Corundum lightweight refractory with low bulk density

Refractories ◽  
1963 ◽  
Vol 4 (5-6) ◽  
pp. 237-243
Author(s):  
I. S. Kainarskii ◽  
A. N. Gaodu

2018 ◽  
Vol 281 ◽  
pp. 150-155
Author(s):  
Dan Yang Zhang ◽  
Ling Qu ◽  
Wen Jie Yuan

Mullite-anorthite as a kind of lightweight refractory combines the low thermal conductivity of anorthite and the excellent thermal properties of mullite. In this work, mullite beads and calcium aluminate cement was used as the main component and bonding agent. The lightweight mullite-anorthite refractory was prepared by difference routes including foaming method, addition of pore-forming agent and sacrificial template method. The phase composition, bulk density, apparent porosity and thermal conductivity of samples were compared. The results showed that anorthite formed as the consequence of the reaction of cement and mullite. Extra addition of foam, cornstarch and polyurethane sponge could decrease the bulk density of samples. The pore size of samples prepared by foaming method was the smallest. The apparent porosity of samples obtained by sacrificial template method was largest, but the thermal conductivity was the highest due to the open pores.



TAPPI Journal ◽  
2015 ◽  
Vol 14 (6) ◽  
pp. 395-402
Author(s):  
FLÁVIO MARCELO CORREIA ◽  
JOSÉ VICENTE HALLAK D’ANGELO ◽  
SUELI APARECIDA MINGOTI

Alkali charge is one of the most relevant variables in the continuous kraft cooking process. The white liquor mass flow rate can be determined by analyzing the chip bulk density fed to the process. At the mills, the total time for this analysis usually is greater than the residence time in the digester. This can lead to an increasing error in the mass of white liquor added relative to the specified alkali charge. This paper proposes a new approach using the Box-Jenkins methodology to develop a dynamic model for predicting chip bulk density. Industrial data were gathered on 1948 observations over a period of 12 months from a Kamyr continuous digester at a bleached eucalyptus kraft pulp mill in Brazil. Autoregressive integrated moving average (ARIMA) models were evaluated according to different statistical decision criteria, leading to the choice of ARIMA (2,0,2) as the best forecasting model, which was validated against a new dataset gathered during 2 months of operations. A combination of predictors has shown more accurate results compared to those obtained by laboratory analysis, allowing a reduction of around 25% of the chip bulk density error to the alkali addition amount.



2011 ◽  
Vol 65 (9) ◽  
pp. 895-899
Author(s):  
Yan Ju
Keyword(s):  


Author(s):  
Jing Li ◽  
Shankar Mahalingam ◽  
David R. Weise
Keyword(s):  






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