ÖZELLİK SEÇİMİ İLE BİRLEŞTİRİLMİŞ DESTEK VEKTÖR MAKİNELERİNİ KULLANARAK KÖMÜRÜN ÜST ISIL DEĞERİNİN KISA VE ELEMENTEL ANALİZ DEĞİŞKENLERİNDEN TAHMİNİ

Year 2020, , 1129 - 1141, 07.08.2020

Mustafa Açıkkar

https://doi.org/10.28948/ngumuh.585596

Cited By: 1

Abstract

Üst
ısıl değer (GCV), kömürün belirli bir miktarı yakıldığında açığa çıkan ısı
enerjisi miktarını gösteren temel bir termal özelliğidir. Sunulan çalışmanın
ana amacı, özellik seçimi algoritması ile destek vektör makineleri (SVM'ler)
kullanarak yeni GCV tahmin modelleri geliştirmektir. Bu amaçla, literatürde ilk
kez, özellik seçici RRelief-F algortiması, GCV'nin her bir tahmin edici
değişkeninin önemini belirlemek için kısa ve elementel analiz değişkenlerinden
oluşan veri kümesine uygulanmıştır. Bu şekilde, yedi farklı karma giriş seti
(veri modelleri) oluşturulmuştur. Sunulan modellerin tahmin performansı, çoklu
korelasyon katsayısının karesi (R²), kök ortalama kare hatası (RMSE)
ve ortalama mutlak yüzde hatası (MAPE) ile hesaplanmıştır. Bu çalışmadan elde
edilen tüm sonuçlar değerlendirildiğinde, kısa analizden elde edilen nem (M) ve
kül (A) ile elementel analizden elde edilen karbon (C), hidrojen (H) ve kükürt
(S) değişkenleri kömürün GCV'sini tahmin etmede en uygun değişkenler olarak
belirlenirken, kısa analizden elde edilen uçucu madde ile elementel analizden
elde edilen nitrojenin tahmin etme doğruluğu üzerinde olumlu bir etkiye sahip
olmadığı görülmüştür. M, A, C, H ve S tahmin edici değişkenlerini kullanan
SVM-tabanlı model, en yüksek R² ve en düşük RMSE ve MAPE değerlerini
sırasıyla 0,998, 0,22 Mj/kg ve % 0,66 olarak vermiştir. Ayrıca, karşılaştırma amacıyla
GCV’yi tahmin etmek için çok katmanlı algılayıcı ve radyal temelli fonksiyon
ağı kullanılmıştır.

Keywords

kömürün üst ısıl değeri, özellik seçimi, destek vektör makinesi, yapay sinir ağları

PREDICTION OF GROSS CALORIFIC VALUE OF COAL FROM PROXIMATE AND ULTIMATE ANALYSIS VARIABLES USING SUPPORT VECTOR MACHINES WITH FEATURE SELECTION

Abstract

The
gross calorific value (GCV) is an essential thermal property of coal which
indicates the amount of heat energy that could be released by burning a
specific quantity. The primary objective of the presented study is to develop
new GCV prediction models using support vector machines (SVMs) combined with
feature selection algorithm. For this purpose, the feature selector RReliefF is
applied to the dataset consisting of proximate and ultimate analysis variables
to determine the importance of each predictor of GCV. In this way, seven
different hybrid input sets (data models) were constructed. The prediction
performance of models was computed by using the square of multiple correlation
coefficient (R²), root mean square error (RMSE), and mean absolute
percentage error (MAPE). Considering all the results obtained from this study,
the predictor variables moisture (M) and ash (A) obtained from the proximate
analysis and carbon (C), hydrogen (H) and sulfur (S) obtained from the ultimate
analysis were found to be the most relevant variables in predicting GCV of
coal, while the predictor variables volatile matter from the proximate analysis
and nitrogen from the ultimate analysis did not have a positive effect on the
prediction accuracy. The SVM-based model using the predictor variables M, A, C,
H, and S yielded the highest R² and the lowest RMSE and MAPE with
0.998, 0.22 MJ/kg, and 0.66%, respectively. For comparison purposes, multilayer
perceptron and radial basis function network were also used to predict GCV.

Keywords

Gross calorific value of coal, feature selection, support vector machines, artificial neural network

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