1. Introduction
Rice is one of the oldest and most important grains on earth. Due to the increase in population and the limitations of increasing the area under rice cultivation, the most important goal in the industry is the processing of this strategic plant and the production of the highest quality crop [
1]. Rice is a very valuable food and plays an important role in human nutrition. In 2020, according to the World Food and Agriculture Organization, the total world rice production was 513 million tons [
2]. The nutritional value of rice has led to the use of this substance as a staple food in most countries and has a significant role in the nutrition of people around the world. Due to the importance of rice in the diet of Iranians after wheat, it is among the strategic goods and is the second most-consumed food by most people [
3].
Parboiling is a hydrothermal process that is applied to the hulls before the conversion operation and consists of three stages: immersion, steaming, and drying. To solve the problem of rice fracture, parboiling is a useful, practical, and safe method that has a significant impact on rice self-sufficiency [
4].
Drying is one of the oldest and most common processes of preserving food and agricultural products, which has greatly affected the quality of the product in terms of color, texture, size, and taste [
5]. For example, it reduces the activity of water in the product and in turn reduces the microbiological activity of the product and also minimizes physical and chemical changes during storage. In drying, in addition to preventing food spoilage due to the invasion of microorganisms or chemical reactions, the weight of the food is reduced due to the loss of moisture in the product, and savings in transportation, packaging, and storage costs [
6].
In the past, solar energy was used to dry a variety of agricultural products and food, but due to inappropriate changes in food quality, lack of enough control during the drying process, long drying time, and unsanitary, the product has many problems in using this method, so it is necessary to use new technology in the drying process [
7].
Hot-air drying is the most common method for drying food and agricultural products. In this method, the whole product is completely and uniformly exposed to drying. Low energy efficiency, long drying time, and low quality of products are among the disadvantages of this method [
8]. On the other hand, the use of IR radiation for drying agricultural products is increasing due to its many advantages (faster heat transfer, uniform heating of the product, and high value of dried food). IR radiations are shone on the product [
9]. Depending on the type of product and the wavelength of the radiated radiation, part of the radiation passes through the product, a percentage of it is reflected, and finally, a part is absorbed and penetrates the product and will be converted into thermal energy. The product then heats up sharply and the thermal gradient inside the body increases sharply over a short period of time. Because air transmits IR radiation energy, IR energy heats the product without heating the ambient air [
10]. Drying of food and agricultural products using combined infrared and hot-air drying can be a good substitute for single hot and infrared drying. The interaction of the two drying leads to an efficient drying process. When materials are exposed to IR radiation, the radiation penetrates into the product and, as a result, increases the molecular vibration, thus facilitating heat dissipation and reducing the drying time [
11,
12]. Rapid heating of the material increases the speed of moisture movement to the surface. The flow of hot and dry air removes moisture from the surface, thus increasing mass transfer. Other advantages of infrared and hot-air drying are high drying speed, high energy efficiency, better product quality, and efficient use of space [
13]. Many products have been dried using IR-HA drying, including shiitake mushroom [
14], mint leaves [
15], white mulberry [
13], Kiwi and Turnip [
16,
17], sweet potato [
12], and savory leaves [
18].
Drying is a very complex and uncertain phenomenon and there are unknown factors for them. This phenomenon has been modeled with different levels of complexity [
19]. Predicting the drying kinetics of agricultural products under different conditions is very important and basic for equipment and process design, quality control, energy and fuel management, and selection of appropriate storage [
20]. In order to model food processing processes such as drying and predicting the desired parameters in the design and development of systems, various methods and equations have been used. In situations where the relationships between independent and dependent parameters are complex, mathematical modeling methods will face limitations such as selecting parameters, the application of assumptions to solve equations, and the complexity of solving equations. Therefore, the best choice is to use intelligent optimization methods [
21]. These methods include artificial neural networks (ANNs), adaptive fuzzy-neural inference system (ANFIS), and support vector regression (SVR), which allow adequate and accurate prediction of the drying process in industrial applications.
Many authors have successfully used ANN, ANFIS, and SVR to describe the drying properties of many agricultural and food products. Kaveh et al. [
22] conducted a study to predict the moisture ratio (
MR), energy, and exergy of onions during drying with a semi-industrial type of continuous dryer by using two different approaches ANNs and ANFIS. The results showed that the ANFIS model provided better results for all predicted parameters. In another study, the
MR, energy, and exergy analysis of drying of blackberry in an IR-HA dryer with ultrasonic pretreatment were predicted by ANNs and ANFIS [
23]. Ziaforoughi et al. [
24] performed comparisons between mathematical and inference models to predict the
MR of quince in the IR drying. According to the results, the ANFIS model with (
R2 = 0.998) had better performance than the mathematical models. SVR has also been used to predict the moisture content (
MC) of wood during the drying process [
25]. Several studies have employed ANNs to estimate the
MR of different products in different dryers, including green peas [
26], apricot [
27], stevia [
28], basil seed [
29], and yam slices [
30]. Therefore, in this study, ANN, ANFIS, and SVR are evaluated to predict the moisture drying ratio of rice in an IR-HA dryer under different operating conditions (two levels of infrared power and three levels of inlet air temperature).
Previous studies have generally focused on neural networks and semi-empirical models. These methods are powerful tools for modeling complex phenomena such as the drying process, but these methods provide district information based on drying behavior, and therefore it is inevitable to consider different models for precise simulation of the drying process. The number of studies comparing ANN, ANFIS, and SVR methods based on drying characteristics is limited in the literature. Therefore, the purposes of the present study are (a) investigation of the effect of drying temperature (40, 50, and 60 °C) at two levels of IR power (0.32 and 0.49 W/cm2) on parboiled hulls moisture ratio (b) the evaluation of the different topologies of ANN models as shaped by the selection of the networks, activation functions, training algorithm, the neuron and the hidden layer number, (c) the evaluation of different first-order Takagi–Sugeno type ANFIS models with different number and types of membership function for each input and output, training algorithm, number of output membership functions and number of fuzzy rules for predicting the drying characteristics of parboiled hulls, (d) the comparison of the various learning algorithms of the support vector regression method for estimating the MR of drying of parboiled hulls.
4. Conclusions
Drying is a useful crop preservation method, increases the shelf-life of the crops, and decreases storage, packaging, and transportation costs. There are several methods for drying and each one of them has a specific procedure. Analyzing and finding the optimum procedure condition could be obtained using modeling and simulation techniques. The data set was obtained at three temperature levels (40, 50, and 60 °C), two levels of IR power (0.32 and 0.49 W/cm2) in an IR-HA drying. Drying time was reduced at higher temperature values of the inlet air between 40 °C and 60 °C. Based on the data set, three nonlinear prediction models (ANN, ANFIS, and SVR) were designed to estimate the parboiled hulls MR. All three models were designed with one output variable (MR) and three inlet variables (inlet air temperature, IR power, and drying time). To select the best model for accurately predicting the MR, the developed models were compared with each other. To select the best model, the coefficient of determination obtained and the total ranking for each model was calculated and examined. According to the experimental data set, the performance prediction of the ANFIS model (R2 = 0.9995) was higher than the ANN model (R2 = 0.9991). Therefore, the ANFIS model provided higher performance in predicting the MR of parboiled hulls in comparison with ANNs with higher R2, lower MSE, and MAE. The SVR method also showed considerable capability in training the model for predicting the MR of parboiled hulls compared to the other two methods.