• RAGAVI. P
• Dr. T. A. ALBINAA

Gas plant Leak detection is an important and persistent problem in the Oil and Gas plant industry. This is very important as pipelines are the most common way of transporting natural gas. This research aims to study the ability of data-driven intelligent models to detect small leaks for a natural gas pipeline using basic operational parameters and then compare the models among themselves using existing performance metrics. This project applies the observer design technique to detect leaks in natural gas pipelines using a regression classification hierarchical model where an intelligent model acts as a Linear regression and logistic regression acts as a classifier. The result shows that while support vector machines and artificial neural networks are better at regression than the others, they do not provide the best results in leak detection due to their internal complexities and the volume of data used while prediction. The developed model was trained and tested using the sequence of concentration profiles generated using open-source simulated data. The model learned successfully to predict the gas leakage and classify its size.

Gas leakage, pipeline, random forest algorithm regression classification.

"GAS PLANT LEAKAGE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.f65-f73, March-2023, Available :http://www.jetir.org/papers/JETIR2303506.pdf

"GAS PLANT LEAKAGE DETECTION USING MACHINE LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppf65-f73, March-2023, Available at : http://www.jetir.org/papers/JETIR2303506.pdf