Study and Analysis of Window Characteristics During Continuous Grain Drying

Mathematical model of grain drying process is the grain drying machine design, the drying operation automatic control and the optimization of the important basis of drying technology. Cross flow drying is one of the forms of grain drying has been widely applied at present, this article is mainly to Page model is applied to the continuous grain in cross flow drying, and briefly introduces the model predictive control method and its application in the research and application of continuous cross-flow grain drying machine, so as to provide reference for similar research in China.

As an indispensable part of textile processing, the fabric drying process has a great impact on product quality and overall energy consumption. To reveal the characteristics of the continuous drying process of various fabrics and optimize process parameters for improving productivity and saving energy, a finite element model is built to simulate the continuous fabric drying process, and an optimization method is applied to optimize process parameters based on the model. Specifically, a finite element model is first built; the model can predict distribution of water content and surface temperature of three kinds of fabric in the continuous drying process under different process parameters. The model is then verified by experiments, and the experimental results agree well with the numerical results: The mean absolute errors of distribution of water content and surface temperature of fabrics are 4.22% and 2.15℃, respectively. The numerical results indicate that wind velocity, wind temperature, and fabric velocity have a significant influence on the drying rate and surface temperature of fabrics in the continuous drying process, which, however, are not affected obviously by initial water content. It is also found that under the same initial and technological conditions, the drying rate and surface temperature of fabrics in the continuous drying process are lower than those in the intermittent drying process. Second, the Taguchi method is applied to design continuous fabric drying schemes, considering the interaction effect of technological parameters on the drying process. The numerical model is then applied to simulate these schemes, and the TOPSIS method is applied to analyze and compare these numerical results. The optimal technological parameters are determined; the optimal parameters can help to save energy by about 27.8 % and enhance energy efficiency by about 16 % in the continuous drying process. It is worth noting that the interaction effect of fabric velocity and wind temperature on the continuous drying process is more significant than their independent effects.

Due to the nonlinearity, strong coupling and hysteresis of the parameters used in measuring the grain drying process, it is always a challenge to perform accurate control of the drying system. The purpose of this paper is to describe the design of a suitable neural network model to control the grain dryer more effectively. First, the operating mechanism of a continuous grain dryer and the principle of the heat and mass transfer that can be used in the process of grain drying was analyzed before an intelligent control system was designed accordingly. Second, an intelligent control system based on the Back Propagation Neural Network (BPNN) was developed. The BPNN was the optimal model selected based on a series of comparative test results. According to the BPNN prediction of the moisture content of dried rice, the system could adjust the rate of grain discharge of the dryer, and then control the drying process accurately. Finally, the neural network control model was simulated using computer simulation technology, and was optimized by comparing analysis results with experimental results. The results showed that the optimized intelligent control system using BPNN has the advantage of strong stability and good noise handling, and could have great potential for future implementation studies.

Grain drying is a complex heat and mass transfer process, which has the characteristics of a significant delay, multidisturbance, nonlinearity, strong coupling, and parameter uncertainty. Artificial intelligence (AI) control technology is suitable for solving such complex control problems. In this paper, the mechanism and data dual-drive with equivalent accumulated temperature (EAT) mutual-window AI-control method for continuous grain drying were proposed, and a control system was established. The experimental verification was carried out on the test platform of continuous grain drying. The results show that the method has the ability of implicit prediction, high accuracy, strong stability and self-adaptive ability, and the maximum control deviation of moisture at the outlet of the dryer is −0.58–0.3%.

To explore the collapse of eucalyptus wood cells during the drying process, continuous and intermittent drying were carried out on Eucalyptus urophylla. The shrinkage throughout the intermittent drying process was less than that of continuous drying. According to observations of cells made using scanning electron microscopy (SEM), there was a large difference in the degree of cell collapse between continuous and intermittent drying. Severe cell collapse was observed after freeze-drying at high moisture content, even after an intermittent drying process. It is clear that collapse recovery via intermittent drying was more extensive than in continuous drying. In particular, ray parenchyma and axial parenchyma recovered from collapse more than did wood fibers.

