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The key reasons that the present method cannot be used to solve the industrial multi- phase flow pattern recognition are clarified firstly. The prerequisite to realize the online recognition is proposed and recognition rules for partial flow pattern are obtained based on the massive experimental data. The standard templates for every flow regime feature are calculated with self-organization cluster algorithm. The multi-sensor data fusion method is proposed to realize the online recognition of multiphase flow regime with the pressure and differential pressure signals, which overcomes the severe influence of fluid flow velocity and the oil fraction on the recognition. The online recognition method is tested in the practice, which has less than 10 percent measurement error. The method takes advantages of high confidence, good fault tolerance and less requirement of single sensor performance.
The key reasons that the present method can not be used to solve the industrial multi- phase flow pattern recognition are clarified first. The prerequisite to realize the online recognition is proposed and recognition rules for partial flow pattern are obtained based on the massive experimental data. The standard templates for every flow regime feature are calculated with self-organization cluster algorithm. The multi-sensor data fusion method is proposed to realize the online recognition of multiphase flow regime with the pressure and differential pressure signals, which overcomes the severe influence of fluid flow velocity and the oil fraction on the recognition. The online recognition method is tested in the practice, which has less than 10 percent measurement error. The method has advantages of high confidence, good fault tolerance and less requirement of single sensor performance.