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Advanced Statistical Analysis as a Novel Tool to Pneumatic Conveying Monitoring and Control Strategy Development
Author(s) -
Romanowski Andrzej,
Grudzien Krzysztof,
Aykroyd Robert G.,
Williams Richard A.
Publication year - 2006
Publication title -
particle and particle systems characterization
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.877
H-Index - 56
eISSN - 1521-4117
pISSN - 0934-0866
DOI - 10.1002/ppsc.200601059
Subject(s) - markov chain monte carlo , electrical capacitance tomography , bayesian probability , computer science , statistical process control , sampling (signal processing) , process (computing) , control engineering , artificial intelligence , engineering , capacitance , computer vision , chemistry , electrode , filter (signal processing) , operating system
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.