Grain drying is an energy-intensive stage of post-harvest processing, where the use of heat pumps is one of the methods to improve energy efficiency. The paper provides concise information on drying technology, types of dryers, and their features. Particular attention is paid to recirculation schemes of the drying agent, which reduce energy consumption by reusing the heat carrier. Modern approaches to integrating heat pumps into continuous flow grain dryers systems are discussed, enabling dual functionality: dehumidifying air in the evaporator and heating it in the condenser. The article proposes a methodology for assessing the energy efficiency of grain drying in heat pump continuous flow grain dryers. The study also analyzes the influence of the non-isenthalpic drying mode in continuous flow dryers, a key feature that differentiates them from chamber systems. Recommendations for optimizing drying parameters to reduce energy consumption and improve dryer efficiency are provided. The research findings may be useful for agricultural enterprises seeking to modernize post-harvest grain processing technologies.

This chapter presents an analytical modeling of mass transfer in wet porous bodies during the continuous and intermittent drying process in fixed bed. The drying process was simulated assuming the liquid diffusion as the only mass transport mechanism and constant mass diffusion coefficient. The presented models involve spherical, cylindrical and prolatespheroidalgeometries. Simulation tests of intermittent drying of ellipsoidal solids were performed and the results were compared with the continuous drying curve in order to evaluate the tempering effect in the drying process optimization. It was possible to simulate the moisture distribution during the tempering period. As an application, the methodology is used to describe intermittent drying of rough rice (BRSMG Conai variety) at temperature of 40°C and tempering periods from 0 to 1 hour. Experimental data were used to estimate the diffusion coefficient. Under the considered operating conditions, it was verified that intermittent drying provides reduction in effective operating time when compared to continuous drying.

Rice drying is commonly used by Indonesian people from generation to generation with manual drying of grain (solar heat) which takes approximately 3 days, and when the rainy season arrives drying grain becomes longer which ranges from (4-7) days, depending on the weather which is not decisive. Currently, many researchers make grain dryers but do not pay attention to the air speed used. Based on these drying problems, this research uses a variable variation of three fan speeds using experimental research methods. The main objective of this research is to understand the effect of airflow speed generated by the blower on two important aspects in the grain drying process, namely the drying process itself and the quality of the final grain. The quality of the grain can be known through the moisture content of the grain and the color of the grain. The grain dryer model made is a tube that has a diameter of 35 cm and a height of 100 cm with a vertical tube position equipped with a stirrer and an air heater inside the tube and a blower placed at the bottom of the tube which is connected to a pipe to channel air into the tube, when the grain dryer is turned on the stirrer will rotate to stir the grain and the heater will emit hot air to dry the grain. The hot air produced by the heater is not well distributed in the tube, so air is needed to distribute the hot air in the tube. The results of the research that has been done state that the grain dryer that uses an air speed of 12 m / s is better than the speed of 8 m / s and 10 m / s. Grain dryers that use 12 m / s air speed produce less water content, grain color that tends to be better and drying time that is faster than air speed 8 m / s and 10 m / s. It can be concluded that air speed affects the quality of the grain produced

Grain drying is a nonlinear process featuring with multi-variables and long delay. Moisture content as one of the key set variables in the drying processes is difficult to be controlled accurately. Since classical control methods based on mathematic model are not suitable for this kind of nonlinear process, a new intelligent control system for continuous-flow dryer was proposed to solve this problem. Based on the human control concept for controlling the outlet moisture content, a human-in-the-loop self-adaptive inversion control model for grain drying process was established, a nonlinear process inversion control algorithm was composed, and consequently a human-in-the-loop controller for continuous-flow grain drying was developed. Experiments have been conducted and the results showed good feasibility of this control method.

