Towards Understanding slag build-up in a Grate-Kiln furnace : A study of what parameters in the Grate-Kiln furnace leads to increased slag build-up, in a modern pellet production kiln

Sammanfattning: As more data is being gathered in industrial production facilities, the interest in applying machine learning models to the data is growing. This includes the iron ore mining industry, and in particular the build-up of slag in grate-kiln furnaces. Slag is a byproduct in the pelletizing process within these furnaces, that can cause production stops, quality issues, and unplanned maintenance. Previous studies on slag build-up have been done mainly by chemists and process engineers. Whilst previous research has hypothesized contributing factors to slag build-up, the studies have mostly been conducted in simulation environments and thus have not used real sensor data utilizing machine learning models. Luossavaara-Kiirunavaara Aktiebolag (LKAB) has provided data from one of their grate-kiln furnaces, a time-series data of sensor readings, that compressed before storage.  A Scala package was built to ingest and interpolate the LKAB data and make it ready for machine learning experiments. The estimation of slag within the kiln was found too arbitrary to make accurate predictions. Therefore, three quality metrics, tightly connected to the build-up of slag, were selected as target variables instead. Independent and identically distributed (IID) units of data were created by isolating fuel usage, product type produced and production rate. Further, another IID criterion was created, adjusting the time for each feature in order to be able to compare feature values for a single pellet in production. Specifically, the time it takes for a pellet to go from the feature sensor to the quality test was added to the original timestamp. This resulted in a table where each row represents multiple features and quality measures for the same small batch of pellets. An IID unit of interest was then used to find the most contributing features by using principal component analysis (PCA) and lasso regression. It was found that using the two mentioned methods, the number of features could be reduced to a smaller set of important features. Further, using decision tree regression with the subset of features, selected from the most important features, it was found that decision tree regression had a similar performance with the subset of features as the lasso regression. Decision tree and lasso regression were chosen for interpretability, which was important in order to be able to discuss the contributing factors with LKAB process engineers.