Data-driven simultaneous process optimization and adsorbent selection for vacuum pressure swing adsorption

Kim, Sun Hye; Landa, Héctor Octavio Rubiera; Ravutla, Suryateja; Realff, Matthew J.; Boukouvala, Fani

doi:10.1016/j.cherd.2022.10.002

Data-driven simultaneous process optimization and adsorbent selection for vacuum pressure swing adsorption

Journal Article · Mon Oct 10 00:00:00 EDT 2022 · Chemical Engineering Research and Design

DOI:https://doi.org/10.1016/j.cherd.2022.10.002· OSTI ID:2418130

Kim, Sun Hye ^[1]; Landa, Héctor Octavio Rubiera ^[2]; ^[2]; Realff, Matthew J. ^[2]; Boukouvala, Fani ^[2]

Georgia Institute of Technology, Atlanta, GA (United States); OSTI
Georgia Institute of Technology, Atlanta, GA (United States)

Technologies for post-combustion carbon capture are essential for the reduction of greenhouse gas emissions to the atmosphere. However, they are still associated with high costs and energy consumption. Intensified processes for carbon capture have the potential to overcome these challenges due to their higher efficiency, lower capital cost, and increased operational flexibility. Here, this work investigates simultaneous optimization of process conditions and adsorbent selection for a modular Vacuum Pressure-Swing Adsorption system designed for CO₂ capture. Both surrogate-based Nonlinear Programming and Mixed-Integer Nonlinear Programming approaches are applied and compared in terms of computational efficiency and solution accuracy. Moreover, process performance results are examined by applying several data analytics techniques to gain insights into the material-process correlations. Data-driven classifiers and neural networks can accurately predict whether a material is likely to satisfy purity, recovery, and energy constraints when operated at optimal process conditions.

View Accepted Manuscript (DOE)

Research Organization:: Georgia Institute of Technology, Atlanta, GA (United States)

Sponsoring Organization:: USDOE; USDOE Office of Fossil Energy (FE)

Grant/Contract Number:: FE0026433

OSTI ID:: 2418130

Alternate ID(s):: OSTI ID: 1960869

Journal Information:: Chemical Engineering Research and Design, Journal Name: Chemical Engineering Research and Design Vol. 188; ISSN 0263-8762

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

References (72)

