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6Cast Big Sillimanite Block

  • Item: Sillimanite Big Block
  • Apparent Prosity : ≤21 (%)
  • Bulk Density : ≥2.5 g/cm3
  • Cold Crushing Strength : ≥60 Mpa
  • Pyrometric Cone Equivalent S.C. SK : 36
  • 0.2Mpa Refractoriness Under Load T0.6 ℃: ≥1550
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  • Cast Big Sillimanite Block
■ Feature

Sillimanite Big Block:

Due to its good chemical stability, high fire resistance, big intensity and good high temperature performance, it is a new top-grade refractory material with high erosion resistant.

■ Technical Data

Physical and chemical index:

Item

TY-CGXS-60

Chemical Composition  %

Al2O3

60

SiO2

38

Fe2O3

1.0

Apparent Porosity

24

Bulk Density g/cm3

2.4

Cold Crushing Strength Mpa

60

0.2Mpa Refractoriness Under Load T0.6

1500

Permanent Linear Change On Reheating

%1500X2h

±0.1

Thermal Shock Resistances 100 water cycles

20

Reversible Thermal Expansion 1000

0.6

Pyrometric Cone Equivalent S.C. SK

36

 

1.The typical technical data shown are based on average results on production samples and are subject to normal variation on individual tests.

2.Sunrise reserve the rights to further technical developments and new editions of technical production information.

 


■ 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.


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Company: Construction Management & Engineering Cell
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