Optimization of Caper Drying Using Response Surface Methodology and Artificial Neural Networks for Energy Efficiency Characteristics

Hasan Demir, Hande Demir, Biljana Lončar, Lato Pezo, Ivan Brandić, Neven Voća () and Fatma Yilmaz
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Hasan Demir: Department of Chemical Engineering, Osmaniye Korkut Ata University, 80000 Osmaniye, Türkiye
Hande Demir: Department of Food Engineering, Osmaniye Korkut Ata University, 80000 Osmaniye, Türkiye
Biljana Lončar: Faculty of Technology Novi Sad, University of Novi Sad, Bul. Cara Lazara 1, 21000 Novi Sad, Serbia
Lato Pezo: Institute of General and Physical Chemistry, University of Belgrade, Studentski Trg 12-16, 11000 Belgrade, Serbia
Ivan Brandić: Faculty of Agriculture, University of Zagreb, Svetosimunska cesta 25, 10000 Zagreb, Croatia
Neven Voća: Faculty of Agriculture, University of Zagreb, Svetosimunska cesta 25, 10000 Zagreb, Croatia
Fatma Yilmaz: Graduate School of Natural and Applied Sciences, Osmaniye Korkut Ata University, 80000 Osmaniye, Türkiye

Energies, 2023, vol. 16, issue 4, 1-14

Abstract: One of the essential factors for the selection of the drying process is energy consumption. This study intended to optimize the drying treatment of capers using convection (CD), refractive window (RWD), and vacuum drying (VD) combined with ultrasonic pretreatment by a comparative approach among artificial neural networks (ANN) and response surface methodology (RSM) focusing on the specific energy consumption (SEC). For this purpose, the effects of drying temperature (50, 60, 70 °C), ultrasonication time (0, 20, 40 min), and drying method (RWD, CD, VD) on the SEC value (MJ/g) were tested using a face-centered central composite design (FCCD). RSM ( R 2 : 0.938) determined the optimum drying-temperature–ultrasonication-time values that minimize SEC as; 50 °C-35.5 min, 70 °C-40 min and 70 °C-24 min for RWD, CD and VD, respectively. The conduct of the ANN model is evidenced by the correlation coefficient for training (0.976), testing (0.971) and validation (0.972), which shows the high suitability of the model for optimising specific energy consumption (SEC).

Keywords: drying of capers; response surface method; vacuum drying; specific energy consumption; artificial neural network; refractive window drying (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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