Drying of mushroom slices in a new type solar drying system and under open sun: Experimental and mathematical investigation

Year 2022, Volume: 6 Issue: 4, 221 - 232, 20.12.2022

Kamil Neyfel Çerçi , Doğan Burak Saydam , Ertaç Hürdoğan

https://doi.org/10.26701/ems.1144456

Cited By: 3

Abstract

Drying is among the beneficial food preservation strategies and this method ensures food products last before they reach consumers. The most used drying method is direct drying under the sun. However, in this method, the negative effects of the external environment damage food products. Recently, solar drying systems have been the main subject of much research as they have been protecting food from the negative effects of the external environment. In this study, a solar drying system (SD), which have a drying chamber with different structure, was used for drying mushroom. At the same time, mushroom slices were dried under open sun (OSD) for observing the performance of drying system. Drying rate (DR) and moisture ratio (MR) values were determined from the experiments. In addition, the MR values obtained from the experiments were estimated by 6 different mathematical models and 6 different machine learning algorithms. According to the results of the experiments, the drying time of the mushroom slices using SD was approximately 12.4 hours less than the drying time under open sun. The best convergence in the results gathered from the mathematical models is Sripinyowanich and Noomhorn and Hii et al. models for SD and OSD, respectively. The best estimation for MR values was realized by the Multilayer Perception algorithm for both drying methods.

Keywords

Drying, Solar Energy, Open Sun, Machine Learning Algorithm, Thin Layer Mathematical Modeling, Mushroom

Supporting Institution

Osmaniye Korkut Ata University, Scientific Research Projects Unit

Project Number

OKUBAP-2014-PT3-032

Thanks

This work was supported by the Osmaniye Korkut Ata University, Scientific Research Projects Unit (OKUBAP) with a project number of OKUBAP-2014-PT3-032. We would like to thank OKÜBAP for their support.

