Adaptive Neural Network Based Fuzzy Inference System for the Determination of Performance in the Solar Tray Dryer

Year 2021, Volume: 5 Issue: 1, 41 - 49, 26.06.2021

Zehra Yıldız Leyla Gokayaz Ercan Köse Aydın Mühürcü

Abstract

This study aims to apply the adaptive neural network based fuzzy inference system (ANFIS) were used to modeling the apple solar drying conditions in the solar tray dryer. Apple slices were dried by solar drying techniques as a solar tray dryer, exposure to direct sunlight and in the shade. Drying air temperature, the air humidity, apple slice load, apple slice thickness and solar drying time has been investigated with the prediction of the drying in the solar tray dryer on water loss, drying rate and shrinkage ratio. The model results clearly showed that the use of ANFIS led to more accurate results. The correlation coefficient (R2) values of the water loss, drying rate and shrinkage ratio were found as 0.9968, 0,9675 and 0,9918, the water loss, drying rate and shrinkage ratio respectively.

Keywords

Anfis,, Drying,, Solar drying,, Solar tray dryer

References

• Abbasov T, Yuceer M, Yildiz Z, 2011. Prediction of Cleaning Efficiency of the Electromagnetic Filtration Based Fuzzy Inference System, International Review of Chemical Engineering Process using Adaptive Neural Network,I.RE.CH.E., 3(2):285-289.
• Aboud A, 2013. Drying Characteristic of Apple Slices Undertaken the Effect of Passive Shelf Solar Dryer and Open Sun Drying. Pakistan Journal of Nutrition, 12(3):250-254.
• Askari GR, Emam-Djomeh Z, Mousavi SM, 2008. Investigation of the Effects of Microwave Treatment on the Optical Properties of Apple Slices During Drying. Drying Technology 26:1362–1368.
• Ceylan İ, Aktas M, Doğan H, 2006. Güneş Enerjili Kurutma Fırınında Elma Kurutması, Politechnics Journal, 289-294.
• Choi IH, Pak JM, Ahn CK, Lee SH, Lim MT, Song MK, 2015. Arbitration Algorithm of Optical Flow Based on ANFIS for Visual Object Tracking. Measurement. 75:338–353.
• Cui Z, Li C, Song C, Song Y, 2008. Combined Microwave-Vacuum and Freeze Drying of Carrot and Apple Chips. Drying Technology. 26:1517–1523.
• Decareau RV, 1992. in: Encyclopedia of Food Science and Technology. John Wiley. New York., No:3, pp. 1772–1778.
• Demirhan E, Ozbek B, 2011. Thin-Layer Drying Characteristics and Modeling of Celery Leaves Undergoing Microwave Treatment. Chemical Engineering Communications 198(7):957-975.
• Esen H, Ozgen F, Esen M, Sengur A, 2009. Artificial neural network and wavelet neural network approaches for modeling of a solar air heater, Expert Systems with Applications, 36(8):11240-11248.
• Jang JR, 1993. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man, 23: 665–685.
• Kharb RK, Shimi SL, Chatterji S, Ansari MF, 2014. Modeling of solar PV module and maximum point tracking using ANFIS. Renew. Sustain. Energy Rev. 33:602–612.
• Ochoa-Martínez LA, García-Quintero M, Morales-Castro J, Gallegos-Infante J, Martínez-Sánchez CE, Herman-Lara E, 2006. Effect of CaCl2 and convective Osmotic Drying on Texture And Preference of Apple. Journal of Food Quality, 29:583–595.
• Öztürk H, 2008. Yenilenebilir Enerji Kaynakları ve Kullanımı. Teknik Yayınevi Mühendislik. Mimarlık Yayınları. Ankara.
• Sharma R, Patterh MS, 2015. A New Pose Invariant Face Recognition System Using PCA Optik.126, 3483–3487.