IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v225y2024ics0960148124004130.html
   My bibliography  Save this article

Incorporating artificial intelligence-powered prediction models for exergy efficiency evaluation in parabolic trough collectors

Author

Listed:
  • Alawi, Omer A.
  • Kamar, Haslinda Mohamed
  • Homod, Raad Z.
  • Yaseen, Zaher Mundher

Abstract

Exergy analysis is essential for evaluating the second law of thermodynamics efficiency in solar thermal applications such as parabolic trough collectors (PTCs). This study creates ML models to tackle complex challenges in renewable energy systems and components. Six prediction models were developed such as Adaptive Boosting (AdaBoost), Multivariate adaptive regression splines (MARS), Stochastic Gradient Descent (SGD), Tweedie Regressor, voting, and stacking ensemble learning, were developed to predict the exergy efficiency of PTCs. The base fluids were three molten salts (Solar Salt, Hitec, and Hitec XL). Three nanoparticle types (Al2O3, CuO, and SiO2) were mixed homogeneously in a single-phase approach to prepare nine nanofluids. The output was predicted based on different input parameters such as molten salts, nanoparticle types, input temperature, volume fraction, Reynolds number (Re), Nusselt number (Nu), and friction factor (f). The results indicated that the stacking regressor efficiently predicted the exergy efficiency using (3-5) input parameters with a coefficient of determination (R2 = 0.963), followed by the AdaBoost algorithm with R2 = 0.947 using the fifth input combination over the testing phase. Further, AdaBoost and Stacking Regressors models were compared with the previously published study and showed an overall accuracy of R2 = 0.9472 and R2 = 0.9634, respectively.

