Comparison of Random Forest and Support Vector Regression Models in Predicting Hydrogen Production Process from Biomass

Gülbahar Bilgiç; Ali Emre Gök

Makale detayları

Comparison of Random Forest and Support Vector Regression Models in Predicting Hydrogen Production Process from Biomass

Yazarlar:

Gülbahar Bilgiç | Ali Emre Gök

Yayıncı:

Cukurova University

Ana Başlıklar:

Biyokütle Enerji Sistemleri

Dergi:

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi Vol.39 Issue 2, pp.475-488

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322879

pISSN:

2757-9255

Anahtar kelimeler:

Özet:

 The need for energy in the world is increasing day by day and various energy production methods are used to meet this need. Production of hydrogen from biomass is one of these methods. Hydrogen production from biomass is a promising process to produce hydrogen and energy which has advantages such as the ability to use sustainable energy sources like biomass and solid waste, being carbon neutral, and increasing energy independence thanks to the variation of resources and the availability of local resources. The catalysts used in this process which can be conducted in three separate ways, affect hydrogen and energy production positively or negatively. One of the most important steps in effectively acquiring the ideal amount of product is predicting the outcomes of this procedure. This article compares a support vector regression SVR and random forest RF model to predict how various inputs used to produce hydrogen from biomass will affect hydrogen output. Additionally, the effect of catalyst addition on hydrogen yield in biomass processes was examined. In this context, 57 experimental studies from the literature were selected as a data set. From this data, 90 was selected for training and 10 for testing. The outputs were evaluated according to parameters such as R2, RMSE and MSE. The results show that RF and SVR models can significantly predict catalyst activity and hydrogen production.

 The need for energy in the world is increasing day by day and various energy production methods are used to meet this need. Production of hydrogen from biomass is one of these methods. Hydrogen production from biomass is a promising process to produce hydrogen and energy which has advantages such as the ability to use sustainable energy sources like biomass and solid waste, being carbon neutral, and increasing energy independence thanks to the variation of resources and the availability of local resources. The catalysts used in this process which can be conducted in three separate ways, affect hydrogen and energy production positively or negatively. One of the most important steps in effectively acquiring the ideal amount of product is predicting the outcomes of this procedure. This article compares a support vector regression SVR and random forest RF model to predict how various inputs used to produce hydrogen from biomass will affect hydrogen output. Additionally, the effect of catalyst addition on hydrogen yield in biomass processes was examined. In this context, 57 experimental studies from the literature were selected as a data set. From this data, 90 was selected for training and 10 for testing. The outputs were evaluated according to parameters such as R2, RMSE and MSE. The results show that RF and SVR models can significantly predict catalyst activity and hydrogen production.

 The need for energy in the world is increasing day by day and various energy production methods are used to meet this need. Production of hydrogen from biomass is one of these methods. Hydrogen production from biomass is a promising process to produce hydrogen and energy which has advantages such as the ability to use sustainable energy sources like biomass and solid waste, being carbon neutral, and increasing energy independence thanks to the variation of resources and the availability of local resources. The catalysts used in this process which can be conducted in three separate ways, affect hydrogen and energy production positively or negatively. One of the most important steps in effectively acquiring the ideal amount of product is predicting the outcomes of this procedure. This article compares a support vector regression SVR and random forest RF model to predict how various inputs used to produce hydrogen from biomass will affect hydrogen output. Additionally, the effect of catalyst addition on hydrogen yield in biomass processes was examined. In this context, 57 experimental studies from the literature were selected as a data set. From this data, 90 was selected for training and 10 for testing. The outputs were evaluated according to parameters such as R2, RMSE and MSE. The results show that RF and SVR models can significantly predict catalyst activity and hydrogen production.

 Dünyadaki enerji ihtiyacı günden güne artış göstermekte ve bu ihtiyacın karşılanması için, çeşitli enerji üretim yöntemleri kullanılmaktadır. Bu yöntemlerden biri biyokütleden hidrojen üretimidir. Biyokütle ve atık benzeri yenilenebilir enerji kaynaklarından yararlanma kabiliyeti, karbon nötr olması, kaynak çeşitliliği ve yerel kaynakların kullanılabilirliği sayesinde enerji bağımsızlığını artırması gibi avantajları bulunan biyokütleden hidrojen üretimi, gelecek vaat eden bir hidrojen ve enerji üretim sürecidir. Üç farklı yöntem kullanılarak gerçekleştirilebilen bu süreçte kullanılan katalizörler, hidrojen üretimine olumlu ve olumsuz etki etmektedir. Bu sürecin sonuçlarını tahmin etmek, optimum miktarda ürünü verimli bir şekilde elde etmede kritik bir adımdır. Bu makalede, biyokütleden hidrojen üretmek için kullanılan çeşitli girdilerin hidrojen çıktısını nasıl etkileyeceğini tahmin etmek için bir destek vektör regresyonunu SVR ve rastgele orman RF modelini karşılaştırılmıştır. Ayrıca biyokütle süreçlerinde katalizör ilavesinin hidrojen verimi üzerinde etkisini incelenmiştir. Bu bağlamda literatürden 57 deneysel çalışma veri seti olarak seçilmiştir. Bu verilerden eğitim için 90 ve test için 10 seçilmiştir. Sonuçlar R2, RMSE ve MSE gibi parametrelere göre değerlendirilmiştir. Sonuç olarak RF ve SVR modellerinin katalizör aktivitesini ve hidrojen üretimini önemli ölçüde tahmin edebildiğini göstermektedir.

Makale detayları

Comparison of Random Forest and Support Vector Regression Models in Predicting Hydrogen Production Process from Biomass

Yazarlar:

Gülbahar Bilgiç | Ali Emre Gök

Yayıncı:

Cukurova University

Ana Başlıklar:

Biyokütle Enerji Sistemleri

Dergi:

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi Vol.39 Issue 2, pp.475-488

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322879

pISSN:

2757-9255

Hydrogen

Biomass

Random forest

Support vector regressionHidrojen

Biyokütle

Rastgele orman

Destek vektör regresyonu

Sayı

Makale detayları

Yazarlar: Gülbahar Bilgiç | Ali Emre Gök

Yayıncı: Cukurova University

Ana Başlıklar: Biyokütle Enerji Sistemleri

Dergi: Çukurova Üniversitesi Mühendislik Fakültesi Dergisi Vol.39 Issue 2, pp.475-488

Belge Tipi: Makale

Yayın Tarihi: 11/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001322879

pISSN: 2757-9255

Sayı

Yazarlar:

Gülbahar Bilgiç | Ali Emre Gök

Yayıncı:

Cukurova University

Ana Başlıklar:

Biyokütle Enerji Sistemleri

Dergi:

Çukurova Üniversitesi Mühendislik Fakültesi Dergisi Vol.39 Issue 2, pp.475-488

Belge Tipi:

Makale

Yayın Tarihi:

11/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001322879

pISSN:

2757-9255