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MACHINE LEARNING DRIVEN DEVELOPMENT OF CORROSION INHIBITORS IN OIL AND GAS INDUSTRY APPLICATIONS

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Date
2025-06
Author
RIYAZ, NAJAMUS SAHAR
Collections
Environmental Engineering
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Najamus Riyaz_ OGS Approved Thesis.pdf
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Abstract
Corrosion inhibitors remain one of the most widely used and effective strategies for mitigating corrosion in the oil and gas industry. This study employed a machine learning (ML)-driven approach to develop green corrosion inhibitors, utilizing a Graph Convolutional Network (GCN) to predict inhibition efficiencies. The model was trained on a dataset of over 100 inhibitors and predicted an inhibition efficiency of 84% for 200 ppm chitosan-grafted polyacrylamide (CsAM). Experimental validation was conducted using electrochemical techniques on CsAM, synthesized with four different polyacrylamide-tochitosan ratios. Characterization of the inhibitors was performed using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), Contact Angle measurements, and Thermogravimetric Analysis (TGA). Electrochemical results showed a maximum inhibition efficiency of 98% for 1:30 CsAM at 200 ppm. Corrosion kinetic analysis revealed that the inhibitor acts as a mixed-type inhibitor, with corrosion prevention primarily governed by physisorption.
URI
http://hdl.handle.net/10576/66438