Journal Publications
2023
123. Bhushan Pawar, Bhavana Bhadriraju, Faisal Khan, Joseph Sang-II Kwon, Qingsheng Wang, “Resilience assessment of chemical processes using operable adaptive sparse identification of systems”, Computers & Chemical Engineering, Volume 177, 2023, 108346, ISSN 0098-1354, DOI.org/10.1016/108346.
122. Bhavana Bhadriraju, Joseph Sang-Il Kwon, Faisal Khan, “An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries”, Computers & Chemical Engineering, Volume 175, 2023, 108275, ISSN 0098-1354, DOI.org/10.1016/108275.
121. Silabrata Pahari, Juhyeon Kim, Hyun-Kyu Choi, Mairui Zhang, Anqi Ji, Chang Geun Yoo, Joseph Sang-Il Kwon, “Multiscale kinetic modeling of biomass fractionation in an experiment: Understanding individual reaction mechanisms and cellulose degradation”, Chemical Engineering Journal, Volume 467, 2023, 143021, ISSN 1385-8947, DOI.org/10.1016/143021.
120. N. Sitapure, J. S. Kwon, “CrystalGPT: Enhancing System-to-System Transferability in Crystallization Prediction and Control Using Time-Series-Transformers”, Computers & Chemical Engineering, Volume 177, 2023, 108339, ISSN 0098-1354, DOI.org/10.1016/108339.
119. Lin. Yu-T, Liu. S, Bhat. B, Kuan. K-Y, Zhou. W, Cobos Yelavives. I, J. S. Kwon, Akbulut. M, “pH- and Temperature-Responsive Supramolecular Assemblies with Highly Adjustable Viscoelasticity: A Multi-Stimuli Binary System”, Soft Matter, 2023. In Press.
118. N. K. Wagh, D-H. Kim, C. Lee, S-H. Kim, H-D. Um, J. S. Kwon, S. S. Shinde, S. Lee, and J-H. Lee, “Heterointerface promoted trifunctional electrocatalysts for all temperature high-performance rechargeable Zn-air batteries”, Nanoscale Horizons, 2023. DOI:10.1039/D3NH00108C.
117. C. Lee, S. Pahari, N. Sitapure, M. Barteau, J. S. Kwon, “Investigating High-Performance Non-Precious Transition Metal Oxide Catalysts for Nitrogen Reduction Reaction: A Multifaceted DFT-kMC-LSTM Approach”, ACS Catalysis, 2023.DOI:10.1021/acscatal.3c01360.
116. J. Kim, S. Pahari, H. Choi, M. Zhang, A. Ji, C. Yoo, J. S. Kwon, “Multiscale kinetic modeling of biomass fractionation in an experiment: understanding individual reaction mechanisms and cellulose degradation”, Chemical Engineering Journal, 2023.DOI:10.3390/pr11030809.
115. N. Sitapure, J. S. Kwon, “Exploring the Potential of Time-series Transformers for Process Modeling and Control in Chemical Systems: An Inevitable Paradigm Shift?”, Chemical Engineering Research & Design, 2023. DOI:10. 1016/j.cherd.2023.04.028.
114. P. Shah, H. Choi, J. S. Kwon, “Achieving optimal paper properties: a layered multiscale kMC and LSTM-ANN-based control approach for Kraft pulping”, Processes, 2023.DOI:10.3390/pr11030809.
2022
113. Y. Choi, B. Bhadriraju, H. Cho, J. Lim, I. Han, I. Moon, J. S. Kwon, J. Kim, “Data-driven modeling of multimode chemical process: Validation with a real-world distillation column”, Chemical Engineering Journal, 2022. DOI:10.1016/j.cej.2022.141025.
112. N. Sitapure, J. S. Kwon, “A unified approach for modeling and control of crystallization of Quantum Dots (QDs)”, Digital Chemical Engineering, 2022. DOI:10.1016/j.dche.2022.100077.
111. K Cao, N. Sitapure, J. S. Kwon, “Exploring the benefits of utilizing small modular device for sustainable and flexible shale gas water management”, Journal of Cleaner Production, 2022. DOI:10.1016/j.jclepro.2022.135282
110. C. Lee, S. Pahari, N. Sitapure, M. Barteau, J. S. Kwon, “A DFT-kMC analysis for identifying novel bimetallic electrocatalysts for enhanced NRR performance by suppressing HER at ambient conditions via active-site separation”, ACS Catalysis, 2022. DOI:10.1021/acscatal.2c04797.
109. M. S. F. Bangi, J. S. Kwon, “Deep hybrid model-based predictive control with guarantees on domain of applicability”, AIChE Journal, 2022. DOI:10.1002/aic.18012.
108. P. Shah, Z. Sheriff, M. S. F. Bangi, C. Kravaris, J. S. Kwon, C. Botre, and J. Hirota, “Multi-rate observer design and optimal control to maximize productivity of an industry-scale fermentation process”, AIChE Journal, 2022. DOI:10.1002/aic.17946
107. P. Kumari, Q. Wang, F. Khan, J. S. Kwon, “A unified causation prediction model for aboveground onshore oil and refined product pipeline incidents using artificial neural network”, Chem. Eng. Res. & Des., 2022. DOI:10.1016/j.cherd.2022.09.022
106. P. Kumari, S. Z. Halim, J. S. Kwon, and N. Quddus, “An integrated risk prediction model for corrosion-induced pipeline incidents using artificial neural network and Bayesian analysis”, Process Safety and Environmental Protection, 2022. DOI:10.1016/j.psep.2022.07.053
105. B. Bhadriraju, F. Khan, and J. S. Kwon, “An adaptive data-driven approach for two-timescale dynamics prediction and remaining useful life estimation of Li-ion batteries”, Computers & Chemical Engineering, 2023. DOI:10.1016/j.compchemeng.2023.108275.
