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.
A detailed chemical kinetic mechanism has been developed to describe the pyrolysis and oxidation of the hydrogen/NOx and syngas/NOx systems. The thermodynamic data of nitrogenous compounds have been updated based on the study of Bugler et al. [
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