Sadaghıanı, Omıd Karımı

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O.K.Sadaghıanı
O., Sadaghiani
O.,Sadaghıanı
O.K.Sadaghiani
S., Omid Karimi
Sadaghıanı,O.K.
Omid Karimi, Sadaghiani
Sadaghıanı, Omıd Karımı
Omıd Karımı, Sadaghıanı
Sadaghiani,O.K.
Sadaghiani, Omid Karimi
S.,Omid Karimi
S.,Omıd Karımı
Karimi Sadaghiani, Omid
Karimi Sadaghiani,O.
Job Title
Doktor Öğretim Üyesi
Email Address
omid.sadaghiani@atilim.edu.tr
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Scholarly Output

5

Articles

2

Citation Count

5

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0

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2023 - 20245
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Now showing 1 - 5 of 5
  • Thumbnail Image
    Review
    Citation Count: 0
    A systematic review on smart waste biomass production using machine learning and deep learning
    (Springer, 2023) Sadaghıanı, Omıd Karımı; Sadaghiani, Omid Karimi; Energy Systems Engineering
    The 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.
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    Review
    Citation Count: 1
    A review on the applications of machine learning and deep learning in agriculture section for the production of crop biomass raw materials
    (Taylor & Francis inc, 2023) Sadaghıanı, Omıd Karımı; Karimi Sadaghiani, Omid; Energy Systems Engineering
    The application of biomass, as an energy resource, depends on four main steps of production, pre-treatment, bio-refinery, and upgrading. This work reviews Machine Learning applications in the biomass production step with focusing on agriculture crops. By investigating numerous related works, it is concluded that there is a considerable reviewing gap in collecting the applications of Machine Learning in crop biomass production. To fill this gap by the current work, the origin of biomass raw materials is explained, and the application of Machine Learning in this section is scrutinized. Then, the kinds and resources of biomass as well as the role of machine learning in these fields are reviewed. Meanwhile, the sustainable production of farming-origin biomass and the effective factors in this issue are explained, and the application of Machine Learning in these areas are surveyed. Summarily, after analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in crop biomass production areas to enhance the crops production quantity, quality, and sustainability, improve the predictions, decrease the costs, and diminish the products losses. According to the statistical analysis, in 19% of the studies conducted about the application of Machine Learning and Deep Learning in crop biomass raw materials, Artificial Neural Network (ANN) algorithm has been applied. Afterward, the Random Forest (RF) and Super Vector Machine (SVM) are the second and third most-utilized algorithms applied in 17% and 15% of studies, respectively. Meanwhile, 26% of studies focused on the applications of Machine Learning and Deep Learning in the sugar crops. At the second and third places, the starchy crops and algae with 23% and 21% received more attention of researchers in the utilization of Machine Learning and Deep Learning techniques.
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    Article
    Citation Count: 4
    Enhancement 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 Engineering
    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 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.
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    Review
    Citation Count: 0
    Machine 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 Engineering
    This 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.
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    Article
    Citation Count: 0
    Thermodynamic Modeling and Multi-Objective Optimization of a New System Presented for Reutilization of the Lost Heat in Combined-Cycle Power Plants
    (John Wiley and Sons Inc, 2023) Sadaghıanı, Omıd Karımı; Karimi Sadaghiani,O.; Energy Systems Engineering
    In combined-cycle power plants, a large amount of thermal energy is lost when the boiler and steam unit are out of order and the gas unit is operated in single mode. For the first time, this work suggests every combined-cycle power plants should be equipped with this kind of energy system to recover the waste heat by producing hydrogen and generating electricity. This system combines a Rankine cycle with a thermoelectric generator, a finned-tube heat exchanger, and a proton exchange membrane to produce hydrogen. Having been designed, the suggested energy system is assessed by energy, exergy, and exergo-economy laws. Furthermore, the impacts of some effective factors on the efficiency and the costs are precisely analyzed. Eventually, the presented system is optimized considering two main purposes of exergy efficiency and costs. The achieved results show that the proposed system can effectively link to the gas unit to restore and even save the lost thermal energy in the single-mode condition. The conducted optimization attenuates the objective parameter of exergy efficiency from 48.39% to 41.65% and diminishes the costs from 550.14 to 480.82 $ GJ−1. Eventually, the optimization causes (Formula presented.) to rise from 1.2 to 1.32 kg h−1. © 2023 The Authors. Energy Technology published by Wiley-VCH GmbH.