■ Feature
Silica brick is made of quartz stone material, the content of SiO2 is above 93%,it has good resistance to acid slag and good heat conductivity. And the refractoriness is 1690~1710℃,RUL is beyond 1620℃.
SiO2 has different types of crystals in different temperatures, and the transform between the crystals will cause the change of the bulk. So there is a close contact between the changes of SiO2 crystals and the produce ,performance and usage of silica bricks. During the heating, there will be changes of the bulk ,and cristobalite changes most, then quartz, phosphorus quartz is the slowest of all and it also has good heat-conductivity. Carbon furnace should use the silica brick with small true density. And the phosphorus quartz is better than others.
SiO2 has different types of crystals in different temperatures, and the transform between the crystals will cause the change of the bulk. So there is a close contact between the changes of SiO2 crystals and the produce ,performance and usage of silica bricks. During the heating, there will be changes of the bulk ,and cristobalite changes most, then quartz, phosphorus quartz is the slowest of all and it also has good heat-conductivity. Carbon furnace should use the silica brick with small true density. And the phosphorus quartz is better than others.
■ Technical Data
|
|
JG-95 |
|
|
Wall |
Other |
|
|
SiO2 ≥ |
95 |
|
|
Al2O3 ≤ |
1.5 |
|
|
Fe2O3 ≤ |
1.5 |
|
|
CaO ≤ |
2.0 |
|
|
|
22 |
24 |
|
|
35 |
35 |
|
|
1670 |
1650 |
|
|
2.32 |
2.34 |
|
|
1.0 |
1.0 |
|
|
0~0.2 |
0~0.2 |
|
|
1.28 |
1.30 |
■ About Us
development history »
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.
Factory strength »
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.
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.
Factory strength »
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.
