Journal ArticleDOI
Recent Advances in Machine Learning Research for Nanofluid-Based Heat Transfer in Renewable Energy System
Prabhakar Sharma,Zafar Said,Anurag Kumar,Sandro Nižetić,Ashok K. Pandey,Anh Le Tuan Hoang,Zuohua Huang,Asif Afzal,Changhe Li,Anh-Tuan Le,Xuan Phuong Nguyen,Viet D. Tran +11 more
- Vol. 36, Iss: 13, pp 6626-6658
TLDR
In this paper , a review of machine learning techniques employed in the nanofluid-based renewable energy system, as well as new developments in machine learning research, is presented.Abstract:
Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required. read more
Citations
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Journal ArticleDOI
Using Bayesian optimization and ensemble boosted regression trees for optimizing thermal performance of solar flat plate collector under thermosyphon condition employing MWCNT-Fe3O4/water hybrid nanofluids
TL;DR: In this article , the performance of a flat plate solar collector operating under thermosyphon conditions using MWCNT + Fe 3 O 4 /Water hybrid nanofluids was investigated.
Journal ArticleDOI
Improving the thermal efficiency of a solar flat plate collector using MWCNT-Fe3O4/water hybrid nanofluids and ensemble machine learning
Zafar Said,Prabhakar Sharma,L. Syam Sundar,Changhe Li,Duy Cuong Tran,Nguyen Dang Khoa Pham,Xuan Phuong Nguyen +6 more
TL;DR: In this paper , the performance of a flat plate solar collector using MWCNT + Fe3O4/Water hybrid nanofluids was examined in an arid environment, where modern ensemble machine learning techniques Boosted Regression Tree and Extreme Gradient Boosting (XGBoost) were used to develop prognostic models for each parameter.
Journal ArticleDOI
Using machine learning approaches to model and optimize a combined solar/natural gas-based power and freshwater cogeneration system
TL;DR: In this paper , the authors proposed an electricity and freshwater cogeneration system using solar energy and natural gas dual sources, which is a combination of a heliostat field with a gas turbine cycle as the top systems and thermal vapor compression-multi effect desalination, steam Rankine cycle, organic Rankine cycles, and thermoelectric generator as subsystems.
Journal ArticleDOI
A Review of Applications, Prospects, and Challenges of Proton-Conducting Zirconates in Electrochemical Hydrogen Devices
M. K. Hossain,S. M. Kamrul Hasan,M. Hossain,R. Das,H. Bencherif,Mirza H. K. Rubel,Md. Ferdous Rahman,Tanvir Emrose,Kenichi Hashizume +8 more
TL;DR: In this article , a review highlights the applications of zirconate-based proton-conducting materials in electrochemical cells, particularly in tritium monitors, triton recovery, hydrogen sensors, and hydrogen pump systems.
Journal ArticleDOI
Optimization of combustion, performance, and emission characteristics of a dual-fuel diesel engine powered with microalgae-based biodiesel/diesel blends and oxyhydrogen
Zafar Said,Duc Trong Nguyen Le,Prabhakar Sharma,Viet Ha Dang,Huu Son Le,Dinh Tuyen Nguyen,Thi Anh Em Bui,Van Giao Nguyen +7 more
TL;DR: In this paper , the authors explored and improved the effects of engine load, injection time, and oxyhydrogen fuel flow rate on the combustion and emissions characteristics of a diesel engine.
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