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

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 (∑SFAMs), monounsaturated (∑MUFAMs), polyunsaturated (∑PUFAMs), degree of unsaturation (DU), long-chain saturated factor (LCSF), very-long-chain fatty acid (VLCFA), and ratio (∑MUFAMs+∑PUFAMs∑SFAMs) fatty acid composition were considered. The results demonstrated that the RBFNN model with the combination of X1, X2, X3, ∑SFAMs, ∑MUFAMs, ∑PUFAMs. VLCFA, DU, LCSF, ∑MUFAMs+∑PUFAMs∑SFAMs, 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.

Survey on Antioxidants Used as Additives to Improve Biodiesel’s Stability to Degradation through Oxidation

“…Depletion of fossil fuel sources requires finding the necessary substitute sources, considering the increase in the rate of energy consumption due to population growth, industrialization, and transportation demands. As a result, this increased demand for energy, together with the increase in greenhouse gas (GHG) emissions from the use of fossil fuels, constitutes a major challenge facing the world's population today [26]. This issue determined the need to find sources of clean, low-cost energy, and led to the intensification of efforts based on research and revision of production paths, using new and optimized techniques, to promote clean energy production [26,27].…”

“…As a result, this increased demand for energy, together with the increase in greenhouse gas (GHG) emissions from the use of fossil fuels, constitutes a major challenge facing the world's population today [26]. This issue determined the need to find sources of clean, low-cost energy, and led to the intensification of efforts based on research and revision of production paths, using new and optimized techniques, to promote clean energy production [26,27]. This will support the maintenance of energy security, limit environmental pollution, and reduce the degree of climate change [28,29].…”

Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters

“…ANNs have many scientific and engineering applications, including process monitoring [15], waste volume prediction [16], and water quality prediction [17]. ANNs have been used for combustion modeling [18], energy consumption prediction [19], and prediction of oxidation stability of biodiesel derived from waste [20]. The application of ANNs for HHV prediction of solid fuels has been examined.…”