IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v252y2022ics0360544222009367.html
   My bibliography  Save this article

Identification of a supercritical fluid extraction process for modelling the energy consumption

Author

Listed:
  • Hämäläinen, Henri
  • Ruusunen, Mika

Abstract

Supercritical carbon dioxide extraction has been established as a promising and clean technology alternative to conventional separation techniques. Despite a high energy demand of extraction processes, their energy analysis has been scarcely considered. In this study, a supercritical carbon dioxide batch extraction process was modelled through system identification, forming a full simulator of its control loops affecting the energy consumption. The modelling was based on data acquired through systematic approach including experimental design and identification of dynamic process responses and energy consumption. Regression analysis and 12 identified models for subprocesses showed feasible performance during simulations with experimental data. The best local model for a subprocesses exhibited a Mean Absolute Percentage Error of 3% with independent test data. Regression model for steady-state electricity consumption showed a Mean Absolute Percentage Error of 7.6%, also suggesting the existence of nonlinearities between the response and other process variables. The identification approach reveals new information on energy consumption and dynamics of energy consumption of supercritical extraction in transient operating conditions. The models can be applied for further developments in real-time energy monitoring and optimization of supercritical extraction processes.

Suggested Citation

  • Hämäläinen, Henri & Ruusunen, Mika, 2022. "Identification of a supercritical fluid extraction process for modelling the energy consumption," Energy, Elsevier, vol. 252(C).
    • Handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222009367
    DOI: 10.1016/j.energy.2022.124033
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222009367
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.124033?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    2. Knez, Ž. & Markočič, E. & Leitgeb, M. & Primožič, M. & Knez Hrnčič, M. & Škerget, M., 2014. "Industrial applications of supercritical fluids: A review," Energy, Elsevier, vol. 77(C), pages 235-243.
    3. Patel, Rajesh N. & Bandyopadhyay, Santanu & Ganesh, Anuradda, 2011. "Extraction of cardanol and phenol from bio-oils obtained through vacuum pyrolysis of biomass using supercritical fluid extraction," Energy, Elsevier, vol. 36(3), pages 1535-1542.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Xincheng & Banks, Jonathan & Wu, Linping & Liu, Wei Victor, 2020. "Numerical modeling of a coaxial borehole heat exchanger to exploit geothermal energy from abandoned petroleum wells in Hinton, Alberta," Renewable Energy, Elsevier, vol. 148(C), pages 1110-1123.
    2. Khan, Waqas & Somers, Ward & Walker, Shalika & de Bont, Kevin & Van der Velden, Joep & Zeiler, Wim, 2023. "Comparison of electric vehicle load forecasting across different spatial levels with incorporated uncertainty estimation," Energy, Elsevier, vol. 283(C).
    3. Kravanja, Gregor & Zajc, Gašper & Knez, Željko & Škerget, Mojca & Marčič, Simon & Knez, Maša H., 2018. "Heat transfer performance of CO2, ethane and their azeotropic mixture under supercritical conditions," Energy, Elsevier, vol. 152(C), pages 190-201.
    4. Vasile Brătian & Ana-Maria Acu & Camelia Oprean-Stan & Emil Dinga & Gabriela-Mariana Ionescu, 2021. "Efficient or Fractal Market Hypothesis? A Stock Indexes Modelling Using Geometric Brownian Motion and Geometric Fractional Brownian Motion," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    5. Andrea Petroselli & Jacek Florek & Dariusz Młyński & Leszek Książek & Andrzej Wałęga, 2020. "New Insights on Flood Mapping Procedure: Two Case Studies in Poland," Sustainability, MDPI, vol. 12(20), pages 1-17, October.
