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Solid Concentration Measurement in Horizontal Gas-Solid Flows: An Adaptive Model Matching Strategy Using Array
Zengyan Zhu1, Dayang Wang2, Yan Li3
1School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China.
Micromachines
|July 28, 2026
Summary
This study introduces an adaptive strategy for measuring solid concentration in horizontal gas-solid flows using an array capacitive sensor. The method accurately determines solid concentration by adapting to changing particle distributions, improving process control.
Area of Science:
- Fluid dynamics
- Sensor technology
- Process control
Background:
- Accurate solid concentration measurement is crucial for horizontal gas-solid flows.
- Gravity-induced particle distribution causes nonlinear sensor signals, hindering precise measurement.
- Existing linear models fail when particle distribution changes.
Purpose of the Study:
- To propose an adaptive model matching strategy for improved solid concentration measurement.
- To address the challenge of nonlinear sensor signals due to uneven particle distribution.
- To enhance accuracy in horizontal gas-solid flow monitoring.
Main Methods:
- Utilized an array capacitive sensor with two excitation modes: concave-ring for distribution and multi-electrode for concentration.
- Employed BP-Adaboost for particle distribution classification and K-means clustering for measurement models.
- Implemented an adaptive model selection based on particle distribution using Euclidean distance.
Main Results:
- The proposed method demonstrated adaptability to various particle distribution states.
- Achieved an average relative measurement error of 4.41% within a solid concentration range of 0.34-14.08%.
- Validated through 3D simulations and laboratory experiments.
Conclusions:
- The adaptive model matching strategy significantly improves measurement accuracy for solid concentration.
- This technique offers reliable process control in horizontal gas-solid flows.
- The sensor system effectively overcomes limitations of single linear models.