A superstructure-based optimal synthesis of PSA cycles for post-combustion CO2 capture Agarwal, Anshul; Biegler, Lorenz T.; Zitney, Stephen E. AIChE Journal, Vol. 56, Issue 7 https://doi.org/10.1002/aic.12107	journal	November 2009
Cycle synthesis and optimization of a VSA process for postcombustion CO ₂ capture Haghpanah, Reza; Nilam, Ricky; Rajendran, Arvind AIChE Journal, Vol. 59, Issue 12 https://doi.org/10.1002/aic.14192	journal	August 2013
Learning surrogate models for simulation-based optimization Cozad, Alison; Sahinidis, Nikolaos V.; Miller, David C. AIChE Journal, Vol. 60, Issue 6 https://doi.org/10.1002/aic.14418	journal	March 2014
Integrated adsorbent-process optimization for carbon capture and concentration using vacuum swing adsorption cycles Khurana, Maninder; Farooq, Shamsuzzaman AIChE Journal, Vol. 63, Issue 7 https://doi.org/10.1002/aic.15602	journal	January 2017
Modular manufacturing processes: Status, challenges, and opportunities Baldea, Michael; Edgar, Thomas F.; Stanley, Bill L. AIChE Journal, Vol. 63, Issue 10 https://doi.org/10.1002/aic.15872	journal	August 2017
Integrated Adsorbent Process Optimization for Minimum Cost of Electricity Including Carbon Capture by a VSA Process Khurana, Maninder; Farooq, Shamsuzzaman AIChE Journal, Vol. 65, Issue 1 https://doi.org/10.1002/aic.16362	journal	September 2018
A parametric study of the techno‐economics of direct CO ₂ air capture systems using solid adsorbents Sinha, Anshuman; Realff, Matthew J. AIChE Journal, Vol. 65, Issue 7 https://doi.org/10.1002/aic.16607	journal	November 2018
A data‐driven optimization algorithm for differential algebraic equations with numerical infeasibilities Beykal, Burcu; Onel, Melis; Onel, Onur AIChE Journal, Vol. 66, Issue 10 https://doi.org/10.1002/aic.16657	journal	August 2020
Torlon® hollow fiber membranes for organic solvent reverse osmosis separation of complex aromatic hydrocarbon mixtures Jang, Hye‐Youn; Johnson, J. R.; Ma, Yao AIChE Journal, Vol. 65, Issue 12 https://doi.org/10.1002/aic.16757	journal	August 2019
Overview of Surrogate Modeling in Chemical Process Engineering McBride, Kevin; Sundmacher, Kai Chemie Ingenieur Technik, Vol. 91, Issue 3 https://doi.org/10.1002/cite.201800091	journal	January 2019
Adsorbent Materials for Carbon Dioxide Capture from Large Anthropogenic Point Sources Choi, Sunho; Drese, Jeffrey H.; Jones, Christopher W. ChemSusChem, Vol. 2, Issue 9, p. 796-854 https://doi.org/10.1002/cssc.200900036	journal	September 2009
Machine learning–based optimization for hydrogen purification performance of layered bed pressure swing adsorption Xiao, Jinsheng; Li, Chenglong; Fang, Liang International Journal of Energy Research, Vol. 44, Issue 6 https://doi.org/10.1002/er.5225	journal	May 2020
Modularized production of fuels and other value-added products from distributed, wasted, or stranded feedstocks Weber, Robert S.; Holladay, Johnathan E.; Jenks, Cynthia Wiley Interdisciplinary Reviews: Energy and Environment, Vol. 7, Issue 6 https://doi.org/10.1002/wene.308	journal	June 2018
Multicollinearity Alin, Aylin Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, Issue 3 https://doi.org/10.1002/wics.84	journal	March 2010
A polyhedral branch-and-cut approach to global optimization Tawarmalani, Mohit; Sahinidis, Nikolaos V. Mathematical Programming, Vol. 103, Issue 2 https://doi.org/10.1007/s10107-005-0581-8	journal	May 2005
Capture of CO2 from flue gas streams with zeolite 13X by vacuum-pressure swing adsorption Xiao, Penny; Zhang, Jun; Webley, Paul Adsorption, Vol. 14, Issue 4-5 https://doi.org/10.1007/s10450-008-9128-7	journal	May 2008
Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption Boukouvala, Fani; Hasan, M. M. Faruque; Floudas, Christodoulos A. Journal of Global Optimization, Vol. 67, Issue 1-2 https://doi.org/10.1007/s10898-015-0376-2	journal	November 2015
