This study considered the impacts of diesel–soybean biodiesel blends mixed with 3% cerium coated zinc oxide (Ce-ZnO) nanoparticles on the performance, emission, and combustion characteristics of a single cylinder diesel engine. The fuel blends were prepared using 25% soybean biodiesel in diesel (SBME25). Ce-ZnO nanoparticle additives were blended with SBME25 at 25, 50, and 75 ppm using the ultrasonication process with a surfactant (Span 80) at 2 vol.% to enhance the stability of the blend. A variable compression ratio engine operated at a 19.5:1 compression ratio (CR) using these blends resulted in an improvement in overall engine characteristics. With 50 ppm Ce-ZnO nanoparticle additive in SBME25 (SBME25Ce-ZnO50), the brake thermal efficiency (BTE) and heat release rate (HRR) increased by 20.66% and 18.1%, respectively; brake specific fuel consumption (BSFC) by 21.81%; and the CO, smoke, and hydrocarbon (HC) decreased by 30%, 18.7%, and 21.5%, respectively, compared to SBME25 fuel operation. However, the oxides of nitrogen slightly rose for all the nanoparticle added blends. As such, 50 ppm of Ce-ZnO nanoparticle in the blend is a potent choice for the enhancement of engine performance, combustion, and emission characteristics.

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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