Effects of three drying methods on kinetics and energy consumption of carrot drying process and modeling with artificial neural networks

Kılıç F.

ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS, cilt.43, sa.12, ss.1468-1485, 2021 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43 Sayı: 12
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1080/15567036.2020.1832163
  • Dergi Adı: ENERGY SOURCES PART A-RECOVERY UTILIZATION AND ENVIRONMENTAL EFFECTS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Aerospace Database, Applied Science & Technology Source, CAB Abstracts, Communication Abstracts, Compendex, Computer & Applied Sciences, Environment Index, Greenfile, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1468-1485
  • Anahtar Kelimeler: Drying oven design, food drying, artificial intelligence, energy consumption, image processing
  • Gazi Üniversitesi Adresli: Evet

Özet

In this study, a multifunctional food drying oven has been designed to compare the energy consumption in drying processes with resistance, infrared-assisted, and infrared (three drying methods) in the food drying process, and it has been aimed to dry carrot slices using three different drying methods. Secondly, in ordinary drying experiments, when the dried food is taken out of the oven, it absorbs moisture and loses heat. In order to make the measurements more precise, a system to be taken from the oven has been designed and a mechanical and electronic mechanism has been created to save them on a computer. Finally, in order to obtain food color change information to be used as a decision-making mechanism for the termination of drying, carrot slices have been photographed at the same time on each measurement and their weight have been detected by the sensor and recorded to the computer. Experiment time and energy consumption values of drying with resistance, infrared assisted and infrared processes have been determined as 180 minutes 1.032 kWh, 210 minutes 1.315, kWh 240 minutes, and 1.691 kWh, respectively. Image processing technology has been used in the drying process with the photographs taken. Color and size analyses of carrot slices whose image processing and drying processes are executed have been performed. Percentages of decrease in the number of pixels in drying with resistance, infrared assisted and infrared processes have been calculated as 69.5%, 53.8%, and 53.1%, respectively. In addition, the moisture content of the carrot slice has been modeled with artificial neural networks. Artificial neural networks infrared assisted drying method has obtained the best regression result among three methods with 0.99837 all regression value. Triple drying method artificial neural networks algorithm, all mean absolute deviation and mean squared error parameters have been obtained as 0.87-2.37 in infrared assisted method, respectively.