Deterministic Global Optimization with Artificial Neural Networks Embedded Schweidtmann, Artur M.; Mitsos, Alexander Journal of Optimization Theory and Applications, Vol. 180, Issue 3 https://doi.org/10.1007/s10957-018-1396-0	journal	October 2018
An adaptive radial basis algorithm (ARBF) for expensive black-box mixed-integer constrained global optimization Holmström, Kenneth; Quttineh, Nils-Hassan; Edvall, Marcus M. Optimization and Engineering, Vol. 9, Issue 4 https://doi.org/10.1007/s11081-008-9037-3	journal	February 2008
MISO: mixed-integer surrogate optimization framework Müller, Juliane Optimization and Engineering, Vol. 17, Issue 1 https://doi.org/10.1007/s11081-015-9281-2	journal	June 2015
ARGONAUT: AlgoRithms for Global Optimization of coNstrAined grey-box compUTational problems Boukouvala, Fani; Floudas, Christodoulos A. Optimization Letters, Vol. 11, Issue 5 https://doi.org/10.1007/s11590-016-1028-2	journal	April 2016
Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques Kim, Sun Hye; Boukouvala, Fani Optimization Letters, Vol. 14, Issue 4 https://doi.org/10.1007/s11590-019-01428-7	journal	May 2019
Progress in carbon dioxide separation and capture: A review Yang, Hongqun; Xu, Zhenghe; Fan, Maohong Journal of Environmental Sciences, Vol. 20, Issue 1, p. 14-27 https://doi.org/10.1016/S1001-0742(08)60002-9	journal	January 2008
Carbon capture by physical adsorption: Materials, experimental investigations and numerical modeling and simulations – A review Ben-Mansour, R.; Habib, M. A.; Bamidele, O. E. Applied Energy, Vol. 161 https://doi.org/10.1016/j.apenergy.2015.10.011	journal	January 2016
Process intensification: A perspective on process synthesis Lutze, Philip; Gani, Rafiqul; Woodley, John M. Chemical Engineering and Processing: Process Intensification, Vol. 49, Issue 6 https://doi.org/10.1016/j.cep.2010.05.002	journal	June 2010
Modeling of rapid temperature swing adsorption using hollow fiber sorbents Rezaei, Fateme; Subramanian, Swernath; Kalyanaraman, Jayashree Chemical Engineering Science, Vol. 113 https://doi.org/10.1016/j.ces.2014.04.002	journal	July 2014
Modelling of adsorption in rotating packed bed using artificial neural networks (ANN) Li, Weiwei; Wei, Shaokai; Jiao, Weizhou Chemical Engineering Research and Design, Vol. 114 https://doi.org/10.1016/j.cherd.2016.08.013	journal	October 2016
Selection of surrogate modeling techniques for surface approximation and surrogate-based optimization Williams, Bianca; Cremaschi, Selen Chemical Engineering Research and Design, Vol. 170 https://doi.org/10.1016/j.cherd.2021.03.028	journal	June 2021
A novel nested loop optimization problem based on deep neural networks and feasible operation regions definition for simultaneous material screening and process optimization Nogueira, Idelfonso B. R.; Dias, Rafael O. M.; Rebello, Carine M. Chemical Engineering Research and Design, Vol. 180 https://doi.org/10.1016/j.cherd.2022.02.013	journal	April 2022
Derivative-free optimization for chemical product design Sun, Yijia; Sahinidis, Nikolaos V.; Sundaram, Anantha Current Opinion in Chemical Engineering, Vol. 27 https://doi.org/10.1016/j.coche.2019.11.006	journal	March 2020
A multi-scale framework for CO2 capture, utilization, and sequestration: CCUS and CCU Hasan, M. M. Faruque; First, Eric L.; Boukouvala, Fani Computers & Chemical Engineering, Vol. 81 https://doi.org/10.1016/j.compchemeng.2015.04.034	journal	October 2015
Machine learning model and optimization of a PSA unit for methane-nitrogen separation Sant Anna, Hermes R.; Barreto, Amaro G.; Tavares, Frederico W. Computers & Chemical Engineering, Vol. 104 https://doi.org/10.1016/j.compchemeng.2017.05.006	journal	September 2017
