article · Industrial Crops and Products
This research evaluates the effects of adding propanol-2 and zinc oxide nanoparticles to a Calophyllum biodiesel and diesel blend, known as CB20, within an internal combustion engine. Testing showed that the additives enhanced engine performance, raising brake thermal efficiency by up to 3.91 percent and decreasing specific fuel consumption by up to 11.53 percent compared to standard CB20. Peak cylinder pressure and heat release rates were highest when using a 120 parts per million concentration of the nanoparticle additive. The formulation also reduced exhaust emissions, cutting carbon monoxide by 38.7 percent, hydrocarbons by 14.9 percent, nitrogen oxides by 4.8 percent, and smoke by 2.48 percent. Additionally, a generalised regression neural network was constructed to model engine behaviours. The network delivered high predictive accuracy using minimal data, achieving correlation coefficients between 0.98284 and 0.99959.
Blending biofuels and nanoparticles into conventional engine systems can lower harmful exhaust emissions and boost energy efficiency. However, identifying the correct chemical balances usually requires exhaustive physical testing. Developing precise computational models that require relatively little data enables engine operators and researchers to simulate and fine-tune cleaner fuel mixtures faster, supporting transitions toward lower-emission transport fuels.
This work demonstrates an applied, experimental-stage fuel enhancement strategy for the biodiesel and automotive sectors. It could enable fuel formulators to design lower-emission blends using Calophyllum biodiesel, propanol-2, and zinc oxide additives. Furthermore, automotive developers and researchers can utilise the demonstrated neural network model to predict engine outputs with reduced data requirements. The technology remains at the stage of laboratory engine testing and computational modelling, prior to commercial fleet deployment.
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Even though higher alcohols (HAs) and nanoparticles have the tendency to enhance engine behaviours (EBs), namely performance, emissions, and combustion characteristics, and ensure a greener environment, the absence of a reliable model to predict and model the appropriate HA dosage to blend with nanoparticles in green diesel (GD) has affected the biodiesel and automotive industries. For the first time, a study adopted a generalized regression neural network (GRNN) to investigate the influence of propanol-2 as one of the HAs, zinc oxide (ZnO) as one of the nanoparticles, and Calophyllum biodiesel (CB) as GD on EBs. The study focused on the effect of adding propanol-2 and ZnO fuel enhancers on the engine features and performance, combustion, and emissions of a CB blend (CB20) in an internal combustion (IC) engine. The results showed improved engine performance, with brake thermal efficiency increasing by 0.06 %, 1.71 %, and 3.91 %, and specific fuel consumption reduced by 5.83 %, 7.4 %, and 11.53 %, respectively, compared to CB20 fuel. The highest cylinder pressure of 70.84 bar was observed at the 120 ppm nano additive blend, while the highest heat release rate (HRR) of 36.65 J/℃A was observed at the same concentration of nano additives. Furthermore, the inclusion of ZnO nano condiments caused a decrease in carbon monoxide (CO), hydrocarbon (HC), nitrogen oxide (NOx), and smoke emissions by 38.7 %, 14.9 %, 4.8 %, and 2.48 %, respectively, at higher dosages of nano additives in the CB20 blend. A computational model based on a GRNN was constructed for further analysis of engine efficiency and emissions behaviour. The GRNN model accurately predicted output variables for various blends, with correlation coefficient (R) values varying from 0.98284 to 0.99959, with lesser RMSE and MAPE values within acceptable boundaries. The highest cylinder pressure of 70.84 bar was observed at the 120 ppm nano additive blend, while the highest heat release rate (HRR) of 36.65 J/℃A was observed at the same concentration of nano additives. Furthermore, the inclusion of ZnO nano condiments caused a decrease in carbon monoxide (CO), hydrocarbon (HC), nitrogen oxide (NOx), and smoke emissions by 38.7 %, 14.9 %, 4.8 %, and 2.48 %, respectively, at higher dosages of nano additives in the CB20 blend. A computational model based on a GRNN was constructed for further analysis of engine efficiency and emissions behaviour. The GRNN model accurately predicted output variables for various blends, with correlation coefficient (R) values varying from 0.98284 to 0.99959, with lesser RMSE and MAPE values within acceptable boundaries. The results also showed that the GRNN models are advantageous for network simplicity and require less data, making them reliable tools for predicting and modelling EP of the latest fuel for researchers and stakeholders in the automotive industry. • Study on IC engines using novel ternary renewable fuels. • 1st term GRNN modeling for ternary fuel (biodiesel/diesel/propanol blend with Zn nanoparticles) in IC engine. • Correlation between GRNN-predicted engine performance and measured values for ternary fuels. • Efforts towards achieving SDG 7 millennium goal.
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DOI: 10.1016/j.indcrop.2025.120812
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