References

• [1] Argyropoulos, D., Heindl, A., Müller, J. (2011). Assessment of convection, hot-air combined with micro-wavevacuum and freeze-drying methods for mushrooms with regard to product quality. International Journal of Food Science and Technology, 333–342, https://doi.org/10.1111/j.1365-2621.2010.02500.x.
• [2] Boroze, T., Desmorieux, H., Méot, J. M., Marouzé, C., Azouma, Y., Napo, K. (2014). Inventory and comparative characteristics of dryers used in the sub-Saharan zone: Criteria influencing dryer choice. Renewable and Sustainable Energy Reviews, 40, 1240-1259, https://doi.org/10.1016/j.rser.2014.07.058.
• [3] Moses, J., Norton, T., Alagusundaram, K., Tiwari, B. (2014). Novel Drying Techniques for the Food Industry. Food Enginering Review, 43-55, DOI 10.1007/s12393-014-9078-7.
• [4] Hnin, K. K., Zhang, M., Mujumdar, A. S., Zhu, Y. (2018). Emerging food drying technologies with energy-saving characteristics: A review. Drying Technology, 1465-1480, https://doi.org/10.1080/07373937.2018.1510417.
• [5] Uthpala, T. G. G., Navaratne, S. B., Thibbotuwawa, A. (2020). Review on low‐temperature heat pump drying applications in food industry: Cooling with dehumidification drying method. Journal of Food Process Engineering, 43(10), e13502, https://doi.org/10.1111/jfpe.13502.
• [6] Duan, X., Yang, X., Ren, G., Pang, Y., Liu, L., Liu, Y. (2016). Technical aspects in freeze-drying of foods. Drying Technology, 34(11), 1271-1285, https://doi.org/10.1080/07373937.2015.1099545.
• [7] Mustayen, A., Mekhilef, S., Saidur, R. (2014). Performance studyofdifferentsolardryers:A review. Renewable and Sustainable Energy Reviews , 463–470, https://doi.org/10.1016/j.rser.2014.03.020.
• [8] Tiwari, S., & Tiwari, G. N. (2016). Exergoeconomic analysis of photovoltaic-thermal (PVT) mixed mode greenhouse solar dryer. Energy, 114, 155-164, https://doi.org/10.1016/j.energy.2016.07.132.
• [9] VijayaVenkataRaman, S., Iniyan, S., & Goic, R. (2012). A review of solar drying technologies. Renewable and sustainable energy reviews, 16(5), 2652-2670, https://doi.org/10.1016/j.rser.2012.01.007.
• [10] Stritih, U., Osterman, E., Evliya, H., Butala, V., Paksoy, H. (2013). Exploiting solar energy potential through thermal energy storage in Slovenia and Turkey. Renewable and Sustainable Energy Reviews, 442–461, https://doi.org/10.1016/j.rser.2013.04.020.
• [11] Mugi, V. R., Das, P., Balijepalli, R., & Chandramohan, V. P. (2022). A review of natural energy storage materials used in solar dryers for food drying applications. Journal of Energy Storage, 49, 104198, https://doi.org/10.1016/j.est.2022.104198.
• [12] Olmuş, U., Güzelel, Y. E., Pınar, E., Özbek, A., Büyükalaca, O. (2022). Performance assessment of a desiccant air-conditioning system combined with dew-point indirect evaporative cooler and PV/T. Solar Energy, 231, 566-577, https://doi.org/10.1016/j.solener.2021.12.004.
• [13] Kutlu, C., Erdinc, M. T., Li, J., Wang, Y., Su, Y. (2019). A study on heat storage sizing and flow control for a domestic scale solar-powered organic Rankine cycle-vapour compression refrigeration system. Renewable Energy, 143, 301-312, https://doi.org/10.1016/j.renene.2019.05.017.
• [14] Srinivasan, G., Muthukumar, P. (2021). A review on solar greenhouse dryer: Design, thermal modelling, energy, economic and environmental aspects. Solar Energy, 229, 3-21, https://doi.org/10.1016/j.solener.2021.04.058.
• [15] Wang, H., Zhang, M., Mujumdar, A. S. (2014). Comparison of Three New Drying Methods for Drying Characteristics and Quality of Shiitake Mushroom (Lentinus edodes). Drying Technology, 1791–1802, https://doi.org/10.1080/07373937.2014.947426.
• [16] Fudholi, A., Sopian, K. (2019). A review of solar air flat plate collector for drying application. Renewable and Sustainable Energy Reviews , 333–345, https://doi.org/10.1016/j.rser.2018.12.032.
• [17] Aktaş, M., Şevik, S., Özdemir, M. B., Gönen, E., (2015). Performance analysis and modeling of a closed-loop heat pump dryer for bay leaves using artificial neural network. Applied Thermal Engineering, 87, 714-723. http://dx.doi.org/ 10.1016/j.applthermaleng.2015.05.049.
• [18] Doymaz, İ. (2011). Thin-layer drying characteristics of sweet potato slices and mathematical modelling. Heat Mass Transfer, 47, 277–285. http://dx.doi.org/ 10.1007/s00231-010-0722-3.
• [19] Kooli. S., Fadhel. A., Farhat. A., Belghith. A. (2007). Drying of red pepper in open sun and greenhouse conditions Mathematical modeling and experimental validation. Journal of Food Engineering 79 (2007) 1094–1103, https://doi.org/10.1016/j.jfoodeng.2006.03.025.
• [20] Chokphoemphuna, S., Chokphoemphunb A. (2018). Moisture content prediction of paddy drying in a fluidized-bed drier with a vortex flow generator using an artificial neural network. Applied Thermal Engineering, 145, 630–636. http://dx.doi.org/ 10.1016/j.applthermaleng.2018.09.087.
• [21] Süfer, Ö., Sezer, S., Demir, H. (2017). Thin layer mathematical modeling of convective, vacuum and microwave drying of intact and brined onion slices. Journal Of Food Processıng and Preservatıon, 41, 1-13. http://dx.doi.org/ 10.1111/jfpp.13239.
