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akaturk Akademik ölçüm

Makale detayı · 2023

A systematic review on smart waste biomass production using machine learning and deep learning

YÖKSİS OpenAlex Açık erişim · hybrid SJR Q2 JCR Q3 Atıf 19 Yüzdelik 80.8% FWCI 1.6
Yıl
2023
Tür
article

Veri kaynağı ayrımı

  • YÖKSİS YÖKSİS makale kaydı
  • YÖKSİS dergi adı Journal of Material Cycles and Waste Management
  • Katalog eşleşmesi (ISSN) Journal of Material Cycles and Waste Management
  • OpenAlex OpenAlex zenginleştirmesi (özet, atıf, konular)

Özet

OpenAlex · İngilizce

Forest is considered a significant source of woody biomass production . Sustainable production of wood, lower emittance of CO2 from burning, and lower amount of sulfur and heavy metals are the advantages of wood rather than fossil fuels . The quality and quantity of woody biomass production are a function of some operations including genetic modifications, high-quality forestry , evaluation, monitoring, storage, and transportation. Due to surveying numerous related works, it was found that there is a considerable reviewing gap in analyzing and collecting the applications of Machine Learning in the quality and quantity of woody biomass. To fill this gap in the current work, the above-mentioned operations are explained followed by the applications of Machine Learning algorithms. Conclusively, Machine Learning and Deep Learning can be employed in estimating main effective factors on trees growth , classification of seeds, trees, and regions, as well as providing decision-making tools for farmers or governors, evaluation of biomass, understanding the relation between the woody bimass internal structure and bio-fuel production, the ultimate and proximate analyses, prediction of wood contents and dimensions, determination of the proportion of mixed woody materials, monitoring for early disease identification and classification, classifying trees diseases, estimating evapotranspiration , collecting information about forest regions and its quality, nitrogen concentration in trees, choosing viable storage sites for storage depots and improving the solution, classifying different filling levels in silage, estimating acetic acid synthesis and aerobic reactions in silage, determining crop quantity in silo, estimating the methane production, and monitoring and predicting water content, quality and quantity of stored biomass, forecasting the demand, path way and on-time performance predicting, truck traffic predicting, and behavioral analysis and facility planning.

Konular

Atıflar

OpenAlex cited_by_count. WoS veya Scopus atıf sayısı değildir; o kaynaklar için ayrı kolon yoktur.

19 atıf

OpenAlex cited_by_count (önbellek / veritabanı)

Yerel katalogda bu makaleye atıf yapan 2 yayın (OpenAlex referans eşleşmesi; tam dünya listesi değildir).

  1. Machine learning integrated solvothermal liquefaction of lignocellulosic biomass to maximize bio-oil yield 2025 Atıf 3 · OpenAlex
  2. Machine learning integrated solvothermal liquefaction of lignocellulosic biomass to maximize bio-oil yield 2025 Atıf 3 · OpenAlex

Yazarlar

  1. OMID KARIMI SADAGHIANI TÜRK HAVA KURUMU ÜNİVERSİTESİ