The traditional mode of transporting natural gas across vast geographic regions entailed building a large network of -metal pipelines. Owing to the corrosiveness of metal, significant security issues remain. They are slowly being converted to polyethylene pipes that result in sturdier, durable, and reliable gas distribution networks, especially for low-pressure pipes near customer premises. However, detecting vulnerabilities across such a vast global network of pipes and replacing them is a humongous task that is prone to inefficiencies with traditional linear approaches.
In this whitepaper, we shall explore how HCLTech uses a digital twin approach to create a comprehensive solution that leverages ML and accurately identifies faults in the network and uses an application integration based geospatial dashboard to review recommendations and create work orders. This streamlines the entire replacement process digitally and ensures efficiency and efficacy.
Implement and Scale Digital Twins for Pipeline Networks