104. B. Bhat, S. Pahari, S. Liu, Y. Lin, J. Kwon, and M. Akbulut, “Nanostructural and rheological transitions of pH-responsive supramolecular systems involving a zwitterionic amphiphile and a triamine”, Colloids and Surfaces, 2022. DOI:10.1016/j.colsurfa.2022.130067
103. J. Yang , M. Lee , M. Park , J. Kim , N. Sitapure, D. Hahm , H. Kim , S. Rhee , D. Lee , J. Kim , T. Shin, D. Lee, K. Kwak, J. Kwon, B. Kim, W. Bae, M. Kang, “Nondestructive photopatterning of heavy-metal-free quantum dots”, Advanced Materials, 2022. DOI:10.1002/adma.202205504
102. P. Kumari, Q. Wang, and J. S. Kwon, “A direct transfer entropy-based multiblock Bayesian network for root cause diagnosis of process faults”, Ind. & Eng. Chem. Res., 2022. DOI:10.1021/acs.iecr.2c02320
101. N. Sitapure, T. Kwon, M. Lee, B. Kim, M. Kang, and J. S. Kwon, “Modeling ligand crosslinking for interlocking quantum dots in thin-films”, J. Mater. Chem. C, 2022. DOI:10.1039/D2TC00548D
100. S. Pahari, S. Liu, C. H. Lee, M. Akbulut, and J. S. Kwon, “SAXS-guided unbiased coarse-grained Monte Carlo simulation for identification of self-assembly nanostructure and dimension”, Soft Matter, 2022. DOI:10.1039/D2SM00601D
99. S. Liu, Y. Lin, B. Bhat, S. Pahari, A. De, J. Kwon, and M. Akbulut, “Dynamic, hollow nanotubular networks with superadjustable pH-responsive and temperature resistant rheological characteristics”, Chemical Engineering Journal, 2023. DOI:10.1016/j.cej.2022.139364
98. N. Sitapure, and J. S. Kwon, “Neural network-based model predictive control for thin-film chemical deposition of quantum dots using data from a multiscale simulation”, Chem. Eng. Res. & Des., 2022. DOI:10.1016/j.cherd.2022.05.041
97. P. Shah, Z. Sheriff, M. S. F. Bangi, J. S. Kwon, C. Kravaris, C. Botre, and J. Hirota, “Deep neural network-based hybrid modeling and experimental validation for an industry-scale fermentation process: identification of time-varying dependencies among parameters”, Chemical Engineering Journal, 2022. DOI:10.1016/j.cej.2022.135643
96. H. Qiang, C. Sellers, J. S. Kwon, and H. Wu, “Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification”, Digital Chemical Engineering, 2022. DOI:10.1016/j.dche.2022.100020
95. G. Hwang, N. Sitapure, J. Moon, H. Lee, S. Hwang, and J. S. Kwon, “Model predictive control of Lithium-ion batteries: Development of optimal charging profile for reduced intracycle capacity fade using an enhanced single particle model (SPM) with first-principled chemical/mechanical degradation mechanisms”, Chemical Engineering Journal, 2022, 134768. DOI:10.1016/j.cej.2022.134768
94. M. S. F. Bangi, K. C. Kao, and J. S. Kwon, “Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae”, Chem. Eng. Res. & Des., 2022. DOI:10.1016/j.cherd.2022.01.041
93. K. Shin, S. Son, J. Moon, Y. Jo, J. S. Kwon, and S. Hwang, “Dynamic modeling and predictive control of boil-off gas generation during LNG loading”, Computers & Chemical Engineering, 2022, 107698. DOI:10.1016/j.compchemeng.2022.107698
92. P. Kumari, B. Bhadriraju, Q. Wang, and J. S. Kwon, “A modified Bayesian network to handle cyclic loops in root cause diagnosis of rare events in the chemical process industry”, Journal of Process Control, 2022, 110: 84-98. DOI:10.1016/j.jprocont.2021.12.011
91. S. Son, H. Choi, J. Moon, and J. S. Kwon, “Hybrid Koopman model predictive control of nonlinear systems by utilizing multiple EDMD models: Application to a batch pulp digester with feed fluctuation”, Control Engineering Practice, 2022, 118: 104956. DOI.org/10.1016/j.conengprac.2021.104956
90. J. Jung, H. Choi, S. Son, J. S. Kwon, and J. H. Lee, “Multiscale modeling of fiber deformation: application to a batch pulp digester for model predictive control of fiber strength ”, Computers & Chemical Engineering, 2022, 107640. DOI:10.1016/j.compchemeng.2021.107640
89. B. Bhat, S. Liu, Y. Lin, M. L. Sentmanat, J. S. Kwon, and M. Akbulut, “Supramolecular viscosity modifiers with salt-responsive and pH-switchable characteristics for hydraulic fracturing applications”, Research Square, 2021. DOI:10.21203/rs.3.rs-453320/v1
88. S. Son, A. Narasingam, and J. S. Kwon, “Handling plant-model mismatch in Koopman Lyapunov-based model predictive control via offset-free control framework”, Journal of Process Control, arXiv:2010.07239, 2020. DOI:10.48550/arXiv.2010.07239
2021
87. S. Cho, D. Kang, J. S. Kwon, M. Kim, H. Cho, I. Moon, and J. Kim, “A framework for economically optimal operation of explosive waste incineration process to reduce NOx emission concentration”, Mathematics, 2021, 9(17), 2174. DOI:10.3390/math9172174 (Invited Paper)
86. S. Pahari, J. Moon, M. Akbulut, S. Hwang, and J. S. Kwon, “Estimation of microstructural properties of wormlike micelles via a multi-scale multi-recommendation batch Bayesian optimization”, Ind. & Eng. Chem. Res., 2021, 60(43), 15669-15678. DOI:10.1021/acs.iecr.1c03045
85. B. Bhadriraju, F. Khan, and J. S. Kwon, “OASIS-P: Operable adaptive sparse identification of systems for fault prognosis of chemical processes”, Journal of Process Control, 2021, 107, 114-126. DOI:10.1016/j.jprocont.2021.10.006
