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The Study of Thermodynamics Analysis and Data Emulation with Stages for Maize Drying Process

Author: JiaXiaoFeng
Tutor: ZhaoChunYu
School: Northeastern University
Course: Mechanical and Electronic Engineering
Keywords: Drying Models Nonlinear regression analysis algorithm Simulation Moisture Forecast Neural Network
CLC: TK123
Type: Master's thesis
Year: 2008
Downloads: 93
Quote: 1
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Abstract

With the strong development of the world economy, an increasingly prominent food crisis in front of us. To reduce the grain in the drying process of the energy loss, thus improving the quality of grain drying to improve the people's living standard is imminent. Therefore, the mechanism of drying to a machine line intelligent control the moisture content of corn has practical significance. Grain drying is a complex of strongly nonlinear, large delay of heat and mass transfer process, it is not only affected by the material properties and parameters of the media, but also with climatic conditions and drying processes have an important relationship, resulting in accurate control of the quality of food is very final drying difficult. But from the viewpoint of thermodynamics, grain dryer is an open thermodynamic system. Thus, according to grain during drying temperature variation, predicted corn drying characteristics and the machine status and plan for the impact of grain discharge rate, take timely measures to adjust the row of corn grain drying speed is the key to solving the problem can be overcome by drying process rather difficult to control because of the lack of large delay. According to corn in the present value of the detected temperature and other parameters, and establishing the actual model, control theory can provide the basis for grain drying. And in the drying section of the current rate of drying corn layer be controlled to control the loss of water to achieve a final moisture content of the control unit, the corn moisture content of the standard machine. Through the establishment of corn drying paper model to analyze the internal temperature distribution of corn grain and moisture migration mechanism to determine the corn samples during drying temperature change can be used as a machine moisture prediction of the important factors. In four sections downstream of the dryer as an experimental model of corn drying process, collecting the various stages of corn drying process the temperature data, using nonlinear regression analysis algorithm for solving the mathematical model, and the obtained model simulation, the line control provide the basis for a machine of water. Finally, retention obtained by the model obtained value as established by the BP neural network algorithm on a machine corn grain moisture prediction model inputs, and then dried by controlling the motor speed to control the four displacement speed in order to achieve control of the machine grain moisture purposes. In a study of the mathematical model, the use of this line developed the measured temperature values ??to predict grain-containing vapor within the method indirectly reflects the drying process the temperature difference between inside and outside the cereal grains. Practical and effective for the drying process provides a theoretical basis to meet the online real-time control requirements.

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CLC: > Industrial Technology > Energy and Power Engineering > Thermal engineering, heat > Thermal Engineering Theory > Engineering Thermodynamics
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