    6. Jose Manuel Barrera & Alejandro Reina & Alejandro Maté & Juan Carlos Trujillo, 2020. "Solar Energy Prediction Model Based on Artificial Neural Networks and Open Data," Sustainability, MDPI, vol. 12(17), pages 1-20, August.
    7. Feng, Junfeng & Hse, Chung-yun & Wang, Kui & Yang, Zhongzhi & Jiang, Jianchun & Xu, Junming, 2017. "Directional liquefaction of biomass for phenolic compounds and in situ hydrodeoxygenation upgrading of phenolics using bifunctional catalysts," Energy, Elsevier, vol. 135(C), pages 1-13.
    8. Bhatia, Kushagra & Mittal, Rajat & Varanasi, Jyothi & Tripathi, M.M., 2021. "An ensemble approach for electricity price forecasting in markets with renewable energy resources," Utilities Policy, Elsevier, vol. 70(C).
    9. Anh Ngoc-Lan Huynh & Ravinesh C. Deo & Duc-Anh An-Vo & Mumtaz Ali & Nawin Raj & Shahab Abdulla, 2020. "Near Real-Time Global Solar Radiation Forecasting at Multiple Time-Step Horizons Using the Long Short-Term Memory Network," Energies, MDPI, vol. 13(14), pages 1-30, July.
    10. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    11. Zhang, Xiaogang & Ranjith, P.G. & Ranathunga, A.S., 2019. "Sub- and super-critical carbon dioxide flow variations in large high-rank coal specimen: An experimental study," Energy, Elsevier, vol. 181(C), pages 148-161.
    12. Ahmed Gowida & Tamer Moussa & Salaheldin Elkatatny & Abdulwahab Ali, 2019. "A Hybrid Artificial Intelligence Model to Predict the Elastic Behavior of Sandstone Rocks," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    13. Indy Man Kit Ho & Anthony Weldon & Jason Tze Ho Yong & Candy Tze Tim Lam & Jaime Sampaio, 2023. "Using Machine Learning Algorithms to Pool Data from Meta-Analysis for the Prediction of Countermovement Jump Improvement," IJERPH, MDPI, vol. 20(10), pages 1-15, May.
    14. Lee, Joseph C.Y. & Draxl, Caroline & Berg, Larry K., 2022. "Evaluating wind speed and power forecasts for wind energy applications using an open-source and systematic validation framework," Renewable Energy, Elsevier, vol. 200(C), pages 457-475.
    15. Gaetano Perone, 2022. "Comparison of ARIMA, ETS, NNAR, TBATS and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(6), pages 917-940, August.
    16. Mostofi Fatemeh & Toğan Vedat & Başağa Hasan Basri, 2022. "Real-estate price prediction with deep neural network and principal component analysis," Organization, Technology and Management in Construction, Sciendo, vol. 14(1), pages 2741-2759, January.
    17. Dariusz Młyński, 2020. "Analysis of Problems Related to the Calculation of Flood Frequency Using Rainfall-Runoff Models: A Case Study in Poland," Sustainability, MDPI, vol. 12(17), pages 1-17, September.
    18. Dalton Garcia Borges de Souza & Erivelton Antonio dos Santos & Francisco Tarcísio Alves Júnior & Mariá Cristina Vasconcelos Nascimento, 2021. "On Comparing Cross-Validated Forecasting Models with a Novel Fuzzy-TOPSIS Metric: A COVID-19 Case Study," Sustainability, MDPI, vol. 13(24), pages 1-25, December.
    19. Jennifer L. Castle & Jurgen A. Doornik & David F. Hendry, 2021. "Forecasting Principles from Experience with Forecasting Competitions," Forecasting, MDPI, vol. 3(1), pages 1-28, February.
    20. Montero-Sousa, Juan Aurelio & Aláiz-Moretón, Héctor & Quintián, Héctor & González-Ayuso, Tomás & Novais, Paulo & Calvo-Rolle, José Luis, 2020. "Hydrogen consumption prediction of a fuel cell based system with a hybrid intelligent approach," Energy, Elsevier, vol. 205(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:252:y:2022:i:c:s0360544222009367. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.