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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hüseyin Çamur & Ahmed Muayad Rashid Al-Ani, 2022. "Prediction of Oxidation Stability of Biodiesel Derived from Waste and Refined Vegetable Oils by Statistical Approaches," Energies, MDPI, vol. 15(2), pages 1-26, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:2:p:407-:d:719178
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    References listed on IDEAS

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    1. Krishna Kumar Gupta & Kanak Kalita & Ranjan Kumar Ghadai & Manickam Ramachandran & Xiao-Zhi Gao, 2021. "Machine Learning-Based Predictive Modelling of Biodiesel Production—A Comparative Perspective," Energies, MDPI, vol. 14(4), pages 1-16, February.
    2. Khozeymeh Nezhad, Marziyeh & Aghaei, Hamidreza, 2021. "Tosylated cloisite as a new heterofunctional carrier for covalent immobilization of lipase and its utilization for production of biodiesel from waste frying oil," Renewable Energy, Elsevier, vol. 164(C), pages 876-888.
    3. Babu, D. & Thangarasu, Vinoth & Ramanathan, Anand, 2020. "Artificial neural network approach on forecasting diesel engine characteristics fuelled with waste frying oil biodiesel," Applied Energy, Elsevier, vol. 263(C).
    4. Babu, D. & Karvembu, R. & Anand, R., 2018. "Impact of split injection strategy on combustion, performance and emissions characteristics of biodiesel fuelled common rail direct injection assisted diesel engine," Energy, Elsevier, vol. 165(PB), pages 577-592.
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    2. Justyna Kujawska & Monika Kulisz & Piotr Oleszczuk & Wojciech Cel, 2023. "Improved Prediction of the Higher Heating Value of Biomass Using an Artificial Neural Network Model Based on the Selection of Input Parameters," Energies, MDPI, vol. 16(10), pages 1-16, May.

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