Prandtl number of optimum biodiesel from food industrial waste oil and diesel fuel blend for diesel engine

• Published in: Fuel (Guildford) 2021-02, Vol.285, p.119049, Article 119049
• Main Authors: David Samuel, Olusegun, Adekojo Waheed, M., Taheri-Garavand, A., Verma, Tikendra Nath, Dairo, Olawale U., Bolaji, Bukola O., Afzal, Asif
• Format: Article
• Language: English
• Subjects: Artificial Neural Network; Artificial neural networks; Biodiesel; Biodiesel fuels; Biofuels; Brakes; Combustion; Diesel; Diesel engines; Diesel fuels; Food industry; Fuel consumption; Heat transfer; Industrial wastes; Modelling; Neural networks; Oil wastes; Optimization; Power consumption; Prandtl number; Prediction; Reaction time; Renewable energy; Response surface methodology; Root-mean-square errors; Specific heat; Standard error; Thermal conductivity; Thermal diffusivity; Thermodynamic efficiency; Thermophysical properties; Torque; Transport properties
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2020.119049

[Display omitted] •ANN provided a higher predicted yield for food industrial waste oil methyl ester (FIWOME) compared to RSM.•Correlations for the specific heat capacity (Cp), thermal diffusivity (TD) and thermal conductivity (TC) of FIWOME established for the first time.•The Cp and TD are correlated with FIWOME percent through the least square regression.•The TC was correlated with the FIWOME fraction through linear regressions.•The major properties of FIWOME concurred agreed with previous studies and compiled with biodiesel standards.•The performance of B22.5 gave a good improvement in term of BTE. Unconventional biodiesel characterization techniques using thermophysical and transport properties have been receiving increasing attention due to its advantages over fundamental combustion and simulation of heat transfer in solving heat transfer, chemical, and bioenergy characteristics of biodiesel combustion. In this study, the optimum production yield of Food Industrial Waste Oil Methyl Ester (FIWOME, B100, FIWOB) was modelled using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) techniques. The basic properties of the fuel types were determined using ASTM test methods, while specific heat capacity (Cp), thermal diffusivity (TD), thermal conductivity (TC) and Prandtl number (Pr) were determined using standard methods. Diesel engine performance indicators such as Engine Torque (ET), Brake Power (BP), Brake Specific Fuel Consumption (BSFC) and Brake Thermal Efficiency (BTE) were determined for different fuel types using a Perkins diesel engine. The estimated Coefficient of Determination (R2) of 0.9820, Root Mean-Square-Error (RMSE) of 1.7403, Standard Error of Prediction (SEP) of 0.0215, Mean Average Error (MAE) of 1.3790, and Average Absolute Deviation (AAD) of 1.6389 for RSM compared to those of R2 (0.9847), RMSE (1.6071), SEP (0.0199), MAE (1.1425), and AAD (1.2583) for ANN exhibited the robustness of the ANN tool over the RSM technique. Optimal biodiesel-- diesel fuel (B0) blend was 22.5% volume ratio called as B22.5. The optimum yield of FIWOME of 92.5% was achieved at the methanol/oil molar ratio of 5.99, KOH of 1.1 wt.%, and a reaction time of 77.6 min. The basic properties of FIWOME determined complied with both ASTM D6751 and EN 14214 specifications. The values of Cp and TD increased and decreased respectively with biodiesel percent in fuel in a quadratic manner. The TC and Pr were correlated with the biodiesel fraction through