Concentration measurement of three-phase flow based on multi-sensor data fusion using adaptive fuzzy inference system

• This paper proposes a multi-sensor data fusion technology for concentration measurement of coal/biomass/air three-phase flow.
• The technology integrates capacitive and electrostatic sensors and incorporates the data fusion model of an adaptive network based on fuzzy inference system (ANFIS).
• The relationships between the solid phase concentrations and the signal features are established by the fusion model.
• Experimental results show that the fiducial errors of biomass and pulverized coal are 1.2% and 0.7% by using ANFIS based on Kalman filter and gradient descent hybrid algorithm.

This paper proposes a new method for the volumetric-concentration measurement of coal/biomass/air three-phase flow using multi-sensor data fusion techniques. The method integrates capacitive and electrostatic sensors and incorporates the data fusion model of an adaptive network based fuzzy inference system (ANFIS), which simulates the human׳s understanding of things. The features of the two sensor signals are extracted as the input of the ANFIS under various experimental conditions. The fusion model of the ANFIS establishes the relationship between the volumetric-concentration of the solid phase and the signal features by training with two different learning rules: the gradient descent method only and the hybrid method combining the Kalman filter algorithm with the gradient descent algorithm. Experimental results show that the ANFIS based on the hybrid learning rule outperforms the system based on the gradient descent learning rule and that the fiducial error for biomass and pulverized coal flows are 1.2% and 0.7%, respectively.

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