Evaluating Modelling Performance: Sensitivity Analysis of Data Volume in Industrial Batch Processes

Abstract

The iron and steel industry, a cornerstone of global industrial development, is accountable for a significant environmental footprint, contributing to 7.2% of global greenhouse gas emissions. This significant portion underscores the sector's substantial impact on climate change. The projected increase in steel production by an estimated 30% by the year 2050, further accentuates the urgent need for innovation and sustainable practices within this industry. Given these considerations, prioritising the development of more efficient and environmentally friendly production methods becomes not only a matter of environmental responsibility but also a crucial aspect of ensuring the industry's long-term viability. This work presents an investigation that evaluate the impact of product series and modelling complexity regarding the prediction of products downstream properties for industrial batch processes. The system under observation is the production of thermocouple wire-rod materials, starting from the smelt-shop and concluding after the hot-rolling mill. The first perspective considered is how to model processes with more than one product of the same product series, in this case different alloy products that are of the same product series, namely thermocouples. In addition, models of escalating complexity are being implemented. This involves examining whether the successful generalisation of simpler models necessitates the adoption of a more sophisticated approach.