  An extremely important part of the finishing line is the pickling process, in
which oxides formed during the hot rolling stage are removed from the surface
of the steel sheets. The efficiency of the pickling process is mainly dependent
on the nature of the oxide present at the surface of the steel, but, also, on
process parameters such as bath composition and time duration are relevant.
When acid concentration, solution temperatures and line speed are not properly
balanced, in fact, sheet defects like under pickling or over pickling may
happen and their occurrence does have a very serious effect on cold-reduction
performance and surface appearance of the finished product. Furthermore,
product damage from handling or improper equipment adjustment can render the
steel unsuitable for further processing. This is the reason why it is important
that process significant parameters are controlled and maintained as accurately
as possible in order to avoid these undesired phenomena. In the present work, a
control algorithm, composed by two different modules, i.e. decision tree and
rectangular Basis Function Network, has been implemented to aim of predicting
pickling defects and suggesting the optimal speed or the admissible speed range
of the steel strip in the process line. In this way the most suitable line
speed value can be set in an automatic way or by the technical personnel.
