Green lubricants production from Nile tilapia waste and prediction of physical properties through molecular dynamics simulations
Fish farming is a worldwide growing activity and a large amount of residues is produced in this process. The present work describes a cleaner and sustainable way to produce new lubricants from fish waste oil. Oil extracted from the Nile tilapia (Oreochromis niloticus) viscera was utilized as raw mat...
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Veröffentlicht in: | Journal of the American Oil Chemists' Society 2022-04, Vol.99 (4), p.341-352 |
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Sprache: | eng |
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Zusammenfassung: | Fish farming is a worldwide growing activity and a large amount of residues is produced in this process. The present work describes a cleaner and sustainable way to produce new lubricants from fish waste oil. Oil extracted from the Nile tilapia (Oreochromis niloticus) viscera was utilized as raw material to produce basic oil for lubricants. The products were synthesized by esterification with polyols, trimethylolpropane (TMP) and pentaerythritol (PE), using p‐toluenesulfonic acid (p‐TSA) as catalyst. The synthesized esters were characterized by infrared (IR) and nuclear magnetic resonance (NMR). Computational methods were used to predict the physical characteristics of the material. In addition, the main physicochemical properties were evaluated, as well as the thermal behavior and toxicity of the products against Artemia salina. The synthesized esters showed high viscosity indexes (VI > 150) and viscosities that fit the degree of application ISO‐46 and 150. Molecular dynamics simulations indicated that at room temperature the lubricants Tilapia fatty acid ‐ trimethylolpropane ester (T‐TMPE) and Tilapia fatty acid ‐ pentaerythritol ester (T‐PEE) are in liquid and gel states, respectively, confirming the experimental data. The products did not present toxicity against A. salina. In this research, we reinforce the potential of using tilapia oil from waste to produce green lubricants as a strategy to reduce damage to the environment, as well as the use of computational methods that collaborate to predict physical properties of lubricants. |
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ISSN: | 0003-021X 1558-9331 |
DOI: | 10.1002/aocs.12580 |