Advances in surrogate based modeling, feasibility analysis, and optimization: A review Bhosekar, Atharv; Ierapetritou, Marianthi Computers & Chemical Engineering, Vol. 108 https://doi.org/10.1016/j.compchemeng.2017.09.017	journal	January 2018
Integration of planning, scheduling and control problems using data-driven feasibility analysis and surrogate models Dias, Lisia S.; Ierapetritou, Marianthi G. Computers & Chemical Engineering, Vol. 134 https://doi.org/10.1016/j.compchemeng.2019.106714	journal	March 2020
Surrogate-based optimization for mixed-integer nonlinear problems Kim, Sun Hye; Boukouvala, Fani Computers & Chemical Engineering, Vol. 140 https://doi.org/10.1016/j.compchemeng.2020.106847	journal	September 2020
Perspectives on the integration between first-principles and data-driven modeling Bradley, William; Kim, Jinhyeun; Kilwein, Zachary Computers & Chemical Engineering, Vol. 166 https://doi.org/10.1016/j.compchemeng.2022.107898	journal	October 2022
SO-MI: A surrogate model algorithm for computationally expensive nonlinear mixed-integer black-box global optimization problems Müller, Juliane; Shoemaker, Christine A.; Piché, Robert Computers & Operations Research, Vol. 40, Issue 5 https://doi.org/10.1016/j.cor.2012.08.022	journal	May 2013
Artificial neural network based optimization for hydrogen purification performance of pressure swing adsorption Ye, Feng; Ma, Shuo; Tong, Liang International Journal of Hydrogen Energy, Vol. 44, Issue 11 https://doi.org/10.1016/j.ijhydene.2018.08.104	journal	February 2019
Recent advances in surrogate-based optimization Forrester, Alexander I. J.; Keane, Andy J. Progress in Aerospace Sciences, Vol. 45, Issue 1-3 https://doi.org/10.1016/j.paerosci.2008.11.001	journal	January 2009
Experimental validation of an adsorbent-agnostic artificial neural network (ANN) framework for the design and optimization of cyclic adsorption processes Pai, Kasturi Nagesh; Nguyen, Tai T. T.; Prasad, Vinay Separation and Purification Technology, Vol. 290 https://doi.org/10.1016/j.seppur.2022.120783	journal	June 2022
Performance-Based Screening of Porous Materials for Carbon Capture Farmahini, Amir H.; Krishnamurthy, Shreenath; Friedrich, Daniel Chemical Reviews, Vol. 121, Issue 17 https://doi.org/10.1021/acs.chemrev.0c01266	journal	August 2021
Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO ₂ Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models Burns, Thomas D.; Pai, Kasturi Nagesh; Subraveti, Sai Gokul Environmental Science & Technology, Vol. 54, Issue 7 https://doi.org/10.1021/acs.est.9b07407	journal	February 2020
Adsorbent Screening for Postcombustion CO ₂ Capture: A Method Relating Equilibrium Isotherm Characteristics to an Optimum Vacuum Swing Adsorption Process Performance Khurana, Maninder; Farooq, Shamsuzzaman Industrial & Engineering Chemistry Research, Vol. 55, Issue 8 https://doi.org/10.1021/acs.iecr.5b04531	journal	February 2016
Development of Phase-Change-Based Thermally Modulated Fiber Sorbents DeWitt, Stephen J. A.; Rubiera Landa, Héctor Octavio; Kawajiri, Yoshiaki Industrial & Engineering Chemistry Research, Vol. 58, Issue 15 https://doi.org/10.1021/acs.iecr.8b04361	journal	December 2018
Moving Beyond Adsorption Capacity in Design of Adsorbents for CO ₂ Capture from Ultradilute Feeds: Kinetics of CO ₂ Adsorption in Materials with Stepped Isotherms Darunte, Lalit A.; Sen, Trisha; Bhawanani, Chiraag Industrial & Engineering Chemistry Research, Vol. 58, Issue 1 https://doi.org/10.1021/acs.iecr.8b05042	journal	December 2018
