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Temperature signal decoupling of heat transfer probe for gas-solids flow measurement | IEEE Conference Publication | IEEE Xplore

Temperature signal decoupling of heat transfer probe for gas-solids flow measurement


Abstract:

A new decouple method of the heat transfer probe for measuring the gas-solid two-phase mass flow is presented. The gas-solid two-phase mass flow can be measured on-line b...Show More

Abstract:

A new decouple method of the heat transfer probe for measuring the gas-solid two-phase mass flow is presented. The gas-solid two-phase mass flow can be measured on-line by a heat transfer probe, and the temperature of the fluid is useful for the data fusion to get high accuracy. the measure range of the temperature was expanded by the new circuit and higher accuracy achieved. Experiments are carried out in a specially designed laboratory scale pneumatic conveyor and the result shows that this method can perform compound measurement.
Date of Conference: 15-17 December 2017
Date Added to IEEE Xplore: 08 February 2018
ISBN Information:
Conference Location: Chengdu, China
School of Information Engineering, Hubei University of Economic
School of Automation, Huazhong University of Science and Technology, Wuhan, China

I. Introduction

The measurement of the gas-solid two-phase flow in pneumatic transmission system is of great significance, especially in a coal-fired plant or the metallurgical industry. But the concentration measurement of gas-solid two-phase flow is one of the most difficult parameter to be measured. The concentration measurement has been the research focus and difficulty in the industrial detection for a long time [1]. Recently, many sensor techniques have been adopted in the research on gas-solid two-phase flow, such as the process tomography technology [2], [3], electrostatic method [4], [5], optical method [6], [7], microwave method [8], [9] and thermal method [10], [11]. A variety of data processing methods have been used in the measured system to improve measuring accuracy, including neural network technique [12], [13], soft-sensing technique [14] and data fusion method [15], [16].

School of Information Engineering, Hubei University of Economic
School of Automation, Huazhong University of Science and Technology, Wuhan, China
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References

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