In order to reduce dangerous air pollution and lessen the hazards associated with climate change, biofuels made from vegetable biomass can be utilized as fuel in diesel engines. However, feedstocks and the crop space needed for their cultivation are unavoidable barriers that hinder its adoption and result in a shortage of farmland for increasing food profits. Nevertheless, microalgae are the most reliable and competent biodiesel supply because they are not edible and do not require cropland. Additionally, the addition of nanoparticles has become popular to improve the negative aspects of biodiesel. Based on these, in this study, multi-purpose optimization research was conducted on the addition of iron oxide (FeO) nanoparticles in three different amounts (25, 50, and 75 ppm) to 20 % synthesized spirulina microalgae biodiesel (SSMB)/80 % diesel mixtures. Tests were conducted at three different compression ratios (14.5:1, 15.5:1, and 16.5:1) (CoR) and four different engine loads (25 %, 50 %, 75 %, and 100 %). The response surface methodology (RSM) optimization process was performed with the test results. According to RSM, a desirability coefficient of 0.8299, 15.80:1 CoR, 68 ppm AoN, and 45 % LoE were the ideal conditions. The optimum responses under these circumstances were 421.9653 g/kWh, 19.4014 %, 0.0939 %, 4.0785 %, 73.1716 ppm and 253.0620 ppm for brake-specific fuel consumption (BSFC), brake-thermal efficiency (BTHE), carbon monoxide (CO), carbon dioxide (CO), hydrocarbon (HC), and nitrogen oxide (NOx), respectively. RSM could be used successfully because the error rates (maximum 6.5 %) when comparing the test results with the RSM optimization outcomes were within reasonable bounds.

中文翻译：

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根据不同的发动机变量，研究将纳米颗粒 (Fe3O4) 添加到第三代生物柴油（螺旋藻微藻）/柴油混合物中作为创新燃料：RSM 优化

为了减少危险的空气污染并减轻与气候变化相关的危害，由植物生物质制成的生物燃料可以用作柴油发动机的燃料。然而，原料和种植所需的作物空间是不可避免的障碍，阻碍了其采用，并导致增加粮食利润的农田短缺。然而，微藻是最可靠和最有能力的生物柴油供应，因为它们不可食用并且不需要耕地。此外，添加纳米粒子已变得流行以改善生物柴油的负面影响。基于此，本研究对20%合成螺旋藻微藻生物柴油（SSMB）/80中添加三种不同量（25、50和75 ppm）的氧化铁（FeO）纳米粒子进行了多用途优化研究。 % 柴油混合物。测试在三种不同的压缩比（14.5:1、15.5:1 和 16.5:1）（CoR）和四种不同的发动机负载（25%、50%、75% 和 100%）下进行。根据测试结果执行响应面法 (RSM) 优化过程。根据 RSM，理想条件为 0.8299、15.80:1 CoR、68 ppm AoN 和 45% LoE。在这些情况下，制动特定燃油消耗 (BSFC)、制动热效率 (BTHE)、一氧化碳 (CO)、分别为二氧化碳 (CO)、碳氢化合物 (HC) 和氮氧化物 (NOx)。 RSM 可以成功使用，因为将测试结果与 RSM 优化结果进行比较时的错误率（最大 6.5%）在合理范围内。