Browsing by Author "Sadaghiani, Omid Karimi"
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Article Citation Count: 4Enhancement of quality and quantity of woody biomass produced in forests using machine learning algorithms(Pergamon-elsevier Science Ltd, 2023) Sadaghıanı, Omıd Karımı; Sadaghiani, Omid Karimi; Energy Systems EngineeringForest 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 identifi-cation 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.Review Citation Count: 0Machine learning for sustainable reutilization of waste materials as energy sources - a comprehensive review(Taylor & Francis inc, 2024) Sadaghıanı, Omıd Karımı; Sadaghiani, Omid Karimi; Energy Systems EngineeringThis work reviews Machine Learning applications in the sustainable utilization of waste materials as energy source so that analysis of the past works exposed the lack of reviewing study. To solve it, the origin of waste biomass raw materials is explained, and the application of Machine Learning in this section is scrutinized. After analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in waste biomass production areas to enhance the quality and quantity of production, improve the predictions, diminish the losses, as well as increase storage and transformation conditions. The positive effects and application with the utilized algorithms and other effective information are collected in this work for the first time. According to the statistical analysis, in 20% out of the studies conducted about the application of Machine Learning and Deep Learning in waste biomass raw materials, Artificial Neural Network (ANN) algorithm has been applied. Afterward, the Super Vector Machine (SVM) and Random Forest (RF) are the second and third most-utilized algorithms applied in 15% and 14% of studies. Meanwhile, 27% of studies focused on the applications of Machine Learning and Deep Learning in the Forest wastes.Review Citation Count: 0A systematic review on smart waste biomass production using machine learning and deep learning(Springer, 2023) Sadaghıanı, Omıd Karımı; Sadaghiani, Omid Karimi; Energy Systems EngineeringThe utilization of waste materials, as an energy resources, requires four main steps of production, pre-treatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the waste biomass production step. By investigating numerous related works, it is concluded that there is a considerable reviewing gap in the surveying and collecting the applications of Machine Learning in the waste biomass. To fill this gap with the current work, the kinds and resources of waste biomass as well as the role of Machine Learning and Deep Learning in their development are reviewed. Moreover, the storage and transportation of the wastes are surveyed followed by the application of Machine Learning and Deep Learning in these areas. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in waste biomass production areas to enhance the waste collecting quality and quality, improve the predictions, diminish the losses, as well as increase storage and transformation conditions.