Modeling of grain drying in continuous cross-flow sliding bed dryers

This study measured the thermal effects of corn drying within a continuous cross-flow grain dryer based on a variety of operating conditions. The dryer contains a column of grain that acts as a packed bed in which the air flows through the voids between the kernels. The study analyzed the following parameters and their effects on corn drying: dryer column thickness, air flow rate per volume of corn, air drying temperature, and incoming and outgoing corn moisture. A pilot-scale cross flow corn dryer with variable column thickness, variable drying air temperature, and variable fan speed was used to experimentally dry corn. The pilot scale dryer has a drying column height of 3.35 m (132 in.), column width of 0.61 m (24 in.) and a variable thickness of 0.203 m to 0.305 m (8 to 12 in.). An array of thermocouples was arranged through the packed bed of corn to measure the thermal profile as the air propagated through the corn. The thermal profiles from the experiments were compared and evaluated among the experiments. In the United States, corn is a primary grain commodity. Improved farming practices, in conjunction with improved grain genetics, have resulted in increased grain yields and the ability to grow crops in locations not possible two decades prior. After harvesting, most grains require supplemental drying to prevent spoilage. Continuous flow grain dryers have become a common method of drying and conditioning large amounts of grain. Grain dryers are required to dry grain faster and more efficiently without sacrificing grain quality. However, higher energy costs and increased crop yields have made grain drying the second largest expense for grain producers due to their high energy consumption of propane or natural gas. The overarching goal of this study is to determine the primary factors that influence heat propagation within the packed bed of grain with the intention of incorporating these effects into numerical grain drying models.

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Parameters Online Detection and Model Predictive Control during the Grain Drying Process

Mathematical Problems in Engineering ◽

10.1155/2013/924698 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Lihui Zhang ◽

Helei Cui ◽

Hongli Li ◽

Feng Han ◽

Yaqiu Zhang ◽

...

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Constant Velocity ◽

Variable Temperature ◽

Structural Characteristics ◽

Cross Flow ◽

System Control ◽

Drying Process ◽

Grain Drying ◽

Set Up

In order to improve the grain drying quality and automation level, combined with the structural characteristics of the cross-flow circulation grain dryer designed and developed by us, the temperature, moisture, and other parameters measuring sensors were placed on the dryer, to achieve online automatic detection of process parameters during the grain drying process. A drying model predictive control system was set up. A grain dry predictive control model at constant velocity and variable temperature was established, in which the entire process was dried at constant velocity (i.e., precipitation rate per hour is a constant) and variable temperature. Combining PC with PLC, and based on LabVIEW, a system control platform was designed.

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Creating an Application to Predict Operational Characteristics and Efficiency of Continuous Cross-Flow Corn Drying

Volume 3: Gas Turbine Heat Transfer; Transport Phenomena in Materials Processing and Manufacturing; Heat Transfer in Electronic Equipment; Symposium in Honor of Professor Richard Goldstein; Symposium in Honor of Prof. Spalding; Symposium in Honor of Prof. Arthur E. Bergles ◽

10.1115/ht2013-17327 ◽

2013 ◽

Author(s):

Matthew L. Olson ◽

Stephen P. Gent ◽

Taylor N. Suess ◽

Michael P. Twedt

Keyword(s):

Cross Flow ◽

Ease Of Use ◽

Operating Conditions ◽

Operational Characteristics ◽

Grain Drying ◽

Improve Accuracy ◽

Drying Models ◽

Drying System ◽

Page Equation ◽

Drying Conditions

The purpose of this study is to create a computer simulation which numerically predicts the drying conditions within a continuous cross-flow grain drying system. The model is based on a system of four partial differential equations using energy and mass balances for the air, grain, and moisture within the column. This simulation includes: (1) a graphical user interface for varying the operating conditions, (2) a numerical scheme for solving the system of equations based on a backwards finite difference scheme, and (3) graphical and tabular output data. The output includes graphs of moisture content, air temperature, and grain temperature inside the column, as well as the predicted energy consumption of the system. Using this program, the grain drying model is analyzed in order to gain insight towards the optimal operating conditions for the grain dryer. The study also makes adjustments to the model in order to improve accuracy and ease of use. In particular, the Page equation for single-kernel drying is implemented. Model assumptions are also analyzed for validity, and the solutions are verified using experimental data collected in a previous study. The overall goal of this research is to improve grain dryer design and optimize operating conditions in order to reduce energy costs, improve grain quality, and increase the understanding of deep bed grain drying models.

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