IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v240y2022ics0360544221027237.html
   My bibliography  Save this article

Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation

Author

Listed:
  • Zhao, Shuchun
  • Guo, Junheng
  • Dang, Xiuhu
  • Ai, Bingyan
  • Zhang, Minqing
  • Li, Wei
  • Zhang, Jinli

Abstract

Developing high-efficient and green energy-saving stirred tank reactors is of vital importance in liquid-liquid processing industries. Therefore, this work revealed the energy consumption, flow characteristics, and explored the energy-saving strategies of novel-designed Cup-shape Blade (CB) stirred tank reactors characterized by easy fabrication, low energy consumption and large pumping flow rate. Computational Fluid Dynamics (CFD) and Artificial Neural Network (ANN) were conducted to investigate the influences of geometrical parameters on liquid-liquid power number (NP), flow number (NQ) and flow patterns. The improved method for hyper-parameter specification indicates that the Bayesian regulation training algorithm and the two-hidden-layer scheme possess the best performance. Back Propagation Neural Network (BPNN) presents the best predictive accuracy for NP and NQ, and Learning Vector Quantization (LVQ) network shows the best classification capability of flow patterns. Analyses based on the ANN modeling and the “Local Interpretable Model-agnostic Explanations” method show the joint and complicated effects of geometrical parameters on energy consumption and flow characteristics. Procedures were proposed to ascertain the energy-efficiency blade configuration based on ANN.

