Publications

135. C. Lee, J. Kim, J. Ryu, W. Won, C. G. Yoo, J. S. Kwon, “Lignin structure dynamics: Advanced real-time molecular sensing strategies”, Chemical Engineering Journal, 2024, 487, 150680, DOI: 10.1016/j.cej.2024.150680

134. N. Sitapure, J. S. Kwon, “Machine Learning Meets Control: Unveiling the Potential of LSTMc”, AIChE Journal, 2024, 70, 7, e18356, DOI: 10.1002/aic.18356

133. P. Shah, S. Pahari, J. S. Kwon, “Unveiling Latent Chemical Mechanisms: Hybrid Modeling for Estimating Spatiotemporally Varying Parameters in Moving Boundary Problems”, Ind. & Eng. Chem. Res., DOI:10.1021/3c03531. (in press)

132. J. Kim, S. Pahari, J. Ryu, M. Zhang, Q. Yang, C. G. Yoo, J. S. Kwon, “Advancing Biomass Fractionation with Real-Time Prediction of Lignin Content and MWd: A kMC-based Multiscale Model for Optimized Lignin Extraction”, Chemical Engineering Journal, 2024, 479, 147226, DOI:10.1016/147226.

2023

131. Q. Hu, D. Kuai, H. Park, P. B. Balbuena, J. S. Kwon, H. J. Wu, “Advancing glycan analysis: A new platform integrating SERS, boronic acids, and machine learning algorithms”, Advanced Sensor Research, 2023, 2300052 DOI:10.1002/adsr.202300052.

130. P. Shah, M. S. F. Bangi, J. S. Kwon, “Order-reduction and controller design of hydraulic fracturing”, Energy Systems and Processes, 2023, 12, 1-32, DOI:10.1063/9780735425743_012.

129. N. Sitapure, J. S. Kwon, “Introducing Hybrid Modeling with Time-series-Transformers: A Comparative Study of Series and Parallel Approach in Batch Crystallization”, Ind. & Eng. Chem. Res., 62, 49, 21278–21291 DOI:10.1021/3c02624 .

128. J. Kim, S. Pahari, J. S. Kwon, “Modeling biomass degradation with multiscale kMC simulations”, Energy Systems and Processes, 2023, 11, DOI:10.1063/9780735425743_011.

127. B. Bhat, S. Pahari, J. S. Kwon, M. Akbulut, “Stimuli-responsive viscosity modifiers”, Advances in Colloid and Interface Science, Volume 321, 2023, 103025, ISSN 0001-8686, DOI:10.1016/103025.

126. S. Pahari, Y. Lin, S. Liu, C. Lee, M. Akbulut, J. S. Kwon, “Stochastic optimal control of mesostructure of supramolecular assemblies using dissipative particle dynamics and dynamic programming with experimental validation”, Chemical Engineering Journal, Volume 475, 2023, 145087, ISSN 1385-8947, DOI:10.1016/145087.

125. B. Pawar, B. Bhadriraju, F. Khan, J. S. Kwon, Q. Wang, “Resilience assessment of chemical processes using operable adaptive sparse identification of systems”, Computers & Chemical Engineering, Volume 177, 2023, 108346, ISSN 0098-1354, DOI:10.1016/108346.

124. B. Bhadriraju, J. S. Kwon, F. 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:10.1016/108275.

123. B. Bhadriraju, J. S. Kwon, F. Khan “Data-driven adaptive sparse modeling of chemical process systems”, Energy Systems and Processes, 2023, 10, DOI:10.1063/9780735425743_010.

122.  S. Pahari, J. Kim, H. Choi, M. Zhang, A. Ji, C. G. Yoo, J. S. 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:10.1016/143021.

121.  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:10.1016/108339.

120.  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, Volume 19, 2023, 5609-5621, DOI:10.1039/D3SM00549F.

119.  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.

118  A. Narasingam, S. Son, and J. S. Kwon, “Data-driven feedback stabilization of nonlinear systems: Koopman-based model predictive control”, International Journal of Control, 2023. DOI:10.1080/00207179.2021.2013541

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. 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.

