The identification of flow pattern is key issue in multiphase flow which encountered in the petrochemical industry. Gas-liquid two-phase flow is difficult to identify the gas-liquid flow regimes objectively. This paper presents a feasibility of a clamp-on instrument for objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and artificial neural network. It is on recording and processing of the ultrasonic signals reflected from the two-phase flow. Experimental data obtained on a horizontal test rig with total pipe length of 21 m long and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer Perceptron Neural Networks (MLPNNs) used for developing the classification model. The classifier requires features as input which is representative of the signals. Ultrasound signal features extracted by applying both power spectral density (PSD) and discrete wavelet transforms (DWT) methods to the flow signals. A classification scheme of "1-of-C coding method for classification" was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using output of a first level networks features as input features. Addition of the two network models provided a combined neural network models which has achieved higher accuracy than single neural network models. Classification accuracies evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed three datasets out of 24 test datasets of the classification and scored 87.5% accuracy. (2) With the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated success of a clamp-on ultrasound sensor for flow regime classification and it would be possible in industry practice. It is considerably more promising than other techniques as it uses of non-invasive and nonradioactive sensor.

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