■ Feature
► Description:
Silimanite brick is a kind of refractoriness which made by silimaniteand other minerals. And silimanite can be transformed to be mullite above 1500℃ high temprature.
► The main applications are:
1,Rider arches;
2,Forehearth superstracture;
3,Bushing for rochwool and fiber glass;
4,High temprature furnaces, such as glass melting furnace,etc .
► Advantages:
1,Corrosion-resistant;
2,Dense structure;
3,Good thermal shock resistance.
Silimanite brick is a kind of refractoriness which made by silimaniteand other minerals. And silimanite can be transformed to be mullite above 1500℃ high temprature.
► The main applications are:
1,Rider arches;
2,Forehearth superstracture;
3,Bushing for rochwool and fiber glass;
4,High temprature furnaces, such as glass melting furnace,etc .
► Advantages:
1,Corrosion-resistant;
2,Dense structure;
3,Good thermal shock resistance.
■ Technical Data
| Item | GXS-50 | GXS-60 | GXS-62 | |
| Chemical composition | Al2O3 | ≥50 | ≥60 | ≥62 |
| SiO2 | ≤45 | ≤38 | ≤33 | |
| Fe2O3 | ≤1.2 | ≤1.0 | ≤1.0 | |
| Apparent Porosity％ | ≤18 | ≤18 | ≤17 | |
| Bulk Density g/cm3 | ≥2.4 | ≥2.5 | ≥2.55 | |
| Cold Crushing Strength Mpa | ≥45 | ≥50 | ≥60 | |
| 0.2Mpa Refractoriness Under Load T0.6 ℃ | ≥1550 | ≥1600 | ≥1650 | |
| Permanent Linear Change On Reheating （%）1500℃X2h | ±0.1 | ±0.1 | ±0.1 | |
| Thermal Shock Resistances 100℃ water cycles | ≥20 | ≥20 | ≥20 | |
| Reversible Thermal Expansion 1000℃ | 0.55 | 0.6 | 0.6 | |
| Pyrometric Cone Equivalent S.C. SK | 35 | 36 | 38 | |
| Range of Application | Rider Arches | Superstructure forehearth | Superstructure forehearth | |
■ 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.
