| The main purpose of this paper is to improve the performance of coal-water slurry and establish high-precision blending CWS performance prediction models, applicate regression models and neural network models and other forecasting models to research on the coal-water slurry preparation and development.First of all, through the experiment effects of the physical and chemical characteristics on coal slurry parameters have been researched, while coals Mad, Aad, and Oad several factors was investigated in coal slurry parameters. From the experimental results we can find that coal-forming slurry is determined by a variety of factors, and also the relationship between several factors is more complex, and a separate analysis of the impact factors of a coal-water slurry-is not scientific.A variety of single coal are different, mixed into a slurry of different coals in the process of mutual influence and·constraints, blending into a slurry of coal characteristics of each component can not be a simple sum, but it shows a very complex non-linear characteristics, while the increasing emergence of neural network technology to solve nonlinear problems is an effective way.By analyzing correlation between various natures of coal and coal slurry ability, 10 kinds of factors are selected for regression analysis prediction which is compared with the neural network model. Through the ten factors, nine factors, five factors, four factors and three factor linear and nonlinear analysis, the five factors is the best linear regression model, the error is 1.69%.The ten factors, nine factors, five factors, four factors and three factor the neural network models are analyzed. By comparing the number of optimal model parameters and errors, we fine a five-factor neural network prediction model is the best result, the error is 0.49%, while the number of each type of input factors of neural network prediction model results are better than the corresponding results of the regression equation.
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