84. S. Pahari, M. Akbulut, and J. S. Kwon, “Model predictive control for wormlike micelles (WLMs): application to a system of CTAB and NaCl”, Chem. Eng. Res. & Des., 2021, 174, 30-41. DOI:10.1016/j.cherd.2021.07.023
83. S. Pahari, B. Bhadriraju, M. Akbulut, and J. S. Kwon, “A slip-spring framework to study relaxation dynamics of entangled wormlike micelles with kinetic Monte Carlo algorithm”, J. Colloid Interface Sci., 2021, 600, 550-560. DOI:10.1016/j.jcis.2021.05.032
82. D. Lee, A. Green, H. Wu, and J. S. Kwon, “Hybrid PDE-kMC modeling approach to simulate multivalent lectin-glycan binding process”, AIChE J., 2021, e17453. DOI:10.1002/aic.17453
81. K. Cao, S. Son, J. Moon, and J. S. Kwon, “A closed-loop integration of scheduling and control for hydraulic fracturing using offset-free model predictive control”, Applied Energy, 2021, 302, 117487. DOI:10.1016/j.apenergy.2021.117487
80. M. S. F. Bangi, and J. S. Kwon, “Deep reinforcement learning control of hydraulic fracturing”, Computers & Chemical Engineering, 2021, 107489. DOI:10.1016/j.compchemeng.2021.107489
79. P. Kumari, B. Bhadriraju, Q. Wang, and J. S. Kwon, “Development of parametric reduced-order model for consequence estimation of rare events”, Chem. Eng. Res. & Des., 2021, 169, 142-152. DOI:10.1016/j.cherd.2021.02.006
78. N. Sitapure, R. Epps, M. Abolholsani, and J. S. Kwon, “CFD-based computational studies of quantum dot size control in slug flow reactors: handling slug-to-slug variation”, Ind. & Eng. Chem. Res., 2021, 60(13), 4930-4941. DOI:10.1021/acs.iecr.0c06323
77. H. Choi, S. Son, and J. S. Kwon, “Inferential model predictive control of continuous pulping under grade transition”, Ind. & Eng. Chem. Res., 2021, 60(9), 3699-3710. DOI:10.1021/acs.iecr.0c06216
76. S. Son, H. Choi, and J. S. Kwon, “Application of offset-free Koopman-based model predictive control to a batch pulp digester”, AIChE J., 2021, e17301. DOI:10.1002/aic.17301
75. H. Lee, N. Sitapure, S. Hwang, and J. S. Kwon, “Multiscale modeling of dendrite formation in Lithium-ion Batteries”, Computers & Chemical Engineering, 2021, 107415. DOI:10.1016/j.compchemeng.2021.107415
74. B. Bhadriraju, J. S. Kwon, and F. Khan, “Risk-based fault prediction of chemical processes using operable adaptive sparse identification of systems (OASIS)”, Computers & Chemical Engineering, 2021, 152,107378. DOI:10.1016/j.compchemeng.2021.107378
73. S. Liu, Y. Lin, B. Bhat, J. S. Kwon, and M. Akbulut, “pH-responsive viscoelastic supramolecular viscosifiers based on dynamic complexation of zwitterionic octadecylamidopropyl betaine and triamine for hydraulic fracturing applications”, Royal Society of Chemistry, 2021, 11, 22517. DOI: 10.1039/d1ra00257k
2020
72. D. Lee, A. Jayaraman, and J. S. Kwon, “Development of a hybrid model for a partially known intracellular signaling pathway through correction term estimation and neural network modeling”, PLOS Computational Biology, 2020, 16(12), e1008472. DOI:10.1371/journal.pcbi.1008472
71. D. Sen, H. Chen, A. Datta-Gupta, J. S. Kwon and M. Srikanta, “Data-driven rate optimization under geologic uncertainty”, SPE Annual Technical Conference Exhibition, 2020, SPE-201325-MS, https://doi.org/10.2118/201325-MS.
70. N. Sitapure, R. Epps, M. Abolhasani, and J. S. Kwon, “Multiscale modeling and optimal operation of microfluidic synthesis of perovskite quantum dots: towards size-controlled continuous manufacturing”, Chemical Engineering Journal, 2020, 413, 127905. DOI:10.1016/j.cej.2020.127905
69. S. Son, H. Choi, and J. S. Kwon, “Multiscale modeling and control of pulp digester under fiber-to-fiber heterogeneity”, Computers & Chemical Engineering, 2020, 143, 107117. DOI:10.1016/j.compchemeng.2020.107117
68. S. Pahari, P. Bhandakkar, M. Akbulut, and J. S. Kwon, “Optimal pumping schedule with high-viscosity gel for uniform distribution of proppant in unconventional reservoirs”, Energy, 2020, 119231. DOI:10.1016/j.energy.2020.119231
67. Y. Jo, M. S. F. Bangi, S. Son, J. S. Kwon, and S. Hwang, “Dynamic modeling and offset-free predictive control of LNG tank”, Fuel, 2020, 285, 119074. DOI:10.1016/j.fuel.2020.119074
66. N. Sitapure, H. Lee, F. O. Acevedo, P. B. Balbuena, S. Hwang, and J. S. Kwon, “A computational approach to characterize formation of a passivation layer in Lithium metal anodes”, AIChE, 2020, e17073. DOI:10.1002/aic.17073
65. S. Mao, P. Siddhamshetty, Z. Zhang, W. Yu, T. Chun, J. S. Kwon, and K. Wu, “Impact of proppant pumping schedule on well production for slickwater fracturing”, SPE Journal, 2020, 1-17, 204235. DOI:10.2118/204235-PA
64. H. Choi and J. S. Kwon, “Multiscale modeling and predictive control of cellulose accessibility in alkaline pretreatment for enhanced glucose yield”, Fuel, 2020, 280, 118540. DOI:10.1016/j.fuel.2020.118546
63. A. Narasingam, S. Son, and J. S. Kwon, “Data-driven feedback stabilization of nonlinear systems: Koopman-based model predictive control”, International Journal of Control, 2021. DOI:10.1080/00207179.2021.2013541
