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Gu Y.,Tongji University | Xu J.,Tongji University | Keller A.A.,University of California at Santa Barbara | Yuan D.,Tongji University | And 9 more authors.
Journal of Cleaner Production | Year: 2015

China is the largest producer of iron and steel in the world. This heavy industry is characterized by significant water consumption and numerous water-related hazards. In this study, we propose the use of water footprint instead of conventional indicators (fresh water consumption (FWC) per tonne of steel or water consumption (WC) per tonne of steel) for the iron and steel industry. Using an iron factory in Eastern China as an example, we develop a water footprint calculation model that includes direct and virtual water footprints. A system boundary analysis method is then proposed to develop a common and feasible industrial water footprint assessment methodology. Specifically, we analyze the characteristics of the iron and steel industry from a life cycle assessment perspective. A water risk assessment was performed based on the results of the water footprint calculations. The selected iron factory has a water consumption (blue water) footprint of 2.24 × 107 m3, including virtual water, and a theoretical water pollution (gray water) footprint of 6.5 × 108 m3 in 2011, indicating that the enterprise poses a serious risk to the water environment. The blue water and gray water footprints are calculated separately to provide more detailed water risk information, instead of adding these two indicators, which has less environmental significance. © 2015 Elsevier Ltd. All rights reserved. Source


Zhang H.-N.,University of Science and Technology Beijing | Xu A.-J.,University of Science and Technology Beijing | He D.-F.,University of Science and Technology Beijing | Cui J.,Ningbo Iron and Steel Co
Journal of Central South University | Year: 2013

In order to obtain better carbonation effect, extraction behavior of slag batch is necessary to study. Relevant parameters like selective extraction yield were originally discussed. The relationship between selective extraction yield and conversion ratio was systemically focused on. The results show that alkaline earth metal conversion ratio is changed with leaching time and NH4Cl concentration by first order exponential, and the maximum conversion for calcium keeps about 68% at 120 min in 0.4 mol/L NH4Cl solution, while leaching temperature and particle size have a linear effect on conversion ratio. Selective extraction yield of calcium is more than 93%, and the value of Mg is less than 5%. Apparent layer bands of silicon and calcium appear in the surface area through morphology detection of slag after leaching, and the case for 38-75 μm slag batch is more obvious than 75-150 μm slag and slag with larger particle size when leaching in 0.4 mol /L NH4Cl solution for 90 min at 60 C. © 2013 Central South University Press and Springer-Verlag Berlin Heidelberg. Source


Zhang H.-N.,University of Science and Technology Beijing | Zhang H.-N.,National Institute of Design | Xu A.-J.,University of Science and Technology Beijing | Xu A.-J.,National Institute of Design | And 4 more authors.
Metalurgia International | Year: 2013

In order to obtain better carbonation effect, extraction behavior of slag batch is necessary to study and parameters related like selective extraction yield are originally discussed in the article. Corresponding experiments are conducted for systemically analyzing selective extraction process of slag batch in CH3COOH solution and discovering the relationship between metal element extraction concentration, selective extraction yield and the conversion ratio of alkaline earth metal and relevant parameters. Results show that metal element concentration is changed with those parameters by first order exponential function. Selective extraction yield of calcium is varied from 70% to 82%, and the value of Mg and Fe is about 10%, Mn is changed on the scale of 2% and Al and Zn can be neglected under experimental conditions. A significant linear relationship is fitted between leaching temperature, L/S ratio, particle size and calcium selective extraction yield, but not obvious for element Fe and Mn. The conversion ratio of calcium fluctuates from 38.76% to 51.32%. And apparent layer bands of silicon and calcium appear in the surface area through morphology detection of slag after extraction, which verifies that extraction reaction was indeed expressed by a core shrinking model. Source


Xu A.-J.,University of Science and Technology Beijing | Zhang H.-N.,University of Science and Technology Beijing | Yang Y.,University of Science and Technology Beijing | Cui J.,Ningbo Iron and Steel Co | And 2 more authors.
Journal of Iron and Steel Research International | Year: 2012

In order to study calcium leaching behavior for the steelmaking slag, factors that influence the leaching yield have been optimized. The results show that granularity of the slag, liquid to solid ratio (in short for L/S), temperature and reaction time have a significant effect on the leaching yield. The optimal conditions for leaching are determined as follows: 1) the granularity at 75 μm, L/S at 100, temperature at 60 °C; 2) the granularity at 75 μm, L/S at 50, temperature at 40 °C. Finally, the optimal leaching yield under these conditions is about 15%. © 2012 Central Iron and Steel Research Institute. Source


Zhang H.-N.,University of Science and Technology Beijing | Xu A.-J.,University of Science and Technology Beijing | Cui J.,Ningbo Iron and Steel Co | He D.-F.,University of Science and Technology Beijing | Tian N.-Y.,University of Science and Technology Beijing
Kang T'ieh/Iron and Steel | Year: 2012

On the basis of optimizing the traditional BP neural network model, a neural network prediction model combined with grey theory was developed by laying correlative degree weights to all input factors which had effects on the output variable. And then simulation experiments of model newly established were conducted based on data from a domestic steel plant. The results show that hit rate arrives at 65.00% when the error modulus is less than 5.00%, and the value is 96.67% when less than 10.00%. Comparing to the traditional neural network prediction model, the accuracy almost increases by 12.50%, Thus, the prediction of end point phosphorus content fits the real perfectly, which accounts for that neural network model for terminative phosphorus content based on grey theory can reflect accurately the practice in hot metal pretreatment. Source

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