Artificial Neural Network Modeling of the Spontaneous Combustion Occurring in the Industrial-scale Coal Stockpiles with 10-18 mm Coal Grain Sizes

Companies consuming large amounts of coal should work with coal stocks in order to not face problems due to production delays. The industrial-scale stockpiles formed for the aforementioned reasons cause environmental problems and economic losses for the companies. This study was performed in a coal stock area of a large company in Konya, which uses large amounts of coal in its manufacturing units. The coal stockpile with 5 m width, 10 m length, 3 m height, and having 120 tons of weight was formed in the coal stock area of the company. The inner temperature data of the stockpile was recorded by 17 temperature sensors placed inside the stockpile at certain points. In order to achieve this goal, the electrical signal conversion of temperatures sensed by 17 temperature sensors placed in certain points inside the coal stockpile, the transfer of these electrical signals into computer media by using analog-digital conversion unit after applying necessary filtration and upgrading processes, and the record of these information into a database in particular time intervals are provided. Additionally, the data relating to the air temperature, air humidity, atmospheric pressure, wind velocity, and wind direction that are the parameters affecting the coal stockpile were also recorded. Afterwards, these measurement values were used for training and testing of an artificial neural network model. Comparison of the experimental and artificial neural network results, accuracy rates of training and testing were found to be 99.5% and 99.17%, respectively. It is shown that possible coal stockpile behavior with this artificial neural network model is powerfully estimated.