Application of artificial neural networks, support vector, adaptive neuro-fuzzy inference systems for the moisture ratio of parboiled hulls

Abstrakt

Drying as an effective method for preservation of crop products is affected by various conditions and to obtain optimum drying conditions it is needed to be evaluated using modeling techniques. In this study, an adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and support vector regression (SVR) was used for modeling the infrared-hot air (IR-HA) drying kinetics of parboiled hull. The ANFIS, ANN, and SVR were fed with 3 inputs of drying time (0–80 min), drying temperature (40, 50, and 60 °C), and two levels of IR power (0.32 and 0.49 W/cm2) for the prediction of moisture ratio (MR). After applying different models, several performance prediction indices, i.e., correlation coefficient (R2), mean square error index (MSE), and mean absolute error (MAE) were examined to select the best prediction and evaluation model. The results disclosed that higher inlet air temperature and IR power reduced the drying time. MSE values for the ANN, ANFIS tests, and SVR training were 0.0059, 0.0036, and 0.0004, respectively. These results indicate the high-performance capacity of machine learning methods and artificial intelligence to predict the MR in the drying process. According to the results obtained from the comparison of the three models, the SVR method showed better performance than the ANN and ANFIS methods due to its higher R2 and lower MSE

Autorzy

Vali Rasooli Sharabiani
Vali Rasooli Sharabiani
Mohammad Kaveh
Mohammad Kaveh
Ebrahim Taghinezhad
Ebrahim Taghinezhad
Rouzbeh Abbaszadeh
Rouzbeh Abbaszadeh
Esmail Khalife
Esmail Khalife
artykuł
Applied Sciences-Basel
Angielski
2022
12
4
1771
otwarte czasopismo
CC BY 4.0 Uznanie autorstwa 4.0
ostateczna wersja opublikowana
w momencie opublikowania
2022-02-09
100
2,7
0
10