Gas-liquid flow regime identification via a non-intrusive optical sensor combined with polynomial regression and linear discriminant analysis

•Non-intrusive optical sensor is designed and setup for detection of gas liquid flow in vertical configuration.•Polynomial regression and Linear discrimination methods combined with sensor response for flow regime identification.•Average and standard deviation of sensor responses are used as features for training, cross validating and testing the ML methods.•Offline testing and real time application provide high accuracy of flow regime identification. The identification of gas-liquid flow regimes for co-current upward flow in a vertical pipe is investigated using Polynomial regression (PR) and Linear Discriminant analysis (LDA) on the response from a non-intrusive optical sensor. The average voltage and associated standard deviation can be extracted as features from the sensor and used as inputs to establish the coefficients of an optimal regression model. Further to this, due to the structural similarity between the slug and churn flow regimes, further discrimination was required in the form of a Linear discriminant analysis to give accurate classification between these two flow regimes. According to the offline test and real-time flow conditions considered, a classification accuracy of 100% is achieved. The results of this paper show the capability of applying a relatively simple supervised regression model and LDA to the response from a non-intrusive optical sensor to identify gas-liquid flow regimes efficiently and accurately.