■ Feature
► Description
Fire clay insulation brick is made of local top grade fireclay materials under the high temperature according to the lasted national standard, which possesses even bulk density, high strength, low thermal conductivity and low impurity.
► Application
Fire clay insulation brick mainly used for the insulating lining of hot surfaces or backing heat-insulating layers of other refractory materials. The refractory linings or heat-insulating materials of the industries, such as, ethylene pyrolysis furnaces, tubular furnaces, reforming furnaces of synthetic ammonia, gas generators and high-temperature shullte kilns,etc
■ Technical Data
| Sunrise Brand | NG-0.5 | NG-0. 6 | NG-0. 8 | NG-1.0 | NG-1.3 | NG-1.5 | |
| USA Brand | ISO125-0.5 | ISO125-0.6 | ISO135-0.8 | ISO135-1.0 | ISO140-1.2 | ISO140-1.5 | |
| Max Service Temperature (℃) | 1150 | 1200 | 1280 | 1300 | 1350 | 1400 | |
| Bulk Density (g/cm3) | 0.55 | 0.6 | 0.8 | 1.0 | 1.3 | 1.5 | |
| Apparent Porosity % | 80 | 70 | 60 | 55 | 50 | 40 | |
| Cold Crushing Strength (Mpa) ≥ | 1.5 | 2.0 | 2.5 | 3.0 | 4.0 | 6.0 | |
| Reheating Linear Change (%) ℃X 12H ≤ | 1250℃ -0.5 | 1300℃ -0.5 | 1350℃ -0.5 | 1350℃ -0.9 | 1350℃ -0.9 | 1350℃ -0.9 | |
| Thermal conductivity（W/m.k） | 400℃ | 0.14 | 0.25 | 0.35 | 0.41 | 0.51 | 0.6 |
| 600℃ | 0.16 | 0.29 | 0.45 | 0.43 | 0.61 | 0.71 | |
| 800℃ | 0.18 | 0.31 | 0.50 | 0.44 | 0.67 | 0.77 | |
| 1000℃ | 0.20 | 0.33 | 0.60 | 0.45 | 0.8 | 0.9 | |
| Al2O3 (%) | 37 | 40 | 40 | 40 | 40 | 42 | |
| Fe2O3 (%) | 1.0 | 1.5 | 1.5 | 1.5 | 2 | 2 | |
| SiO2 | 44 | 55 | 55 | 55 | 55 | 55 | |
Meet us at The 26th china international exhibition
booth NO： E3-65
Add: china international exhibition center , beijing
Time: 2015-5-20 - 23
■ 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.
