Advanced statistical analysis as a novel tool to pneumatic conveying monitoring and control strategy development

Behaviour of powder flow in pneumatic conveying has been investigated for many years, though it still remains a challenging task both practically and theoretically, especially when considering monitoring and control issues. Better understanding of the gas-solids flow structures can be beneficial for the design and operation of pneumatic transport installations. This paper covers a novel approach for providing the quantitative description in terms of parameter values useful for monitoring and control of this process with the use of Electrical Capacitance Tomography (ECT). The use of Bayesian statistics for analysis of ECT data allows the direct estimation of control parameters. This paper presents how this characteristic parameters estimation can be accomplished without the need for reconstruction and image post processing, which was a classical endeavour whenever tomography was applied. It is achieved using a 'high-level' statistical Bayesian modelling combined with a Markov chain Monte Carlo (MCMC) sampling algorithm. Advanced statistics is applied to data analysis for measurements coming from the part of phenomena present in the horizontal section of pneumatic conveyor during slug formation.