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Simulating and Anticipating the Technology Process During Vacuum Freeze-drying

Author: WenHaiJun
Tutor: GuoYuMing
School: Shanxi Agricultural University
Course: Agricultural Mechanization Engineering
Keywords: Vacuum freeze-drying Artificial Neural Networks Process Simulation analysis
CLC: TQ028
Type: Master's thesis
Year: 2004
Downloads: 524
Quote: 6
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Abstract

Vacuum freeze-drying technology fully retains the nutrients and active substances of the material to be dried, and good to keep to its original natural color and flavor and shape. Has been widely used in the food, pharmaceutical and biological products industry, but because of high energy consumption, high cost, limiting its development in the agricultural product processing industry. To do this study and exploration of low power consumption freeze-drying process has important practical significance. Most of the research in this area using the test method, time-consuming due to the diversity of influencing factors and testing process, making slow progress and is not easy. Therefore, this study, based on this background, the study aimed at finding a simulation analysis of the process of freeze-drying method. First in the Summary of neural network and applications based on sets of neural network for the rationality and feasibility of the simulation analysis. And then apply the theory of artificial neural network system to vacuum freeze-drying process modeling and process prediction of MATLAB programming, the use of neural network toolbox to establish a BP artificial neural network model, the network simulation of the freeze-drying process. On the basis of experience of the freeze-drying process conditions of the test material obtained on the Internet, the use of neural network to predict the freeze-drying chamber pressure, material thickness and the heating plate temperature of lyophilized energy and time. The results show that the neural network to predict the vacuum freeze-drying process is feasible for the study of the vacuum freeze-drying process parameters impact on energy consumption and optimize the process of guiding significance and practical value. Carrots as a model material, in-depth and systematic study of the impact of various process parameters on the drying process. The results showed that: the thickness of the freeze-dried, the temperature of the heating plate, the lyophilization chamber pressure is the most important of the three process parameters; thickness of material is a key factor affecting the freeze-drying process; the heated mixed lyophilised time was significantly less than pure radiant heating, with a more high economy. Experimental results as a neural network training and testing samples, the freeze-drying process neural network model training and testing. The physical model under the test conditions, the establishment of a vacuum freeze-drying the non-steady-state mathematical model, based on appropriate assumptions. The analytic solution of the model simplifies the quasi-steady-state model. And neural network simulation results and experimental results are compared and analyzed, the results show that the neural network simulation value under certain conditions, instead of the mathematical model and the freeze-drying process characteristics. Forecast freeze-dried and the actual match.

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