Al Awadh Mohammed, Goh Kah Ong Michael
Department of Industrial Engineering, College of Engineering, King Khalid University, P.O. Box 394, Abha, 61421, Saudi Arabia.
Center for Engineering and Technology Innovations, King Khalid University, Abha, 61421, Saudi Arabia.
Sci Rep. 2025 May 6;15(1):15812. doi: 10.1038/s41598-025-00523-3.
This research explores the performance, exhaust emissions, and combustion properties of a Premixed Charged Compression Ignition (PCCI) engine using combinations of Andropogon narudus (AN) and Sapota Oil Methyl Ester (SOME) blended as substitute fuels. A split-fuel injection system was used, supplying 70% of the fuel with direct injection and 30% through the intake air manifold. The test fuels considered were D100 (commercial diesel), AN20 + D80, SOME20 + D80, and their corresponding mixture with nano-additives CeO₂ and Al₂O₃ (10 ppm).Performance analysis showed that the highest brake thermal efficiency (BTE) was attained by the SOME20 + D80 mixture with Al₂O₃, which rose by 2.5% with respect to diesel, and AN20 + D80 with Al₂O₃ with a 2.3% rise in BTE. Brake-specific fuel consumption (BSFC) was reduced by 0.10 g/kWh for AN20 + D80 with Al₂O₃ with respect to diesel due to its lower viscosity. Emission analysis showed a hydrocarbon (HC) emission decrease of up to 7 ppm for all the blends tested, although CO₂ and NO emissions were higher in AN and SOME fuel blends with nano-additives. Combustion studies revealed that AN20 + D80 with Al₂O₃ had the maximum peak pressure and net heat release rate, which supports the effect of fuel properties on combustion behavior. For improving predictive accuracy, machine learning-augmented modeling was utilized using Multiple Linear Regression (MLR), Random Forest Regression (RF), and Support Vector Machine Regression (SVMR). The RF model performed better with predictive efficiency, giving R² = 0.97 for NO, 0.99 for Smoke, and 0.95 for CO, capturing nonlinear relationships well. MLR had good fits for BTE (R² = 0.99) and BSFC (R² = 0.94), whereas SVMR had poorer predictions (e.g., Smoke: R² = 0.19, CO₂: R² = 0.30).A sustainability ranking situated AN20 + D80 at the most viable biofuel position, particularly with the addition of Al₂O₃. Predictive analytics derived from ML in the study focus on the role of achieving maximal alternative fuel mixtures, less reliance on huge experimental trials, and more cleaner and efficient burning systems.
本研究探讨了使用香根草（AN）和人心果油甲酯（SOME）混合作为替代燃料的预混合充量压缩点火（PCCI）发动机的性能、尾气排放和燃烧特性。采用了分体式燃油喷射系统，通过直接喷射供应70%的燃料，通过进气歧管供应30%的燃料。所考虑的测试燃料为D100（商用柴油）、AN20 + D80、SOME20 + D80，以及它们与纳米添加剂CeO₂和Al₂O₃（10 ppm）的相应混合物。性能分析表明，添加Al₂O₃的SOME20 + D80混合物实现了最高的制动热效率（BTE），相对于柴油提高了2.5%，添加Al₂O₃的AN20 + D80的BTE提高了2.3%。由于其较低的粘度，添加Al₂O₃的AN20 + D80相对于柴油的制动比油耗（BSFC）降低了0.10 g/kWh。排放分析表明，所有测试混合物的碳氢化合物（HC）排放最多降低了7 ppm，不过添加纳米添加剂的AN和SOME燃料混合物中的CO₂和NO排放较高。燃烧研究表明，添加Al₂O₃的AN20 + D80具有最大的峰值压力和净热释放率，这支持了燃料特性对燃烧行为的影响。为了提高预测精度，利用了基于多元线性回归（MLR）、随机森林回归（RF）和支持向量机回归（SVMR）的机器学习增强建模。RF模型在预测效率方面表现更好，NO的R² = 0.97，烟度的R² = 0.99，CO的R² = 0.95，能很好地捕捉非线性关系。MLR对BTE（R² = 0.99）和BSFC（R² = 0.94）拟合良好，而SVMR的预测较差（例如，烟度：R² = 0.19，CO₂：R² = 0.30）。可持续性排名将AN20 + D80置于最可行的生物燃料位置，特别是添加Al₂O₃的情况下。该研究中基于机器学习的预测分析侧重于实现最佳替代燃料混合物的作用，减少对大量实验试验的依赖，以及更清洁高效的燃烧系统。