115. 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.

114. B. Bhat, S. Pahari,  J. S. Kwon, M. Akbulut, “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.

113. 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.

112. B. Bhat, S. Pahari, J. Kwon, and M. Akbulut, “Rheological dynamics and structural characteristics of supramolecular assemblies of beta-cyclodextrin and sulfonic surfactants”, Soft Matter 2023, 19, 2231-2240, DOI:10.1039/D3SM00132F.

111. 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

110. 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.

109. 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.

108. 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.

107. 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

106. 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.

105. 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

104. 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

103. 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

102. 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

101. 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

100. 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

99. 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

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: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, “Development of offset-free Koopman Lyapunov-based model predictive control and mathematical analysis for zero steady-state offset condition considering influence of Lyapunov constraints on equilibrium point”, Journal of Process Control, 2022, 118, 26-36, DOI:10.1016/j.jprocont.2022.08.005.

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, DOI: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

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

2024

57. S. Nagpal, J. S. Kwon, “Enhancing Protein Crystal Purity through Adaptive Kinetic Monte Carlo Modeling and Control of Surface Morphology”, Proceedings of the American Control Conference, Toronto, Canada, 2024.

56. S. Pahari, P. Shah, J. S. Kwon, “Integrating Deep Neural Networks for Hybrid Modeling of Complex Chemical Processes: Estimation of Spatiotemporally Varying Parameters in Moving Boundary Problems”, Proceedings of the American Control Conference, Toronto, Canada, 2024.

55. S. Pahari, P. Shah, C. H. Lee, J. S. Kwon, “A Hybrid Modeling Framework for Catalytic Systems: Sensitivity Analysis and Estimation of Activation Energies”, Proceedings of the American Control Conference, Toronto, Canada, 2024.

54. J. Kim, S. Pahari, M. Zhang, J. Ryu, C. G. Yoo, J. S. Kwon, “Adaptive Control for Lignin-First Biomass Fractionation: An Experimentally Verified Multiscale kMC Approach”, Proceedings of the American Control Conference, Toronto, Canada, 2024.

53. N. Sitapure, J. S. Kwon, “Empowering Hybrid Models with Attention-based Time-series Transformers: A Case Study in Batch Crystallization”, Proceedings of the American Control Conference, Toronto, Canada, 2024.

52. N. Sitapure, J. S. Kwon, “Integrating Machine Learning in Process Control with LSTMc: A Case Study in Batch Crystallization”, Proceedings of the American Control Conference, Toronto, Canada, 2024.

51. B. Bhadriraju,  J. S. Kwon, F. Khan, “Lyapunov-based Model Predictive Control using Operable Adaptive Sparse Identification of Systems (OASIS)”, Proceedings of the American Control Conference, Toronto, Canada, 2024.

2023

50. P. Shah, H. K. Choi, J. S. Kwon, “LSTM-based control of cellulose degree of polymerization in a batch pulp digester”, Proceedings of the American Control Conference, San Diego, CA, 2023.

49. 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, San Diego, CA, 2023.

48. S. Pahari, J. Kim, M. Zhang, A. Ji, C. G. Yoo, J. S. Kwon, “Multiscale modeling, experimental validation, and optimal operation for a batch pulp digester with a novel solvent”, Proceedings of the American Control Conference, San Diego, CA, 2023.

47. N. Sitapure, J. S. Kwon, “Model Predictive Control of Cadmium Telluride (CdTe) Quantum Dot (QD) Crystallization”, Proceedings of the American Control Conference, San Diego, CA, 2023.

46. M. S. F. Bangi, J. S. Kwon, “Control Lyapunov-Barrier function-based predictive control using a deep hybrid model with guarantees on domain of applicability”, Proceedings of the American Control Conference, San Diego, CA, 2023.

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.

