Parametric Modelling Algorithms in Electrical Capacitance Tomography for Multiphase Flow Monitoring

Bayesian statistics is a powerful physical phenomena modelling tool. However it usually demands highly iterative algorithms, hence it is was not widely used so far. Recently, rapid development of computing capabilities enables use of such methods. Computing methodology here presented features Markov chain Monte Carlo (MCMC) methods applied to Bayesian modelling. The essential aspect is enabling direct characteristic parameters estimation, hence omitting the phase of image reconstruction widely produced whenever process tomography is applied. This property has an important feature of making feasible implementation of automatic industrial process control systems based on electrical capacitance tomography (ECT)