823
Views
6
CrossRef citations to date
0
Altmetric
Research Article

Experimental and Modeling Study of Peanut Drying in a Solar Dryer with a Novel Type of a Drying Chamber

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 5586-5609 | Received 16 Mar 2021, Accepted 23 Aug 2021, Published online: 14 Sep 2021

References

  • Abakar, K. A., and C. Yu. 2014. Performance of SVM based on PUK kernel in comparison to SVM based on RBF kernel in prediction of yarn tenacity. Indian Journal of Fibre & Textile Research 39:55–59.
  • Akman, H. 2018. Thermodynamic Analysis of a Solar Energy Assisted Drying System. Osmaniye Korkut Ata University, Institude of Natural and Applied Sciences, MSc Thesis (in Turkish).
  • Akman, H., K. N. Çerçi, E. Hürdoğan, and O. Büyükalaca. 2018. Design and Manufacture of a Solar Energy Assisted Drying System and Evaluation of First Experiment Results. Osmaniye Korkut Ata Univ. J. Nat. Appl. Sci 1:1–9.
  • Akpinar, E. K. 2004. Experimental determination of convective heat transfer coefficient of some agricultural products in forced convection drying. International Communications in Heat and Mass Transfer 31 (4):585–95. doi:10.1016/S0735-1933(04)00038-7.
  • Akpinar, E. K., and S. Toraman. 2016. Determination of drying kinetics and convective heat transfer coefficients of ginger slices. Heat and Mass Transfer/Waerme- Und Stoffuebertragung 52 (10):2271–81. doi:10.1007/s00231-015-1729-6.
  • Anwar, S. I., and G. N. Tiwari. 2001a. Convective heat transfer coefficient of crops in forced convection drying – An experimental study. Energy Conversion and Management 42 (14):1687–98. doi:10.1016/S0196-8904(00)00160-6.
  • Anwar, S. I., and G. N. Tiwari. 2001b. Evaluation of convective heat transfer coefficient in crop drying under open sun drying conditions. Energy Conversion and Management 42 (5):627–37. doi:10.1016/S0196-8904(00)00065-0.
  • Babu, A. K., G. Kumaresan, V. Antony Aroul Raj, and R. Velraj. 2020. CFD studies on different configurations of drying chamber for thin-layer drying of leaves. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 42 (18):2227–39. doi:10.1080/15567036.2019.1607935.
  • Beigi, M. 2017. Mathematical modelling and determination of mass transfer characteristics of celeriac slices under vacuum drying. Periodica Polytechnica Chemical Engineering 61 (2):109–16. doi:10.3311/PPch.9271.
  • Bruce, D. M. 1985. Exposed-layer barley drying: Three models fitted to new data up to 150°C. Journal of Agricultural Engineering Research 32 (4):337–48. doi:10.1016/0021-8634(85)90098-8.
  • Çerçi, K. N., Ö. Sufer, M. Söyler, E. Hüdroğan, and C. Özalp. 2018. Thin layer drying of zucchini in solar dryer located in Osmaniye region. Tehnicki Glasnik 12 (2):79–85. doi:10.31803/tg-20180126094515.
  • Çerçi, K. N., and M. Daş. 2019. Modeling of Heat Transfer Coefficient in Solar Greenhouse Type Drying Systems. Sustainability 11 (18):5127. doi:10.3390/su11185127.
  • Chapaneri, S., R. Lopes, and D. Jayaswal. 2015. Evaluation of Music Features for PUK Kernel based Genre Classification. Procedia Computer Science 45:186–96. doi:10.1016/j.procs.2015.03.119.
  • Chioma, O. P., C. D. Chukwunonye, and N. G. Ifeanyichukwu. 2018. The Mathematical Modelling of the Effects of Thin Layer Drying of Groundnut (Kerstigiella geocarpa harms). Greener Journal of Science, Engineering and Technological Research 8 (3):22–32. doi:10.15580/GJSETR.2018.3.062818070.
  • Corzo, O., N. Bracho, A. Pereira, and A. Vásquez. 2008. Weibull distribution for modeling air drying of coroba slices. LWT - Food Science and Technology 41 (10):2023–28. doi:10.1016/j.lwt.2008.01.002.
  • Csépe, Z., L. Makra, D. Voukantsis, I. Matyasovszky, G. Tusnády, K. Karatzas, and M. Thibaudon. 2014. Science of the Total Environment Predicting daily ragweed pollen concentrations using Computational Intelligence techniques over two heavily polluted areas in Europe. Science of the Total Environment, The 42:1687–98. doi:10.1016/S0196-8904(00)00160-6.
  • Daş, M., E. Alıç, and E. K. Akpinar. 2021. Numerical and experimental analysis of heat and mass transfer in the drying process of the solar drying system. Engineering Science and Technology, an International Journal 24 (1):236–46. doi:10.1016/j.jestch.2020.10.003.
