■ Feature
► Description:
Using big vibrating table and adopting special castables, the company produces furnace large fireclay blocks with vibrate moulding technology. The physical and chemical properties and appearance of the blocks have exceeded the national standards. The resistant ability to molten glass of the products is better than that of fanditional casting process products and ramming products. The products are especially suitable to build the bottom of furnace, cooling and working end side wall of glass furnace.
► Specifications:
1. Low permanent linear change on reheating
2. High dense structure
3. Good Thermal Shock Stability
► Advantages:
1. Good wear-resistance;
2. Corrosion-resistant;
3. Various shapes;
4. High quality;
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■ Technical Data
| Item | Behaviors | |
| Physical properties | Refractoriness (℃) | ≧1750 |
| Bulk Density (g/cm3) | ≧2.3 | |
| Apparent Porosity （%） | ＜18 | |
| Cold Crushing Strength （Mpa） | ＞50 | |
| 0.2Mpa Softening Temp Under Load (℃) | ≧1450 | |
| 1400℃x2h Permanent Linear Change （%） | ±0.2 | |
| Chemical Composition （%） | Al2O3 | ≧40 |
| Fe2O3 | ≦1.5 | |
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.
