Neural Network Correction for Multi-Phase Flowmeter Accuracy
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Solution Overview
Problem
Conventional flowmeters inaccurately measure properties like mass flow rate and density when dealing with multi-phase fluids, as they assume single-phase fluids, leading to incorrect measurements even when operating with multi-phase flows.
Innovation Solution
A digital flowmeter system that uses a neural network to correct intermediate values derived from apparent properties, accounting for the presence of multiple phases by determining phase-specific properties such as mass flow rates and densities of individual phases within the multi-phase fluid.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional flowmeters assume single-phase fluids for measurement, then the device complexity remains simple, but the measurement precision deteriorates when dealing with multi-phase fluids
Solution Approach 1:
The patent introduces a neural network as an intermediary computational layer between the flowmeter sensor and the measurement output. The neural network processes apparent property measurements and applies corrections based on trained multi-phase flow characteristics, enabling accurate multi-phase measurement without redesigning the physical flowmeter structure.
Solution Approach 2:
The patent transforms the measurement approach by changing from direct single-phase property measurement to measuring apparent properties and then applying parameter transformations through neural network corrections. This allows the system to adapt to multi-phase conditions by modifying the interpretation of measured parameters rather than the physical measurement process.
2Reliability
If conventional flowmeters operate with multi-phase fluids using single-phase assumptions, then the device complexity remains low, but the reliability of measurements deteriorates
Solution Approach 1:
The patent implements preliminary action by training the neural network offline with extensive multi-phase flow data before deployment. This pre-training establishes correction algorithms that automatically compensate for multi-phase effects during operation, ensuring reliable measurements without adding complex real-time processing hardware.
Solution Approach 2:
The system incorporates feedback mechanisms where the neural network continuously refines its corrections based on apparent property measurements. The measured apparent properties feed into the neural network, which outputs corrected values that account for multi-phase conditions, creating a closed-loop measurement system that adapts to actual flow conditions.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Improves the accuracy of mass flow rate and density measurements for multi-phase fluids by correctly accounting for the presence of multiple phases, enhancing the reliability of flowmeter readings.
Implementation Method 1
Coriolis-type mass flowmeters are based on the well-known Coriolis effect, in which material flowing through a rotating conduit becomes a radially traveling mass that is affected by a Coriolis force and therefore experiences an acceleration
Implementation Method 2
The period of the input voltage is chosen so that the motion of the conduit matches a resonant mode of vibration of the conduit. This reduces the energy needed to sustain oscillation
Data Source
AI summary
A multi-phase process fluid is passed through a vibratable flowtube. Motion is induced in the vibratable flowtube. A first apparent property of the multi-phase process fluid based on the motion of the vibratable flowtube is determined, and an apparent intermediate value associated with the multi-phase process fluid based on the first apparent property is determined. A corrected intermediate value is determined based on a mapping between the intermediate value and the corrected intermediate value. A phase-specific property of a phase of the multi-phase process fluid is determined based on the corrected intermediate value.