  Data-driven model interpretability is a requirement to gain the acceptance of
process engineers to rely on the prediction of a data-driven model to regulate
industrial processes in the ironmaking industry. In the research presented in
this paper, we focus on the development of an interpretable multivariate time
series forecasting deep learning architecture for the temperature of the hot
metal produced by a blast furnace. A Long Short-Term Memory (LSTM) based
architecture enhanced with attention mechanism and guided backpropagation is
proposed to accommodate the prediction with a local temporal interpretability
for each input. Results are showing high potential for this architecture
applied to blast furnace data and providing interpretability correctly
reflecting the true complex variables relations dictated by the inherent blast
furnace process, and with reduced prediction error compared to a
recurrent-based deep learning architecture.
