The Analysis And The Calorific Value Prediction Of Coal Quality Test Data

Coal quality data runs through all aspects of the operation of the coal business,So the data analysis and prediction can maximize not only the development offunctional data, but the role of it. A large number of coal quality test data can findsome abnormal data, which can provide accurate, timely, and reliable analytical data.To analyze the correlation between coal calorific value ash, moisture and volatilegrading can provide important theoretical basis for predict the heat prediction and thedata review. It is an important task and direction for coal quality data analysis toresearch reasonable prediction algorithms and means of achieving the calorific valueof coal.The main object of this study is indicators analysis data of a Colliery CoalPreparation Plant Industry. The issue study the algorithm of error coal quality dataanalysis, correlation analysis, calorific value prediction in-depth, and the algorithmintegrated in a network environment implementation. It establishes a systematicprogram of coal quality data analysis processing. At the same time, It solves theproblem of the data transmission lag, sharing and poor problem. It meets therequirements of coal enterprise information construction. The details are as follows:First, the issue selects a new simple structure to analysis of distribution grosserror by analyzing and comparing different outlier analysis algorithms; Secondly,itanalyzes the calorific value of coal and ash, moisture, volatile matter and correlation,and it improves the traditional gray correlation analysis. It uses a gradient of grayrelational analysis to analysis. Then, It uses the same sample data and test data tocompare the regression analysis with neural network, getting an conclusions that theprediction of neural network is better. Finally, the analysis algorithm, the predictionalgorithm and data processing achieve in the network system.