| Since Chinese medicine extraction is happened in closed container,conventional methods are difficult to detect internal material saturation, thedistribution of flow pattern, flow, and particle size. Electrical capacitancetomography can detect parameters of sealed containers and with other measuringinstruments or technologies can detect real-time velocity. It’s important to automaticcontrol of Chinese medicine extraction.In this paper, Chinese medicine extraction process is studied based on ECT, andthe main contents are as follows:Fistly, the composition of ECT system, technical characteristics and ECTworking priciple are described. The use of finite element method proved sensitive forhigh-k dielectric field effect.Secondly, Chinese medicine extraction process parameter detectors areoptimized. Analyze the sensitive field of uniformity and structural parameter basedon dynamic countercurrent extraction process, the optimal function is established,and the optimization of the sensor is determined. It provides theoretical support tostudy detector and data acquisition system.Thirdly, the flow regime identification and image reconstruction duringextraction process is depth studied. Analyzed capacitance distribution characteristicsand proposed flow pattern identification method based on feature extraction and BPneural network. For the slow rate of convergence in image reconstruction process, animproved polynomial acceleration algorithm is proposed. It allows the distortion issmall of reconstruction image and with the anti-noise ability.Forthly, complex process parameters to be measured in Chinese medicineextraction are detected. It needs to measure the distribution of flow pattern, internal material saturation, particle size and flow during extracting Salvia. In this paper, themeasure capacitance, the design of sensor, analysis of flow pattern recognition andimage reconstruction are simulated, which as a theoretical foundation for the actualproduction process. |
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