Estimation of the Experimental Drying Performance Parameters Using Polynomial SVM and ANN Models

Abstract

The utilization of solar energy in Turkey is very popular because of yearly high solar radiation compared to other countries. One of the common usage area of solar energy is food drying processes. Foods are generally dried under direct sunlight. However, the quality of the dried product exposed to solar radiation reduces. Additionally, the food product dried in outdoors is also exposed to the negative effects of the external environment and thus adversely affects the product quality. In order to overcome these problems, many studies are carried out on solar assisted drying systems. It is very important to calculate or modeling the drying parameters for the design of solar assisted drying systems. In recent years, interest on calculative intelligence methods increases due to the fact that it has high predictive power in modeling of systems. In this study, performance parameters such as solar collector efficiency (ηc), drying rate (DR) and convective heat transfer coefficient (hc) obtained from a solar energy assisted dryer for different products were estimated by Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The accuracy criteria of the predicted results for each model were determined and compared. It was shown from the results that the best converging models of DR and ηc parameters were ANN and SVMC, respectively. However, it was observed that SVML was the best convergent model for hc values obtained from apple product, and ANN model was the best convergent model for hc values obtained from other products.

Keywords

• Drying,Solar Energy,Performance parameter,SVM,ANN

References

1. Kant, K., Shukla, A., Sharma, A., Kumar, A., Jain, A. (2016). Thermal energy storage based solar drying systems: A review, Innovative Food Science and Emerging Technologies, 34: 86–99. doi: 10.1016/j.ifset.2016.01.007
2. Akman, H. (2017). Thermodynamic Analysis of a Solar Energy Assisted Drying System (MSc Thesis), Osmaniye Korkut Ata University, Osmaniye.
3. Akman, H., Çerci, K., and Hürdoğan, E. (2017). Design and Manufacture of a Solar Energy Assisted Drying System and Evaluation of First Experiment Results, International Advanced Researches & Engineering Congress, 1-14. Osmaniye.
4. Mustayen, A. G. M. B., Mekhilef, S., Saidur, R. (2014). Performance study of different solar dryers: A review. Renewable and Sustainable Energy Reviews, 34, 463-470. doi: 10.1016/j.rser.2014.03.020.
5. 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, 25, 442-461.
6. 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, 32(15): 1791-1802.
7. Çerçi, K. N., Süfer, Ö., Söyler, M., Hürdoğan, E., Özalp, C. (2018). Thin layer drying of zucchini in solar dryer located in Osmaniye region. Tehnički glasnik, 12(2): 79-85.
8. Togrul, İ. T. (2005). Convective heat transfer coefficient of apricots under open sun drying conditions. Chemical Engineering Communications, 192(8), 1036-1045.

