{'fraction_non_alphanumeric': 0.01780185758513932, 'fraction_numerical': 0.005417956656346749, 'mean_word_length': 4.986111111111111, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 1, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Estimation of Calorific Values of Some of the Turkish Lignites by Artificial Neural Network and Multiple Regressions



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.



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.



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.



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



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.
