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Modeling and On-line Measurement System Design of Infrared Radiation Drying for Fruits and Vegetables

Author: LinXiNa
Tutor: WangXiangYou
School: Shandong University of Technology
Course: Agricultural Mechanization Engineering
Keywords: Infrared Drying model Regression analysis Neural Networks Dynamics
CLC: TS255.3
Type: Master's thesis
Year: 2010
Downloads: 114
Quote: 1
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Abstract

Infrared drying of fruits and vegetables is a complex, non-steady-state heat transfer, mass transfer process, it is not only affected by the impact of the dry conditions, and with the type of material, there are significant differences in the different internal structure, physical and chemical properties and external shape. Infrared drying process influenced by many factors, we do a more in-depth study of the fruits and vegetables infrared drying model, in order to achieve accurate prediction and control of fruit and vegetable infrared drying process. 1, infrared drying characteristics of apple slices and dry quality. Explore the radiated power, radiation distance, material temperature, material thickness, type of material and state infrared drying rate orthogonal experiment to determine the optimal drying process parameters seek drying regular basis, determining the optimal heating program drying cycle, to provide a basis for the design of new technology and traditional equipment improvements, in order to achieve the purpose of energy conservation and improve the drying quality. 2 to determine the moisture content between the dominant factor in the network model structure based on BP neural network. The input layer of the network, the number of neurons in the hidden layer and output layer respectively 5,11,1 distribution of neurons, neural network toolbox in Matlab drying test data as the value of the training and testing samples, after limited times iterative calculations obtained by a reaction of the mathematical model of the internal links of the test data, and realization of the model training and system simulation. The results show that: in the test range, BP neural network can be efficiently and accurately, model quickly, and the predicted and measured values ??of the model fitting better. 3, the traditional drying model for solving nonlinear least squares data fitting, to determine the drying coefficient. Select the heating temperature of 60 ° C, the radiated power is 750W, the radiation distance of 100 mm material thickness of infrared dried apple slices 5mm when test data as the measured value of the sample based on Matlab software, using the Gauss - Newton algorithm, non-traditional drying model linear least squares data fitting solution to determine the drying coefficient. By the coefficient of determination R2 root mean square error, error sum of squares SSE and RMSE goodness of fit evaluation of various traditional drying model evaluation. At the same time, the infrared drying of fruits and vegetables with BP neural network model for comparison. The results show that, with the Modified Page equation-Ⅱ model can better predict and control fruit and vegetable infrared drying process. 4, to improve the test bench design a set of fruits and vegetables infrared drying online real-time detection system. The hardware part of the resistance strain gauge quality sensor AD590 temperature sensor, and the quality and temperature of two physical signal into a weak electrical signals through analog circuit amplifies the electrical signal converted, then PCI8310 data acquisition boards convert analog signals to digital signals and transmitted to a PC store, using VB software design, function transformation voltage value corresponding to mass and temperature through the program, debug and generate drying curves the radiation temperature curve and the temperature of the material change curve, which is accurate, timely reflect the process characteristics of infrared drying of fruits and vegetables.

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