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A Revised KDD Procedure for the Modeling of Continuous Production in Powder Processing

 
: Vernickel, K.; Weber, J.; Li, X.; Berg, J.; Reinhart, G.

:

Institute of Electrical and Electronics Engineers -IEEE-; Institute of Electrical and Electronics Engineers -IEEE-, Singapore Section:
IEEE International Conference on Industrial Engineering and Engineering Mangement, IEEM 2019 : 15-18 December 2019, Macau
Piscataway, NJ: IEEE, 2019
ISBN: 978-1-7281-3804-6
ISBN: 978-1-7281-3803-9
ISBN: 978-1-7281-3805-3
S.340-344
International Conference on Industrial Engineering and Engineering Management (IEEM) <2019, Macau>
Englisch
Konferenzbeitrag
Fraunhofer IGCV ()

Abstract
In this paper, a revised Knowledge Discovery in Databases (KDD) procedure is proposed, which is designed especially for data mining in powder processing and other types of continuous production. The revised KDD procedure includes data preprocessing, feature engineering, machine learning and model evaluation. The proposed methods are implemented and evaluated using a dataset from a fluidized bed opposed jet mill. The evaluation results show that the machine learning model can accurately predict the product quality in this scenario and capture the internal relations between processing parameters and product quality.

: http://publica.fraunhofer.de/dokumente/N-629169.html