9. Cerci, K. N., & Akpinar, E. K. (2016). Experimental determination of convective heat transfer coefficient during open sun and greenhouse drying of apple slices. Journal of Thermal Engineering, 2: 741-747.
10. Jain, D., Mridula, D., Patil, R. T., Barnwal, P., Kumar, R. (2010). Kinetics of convective heat and mass transfer coefficient of green chilli during open-sun and greenhouse drying. Desalination and water treatment, 24(1-3): 38-46. doi: 10.5004/dwt.2010.1152
11. Zhu, A. (2018). The convective hot air drying of Lactuca sativa slices. International Journal of Green Energy, 15(3): 201-207. doi: 10.1080/15435075.2018.1434523
12. Oko, C. O. C., Nnamchi, S. N. (2013). Coupled heat and mass transfer in a solar grain dryer. Drying Technology, 31(1): 82-90. doi: 10.1080/07373937.2012.719561
13. Amer, B. M., Gottschalk, K., & Hossain, M. A. (2018). Integrated hybrid solar drying system and its drying kinetics of chamomile. Renewable Energy, 121: 539-547. doi: 10.1016/j.renene.2018.01.055
14. 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(3): 1094-1103. doi: 10.1016/j.jfoodeng.2006.03.025
15. Anwar, S. I., Tiwari, G. N. (2001). Heat and mass transfer coefficients in a four-tray solar crop drying system. International Journal of Ambient Energy, 22: 48-56. doi: 10.1080/01430750.2001.9675386
16. Sagia, A. S,. Fragkou, D.V. (2014). Influence of drying conditions and mathematical models on the drying curves and the moisture diffusivity of mushrooms. Journal of Thermal Engineering, 1: 236-244.
17. Doymaz, İ., Kipcak, A.S. (2018). Investigation of infrared drying of pomegranate by-products. Journal of Thermal Engineering, 4: 1821-1827.
18. Çerçi, K. N., Daş, M. (2019). Modeling of heat transfer coefficient in solar greenhouse type drying systems. Sustainability, 11(18): 5127.doi: 10.3390/su11185127
19. Kaveh, M., Sharabiani, V.R., Chayjan, R.A., Taghinezhad, E., Abbaspour-Gilandeh, Y., Golpour, I. (2018). ANFIS and ANNs model for prediction of moisture diffusivity and specific energy consumption potato, garlic and cantaloupe drying under convective hot air dryer. Information Processing in Agriculture, 5: 372-387.
20. Nadian, M.H., Abbaspour-Fard, M.H., Martynenko, A., Golzarian, M.R. (2017). An intelligent integrated control of hybrid hot air-infrared dryer based on fuzzy logic and computer vision system. Computers and Electronics in Agriculture, 137: 138-149.
21. Mashaly, A.F., Alazba A.A. (2016). Comparison of ANN, MVR, and SWR models for computing thermal efficiency of a solar still. International Journal of Green Energy, 10: 1016–1025.
22. Celebi, K., Uludamar, E., Tosun, E., Yildizhan, S., Aydin, K., Ozcanli, M. (2017). Experimental and artificial neural network approach of noise and vibration characteristic of an unmodified diesel engine fuelled with conventional diesel, and biodiesel blends with natural gas addition. Fuel, 197:159-173. doi: 10.1016/j.fuel.2017.01.113
23. Bilgili, M., Şahin, B., Yaşar, A. (2007). Application of artificial neural networks for the wind speed prediction of target station using reference station data. Renewable Energy, 32: 2350-2360. doi: 10.1016/j.renene.2006.12.001
24. Çerçi, K.N., Saydam, D.B., Hürdoğan, E. (2018). Evaluating the Performance of a Solar Energy Assisted Drying System for Different Food Products in Osmaniye Climatic Conditions, 4th International Conference on Advances in Mechanical Engineering (ICAME-2018), İstanbul, Turkey.
25. Holman J.P. (2001). Experimental methods for engineers, 8th ed. USA: McGraw Hill.
26. Oztop. H.F., Bayrak. F., Hepbaslı. A. (2013). Energetic and exergetic aspects of solar air heating (solar collector) systems. Renewable and Sustainable Energy Reviews, 21, 59–83,. doi: 10.1016/j.rser.2012.12.019.
27. Akpinar, E. K., Toraman, S. (2016). Determination of drying kinetics and convective heat transfer coefficients of ginger slices, Heat Mass Transfer, 52, 2271–2281. doi: 10.1007/s00231-015-1729-6
28. Anwar, S.I. Tiwari, G.N. (2001). Convective heat transfer coefficient of crop in forced convection drying-an experimental study. Energy Conversion Management, 42: 1687–1698. doi: 10.1016/S0196-8904(00)00160-6
29. Li, L.L., Zhao, X., Tseng, M.L., Tan, R.R. (2019). Short-term wind power forecasting based on support vector machine with improved dragonfly algorithm. Journal of Cleaner Production, 118447. doi: 10.1016/j.jclepro.2019.118447
30. Leong, W.C., Kelani, R.O., Ahmad, Z. (2019). Prediction of air pollution index (API) using support vector machine (SVM), Journal of Environmental Chemical Engineering, 103208. doi: 10.1016/j.jece.2019.103208
31. Ma, Z., Ye, C., Li, H., Ma, W. (2018). Applying support vector machines to predict building energy consumption in China. Energy Procedia, 152: 780-786. doi: 10.1016/j.egypro.2018.09.245
32. Fan, J., Wang, X., Wu, L., Zhou, H., Zhang, F., Yu, X., Lu, X., Xiang, Y. (2018). Comparison of support vector machine and extreme gradient boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China. Energy Conversion and Management, 164: 102-111. doi: 10.1016/j.enconman.2018.02.087
33. 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 Sciences, 8: 215, 1-16. doi:10.3390/app8020215
34. Vapnik, V. N. (1999). The Nature of Statistical Learning Theory (2nd edition). New York: Springer-Verlag.
35. Subaira, A.S., and Anitha, P. (2014). Efficient classification mechanism for network intrusion detection system based on data mining techniques: a survey, in: Intelligent Systems and Control (ISCO), 2014 IEEE 8th International Conference On. IEEE, 274–280.
36. Dos Santos, E. M., Gomes, H. M. (2002). A Comparative Study of Polynomial Kernel SVM Applied to Appearance-Based Object Recognition Pattern Recognition with Support Vector Machines, 408-418, Berlin: Springer.
37. Ghritlahre, H. K., Prasad, R. K. (2018). Exergetic performance prediction of solar air heater using MLP, GRNN and RBF models of artificial neural network technique. Journal of Environmental Management, 223: 566-575. doi: 10.1016/j.jenvman.2018.06.033
38. Chai, T., Draxler, R. R. (2014). Root Mean Square Error (RMSE) or Mean Absolute Error (MAE)?-Arguments against avoiding RMSE in literature. Geoscientific Model Development Discussions, 7: 1247-1250. doi: 10.5194/gmd-7-1247-2014