The mathematical model of grain drying process is an important basis for grain drying equipment design, automatic control of drying operation and optimization of drying process. Continuous drying is one of the most widely used forms of grain drying, In the drying process, there are many factors that affect the water content of the machine, such as food temperature, food humidity, hot air wind speed, the initial moisture into the food. It is very important to realize the automatic control of the grain dryer, to ensure the uniformity of the food moisture, dry grain quality, reduce the labor intensity of the operator and give full play to the dryer production capacity is of great significance. In this paper, the accumulated temperature control model of corn is put forward, and the rate of grain discharge dryer is determined by the accumulated temperature value to control the water content of grain. The experimental results show that the mathematical model of the accumulated temperature can be used for the automatic control of the continuous dryer. The accumulated temperature can effectively control the drying process and can meet the requirements of the operation. It has the characteristics of simple algorithm, fast response and overcoming the influence of the same lag on the measurement and control.

Grain moisture modeling and control framework for in-silo storage

In agricultural production, numerous nonlinear, time-lagged, and continuously disturbed deterministic processes require effective regulation. This study proposed a window AI control method driven by mechanism and data, which was studied and applied in continuous grain drying process based on the concepts of micro-environment absolute water potential and water potential accumulation. A control system for continuous grain drying was established based on absolute water potential accumulation, and three sets of experiments were conducted: constant temperature drying (water potential accumulation control window, hot air temperatures of three drying sections: 40 °C, 40 °C, 40 °C; relative humidity: 35–40%), increasing temperature drying (water potential accumulation control window, hot air temperatures of three drying sections: 35 °C, 40 °C, 45 °C; relative humidity: 35–40%), and a control experiment (equivalent accumulated temperature control window, hot air temperatures of three drying sections: 40 °C, 40 °C, 40 °C; relative humidity: 35–40%). The results showed that the outlet moisture content ranged from 15.08% to 15.86%, 15.30% to 15.91%, and 15.10% to 15.95%, respectively. The outlet moisture control accuracy ranged from −0.42% to 0.36%, −0.2% to 0.41%, and −0.4% to 0.45%, respectively. Analysis of grain quality indicators (damage percentage, germination percentage, fatty acid value) and microscopic structure revealed the following order: increasing temperature drying > constant temperature drying > control experiment. Compared with the control experiment adopting the equivalent accumulated temperature window control method, the proposed method exhibited higher control accuracy and stability. By integrating coupled temperature and humidity parameters into the variables, the quality of dried grains was effectively guaranteed.

A comparative study on intermittent heat pump drying process of Chinese cabbage (Brassica campestris L.ssp) seeds

Modelling of moving drying process and analysis of drying characteristics for germinated brown rice under continuous microwave drying

Most of the exergy studies reported were in batch fluidized bed drying process. The continuous dryer plays a pivotal role in industrial usage. In this study, exergy and energy analyses of multistage fluidized bed drying of Barnyard millet with stages connected externally were examined. The dryer was operated by changing wall temperature of stages (313–328 K), air inlet velocity (1.01–1.3 m/s), downcomer height (50–70 mm), and flow rate of solids (5–10 kg/h). Drying characteristics of Barnyard millet by changing process operating conditions of the dryer, were reported and the energy utilization ratio, exergy loss and exergy efficiency of dryer were also reported. The EUR of dryer at steady state has been found to be in the range of 0.25–0.46 and the maximum exergy efficiency of dryer was observed as 0.64 and lowest as 0.371 in the continuous drying process.Practical ApplicationsFluidized beds offer many distinct features and advantages for processing of particulate food materials. To decrease the drying time than that for single stage dryers, continuous multistage fluidized bed dryer was introduced. Reducing moisture content from feed materials in continuous dryers while discharging continuously, helps to process large quantity of solid materials with good quality of product. Several dryers with different modifications and designs have been suggested by many earlier investigators but continuous dryers play significant role in particulate industry. The multistage dryer is designed by connecting two or more fluidized beds internally or externally, where each bed is termed as a stage. Advantages of the multistage dryer are improvement in gas–solid axial mixing, which results in good quality of product. Due to stage‐wise contact of solids with gas, dried product with uniform moisture content can be obtained.