| Paper Name |
Multisensor Data Fusion Techniques With ELM for Pulverized-Fuel Flow Concentration Measurement in Cofired Power Plant |
| Author |
Wang, XX; Hu, HL*; Liu, X |
| Publication/Completion Time |
2015-10-04 |
| Magazine Name |
IEEE Transactions on Instrumentation and Measurement |
| Vol |
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| Related articles |
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| Paper description |
Due to the phase concentration, measurement of coal/biomass/air three-phase flow is closely associated with the quantity of pollutant discharged and combustion efficiency in the pneumatic conveying system. This paper proposed a multisensor data fusion technique for the online volume concentration measurement of coal/biomass pulverized-fuel flow in cofired power plant. The techniques combine electrostatic sensors with
capacitance sensors and incorporates a data fusion technique based on an adaptive wavelet neural network (AWNN), and gradient descent learning algorithm and genetic learning algorithm are used for training of the network parameters. The flow regime is first identified by performing an extreme learning machine on the electrostatic fluctuation signals, thus making the concentration measurement less affected by variations in the flow regimes. Then under the certain identified flow regime, the AWNN data fusion method was applied to
determine the phase concentration. An experimental platform was built for phase concentration measurement of pulverized coal (PC)/biomass/air three-phase flow, and the test results confirmed that the technology of the concentration measurement with flow regime identification is better than that of the one without, and the maximum fiducial errors of sawdust and PC are 2.1% and 1.2%. |
(创新港)
