This work explores the underlying correlation between fatty acid composition and biodiesel fuel properties using artificial neural network (ANN) and establishes the complex non‐linear relationship between the fuel parameters and the significant fatty acids present in the composition of biodiesel. Six physico‐chemical properties of biodiesel, that is, specific gravity, ^oAPI, aniline point, cetane number, flash point, and calorific value have been assessed and a modeling framework has been developed. High value of R² and low value of RMSE signify that ANN model captures the inherent relationship. Results demonstrate that with 1% change in arachidic acid, an increment of 21.74 MJ/Kg in heating value and a decrease of 7.17 in cetane number of biodiesel fuel take place, respectively. This suggests that the presence of a regulated amount of arachidic acid in biodiesel composition would assist in obtaining desired heat value and diesel index of biodiesel that would ultimately aid in improving fuel quality and hence, would help in obtaining higher grade biodiesel fuel. Furthermore, ANN‐based stochastic optimization techniques, genetic algorithm, and particle swarm optimization have been used to predict the maximum possible biodiesel blend that can be used in diesel engines retaining the fuel qualities.

中文翻译：

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利用人工智能和优化方法评估生物柴油的脂肪酸组成与燃料特性之间的相关性

这项工作使用人工神经网络（ANN）探索了脂肪酸成分与生物柴油燃料特性之间的潜在相关性，并建立了燃料参数与生物柴油成分中存在的重要脂肪酸之间的复杂非线性关系。生物柴油，即，比重，六理化性质^Ø API，苯胺点，十六烷值，闪点和热值进行了评估和建模框架已经形成。高R ²值RMSE值低表明ANN模型可以捕获固有关系。结果表明，在花生酸变化1％的情况下，生物柴油燃料的发热量分别增加21.74 MJ / Kg和十六烷值减少7.17。这表明在生物柴油组合物中存在调节量的花生酸将有助于获得所需的生物柴油热值和柴油指数，这最终将有助于改善燃料质量，因此将有助于获得更高等级的生物柴油燃料。此外，基于神经网络的随机优化技术，遗传算法和粒子群优化技术已被用于预测可用于柴油发动机的最大生物柴油混合物，从而保持燃油质量。