Premium
Application of a Bayesian Approach to the Tomographic Analysis of Hopper Flow
Author(s) -
Grudzien Krzysztof,
Romanowski Andrzej,
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.200500951
Subject(s) - markov chain monte carlo , computer science , electrical capacitance tomography , bayesian probability , sampling (signal processing) , flow (mathematics) , bayesian inference , measure (data warehouse) , monte carlo method , markov chain , data mining , algorithm , artificial intelligence , machine learning , statistics , mathematics , computer vision , capacitance , chemistry , geometry , electrode , filter (signal processing)
Abstract This paper presents a new approach to the analysis of data on powder flow from electrical capacitance tomography (ECT) using probability modelling and Bayesian statistics. The methodology is illustrated for powder flow in a hopper. The purpose, and special features, of this approach is that ‘high‐level’ statistical Bayesian modelling combined with a Markov chain Monte Carlo (MCMC) sampling algorithm allows direct estimation of control parameters of industrial processes in contrast to usually applied ‘low‐level’, pixel‐based methods of data analysis. This enables reliable recognition of key process features in a quantitative manner. The main difficulty when investigating hopper flow with ECT is due to the need to measure small differences in particle packing density. The MCMC protocol enables more robust identification of the responses of such complex systems. This paper demonstrates the feasibility of the approach for a simple case of particulate material flow during discharging of a hopper. It is concluded that these approaches can offer significant advantages for the analysis and control of some industrial powder and other multi‐phase flow processes.