  We investigate the ability to detect slag formations in images from inside a
Grate-Kiln system furnace with two deep convolutional neural networks. The
conditions inside the furnace cause occasional obstructions of the camera view.
Our approach suggests dealing with this problem by introducing a convLSTM-layer
in the deep convolutional neural network. The results show that it is possible
to achieve sufficient performance to automate the decision of timely
countermeasures in the industrial operational setting. Furthermore, the
addition of the convLSTM-layer results in fewer outlying predictions and a
lower running variance of the fraction of detected slag in the image time
series.
