Prediction of Oxidation Stability of Biodiesel Derived from Waste and Refined Vegetable Oils by Statistical Approaches

Hüseyin Çamur and Ahmed Muayad Rashid Al-Ani
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Hüseyin Çamur: Department of Mechanical Engineering, Faculty of Engineering, Near East University, 99138 Nicosia, Cyprus
Ahmed Muayad Rashid Al-Ani: Department of Mechanical Engineering, Faculty of Engineering, Near East University, 99138 Nicosia, Cyprus

Energies, 2022, vol. 15, issue 2, 1-26

Abstract: The oxidation stability (OX) of the biodiesel is an essential parameter mainly during storage, which reduces the quality of the biodiesel, thus affecting the engine performance. Moreover, many factors affect oxidation stability. Therefore, determining the most significant parameter is essential for achieving accurate predictions. In this paper, an empirical equation (Poisson Regression Model (PRM)), machine learning models (Multilayer Feed-Forward Neural Network (MFFNN), Cascade Feed-forward Neural Network (CFNN), Radial Basis Neural Network (RBFNN), and Elman neural network (ENN)) with various combinations of input parameters are utilized and employed to identify the most relevant parameters for prediction of the oxidation stability of biodiesel. This study measured the physicochemical properties of 39 samples of waste frying methyl ester and their blends with various percentages of palm biodiesel and refined canola biodiesel. To this aim, 14 parameters including concentration amount of WFME (X1), PME (X2), and RCME (X3) in the mixture, kinematic viscosity (KV) at 40 °C, density at 15 °C (D), cloud point (CP), pour point (PP), the estimation value of the sum of the saturated ( ∑ S F A M s ), monounsaturated ( ∑ M U F A M s ), polyunsaturated ( ∑ P U F A M s ), degree of unsaturation ( D U ), long-chain saturated factor ( L C S F ), very-long-chain fatty acid ( V L C F A ), and ratio ( ∑ M U F A M s + ∑ P U F A M s ∑ S F A M s ) fatty acid composition were considered. The results demonstrated that the RBFNN model with the combination of X1, X2, X3, ∑ S F A M s , ∑ M U F A M s , ∑ P U F A M s . VLCFA, DU, LCSF , ∑ M U F A M s + ∑ P U F A M s ∑ S F A M s , KV, and D has the lowest value of root mean squared error and mean absolute error. In the end, the results demonstrated that the RBFNN model performed well and presented high accuracy in estimating the value of OX for the biodiesel samples compared to PRM, MFFNN, CFNN, and ENN.

Keywords: multilayer feed-forward neural network; cascade feed-forward neural network; radial basis neural network; Elman neural network; poisson regression model; oxidation stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (2)

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