IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v276y2023ics036054422300912x.html
   My bibliography  Save this article

Modelling development and optimization on hydrodynamics and energy utilization of fish culture tank based on computational fluid dynamics and machine learning

Author

Listed:
  • Zhang, Shanhong
  • Yu, Guanghui
  • Guo, Yu
  • Wang, Yang

Abstract

Hydrodynamics of culture tank plays an essential role in the recirculating aquaculture system (RAS), maintaining the maximum effective energy utilization rate and uniform vortex distributions for fish growth is still a great challenge. To solve this problem, a novel approach to optimize physical parameters of octagonal tank using computational fluid dynamics (CFD) and machine learning (ML) is proposed. The initial tank is regarded as the benchmark, six vital parameters of octagonal tank including inlet and outlet diameters, fillet radius, inlet height, inlet angle and inlet velocity have been numerically investigated by CFD. Modelling development and optimization based on the Artificial Neural Network (ANN) and Nondominated Sorting Genetic Algorithm ΙΙ (NSGA-Ⅱ) are developed to obtain the Pareto front for maximizing effective energy utilization rate and minimizing the vortex STD. Some key findings are found that: 1) The model provides high predictive capability: RMSE of average velocity is 0.002.2) NSGA-Ⅱ combining ANN is applied in the optimization process to obtain 78 groups of optimal Pareto front.3) A series of optimal parameters are achieved by LINMAP, the optimal parameter combination could improve the energy utilization rate up to 12.32 corresponding with the benchmark. 4) Inlet velocity of 0.1 m/s and inlet diameter of 103.9 mm in the culture tank can be more significant for improving flow uniformity but also raising effective energy utilization rate. The proposed CFD-ML model prediction and optimization have satisfactory performance in hydrodynamics of culture tank.