AIChE

2023

16. P. Shah, N. Sitapure, J. S. Kwon, “Next-Generation Hybrid Models: Combining Attention Mechanisms and Lstm for Improved Predictions and Process Control in the Chemical Industry”, AIChE.2023 AIChE Annual Meeting

15. A. Gandhi, S. Pahari, F. Hasan, J. S. Kwon, “Computational Quantification of Variation in CO2 Adsorption on Aluminum Substituted Zeolite Frameworks”, AIChE.2023 AIChE Annual Meeting

14. C. H. Lee, S. Pahari, J. S. Kwon, “Bridging the Gap in Catalysis Research: A Multifaceted DFT-Kmc-Lstm Approach to Investigate Heterogeneous Catalytic Reactions”, AIChE.2023 AIChE Annual Meeting

13. J. Kim, S. Pahari, P. Shah, J. S. Kwon, “Enhanced Polyhydroxyalkanoate (PHA) Production through Multiscale Modeling and Process Control Strategies: A Novel Approach to Bio-Based Polymer Synthesis”, AIChE.2023 AIChE Annual Meeting

12. J. S. Kwon, “CrystalGPT: Enhancing System-to-System Model Interchangeability in Crystallization Prediction and Control Using a Time-Series-Transformer Model”, AIChE.2023 AIChE Annual Meeting

11. S. Nagpal, N. Sitapure, J. S. Kwon, “Advancing Protein Crystallization: A Groundbreaking Kinetic Monte Carlo Model for Accurate Prediction of Morphology and Growth across Diverse Operating Conditions”, AIChE.2023 AIChE Annual Meeting

10. J. Kim, S. Pahari, A. Ji, M. Zhang, C. G. Yoo, J. S. Kwon, “Multiscale Kinetic Modeling and Optimization: In-Depth Analysis of Cellulose Degradation for Enhanced Pulping Process and Superior Paper Quality”, AIChE.2023 AIChE Annual Meeting

9. P. Shah, J. S. Kwon, “Improving Industrial-Scale Bioreactor Performance: Development and Validation of Computationally Efficient Compartment-Based Models Using Real Plant Data”, AIChE.2023 AIChE Annual Meeting

8. N. Sitapure, J. S. Kwon, “CrystalGPT: Revolutionizing Crystallization Process Prediction and Control with a Multivariate Time-Series Approach Leveraging Transformer Networks”, AIChE.2023 AIChE Annual Meeting

7. B. Bhadriraju, F. Khan, J. S. Kwon, “A Systematic Approach for Controlling Processes Outside the Training Region of Data-Driven Models”, AIChE.2023 AIChE Annual Meeting

6. Y. Choi, B. Bhadriraju, H. Cho, J. Lim, I. Moon, J. Kim, J. S. Kwon, “Data-Driven Adaptive Sparse Identification of Time-Varying Nonlinear Dynamics for 2, 3-Bdo Distillation Column”, AIChE.2023 AIChE Annual Meeting

5. S. Pahari, J. S. Kwon, “Integrating Experimental Data into Molecular Simulations: A Hybrid Modeling Approach for Bridging Theory and Experiment”, AIChE.2023 AIChE Annual Meeting

4. S. Pahari, Y. T. Lin, C. H. Lee, M. Akbulut, J. S. Kwon, “Overcoming Challenges in Self-Assembly Control: Experimental Validation of an Integrated Framework with a Thermosensitive System”, AIChE.2023 AIChE Annual Meeting

3. R. P. Bhavsar, B. Bhadriraju, G. H. Lee, A. H. Park, J. S. Kwon, “Modeling of Spatiotemporal Temperature Distribution in Hybrid Nanoscale Multifunctional Material for Desorption of CO2”, AIChE.2023 AIChE Annual Meeting

2. J. Kim, J. Ryu, M. Zhang, C. G. Yoo, J. S. Kwon, “A Multi-Scale Kinetic Modeling and Optimal Control Strategy for an Effective Lignin Fractionation Process”, AIChE.2023 AIChE Annual Meeting

1. N. Sitapure, J. S. Kwon, “Advancing Digital Twins in Chemical Systems: A Novel Time-Series Transformer (TST)-Based Hybrid Modeling Approach”, AIChE.2023 AIChE Annual Meeting