IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v165y2018ipbp836-845.html
   My bibliography  Save this article

Application of artificial neural network method for prediction of osmotic pretreatment based on the energy and exergy analyses in microwave drying of orange slices

Author

Listed:
  • Azadbakht, Mohsen
  • Torshizi, Mohammad Vahedi
  • Noshad, Fatemeh
  • Rokhbin, Arash

Abstract

In the present study, artificial neural network (ANN) method was applied for predicting osmotic pretreatment based on the energy and exergy analyses in microwave drying of orange slices. For this purpose, the oranges were cut into slices with a thickness of 4 mm and treated with salt (NaCl) and distilled water solution (7% by weight) for 30, 60, and 90 min as osmosis pre-treatment. Then, they were dried in three replicates using a microwave dryer and at three powers of 90, 360, and 900 W. The statistical analysis results showed that the osmosis time is significant for the energy efficiency and exergy efficiency and specific exergy loss at 1% level. The highest energy and exergy efficiency was observed at 900 W and in the osmosis time of 90 min. The highest energy and exergy efficiency was observed at 42.1% and 31.08%, respectively. The maximum exergy loss was seen at 360 W and osmosis time of 60 min. The osmosis time did not affect the specific energy loss. The microwave power was statistically significant for all the parameters (energy and exergy) such that with increasing the microwave power, the energy and exergy efficiency increased, while the specific exergy and energy loss decreased. Overall, with increasing osmosis time and microwave power, the energy and exergy levels of the microwave dryer increased. The maximum coefficient of determination (R2) in a network containing 6 neurons in the hidden layer was 0.999 for energy efficiency, R2 = 0.871 for specific energy loss, R2 = 0.999 for specific exergy loss, and R2 = 0.972 for exergy efficiency. This amount was seen in a network containing 4 neurons in the hidden layer.

Suggested Citation

  • Azadbakht, Mohsen & Torshizi, Mohammad Vahedi & Noshad, Fatemeh & Rokhbin, Arash, 2018. "Application of artificial neural network method for prediction of osmotic pretreatment based on the energy and exergy analyses in microwave drying of orange slices," Energy, Elsevier, vol. 165(PB), pages 836-845.
    • Handle: RePEc:eee:energy:v:165:y:2018:i:pb:p:836-845
    DOI: 10.1016/j.energy.2018.10.017
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218319984
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2018.10.017?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. Sarker, Md. Sazzat Hossain & Ibrahim, Mohd Nordin & Abdul Aziz, Norashikin & Punan, Mohd Salleh, 2015. "Energy and exergy analysis of industrial fluidized bed drying of paddy," Energy, Elsevier, vol. 84(C), pages 131-138.
    2. Koukouch, Abdelghani & Idlimam, Ali & Asbik, Mohamed & Sarh, Brahim & Izrar, Boujemaa & Bostyn, Stéphane & Bah, Abdellah & Ansari, Omar & Zegaoui, Omar & Amine, Amina, 2017. "Experimental determination of the effective moisture diffusivity and activation energy during convective solar drying of olive pomace waste," Renewable Energy, Elsevier, vol. 101(C), pages 565-574.
    3. Sharma, G.P. & Prasad, Suresh, 2006. "Specific energy consumption in microwave drying of garlic cloves," Energy, Elsevier, vol. 31(12), pages 1921-1926.
    4. Yogendrasasidhar, D. & Pydi Setty, Y., 2018. "Drying kinetics, exergy and energy analyses of Kodo millet grains and Fenugreek seeds using wall heated fluidized bed dryer," Energy, Elsevier, vol. 151(C), pages 799-811.
    5. Azadbakht, Mohsen & Aghili, Hajar & Ziaratban, Armin & Torshizi, Mohammad Vahedi, 2017. "Application of artificial neural network method to exergy and energy analyses of fluidized bed dryer for potato cubes," Energy, Elsevier, vol. 120(C), pages 947-958.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Safoura Zadhossein & Yousef Abbaspour-Gilandeh & Mohammad Kaveh & Mariusz Szymanek & Esmail Khalife & Olusegun D. Samuel & Milad Amiri & Jacek Dziwulski, 2021. "Exergy and Energy Analyses of Microwave Dryer for Cantaloupe Slice and Prediction of Thermodynamic Parameters Using ANN and ANFIS Algorithms," Energies, MDPI, vol. 14(16), pages 1-19, August.

