This study investigates the effects of using biodiesel from Mesua ferrea (BD20) and chromium oxide (Cr2O3) nanoparticles in diesel engines. The Response Surface Methodology (RSM) model and artificial neural networks (ANNs) were developed to make precise predictions of the operating parameters. The amount of Cr2O3 nanoparticles was set at 80 mg/L, and surfactant and dispersant were applied to the nanoparticles in the same amounts. The study was carried out with different compression ratios and load conditions. The parameters evaluated were engine load, fuel samples and compression ratio as inputs and BTE, BSFC, CP, NHRR, CO, UHC, NO x and smoke opacity as outputs. The addition of the QPAN80 additive at the same dosage of 80 mg/L together with the BD20 fuel blend containing Cr2O3 at a concentration of 80 mg/L resulted in a significant increase in BTE by 16.58 % and a reduction in BSFC by 0.58 %. While the NHRR increased by 85.40 %, the CP increased sharply by 24.47 %. The CO concentration decreased by 31.85 %, the UHC concentration by 22.22 %, the NO x concentration by 6.16 % and the smoke emission by 62.61 %. For each output parameter, the correlation coefficient (R 2), calculated using ANNs and RSM was between 0.96 and 0.98. The observed range of values demonstrates a robust correlation between the experimental data and the predicted outcomes.

中文翻译：

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研究分散在 Mesua ferrea 生物柴油中的氧化铬 (Cr2O3) 纳米粒子的柴油发动机的运行特性：使用 ANN 和 RSM 的实验和预测方法


这项研究调查了使用生物柴油的影响铁花木(BD20)和氧化铬(Cr 2氧3 ）柴油发动机中的纳米颗粒。开发响应面方法 (RSM) 模型和人工神经网络 (ANN) 来精确预测运行参数。 Cr含量2氧3纳米粒子的浓度设定为80 mg/L，并且将相同量的表面活性剂和分散剂施加到纳米粒子上。该研究是在不同的压缩比和负载条件下进行的。评估的参数包括作为输入的发动机负载、燃料样本和压缩比以及 BTE、BSFC、CP、NHRR、CO、UHC、NO x和烟雾不透明度作为输出。添加相同剂量 80 mg/L 的 QPAN80 添加剂以及含 Cr 的 BD20 燃料混合物2氧3浓度为 80 mg/L 时，BTE 显着增加 16.58%，BSFC 减少 0.58%。 NHRR 增加了 85.40%，而 CP 则大幅增加了 24.47%。 CO浓度下降31.85%，UHC浓度下降22.22%，NO浓度下降x浓度降低6.16%，烟气排放量降低62.61%。对于每个输出参数，相关系数 (右2 ），使用 ANN 和 RSM 计算得出的结果在 0.96 到 0.98 之间。观察到的值范围表明实验数据和预测结果之间存在很强的相关性。