Blockage Detection Of Coal Slurry Pipeline

As waste of coal washing industry the coal slurry will cause great pollution to the environment if handled it improperly. The coal slurry is used as fuel in the gangue power plant, and is mixed burning in the circulating fluidized bed boiler. Through the laying of pipe processing method has the great advantage of coal slurry transportation.The conveying system monitoring data of slurry pipeline in Huangling coal gangue power plant is analyzed in this paper. According to the results of the analysis the experimental platform of slurry pipeline conveying system is built. And the blockage fault process of coal slurry in the pipe transportation is simulated, launches the research in view of the following work. The following problem related blockage fault is researched in this paper.According to the different laying type of the coal slurry pipeline that are the straight horizontal, inclined pipe and vertical pipe, analyzing the effect of pressure distribution on characteristic signal of the complex pipeline, and arranging the sensor position reasonable in building simulation system of the coal slurry pipeline conveying.Acquainting and preprocessing the characteristic signal of coal slurry conveying pipeline, comparing the influence of different parameters through the wavelet threshold de-noising, and selecting the appropriate de-noising parameter, and combining with the method of lifting wavelet transform completed de-noising preprocessing and lay the foundation for the follow-up research.Aiming at the blockage fault in the coal slurry pipeline conveying process, putting forward the algorithm of wavelet neural network combined weight and the activation function type, and making this algorithm used to the fault detection of slurry pipeline conveying system. Analyzing location principle of pipeline based on the pressure wave method, and combined with the wavelet transform method, to establish the positioning method combined wavelet and pressure wave, determining the position of fault accurately.