Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms-Reference-Cited by-同舟云学术

Forecasting of energy-related carbon dioxide emission using ANN combined with hybrid metaheuristic optimization algorithms

Published:2024-02-29 Issue:1 Volume:18 Page:
ISSN:1994-2060
Container-title:Engineering Applications of Computational Fluid Mechanics
language:en
Short-container-title:Engineering Applications of Computational Fluid Mechanics

Author:

Moayedi Hossein¹²,Mukhtar Azfarizal³,Khedher Nidhal Ben⁴⁵,Elbadawi Isam⁶,Amara Mouldi Ben⁷,TT Quynh¹²,Khalilpoor Nima⁸

Affiliation:

1. Institute of Research and Development, Duy Tan University, Da Nang, Vietnam

2. School of Engineering & Technology, Duy Tan University, Da Nang, Vietnam

3. Institute of Sustainable Energy, Putrajaya Campus, Universiti Tenaga Nasional, Kajang, Malaysia

4. Department of Mechanical Engineering, College of Engineering, University of Ha'il, Ha'il, Saudi Arabia

5. Laboratory of Thermal and Energetic Systems Studies (LESTE) at the National School of Engineering of Monastir, University of Monastir, Monastir, Tunisia

6. Department of Industrial Engineering, College of Engineering, University of Ha'il, Ha'il, Saudi Arabia

7. Applied college, University of Ha'il, Ha'il, Saudi Arabia

8. Department of Energy Engineering, Graduate School of the Environment and Energy, Science and Research Branch, Islamic Azad University, Tehran, Iran

Funder

Scientific Research Deanship at the University of Ha'il Saudi Arabia

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/19942060.2024.2322509

Reference70 articles.

1. Modelling carbon emission intensity: Application of artificial neural network

2. Current Status Investigation and Predicting Carbon Dioxide Emission in Latin American Countries by Connectionist Models

3. Application of the multi-objective optimization method for designing a powered Stirling heat engine: Design with maximized power, thermal efficiency and minimized pressure loss

4. The New Prediction Methodology for CO2 Emission to Ensure Energy Sustainability with the Hybrid Artificial Neural Network Approach

5. An optimized neuro-fuzzy system using advance nature-inspired Aquila and Salp swarm algorithms for smart predictive residual and solubility carbon trapping efficiency in underground storage formations

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