Analysis of 3D surface data for on-line determination of the size distribution of iron ore pellet piles on conveyor belt
2007 (English)Licentiate thesis, comprehensive summary (Other academic)
Size measurement of iron ore pellets in industry is usually performed by manual sampling and sieving techniques. The manual sampling is performed infrequently and is inconsistent, invasive and time-consuming. Iron ore pellet's sizes are critical to the efficiency of the blast furnace process in the production of steel. Overly coarse pellets affect the blast furnace process negatively, however this affect can be minimized by operating the furnace with different parameters. An on-line system for measurement of pellet sizes would improve productivity through fast feedback and efficient control of the blast furnace. Also, fast feedback of pellet sizes would improve pellet quality in pellet production. Image analysis techniques promise a quick, inexpensive, consistent and non-contact solution to determining the size distribution of a pellet pile. Such techniques capture information of the surface of the pellet pile which is then used to infer the pile size distribution. However, there are a number of sources of error relevant to surface analysis techniques. The objective of this thesis is to address and overcome aspects of these sources of error relevant to surface analysis techniques. The research problem is stated as: How can the pellet pile size distribution be estimated with surface analysis techniques using image analysis? This problem is addressed by dividing the problem into sub-problems. The focus of the presented work is to develop techniques to overcome, or minimize, two of these sources of error; overlapped particle error and profile error. Overlapped particle error describes the fact that many pellets on the surface of a pile are only partially visible and a large bias results if they are sized as if they were smaller entirely visible pellets. No other researchers make this determination. Profile error describes the fact that only one side of an entirely visible pellet can be seen making it difficult to estimate pellets size. Statistical classification methods are used to overcome these sources of error. The thesis is divided into two parts. The first part contains an introduction to the research area together with a summary of the contributions, and the second part is a collection of four papers describing the research.
Place, publisher, year, edition, pages
Luleå: Luleå tekniska universitet, 2007. , 113 p.
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757 ; 2007:48
Research subject Industrial Electronics
IdentifiersURN: urn:nbn:se:ltu:diva-17046Local ID: 14ab2a80-7d80-11dc-b50c-000ea68e967bOAI: oai:DiVA.org:ltu-17046DiVA: diva2:990040
Godkänd; 2007; 20071018 (ysko)2016-09-292016-09-29Bibliographically approved