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Title: Probabilistic analysis of CO 2 storage mechanisms in a CO 2 -EOR field using polynomial chaos expansion

Authors:
ORCiD logo; ; ; ;
Publication Date:
Sponsoring Org.:
USDOE
OSTI Identifier:
1325349
Resource Type:
Journal Article: Publisher's Accepted Manuscript
Journal Name:
International Journal of Greenhouse Gas Control
Additional Journal Information:
Journal Volume: 51; Journal Issue: C; Related Information: CHORUS Timestamp: 2017-10-03 15:44:47; Journal ID: ISSN 1750-5836
Publisher:
Elsevier
Country of Publication:
Netherlands
Language:
English

Citation Formats

Jia, Wei, McPherson, Brian J., Pan, Feng, Xiao, Ting, and Bromhal, Grant. Probabilistic analysis of CO 2 storage mechanisms in a CO 2 -EOR field using polynomial chaos expansion. Netherlands: N. p., 2016. Web. doi:10.1016/j.ijggc.2016.05.024.
Jia, Wei, McPherson, Brian J., Pan, Feng, Xiao, Ting, & Bromhal, Grant. Probabilistic analysis of CO 2 storage mechanisms in a CO 2 -EOR field using polynomial chaos expansion. Netherlands. doi:10.1016/j.ijggc.2016.05.024.
Jia, Wei, McPherson, Brian J., Pan, Feng, Xiao, Ting, and Bromhal, Grant. 2016. "Probabilistic analysis of CO 2 storage mechanisms in a CO 2 -EOR field using polynomial chaos expansion". Netherlands. doi:10.1016/j.ijggc.2016.05.024.
@article{osti_1325349,
title = {Probabilistic analysis of CO 2 storage mechanisms in a CO 2 -EOR field using polynomial chaos expansion},
author = {Jia, Wei and McPherson, Brian J. and Pan, Feng and Xiao, Ting and Bromhal, Grant},
abstractNote = {},
doi = {10.1016/j.ijggc.2016.05.024},
journal = {International Journal of Greenhouse Gas Control},
number = C,
volume = 51,
place = {Netherlands},
year = 2016,
month = 8
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1016/j.ijggc.2016.05.024

Citation Metrics:
Cited by: 5works
Citation information provided by
Web of Science

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  • This paper presents a robustness analysis of an air heating plant with a multivariable closed-loop control law by using the polynomial chaos methodology (MPC). The plant consists of a PVC tube with a fan in the air input (that forces the air through the tube) and a mass flux sensor in the output. A heating resistance warms the air as it flows inside the tube, and a thermo-couple sensor measures the air temperature. The plant has thus two inputs (the fan's rotation intensity and heat generated by the resistance, both measured in percent of the maximum value) and two outputsmore » (air temperature and air mass flux, also in percent of the maximal value). The mathematical model is obtained by System Identification techniques. The mass flux sensor, which is nonlinear, is linearized and the delays in the transfer functions are properly approximated by non-minimum phase transfer functions. The resulting model is transformed to a state-space model, which is used for control design purposes. The multivariable robust control design techniques used is the LQG/LTR, and the controllers are validated in simulation software and in the real plant. Finally, the MPC is applied by considering some of the system's parameters as random variables (one at a time, and the system's stochastic differential equations are solved by expanding the solution (a stochastic process) in an orthogonal basis of polynomial functions of the basic random variables. This method transforms the stochastic equations in a set of deterministic differential equations, which can be solved by traditional numerical methods (That is the MPC). Statistical data for the system (like expected values and variances) are then calculated. The effects of randomness in the parameters are evaluated in the open-loop and closed-loop pole's positions.« less
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