Fractional order calculus approach for drying modeling of eggplants

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Erentürk K., Kose B., Erentürk S.

FOOD SCIENCE AND TECHNOLOGY INTERNATIONAL, cilt.26, ss.379-387, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 26
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1177/1082013219895852
  • Dergi Adı: FOOD SCIENCE AND TECHNOLOGY INTERNATIONAL
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, CAB Abstracts, Compendex, EMBASE, Food Science & Technology Abstracts, INSPEC, MEDLINE, Veterinary Science Database, DIALNET
  • Sayfa Sayıları: ss.379-387
  • Anahtar Kelimeler: Drying, eggplants, fractional order calculus, modeling, regression analysis, KINETICS, ALGORITHM, SYSTEMS, L.
  • Atatürk Üniversitesi Adresli: Evet

Özet

In order to determine the drying modeling of eggplants, fractional order calculus modeling was applied and compared to regression analysis in this study. The fractional order calculus based on the Caputo derivative approach was considered and applied for modeling of eggplant drying. The drying experiments were performed on three levels of drying air temperatures (60, 70, and 80 celcius), with two different air flow velocity levels (0.5 and 1 m/s), and three levels of thickness (3.5, 6.5, and 9.5 mm) to measure the effects of different drying conditions for eggplants in a convective dryer. Four different commonly used mathematical models from the literature were fitted to the experimental data with regression analysis. Based on obtained results, the coefficient of determination (R-2) was found as 0.9956 for the values of 80 celcius, 0.5 m/s, and 3.5 mm, while sum squared error was 0.0061 and root mean square error was 0.0235 for the Page model. However, better and more accurate results were obtained using fractional order modeling with the values of R-2 found as 0.9981, sum squared error as 0.0012, and root mean square error as 0.0099 for the considered case.