Suggested Citation

  • Zhang, Shanhong & Yu, Guanghui & Guo, Yu & Wang, Yang, 2023. "Modelling development and optimization on hydrodynamics and energy utilization of fish culture tank based on computational fluid dynamics and machine learning," Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s036054422300912x
    DOI: 10.1016/j.energy.2023.127518
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422300912X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127518?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. Richmond, M. & Sobey, A. & Pandit, R. & Kolios, A., 2020. "Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning," Renewable Energy, Elsevier, vol. 161(C), pages 650-661.
    2. Kocakulak, Tolga & Babagiray, Mustafa & Nacak, Çağatay & Safieddin Ardebili, Seyed Mohammad & Calam, Alper & Solmaz, Hamit, 2022. "Multi objective optimization of HCCI combustion fuelled with fusel oil and n-heptane blends," Renewable Energy, Elsevier, vol. 182(C), pages 827-841.
    3. Habibi Khalaj, Ali & Scherer, Thomas & Siriwardana, Jayantha & Halgamuge, Saman K., 2015. "Multi-objective efficiency enhancement using workload spreading in an operational data center," Applied Energy, Elsevier, vol. 138(C), pages 432-444.
    4. Sui, Zengguang & Sui, Yunren & Wu, Wei, 2022. "Multi-objective optimization of a microchannel membrane-based absorber with inclined grooves based on CFD and machine learning," Energy, Elsevier, vol. 240(C).
    5. Noël, H. & Glouannec, P. & Velly, J. P., 1996. "Mathematical modelling and experimental validation of the thermal behaviour of an aquacultural pond," Applied Energy, Elsevier, vol. 55(1), pages 47-64, September.
    6. Chiaramonti, David & Prussi, Matteo & Casini, David & Tredici, Mario R. & Rodolfi, Liliana & Bassi, Niccolò & Zittelli, Graziella Chini & Bondioli, Paolo, 2013. "Review of energy balance in raceway ponds for microalgae cultivation: Re-thinking a traditional system is possible," Applied Energy, Elsevier, vol. 102(C), pages 101-111.
    7. Hojjat, Mohammad, 2020. "Nanofluids as coolant in a shell and tube heat exchanger: ANN modeling and multi-objective optimization," Applied Mathematics and Computation, Elsevier, vol. 365(C).
    8. Li, Wenqiang & Gong, Guangcai & Fan, Houhua & Peng, Pei & Chun, Liang, 2020. "Meta-learning strategy based on user preferences and a machine recommendation system for real-time cooling load and COP forecasting," Applied Energy, Elsevier, vol. 270(C).
    9. Zhong, Hanbin & Xiong, Qingang & Yin, Lina & Zhang, Juntao & Zhu, Yuqin & Liang, Shengrong & Niu, Ben & Zhang, Xinyu, 2020. "CFD-based reduced-order modeling of fluidized-bed biomass fast pyrolysis using artificial neural network," Renewable Energy, Elsevier, vol. 152(C), pages 613-626.
    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. Sui, Zengguang & Sui, Yunren & Wu, Wei, 2022. "Multi-objective optimization of a microchannel membrane-based absorber with inclined grooves based on CFD and machine learning," Energy, Elsevier, vol. 240(C).
    2. Wang, Fengjuan & Lv, Chengwei & Xu, Jiuping, 2023. "Carbon awareness oriented data center location and configuration: An integrated optimization method," Energy, Elsevier, vol. 278(C).
    3. Yan Bai & Lijun Gu & Xiao Qi, 2018. "Comparative Study of Energy Performance between Chip and Inlet Temperature-Aware Workload Allocation in Air-Cooled Data Center," Energies, MDPI, vol. 11(3), pages 1-23, March.
    4. Ma, Ting & Guo, Zhixiong & Lin, Mei & Wang, Qiuwang, 2021. "Recent trends on nanofluid heat transfer machine learning research applied to renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    5. Silva-Llanca, Luis & Ortega, Alfonso & Fouladi, Kamran & del Valle, Marcelo & Sundaralingam, Vikneshan, 2018. "Determining wasted energy in the airside of a perimeter-cooled data center via direct computation of the Exergy Destruction," Applied Energy, Elsevier, vol. 213(C), pages 235-246.
    6. Abdellatif Elmouatamid & Brian Fricke & Jian Sun & Philip W. T. Pong, 2023. "Air Conditioning Systems Fault Detection and Diagnosis-Based Sensing and Data-Driven Approaches," Energies, MDPI, vol. 16(12), pages 1-20, June.
    7. Borkowski, Mateusz & Piłat, Adam Krzysztof, 2022. "Customized data center cooling system operating at significant outdoor temperature fluctuations," Applied Energy, Elsevier, vol. 306(PB).
    8. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    9. José Luis de Andrés Honrubia & José Gaviria de la Puerta & Fernando Cortés & Urko Aguirre-Larracoechea & Aitor Goti & Jone Retolaza, 2021. "Development and Application of a Multi-Objective Tool for Thermal Design of Heat Exchangers Using Neural Networks," Mathematics, MDPI, vol. 9(10), pages 1-23, May.
    10. Salama, El-Sayed & Kurade, Mayur B. & Abou-Shanab, Reda A.I. & El-Dalatony, Marwa M. & Yang, Il-Seung & Min, Booki & Jeon, Byong-Hun, 2017. "Recent progress in microalgal biomass production coupled with wastewater treatment for biofuel generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 1189-1211.
    11. Babagiray, Mustafa & Kocakulak, Tolga & Safieddin Ardebili, Seyed Mohammad & Solmaz, Hamit & Çınar, Can & Uyumaz, Ahmet, 2022. "Experimental and statistical investigation of different valve lifts on HCCI combustion, performance and exhaust emissions using response surface method," Energy, Elsevier, vol. 244(PB).
    12. El Shenawy, E.A. & Elkelawy, Medhat & Bastawissi, Hagar Alm-Eldin & Taha, Mohammed & Panchal, Hitesh & Sadasivuni, Kishor kumar & Thakar, Nishant, 2020. "Effect of cultivation parameters and heat management on the algae species growth conditions and biomass production in a continuous feedstock photobioreactor," Renewable Energy, Elsevier, vol. 148(C), pages 807-815.
    13. Mingyu Li & Dongxiao Niu & Zhengsen Ji & Xiwen Cui & Lijie Sun, 2021. "Forecast Research on Multidimensional Influencing Factors of Global Offshore Wind Power Investment Based on Random Forest and Elastic Net," Sustainability, MDPI, vol. 13(21), pages 1-19, November.
    14. John J. Milledge & Birthe V. Nielsen & Supattra Maneein & Patricia J. Harvey, 2019. "A Brief Review of Anaerobic Digestion of Algae for Bioenergy," Energies, MDPI, vol. 12(6), pages 1-22, March.
    15. Bagchi, Sourav Kumar & Patnaik, Reeza & Sonkar, Sashi & Koley, Shankha & Rao, P. Srinivasa & Mallick, Nirupama, 2019. "Qualitative biodiesel production from a locally isolated chlorophycean microalga Scenedesmus obliquus (Turpin) Kützing GA 45 under closed raceway pond cultivation," Renewable Energy, Elsevier, vol. 139(C), pages 976-987.
    16. Farrell, C.C. & Osman, A.I. & Doherty, R. & Saad, M. & Zhang, X. & Murphy, A. & Harrison, J. & Vennard, A.S.M. & Kumaravel, V. & Al-Muhtaseb, A.H. & Rooney, D.W., 2020. "Technical challenges and opportunities in realising a circular economy for waste photovoltaic modules," Renewable and Sustainable Energy Reviews, Elsevier, vol. 128(C).
    17. Pawar, Sanjay, 2016. "Effectiveness mapping of open raceway pond and tubular photobioreactors for sustainable production of microalgae biofuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 640-653.
    18. Sui, Zengguang & Wu, Wei, 2022. "A comprehensive review of membrane-based absorbers/desorbers towards compact and efficient absorption refrigeration systems," Renewable Energy, Elsevier, vol. 201(P1), pages 563-593.
    19. Costa, Jorge Alberto Vieira & Freitas, Bárbara Catarina Bastos de & Lisboa, Cristiane Reinaldo & Santos, Thaisa Duarte & Brusch, Lucio Renato de Fraga & de Morais, Michele Greque, 2019. "Microalgal biorefinery from CO2 and the effects under the Blue Economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 99(C), pages 58-65.
    20. Habibi Khalaj, Ali & Abdulla, Khalid & Halgamuge, Saman K., 2018. "Towards the stand-alone operation of data centers with free cooling and optimally sized hybrid renewable power generation and energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 451-472.

    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:276:y:2023:i:c:s036054422300912x. 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.