Advanced statistical computing for capacitance tomography as a monitoring and control tool

Advanced statistical modelling such as Bayesian framework is a powerful methodology and gives great flexibility in terms of physical phenomena modelling. Unfortunately it is usually associated with very time and resource consuming computing. Therefore it was avoided by engineers in the past. Nowadays, rapid development of computer capabilities enables use of such methods. Algorithms reported here are based on Markov chain Monte Carlo (MCMC) methods applied to Bayesian modelling. The important factor is highly iterative approach enabling direct desired parameters estimation, hence omitting the phase of image reconstruction. This property has an important feature of making feasible implementation of automatic industrial process control systems based on process tomography.