Effects of slag composition on H2 generation and magnetic precipitation from molten steelmaking slag-steel reaction

In this paper, the effects of slag composition (slag basicity CaO/SiO2 and FeO concentration) on the amounts of H2 gas generated and the magnetic spinel phase precipitated as a result of the reaction between synthetic steelmaking slag and steam at 1873 K (1600 °C) were studied by thermodynamic simulation (using Thermodynamic Package FactSage 7.0) and laboratory experiments. The thermodynamic calculation showed that, upon increasing slag basicity (CaO/SiO2) from 1.0 to 2.5, for the reaction of 100 g of slags with 100 g of H2O gas, the accumulated amount of the produced H2 gas increased from 0.17 to 0.27 g, while the amount of magnetic spinel phase first increased and then decreased, with the maximum precipitation of 16.71 g at the basicity of 1.5. When the FeO concentration increased from 15 to 30 pct for the slag with basicity of 2.0, the accumulated amount of the produced H2 gas increased from 0.17 to 0.28 g, and the amount of magnetic spinel phase increased from 5.88 to 10.59 g. The laboratory experiments were conducted in confocal laser scanning microscope to verify the reaction between 0.2 g of slag and 3.75 L of H2O-Ar gas (PH2O=0.2atm). The results indicated that, for 100 g of slags, upon increasing slag basicity (CaO/SiO2) from 1.0 to 2.5, both the produced H2 gas and magnetic spinel phase first increased and then decreased, with the maximum amounts being 0.09 g of gas and 37.00 g of magnetic spinel phase at the slag basicity of 1.50. For the FeO concentration increasing from 15 to 30 pct, the amounts of both the produced H2 gas and magnetic spinel phase increased from 0.04 to 0.10 g and from 18.00 to 27.00 g, respectively. The reaction rate between the molten CaO-SiO2-FeO-MnO-Al2O3-MgO slag and the moisture (PH2O=0.2atm) increased with the increasing FeO activity in the slag. The dependence of the reaction rate (mol/cm2/s) on FeO content can be expressed as r=(7.67(aFeO)−2.99)×10−7

Development of a novel process for energy and materials recovery in steelmaking slags

This work aims at gathering fundamental knowledge for the development of a novel process for energy (H2 gas) and materials (magnetite Fe3O4) recovery in hotsteelmaking slags by reacting molten steelmaking slag with steam. Thermodynamic simulation was carried out to calculate the accumulated amount of produced H2 gas as a function of the volume of H2O-Ar gas introduced and the precipitated phases of the molten slags during controlled cooling. Laboratory experiments of crystallisation behaviours of molten slags during cooling were visualized in situ through a confocal laser scanning microscope (CSLM), and the cooled slags obtained were characterised by using SEM-EDS and XRD. CCT diagrams for different slags were created showing the slag crystallisation/phase transformation at different cooling rates. The recovery ratio of H2 gas and the maximum potential recovery ratio of iron oxide in the oxidised slags were calculated, which concludes that with increasing the slag basicity from 1.0 to 1.5 and 2.0, the recovery ratio of H2 was found to increase from 12.6% to 23.7% and 22.6%, and the maximum potential recovery ratio of iron oxide was found to increase from 18.3% to 34.4% and 32.8% under the investigated conditions

Asymmetric rolling of interstitial-free steel using differential roll diameters. Part II : microstructure and annealing effects

The effects of annealing on the microstructure, texture, tensile properties, and R value evolution of an IF steel sheet after room-temperature symmetric and asymmetric rolling were examined. Simulations were carried out to obtain R values from the experimental textures using the viscoplastic self-consistent polycrystal plasticity model. The investigation revealed the variations in the textures due to annealing and symmetric/asymmetric rolling and showed that the R values correlate strongly with the evolution of the texture. An optimum heat treatment for the balance of strength, ductility, and deep drawability was found to be at 873 K (600 _C) for 30 minutes

Adaptive estimation of O-D demands for an incident-induced congested freeway under ATIS environment

An integrated freeway traffic management system requires a dynamic traffic control model that operates in real-time and efficiently allocates freeway traffic diversion onto less congested arterials. The distribution of congested freeway traffic onto neighboring arterials is often a cost-effective and practical way to mitigate the effect of non-recurrent congestion on urban freeways. However, the key tasks associated with the redistribution of traffic in the network involve reliable information provision, realistic modeling of driver behavior, and projecting future traffic conditions in the network. All three of these tasks are interdependent, and the accuracy of the driver behavior model contributes to the efficiency and precision of the other two tasks. Hence, it is essential to construct a reliable, realistic, and real-time driver behavior modeling framework that can efficiently represent drivers\u27 dynamic route switching behavior. While it is difficult to capture individual travel behavior characteristics in the field using currently available technology, it is relatively easy to obtain aggregate level responses in real-time. This research proposes an aggregate level dynamic route diversion model framework that can adapt to rapidly varying traffic characteristics as well as to short term and long term changes in drivers\u27 route choice behaviors. A Bayesian updating framework was developed to update the time varying parameters of the aggregate diversion model. The proposed route diversion model updating strategy was integrated with an adaptive freeway origin-destination (O-D) flow estimation framework. The framework was applied to estimate time-dependent O-D flow matrices in a test freeway network in northern Indiana under incident induced congested situations. Data required for the models were generated using INTEGRATION, a traffic simulation software developed at Queen\u27s University, Kingston, Canada. Experimental results showed significant improvement in O-D estimates when the route diversion propensity of drivers was properly accounted for. The adaptive characteristics of the route diversion model were captured using a Dynamic Linear Model which proved to be a computationally efficient approach yielding satisfactory predictions