• [22] Çerçi, K., N., Süfer, Ö., Söyler , M., Hürdoğan , E., Özalp , C. (2018). Thın Layer Dryıng of Zucchını In Solar Dryer Located In Osmanıye Regıon, Tehnıčkı Glasnık, 12, 79-85, http://dx.doi.org/ 10.31803/tg-20180126094515.
• [23] Akman, H. (2017). Thermodynamic Analysis of a Solar Energy Assisted Drying System, MSc Thesis (in Turkish) , Osmaniye Korkut Ata University, Osmaniye.
• [24] Hürdoğan, E., Çerçi, K. N., Saydam, D. B., Ozalp, C. (2022). Experimental and modeling study of peanut drying in a solar dryer with a novel type of a drying chamber. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(2), 5586-5609, https://doi.org/10.1080/15567036.2021.1974126.
• [25] Holman JP. (2001). Experimental methods for engineers. 8th ed. McGraw Hill.
• [26] Kavak Akpinar, E., Toraman, S., (2016). Determination of drying kinetics and convective heat transfer coefficients of ginger slices. Heat Mass Transfer, 52, 2271–2281. http://dx.doi.org/ 10.1007/s00231-015-1729-6.
• [27] Hii, C. L., Law, C. L., Cloke, W. (2009). Modeling using a new thin layer drying model and product quality of cocoa. Journal of Food Engineering, 90, 191-198. https://doi.org/10.1016/j.jfoodeng.2008.06.022
• [28] Figiel, A. (2009). Drying kinetics and quality of vacuum-microwave dehydrated garlic cloves and slices. Journal of Food Engineering, 94, 98-104. https://doi.org/10.1016/j.jfoodeng.2009.03.007
• [29] Erbay, Z. Icier, F., (2010). A review of thin-layer drying of foods: theory, modeling, and experimental results. Critical Reviews in Food Science and Nutrition, 50, 441-464. https://doi.org/10.1080/10408390802437063
• [30] Sripinyowanich, J. Noomhorm, A. (2011). A new model and quality of unfrozen and frozen cooked rice dried in a microwave vibro-fluidized bed dryer. Drying Technology, 29, 735-748. https://doi.org/10.1080/07373937.2010.535399.
• [31] Noomhorn, A. Verma, L. R. (1986). Generalized single-layer rice drying models. Transactions of the ASAE, 29, 587-591. https://doi.org/10.13031/2013.30194.
• [32] Yaldız, O., Ertekin, C., (2001). Thin layer solar drying of some vegetables. Drying Technology, 19, 583–596. http://dx.doi.org/ 10.1081/DRT-100103936.
• [33] Sun, Y., Zhang, M., Mujumdar, A. S., & Yu, D. (2021). Pulse-spouted microwave freeze drying of raspberry: Control of moisture using ANN model aided by LF-NMR. Journal of Food Engineering, 292, 110354, https://doi.org/10.1016/j.jfoodeng.2020.110354.
• [34] Şanlitürk, E. (2018). Prediction of Defective Product with Machine Learning Algorithms, MSc Thesis (in Turkish) Istanbul: Istanbul Teknik University.
• [35] Ayhan, S., Erdoğmuş, Ş. (2014). Kernel Function Selection for the Solution of Classification Problems via Support Vector Machines. Eskişehir Osmangazi University Journal of Economics and Administrative Sciences, 9(1), 175-201.
• [36] Deka, P. C. (2014). Support vector machine applications in the field of hydrology: a review. Applied soft computing, 19, 372-386, https://doi.org/10.1016/j.asoc.2014.02.002.
• [37] Henderson, S. M. Pabis, S. (1961). Grain drying theory I: temperature effect on drying coefficient. Journal of Agricultural Engineering Research, 6, 169-74.
• [38] Karimipour, A., Bagherzadeh, S., A., Taghipour, A., Abdollahi, A., Safae, M., R. (2019). A novel nonlinear regression model of SVR as a substitute for ANN to predict conductivity of MWCNT-CuO/water hybrid nanofluid based on empirical data. Physica A, 521, 89-97. https://doi.org/10.1016/j.physa.2019.01.055
• [39] Das, M., Akpinar, E., K. (2018). Investigation of Pear Drying Performance by Different Methods and Regression of Convective Heat Transfer Coefficient with Support Vector Machine. Applied Science, 8, 215. doi:10.3390/app8020215.
• [40] Sattari, M. T., Feizi, H., Colak, M. S., Ozturk, A., Apaydin, H., Ozturk, F. (2020). Estimation of sodium adsorption ratio in a river with kernel-based and decision-tree models. Environmental Monitoring and Assessment, 192(9), 1-13, https://doi.org/10.1007/s10661-020-08506-9.
• [41] Quinlan, J. R. (2014). C4. 5: programs for machine learning. Elsevier.
• [42] Zhan, C., Gan, A., Hadi, M. (2011). Prediction of lane clearance time of freeway incidents using the M5P tree algorithm. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1549-1557, Doi: 10.1109/TITS.2011.2161634.
• [43] Güzelel, Y. E., Olmuş, U., Çerçi, K. N., Büyükalaca, O. (2021). Comprehensive modelling of rotary desiccant wheel with different multiple regression and machine learning methods for balanced flow. Applied Thermal Engineering, 199, 117544, https://doi.org/10.1016/j.applthermaleng.2021.117544.
• [44] Behnood, A., Behnood, V., Gharehveran, M. M., Alyamac, K. E. (2017). Prediction of the compressive strength of normal and high-performance concretes using M5P model tree algorithm. Construction and Building Materials, 142, 199-207, https://doi.org/10.1016/j.conbuildmat.2017.03.061
• [45] Akman, M. (2010). An overview of data mining techniques and analysis of Random Forests method: An application on medical field, MSc Thesis (in Turkish), Ankara University, Ankara.
• [46] Messikha,N., Bousbaa, S., Bougdaha N. (2017). The use of a multilayer perceptron (MLP) for modelling the phenol removal by emulsion liquid membrane. Journal of Environmental Chemical Engineering. 5, 3483–3489. http://dx.doi.org/10.1016/j.jece.2017.06.053.