Glass structure of industrial ground granulated blast furnace slags (GGBS) investigated by time-resolved Raman and NMR spectroscopies

Abstract

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Titanium in GGBS-like calcium-magnesium-aluminosilicate glasses: Its role in the glass network, dissolution at alkaline pH and surface layer formation

“…39 This is supported by experimental results on GGBS glasses that reported the large majority of Al in 4-coordinate positions. 10,14 In tetrahedral coordination, Al contributes one tetrahedron per atom, but also requires charge compensation due to its 3+ oxidation state. Ti was not included in NBO/T calculation here, because the necessary structural hypothesis will be experimentally examined in this study and discussed below.…”

“…8,9 These differences can partially be explained by the polymerization degree of the SiO2 network that controls dissolution rates and subsequently short-term compressive strength. 8,[10][11][12][13] In GGBS glasses, mainly SiO2 and, to a smaller extent, Al2O3 are the principal network formers whereas CaO, together with small amount of MgO, are the main network modifiers. 2,6,10,[12][13][14] TiO2 is present in GGBS at levels of up to a few weight percent (wt%) but its presence has been reported to have a negative effect starting from 1 wt% TiO2 in blended cements.…”

“…8,[10][11][12][13] In GGBS glasses, mainly SiO2 and, to a smaller extent, Al2O3 are the principal network formers whereas CaO, together with small amount of MgO, are the main network modifiers. 2,6,10,[12][13][14] TiO2 is present in GGBS at levels of up to a few weight percent (wt%) but its presence has been reported to have a negative effect starting from 1 wt% TiO2 in blended cements. 9,13,15,16 Standard mortars containing 75 % GGBS have been observed to lose more than 50% of their compressive strength after 2 days of curing when 1.9 wt% TiO2 is added to the slag.…”

Experimental Study of Slag Changes during the Very Early Stages of Its Alkaline Activation

“…It is known [ 27 , 28 ] that the dissolution of slag glass is strongly dependent on the disorder of the aluminosilicate framework due to its alkali content (K + , Na + )—especially alkaline earth metal cations (Ca 2+ , Mg 2+ ). This can be expressed nicely in terms of the ratio of non-bridging oxygens to those that are tetragonally coordinated, which typically reaches values of 1.7 to 2.0 for industrial slags [ 29 , 30 ]. Additionally, the reactivity of the slag is significantly influenced by the presence of minor elements, of which, network modifiers such as Ba and Sr increase the reactivity of the slag, while others such as Ti, Ce, Cr, V, and Zr have a dramatic adverse effect [ 31 ].…”

Prediction of silicon content in the hot metal using Bayesian networks and probabilistic reasoning

“…In this context, the main objective of this work was to build the source code of a Bayesian artificial neural network to determine the number of neurons with the best results for predicting the silicon content in cast iron, varying the number of neurons in the hidden layer by 10,20,25,30,40,50, 75 and 100 neurons.…”