  Machine learning applied to generate data-driven models are lacking of
transparency leading the process engineer to lose confidence in relying on the
model predictions to optimize his industrial process. Bringing processes in the
industry to a certain level of autonomy using data-driven models is
particularly challenging as the first user of those models, is the expert in
the process with often decades of experience. It is necessary to expose to the
process engineer, not solely the model predictions, but also their
interpretability. To that end, several approaches have been proposed in the
literature. The Local Interpretable Model-agnostic Explanations (LIME) method
has gained a lot of interest from the research community recently. The
principle of this method is to train a linear model that is locally
approximating the black-box model, by generating randomly artificial data
points locally. Model-agnostic local interpretability solutions based on LIME
have recently emerged to improve the original method. We present in this paper
a novel approach, VAE-LIME, for local interpretability of data-driven models
forecasting the temperature of the hot metal produced by a blast furnace. Such
ironmaking process data is characterized by multivariate time series with high
inter-correlation representing the underlying process in a blast furnace. Our
contribution is to use a Variational Autoencoder (VAE) to learn the complex
blast furnace process characteristics from the data. The VAE is aiming at
generating optimal artificial samples to train a local interpretable model
better representing the black-box model in the neighborhood of the input sample
processed by the black-box model to make a prediction. In comparison with LIME,
VAE-LIME is showing a significantly improved local fidelity of the local
interpretable linear model with the black-box model resulting in robust model
interpretability.
