Time filter

Source Type

Wang B.,University of Science and Technology Beijing | Mu Y.-Q.,University of Science and Technology Beijing | Ju Q.-P.,Fangda Special Steel Science and Technology Co. | Xie F.-M.,Fangda Special Steel Science and Technology Co. | And 3 more authors.
Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing | Year: 2013

To solve contradictions between market demands and productivity, the problem how to arrange the production, which not only meets the needs of many customers for all types of products, but also gives full play to productivity was discussed under the premise of smooth running in iron and steel plants. A domestic special steel plant, which produces long products, was chosen as an example, and the reasonable proportion of different type of products was analyzed in combination with the concepts of product structure and productivity. The relationship between product structure and productivity was studied from three aspects of the equipment, the processes and the steelmaking plant. Finally, a method was proposed to calculate the reasonable product structure and productivity.


Mu Y.-Q.,University of Science and Technology Beijing | Yin J.,University of Science and Technology Beijing | Xie F.-M.,Fangda Special Steel Science and Technology Co. | Wang B.,University of Science and Technology Beijing | And 6 more authors.
Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing | Year: 2013

Nowadays, the complex organization mode of multi-steel grade production exists in many steelmaking plants in China. The matching between metallurgical furnaces and casters has effect on the smooth running of production in the workshops. With a typical steel plant in China as an example, the issue about furnace-caster matching was analyzed based on previous studies. The concept of equivalent period was proposed to solve the furnace-caster matching mode in combination with product structure. The index of furnace-caster matching degree was suggested to evaluate the furnace-caster matching mode.


Fu G.-Q.,University of Science and Technology Beijing | Liu Q.,University of Science and Technology Beijing | Wang Z.,University of Science and Technology Beijing | Chang J.,Fangda Special Steel Science and Technology Co. | And 5 more authors.
Beijing Keji Daxue Xuebao/Journal of University of Science and Technology Beijing | Year: 2013

LF refining process plays an important role in the temperature adjustment of molten steel, and precisely predicting the LF end-point temperature of molten steel is of great importance to actual production. Generally speaking, the prediction models of LF end-point temperature include the mechanism model and the black box model. The mechanism model can reflect the influence of each factor on the end-point temperature of molten steel, but it is difficult to obtain the expected prediction accuracy due to the limited comprehension of heat transfer in LF refining process. The black box model can usually achieve high prediction accuracy, whereas it does not reveal the effect of each factor. Moreover, the black box model has limited applications when process conditions are changed. Taking LF refining process in Fangda special steel plants as an object of study, this paper establishes a grey box model for predicting the LF end-point temperature of molten steel based on the mechanism model and the black box model. The grey box prediction model can not only indicate the impact of each factor, but also provide the precise prediction of LF end-point temperature. Verification results show that the hit rate of the grey box model is greater than 95% while the predictive error is within ±5°C.


Liu Q.,State Key Laboratory of Advanced Metallurgy | Liu Q.,University of Science and Technology Beijing | Wang Z.,State Key Laboratory of Advanced Metallurgy | Wang Z.,University of Science and Technology Beijing | And 4 more authors.
Kang T'ieh/Iron and Steel | Year: 2013

As the key procedure during steelmaking process, the production in steelmaking plant involving four processes: hot metal pretreatment, steelmaking, secondary refining and continuous casting. Such as steelmaking process, the fine production in steel manufacturing process was described. Fine production for steelmaking process is elaborated from the fine control of productive technology, the fine configuration and operation of process facilities, the fine control of production process. Meanwhile, recent research progress concerning fine production for steelmaking process was reviewed. Fine production for steelmaking process was of great importance to the high quality, high efficiency, low cost, and energy saving production in steel manufacturing process.


Liu Q.,University of Science and Technology Beijing | Wang B.,University of Science and Technology Beijing | Wang Z.,University of Science and Technology Beijing | Xie F.,Fangda Special Steel Science and Technology Co. | Chang J.,Fangda Special Steel Science and Technology Co.
Materials Today: Proceedings | Year: 2015

As the key component during steelmaking process, the production in steelmaking plants involves four stages in process: Hot metal pretreatment, steelmaking, secondary metallurgy and continuous casting. This paper is to describe the fine production in steelmaking plants, which is elaborated from fine control of productive technology, fine configuration and reliable operation of process facilities, and fine control of production process. Meanwhile, recent research progress concerning fine production in steelmaking plants will be reviewed. The technology of fine production in steelmaking plants will be introduced combined with research cases on integration of process models and key techniques. Moreover, fine production in steelmaking plants is of great importance to the high-quality, high-efficiency, low-cost, and energy-saving production in steel manufacturing process. © 2015 The Authors.


Cao L.,University of Science and Technology Beijing | Wang Z.,University of Science and Technology Beijing | Liu Q.,University of Science and Technology Beijing | Lu X.,University of Science and Technology Beijing | And 2 more authors.
Proceedings of the 6th International Congress on the Science and Technology of Steelmaking, ICS 2015 | Year: 2015

With the purpose of optimizing the mixing effect of the steel bath, hydrodynamic model experiments which were designed based on the similarity theory have been carried out in a 1/6th scaled down model of an 80t converter. The flow characteristics under the condition of top blowing, bottom blowing and top-bottom blowing process were discussed, and the influence rules of different technical parameters including the oxygen lance height, the flow rate of the top-blown oxygen, the number of the nozzles of the oxygen lance, the configurations of bottom nozzle and the flow rate of bottom-blown gas on the mixing effect of the steel bath were also investigated. The results showed that a better performance of top blown converter can be achieved by the means of using oxygen lance with four nozzles and controlling the oxygen lance height ranging from 1000mm to 1360mm and keeping the flow rate of topblown oxygen within 15000Nm3/h to16000Nm3/h. As for bottom blown converter, the asymmetric configurations are more beneficial to obtain a desired agitation effect for the converter bath than the symmetric ones, at the same time the flow rate of bottom-blown gas should be controlled within the scope of 45-76Nm3/h. Furthermore, through variance analysis, the optimal operating parameters of combined blown converter were obtained as follows: lance height 950mm, oxygen flow rate 14000Nm3/h, five nozzles oxygen lance, bottom nozzle configuration B (the layout of bottom nozzles on 0.5D with the axis angle of 30°) and bottom gas flow rate 100Nm3/ h. In addition, the agitation energy in the process of top blowing, bottom blowing or combined blowing and its effect on bath mixing were analyzed, and then the estimation model for mixing time of the steel bath in basic oxygen furnace was proposed using the method of nonlinear curve fitting to analyze the experiment data, and its expression is τ=169.72 ϵA -0.35.

Loading Fangda Special Steel Science and Technology Co. collaborators
Loading Fangda Special Steel Science and Technology Co. collaborators