Selecting suitable biodiesel for the intended application is challenging due to the significant variations in the feedstock for producing biodiesel. The available models to predict biodiesel properties have limited applicability and reliability. The present work addresses these two challenges by developing reliable models based on machine learning algorithms for predicting engine fuel properties of biodiesel and optimizing biodiesel composition for better fuel properties. The models are developed using multilinear regression (MLR), artificial neural networks (ANN), support vector machine regression with grid search (SVMGS), Bayesian optimization (SVMBO) and grey-wolf optimization (SVMGWO) for hyperparameter tuning, Gaussian process regression (GPR), random forest (RF), and adaptive neuro-fuzzy inference system (ANFIS) algorithms. The models are trained to predict viscosity, cetane number, and calorific value from 13 methyl ester constituents of 70 biodiesels. SVMGS models predicted the viscosity, cetane number, and calorific value of 33 validation samples with a mean absolute percentage error of 1.54%, 1%, and 0.43%. Biodiesel composition was optimized to minimize viscosity and maximize cetane number and calorific value. The optimized composition exhibits 3.72 cSt viscosity, 57 cetane number, and 43 MJ/kg calorific value, which can be prepared by blending 68% ± 1% camelina and 32% ± 1% coconut oil. Applying machine learning algorithms to predict biodiesel properties yielded more accurate predictions than available models. It helped find the optimal composition for improved engine characteristics.

中文翻译：

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研究机器学习算法优化生物柴油成分以改善发动机燃料性能的适用性

由于生产生物柴油的原料存在显着差异，因此为预期应用选择合适的生物柴油具有挑战性。预测生物柴油特性的可用模型的适用性和可靠性有限。目前的工作通过开发基于机器学习算法的可靠模型来解决这两个挑战，用于预测生物柴油的发​​动机燃料特性并优化生物柴油成分以获得更好的燃料特性。这些模型是使用多线性回归（MLR）、人工神经网络（ANN）、网格搜索支持向量机回归（SVMGS）、用于超参数调整的贝叶斯优化（SVMBO）和灰狼优化（SVMGWO）、高斯过程回归（ GPR）、随机森林（RF）和自适应神经模糊推理系统（ANFIS）算法。这些模型经过训练可以预测 70 种生物柴油的 13 种甲酯成分的粘度、十六烷值和热值。SVMGS 模型预测了 33 个验证样品的粘度、十六烷值和热值，平均绝对百分比误差分别为 1.54%、1% 和 0.43%。生物柴油成分经过优化，可最大限度地降低粘度并最大限度地提高十六烷值和热值。优化后的组合物具有3.72 cSt粘度、57十六烷值和43 MJ/kg热值，可由68%±1%亚麻荠油和32%±1%椰子油混合制备。应用机器学习算法来预测生物柴油特性比现有模型产生了更准确的预测。它有助于找到改善发动机特性的最佳成分。