Study on the reducibility of iron ore pellets at high temperature

The behaviour of iron ore pellets in a blast furnace must be considered to improve ironmaking operations, especially when a large amount of the pellets is used. This study presents the reduction degree, mineralogical composition, and morphology of the pellet reduced in a gas mixture of 60% CO and 40% Ar at temperatures between 900 and 1,100oC. The pellet was prepared from iron ore from the Cao Bang province, Vietnam, by rotary drum. The obtained results showed that the reduction degree of the pellet increased with increasing reduction time and temperature. The activation energy of the reducing reaction was calculated to be 63.2 kJ/mol, which indicated that reduction occurred more easily in the present condition. X-ray diffraction (XRD) results revealed mineralogical phases such as hematite (Fe2O3), magnetite (Fe3O4), wüstite (FeO), metallic iron (Fe), and fayalite (Fe2SiO4) existing in the pellets when reduced for different times and temperatures. Fe and Fe2SiO4 were found to be the majority in the pellet that was reduced for 90 min at 1,100oC. Scanning electron microscopy (SEM) observations suggested the formation of a liquid phase, e.g., Fe2SiO4, which retarded the reducing reaction because it hindered the diffusion of gas flow inside the pellet. This phenomenon is essential to blast furnace ironmaking because pellets must be completely reduced before they move down to the liquid zone.

An Accurate Carbon Accounting Model Based on the Micro-level Process Principles for the Lean Carbon Management in the Blast Furnace Ironmaking

Background:The carbon accounting plays a critical role in the lean carbon management and the policy formulation for industrial entities. The carbon accounting method based on emission factors offered by IPCC usually leads significant errors on the micro-level of the enterprise. For achieving a bottom-up lean carbon management with higher accounting accuracy, a micro-carbon accounting model based on the micro-level process principles is established on a S-system of dynamics with using the approximation method of power law and Michaelis-Menten law. It is used to predict the amounts of various resources and output products at the process nodes under predetermined simulation conditions.Results:In the case study on a blast furnace ironmaking system, it succeeds in accurately predicting the amount of products including carbon emissions depending on the massive variables of materials and fuels. Further study on the residual analysis shows that mean errors of the CO2 and CO emissions are respectively 5.23% and 6.77% while using the micro-carbon accounting model.Conclusions:This method better addresses the challenge of severely overestimation on carbon emissions in the carbon accounting of the ironmaking industry. It offers a prospective and accurate carbon accounting model further for formulating more targeted policies of the lean carbon management at a micro-level of an enterprise.

Pellet Roasting Management System Based on Deep Learning and Internet of Things

Pellet is widely used in blast furnace ironmaking. Pellet quality affects the effect of ironmaking, the existing control system of grating-rotary kiln mainly adopts manual control mode, and the quality of pellet production largely depends on the experience, fatigue, and sense of responsibility of the site operators. The use of the Internet of things (IoT) technology in the integration and improvement of enterprise information level, to achieve fine, intelligent production management, at the same time, is conducive to promoting steel enterprises to reduce costs and increase efficiency, energy conservation and emission reduction, transformation and upgrading, and taking a new road to industrialization. According to the working principle and technological characteristics of the grate-rotary kiln at all stages, this paper designs the management system of firing pellets based on convolutional neural network (CNN) and IoT technology, so as to realize automatic recognition of image data obtained by the perceptual layer and make an intelligent analysis of it. The system can classify the working conditions of the current equipment, so as to judge whether the production process parameters of the grate-rotary kiln are up to the standard, thus achieving the goal of controlling the quality of the finished pellet.

Optimizing MgO distribution between sinter and pellet based on melting behavior and interaction

During blast furnace ironmaking, MgO flux is added to the sinter or pellets to reduce the viscosity and improve the desulfurization capacity of the slag. The distribution of total MgO addition between the sinter and pellets affects the interaction between the ferrous burdens. Inappropriate interaction between different burdens promotes the formation of an excessive amount of low-melting-point liquid phases, which reduces the permeability of the cohesive zone. In this study, the effect of MgO on the interaction between the sinter and pellets was visualized during heating and further investigated using thermodynamic calculations. A new concept named the distribution index of MgO was proposed for optimizing the MgO distribution between the sinter and pellets. A trade-off between the permeability and interaction between burdens in the cohesive zone was observed, which can be addressed by postponing the interaction between burdens to a higher temperature (i.e., a lower part of the cohesive zone).