| Summary: | In the current study, our goal was to obtain a robust model to predict the speed of sound in biodiesel. For this purpose, an extensive databank has been extracted from previously published papers. Then, a Support Vector Machine (SVM) has been optimized by Grey Wolf Optimization (GWO) method to analyze these data and determine the correlation between speed of sound in biodiesel and its related properties including pressure, temperature, molecular weight, and normal melting point. The results were very satisfactory because the values of statistical parameters R2 and RMSE were obtained 1 and 1.4024, respectively. Here, this is the first time that the sensitivity analysis is used to estimate this target value. This analysis shows that the pressure widely affects the output values with relevancy factor 87.92. Also, our proposed method is highly accurate than other machine learning methods used in papers employed for this objective.
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