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In the present study, the freeze drying behavior of apples have been modeled and predicted. Because freeze-drying is a very expensive and complex process, modeling of the freeze-drying process is a challenging task. In this study, a novel data scaling method called multiple output–dependent data scaling (MODDS) has been proposed and combined with an adaptive neuro-fuzzy inference system (ANFIS) to forecast the moisture content (MC), moisture ratio (MR), and drying rate (DR) values, which are outputs of freeze-drying behavior of apples. The input parameters of the freeze drying system are the sample thicknesses, drying time, pressure, relative humidity, chamber temperature, and sample temperature. Using the input parameters, the outputs of the freeze-drying process of apples were predicted using a hybrid system based on MODDS and ANFIS. In the first stage, only input parameters were scaled using MODDS. In the second stage, the outputs of freeze drying of apples were predicted with the scaled input parameters using ANFIS algorithm. Ninety-two samples were included in the data set, including 10-, 7-, and 5-mm samples. In order to evaluate the performance of the proposed model, the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), coefficient of determination (R 2), index of agreement (IA), and mean absolute percentage error (MAPE) were used. Though MSE values of 2.48, 0.035, and 0.011 and IA values of 0.887, 0.887, and 0.466 were obtained for MC, MR, and DR, respectively, using the ANFIS prediction algorithm the hybrid MODDS-ANFIS model achieved MSE values of 0.003, 0.00005, and 0.00007 and IA values of 0.999, 0.999, and 0.993 for the prediction of MC, MR, and DR, respectively. The results obtained demonstrate that the proposed hybrid system is a robust and efficient method for the modeling and prediction of freeze-drying behavior of apples. [ABSTRACT FROM AUTHOR]

The freeze drying process is based on different parameters, such as drying time, pressure, sample thickness, chamber temperature, sample temperature and relative humidity. Hence, the determination of the drying behaviors, such as MC and MR, of the freeze drying process are too complex. In this paper, to simplify this complex process, the use of artificial neural networks has been proposed. An artificial neural networks model has been developed for the prediction of drying behaviors, such as MC and MR, of strawberries in the freeze drying process. The back-propagation learning algorithm with variant which is Levenberg-Marquardt (LM) and Fermi transfer function have been used in the network. In addition, the statistical validity of the developed model has been determined by using the coefficient of determination (R²), the root means square error (RMSE) and the mean absolute percentage error (MAPE). R², RMSE and MAPE have been determined for MC and MR as 0.999, 0.001924, 0.152284 and 0.999, 1.87E-05, 0.13393, respectively. [ABSTRACT FROM AUTHOR]

In this study, heating and cooling performances of two vortex tubes connected in parallel using different working fluids were compared. In experimental studies, oxygen, nitrogen, and air were used as working fluids in counterflow Ranque--Hilsch vortex tube (RHVT) and performance evaluation was performed. Nozzles made of polyamide and brass are used, and the number of these nozzles is 2, 4, and 6. Compressed working fluids were used to operate the vortex tube system at different inlet pressure values varying from 150 kPa to 600 kPa with 50 kPa increment. The geometric characteristics of the vortex tube are the length of the hot tube and the diameter of the orifice, which are 100 mm and 7 mm, respectively. Experiments were performed with the hot flow outlet valve fully open. The thermodynamic performance of the parallel connected vortex tube system was determined by performing exergy analysis. As a result of experimental studies, the highest performance of parallel connected RHVT system was obtained when nitrogen was used as a working fluid with brass-six-nozzle at 600 kPa. [ABSTRACT FROM AUTHOR]