  A methodology for building pragmatic physics based models (Zoric et al.,
2015b) is here adapted to a use-case in the steel industry. The challenge is to
predict the erosion of steel ladle linings, such that the model can support
operators to decide if the lade lining can be used one more time or not. If the
ladle has too thin lining 140 tons of hot liquid steel may escape out of the
ladle, with huge consequences for workers and plant. The development was done
with a very small core team (two developers), which is typical for many
industrial developments. The adopted workflow for the development, challenges
that were faced, and some model results are presented. One key learning is that
development of models should allow time for maturing the process understanding,
and time should be given for many iterations by "questions-responses and
actions" at the various levels in the model development. The good interactions
between development team and industry case owner is an important success
factor. In this case the results of using the PPBM (Pragmatism in physics-based
modelling) were good thanks to very successful interaction between development
team and industry case owner. Combining or extending the model with use of ML
methods and cognition-related methods, such as knowledge graphs and
self-adaptive algorithms is discussed.