  • Dalvand, M. J., S. S. Mohtasebi, and S. Rafiee. 2012. Determining the influence of drying conditions on EHD drying process. Journal of Agricultural and Biological Science 7:396–401.
  • Darabi, H., A. Zomorodian, M. H. Akbari, and A. N. Lorestani. 2015. Design a cabinet dryer with two geometric configurations using CFD. Journal of Food Science and Technology 52 (1):359–66. doi:10.1007/s13197-013-0983-1.
  • Devan, P. K., C. Bibin, I. Asburris Shabrin, R. Gokulnath, and D. Karthick. 2020. Solar drying of fruits – A comprehensive review. Materials Today: Proceedings 33:253–60. doi:10.1016/j.matpr.2020.04.041.
  • Ding, S., N. Zhang, X. Zhang, and F. Wu. 2017. Twin support vector machine: Theory, algorithm and applications. Neural Computing & Applications 28 (11):3119–30. doi:10.1007/s00521-016-2245-4.
  • Ekka, J. P., and M. Palanisamy. 2020. Determination of heat transfer coefficients and drying kinetics of red chilli dried in a forced convection mixed mode solar dryer. Thermal Science and Engineering Progress 19:100607. doi:10.1016/j.tsep.2020.100607.
  • El Hage, H., A. Herez, M. Ramadan, H. Bazzi, and M. Khaled. 2018. An investigation on solar drying: A review with economic and environmental assessment. Energy 157:815–29. doi:10.1016/j.energy.2018.05.197.
  • ELkhadraoui, A., S. Kooli, I. Hamdi, and A. Farhat. 2015. Experimental investigation and economic evaluation of a new mixed-mode solar greenhouse dryer for drying of red pepper and grape. Renewable Energy 77:1–8. doi:10.1016/j.renene.2014.11.090.
  • Goyal, R. K., and G. N. Tiwari. 1998. Heat and mass transfer relations for crop drying. Drying Technology 16 (8):1741–54. doi:10.1080/07373939808917490.
  • Guo, H., J. Zhao, J. Yin, and L. Yao. 2018. Structural testing of polyimide nanocomposite films with SAXS and SVM-PUK. Polymer Testing 70 (May):30–38. doi:10.1016/j.polymertesting.2018.06.025.
  • Henderson, S. M. 1974. Progress in Developing the Thin Layer Drying Equation. Transactions of the ASAE 17 (6):1–3. doi:10.1016/j.polymertesting.2018.06.025.
  • Henderson, S. M., and S. Pabis. 1961. Grain drying theory I: Temperature effect on drying coefficient. Journal of Agricultural Engineering Research 6:169–74.
  • Hii, C. L., C. L. Law, and M. Cloke. 2009. Modeling using a new thin layer drying model and product quality of cocoa. Journal of Food Engineering 90 (2):191–98. doi:10.1016/j.jfoodeng.2008.06.022.
  • Holman, J. P. 2001. Experimental methods for engineers: 8th ed.Singapore:McGraw Hill.
  • Houda, S., R. Belarbi, and N. Zemmouri. 2017. A CFD Comsol model for simulating complex urban flow. Energy Procedia 139:373–78. doi:10.1016/j.egypro.2017.11.224.
  • Kalmegh, S. 2015. Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News. International Journal of Innovative Science, Engineering & Technology 2 (2):438–46.
  • Kaveh, M., R. Amiri, C. Ali, and M. Nikbakht. 2017. Mass transfer characteristics of eggplant slices during length of continuous band dryer. Heat and Mass Transfer 53 (6):2045–59. doi:10.1007/s00231-016-1961-8.
  • Kooli, S., A. Fadhel, A. Farhat, and A. Belghith. 2007. Drying of red pepper in open sun and greenhouse conditions Mathematical modeling and experimental validation. Journal of Food Engineering 79 (3):1094–103. doi:10.1016/j.jfoodeng.2006.03.025.
  • Kumar, A., and G. N. Tiwari. 2007. Effect of mass on convective mass transfer coefficient during open sun and greenhouse drying of onion flakes. Journal of Food Engineering 79 (4):1337–50. doi:10.1016/j.jfoodeng.2006.04.026.
  • Liu, M., M. Wang, J. Wang, and D. Li. 2013. Comparison of random forest, support vector machine and back propagation neural network for electronic tongue data classification: Application to the recognition of orange beverage and Chinese vinegar. Sensors and Actuators. B, Chemical 177:970–80. doi:10.1016/j.snb.2012.11.071.
  • Midilli, A., H. Kucuk, and Z. Yapar. 2002. A new model for single-layer drying. Drying Technology 20 (7):1503–13. doi:10.1081/DRT-120005864.
  • Onwude, D. I., N. Hashim, R. B. Janius, N. M. Nawi, and K. Abdan. 2016. Modeling the Thin-Layer Drying of Fruits and Vegetables: A Review. Comprehensive Reviews in Food Science and Food Safety 15 (3):599–618. doi:10.1111/1541-4337.12196.
