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Enlarging the Domain of Attraction of the Local Dynamic Mode Decomposition with Control Technique: Application to Hydraulic Fracturing

  • Mohammed Saad Faizan Bangi
    Mohammed Saad Faizan Bangi
    Artie McFerrin Department of Chemical Engineering and Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
  • Abhinav Narasingam
    Abhinav Narasingam
    Artie McFerrin Department of Chemical Engineering and Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
  • Prashanth Siddhamshetty
    Prashanth Siddhamshetty
    Artie McFerrin Department of Chemical Engineering and Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
  • , and 
  • Joseph Sang-Il Kwon*
    Joseph Sang-Il Kwon
    Artie McFerrin Department of Chemical Engineering and Texas A&M Energy Institute, Texas A&M University, College Station, Texas 77843, United States
    *E-mail: [email protected]
Cite this: Ind. Eng. Chem. Res. 2019, 58, 14, 5588–5601
Publication Date (Web):March 7, 2019
https://doi.org/10.1021/acs.iecr.8b05995
Copyright © 2019 American Chemical Society
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Abstract

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The local dynamic mode decomposition with control (LDMDc) technique combines the concept of unsupervised learning and the DMDc technique to extract the relevant local dynamics associated with highly nonlinear processes to build temporally local reduced-order models (ROMs). But the limited domain of attraction (DOA) of LDMDc hinders its widespread use in prediction. To systematically enlarge the DOA of the LDMDc technique, we utilize both the states of the system and the applied inputs from the data generated using multiple “training” inputs. We implement a clustering strategy to divide the data into clusters, use DMDc to build multiple local ROMs, and implement the k-nearest neighbors technique to make a selection among the set of ROMs during prediction. The proposed algorithm is applied to hydraulic fracturing to demonstrate the enlarged DOA of the LDMDc technique.

Cited By

This article is cited by 14 publications.

  1. Sang Hwan Son, Abhinav Narasingam, Joseph Sang-Il 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. https://doi.org/10.1016/j.jprocont.2022.08.005
  2. Parth Shah, M. Ziyan Sheriff, Mohammed Saad Faizan Bangi, Costas Kravaris, Joseph Sang-Il Kwon, Chiranjivi Botre, Junichi 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, 441 , 135643. https://doi.org/10.1016/j.cej.2022.135643
  3. Mohammed Saad Faizan Bangi, Joseph Sang-Il Kwon. Universal hybrid modeling of batch kinetics of aerobic carotenoid production using Saccharomyces Cerevisiae. 2022,,, 3716-3721. https://doi.org/10.23919/ACC53348.2022.9867779
  4. Mohammed Saad Faizan Bangi, Katy Kao, Joseph Sang-Il Kwon. Physics-informed neural networks for hybrid modeling of lab-scale batch fermentation for β-carotene production using Saccharomyces cerevisiae. Chemical Engineering Research and Design 2022, 179 , 415-423. https://doi.org/10.1016/j.cherd.2022.01.041
  5. Sang Hwan Son, Hyun-Kyu Choi, Jiyoung Moon, Joseph Sang-Il Kwon. Hybrid Koopman model predictive control of nonlinear systems using multiple EDMD models: An application to a batch pulp digester with feed fluctuation. Control Engineering Practice 2022, 118 , 104956. https://doi.org/10.1016/j.conengprac.2021.104956
  6. Mohammed Saad Faizan Bangi, Joseph Sang-Il Kwon. Deep reinforcement learning control of hydraulic fracturing. Computers & Chemical Engineering 2021, 154 , 107489. https://doi.org/10.1016/j.compchemeng.2021.107489
  7. Sang Hwan Son, Hyun‐Kyu Choi, Joseph Sang‐Il Kwon. Application of offset‐free Koopman‐based model predictive control to a batch pulp digester. AIChE Journal 2021, 67 (9) https://doi.org/10.1002/aic.17301
  8. Pallavi Kumari, Bhavana Bhadriraju, Qingsheng Wang, Joseph Sang-Il Kwon. Development of parametric reduced-order model for consequence estimation of rare events. Chemical Engineering Research and Design 2021, 169 , 142-152. https://doi.org/10.1016/j.cherd.2021.02.006
  9. Abhinav Narasingam, Joseph Sang-Il Kwon. Koopman operator-based model identification and control of hydraulic fracture propagation. 2020,,, 4533-4538. https://doi.org/10.23919/ACC45564.2020.9147687
  10. Abhinav Narasingam, Joseph Sang-Il 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. https://doi.org/10.1016/j.jprocont.2020.05.003
  11. Mohammed Saad Faizan Bangi, Joseph Sang-Il Kwon. Deep hybrid modeling of chemical process: Application to hydraulic fracturing. Computers & Chemical Engineering 2020, 134 , 106696. https://doi.org/10.1016/j.compchemeng.2019.106696
  12. Abhinav Narasingam, Joseph Sang‐Il Kwon. Koopman Lyapunov‐based model predictive control of nonlinear chemical process systems. AIChE Journal 2019, 65 (11) https://doi.org/10.1002/aic.16743
  13. Anqi Bao, Eduardo Gildin, Abhinav Narasingam, Joseph S. Kwon. Data-Driven Model Reduction for Coupled Flow and Geomechanics Based on DMD Methods. Fluids 2019, 4 (3) , 138. https://doi.org/10.3390/fluids4030138
  14. Ashish Yewale, Ravi Methekar, Shailesh Agrawal. Dynamic analysis and multiple model control of continuous microbial fuel cell (CMFC). Chemical Engineering Research and Design 2019, 148 , 403-416. https://doi.org/10.1016/j.cherd.2019.06.007

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