ABSTRACT
This investigation uses the artificial neural network model to classify the
energy and exergy of the kiwi drying process in a microwave dryer. In this
experiment, classification was carried out separately for various pretreatments and microwave powers using three pretreatments (oven, ohmic,
and control treatments) and microwave power values (360, 600, and 900W),
and the artificial neural network model. Classification was done using 5
different input data groups. The first group included the overall data (energy
efficiency, special energy loss, exergy efficiency, and exergy loss), while the
second to fifth groups included the data on the exergy efficiency, special
energy loss, energy efficiency and special exergy loss in the order
mentioned, which served as the classification inputs. Considering the results,
the best R and Percent Correct values for the oven (Percent Correct=90 –
R=0.709) and ohmic (Percent Correct=83.33– R=0.846) pretreatments were
obtained. The values of this parameters were also calculated for the control
(Percent Correct=71.43 – R=0.843), the 360W power (Percent
Correct=92.86 – R=0.9975), the 600W power (Percent Correct=100 –
R=0.9124), and the 900W power (Percent Correct=100 – R=0.9685). The
overall data was used in the classification phase. In addition, the maximum
correctly detected data for the oven, ohmic, and pretreatment was 18 (20
items), 15 (18 items), and 5 (7 items), respectively. The maximum correctly
detected data for the 360W power, 600W power, and 900W power levels
was 13 (14 items), 15 (15 items), and 16 (16 items), respectively. In sum,
the neural network using the overall data input displayed acceptable
efficiency in classifying the energy and exergy data of the kiwi drying
process in microwave dryers
Energy efficiency optimisation for distillation column using artificial neural network models
