IEEE Trans. Autom. Control. 43(10): 1469-1475 (1998) - 1997
- [j5]Jay H. Lee, Zhenghong Yu:
Worst-case formulations of model predictive control for systems with bounded parameters. Autom. 33(5): 763-781 (1997) - [j4]Jay H. Lee, Parthasarathy Kesavan, Manfred Morari:
Control structure selection and robust control system design for a high-purity distillation column. IEEE Trans. Control. Syst. Technol. 5(4): 402-416 (1997) - 1995
- [j3]Jay H. Lee, Richard D. Braatz, Manfred Morari, Andrew K. Packard:
Screening tools for robust control structure selection. Autom. 31(2): 229-235 (1995) - 1994
- [j2]Jay H. Lee, Manfred Morari, Carlos E. Garcia:
State-space interpretation of model predictive control. Autom. 30(4): 707-717 (1994) - 1991
- [j1]Jay H. Lee, Manfred Morari:
Robust measurement selection. Autom. 27(3): 519-527 (1991)
Coauthor Index
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