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.

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Pollution in the maritime field is considered as such a serious problem that scientists, policymakers, and managers are always urged with a desire to find solutions and strategies one way or another to minimize its negative impacts on the environment and the life. Among the effective solutions, the use of fuels with limited-sulfur content or bio-based fuels has been paid much attention to due to their advantages in the use of marine diesel engines. In the current study, a type of fossil diesel fuel with ultra-low sulfur content (ULSD) was mixed with coconut oil (CO) through the ultrasonic treatment under the changes in volume fraction of as-used fuels, the correlation on viscosity selected the rate mixing between ULSD and coconut oil. After 12 minutes of ultrasound treatment, the highest stability of the ULSD-CO emulsion was achieved correspondingly to 97.8% for a mixing rate of 76%(ULSD):24%(CO) along with the distance of 85mm from the containing-vessel bottom to the ultrasound horn tip. Moreover, the spray characteristics including penetration length and a cone angle of ULSD, CO, and ULSD-CO emulsion were also examined and compared to diesel fuel. The similarity of spray characteristics to diesel fuel, containing ultra-low sulfur content and oxygen content, renewability are considered as some advantages of ULSD-CO emulsion as used for diesel engines aiming to meet the stricter requirements of IMO regulations about the strategies of environmental protection.

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