• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.43.2-43.2
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• 2010

In this study, metallurgical grade (MG) silicon with 99% purity produced by arc furnace process was systematically investigated for slag refining. The most problematic impurities to remove from MG silicon are boron (B) and phosphorus (P). To remove B and P from MG-silicon, we used synthetic slag in the molten state. MG-silicon with synthetic slag of CaO, $SiO_2$, and $CaF_2$ was melted using by high-frequency induction furnace with electrical output of 50kW. Specimens prepared by various refining process conditions(holding time, mixture ratio) were inspected by combined analysis of ICP-MS and XRF. With this approach, B has been reduced to <5ppm, P to <1ppm and other impurities to 0.1~0.2% except for Calcium. Calcium has been increased from 17ppm to 1500ppm. Problem of calcium contamination will be resolved by additional refining processes.

• Computers and Concrete
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• v.21 no.5
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• pp.559-567
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• 2018

In this study alkali activation of Electric Arc Furnace Slag (EAFS) is studied with a comprehensive test program. Three different silicate moduli (1-1,5-2), three different sodium concentrations (4%-6%-8%) for each silicate module, two different curing conditions (45%-98% relative humidity) for each sodium concentration, two different curing temperatures ($400^{\circ}C-800^{\circ}C$) for each relative humidity condition and two different curing time (6h-12h) for each curing temperature variables are selected and their effects on compressive strength was evaluated then regression equations using multiple linear regressions methods are fitted. And then to select the best regression models confirm with using the variables, the regression models compared between itself. An Artificial Neural Network (ANN) models that use silicate moduli, sodium concentration, relative humidity, curing temperature and curing time variables, are formed. After the investigation of these ANN models' results, ANN and multiple linear regressions based models are compared with each other. After that, an explicit formula is developed with values of the ANN model. As a result of this study, the fluctuations of data set of the compressive strength were very well reflected using both of the methods, multiple linear regression with quadratic terms and ANN.