Single-Parameter Thin-Layer Drying Equations for Long-Grain Rice

Abstract

Abstract. The use of multiple parameters in thin-layer drying equations makes it difficult to compare and quantify the impact of drying air temperature, relative humidity, and other factors on the drying characteristics of an agricultural crop. In this study, two single-parameter equations are proposed to quantify thin-layer drying characteristics of contemporary long-grain rice cultivars grown in the Mid-South U.S. Drying runs were first performed to obtain drying curves for cultivar ‘Roy J’ under 18 air conditions; several drying equations were evaluated for their fit to each drying curve. The proposed single-parameter equations (modified Page equation and infinite-series diffusion equation) described the experimental drying data very accurately; the root mean square errors in moisture ratio obtained for the modified Page and infinite-series diffusion equations varied in the ranges of 0.2 to 1.4 and 0.3 to 1.5 percentage points, respectively. The dependence of drying air temperature and relative humidity on drying parameters of the modified Page and infinite-series diffusion equations was described by second-order polynomial regression equations. The impact of harvest moisture content on the drying characteristics of rice was observed to be negligible. The validity of the developed single-parameter equations was also evaluated for five other long-grain rice cultivars; for these cultivars, the maximum errors in the moisture ratio prediction using the modified Page and infinite-series diffusion equations were 2.9 and 3.4 percentage points, respectively. This study provides thin-layer drying data for contemporary rice cultivars in the Mid-South U.S. The resulting thin-layer drying equations are expected to improve the accuracy of deep-bed drying models. While the proposed single-parameter equations were tested only for long-grain rice, the methodology presented in this research could be used to develop similar single-parameter thin-layer drying equations for short-grain and medium-grain rice, as well as other agricultural crops. As such, these equations could be readily used in quantifying the impacts of air and rice variables on drying rates. Keywords: Mathematical modeling, Rice, Thin-layer drying.

Influence of harvest moisture content and milling duration on microwave‐dried rice physicochemical properties

“…Contrarily, for XL753, the HRY for microwave‐dried rice with 20% initial HMC was significantly lower than the control sample. The differences in the two cultivars may be due to their inherent biological variations in size, shape, morphology, and kernel composition (Bruce et al, 2021; Prakash & Siebenmorgen, 2018). The pubescent nature (hair‐like microstructure on the hull) of long grain hybrids may also influence their drying property and HRY (Atungulu et al, 2015).…”

Drying Kinetics of Wheat (Triticum aestivum L., cv. ‘Pionier’) during Thin-Layer Drying at Low Temperatures

“…Once the n value was fixed at the mean value (n = 0.784), the experimental data were fitted again for each drying condition in order to re-adjust the k value. This modification was proposed by Prakash and Siebenmorgen [63] and it was concluded that the model predictability was slightly reduced whereas the complexity of the generalized model was condensed. Hence, a variation of k between 3.660 × 10 −3 and 2.998 × 10 −2 for T = 10-50 • C, 9.820 × 10 −3 and 8.025 × 10 −3 for RH = 20-60% and 8.904 × 10 −3 and 9.940 × 10 −3 for v = 0.15-1.00 ms −1 was ascertained accordingly.…”

Mathematical modeling for drying flint corn in a thin layer

“…The thin layer is characterized as a very thin grain surface that shows a maximum exposure in relation to the air flow (Bala, 2017). The models developed by Lewis, Page, Thompson, Overhults, Brooker, and Midilli were chosen for the analysis in this work because they showed good results in previous studies on grains of different types (Liu et al, 2015;Souza et al, 2015;Sun et al, 2016;Abasi et al, 2017;Nejadi & Nikbakht, 2017;Jian & Jayas, 2018;Prakash & Siebenmorgen, 2018).…”

“…Although the model showed satisfactory data, it is limited, as the diffusion of the product's internal moisture was neglected. The drying of different rice cultivars was analyzed, using the Page, Lewis, and Midilli's models, which were considered valid for predicting the drying of rice after adjustments (Prakash & Siebenmorgen, 2018). The drying of saffron roots was evaluated using eleven exponential models that showed statistically satisfactory results in relation to the experimental data (Karthikeyan & Murugavelh, 2018).…”