Journal of Computational and Applied Mathematics2022,Vol.40817.DOI:10.1016/j.cam.2021.114059

Vapor-liquid equilibrium estimation of n-alkane/nitrogen mixtures using neural networks

Chakraborty, Suman Sun, Yixuan Lin, Guang Qiao, Li
Journal of Computational and Applied Mathematics2022,Vol.40817.DOI:10.1016/j.cam.2021.114059

Vapor-liquid equilibrium estimation of n-alkane/nitrogen mixtures using neural networks

Chakraborty, Suman 1Sun, Yixuan 1Lin, Guang 1Qiao, Li1
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作者信息

  • 1. Purdue Univ
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Abstract

Understanding fluid phase behavior, like VLE, in high P & T conditions is crucial for developing high-fidelity simulations of chemically reacting flows in liquid-fueled combustion systems and also forms an integral part of the design-modeling of the control processes in chemical industries. Two data-driven models have been proposed in this study, each of which was competent in estimating VLE for the Type III binary systems of C-10/N-2 and C-12/N-2, at pressures ranging up to 50-60 MPa. Both models showed better performance in predicting equilibrium pressure as compared to VLE modeled using PREOS. A modified model has also been proposed, capable of estimating the full phase envelope for the binary systems of C-10/N-2 and C-12/N-2 across a wide range of temperatures, and thus exhibit the mixture critical pressure at the concerned temperature. The diverse applicability of the proposed network architecture was further exhibited while estimating the VLE of a ternary system of C-1/C-10/N-2. (C)& nbsp;2022 Elsevier B.V. All rights reserved.

Key words

Vapor-liquid equilibrium (VLE)/Neural networks/Data-driven learning/Peng Robinson equation of state (PR-EOS)/Binary mixtures/Ternary mixtures/BINARY INTERACTION PARAMETERS/EQUATION-OF-STATE/CARBON-DIOXIDE/HYDROCARBON SYSTEMS/PPR78 MODEL/NITROGEN/PREDICTION/PRESSURES/SOLUBILITIES/HYDROGEN

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出版年

2022
Journal of Computational and Applied Mathematics

Journal of Computational and Applied Mathematics

EISCI
ISSN:0377-0427
被引量1
参考文献量51
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