In this first period of COGNIPLANT project, as part of the task on machine learning for industrial process performance prediction and optimization support, a set of deep (reinforcement) learning methods (approaches, algorithms and platforms) are investigated that are most appropriate for predicting and optimizing use case partners KPIs such as energy and emission efficiency, circularity and maintenance in the abrasive, stainless steel, aluminum and lime production industries.
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