Multiphase Flow Meter Calibration

DeepCI introduces a novel deep learning driven time-series predictive and optimization model for uncertainty growth prediction and calibration intervals optimization

DeepCI’s developed technology addresses the limitations of state-of-the-art mathematical/statistical uncertainty growth and calibration intervals predictive methods such as limited modelling assumptions, limited learning, lack of ability to deal with non-linear complex behaviours, and poor scalability. State-of-the-art literature reveals that it is difficult to solve the calibration optimization equation in closed form.

DeepCI’s deep learning and optimization-driven method is capable of acquiring global or near optimal calibration intervals - achieving desired performance with minimum total end-to-end cost or utility function (i.e. sum of financial and measurement cost)). Our developed model learns instrument uncertainty behaviour over time and acquire optimal calibration intervals - meeting in-tolerance percentage or equipment functioning capability within expected tolerance limits at the time of use. Contact us for more details!