The complex processing of titanomagnetite concentrates of the Gremyakha-vyrmes deposit with extraction of vanadium and titanium

Titanomagnetites are a complex raw material with a high content of valuable components: iron (35–65 %), vanadium (0.5–1.5 %) and titanium (2–14 %). Today, titanium–magnetite concentrates are processed in two ways: blast furnace (Russia, China) and using electric smelting (South Africa). The blast–furnace method is applicable only for low–titanium titanomagnetites. In the case of using titanomagnetite concentrates with a titanium dioxide content of more than 4 %, the method of electric smelting with preliminary reduction is applicable. Both technologies aim to recover the two components iron and vanadium, while titanium is not recovered. In this regard, the development of a complex technology for processing titanomagnetite concentrate to obtain iron in granular form, vanadium pentoxide and titanium is urgent.

Preliminary reduction of chromium ore using Si sludge generated in silicon wafer manufacturing process

In order to promote the recycling of by-product from Si wafer manufacturing process and to develop environment-friend and low cost process for ferrochrome alloy production, a basic study was performed on the preliminary reduction reaction between chromium ore and the Si sludge, comprised of SiC and Si particles, which is recovered from the Si wafer manufacturing process for the semiconductor and solar cell industries. Pellets were first made by mixing chromium ore, Si sludge, and some binders in the designed mixing ratios and were then treated at different temperatures in the 1116?C?1388?C range in an ambient atmosphere. Cordierite and SiO2 were confirmed to be formed in the products after the reduction. Additionally, metal particles were observed in the product with Fe, Cr, and Si components. It is found that temperatures above 1300?C are necessary for the reduction of the chromium ore by the Si sludge. The reduction ratio for Fe was evaluated quantitatively for our experimental conditions, and the proper mixing ratio was suggested for the pre-reduction of the chromium ore by the Si sludge. This study provides basic information for the production of ferrochrome alloys on the pre-reduction of chromium ore using Si sludge.

A Design Space Exploration Method Using Artificial Neural Networks and Metamodeling

This paper suggests a design space exploration method using Artificial Neural Networks and metamodeling to systematically reduce the design space to a relatively small region. This method consists of three major steps. Firstly, self-organizing maps is employed to analyze design variables and objective function(s) with the original samples as preliminary reduction optimization of the initial large design space. Successively, resampling within the preliminary reduction space, clustering sample points using the fuzzy c-means clustering method with the given number of cluster, and choosing the most attractive cluster to construct kriging model and identify the design optimum within the reduced design space in the last step. The accuracy and validity of proposed methodology is proved by a heat exchanger design problem. It is found that the proposed method can intuitively capture promising design regions in which it is efficient to acquire the global or near-global desigm optimum.