This article is purposed mainly to analyze the drying characteristics in the internal moisture of rice at different drying temperatures using low-field NMR. The signals of the relaxation time were applied to determine the moisture content and dynamic characteristics of various phases. According to the experimental results, the migration of strongly chemically bound water and other phases of water was a bi-directional process. The first half of the process can be dried at high temperatures to remove moisture rapidly, while the second half can be lowered to reduce the moisture gradient. Besides, the first half was dried at 65 C and the second half at 35, 45 and 55 C, which led to an increase in energy consumption by 9.62%, 6.99%, and 2.54% as well as an extension to the drying time by 65, 40, and 18 min. The results will provide a theoretical basis for optimizing the drying process for rice. Practical ApplicationsLow field nuclear magnetic resonance was applied to study the drying characteristics of rice, and to determine the variation pattern and migration characteristics of water in different phases of rice with varying temperatures during the drying process. On this basis, a high-quality variable temperature drying method was proposed. Moreover, this method is applicable to the variable temperature of the rice drying process.
In this study, a rapid real-time nondestructive method for detecting the moisture content of rice during hot-air drying was investigated. Intelligent techniques of lowfield nuclear magnetic resonance (LF-NMR) and back propagation artificial neural network (BP-ANN) were applied to monitor the moisture content of rice. The effect of different hot-air temperatures (35, 45, 55, and 65 C) on the moisture content and water migration within rice was studied. The results showed that the drying temperature promoted the diffusion and transfer of water within the rice, and was positively proportional to the drying rate. The binding energy of the different states of water within rice increased with the drying process, and the variation in relaxation time and peak area was consistent for each stage at different temperatures. In addition, the amount of LF-NMR signals was used as an indicator to build a predictive model for the moisture content of rice during hot-air drying. A BP-ANN prediction model optimized by transfer function, training function and number of neurons was used to monitor the moisture content of rice using the amount of LF-NMR signals of different states of water as input variables. The optimized neural network model had the excellent predictive ability with an MSE of 6.02 Â 10 À6 and R 2 of 0.996. These results provide a reference for combining LF-NMR and BP-ANN in the application of intelligent online monitoring of hot-air drying of rice. Practical ApplicationsThe monitoring of moisture content during hot-air drying of rice is an essential parameter for optimizing the drying process. The combined approach of LF-NMR and BP-ANN for rapid real-time nondestructive monitoring is well suited to hot-air drying of rice, allowing for improved product quality and operational processes. In addition, the model developed in this study has the good predictive performance to meet the current industry and production needs, providing new research ideas and technical references for the optimization of the drying process of rice.
This research was conducted to determine the effective moisture diffusion coefficient and activation energy of rice using low‐field nuclear magnetic resonance (LF‐NMR). The results showed that the evaporable water was rapidly removed under high‐temperature drying in the early stages of drying. The diffusion rate of evaporable water at different temperatures in the later stages of drying was not significant. Besides, the activation energy in rice was divided into the activation energy of the conversion of the chemically bound water (15.04–25.69 kJ/mol) to evaporable water and the activation energy of the diffusion of evaporable water from rice (6.42–16.42 kJ/mol), indicating that more energy was needed to dry the chemically bound water from rice. A new method for obtaining the effective moisture diffusion coefficient and the activation energy was proposed based on the relaxation time distribution profiles, revealing the internal relationship between various phase moisture and activation energy. These results will provide a theoretical guidance for the drying process. Practical applications The moisture diffusion characteristics and activation energy of rice were determined using low‐field NMR signal quantities, which were important parameters for optimizing the drying process. In addition, the variable temperature drying method was conducive to the rapid diffusion of moisture in the rice. The chemically bound water within rice required more energy to diffuse than other forms of water components. An internal relationship between moisture content and the activation energy was established to provide a theoretical reference for high‐quality drying techniques.
To solve the problems of poor stability and low monitoring precision in the online detection of rice moisture in the drying tower, we designed an online detection device for rice moisture at the outlet of the drying tower. The structure of a tri-plate capacitor was adopted, and the electrostatic field of the tri-plate capacitor was simulated using COMSOL software. A central composite design of three factors and five levels was carried out with the thickness, spacing, and area of the plates as the influencing factors and the capacitance-specific sensitivity as the test index. This device was composed of a dynamic acquisition device and a detection system. The dynamic sampling device was found to achieve dynamic continuous sampling and static intermittent measurements of rice using a ten-shaped leaf plate structure. The hardware circuit of the inspection system with STM32F407ZGT6 as the main control chip was designed to realize stable communication between the master and slave computers. Additionally, an optimized BP neural network prediction model based on the genetic algorithm was established using the MATLAB software. Indoor static and dynamic verification tests were also carried out. The results showed that the optimal plate structure parameter combination includes a plate thickness of 1 mm, plate spacing of 100 mm, and relative area of 18,000.069 mm2 while satisfying the mechanical design and practical application needs of the device. The structure of the BP neural network was 2-90-1, the length of individual code in the genetic algorithm was 361, and the prediction model was trained 765 times to obtain a minimum MSE value of 1.9683 × 10−5, which was lower than that of the unoptimized BP neural network with an MSE of 7.1215 × 10−4. The mean relative error of the device was 1.44% under the static test and 2.103% under the dynamic test, which met the accuracy requirements for the design of the device.
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