62. K. Cao, P. Siddhamshetty, Y. Ahn, M. El-Halwagi, and J. S. Kwon, “Evaluating the spatiotemporal variability of water recovery ratios of shale gas wells and their effects on shale gas development”, Journal of Cleaner Production, 2020, 276, 123171. DOI:10.1016/j.jclepro.2020.123171
61. P. Siddhamshetty, S. Mao, K. Wu, and J. S. Kwon, “Multi-Size Proppant Pumping Schedule of Hydraulic Fracturing: Application to a MP-PIC Model of Unconventional Reservoir for Enhanced Gas Production”, Processes, 2020, 8(5), 570. DOI:10.3390/pr8050570
60. B. Bhadriraju, M. S. F. Bangi, A. Narasingam, and J. S. Kwon, “Operable adaptive sparse identification of systems (OASIS): application to chemical processes”, AIChE J., 2020, 66, e16980. DOI:10.1002/aic.16980
59. P. Bhandakkar, P. Siddhamshetty, and J. S. Kwon, “Numerical study of the effect of propped surface area and fracture conductivity on shale gas production: application for multi-size proppant pumping schedule design”, Journal of Natural Gas Science & Engineering, 2020, 79, 103349. DOI:10.1016/j.jngse.2020.103349
58. P. Kumari, D. Lee, Q. Wang, M. Karim, and J. S. Kwon, “Root cause analysis of key process variable deviation for rare event in chemical process industry”, Ind. & Eng. Chem.Res., 2020. DOI:10.1021/acs.iecr.0c00624
57. Y. Ahn, J. Kim, and J. S. Kwon, “Optimal design of supply chain network with carbon dioxide injection for enhanced shale gas recovery”, Applied Energy, 2020, 274, 115334. DOI:10.1016/j.apenergy.2020.115334
56. N. Sitapure, T. Qiao, D. H. Son, and J. S. Kwon, “Kinetic Monte Carlo modelling of the equilibrium-based size control of CsPbBr3 perovskite quantum dots in strongly confined regime”, Comp. & Chem. Eng., 2020, 139, 106872. DOI:10.1016/j.compchemeng.2020.106872
55. A. Narasingam and J. S. Kwon, “Application of Koopman operator for model-based control of fracture propagation and proppant transport in hydraulic fracturing operation”, Journal of Process Control, 2020, 91, 25-36. DOI:10.1016/j.jprocont.2020.05.003
54. H. Choi and J. S. Kwon, “Multiscale modeling and multiobjective control of wood fiber morphology in batch pulp digester”, AIChE J., 2020, 66, e16972. DOI: 10.1002/aic.16972
53. M. S. F. Bangi and J. S. Kwon, “Deep hybrid modeling of chemical process: Application to hydraulic fracturing”, Comp. & Chem. Eng., 2020, 134, 106696. DOI: 10.1016/j.compchemeng.2019.106696
52. D. Lee, A. Jayaraman, and J. S. Kwon, “Identification of cell‐to‐cell heterogeneity through systems engineering approaches”, AIChE J., 2020, 66, e16915. (selected as AIChE Journal’s Editor’s Choice paper from the May 2020 issue.) DOI: 10.1002/aic.16925
51. S. Yuan, Z. Zhang, Y. Sun, J. S. Kwon, and C. Mashuga, “Liquid flammability ratings predicted by machine learning considering aerosolization”, Journal of Hazardous Materials, 2020, 386, 121640. DOI: 10.1016/j.jhazmat.2019.121640
50. P. Siddhamshetty, P. Bhandakkar, and J. S. Kwon, “Enhancing total fracture surface area in naturally fractured unconventional reservoirs via model predictive control”, Journal of Petroleum Science and Engineering, 2020, 184, 106525. DOI: 10.1016/j.petrol.2019.106525
2019
49. B. Bhadriraju, A. Narasingam, and J. S. Kwon, “Machine learning based adaptive model identification of systems: Application to a chemical process”, Chem. Eng. Res. & Des., 2019, 152, 372-383. DOI:10.1016/j.cherd.2019.09.009
48. A. Bao, E. Gildin, A. Narasingam, and J. S. Kwon, “Data-driven model reduction for coupled flow and geomechanics based on DMD methods”, Fluids, 2019, 4(3), 138. DOI: 10.3390/fluids4030138
47. H. Choi and J. S. Kwon, “Modeling and control of cell wall thickness in batch delignification”, Comp. & Chem. Eng., 2019, 128, 512-523. DOI: 10.1016/j.compchemeng.2019.06.025
46. P. Siddhamshetty and J. S. Kwon, “Simultaneous measurement uncertainty reduction and proppant bank height control of hydraulic fracturing”, Comp. & Chem. Eng., 2019, 127, 272-281. DOI: 10.1016/j.compchemeng.2019.05.025
45. Y. Ahn, P. Siddhamshetty, K. Cao, and J. S. Kwon, “Optimal design of shale gas supply chain network considering MPC-based pumping schedule of hydraulic fracturing in unconventional reservoirs”, Chem. Eng. Res. & Des., 2019, 174, 412-429. DOI: 10.1016/j.cherd.2019.05.016
44. K. Cao, P. Siddhamshetty, Y. Ahn, R. Mukherjee, and J. S. Kwon, “Economic model-based controller design framework for hydraulic fracturing to optimize shale gas production and water usage”, Ind. & Eng. Chem.Res., 2019, 58, 12097-12115. DOI: 10.1021/acs.iecr.9b01553
43. A. Narasingam and J. S. Kwon, “Koopman Lyapunov-based model predictive control of nonlinear chemical process systems”, AIChE J., 2019, 65, e16743. DOI: 10.1002/aic.16743
42. M. S. F. Bangi, A. Narasingam, P. Siddhamshetty, and J. S. Kwon, “Enlarging the domain of attraction of local dynamic mode decomposition with control technique: application to hydraulic fracturing”, Ind. & Eng. Chem. Res., 2019, 58(14), 5588-5601. DOI: 10.1021/acs.iecr.8b05995
