Indian Journal of Chemical Technology

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Value Added Biodiesel for Corrosion Inhibition


Affiliations
1 Department of Mechanical Engineering, Ponjesly College of Engineering, Nagercoil, Tamil Nadu 629 003, India
2 Department of Chemistry, University College of Engineering, Nagercoil, Tamil Nadu 629 004, India
3 Department of Electronics and Communication Engineering, Amrita College of Engineering and Technology, Nagercoil, Tamil Nadu 629 901, India

Biodiesel is one of the prospective fuels, potentially capable of substituting petroleum fuel. Nevertheless, systems using this bioenergy resource are prone to corrosion than conventional fuels. Here, corrosion of copper metal in biodiesel with value added green coffee bean inhibitor has been evaluated by weight loss method, which produced 95.92% inhibition efficiency. Theoretically, corrosion is evaluated by artificial intelligence. The images of the surface obtained using CCD are augmented to 699 image samples. These augmented images are fed to back-propagation based neural network system for training, validation and classification for prediction of corrosion behaviour of copper in biodiesel with and without inhibitor. The neural network system has training, validation & testing prediction accuracies of 97.1%, 96.2% & 98.1%, respectively, and an overall accuracy of 97.1%. The proposed tool can be used to assess the corrosion behaviour dynamically in real time for futuristic prediction of corrosion behaviour of various metals including copper.

Keywords

Artificial intelligence, Biodiesel, Corrosion inhibition, Corrosion prediction, Deep learning
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  • Value Added Biodiesel for Corrosion Inhibition

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Authors

Iyyappan S
Department of Mechanical Engineering, Ponjesly College of Engineering, Nagercoil, Tamil Nadu 629 003, India
Vinod Kumar K P
Department of Chemistry, University College of Engineering, Nagercoil, Tamil Nadu 629 004, India
Ponram P
Department of Electronics and Communication Engineering, Amrita College of Engineering and Technology, Nagercoil, Tamil Nadu 629 901, India

Abstract


Biodiesel is one of the prospective fuels, potentially capable of substituting petroleum fuel. Nevertheless, systems using this bioenergy resource are prone to corrosion than conventional fuels. Here, corrosion of copper metal in biodiesel with value added green coffee bean inhibitor has been evaluated by weight loss method, which produced 95.92% inhibition efficiency. Theoretically, corrosion is evaluated by artificial intelligence. The images of the surface obtained using CCD are augmented to 699 image samples. These augmented images are fed to back-propagation based neural network system for training, validation and classification for prediction of corrosion behaviour of copper in biodiesel with and without inhibitor. The neural network system has training, validation & testing prediction accuracies of 97.1%, 96.2% & 98.1%, respectively, and an overall accuracy of 97.1%. The proposed tool can be used to assess the corrosion behaviour dynamically in real time for futuristic prediction of corrosion behaviour of various metals including copper.

Keywords


Artificial intelligence, Biodiesel, Corrosion inhibition, Corrosion prediction, Deep learning