Sadaghıanı, Omıd Karımı

Profile Picture
Profile URL
https://hdl.handle.net/20.500.14411/8274
Name Variants
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
Main Affiliation
Energy Systems Engineering
Status
Former Staff
Website
ORCID ID
Scopus Author ID
Turkish CoHE Profile ID
Google Scholar ID
WoS Researcher ID

Sustainable Development Goals

2

ZERO HUNGER
ZERO HUNGER Logo

2

Research Products

11

SUSTAINABLE CITIES AND COMMUNITIES
SUSTAINABLE CITIES AND COMMUNITIES Logo

0

Research Products

14

LIFE BELOW WATER
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1

Research Products

6

CLEAN WATER AND SANITATION
CLEAN WATER AND SANITATION Logo

0

Research Products

1

NO POVERTY
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0

Research Products

5

GENDER EQUALITY
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0

Research Products

9

INDUSTRY, INNOVATION AND INFRASTRUCTURE
INDUSTRY, INNOVATION AND INFRASTRUCTURE Logo

0

Research Products

16

PEACE, JUSTICE AND STRONG INSTITUTIONS
PEACE, JUSTICE AND STRONG INSTITUTIONS Logo

0

Research Products

17

PARTNERSHIPS FOR THE GOALS
PARTNERSHIPS FOR THE GOALS Logo

0

Research Products

15

LIFE ON LAND
LIFE ON LAND Logo

3

Research Products

10

REDUCED INEQUALITIES
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0

Research Products

7

AFFORDABLE AND CLEAN ENERGY
AFFORDABLE AND CLEAN ENERGY Logo

4

Research Products

8

DECENT WORK AND ECONOMIC GROWTH
DECENT WORK AND ECONOMIC GROWTH Logo

0

Research Products

4

QUALITY EDUCATION
QUALITY EDUCATION Logo

0

Research Products

12

RESPONSIBLE CONSUMPTION AND PRODUCTION
RESPONSIBLE CONSUMPTION AND PRODUCTION Logo

4

Research Products

3

GOOD HEALTH AND WELL-BEING
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0

Research Products

13

CLIMATE ACTION
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0

Research Products
This researcher does not have a Scopus ID.
This researcher does not have a WoS ID.
Scholarly Output

5

Articles

5

Views / Downloads

12/0

Supervised MSc Theses

0

Supervised PhD Theses

0

WoS Citation Count

27

Scopus Citation Count

39

WoS h-index

3

Scopus h-index

4

Patents

0

Projects

0

WoS Citations per Publication

5.40

Scopus Citations per Publication

7.80

Open Access Source

2

Supervised Theses

0

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JournalCount
Biomass and Bioenergy1
Energy Sources, Part A: Recovery, Utilization, and Environmental Effects1
Energy Technology1
International Journal of Green Energy1
Journal of Material Cycles and Waste Management1
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Scholarly Output Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Review
    Citation - WoS: 2
    Citation - Scopus: 5
    Machine Learning for Sustainable Reutilization of Waste Materials as Energy Sources - a Comprehensive Review
    (Taylor & Francis inc, 2024) Peng, Wei; Sadaghiani, Omid Karimi
    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.
  • Thumbnail Image
    Article
    Citation - Scopus: 1
    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) Peng,W.; Karimi Sadaghiani,O.
    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.
  • Thumbnail Image
    Article
    Citation - WoS: 14
    Citation - Scopus: 17
    Enhancement of Quality and Quantity of Woody Biomass Produced in Forests Using Machine Learning Algorithms
    (Pergamon-elsevier Science Ltd, 2023) Peng, Wei; Sadaghiani, Omid Karimi
    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.
  • Thumbnail Image
    Review
    Citation - WoS: 3
    Citation - Scopus: 5
    A Systematic Review on Smart Waste Biomass Production Using Machine Learning and Deep Learning
    (Springer, 2023) Peng, Wei; Sadaghiani, Omid Karimi
    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.
  • Thumbnail Image
    Review
    Citation - WoS: 8
    Citation - Scopus: 11
    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) Peng, Wei; Karimi Sadaghiani, Omid
    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.