Performance comparison of empirical model and Particle Swarm Optimization & its boiling point prediction models for waste sunflower oil biodiesel

Performance comparison of empirical model and Particle Swarm Optimization & its boiling point prediction models for waste sunflower oil biodiesel

The absence of correlations for predicting the boiling point of biodiesels prevents fuel users to achieve effective engine performance. Among the quality regulator of rudimentary fuel properties is the boiling point and its absence in literature is preventing fuel handlers to achieve actual engine p...

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Bibliographic Details
Main Authors: Afzal, A. (Author), Elumalai, P.V (Author), Enweremadu, C.C (Author), Fayomi, O.S.I (Author), Kaveh, M. (Author), Nisar, K.S (Author), Owamah, H.I (Author), Oyejide, O.J (Author), Saleel, C.A (Author), Samuel, O.D (Author), Sarıkoç, S. (Author), Verma, T.N (Author)
Format: Article
Language:English
Published: Elsevier Ltd 2022
Subjects:
Biodiesel
Biodiesel production
Boiling point
Empirical model
Engine performance
Engines
Esters
Forecasting
Fuel properties
Kinematics
Mean square error
Methanolysis
Mid-boiling point
Particle swarm optimization
Particle swarm optimization (PSO)
Performance comparison
Potassium hydroxide
Prediction modelling
Response surface methodology
Response-surface methodology
Sunflower oil
Sunflower oil methyl ester
Surface properties
Viscosity of liquids
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