This study presents a state estimation model designed to identify and minimize errors in flowmeter measurements, ensuring greater accuracy in measurement data. These errors can stem from various sources, including equipment malfunctions, human errors, instrument rigidity, vibrations, and flow medium-related factors. While several methods exist to analyze these inaccuracies, state estimation proves highly effective in detecting and reducing them, achieving nearly zero percent error and a 95% improvement in flow meter performance assessment. Implemented in Python, the model leverages the Kalman filter to generate estimated values, which are then compared with actual meter readings to assess meter reliability and performance. The performance results offer a comprehensive assessment history, aiding in meter calibration and proving through numerical simulations. To achieve the study's objectives, calibration data—whether individual readings or sequential datasets—are analyzed and benchmarked against performance criteria established by international or local regulatory bodies. The results are further validated using the state estimation model, ensuring that measurement series remain accurate, consistent, and reliable.
