4.7 Article

A calibrated organic Rankine cycle dynamic model applying to subcritical system and transcritical system

Journal

ENERGY
Volume 237, Issue -, Pages -

Publisher

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.energy.2021.121494

Keywords

Organic Rankine cycle; Model calibration; Subcritical system; Transcritical system; Evaporator

Funding

  1. National Key R&D Program of China [2017YFE0102800]
  2. National Natural Science Foundation of China [51906173]
  3. Natural Science Foundation of Shandong Province [ZR2020ME171]

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The study presents a detailed calibration mechanism for the ORC dynamic model, which can capture the system's dynamic behavior well and achieve good predictions for key system state parameters. The findings enhance confidence in model application for similar thermodynamic systems.
The organic Rankine cycle (ORC) technology is an attractive candidate for waste heat recovery and clean energy utilization. The operating condition of ORC system would change constantly under fluctuant heat source and a dynamic model is able to predict the transient behavior. The ORC dynamic model generally contains some unknown parameters while their identification is often ignored. This paper proposes a detailed calibration mechanism for ORC dynamic model including subcritical and transcritical system. Component models including pump, expansion valve and evaporator are well calibrated and the integrated calibrated system model is validated based on experimental data. Results indicate that calibrated model can capture the system dynamic behavior well. For key system state parameters such as working fluid evaporator pressure and outlet temperature, model prediction can achieve mean absolute relative deviation of less than 2% for both subcritical and transcritical system. Meanwhile, the conventional expansion valve model for transcritical system is found not efficient enough and a modification form is provided. This work can contribute to the calibration mechanism of similar thermodynamic system and enhance the confidence in model application. (c) 2021 Elsevier Ltd. All rights reserved.

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