41. H. Choi, D. Lee, A. Singla, H. Wu, and J. S. Kwon, “The influence of hetero-multivalency on lectin-glycan binding”, Glycobiology, 2019, 29(5), 397-408. (Cover page of the “Official Journal of the Society for Glycobiology”) DOI: 10.1093/glycob/cwz010 
40. H. Choi and J. S. Kwon, “Multiscale modeling and control of Kappa number and porosity in a batch pulp digester”, AIChE J. 2019, 65(6), e16589. DOI: 10.1002/aic.16589
39. S. Yuan, C. Ji, A. Monhollen, J. S. Kwon, and C. Mashuga, “Experimental and thermodynamic study of aerosol explosions in a 36 L apparatus”, Fuel, 2019, 245, 467-477. DOI: 10.1016/j.fuel.2019.02.078
38. D. Lee, A. Jayaraman, and J. S. Kwon, “Identification of a time-varying intracellular signaling model through data clustering and parameter selection: application to NFκB signaling pathway induced by LPS in the presence of BFA”, IET Systems Biology, 2019, 13(4), 169-179. (selected as the best paper for “The IET Premium Awards 2021” by IET Systems Biology Journal.) DOI: 10.1049/iet-syb.2018.5079
37. S. Yuan, Z. Jiao, N. Quddus, J. S. Kwon, and C. Mashuga, “Developing quantitative structure-property relationship models to predict the upper flammability limit using machine learning”, Ind. & Eng. Chem. Res. 2019, 58(8), 3531-3573. DOI: 10.1021/acs.iecr.8b05938
36. P. Siddhamshetty, M. Ahammad, R. Hasan, and J. S. Kwon, “Understanding well head ignition as a blowout response”, Fuel, 2019, 243, 622-629. DOI: 10.1016/j.fuel.2019.01.142
35. P. Siddhamshetty, K. Wu, and J. S. Kwon, “Modeling and control of proppant distribution of multi-stage hydraulic fracturing in horizontal shale wells”, Ind. & Eng. Chem.Res. 2019, 58(8), 3159-3169. DOI: 10.1021/acs.iecr.8b05654
2018
34. P. Etoughe, P. Siddhamshetty, K. Cao, R. Mukherjee, and J. S. Kwon, “Incorporation of sustainability in process control of hydraulic fracturing in unconventional reservoirs”, Chem. Eng. Res. & Des., 2018, 139, 62-76. DOI: 10.1016/j.cherd.2018.09.016
33. A. Narasingam and J. S. Kwon, “Data-driven identification of interpretable reduced-order models using sparse regression”, Comp. & Chem. Eng., 2018, 119, 101-111. DOI: 10.1016/j.compchemeng.2018.08.010
32. P. Siddhamshetty, K. Wu, and J. S. Kwon, “Optimization of simultaneously propagating multiple fractures in hydraulic fracturing to achieve uniform growth using data-based model reduction”, Chem. Eng. Res. & Des., 2018, 136, 675-686. DOI: 10.1016/j.cherd.2018.06.015
31. H. S. Sidhu, P. Siddhamshetty, and J. S. Kwon, “Approximate dynamic programming based control of proppant concentration in hydraulic fracturing”, Mathematics, 2018, 6, 132 (Cover page of the Special Issue “New Directions on Model Predictive Control”) DOI: 10.3390/math6080132
30. D. Lee, A. Mohr, J. S. Kwon, and H. Wu, “Kinetic Monte Carlo modeling of multivalent binding of CTB proteins with GM1 receptors”, Comp. & Chem. Eng., 2018, 118, 283-295. DOI: 10.1016/j.compchemeng.2018.08.011
29. N. C. Worstell, A. Singla, P. Saenkham, T. Galbadage, P. Sule, D. Lee, A. Mohr, J. S. Kwon, J. D. Cirillo and H. Wu ”Hetero-multivalency of Pseudomonas aeruginosa lectin LecA binding to model membranes”, Scientific Reports, 2018, 8, 8419. DOI: 10.1038/s41598-018-26643-7
28. D. Lee, A. Singla, H. Wu and J. S. Kwon, “An Integrated numerical and experimental framework for modeling of cholera toxin subunit B and GD1b ganglioside binding kinetics”, AIChE J., 2018, 64, 3882-3893. DOI: 10.1002/aic.16209
27. A. Narasingam, P. Siddhamshetty and J. S. Kwon, “Handling of spatial heterogeneity using POD-based EnKF in model-based feedback control of hydraulic fracturing”, Ind. & Eng. Chem. Res., 2018, 57, 3977–3989. DOI: 10.1021/acs.iecr.7b04927
26. H. S. Sidhu, A. Narasingam, P. Siddhamshetty and J. S. Kwon, “Model order reduction of nonlinear parabolic PDE systems with moving boundaries using sparse proper orthogonal decomposition: Application to hydraulic fracturing”, Comp. & Chem. Eng., 2018, 112, 92-100. DOI: 10.1016/j.compchemeng.2018.02.004
25. P. Siddhamshetty and J. S. Kwon, “Model-based feedback control of oil production in oil-rim reservoirs under gas coning conditions”, Comp. & Chem. Eng., 2018, 112, 112-120. DOI: 10.1016/j.compchemeng.2018.02.001
24. D. Lee, Y. Ding, A. Jayaraman and J. S. Kwon, “Mathematical modeling and parameter estimation of intracellular signaling pathway: application to LPS-induced NFκB activation and TNFα production in macrophages”, Processes, 2018, 6(3), 21 (Feature Paper) DOI: 10.3390/pr6030021
23. C. Curitiba, H. Durand, J. S. Kwon, A. Gomes Barreto, P. Lage, M. Bezerra de Souza, A. Secchi and P. D. Christofides, “Optimal Operation of Batch Enantiomer Crystallization: From Ternary Diagrams to Predictive Control”, AIChE J., 2018, 64, 1618-1637. DOI: 10.1002/aic.16028
22. P. Siddhamshetty, S. Liu, P. P. Valkó and J. S. Kwon, “Feedback control of proppant bank heights during hydraulic fracturing for enahnced productivity in shale formations”, AIChE J., 2018, 64,1638-1650. DOI: 10.1002/aic.16031
