Aswathanrayan M S, Santhosh N, Venkataramana Srikanth Holalu, Kumar Kurugundla Sunil, Kamangar Sarfaraz, Arabi Amir Ibrahim Ali, Algburi Sameer, Al-Sareji Osamah J, Bhowmik A
Department of Mechanical Engineering, ICEAS, Bangalore, Karnataka, India.
Department of Mechanical Engineering, MVJ College of Engineering, Bangalore, 560 067, India.
Sci Rep. 2025 Apr 12;15(1):12683. doi: 10.1038/s41598-025-97092-2.
The growing need for sustainable energy sources and stricter environmental regulations necessitate the development of alternative fuels with lower emissions and improved performance. This study addresses these challenges by optimizing the performance and emission characteristics of a single-cylinder diesel engine powered by neem oil biodiesel blends enhanced with alumina nanoparticlesusing the powerful desirability-based optimization. Neem oil, a non-edible feedstock, was selected to avoid competition with food resources, while alumina nanoparticles were utilized for their catalytic properties to enhance combustion efficiency. The process involved experimental evaluation of biodiesel blends (B10, B20, and B30) combined with alumina nanoparticles at concentrations of 100 ppm, 150 ppm, and 200 ppm using a design of experiments approach. With the engine running at maximum load of 100% and an aluminum oxide concentration of 100 parts per million, the optimal fuel mix comprises of 89.85% diesel and 30% biodiesel. The lowest brake-specific fuel consumption of 0.45 kg per kilowatt-hour that the optimization produced points to effective fuel use. With a little variance of 3.33%, the brake thermal efficiency was maximized at 38.18%, quite near to the validation result of 37.89%. The alumina nanoparticles enhanced combustion through improved fuel atomization and oxidation due to their high surface area and catalytic effects. To further validate the effectiveness of RSM, the results are compared with the performance of several advance machine learning algorithms, including linear regression, decision tree, and random forest. The random forest model demonstrated the highest predictive accuracy for performance (test R = 0.9620, Test MAPE = 3.6795%), making it the most reliable statistical approach for predicting BSFC compared to linear regression and decision Tree models. The random forest model also outperformed other approaches in predicting emissions, achieving the highest accuracy with a test R of 0.9826 and the lowest test MAPE of 9.3067%.This integrated experimental and predictive approach provided a robust framework for optimizing biodiesel formulations, identifying the ideal combination of biodiesel blend ratio and nanoparticle concentration. The findings highlight the potential of neem oil biodiesel blends enhanced with alumina nanoparticles to achieve a sustainable balance between improved engine performance and reduced emissions in CI engines.
对可持续能源的需求不断增长以及更严格的环境法规，使得开发具有更低排放和更好性能的替代燃料成为必要。本研究通过使用强大的基于期望度的优化方法，优化由添加了氧化铝纳米颗粒的印楝油生物柴油混合物驱动的单缸柴油发动机的性能和排放特性，来应对这些挑战。选择印楝油这种不可食用的原料，以避免与粮食资源竞争，同时利用氧化铝纳米颗粒的催化特性来提高燃烧效率。该过程涉及采用实验设计方法，对浓度为100 ppm、150 ppm和200 ppm的生物柴油混合物（B10、B20和B30）与氧化铝纳米颗粒的组合进行实验评估。在发动机以100%的最大负载运行且氧化铝浓度为百万分之一百时，最佳燃料混合物由89.85%的柴油和30%的生物柴油组成。优化产生的最低制动比油耗为每千瓦时0.45千克，这表明燃料使用效率高。制动热效率在38.18%时达到最大值，与验证结果37.89%相差不大，仅为3.33%。氧化铝纳米颗粒因其高表面积和催化作用，通过改善燃料雾化和氧化来增强燃烧。为了进一步验证响应面法的有效性，将结果与包括线性回归、决策树和随机森林在内的几种先进机器学习算法的性能进行比较。随机森林模型在性能预测方面表现出最高的准确性（测试R = 0.9620，测试平均绝对百分比误差 = 3.6795%），与线性回归和决策树模型相比，它是预测制动比油耗最可靠的统计方法。随机森林模型在排放预测方面也优于其他方法，测试R为0.9826，达到最高准确性，测试平均绝对百分比误差最低，为9.3067%。这种综合的实验和预测方法为优化生物柴油配方提供了一个强大的框架，确定了生物柴油混合比和纳米颗粒浓度的理想组合。研究结果突出了添加氧化铝纳米颗粒的印楝油生物柴油混合物在实现压燃式发动机性能提升和排放降低之间可持续平衡方面的潜力。
