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

摘要

Background: The calorific value is the most important and effective factors oflignites in terms of energy resources. Humidity, ash content, volatile matter and sulfur contentare 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 otherparameters will benefit enterprises in term of time, cost and labor.Method: In this study calorific values were estimated by using artificial neural networkand 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 meanabsolute percentage error and the significance coefficient values were determined.Results: Mean absolute percentage error values were found to be below 10%. There is astrong relationship between calorific values and other properties (R2> 90).Conclusion: As a result, artificial neural network and multiple regression models proposedin this study was shown to successfully estimate the calorific value of lignites without performinglaboratory analyses.