Mr. NeC has recently developed a #deeplearning approach for understanding and predicting required changes in set points and controls such that production processes of Cogniplant’s use case partners can be optimized. 

In the FORNA use case, for example, the challenge is to predict changes in set #points and controls such that the number of kiln stops can be reduced and therewith quick lime production can be increased and waste gas #emissions reduced.
This challenge is complicated by a very complex interplay between those set points and controls and the key performance indicators that are influenced by various process parameters.

Rescheduling the multiple set points / controls, each at the right time for appropriate durations, with proper intensity and consistent with the observed other process #parameters – all in combination with the size and peculiar plant architecture and health conditions – is the hard problem in optimising the sometimes-conflicting key performance indicators. It is certainly no toy problem and has hardly ever been addressed in #science

For further details on MRNEC’s approach click here:

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