Yıkanmış Türk Linyit Kömürlerinin Üst Isıl Değerinin Destek Vektör Regresyonu ile Tahmini

Yıl 2020, Sayı: 18, 16 - 24, 15.04.2020

Mustafa Açıkkar Osman Sivrikaya

https://doi.org/10.31590/ejosat.642676

Cited By: 1

Öz

Bu çalışmada yıkanmış Türk
linyit kömürlerinin üst ısıl değeri (GCV), makine öğrenmesi yöntemleri ile
kömür numunelerinin kuru baz kısa analiz sonuçları kullanılarak tahmin
edilmiştir. Laboratuvar kömür analiz sonuçlarından elde edilen kül (A), uçucu
madde (VM), kükürt (S) ve GCV değişkenleri kullanılarak veri kümesi
oluşturulmuştur. Veri kümesine, Destek Vektör Regresyonu (SVR) ile Çok Katmanlı
Algılayıcı (MLP), Genel Regresyon Sinir Ağı (GRNN) ve Radyal Temelli Fonksiyon
Sinir Ağı (RBFN) olmak üzere üç farklı Yapay Sinir Ağı (ANN) uygulanarak GCV
tahmin modelleri geliştirilmiştir. Geliştirilen modellerin performans
genelleştirme kabiliyeti 10-katlı çapraz-doğrulama kullanılarak sağlanmış ve
modellerin tahmin doğruluğu, performans ölçütleri Çoklu Korelasyon Katsayısı (R), Kök Ortalama Kare Hatası
(RMSE), Ortalama Mutlak Hata (MAE) ve Ortalama Mutlak Yüzde Hata (MAPE)
kullanılarak hesaplanmıştır. Sonuçlar, GCV tahmini için, tüm modeller arasında
SVR tabanlı modelin ANN tabanlı modellere göre biraz daha iyi, ANN tabanlı
modeller arasında ise RBFN tabanlı modelin MLP ve GRNN tabanlı modellere göre
daha iyi performans gösterdiğini ortaya koymuştur.

Anahtar Kelimeler

Üst ısıl değer, destek vektör regresyonu, çok katmanlı algılayıcı, genel regresyon sinir ağı, radyal temelli fonksiyon sinir ağı

Destekleyen Kurum

Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi Bilimsel Araştırma Projesi Birimi

Proje Numarası

17119001

Prediction of Gross Calorific Value of Washed Turkish Lignite Coals with Support Vector Regression

Öz

In this study, the gross calorific value (GCV)
of washed Turkish lignite coals was predicted by using dry-basis proximate
analysis data of coal samples with machine learning methods. The data set was
generated by using ash (A), volatile matter (VM), sulfur (S) and GCV variables
obtained from the analysis results. The GCV prediction models were developed by
applying Support Vector Regression (SVR) and three different Artificial Neural
Networks (ANNs), namely Multi-Layer Perceptron (MLP), General Regression Neural
Network (GRNN) and Radial Basis Function Neural Network (RBFN), separately to
the data set. The generalization capability of the developed models was ensured
by using 10-fold cross-validation, and the prediction accuracy of the models
was calculated by using performance metrics Multiple Correlation Coefficient (R), Root Mean Square Error
(RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE).
For GCV prediction, the results reveal that the SVR-based model performed
slightly better than the ANN-based models and among the ANN-based models, the
RBFN-based model performed better than MLP- and GRNN-based models.

Anahtar Kelimeler

Gross calorific value, support vector regression, multi-layer perceptron, general regression neural network, radial basis function neural network

Proje Numarası

17119001

Kaynakça

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