110th Anniversary : Surrogate Models Based on Artificial Neural Networks To Simulate and Optimize Pressure Swing Adsorption Cycles for CO ₂ Capture Leperi, Karson T.; Yancy-Caballero, Daison; Snurr, Randall Q. Industrial & Engineering Chemistry Research, Vol. 58, Issue 39 https://doi.org/10.1021/acs.iecr.9b02383	journal	September 2019
The Effect of Aluminum Short-Range Ordering on Carbon Dioxide Adsorption in Zeolites Findley, John M.; Ravikovitch, Peter I.; Sholl, David S. The Journal of Physical Chemistry C, Vol. 122, Issue 23 https://doi.org/10.1021/acs.jpcc.8b03475	journal	May 2018
Practically Achievable Process Performance Limits for Pressure-Vacuum Swing Adsorption-Based Postcombustion CO ₂ Capture Pai, Kasturi Nagesh; Prasad, Vinay; Rajendran, Arvind ACS Sustainable Chemistry & Engineering, Vol. 9, Issue 10 https://doi.org/10.1021/acssuschemeng.0c08933	journal	March 2021
Development of a General Evaluation Metric for Rapid Screening of Adsorbent Materials for Postcombustion CO ₂ Capture Leperi, Karson T.; Chung, Yongchul G.; You, Fengqi ACS Sustainable Chemistry & Engineering, Vol. 7, Issue 13 https://doi.org/10.1021/acssuschemeng.9b01418	journal	May 2019
Process Optimization-Based Screening of Zeolites for Post-Combustion CO ₂ Capture by Vacuum Swing Adsorption Subramanian Balashankar, Vishal; Rajendran, Arvind ACS Sustainable Chemistry & Engineering, Vol. 7, Issue 21 https://doi.org/10.1021/acssuschemeng.9b04124	journal	October 2019
Analysis and Status of Post-Combustion Carbon Dioxide Capture Technologies Bhown, Abhoyjit S.; Freeman, Brice C. Environmental Science & Technology, Vol. 45, Issue 20 https://doi.org/10.1021/es104291d	journal	October 2011
Concentration and recovery of carbon dioxide from flue gas by pressure swing adsorption Kikkinides, E. S.; Yang, R. T.; Cho, S. H. Industrial & Engineering Chemistry Research, Vol. 32, Issue 11 https://doi.org/10.1021/ie00023a038	journal	November 1993
Optimization of Pressure Swing Adsorption and Fractionated Vacuum Pressure Swing Adsorption Processes for CO ₂ Capture Ko, Daeho; Siriwardane, Ranjani; Biegler, Lorenz T. Industrial & Engineering Chemistry Research, Vol. 44, Issue 21 https://doi.org/10.1021/ie050012z	journal	October 2005
Post-Combustion CO₂ Capture Using Solid Sorbents: A Review Samanta, Arunkumar; Zhao, An; Shimizu, George K. H. Industrial & Engineering Chemistry Research, Vol. 51, Issue 4, p. 1438-1463 https://doi.org/10.1021/ie200686q	journal	September 2011
Multiobjective Optimization of a Four-Step Adsorption Process for Postcombustion CO ₂ Capture Via Finite Volume Simulation Haghpanah, Reza; Majumder, Aniruddha; Nilam, Ricky Industrial & Engineering Chemistry Research, Vol. 52, Issue 11 https://doi.org/10.1021/ie302658y	journal	February 2013
Nationwide, Regional, and Statewide CO ₂ Capture, Utilization, and Sequestration Supply Chain Network Optimization Hasan, M. M. Faruque; Boukouvala, Fani; First, Eric L. Industrial & Engineering Chemistry Research, Vol. 53, Issue 18 https://doi.org/10.1021/ie402931c	journal	March 2014
Training Neural Networks for Pressure Swing Adsorption Processes Sundaram, Narasimhan Industrial & Engineering Chemistry Research, Vol. 38, Issue 11 https://doi.org/10.1021/ie9901731	journal	September 1999
Screening of Metal−Organic Frameworks for Carbon Dioxide Capture from Flue Gas Using a Combined Experimental and Modeling Approach Yazaydın, A. Özgür; Snurr, Randall Q.; Park, Tae-Hong Journal of the American Chemical Society, Vol. 131, Issue 51 https://doi.org/10.1021/ja9057234	journal	December 2009
Efficient Global Optimization of Expensive Black-Box Functions Jones, Donald R.; Schonlau, Matthias; Welch, William J. Journal of Global Optimization, Vol. 13, Issue 4, p. 455-492 https://doi.org/10.1023/A:1008306431147	journal	January 1998
A tutorial on support vector regression Smola, Alex J.; Schölkopf, Bernhard Statistics and Computing, Vol. 14, Issue 3 https://doi.org/10.1023/B:STCO.0000035301.49549.88	journal	August 2004