21. P. Siddhamshetty, S. Yang and J. S. Kwon, “Modeling of hydraulic fracturing and designing of online pumping schedules to achieve uniform proppant concentration in conventional reservoirs”, Comp. & Chem. Eng., 2018, 114, 306-317. DOI: 10.1016/j.compchemeng.2017.10.032
2017
20. A. Narasingam and J. S. Kwon,”Development of local dynamic mode decomposition with control: Application to model predictive control of hydraulic fracturing”, Comp. & Chem. Eng., 2017, 106, 501-511. DOI: 10.1016/j.compchemeng.2017.07.002
19. A. Narasingam, P. Siddhamshetty and J. S. Kwon, “Temporal clustering for order reduction of nonlinear parabolic PDE systems with time-dependent spatial domains: Application to a hydraulic fracturing process”, AIChE J., 2017, 63, 3818-3831. DOI: 10.1002/aic.15733
18. S. Yang, P. Siddhamshetty and J. S. Kwon, “Optimal pumping schedule design to achieve a uniform proppant concentration level in hydraulic fracturing”, Comp. & Chem. Eng., 2017, 101, 138–147. DOI: 10.1016/j.compchemeng.2017.02.035
17. M. Crose, J. S. Kwon, A. Tran and P. D. Christofides, “Multiscale modeling and run-to-run control of PECVD of thin film solar cells”, Renewable Energy, 2017, 100, 129-140. DOI: 10.1016/j.renene.2016.06.065
2015
16. M. Crose, J. S. Kwon, D. Ni and P. D. Christofides, “Multiscale modeling and operation of PECVD of thin film solar cells”, Chem. Eng. Sci., 2015,136,50-61 DOI: 10.1016/j.ces.2015.02.027
15. J. S. Kwon,M. Nayhouse and P. D. Christofides, “Multiscale, Multidomain Modeling and Parallel Computation: Application to Crystal Shape Evolution in Crystallization”, Ind. & Eng. Chem. Res., 2015, 54, 11903−11914 DOI: 10.1021/acs.iecr.5b02942
14. M. Nayhouse, A. Tran, J. S. Kwon, M. Crose, G. Orkoulas and P. D. Christofides, “Modeling and control of Ibuprofen crystal growth and size distribution”, Chem. Eng. Sci., 2015, 134, 414-422 DOI: 10.1016/j.ces.2015.05.033
13. J. S. Kwon, M. Nayhouse and P. D. Christofides, “Detection and isolation of batch-to-batch parametric drift and drift-tolerant model predictive control of batch crystallization process”, Ind. & Eng. Chem. Res., 2015, 54, 5514-5526 DOI: 10.1021/acs.iecr.5b00422
12. J. S. Kwon, M. Nayhouse, D. Ni, G. Orkoulas and P. D. Christofides, “Run-to-run based model predictive control of protein crystal shape in batch crystallization”, Ind. & Eng. Chem. Res., 2015, 54, 4293-4302 DOI: 10.1021/ie502377a
11. J. S. Kwon, M. Nayhouse, D. Ni, G. Orkoulas and P. D. Christofides, “A method for handling batch-to-batch parametric drift using moving horizon estimation: application to run-to-run MPC of batch crystallization”, Chem. Eng. Sci., 2015, 127, 210-219 DOI: 10.1016/j.ces.2015.01.033
2014
10. J. S. Kwon, M. Nayhouse, G. Orkoulas and P. D. Christofides, “Crystal shape and size control using a plug flow crystallization configuration”, Chem. Eng. Sci., 2014, 119, 30 – 39 DOI: 10.1016/j.ces.2014.07.058
9. J. S. Kwon, M. Nayhouse, P. D. Christofides and G. Orkoulas, “Enhancing crystal production rate and reducing polydispersity in continuous protein crystallization”, Ind. & Eng. Chem. Res., 2014, 53, 15538 – 15548 DOI: 10.1021/ie5008163
8. J. S. Kwon, M. Nayhouse, P. D. Christofides and G. Orkoulas, “Protein crystal shape and size control in batch crystallization: comparing model predictive control with conventional operating policies”, Ind. & Eng. Chem. Res., 2014, 53, 5002 – 5014 DOI: 10.1021/ie400584g
7. J. S. Kwon, M. Nayhouse, P. D. Christofides and G. Orkoulas, “Modeling and control of crystal shape in continuous protein crystallization”, Chem. Eng. Sci., 2014, 107, 47 – 57 DOI: 10.1016/j.ces.2013.12.005
6. J. S. Kwon, M. Nayhouse, P. D. Christofides and G. Orkoulas, “Protein crystal shape and size control in batch crystallization: Comparing model predictive control with conventional operating policies”, Ind. & Eng. Chem. Res., 2014, 53 (13), 5002-5014. DOI: 10.1021/ie400584g
5. M. Nayhouse, J. S. Kwon, V. R. Herg, A. M. Amlami and G. Orkoulas, “Freezing transition studies through constrained cell model simulation”, Int. J. Thermophysics, 2014, 35, 1661-1676. DOI: 10.1007/s10765-013-1430-2
~2013
4. J. S. Kwon, M. Nayhouse, P. D. Christofides and G. Orkoulas, “Modeling and control of shape distribution of protein crystal aggregates”, Chem. Eng. Sci., 2013, 104, 484-497. DOI: 10.1016/j.ces.2013.09.026
3. J. S. Kwon, M. Nayhouse, P. D. Christofides and G. Orkoulas, “Modeling and control of protein crystal shape and size in batch crystallization”, AIChE J., 2013, 59(7), 2317-2327. DOI: 10.1002/aic.14039
2. M. Nayhouse, J. S. Kwon, P. D. Christofides and G. Orkoulas, “Crystal shape modeling and control in protein crystal growth”, Chem. Eng. Sci., 2013, 87, 216-223. DOI: 10.1016/j.ces.2012.10.020
1. M. Nayhouse, J. S. Kwon and G. Orkoulas, “Communication: Phase transitions, criticality, and three-phase coexistence in constrained cell models”, J. Chem. Phys., 2012, 136(20), 201101. DOI: 10.1063/1.4725768
Refereed Conference Proceedings
2022
45. J. Jung, H. Choi, S. Son, J. S. Kwon, J. Lee, “Model Predictive Control of Fiber Deformation in a Batch Pulp Digester”, Proceedings of the American Control Conference, Atlanta, Georgia, 2022.
