The Detection Method Of Multi-parameter Of Gas-solid Two-phase Flow Based On Flow Regime Image In Fluidized Bed

As one of most important forms in multiphase flow, gas-solid two-phase flow widely exists in modern industrial process, such as non-ferrous metal, metallurgy, building material, electric power, chemical engineering and food industry, etc. For the complex characters of two-phase flow, it's really difficult to accurately measure those flow parameters. Many detection techniques and methods of two-phase flow are still at laboratory research stage, which is extremely incompact with universality of two-phase flow in the engineering field. Therefore, developing the new technique for the measure and analysis of multi-phase flow parameters are vital to the analysis of moving mechanism of gas-solid two-phase flow, and to the design and operation of related instruments.High-speed photography method is applied to the flow parameters measurement in fluidized bed, and achieves a visual, non-contact measurement, which does not interfere with the normal operation of the production equipment. Moreover, in the high speed gas-solid two-phase flow, we can adjust the frequency of snapping to obtain more clear flow images. Therefore, it has a larger scope of application. Based on a large amount of experimental data, image processing, artifical neural network, optical analysis method and multi-fractal theory have been employed in flow parameters detection. Flow image-based multi-parameters detection method is discussed systematically from the aspects of theory and experiment.Firstly, the gas-solid two-phase flow images are captured by high-speed digital video systems in the fluidized bed. After pretreatment of flow image samples, the different eigenvectors of flow regime are obtained by extracting statistical characteristics of the gray histogram of images, texture characteristics by Fourier transformation, wavelet-fractal characteristics and multi-fractal characteristics. Next, flow regime identification model by utilizing BP (Back Propagation) neutral network, probabilistic neutral network and genetic neutral network are trained by using those eigenvectors as flow regime samples. So, the intelligent identification of flow regime is realized. The test results show that the combination of image texture characteristics by Fourier transformation and probabilistic neutral network is the best model among these models, but the difference of identifying rate is not distinct. Wavelet-fractal characteristics and multi-fractal characteristics can expose the laws of internal movement of gas-solid two-phase flow. Thus, it's more suitable for the analysis of movement mechanism of gas-solid two-phase flow in fluidized bed.Secondly, we expand the scope of detectable flow parameters by using image method in dilute phase flow regime, and achieve measurement of volume voidage.Finally, we introduce the analytic optical flow method into measurement of flow field, velocity field and vorticity field in gas-solid fluidized bed, and discuss velocity distribution of typical flow regime in fluidized bed. We provide an effective diagnostic tool to analyse moving mechanism of gas-solid two-phase flow, and guide the design of related equipments. Therefore, we develop a new method in the measuremnet of flow parameters of gas-solid flow form the views of theory and technique.