Dr. Sander Arnout, Dr. Els Nagels Michel Bridy, Dr. Mark W. Kennedy, Christian Landaas, Paul Hellinckx
Business intelligence tools translate data into information, and can be especially valuable in metallurgical
plants. Indeed, a continuous stream of process data is often available, without conclusive
information being led to all relevant personnel. Besides analysing past behaviour, real intelligence
also requires the ability to make decisions, based on the prediction of expected performance. This
can be achieved by integrating data management with process and flow sheet modelling, based on
physics and chemistry. The use of models ensures a closed mass and energy balance, as well as explicit
assumptions, which can be calibrated to real results. Furthermore, virtual instruments can be
created, even in places where measurements are cumbersome or impossible.
Thermodynamics have an important role in these process models, because of their predictive response
to changes, even outside the normal operating window. This gives the process owner a large
additional flexibility. The possibility to pre-assess materials before treating them, and to see the
effect of settings changes before applying them, is important to keep the process in stable conditions
and is therefore also a matter of safety. As a result, chemical equilibria, melting behaviour, etc. are
highly important to select optimal process settings, as well as to determine potential margins when
treating new materials.
In this paper, we will illustrate how the use of models can enlighten relations between otherwise
noisy data. A first example is the use of the composition dependent melting point as a predictor for
furnace tap temperature. A second is the use of smelting results to predict time and resources needed
downstream in refinery or converting. This is also important to select materials for which the
margin of the whole production process is the highest. A third is the use of production data and theoretical
relationships on fumed product quality for design of a new process. These examples show
that data management and simple or advanced statistics are many times more powerful, when process
understanding is integrated.