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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] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Son SH, Choi H, Kwon JS. Application of offset‐free Koopman‐based model predictive control to a batch pulp digester. AIChE J 2021. [DOI: 10.1002/aic.17301] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Affiliation(s)
- Sang Hwan Son
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
| | - Hyun‐Kyu Choi
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
| | - Joseph Sang‐Il Kwon
- Artie McFerrin Department of Chemical Engineering Texas A&M University College Station Texas USA
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Kumari P, Bhadriraju B, Wang Q, Kwon JSI. Development of parametric reduced-order model for consequence estimation of rare events. Chem Eng Res Des 2021. [DOI: 10.1016/j.cherd.2021.02.006] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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Narasingam A, Kwon JS. Koopman Lyapunov‐based model predictive control of nonlinear chemical process systems. AIChE J 2019. [DOI: 10.1002/aic.16743] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Abhinav Narasingam
- Artie McFerrin Department of Chemical Engineering Texas A&M University College station Texas
| | - Joseph Sang‐Il Kwon
- Artie McFerrin Department of Chemical Engineering Texas A&M University College station Texas
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Yewale A, Methekar R, Agrawal S. Dynamic analysis and multiple model control of continuous microbial fuel cell (CMFC). Chem Eng Res Des 2019. [DOI: 10.1016/j.cherd.2019.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Data-Driven Model Reduction for Coupled Flow and Geomechanics Based on DMD Methods. FLUIDS 2019. [DOI: 10.3390/fluids4030138] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Learning reservoir flow dynamics is of primary importance in creating robust predictive models for reservoir management including hydraulic fracturing processes. Physics-based models are to a certain extent exact, but they entail heavy computational infrastructure for simulating a wide variety of parameters and production scenarios. Reduced-order models offer computational advantages without compromising solution accuracy, especially if they can assimilate large volumes of production data without having to reconstruct the original model (data-driven models). Dynamic mode decomposition (DMD) entails the extraction of relevant spatial structure (modes) based on data (snapshots) that can be used to predict the behavior of reservoir fluid flow in porous media. In this paper, we will further enhance the application of the DMD, by introducing sparse DMD and local DMD. The former is particularly useful when there is a limited number of sparse measurements as in the case of reservoir simulation, and the latter can improve the accuracy of developed DMD models when the process dynamics show a moving boundary behavior like hydraulic fracturing. For demonstration purposes, we first show the methodology applied to (flow only) single- and two-phase reservoir models using the SPE10 benchmark. Both online and offline processes will be used for evaluation. We observe that we only require a few DMD modes, which are determined by the sparse DMD structure, to capture the behavior of the reservoir models. Then, we applied the local DMDc for creating a proxy for application in a hydraulic fracturing process. We also assessed the trade-offs between problem size and computational time for each reservoir model. The novelty of our method is the application of sparse DMD and local DMDc, which is a data-driven technique for fast and accurate simulations.
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