Study On Mathematical Models Of Predicting Coal Spontaneous Combustion

Prediction of coal spontaneous combustion is very important for preventing coal self-ignition. Testing of self-ignition trend and time is the basis of prediction technique of coal spontaneous combustion. The way to test self-ignition time accurately is spontaneous combustion simulating experiment, which takes more than one month and over 1 ton of coal sample. In this text, an improved oil-bath programmed temperature oxidation experimental device is adopted, which can test oxygen consumption and gas generation rate of coal during self-heating process precisely. It is thinked that there is corresponding relation between self-ignition duration and oxygen consumption rate, carbon monoxide as well as carbon dioxide generation rate of the coal at different temperature of self heating process. According to the corresponding relation,three prediction models are built in this text which are fuzzy clustering and fuzzy model identification method,minimal two multiplication method and artificial neural networks method.These three models can predict the coal spontaneous combustion.Fuzzy clustering and fuzzy model identification method :in the first ,using fuzzy clustering classfies the known coal self-ignition duration data,then according to the programmed temperature oxidation experimental data, using fuzzy model identification predicts the unknown coal self-ignition duration.Because using this method predicts according to the classfication result, so the calssfication result is the fuzzy result and we can not receive the precise self-ignition time.On the basis of theoretical analysis ,the theoretical model of corresponding relation between self-ignition duration and oxygen consumption rate, carbon monoxide as well as carbon dioxide generation rate of the coal at different temperature is built.Using minimal two multiplication predicting method can obtain coefficient of the model. So we can predict the coal self-ignition time and analyse the coal self-ignition trend. Because the model build on the theory basis,using this method we can receive preciser result ,but the calculation process is intricacy.Artificial neural network method is built according to the corresponding relation between oxygen consumption rate, carbon monoxide as well as carbon dioxide generation rate of the coal at different temperature and self-ignition duration .Train the artificial neural network using known coal self-ignition duration experiment data, so we can receive the joint strenght of nerve cell. Substitute the programmed temperature oxidation experimental data and coal quality analysis data in the artificial neural network,so we can calculate the coal experiment self-ignition time.This method is convenience and we can estimate precision of prediction.But abundant sample is needed to train the net.In practical work,we can apply different mathematicl predicting methods to predict the coal spontaneous combustion according to the known data and requirement.