A Novel Method for Quantifying the Composition of Mineralogical Phase in Iron Ore Sinter

Abstract

This paper presents a new method to calculate accurately the composition of the mineralogical phase in iron ore sinter, especially the minor phases. This model is based on the combined model of gray-level distribution for a multi-phase system. The composition of the mineralogical phase is calculated by minimizing the difference between the theoretical gray-level histogram and the actual gray-level histogram of a micrograph of the iron ore sinter. A genetic algorithm and an improved genetic algorithm are introduced to solve this extreme value problem. Suitable parameters for the algorithm were determined by comparing the solutions for each of several cases. The example provided demonstrates that this model is valid, especially for the minor phases, and the solution is credible.

Review on the Phase Equilibria in Iron Ore Sinters

“…Different phases verified in the sinter have been collected from literature to Table 3, in which the sinter phases have been classified into iron oxides, calcium ferrites, amorphous silicates, crystalline silicates and non-assimilated non-ferrous particles. The classification has been modified after Mezibricky et al 35) In addition to mineral phases, there are different-sized pores 8,12,27,28,[36][37][38] in the sinter structure, which also have a marked effect on the sinter properties. 14) As earlier stated, the chemical composition together with the process conditions determine the mineral compounds in the sinter.…”

Quantitative Analysis of Mineral Composition of Iron Ore Sinter Based on Comprehensive Image Processing Techniques

“…However, sometimes the large pores existing in the sinter microstructure render this system produce errors inevitably. [10][11][12] To solve this problem, this paper is aimed at applying comprehensive Image Processing Method not only to quantify the mineral composition of sinter but also work out the proportion of the pores.…”

“…It can be classified into two categories according to references: [7][8][9][10][11] 1) both mineral composition recognition and quantification are realized according to characteristics of the distribution of gray level derived from the difference of reflectivity for different minerals; 2) mineral composition recognition is carried out by making use of texture features. However, the first method might fail when two kinds of minerals with © 2014 ISIJ similar reflectivity are identified.…”

A Genetic Algorithm Application to Minimize Pig Iron Cost

“…Genetic algorithms, which became popular through the work of Holland in early 1970s, is a technique used in computing to find exact or approximate solutions to optimization and search problems. 8) Detailed solving process of the model is shown in Fig. 1.…”