Deep learning-based modelling of pyrolysis

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dc.contributor.authorÖzcan, Alper
dc.contributor.authorKasif, Ahmet
dc.contributor.authorSezgin, İsmail Veli
dc.contributor.authorCatal, Cagatay
dc.contributor.authorSanwal, Muhammad
dc.contributor.authorMerdun, Hasan
dc.date.accessioned2026-02-08T15:11:04Z
dc.date.available2026-02-08T15:11:04Z
dc.date.issued2024
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractPyrolysis is one of the thermochemical methods used to produce value-added products from biomass. Thermogravimetric analysis (TGA) is frequently used to examine the energy potential and thermal behavior of biomass, coal, and their blends. The investigation of the TGA data using Artificial Neural Networks (ANN) is one of the most important research areas in recent years. While there are different research papers on the use of Machine Learning (ML) in this field, there is a lack of systematic application of deep learning (DL) algorithms. As such, we applied DL algorithms together with ML algorithms to evaluate the predictive performance of thermal behaviors of proposed bioenergy sources. Thermal behavior of tomato, pepper, eggplant, squash, and cucumber harvest wastes, the equal mass (20%) mixture of them, and the blends of the mixture with coal in the ratios of 20, 33, and 50% under nitrogen atmosphere were investigated by the TGA and ML models. Based on the pyrolysis thermal behavior of the harvest wastes, the eggplant, pepper, tomato, and 5-biomass mixture had the highest conversion potential. According to the thermal behavior of co-pyrolysis of coal and harvest waste mixtures, it had positive effects on pyrolysis conversion degrees and temperature range compared to the coal, and therefore, they can be used as alternative sources for energy production. The MSE and R2 scores of Bi-directional LSTM demonstrate that an improved performance can be obtained with DL based solutions. Promising results were obtained when the Bi-directional LSTM is applied for modeling the pyrolysis. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.
dc.identifier.doi10.1007/s10586-023-04096-6
dc.identifier.endpage1108
dc.identifier.issn1386-7857
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85164964297
dc.identifier.scopusqualityQ1
dc.identifier.startpage1089
dc.identifier.urihttps://doi.org/10.1007/s10586-023-04096-6
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5225
dc.identifier.volume27
dc.identifier.wosWOS:001032489300001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofCluster Computing
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzScopus_KA_20260207
dc.subjectANN
dc.subjectBi-LSTM
dc.subjectCo-pyrolysis
dc.subjectCoal
dc.subjectDeep learning
dc.subjectGreenhouse wastes
dc.subjectTGA
dc.titleDeep learning-based modelling of pyrolysis
dc.typeArticle

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