44. H. Choi, A. Ji, M. Zhang, J. Kim, J. S. Kwon, C. Yoo, “Kinetic modeling study of lignocellulose fractionation using 4-phenolsulfonic acid”, Proceedings of the Dynamics and Control of Process Systems, Busan, Republic of Korea, 2022.
43. S. Pahari, J. Moon, M. Akbulut, S. Hwang, J. S. Kwon, “Development of a soft sensor to estimate the rheological properties of self-assembled systems: application to wormlike micelles (WLMs)”, Proceedings of the Dynamics and Control of Process Systems, Busan, Republic of Korea, 2022.
42. M. S. F. Bangi, J. S. Kwon, “Universal Hybrid Modeling of Batch Kinetics of Aerobic Carotenoid Production Using Saccharomyces Cerevisiae”, Proceedings of the American Control Conference, Atlanta, Georgia, 2022.
41. P. Kumari, B. Bhadriraju, Q. Wang, J. S. Kwon, “Handling cyclic loops for accurate root cause diagnosis of rare events in chemical process industry”, Proceedings of the American Control Conference, Atlanta, Georgia, 2022.
40. B. Bhadriraju, J. S. Kwon, F. Khan, “Prediction and isolation of process faults using operable adaptive sparse identification of systems (OASIS) and contribution plots”, Proceedings of the American Control Conference, Atlanta, Georgia, 2022.
39. G. Hwang, N. Sitapure, J. Moon, S. Hwang, J. S. Kwon, “Mitigation of Intra-Cycle Mechano-Chemical Degradation-Based Capacity Fade in Lithium-Ion Batteries: Application of a Model Predictive Controller”, Proceedings of the American Control Conference, Atlanta, Georgia, 2022.
38. N. Sitapure, J. S. Kwon, “Model predictive control of spray coating of perovskite quantum dots for application in perovskite solar cells”, Proceedings of the American Control Conference, Atlanta, Georgia, 2022.
2021
37. H. Choi, S. Son, J. S. Kwon, “Inferential model predictive control of blow-line fiber morphology in a continuous pulp digester via multiscale modeling”, Proceedings of the American Control Conference, New Orleans, Louisiana, 2021.
36. B. Bhadriraju, J. S. Kwon, F. Khan, “Dynamic risk-based fault prediction of chemical processes using online sparse model identification”, Proceedings of the American Control Conference, New Orleans, Louisiana, 2021.
35. S. Son, A. Narasingam, J. S. Kwon, “Integration of offset-free control framework with Koopman Lyapunov-based model predictive control”, Proceedings of the American Control Conference, New Orleans, Louisiana, 2021.
34. D. Lee, A. Jayaraman, J. S. Kwon, “A hybrid mechanistic data-driven approach for modeling uncertain intracellular signaling pathways”, Proceedings of the American Control Conference, New Orleans, Louisiana, 2021.
33. N. Sitapure, R. Epps, M. Abolhasani, J. S. Kwon, “Multiscale CFD modeling and optimal control of a continuous slug flow crystallizer for quantum dot production”, Proceedings of the American Control Conference, New Orleans, Louisiana, 2021.
32. S. Son, H. Choi, J. S. Kwon, “Application of learning-based offset-free model predictive control to environmental grade transition of pulping process”, Proceedings of MIT Applied Energy “A+B” Symposium, Boston, Massachusetts, 2021.
31. K. Cao, S. Son, J. S. Kwon, “Integrated Scheduling and Offset-free Model Predictive Control of Hydraulic Fracturing Operations with Closed-loop Implementation”, Proceedings of MIT Applied Energy “A+B” Symposium, Boston, Massachusetts, 2021.
30. S. Pahari, M. Akbulut, J. S. Kwon, “Modeling linear rheology of nanoparticle-enhanced viscoelastic fracturing fluids”, Proceedings of MIT Applied Energy “A+B” Symposium, Boston, Massachusetts, 2021.
29. N. Sitapure, J. S. Kwon, “Multiscale modeling of spray coating of perovskite quantum dots (QDs) for application in solar cells”, Proceedings of MIT Applied Energy “A+B” Symposium, Boston, Massachusetts, 2021.
28. H. Choi, C. Yoo, J. S. Kwon, “Strategies to overcome lignocellulose recalcitrance during acid fractionation via multiscale modeling and economic model predictive control”, Proceedings of MIT Applied Energy “A+B” Symposium, Boston, Massachusetts, 2021.
2020
27. A. Narasingam, J. S. Kwon, “Closed-loop stabilization of nonlinear systems using Koopman Lyapunov-based model predictive control,” Proceedings of 59th IEEE Conference on Decision and Control (CDC), Jeju, South Korea, 2020.
26. H. Choi, J. S. Kwon, “Model-based control of alkaline pretreatment for enhanced cellulose accessible surface area”, Proceedings of MIT Applied Energy “A+B” Symposium, Boston, Massachusetts, 2020.
25. K. Cao, P. Siddhamshetty, Y. Ahn, M. El-Halwagi and J. S. Kwon, “Study of time-varying wastewater recovery ratio across multiple counties in Eagle Ford and Marcellus shales”, Proceedings of MIT Applied Energy “A+B” Symposium, Boston, Massachusetts, 2020.
24. P. Siddhamshetty, P. Bhandakkar, J. S. Kwon, “Design of Online Pumping Schedules in Naturally Fractured Shale Formations to Enhance Total Fracture Surface Area (I),” Proceedings of 2020 American Control Conference, Denver, Colorado, 2020.
23. N. Sitapure, T. Qiao, D. H. Song, J. S. Kwon, “Modeling of CsPbBr3 Perovskite Quantum Dots for Equilibrium-Based Crystal Size Control,” Proceedings of 2020 American Control Conference, Denver, Colorado, 2020.
