A moisture content prediction model for deep bed peanut drying using support vector regression

Abstract

In order to make the moisture content monitoring more convenient and rapid during peanut drying process, the drying characteristics of peanut were investigated and a real‐time SVR moisture content monitoring model was established in this paper. The results showed that hot air temperature, initial moisture content, airflow rate and the layer height were the key factors on peanut drying, and the peanut variety showed little effect on drying. The SVR model exhibited a good performance with R2: 0.91, RMSE: 4.38, and bias: −7.5e‐3. Compared with the results of linear regression models and multilayer perceptron model, SVR model showed a better performance. In addition, the SVR model was validated by the drying data of other three varieties of peanuts. And the relative errors between the predicted values by SVR model and the measured values were within 20%, which suggested that SVR was a promising modeling algorithm for peanut drying. Practical application Peanut drying is essential for peanut production due to its high moisture content at harvest (about 30–50% on the wet basis), which makes it susceptible to mildew, or even produces aflatoxins. Peanut has a special physiological structure. In the drying process, the peanut kernels are gradually shrunk, which makes the air layer volume between the shell and the kernel gradually increases, thus hindering mass and heat transfer and leading to its different drying characteristics from other grain and oil crops. Furthermore, it is necessary to monitor the moisture content changes of peanut during deep bed drying to ensure the drying uniformity and prevent energy waste. Therefore, the drying characteristics of peanuts were studied and a moisture content prediction model for deep bed drying was established to assist the actual drying process.

Moisture content‐dependent mechanical behaviors of rice by experiments and composite models in finite element simulation

“…The rice variety named NJ5055 is used in this study and obtained by manual hulling. The moisture content of brown rice can be obtained using several approaches (Lu et al, 2018; Qu et al, 2020), for example, the thermogravimetric method, as shown in Figure 1a. The rice specimens are stored at constant temperature and humidity chamber with the relative humidity of 65% and the air temperature of 25°C.…”

A Heat and Mass Transfer Model of Peanut Convective Drying Based on a Two-Component Structure

“…Freshly harvested peanuts have a moisture content of up to 50% [8], but they are highly susceptible to mold and even toxins if they are not dried in time, thereby resulting in economic losses [9][10][11]. Peanuts with less than 10% moisture can be stored for a long time [12]. With 9% moisture content, it can be stored safely for nearly 1 year at 25-27 • C and 70% RH [13].…”

Study on drying characteristics and moisture content prediction model of Panax notoginseng taproot by using segmented drying of microwave vacuum combined with hot air

“…Duan et al (2022) explored the moisture variation law of tea during hot air drying and established the particle swarm optimization‐Elman (PSO‐Elman) model to predict the moisture content of tea in the drying process by using drying temperature, drum velocity, initial drying moisture, and drying time as input parameters. Qu et al (2020) explored the drying characteristics of peanuts during hot air drying, and established a support vector regression (SVR) model with hot air temperature, initial moisture content, air flow rate, and floor height as input parameters to predict the moisture content of peanuts in the drying process. This study conducted the experimental study in a more complex environment (segmental drying) and proposed a new model for predicting the moisture variation of PNT in the process of segmental drying.…”