  • Page, G. (1949). Factors influencing the maximum rates of air drying shelled corn in thin layers. MSc Thesis, Department of Mechanical Engineering. Purdue University, USA.
  • Pal, M., and P. M. Mather. 2003. An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment 86 (4):554–65. doi:10.1016/S0034-4257(03)00132-9.
  • Patil, R., and R. Gawande. 2016. A review on solar tunnel greenhouse drying system. Renewable and Sustainable Energy Reviews 56:196–214. doi:10.1016/j.rser.2015.11.057.
  • Ponkham, K., N. Meeso, S. Soponronnarit, and S. Siriamornpun. 2012. Modeling of combined far-infrared radiation and air drying of a ring shaped-pineapple with/without shrinkage. Food and Bioproducts Processing 90 (2):155–64. doi:10.1016/j.fbp.2011.02.008.
  • Prestes, F. S., A. A. M. Pereira, A. C. M. Silva, P. O. Pena, and M. S. Nascimento. 2019. Effects of peanut drying and blanching on Salmonella spp. Food Research International 119:411–16. doi:10.1016/j.foodres.2019.02.017.
  • Quinlan, J. R. 1992. Learning with continuous classes. Proceedings 5th Australian Joint Conference on Artificial Intellegence, World Scientific, 343–48. Hobart, Tasmania.
  • Sahdev, R. K., M. Kumar, and A. K. Dhingra. 2017. Development of empirical expression for thin layer groundnut drying under open sun and forced convection modes. Agricultural Engineering International: CIGR Journal 19 (4):152–58.
  • Sanghi, A., R. K. Ambrose, and D. Maier. 2018. CFD simulation of corn drying in a natural convection solar dryer. Drying Technology 36 (7):859–70. doi:10.1080/07373937.2017.1359622.
  • Sethi, V. P., and M. Dhiman. 2020. Design, space optimization and modelling of solar-cum-biomass hybrid greenhouse crop dryer using flue gas heat transfer pipe network. Solar Energy 206:120–35. doi:10.1016/j.solener.2020.06.006.
  • Süfer, Ö., S. Sezer, and H. Demir. 2017. Thin layer mathematical modeling of convective, vacuum and microwave drying of intact and brined onion slices. Journal of Food Processing and Preservation 41 (6):6. doi:10.1111/jfpp.13239.
  • Togrul, I. T., and D. Pehlivan. 2002. Mathematical modelling of solar drying of apricots in thin layers. Journal of Food Engineering 55 (3):209–16. doi:10.1016/S0260-8774(02)00065-1.
  • Wang, Z., N. Zhao, W. Wang, R. Tang, and S. Li. 2015. A Fault Diagnosis Approach for Gas Turbine Exhaust Gas Temperature Based on Fuzzy C-Means Clustering and Support Vector Machine. Mathematical Problems in Engineering (2015:1–11. doi:10.1155/2015/240267.
  • White, G. M., I. J. Ross, and C. G. Poneleit. 1981. Fully-Exposed Drying of Popcorn. Transactions of the ASAE 24 (2):466–68. doi:10.13031/2013.34276.
  • Yabanova, İ., and M. Yumurtacı. 2018. Classification of dynamic egg weight using support vector machine. Journal of the Faculty of Engineering and Architecture of Gazi University 33:2 (2018):393–402. doi:10.17341/gazimmfd.416348.
  • Yin, S., and J. Yin. 2016. Tuning kernel parameters for SVM based on expected square distance ratio. Information Sciences 370–371:370–71. doi:10.1016/j.ins.2016.07.047.
  • Yu, Z., K. Yousaf, M. Ahmad, M. Yousaf, Q. Gao, and K. Chen. 2020. Efficient pyrolysis of ginkgo biloba leaf residue and pharmaceutical sludge (mixture) with high production of clean energy: Process optimization by particle swarm optimization and gradient boosting decision tree algorithm. Bioresource Technology 304:123020. doi:10.1016/j.biortech.2020.123020.
  • Zeng, S., Z. Du, W. Lv, D. Li, D. Su, and H. Lv. 2021. Experimental study on the hygrothermal dynamics of peanut (Arachis hypogaea Linn.) in the process of superposition and variable temperature drying. Drying Technology 1–17. https://doi.org/10.1080/07373937.2021.1873359
  • Zhang, B., J. Zhu, and L. Gao. 2020. Topology optimization design of nanofluid-cooled microchannel heat sink with temperature-dependent fluid properties. Applied Thermal Engineering 176:115354. doi:10.1016/j.applthermaleng.2020.115354.
  • Zhang, S., L. Zhou, B. Ling, and S. Wang. 2016. Dielectric properties of peanut kernels associated with microwave and radio frequency drying. Biosystems Engineering 145:108–17. doi:10.1016/j.biosystemseng.2016.03.002.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.