22. H. Choi, J. S. Kwon, “Multiscale Modeling and Control of Fiber Length in Pulp Digester,” Proceedings of 2020 American Control Conference, Denver, Colorado, 2020.
21. A. Narasingam, J. S. Kwon, “Application of Koopman Operator for Model-Based Control of Fracture Propagation,” Proceedings of 2020 American Control Conference, Denver, Colorado, 2020.
20. D. Lee, Y. Ding, A. Jayaraman, J. S. Kwon, “Derivation of a Dynamic Model for Palmitate-Induced NFκB Signaling Pathway through Systems Biology Approach,” Proceedings of 2020 American Control Conference, Denver, Colorado, 2020.
2019
19. D. Lee, A. Jayaraman, and J. S. Kwon, “Identification of heterogeneous parameters in an intracellular reaction network from population snapshot measurements through sensitivity analysis and neural network”, Proceedings of the 8th IFAC Conference on Foundations of Systems Biology in Engineering (FOSBE), Valencia, Spain, 2019.
18. H. Choi and J. S. Kwon, “Multiscale modeling and model-based feedback control of pulp digester”, Proceedings of the American Control Conference, Philadelphia, Pennsylvania, 2019.
17. Y. Ahn, P. Siddhamshetty, K. Cao and J. S. Kwon, “Optimization framework for integration of shale gas supply chain network and dynamic model of hydraulic fracturing”, Proceedings of MIT Applied Energy “A+B” Symposium, Boston, Massachusetts, 2019.
2018
16. A. Narasingam, P. Siddhamshetty and J. S. Kwon, “POD-EnKF based estimation of heterogeneous reservoir parameters for feedback control of hydraulic fracture geometry”, Proceedings of 13th International Symposium on Process Systems Engineering – PSE 2018, San Diego, California, 2018.
15. P. Siddhamshetty, S. Liu, J. S. Kwon and P. P. Valkó, “Feedback control of proppant bank heights during hydraulic fracturing for enhanced productivity in ultra-low permeability oil reservoirs”, Proceedings of 13th International Symposium on Process Systems Engineering – PSE 2018, San Diego, California, 2018.
14. D. Lee, J. S. Kwon, A. Singla and H. Wu, “Stochastic modeling of CTB-GD1b binding kinetics”, Proceedings of 13th International Symposium on Process Systems Engineering – PSE 2018, San Diego, California, 2018.
13. C. Curitiba, H. Durand, J. S. Kwon, A. Barreto Jr., P. Laranjeira da Cunha Lage, M. Bezerra de Souza Jr., A. Resende Secchi and P. D. Christofides, “Optimal enantiomer crystallization operation using ternary diagram Information”, Proceedings of 13th International Symposium on Process Systems Engineering – PSE 2018, San Diego, California, 2018.
12. P. Siddhamshetty and J. S. Kwon, “Model-based feedback control of oil production in oil-rim reservoirs under gas coning conditions”, Proceedings of the American Control Conference, Milwaukee, Wisconsin, 2018.
11. C. F. Curitiba Marcellos, H. Durand, J. S. Kwon, A. G. Baretto Jr., P. Laranjeira da Cunha Lage, M. Souza, Jr.,, A. R. Secchi and P. D. Christofides, “Model predictive control of batch enantiomer crystallization using ternary diagram information”, Proceedings of the American Control Conference, Milwaukee, Wisconsin, 2018.
10. D. Lee, A. Singla, H. Wu and J. S. Kwon, “Dynamic modeling of binding kinetics between GD1b ganglioside and cholera toxin subunit B”, Proceedings of the American Control Conference, Milwaukee, Wisconsin, 2018.
9. D. Lee, Y. Ding, A. Jayaraman and J. S. Kwon, “Integrative approach to extract the single-cell dynamics of LPS-induced NF-kappaB signal pathway through flow cytometry measurements and parameter estimation”, Proceedings of the American Control Conference, Milwaukee, Wisconsin, 2018.
8. H. S. Sidhu, A. Narasingam and J. S. Kwon, “Model order reduction of nonlinear parabolic PDE systems with moving boundaries using sparse proper orthogonal decomposition methodology”, Proceedings of the American Control Conference, Milwaukee, Wisconsin, 2018.
7. A. Narasingam, P. Siddhamshetty and J. S. Kwon, “Identification of spatially varying geological properties in a heterogeneous reservoir using EnKF and POD based parameterization”, Proceedings of the American Control Conference, Milwaukee, Wisconsin, 2018.
2017
6. P. Siddhamshetty, S. Yang and J. S. Kwon, “Modeling of hydraulic fracturing and developing a new pumping schedule to achieve uniform proppant concentration”, Proceedings of the American Control Conference, Seattle, Washington, 2017.
5. P. Siddhamshetty, A. Narasingam and J. S. Kwon, “Modeling of hydraulic fracturing and design of online optimal pumping schedule”, Proceedings of Foundations of Computer Aided Process Operations / Chemical Process Control – FOCAPO/CPC 2017, Tuscon, Arizona, 2017 (invited talk).
4. J. S. Kwon and P. D. Christofides, “Multiscale model parallelization and run-to-run control of batch protein crystallization”, Proceedings of Foundations of Computer Aided Process Operations / Chemical Process Control – FOCAPO/CPC 2017, Tuscon, Arizona, 2017.
~2016
3. J. S. Kwon, M. Nayhouse, D. Ni and P. D. Christofides, “Handling parametric drift in batch crystallization using predictive control with R2R model parameter estimation”, Proceedings of Advanced Control of Chemical Processes, British Columbia, Canada, 2015.
2. J. S. Kwon, M. Crose, M. Nayhouse, and P. D. Christofides, “Modeling and operation of PECVD for thin film solar cells”, Proceedings of Advanced Control of Chemical Processes, British Columbia, Canada, 2015.
1. M. Nayhouse, J. S. Kwon, G. Orkoulas and P. D. Christofides, “Modeling and control of protein crystal shape distribution”, Proceedings of the American Control Conference, Washington, DC, 2013.