Multiphase Flow Measurement Using Coriolis Flowmeters Incorporating Soft Computing Techniques

This thesis describes a novel measurement methodology for two-phase or multiphase flow using Coriolis flowmeters incorporating soft computing techniques. A review of methodologies and techniques for two-phase and multiphase flow measurement is given, together with the discussions of existing problems and technical requirements in their applications. The proposed measurement system is based on established sensors and data-driven models. Detailed principle and implementation of input variable selection methods for data-driven models and associated data-driven modelling process are reported. Three advanced input variable selection methods, including partial mutual information, genetic algorithm-artificial neural network and tree-based iterative input selection, are implemented and evaluated with experimental data. Parametric dependency between input variables and their significance and sensitivity to the desired output are discussed. Three soft computing techniques, including artificial neural network, support vector machine and genetic programming, are applied to data-driven modelling for two-phase flow measurement. Performance comparisons between the data-driven models are carried out through experimental tests and data analysis. Performance of Coriolis flowmeters with air-water, air-oil and gas-liquid two-phase carbon dioxide flows is presented through experimental assessment on one-inch and two-inch bore test rigs. Effects of operating pressure, temperature, installation orientation and fluid properties (density and viscosity) on the performance of Coriolis flowmeters are quantified and discussed. Experimental results suggest that the measurement system using Coriolis flowmeters together with the developed data-driven models has significantly reduced the original errors of mass flow measurement to within ±2%. The system also has the capability of predicting gas volume fraction with the relative errors less than ±10%