Suggested Citation

  • Zhao, Shuchun & Guo, Junheng & Dang, Xiuhu & Ai, Bingyan & Zhang, Minqing & Li, Wei & Zhang, Jinli, 2022. "Energy consumption, flow characteristics and energy-efficient design of cup-shape blade stirred tank reactors: Computational fluid dynamics and artificial neural network investigation," Energy, Elsevier, vol. 240(C).
    • Handle: RePEc:eee:energy:v:240:y:2022:i:c:s0360544221027237
    DOI: 10.1016/j.energy.2021.122474
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221027237
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.122474?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. Fathy, Ahmed & Rezk, Hegazy & Mohamed Ramadan, Haitham Saad, 2020. "Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process," Energy, Elsevier, vol. 207(C).
    2. Mohammadrezaei, Rashed & Zareei, Samira & Behroozi- Khazaei, Nasser, 2018. "Optimum mixing rate in biogas reactors: Energy balance calculations and computational fluid dynamics simulation," Energy, Elsevier, vol. 159(C), pages 54-60.
    3. Navarkar, Abhishek & Hasti, Veeraraghava Raju & Deneke, Elihu & Gore, Jay P., 2020. "A data-driven model for thermodynamic properties of a steam generator under cycling operation," Energy, Elsevier, vol. 211(C).
    4. Ballestrín, J. & Carra, E. & Alonso-Montesinos, J. & López, G. & Polo, J. & Marzo, A. & Fernández-Reche, J. & Barbero, J. & Batlles, F.J., 2020. "Modeling solar extinction using artificial neural networks. Application to solar tower plants," Energy, Elsevier, vol. 199(C).
    5. Noriega, M.A. & Narváez, P.C., 2020. "Scale-up and cost analysis of biodiesel production using liquid-liquid film reactors: Reduction in the methanol consumption and investment cost," Energy, Elsevier, vol. 211(C).
    6. Kirilova, Elisaveta G. & Vladova, Rayka K. & Vaklieva-Bancheva, Natasha Gr, 2020. "Heat integration of two-stage autothermal thermophilic aerobic digestion system for reducing the impact of uncertainty," Energy, Elsevier, vol. 208(C).
    7. Jufri, Fauzan Hanif & Oh, Seongmun & Jung, Jaesung, 2019. "Development of Photovoltaic abnormal condition detection system using combined regression and Support Vector Machine," Energy, Elsevier, vol. 176(C), pages 457-467.
    8. Shi, Lingli & He, Yong & Lu, Jingsheng & Liang, Deqing, 2020. "Effect of dodecyl dimethyl benzyl ammonium chloride on CH4 hydrate growth and agglomeration in oil-water systems," Energy, Elsevier, vol. 212(C).
    9. Wang, Dan & Tang, Yu-Ting & He, Jun & Yang, Fei & Robinson, Darren, 2021. "Generalized models to predict the lower heating value (LHV) of municipal solid waste (MSW)," Energy, Elsevier, vol. 216(C).
    10. Xu, Yingjie & Mao, Chengbin & Huang, Yuangong & Shen, Xi & Xu, Xiaoxiao & Chen, Guangming, 2021. "Performance evaluation and multi-objective optimization of a low-temperature CO2 heat pump water heater based on artificial neural network and new economic analysis," Energy, Elsevier, vol. 216(C).
    11. Herrera, Gabriel Paes & Constantino, Michel & Tabak, Benjamin Miranda & Pistori, Hemerson & Su, Jen-Je & Naranpanawa, Athula, 2019. "Long-term forecast of energy commodities price using machine learning," Energy, Elsevier, vol. 179(C), pages 214-221.
    12. Ameur, Houari & Bouzit, Mohamed, 2013. "Power consumption for stirring shear thinning fluids by two-blade impeller," Energy, Elsevier, vol. 50(C), pages 326-332.
    13. Ameur, Houari, 2015. "Energy efficiency of different impellers in stirred tank reactors," Energy, Elsevier, vol. 93(P2), pages 1980-1988.
    14. Bi, Haobo & Wang, Chengxin & Lin, Qizhao & Jiang, Xuedan & Jiang, Chunlong & Bao, Lin, 2020. "Combustion behavior, kinetics, gas emission characteristics and artificial neural network modeling of coal gangue and biomass via TG-FTIR," Energy, Elsevier, vol. 213(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. Feng, Zongbao & Wu, Xianguo & Chen, Hongyu & Qin, Yawei & Zhang, Limao & Skibniewski, Miroslaw J., 2022. "An energy performance contracting parameter optimization method based on the response surface method: A case study of a metro in China," Energy, Elsevier, vol. 248(C).
    2. Dimitrios Mouchtaris & Emmanouil Sofianos & Periklis Gogas & Theophilos Papadimitriou, 2021. "Forecasting Natural Gas Spot Prices with Machine Learning," Energies, MDPI, vol. 14(18), pages 1-13, September.
    3. Wang, Jun & Xue, Qingwen & Guo, Ting & Mei, Zili & Long, Enshen & Wen, Qian & Huang, Wei & Luo, Tao & Huang, Ruyi, 2018. "A review on CFD simulating method for biogas fermentation material fluid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 64-73.
    4. Anand, Vishal, 2014. "Slip law effects on heat transfer and entropy generation of pressure driven flow of a power law fluid in a microchannel under uniform heat flux boundary condition," Energy, Elsevier, vol. 76(C), pages 716-732.
    5. Salmon, Aloïs & Marzo, Aitor & Polo, Jesús & Ballestrín, Jesús & Carra, Elena & Alonso-Montesinos, Joaquín, 2022. "World map of low-layer atmospheric extinction values for solar power tower plants projects," Renewable Energy, Elsevier, vol. 201(P1), pages 876-888.
    6. El Ibrahimi, Mohammed & Khay, Ismail & El Maakoul, Anas & Bakhouya, Mohamed, 2022. "Effects of the temperature range on the energy performance of mixed and unmixed digesters with submerged waste: An experimental and CFD simulation study," Renewable Energy, Elsevier, vol. 200(C), pages 1092-1104.
    7. Vlasopoulos, Antonis & Malinauskaite, Jurgita & Żabnieńska-Góra, Alina & Jouhara, Hussam, 2023. "Life cycle assessment of plastic waste and energy recovery," Energy, Elsevier, vol. 277(C).
    8. Miao, Hengyang & Wang, Zhiqing & Wang, Zhefan & Sun, Haochen & Li, Xiangyu & Liu, Zheyu & Dong, Libo & Zhao, Jiantao & Huang, Jiejie & Fang, Yitian, 2022. "Effects of Na2CO3/Na2SO4 on catalytic gasification reactivity and mineral structure of coal gangue," Energy, Elsevier, vol. 255(C).
    9. Qin Lu & Jingwen Liao & Kechi Chen & Yanhui Liang & Yu Lin, 2024. "Predicting Natural Gas Prices Based on a Novel Hybrid Model with Variational Mode Decomposition," Computational Economics, Springer;Society for Computational Economics, vol. 63(2), pages 639-678, February.
    10. Chen, Xiaoling & Zhang, Yongxing & Xu, Baoshen & Li, Yifan, 2022. "A simple model for estimation of higher heating value of oily sludge," Energy, Elsevier, vol. 239(PA).
    11. Tuo He & Dongjie Niu & Gan Chen & Fan Wu & Yu Chen, 2022. "Exploring Key Components of Municipal Solid Waste in Prediction of Moisture Content in Different Functional Areas Using Artificial Neural Network," Sustainability, MDPI, vol. 14(23), pages 1-14, November.
    12. Fathy, Ahmed & Babu, Thanikanti Sudhakar & Abdelkareem, Mohammad Ali & Rezk, Hegazy & Yousri, Dalia, 2022. "Recent approach based heterogeneous comprehensive learning Archimedes optimization algorithm for identifying the optimal parameters of different fuel cells," Energy, Elsevier, vol. 248(C).
    13. Ahmed Al-Zahrani, 2023. "Investigating New Environmentally Friendly Zeotropic Refrigerants as Possible Replacements for Carbon Dioxide (CO 2 ) in Car Air Conditioners," Sustainability, MDPI, vol. 16(1), pages 1-28, December.
    14. Foroutan, Shima & Mohsenzade, Hanie & Dashti, Ali & Roosta, Hadi, 2021. "New insights into the evaluation of kinetic hydrate inhibitors and energy consumption in rocking and stirred cells," Energy, Elsevier, vol. 218(C).
    15. Ge, Lichao & Zhao, Can & Chen, Simo & Li, Qian & Zhou, Tianhong & Jiang, Han & Li, Xi & Wang, Yang & Xu, Chang, 2022. "An analysis of the carbonization process and volatile-release characteristics of coal-based activated carbon," Energy, Elsevier, vol. 257(C).
    16. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    17. Yin, Kaili & Zhang, Xiaojing & Xie, Jingchao & Hao, Ziyang & Xiao, Guofeng & Liu, Jiaping, 2023. "Modeling hourly solar diffuse fraction on a horizontal surface based on sky conditions clustering," Energy, Elsevier, vol. 272(C).
    18. Long, Zhen & Zhou, Xuebing & Lu, Zhilin & Liang, Deqing, 2022. "Kinetic inhibition performance of N-vinyl caprolactam/isopropylacrylamide copolymers on methane hydrate formation," Energy, Elsevier, vol. 242(C).
    19. Buta Singh & Narinder Singh & Zsolt Čonka & Michal Kolcun & Zoltán Siménfalvi & Zsolt Péter & Zoltán Szamosi, 2021. "Critical Analysis of Methods Adopted for Evaluation of Mixing Efficiency in an Anaerobic Digester," Sustainability, MDPI, vol. 13(12), pages 1-27, June.
    20. Hajirahimi, Zahra & Khashei, Mehdi & Etemadi, Sepideh, 2022. "A novel class of reliability-based parallel hybridization (RPH) models for time series forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 156(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:240:y:2022:i:c:s0360544221027237. 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.