Coal Cleaning Products Ash Content Distribution Forecasting Method Research And Application

Coal preparation plant reasonably arrange products structure in practical production needs predict product index in time, in the traditional coal cleaning calculation, whatever method we use, whatever index according to, predict product ash equals to the raw coal ash, changing just production rate. However lots of experiment results have proved, in different ways, index, the product ash is different to the raw coal ash. This makes the whole prediction result much error, and thus cannot accurately represent the actual situation.From a large number of production data can be obtained that product ash is influenced by the raw coal ash and production condition.Through the original data sorting, we calculate distribution rate, the possible deviation (mechanical error), theoretical sorting density, Analysis found that a relationship between product ash and distribution rate, the possible deviation, theoretical sorting density. Using the regression analysis method we establish multivariate linear regression equation of product ash and distribution rate, the possible deviation, theoretical sorting density. Through calculating statistic F we know regression equation have high significant, trough testing the regression coefficients significant, we get the raw coal ash has the biggest influence. Compare the traditional forecasting method to regression analysis method,we find forecasting results of regression analysis method is closer to the experimental values, so this method can be applied in practical production.In the actual production of coal preparation plant process we often encounter block-fine coal separation problem, ash prediction model is applied to calculating block-fine coal separation density and maximum washed coal production rate, so we can get closer prediction result to the objective fact.The application of product ash prediction model in coal washing plant, making product index forecasting results accurately. It has great significance in formulating correct production decision-making and can ensure the maximum comprehensive economic benefit in practical production. Figure 14; table 25; reference 24...