Enhancing the Performance of Pipeline Leakage Detection System Using Artificial Neural Network

This research presents enhancing the performance of pipeline leakage detection system using artificial neural network. The study reviewed many literatures and singled out electrochemical sensor as the best when compared to the rest in sensing gas leakages. This was used as a sensing element to collected data of the gas leakages and then a feed forward neural network was modeled and trained with the gas data using back propagation algorithm. A gas reference model was generated after the training and deployed with the sensing element as an improved gas leakage detection system using Matlab. The system was simulated and the result showed high gas leakage detection accuracy of 98.95% was achieved, near real time detection of 11.23ms, MSE value of 0.000103Mu and a regression 0.999 was recorded. The result was also compared with another work developed with neural network which recorded 94.7% and the result showed that the new system achieved 4.75% improvement in detection accuracy which may look small but is very good when considering the delicate nature of natural gas leakages and the big problem it can cause.