İstatistiksel ve Makine Öğrenmeye Dayalı Yaklaşımlarla Kobalt Katalizör Üzerinden Metan Kuru Reformundan Elde Edilen Sentez Gazının Tahmini Modellemesi

Year 2020, Volume: 32 Issue: 1, 8 - 14, 31.03.2020

Furkan Elmaz Özgün Yücel , Ali Yener Mutlu

https://doi.org/10.7240/jeps.558373

Cited By: 3

Abstract

Metanın kuru reformlanması, CO₂
emisyonunu azaltmak ve çeşitli Fischer-Tropsch sentezlerinde ve sentez
gazlarının üretiminde kullanmak için umut verici bir yöntemdir. İstenen
ürünleri verimli bir şekilde elde etmek için, reaktantların ürünler üzerindeki
etkisi kesin olarak bilinmelidir. Bu amaçla, yapay-zeka bazlı veri odaklı
tahmin modelleri ile metan kuru reformunun modellenmesi için çeşitli çalışmalar
yayınlanmıştır. Önerilen metotlar, aşırı uyum probleminin araştırılmamasından,
eksik ve/veya yanlı performans değerlendirmelerinden dolayı, sürecin belirli
çıktılarını tahmin etmek için yetersiz kalmıştır. Bu çalışmada 57 örnek içeren
bir veri seti kullanarak üç regresyon yöntemi kullandık ve tahmin modelleri
geliştirdik. Modellerin performans değerlendirmeleri, tarafsız sonuçlar elde
etmek için, 10 katlı çapraz doğrulama ile gerçekleştirilmiştir. Önerilen
yöntemlerin hem eğitim hem de test performansları ayrı ayrı incelenmiş ve pratikte
uygulanabilirliği tartışılmıştır.

Keywords

metan, kuru reformlama, destek vektör makineleri, yapay sinir ağları, çapraz-doğrulama

Predictive Modeling of the Syngas Production from Methane Dry Reforming over Cobalt Catalyst with Statistical and Machine Learning Based Approaches

Abstract

Dry reforming of methane is a promising method
to reduce the emission of CO₂ and to use it in various type of
Fischer–Tropsch synthesis and production of syngas. In order to obtain
desirable products efficiently, the effect of reactants on the products must be
known precisely. For this purpose, several studies have published for modeling
the dry reforming of methane process with artificial intelligence-based
data-driven prediction models. Due to lack of investigating overfitting problem
and deficient and/or biased performance evaluations, actual potential of
proposed methods have not been revealed for predicting certain outputs of the
process. In this paper, we employed three regression methods and developed
prediction models using a dataset with 57 observations. Performance evaluations
of the models are performed with 10-fold cross-validation to ensure unbiased
results. Proposed methods’ both training and testing performances are
separately investigated, further applicability is discussed.

Keywords

methane, dry reforming, support vector machine, artificial neural networks, cross-validation

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