Suggested Citation

  • Alawi, Omer A. & Kamar, Haslinda Mohamed & Homod, Raad Z. & Yaseen, Zaher Mundher, 2024. "Incorporating artificial intelligence-powered prediction models for exergy efficiency evaluation in parabolic trough collectors," Renewable Energy, Elsevier, vol. 225(C).
    • Handle: RePEc:eee:renene:v:225:y:2024:i:c:s0960148124004130
    DOI: 10.1016/j.renene.2024.120348
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148124004130
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.120348?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. Li, Danny H.W. & Chen, Wenqiang & Li, Shuyang & Lou, Siwei, 2019. "Estimation of hourly global solar radiation using Multivariate Adaptive Regression Spline (MARS) – A case study of Hong Kong," Energy, Elsevier, vol. 186(C).
    2. Kaood, Amr & Abubakr, Mohamed & Al-Oran, Otabeh & Hassan, Muhammed A., 2021. "Performance analysis and particle swarm optimization of molten salt-based nanofluids in parabolic trough concentrators," Renewable Energy, Elsevier, vol. 177(C), pages 1045-1062.
    3. Alizamir, Meysam & Kim, Sungwon & Kisi, Ozgur & Zounemat-Kermani, Mohammad, 2020. "A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions," Energy, Elsevier, vol. 197(C).
    4. El Bilali, Ali & Taleb, Abdeslam & Brouziyne, Youssef, 2021. "Groundwater quality forecasting using machine learning algorithms for irrigation purposes," Agricultural Water Management, Elsevier, vol. 245(C).
    5. Ngugi Mwenda & Mathew Kosgei & Gregory Kerich & Ruth Nduati & Xiaofeng Zong, 2022. "Tweedie Model for Predicting Factors Associated with Distance Traveled to Access Inpatient Services in Kenya," Journal of Probability and Statistics, Hindawi, vol. 2022, pages 1-10, April.
    6. Chiou-Jye Huang & Ping-Huan Kuo, 2018. "A Short-Term Wind Speed Forecasting Model by Using Artificial Neural Networks with Stochastic Optimization for Renewable Energy Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    7. Zhang, Shunqi & Liu, Ming & Zhao, Yongliang & Liu, Jiping & Yan, Junjie, 2022. "Energy and exergy analyses of a parabolic trough concentrated solar power plant using molten salt during the start-up process," Energy, Elsevier, vol. 254(PC).
    8. Hoxha, Julian & Çodur, Muhammed Yasin & Mustafaraj, Enea & Kanj, Hassan & El Masri, Ali, 2023. "Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis," Applied Energy, Elsevier, vol. 350(C).
    9. Heng, Shye Yunn & Asako, Yutaka & Suwa, Tohru & Nagasaka, Ken, 2019. "Transient thermal prediction methodology for parabolic trough solar collector tube using artificial neural network," Renewable Energy, Elsevier, vol. 131(C), pages 168-179.
    10. Jamei, Mehdi & Ali, Mumtaz & Karbasi, Masoud & Xiang, Yong & Ahmadianfar, Iman & Yaseen, Zaher Mundher, 2022. "Designing a Multi-Stage Expert System for daily ocean wave energy forecasting: A multivariate data decomposition-based approach," Applied Energy, Elsevier, vol. 326(C).
    11. Subramani, J. & Nagarajan, P.K. & Mahian, Omid & Sathyamurthy, Ravishankar, 2018. "Efficiency and heat transfer improvements in a parabolic trough solar collector using TiO2 nanofluids under turbulent flow regime," Renewable Energy, Elsevier, vol. 119(C), pages 19-31.
    12. Ghazouani, Mokhtar & Bouya, Mohsine & Benaissa, Mohammed, 2020. "Thermo-economic and exergy analysis and optimization of small PTC collectors for solar heat integration in industrial processes," Renewable Energy, Elsevier, vol. 152(C), pages 984-998.
    13. Ebrahimi-Moghadam, Amir & Mohseni-Gharyehsafa, Behnam & Farzaneh-Gord, Mahmood, 2018. "Using artificial neural network and quadratic algorithm for minimizing entropy generation of Al2O3-EG/W nanofluid flow inside parabolic trough solar collector," Renewable Energy, Elsevier, vol. 129(PA), pages 473-485.
    14. Muñoz-Sánchez, Belén & Nieto-Maestre, Javier & Iparraguirre-Torres, Iñigo & García-Romero, Ana & Sala-Lizarraga, Jose M., 2018. "Molten salt-based nanofluids as efficient heat transfer and storage materials at high temperatures. An overview of the literature," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3924-3945.
    15. Sahraei, Mohammad Ali & Duman, Hakan & Çodur, Muhammed Yasin & Eyduran, Ecevit, 2021. "Prediction of transportation energy demand: Multivariate Adaptive Regression Splines," Energy, Elsevier, vol. 224(C).
    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. Ajbar, Wassila & Parrales, A. & Huicochea, A. & Hernández, J.A., 2022. "Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    2. Abubakr, Mohamed & Amein, Hamza & Akoush, Bassem M. & El-Bakry, M. Medhat & Hassan, Muhammed A., 2020. "An intuitive framework for optimizing energetic and exergetic performances of parabolic trough solar collectors operating with nanofluids," Renewable Energy, Elsevier, vol. 157(C), pages 130-149.
    3. Amein, Hamza & Akoush, Bassem M. & El-Bakry, M. Medhat & Abubakr, Mohamed & Hassan, Muhammed A., 2022. "Enhancing the energy utilization in parabolic trough concentrators with cracked heat collection elements using a cost-effective rotation mechanism," Renewable Energy, Elsevier, vol. 181(C), pages 250-266.
    4. Kaood, Amr & Abubakr, Mohamed & Al-Oran, Otabeh & Hassan, Muhammed A., 2021. "Performance analysis and particle swarm optimization of molten salt-based nanofluids in parabolic trough concentrators," Renewable Energy, Elsevier, vol. 177(C), pages 1045-1062.
    5. Hassan, Muhammed A. & Al-Ghussain, Loiy & Khalil, Adel & Kaseb, Sayed A., 2022. "Self-calibrated hybrid weather forecasters for solar thermal and photovoltaic power plants," Renewable Energy, Elsevier, vol. 188(C), pages 1120-1140.
    6. Thananya Janhuaton & Vatanavongs Ratanavaraha & Sajjakaj Jomnonkwao, 2024. "Forecasting Thailand’s Transportation CO 2 Emissions: A Comparison among Artificial Intelligent Models," Forecasting, MDPI, vol. 6(2), pages 1-23, June.
    7. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    8. Afzal, Asif & Buradi, Abdulrajak & Jilte, Ravindra & Shaik, Saboor & Kaladgi, Abdul Razak & Arıcı, Muslum & Lee, Chew Tin & Nižetić, Sandro, 2023. "Optimizing the thermal performance of solar energy devices using meta-heuristic algorithms: A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    9. Adrián Caraballo & Santos Galán-Casado & Ángel Caballero & Sara Serena, 2021. "Molten Salts for Sensible Thermal Energy Storage: A Review and an Energy Performance Analysis," Energies, MDPI, vol. 14(4), pages 1-15, February.
    10. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
    11. Saranprabhu, M.K. & Rajan, K.S., 2019. "Magnesium oxide nanoparticles dispersed solar salt with improved solid phase thermal conductivity and specific heat for latent heat thermal energy storage," Renewable Energy, Elsevier, vol. 141(C), pages 451-459.
    12. Chun-Wei Chen, 2023. "A Feasibility Discussion: Is ML Suitable for Predicting Sustainable Patterns in Consumer Product Preferences?," Sustainability, MDPI, vol. 15(5), pages 1-21, February.
    13. Ninoslav Holjevac & Tomislav Baškarad & Josip Đaković & Matej Krpan & Matija Zidar & Igor Kuzle, 2021. "Challenges of High Renewable Energy Sources Integration in Power Systems—The Case of Croatia," Energies, MDPI, vol. 14(4), pages 1-20, February.
    14. Lu, Yilin & Xu, Jingxuan & Chen, Xi & Tian, Yafen & Zhang, Hua, 2023. "Design and thermodynamic analysis of an advanced liquid air energy storage system coupled with LNG cold energy, ORCs and natural resources," Energy, Elsevier, vol. 275(C).
    15. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    16. Nunes, V.M.B. & Lourenço, M.J.V. & Santos, F.J.V. & Nieto de Castro, C.A., 2019. "Molten alkali carbonates as alternative engineering fluids for high temperature applications," Applied Energy, Elsevier, vol. 242(C), pages 1626-1633.
    17. Kumar, Laveet & Hasanuzzaman, M. & Rahim, N.A. & Islam, M.M., 2021. "Modeling, simulation and outdoor experimental performance analysis of a solar-assisted process heating system for industrial process heat," Renewable Energy, Elsevier, vol. 164(C), pages 656-673.
    18. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
    19. Guijo-Rubio, D. & Durán-Rosal, A.M. & Gutiérrez, P.A. & Gómez-Orellana, A.M. & Casanova-Mateo, C. & Sanz-Justo, J. & Salcedo-Sanz, S. & Hervás-Martínez, C., 2020. "Evolutionary artificial neural networks for accurate solar radiation prediction," Energy, Elsevier, vol. 210(C).
    20. Martínez-Merino, Paloma & Alcántara, Rodrigo & Gómez-Larrán, Pedro & Carrillo-Berdugo, Iván & Navas, Javier, 2022. "MoS2-based nanofluids as heat transfer fluid in parabolic trough collector technology," Renewable Energy, Elsevier, vol. 188(C), pages 721-730.

    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:renene:v:225:y:2024:i:c:s0960148124004130. 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/renewable-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.