Case Study
Snam: AI & Advanced Analytics for Gas transportation
Our new approach increases the efficiency of "unaccounted-for gas" (UFG), enabling actions for the improvement of the whole system.
Where: Italy
Where: Italy
With Engineering, we have innovated the approach to the analysis of Unaccounted for Gas. The application of AI and Machine Learning algorithms makes it easier to analyze some of the underlying phenomena of UFG and consequently enables users to take necessary corrective actions.
Challenge
Within the scope of the SUMMER programme, aiming at providing better access to commercial and plant data of redelivery points, Snam needed to find a new approach to manage the issue of Unaccounted-for Gas (UFG). It is the quantity of gas remaining between the gas injected and the gas delivered to end users, net of line-pack variations and the consumption related to the network op. management.
Approach
UFG has a highly variable trend over time and, hence, Snam has carried out several analyses of UFG determinants throughout the years, in order to address actions to reduce such variability. Continuous discussions and the skills introduced in the measurement sector, which also relate to the chemical / physical analysis of the quality of gas, had identified several variables that influenced the thresholds of the meters. It was therefore necessary to adopt a more scientific approach, using AI & Advanced Analytics techniques to solve the problem.
Digital Ecosystem
Solution
Digital Ecosystem
An initial prototyping activity lasting for one year was useful to collect data for the next project phases. We identified six main use cases and adopted a methodological approach to build the data mining model (Crisp-DM). It progressively allowed to identify correlations between UFG & sub-threshold measures. By discussing the outcomes of our analyses with Snam, which we carried out with the help of machine learning activities, we improved the understanding of the phenomena. Hence, we began an industrialisation plan of the models implemented and defined a roadmap to complete the planned cases in 2022. The architecture is based on Microsoft stack: Data Lake Gen2 for data storage, Synapse for data structures, Azure Machine Learning for FE Analytics. UI is designed according to SUMMER guidelines and to market best practices. It allows users to create standard reports independently.
Results
Reduced Unaccounted for Gas
Improved quality of data analysis
Technologies
AI & Data
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