■ Feature
►Specifications:
1.Magnesia Brick Refractory
2.CCS:60mpa,RUL:1700
3.Good erosion resistance
4.High quality
5.Reasonable price
Excellent strength of pressure bending, abrasion proof
Excellent performance of high-temperature firing, high rates of associative property, anti-erosion ability
Excellent thermal shock resistance and spalling resistance
Excellent ability of high refractoriness, anti-erosion of alkaline residue;
High temperature load of refractoriness under load. High mechanical strength
High temperature resistance excellent corrosion resistance
Resistance to spalling
It is basic refractory which the main phase is periclase. The product has such characteristics as high temperature resistance. Good slag resistance. strong corrosion resistance and steady volume in high temperature.
1.Magnesia Brick Refractory
2.CCS:60mpa,RUL:1700
3.Good erosion resistance
4.High quality
5.Reasonable price
►Magnesia Brick Features:
Excellent strength of pressure bending, abrasion proof
Excellent performance of high-temperature firing, high rates of associative property, anti-erosion ability
Excellent thermal shock resistance and spalling resistance
Excellent ability of high refractoriness, anti-erosion of alkaline residue;
High temperature load of refractoriness under load. High mechanical strength
High temperature resistance excellent corrosion resistance
Resistance to spalling
It is basic refractory which the main phase is periclase. The product has such characteristics as high temperature resistance. Good slag resistance. strong corrosion resistance and steady volume in high temperature.
►Application:
■ Technical Data
►Physical and chemical index:
| Item | MZ-91 | MZ-92 | MZ-93 | MZ-94 | |
| Chemical composition % | MgO ≥ | 91 | 92 | 93 | 94.5 |
| SiO2 ≤ | 4.0 | 3.5 | 2.5 | 2.0 | |
| Fe2O3 ≤ | 1.3 | - | - | 1.2 | |
| CaO ≤ | 2.5 | 2.5 | 2.0 | 1.8 | |
| Apparent Porosity％ ≤ | 18 | 18 | 18 | 18 | |
| Bulk Density g/cm3 ≥ | 2.86 | 2.90 | 2.95 | 2.92 | |
| Cold Crushing Strength Mpa ≥ | 60 | 60 | 50 | 60 | |
| 0.2Mpa Refractoriness Under Load T0.6 ℃ | ≥1570 | ≥1560 | ≥1620 | ≥1650 | |
| Permanent Linear Change On Reheating（%）1500℃X2h | 0～+0.4 | 0～+0.4 | 0～+0.4 | 0～+0.4 | |
| Thermal Shock Resistances 100℃ water cycles | ≥18 | ≥18 | ≥18 | ≥18 | |
■ 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.
