IDEAS home Printed from https://ideas.repec.org/a/oup/ijlctc/v17y2022ip321-326..html
   My bibliography  Save this article

Modeling carbon dioxide emission of countries in southeast of Asia by applying artificial neural network
[Energy and exergy analyses of single flash geothermal power plant at optimum separator temperature]

Author

Listed:
  • Ali Komeili Birjandi
  • Morteza Fahim Alavi
  • Mohamed Salem
  • Mamdouh El Haj Assad
  • Natarajan Prabaharan

Abstract

Energy and economy play a substantial role in environmental issues such as the emission of greenhouse gases. CO2 is one of the greenhouses that is hugely produced in industrial processes and other human being activities. The major share of CO2 emission is related to the energy-related activities. As a result, modeling the amount of produced CO2 by utilization of different energy sources must be considered. Moreover, by considering economic indicators such as gross domestic product, the accuracy of the model could be improved. In the present work, artificial neural network (ANN) with two transfer functions including normalized radial basis and tansig is used to model CO2 production of different countries in Southeast Asia including Malaysia, Indonesia, Singapore and Vietnam. It is observed that using the network with normalized radial basis and 11 neurons in the hidden layer provides the model with the highest precision with an R2 of 0.9997 while the optimal architecture of the network using tansig function provides a model with R2 of 0.9996.

Suggested Citation

  • Ali Komeili Birjandi & Morteza Fahim Alavi & Mohamed Salem & Mamdouh El Haj Assad & Natarajan Prabaharan, 2022. "Modeling carbon dioxide emission of countries in southeast of Asia by applying artificial neural network [Energy and exergy analyses of single flash geothermal power plant at optimum separator temp," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 321-326.
    • Handle: RePEc:oup:ijlctc:v:17:y:2022:i::p:321-326.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/ijlct/ctac002
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Mamdouh El Haj Assad & Yashar Aryanfar & Salar Radman & Bashria Yousef & Mohammadreza Pakatchian, 2021. "Energy and exergy analyses of single flash geothermal power plant at optimum separator temperature," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 16(3), pages 873-881.
    2. Mahdi Ramezanizadeh & Mohammad Alhuyi Nazari, 2019. "Modeling thermal conductivity of Ag/water nanofluid by applying a mathematical correlation and artificial neural network," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 14(4), pages 468-474.
    3. Mohammad Hossein Rezaei & Milad Sadeghzadeh & Mohammad Alhuyi Nazari & Mohammad Hossein Ahmadi & Fatemeh Razi Astaraei, 2018. "Applying GMDH artificial neural network in modeling CO2 emissions in four nordic countries," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 13(3), pages 266-271.
    4. Milad Ashouri & Fatemeh Razi Astaraei & Roghaye Ghasempour & M.H. Ahmadi & Michel Feidt, 2017. "Thermodynamic and economic evaluation of a small-scale organic Rankine cycle integrated with a concentrating solar collector," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 12(1), pages 54-65.
    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. Moein Shamoushaki & Mehdi Aliehyaei & Farhad Taghizadeh-Hesary, 2021. "Energy, Exergy, Exergoeconomic, and Exergoenvironmental Assessment of Flash-Binary Geothermal Combined Cooling, Heating and Power Cycle," Energies, MDPI, vol. 14(15), pages 1-24, July.
    2. Mahmoudan, Alireza & Samadof, Parviz & Hosseinzadeh, Siamak & Garcia, Davide Astiaso, 2021. "A multigeneration cascade system using ground-source energy with cold recovery: 3E analyses and multi-objective optimization," Energy, Elsevier, vol. 233(C).
    3. Anwar Saeed & Ebrahem A Algehyne & Musaad S Aldhabani & Abdullah Dawar & Poom Kumam & Wiyada Kumam, 2022. "Mixed convective flow of a magnetohydrodynamic Casson fluid through a permeable stretching sheet with first-order chemical reaction," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-15, April.
    4. Vaccari, Marco & Pannocchia, Gabriele & Tognotti, Leonardo & Paci, Marco, 2023. "Rigorous simulation of geothermal power plants to evaluate environmental performance of alternative configurations," Renewable Energy, Elsevier, vol. 207(C), pages 471-483.
    5. Jiansheng, Wang & Lide, Su & Qiang, Zhu & Jintao, Niu, 2022. "Numerical investigation on power generation performance of enhanced geothermal system with horizontal well," Applied Energy, Elsevier, vol. 325(C).
    6. Mehrpooya, Mehdi & Ashouri, Milad & Mohammadi, Amin, 2017. "Thermoeconomic analysis and optimization of a regenerative two-stage organic Rankine cycle coupled with liquefied natural gas and solar energy," Energy, Elsevier, vol. 126(C), pages 899-914.
    7. Aryanfar, Yashar & Mohtaram, Soheil & García Alcaraz, Jorge Luis & Sun, HongGuang, 2023. "Energy and exergy assessment and a competitive study of a two-stage ORC for recovering SFGC waste heat and LNG cold energy," Energy, Elsevier, vol. 264(C).
    8. Xiaodong Li & Ai Ren & Qi Li, 2022. "Exploring Patterns of Transportation-Related CO 2 Emissions Using Machine Learning Methods," Sustainability, MDPI, vol. 14(8), pages 1-21, April.
    9. Moein Shamoushaki & Giampaolo Manfrida & Lorenzo Talluri & Pouriya H. Niknam & Daniele Fiaschi, 2021. "Different Geothermal Power Cycle Configurations Cost Estimation Models," Sustainability, MDPI, vol. 13(20), pages 1-19, October.
    10. Ramezanizadeh, Mahdi & Ahmadi, Mohammad Hossein & Nazari, Mohammad Alhuyi & Sadeghzadeh, Milad & Chen, Lingen, 2019. "A review on the utilized machine learning approaches for modeling the dynamic viscosity of nanofluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    11. Behzad Maleki & Mahyar Ghazvini & Mohammad Hossein Ahmadi & Heydar Maddah & Shahaboddin Shamshirband, 2019. "Moisture Estimation in Cabinet Dryers with Thin-Layer Relationships Using a Genetic Algorithm and Neural Network," Mathematics, MDPI, vol. 7(11), pages 1-12, November.
    12. Mamdouh El Haj Assad & Yashar Aryanfar & Amirreza Javaherian & Ali Khosravi & Karim Aghaei & Siamak Hosseinzadeh & Juan Pabon & SMS Mahmoudi, 2021. "Energy, exergy, economic and exergoenvironmental analyses of transcritical CO2 cycle powered by single flash geothermal power plant [Modeling of geothermal power system equipped with absorption ref," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 16(4), pages 1504-1518.
    13. Man-Hoe Kim, 2022. "Energy and Exergy Analysis of Solar Organic Rankine Cycle Coupled with Vapor Compression Refrigeration Cycle," Energies, MDPI, vol. 15(15), pages 1-16, August.
    14. MaryamNooman AlMallahi & Mamdouh El Haj Assad & Sameh AlShihabi & Reza Alayi, 2022. "Multi-criteria decision-making approach for the selection of cleaning method of solar PV panels in United Arab Emirates based on sustainability perspective [Mind the gap: a social sciences review o," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 380-393.
    15. Seyed Mohammad Seyed Alavi & Akbar Maleki & Ali Khaleghi, 2022. "Optimal site selection for wind power plant using multi-criteria decision-making methods: A case study in eastern Iran [Selection of optimal location and design of a stand-alone photovoltaic scheme," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 17, pages 1319-1337.

    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:oup:ijlctc:v:17:y:2022:i::p:321-326.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/ijlct .

    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.