Contents lists available at ScienceDirect Fuel journal homepage: www.elsevier.com/locate/fuel Full Length Article Prandtl number of optimum biodiesel from food industrial waste oil and diesel fuel blend for diesel engine Olusegun David Samuel a, ⁎ , M. Adekojo Waheed b , A. Taheri-Garavand c , Tikendra Nath Verma d , Olawale U. Dairo e , Bukola O. Bolaji f , Asif Afzal g a Department of Mechanical Engineering, Federal University of Petroleum Resources, Effurun, Delta State P.M.B 1221, Nigeria b Department of Mechanical Engineering, Federal University of Agriculture, Abeokuta, Nigeria c Mechanical Engineering of Biosystems Department, Lorestan University, Khorramabad, Iran d Department of Mechanical Engineering, Maulana Azad National Institute of Technology, Bhopal, MP 462003, India e Department of Agricultural Engineering, Federal University of Agriculture, Abeokuta, Nigeria f Department of Mechanical Engineering, Faculty of Engineering, Ikole-Ekiti Campus, Federal University Oye-Ekiti, P.M.B. 373, Oye-Ekiti, Nigeria g Department of Mechanical Engineering, P. A. College of Engineering Affiliated to Visvesvaraya Technological University, Belagavi, Mangaluru, India GRAPHICAL ABSTRACT Methanolysis of FIWO RSM Design Reaction temperature Methanol/FIWO molar ratio Catalyst amount Yield CCD Three levels B0 B22.5 B100 Fuel types for thermophysical characterization & Diesel engine performance ANN modelling Schematic setup of Perkins diesel test bed ARTICLE INFO Keywords: Biodiesel Response surface methodology Artifcial Neural Network Prandtl number Modelling Prediction ABSTRACT 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 Artifcial Neural Network (ANN) techniques. The basic prop- erties of the fuel types were determined using ASTM test methods, while specifc heat capacity (C p ), thermal difusivity (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 Specifc Fuel Consumption (BSFC) and Brake Thermal Efciency (BTE) were determined for diferent fuel types using a Perkins diesel engine. The estimated Coefcient of Determination (R 2 ) of 0.9820, Root Mean-Square-Error https://doi.org/10.1016/j.fuel.2020.119049 Received 31 January 2020; Received in revised form 3 August 2020; Accepted 20 August 2020 ⁎ Corresponding author. E-mail address: samuel.david@fupre.edu.ng (O. David Samuel). Fuel 285 (2021) 119049 0016-2361/ © 2020 Elsevier Ltd. All rights reserved. T