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Estimation of Calorific Values of Some of the Turkish Lignites by Artificial Neural Network and Multiple Regressions


Engin Özdemir* and Didem Eren Sarici   Pages 1 - 9 ( 9 )


Background: The calorific value is the most important and effective factors of lignites in terms of energy resources. Humidity, ash content, volatile matter and sulfur content are the main factors affecting lignite's calorific values.

Objective: Determination of calorific value is a process that takes time and cost for businesses. Therefore, estimating the calorific value from the developed models by using other parameters will benefit enterprises in term of time, cost and labor

Method: In this study calorific values were estimated by using artificial neural network and multiple regression models by using lignite data of 30 different regions. As input parameters, humidity, ash content and volatile matter values are used. In addition, the mean absolute percentage error and the significance coefficient values were determined.

Results: Mean absolute percentage error values were found to be below 10%. There is a strong relationship between calorific values and other properties (R2> 90).

Conclusion: As a result, artificial neural network and multiple regression models proposed in this study was shown to successfully estimate the calorific value of lignites without performing laboratory analyses.


Energy, multiple artificial neural network, calorific value regression, Turkish lignite


Department of Mining Engineering, Inonu University, Malatya, Department of Mining Engineering, Inonu University, Malatya

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