oa Gas Transmission Optimization via Machine Learning

This paper focuses on leveraging machine learning approach to optimize fuel consumption in compressor stations in Malaysia. The main objective is to predict the fuel consumption where the model recommends the optimal setpoint, thereby enhancing fuel efficiency and minimizing environmental effect in line with the goal of achieving net zero carbon emissions by 2050. A random forest regression model is used to train the input features that significantly affects the accuracy of the model. Extensive measures and factors were considered during model training in achieving the goal to ease or help operators in the decision-making processes. This study is divided into two processes which is developing regression model to predict fuel consumption and then proceed with optimizing the compressor stations with some identified constraint. The model produced makes a valuable contribution to the gas business sector, exhibiting a high accuracy rate with a correlation coefficient of approximately 95%. Furthermore, the findings demonstrate the potential for substantial cost savings by adopting the machine learning optimization model, with a notable 0.65% reduction in CO2 emissions attributed to fuel consumption over a six-month period.