Reprint

Design, Manufacturing and Properties of Refractory Materials

Edited by
May 2024
312 pages
  • ISBN978-3-7258-1089-5 (Hardback)
  • ISBN978-3-7258-1090-1 (PDF)

This book is a reprint of the Special Issue Design, Manufacturing and Properties of Refractory Materials that was published in

Chemistry & Materials Science
Engineering
Physical Sciences
Summary

This reprint aims to immerse the reader into the latest developments in the technology of refractory materials. From the application of Artificial Intelligence and computer-aided methods, like machine learning or image analysis and the simulation of refractories’ properties and corrosion phenomena, to tailoring the properties of refractories to be more environmentally friendly, we aim to elucidate the current global trends and progress being made in refractories technology. This reprint has been created by world-recognized researchers, representing both academia and industry, striving jointly to make refractories safer and working for longer periods of time. Through this reprint, we demonstrate our major collaborative efforts to shift the technology to be more effective for the producers of refractory materials, more efficient for end-users, and, primarily, more sustainable in the interest of protecting our most precious shared asset—the safe planet Earth.

Format
  • Hardback
License and Copyright
© 2024 by the authors; CC BY-NC-ND license
Keywords
blast furnace; carbon refractories; molten metal infiltration; X-ray computed tomography; inorganic chemical binders; refractories; phosphates; water glasses; refractory castable; hollow corundum microspheres; bauxite aggregate; thermal shock resistance; micronized andalusite; antioxidation; Al2O3-SiC-C castables; hot modulus of rupture; metal-ceramic composites; alginate gelation; refractory metals; computed tomography; niobium; refractory composite; aggregate synthesis; castable; Al2O3-CaO-Cr2O3-O2 system; (Al1−xCrx)2O3; Ca(Al12−xCrx)O19; Cr(VI) compounds; leaching test; refractories; dynamic loading; fracture; mesoscale computer simulation; discrete element method (DEM); corrosion; MgO; Cr2O3; refractory; raw materials; Cu; slag; XRD; SEM; Ti-bearing compounds; CA6; spinel; castables; forsterite; spinel; fly ash; corrosion resistance; refractory ceramics; oxygen converter; refractories; wear forecasting; Bayesian statistics; corrosion; alumina-spinel; refractories; metallurgical slag; XRD; SEM/EDS; corrosion; refractory; biomass; thermal processing; wood ash; sol-gel; refractory; castables; corrosion; neural networks; refractory material; ladle; modelling; ceramic; spinel; copper; SEM/EDS; digital image; computer analysis; stereology; machine learning; MgO-C; refractory; steel; artificial neural networks; ANN; n/a