A robust uncertainty quantification framework for machine learning–based wet-gas flow metering

Key Points

• Improved accuracy in wet-gas flow metering enhances measurement reliability and reduces operational risks.
• Key evidence shows the framework reduces uncertainty by 30% in flow measurements, improving real-time decision-making.
• The study employed advanced machine learning algorithms to assess the efficiency of the proposed framework.
• Findings highlight the significance of uncertainty quantification in allowing better performance of metering devices.