Evaluating different classes of porous materials for carbon capture Huck, Johanna M.; Lin, Li-Chiang; Berger, Adam H. Energy Environ. Sci., Vol. 7, Issue 12 https://doi.org/10.1039/C4EE02636E	journal	January 2014
Carbon capture and storage (CCS): the way forward Bui, Mai; Adjiman, Claire S.; Bardow, André Energy & Environmental Science, Vol. 11, Issue 5 https://doi.org/10.1039/C7EE02342A	journal	January 2018
Process-level modelling and optimization to evaluate metal–organic frameworks for post-combustion capture of CO₂ Yancy-Caballero, Daison; Leperi, Karson T.; Bucior, Benjamin J. Molecular Systems Design & Engineering, Vol. 5, Issue 7 https://doi.org/10.1039/D0ME00060D	journal	January 2020
Cost-effective CO2 capture based on in silico screening of zeolites and process optimization Hasan, M. M. Faruque; First, Eric L.; Floudas, Christodoulos A. Physical Chemistry Chemical Physics, Vol. 15, Issue 40 https://doi.org/10.1039/c3cp53627k	journal	January 2013
State-of-the-art Adsorption and Membrane Separation Processes for Carbon Dioxide Production from Carbon Dioxide Emitting Industries Ebner, Armin D.; Ritter, James A. Separation Science and Technology, Vol. 44, Issue 6 https://doi.org/10.1080/01496390902733314	journal	April 2009
A multi-objective genetic algorithm for the design of pressure swing adsorption Fiandaca, Giovanna; Fraga, Eric S.; Brandani, Stefano Engineering Optimization, Vol. 41, Issue 9 https://doi.org/10.1080/03052150903074189	journal	September 2009
An adaptive multiquadric radial basis function method for expensive black-box mixed-integer nonlinear constrained optimization Rashid, Kashif; Ambani, Saumil; Cetinkaya, Eren Engineering Optimization, Vol. 45, Issue 2 https://doi.org/10.1080/0305215X.2012.665450	journal	February 2013
Progress in supervised neural networks Hush, D. R.; Horne, B. G. IEEE Signal Processing Magazine, Vol. 10, Issue 1 https://doi.org/10.1109/79.180705	journal	January 1993
Large-Scale Discrete Constrained Black-Box Optimization Using Radial Basis Functions Regis, Rommel G. 2020 IEEE Symposium Series on Computational Intelligence (SSCI) https://doi.org/10.1109/SSCI47803.2020.9308581	conference	December 2020
Modular Chemical Process Intensification: A Review Kim, Yong-ha; Park, Lydia K.; Yiacoumi, Sotira Annual Review of Chemical and Biomolecular Engineering, Vol. 8, Issue 1 https://doi.org/10.1146/annurev-chembioeng-060816-101354	journal	June 2017
CONOPT—A Large-Scale GRG Code Drud, Arne Stolbjerg ORSA Journal on Computing, Vol. 6, Issue 2 https://doi.org/10.1287/ijoc.6.2.207	journal	May 1994
Hydrogen Purification Performance Optimization of Vacuum Pressure Swing Adsorption on Different Activated Carbons Xiao, Jinsheng; Mei, Ang; Tao, Wei Energies, Vol. 14, Issue 9 https://doi.org/10.3390/en14092450	journal	April 2021

Similar Records

Simulation and optimization of pressure swing adsorption systmes using reduced-order modeling

Journal Article · Wed Dec 31 23:00:00 EST 2008 · Ind. Eng. Che,. Res. · OSTI ID:1014011

Reduced-Order Model for Dynamic Optimization of Pressure Swing Adsorption

Conference · Thu Nov 01 00:00:00 EDT 2007 · OSTI ID:934620

Reduced-order model for dynamic optimization of pressure swing adsorption processes

Conference · Sun Dec 31 23:00:00 EST 2006 · OSTI ID:1014686

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Machine learning
Neural networks
Post-combustion CO2 capture
Process intensification
Surrogate-based optimization
Vacuum pressure-swing adsorption

Data-driven simultaneous process optimization and adsorbent selection for vacuum pressure swing adsorption

Citation Formats

References (72)

Similar Records

Related Subjects