All industrial processes are economically driven, resulting in the ever
increasing need for process optimization in order to stay competitive
and profitable. Numerous models has been published depicting the
optimization of the raw material usage in an arc furnace, as well as
the thermal and electrical efficiency; however, to obtain a holistic
view of the cost-effectiveness of the process one needs to expand the
model to include the cost of all the operational variables.
This project therefore aimed to not only include the raw material
feeds and power consumption as input variables, but also the labour,
maintenance, and environmental costs to generate an all-inclusive
financial balance. In the end, the model was able to mathematically
optimize the ferrochrome (FeCr) production process through
evaluating the effect of a specified combination of input parameters.
It could therefore be used as a tool to aid in managerial decisionmaking;
to appraise and correspondingly adjust certain operational
variables in order to achieve a maximum return on investment.
This paper fully describes the reasoning behind the choice of the
essential model components, the assumptions made during the
models development, the calculation approach, as well as the
advantages, limitations, possible applications, and areas for
expansion of the final model.
Through running simulations with plant data, it was found that
the accuracy of final model remained fairly constant when applied to
stable furnace conditions, but was met with a number of limitations
when applied to more complex situations. These limitations could,
however, be overcome through further development and expansion of
the existing model to include a larger number of process variables.