The efficiency of natural gas transportation over long distances is heavily dependent on the synchronization of compression assets and pipeline dynamics. This article analyzes the role of artificial intelligence (AI) and machine learning (ML) algorithms in optimizing operational parameters, reducing energy consumption, and enhancing the throughput of complex gas grid systems. Special attention is given to predictive algorithms for load forecasting and real-time process optimization at gas compressor stations.
Sayylova et al. (2026) studied this question.