The Role of Nanotechnology and Artificial Intelligence in Optimizing Thermal Energy Systems

dc.authorwosid A. Hammoodi, Karrar/M-8021-2019
dc.authorwosid Agyekum, Ephraim/Aas-8919-2020
dc.authorwosid Mohammed, Hayder/Aab-1114-2020
dc.authorwosid Rashid, Farhan/M-6680-2017
dc.authorwosid Togun, Hussein/J-5647-2014
dc.authorwosid Kadhim, Saif/E-1009-2019
dc.contributor.author Mohammed, Hayder I.
dc.contributor.author Rashid, Farhan Lafta
dc.contributor.author Togun, Hussein
dc.contributor.author Agyekum, Ephraim Bonah
dc.contributor.author Ameen, Arman
dc.contributor.author Hammoodi, Karrar A.
dc.contributor.author Abbas, Walaa N.
dc.date.accessioned 2025-09-15T18:35:29Z
dc.date.available 2025-09-15T18:35:29Z
dc.date.issued 2025
dc.department Okan University en_US
dc.department-temp [Mohammed, Hayder I.] Imam Jaafar Al Sadiq Univ, Dept Cooling & Air Conditioning Engn, Baghdad, Iraq; [Rashid, Farhan Lafta; Abbas, Walaa N.] Univ Kerbala, Coll Engn, Dept Petr Engn, Karbala, Iraq; [Togun, Hussein] Southern Tech Univ, Thi Qar Tech Coll, Basrah, Iraq; [Agyekum, Ephraim Bonah] Ural Fed Univ, Dept Nucl & Renewable Energy, 19 Mira St, Ekaterinburg 620002, Russia; [Agyekum, Ephraim Bonah] Western Caspian Univ, 31 Istiglaliyyat St, Baku AZ-1001, Azerbaijan; [Ameen, Arman] Univ Gavle, Dept Bldg Engn Energy Syst & Sustainabil Sci, S-80176 Gavle, Sweden; [Hammoodi, Karrar A.] Univ Al Maarif, Dept Coll Engn, Al Anbar 31001, Iraq; [Kadhim, Saif Ali] Univ Technol Iraq, Coll Mech Engn, Baghdad, Iraq; [Abbas, Walaa N.] Warith Al Anbiyaa Univ, Fac Engn, Air Conditioning Engn Dept, Karbala, Iraq; [Agyekum, Ephraim Bonah] Istanbul Okan Univ, Tuzla Campus, TR-34959 Istanbul, Turkiye; [Parveen, Rujda] Shanghai Jiao Tong Univ, Chongqing Inst Artificial Intelligence, Chongqing 401329, Peoples R China en_US
dc.description.abstract The growing demand for clean energy and the limitations of conventional thermal systems necessitates the integration of advanced technologies to enhance efficiency, adaptability, and sustainability. This review critically examines recent advancements in the application of nanotechnology and artificial intelligence for optimizing thermal energy systems, including solar collectors, heat exchangers, and latent heat storage units. Nanotechnology (particularly the use of nano-enhanced phase change materials and nanofluids such as Al2O3 and CuO) has shown to improve thermal conductivity by up to 28.8 %, accelerating energy absorption and storage rates. Concurrently, artificial intelligence algorithms, especially artificial neural networks and particle swarm optimization, enable predictive modelling, real-time system control, and fault detection, with some models achieving prediction accuracies above 97 % under complex operational conditions. The review emphasizes the synergistic potential of combining these technologies to create intelligent, self-regulating thermal energy systems. However, the paper also identifies critical challenges including computational overhead, cost of nanoparticle synthesis, lack of reproducibility in artificial intelligence implementations, and insufficient validation under extreme scenarios. Commercial deployment case studies (such as artificial intelligence-driven phase change material-based heating, ventilation, and air conditioning systems in smart buildings) are discussed to illustrate practical viability, reporting energy savings of up to 28 % with return-on-investment periods under three years. The paper concludes by proposing integrated research directions that combine multiscale material innovation with robust artificial intelligence training on dynamic datasets. This dual approach is essential to developing scalable, cost-effective, and resilient thermal energy systems capable of supporting global energy transitions. en_US
dc.description.woscitationindex Science Citation Index Expanded
dc.identifier.doi 10.1016/j.apenergy.2025.126576
dc.identifier.issn 0306-2619
dc.identifier.issn 1872-9118
dc.identifier.scopus 2-s2.0-105012614220
dc.identifier.scopusquality Q1
dc.identifier.uri https://doi.org/10.1016/j.apenergy.2025.126576
dc.identifier.volume 400 en_US
dc.identifier.wos WOS:001581098600002
dc.identifier.wosquality Q1
dc.language.iso en en_US
dc.publisher Elsevier Sci Ltd en_US
dc.relation.ispartof Applied Energy en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Energy Efficiency en_US
dc.subject Nanofluids en_US
dc.subject Phase Change Materials en_US
dc.subject Machine Learning in Energy Systems en_US
dc.subject Energy Storage Optimization en_US
dc.title The Role of Nanotechnology and Artificial Intelligence in Optimizing Thermal Energy Systems en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.coar.access metadata only access
gdc.coar.type text::journal::journal article

Files