    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. Liu, Zi-Liang & Zielinska, Magdalena & Yang, Xu-Hai & Yu, Xian-Long & Chen, Chang & Wang, Hui & Wang, Jun & Pan, Zhongli & Xiao, Hong-Wei, 2021. "Moisturizing strategy for enhanced convective drying of mushroom slices," Renewable Energy, Elsevier, vol. 172(C), pages 728-739.
    2. Andrea Aquino & Pietro Poesio, 2021. "Off-Design Exergy Analysis of Convective Drying Using a Two-Phase Multispecies Model," Energies, MDPI, vol. 14(1), pages 1-36, January.
    3. Bin Li & Changyou Li & Tao Li & Zhiheng Zeng & Wenyan Ou & Chengjie Li, 2019. "Exergetic, Energetic, and Quality Performance Evaluation of Paddy Drying in a Novel Industrial Multi-Field Synergistic Dryer," Energies, MDPI, vol. 12(23), pages 1-19, December.
    4. El Hallaoui, Zhor & El Hamdani, Fayrouz & Vaudreuil, Sébastien & Bounahmidi, Tijani & Abderafi, Souad, 2022. "Identifying the optimum operating conditions for the integration of a solar loop to power an industrial flash dryer: Combining an exergy analysis with genetic algorithm optimization," Renewable Energy, Elsevier, vol. 191(C), pages 828-841.
    5. Abiodun Okunola & Timothy Adekanye & Endurance Idahosa, 2021. "Energy and exergy analyses of okra drying process in a forced convection cabinet dryer," Research in Agricultural Engineering, Czech Academy of Agricultural Sciences, vol. 67(1), pages 8-16.
    6. Dario Giuseppe Urbano & Andrea Aquino & Flavio Scrucca, 2023. "Energy Performance, Environmental Impacts and Costs of a Drying System: Life Cycle Analysis of Conventional and Heat Recovery Scenarios," Energies, MDPI, vol. 16(3), pages 1-12, February.
    7. Erick César, López-Vidaña & Ana Lilia, César-Munguía & Octavio, García-Valladares & Orlando, Salgado Sandoval & Alfredo, Domínguez Niño, 2021. "Energy and exergy analyses of a mixed-mode solar dryer of pear slices (Pyrus communis L)," Energy, Elsevier, vol. 220(C).
    8. Saurabh Sharma & Vijay Kumar Gahlawat & Kumar Rahul & Rahul S Mor & Mohit Malik, 2021. "Sustainable Innovations in the Food Industry through Artificial Intelligence and Big Data Analytics," Logistics, MDPI, vol. 5(4), pages 1-16, September.
    9. Bhattacharya, Madhuchhanda & Basak, Tanmay, 2013. "A theoretical study on the use of microwaves in reducing energy consumption for an endothermic reaction: Role of metal coated bounding surface," Energy, Elsevier, vol. 55(C), pages 278-294.
    10. Zahra Parhizi & Hamed Karami & Iman Golpour & Mohammad Kaveh & Mariusz Szymanek & Ana M. Blanco-Marigorta & José Daniel Marcos & Esmail Khalife & Stanisław Skowron & Nashwan Adnan Othman & Yousef Darv, 2022. "Modeling and Optimization of Energy and Exergy Parameters of a Hybrid-Solar Dryer for Basil Leaf Drying Using RSM," Sustainability, MDPI, vol. 14(14), pages 1-27, July.
    11. Di Marco, Paolo & Frigo, Stefano & Gabbrielli, Roberto & Pecchia, Stefano, 2016. "Mathematical modelling and energy performance assessment of air impingement drying systems for the production of tissue paper," Energy, Elsevier, vol. 114(C), pages 201-213.
    12. Dhayaneswaran, Y. & Ashok Kumar, L., 2014. "A study on current characteristics of induction motor while operating at its base frequency in textile industry," Energy, Elsevier, vol. 74(C), pages 340-345.
    13. Şöhret, Yasin & Dinç, Ali & Karakoç, T. Hikmet, 2015. "Exergy analysis of a turbofan engine for an unmanned aerial vehicle during a surveillance mission," Energy, Elsevier, vol. 93(P1), pages 716-729.
    14. Acevedo, Luis & Usón, Sergio & Uche, Javier, 2014. "Exergy transfer analysis of microwave heating systems," Energy, Elsevier, vol. 68(C), pages 349-363.
    15. Zheng, Ying & Cai, Jiu-ju & Dong, Hui & Feng, Jun-sheng & Liu, Jing-yu, 2019. "Experimental investigation of volumetric exergy transfer coefficient in vertical moving bed for sinter waste heat recovery," Energy, Elsevier, vol. 167(C), pages 428-439.
    16. Erbay, Zafer & Hepbasli, Arif, 2017. "Assessment of cost sources and improvement potentials of a ground-source heat pump food drying system through advanced exergoeconomic analysis method," Energy, Elsevier, vol. 127(C), pages 502-515.
    17. Feng, Jun-sheng & Dong, Hui & Gao, Jian-ye & Liu, Jing-yu & Liang, Kai, 2016. "Exergy transfer characteristics of gas-solid heat transfer through sinter bed layer in vertical tank," Energy, Elsevier, vol. 111(C), pages 154-164.
    18. Anubhav Pratap Singh & Ronit Mandal & Maryam Shojaei & Anika Singh & Przemysław Łukasz Kowalczewski & Marta Ligaj & Jarosław Pawlicz & Maciej Jarzębski, 2020. "Novel Drying Methods for Sustainable Upcycling of Brewers’ Spent Grains as a Plant Protein Source," Sustainability, MDPI, vol. 12(9), pages 1-17, May.
    19. Samimi-Akhijahani, Hadi & Arabhosseini, Akbar, 2018. "Accelerating drying process of tomato slices in a PV-assisted solar dryer using a sun tracking system," Renewable Energy, Elsevier, vol. 123(C), pages 428-438.
    20. Beigi, Mohsen & Tohidi, Mojtaba & Torki-Harchegani, Mehdi, 2017. "Exergetic analysis of deep-bed drying of rough rice in a convective dryer," Energy, Elsevier, vol. 140(P1), pages 374-382.

    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:165:y:2018:i:pb:p:836-845. 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.