Silica bricks for glass furnace refers to the silicon refractory product used in the high temperature part of the glass kiln (not in contact with the glass liquid), it is a kind of acid refractory mainly made from tridymite, cristobalite and a small amount of residual quartz as well as glass phase. The raw refractory material is natural silica sand whose SiO2 content should be more than 96%. The higher SiO2 content in silica sand, the higher temperature silica bricks resist.
Glass kiln silica brick has high temperature and volume stability, no pollution to glass liquid, chemical corrosion resistance, small bulk density, and can reduce the weight of furnace body.
►Silica Bricks for Glass Melting Furnaces Feature
1.The high temperature volume is stable, and the furnace body does not change due to the temperature fluctuation surface. Because the silicon brick has a high load softening temperature and a small creep rate, the glass kiln can keep the furnace body from deforming at 1600 ° C, and the structure is stable;
2.No pollution to the glass liquid. The main component of silica brick is SiO2. If it is used, if there is any drop or surface droplet, silica bricks for glass melting furnace will not affect the quality of the glass liquid;
3.Resistance to chemical attack. The superstructure silicon brick is eroded by R2O gas in the glass batch, and a smooth metamorphic layer is formed on the surface, which makes the erosion speed lower and protects;
4. The bulk density is small, which can reduce the weight of the furnace body;
Silica bricks are used in the roof and superstructure of glass melting tanks as well as for hot repairs.
►How to Reduce the Consumption of Silica Bricks in Glass Furnaces
Due to the high load-softening temperature, light weight and low price advantage of silica bricks,in order to increase the melting temperature and extend the overall life of the kiln, the small furnace mouth and the chest wall are changed to cast bricks, and some of the domes or parts thereof are changed to other materials. The consumption ratio of silica bricks may be reduce.
|
|
BG-95 |
BG-96B |
BG-96A |
|
|
SiO2 ≥ |
95 |
96 |
96 |
|
|
Fe2O3 ≤ |
1.2 |
1.0 |
0.8 |
|
|
Flux index ≤ |
0.7 |
0.6 |
0.5 |
|
|
0.2MPa refractoriness under load （℃） ≥ |
1660 |
1670 |
1680 |
|
|
Apparent porosity （%） ≤ |
22 |
22 |
22 |
|
|
Cold crushing strength（MPa） |
Unit weight＜20kg ≥
|
35 |
35 |
35 |
|
Unit weight ≥ 20kg ≥
|
30 |
30 |
30 |
|
|
True density （g/cm3 |
2.35 |
2.34 |
2.34 |
|
As adaptive case management (ACM) systems mature, we are moving beyond simple systems that allow knowledge workers to define ad hoc processes, to creating more intelligent systems that support and guide them. Knowledge workers still need to dynami-cally add information, define activities and collaborate with others in order to get their work done, but those are now just the table stakes in a world of big data and intelligent agents. To drive innovation and maintain operational efficiencies, we need to augment case work typically seen as relying primarily on human intelligence with machine intelligence. In other words, we need intelligent ACM.
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As adaptive case management (ACM) systems mature, we are moving beyond simple systems that allow knowledge workers to define ad hoc processes, to creating more intelligent systems that support and guide them. Knowledge workers still need to dynami-cally add information, define activities and collaborate with others in order to get their work done, but those are now just the table stakes in a world of big data and intelligent agents. To drive innovation and maintain operational efficiencies, we need to augment case work typically seen as relying primarily on human intelligence with machine intelligence. In other words, we need intelligent ACM.
Highly predictable work is easy to support using traditional programming techniques, while unpredictable work cannot be accurately scripted in advance, and thus requires the involvement of the knowledge workers themselves. The core element of Adaptive Case Management (ACM) is the support for real-time decision-making by knowledge workers.
