TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Rose, Alexander
A1  - Grotjahn, Martin
T1  - LSTM based Time-series Prediction for Optimal Scheduling in the Foundry Industry
T2  - 2022 International Joint Conference on Neural Networks (IJCNN)
N2  - We present a novel long short-term memory (LSTM) approach for time-series prediction of the sand demand which arises from preparing the sand moulds for the iron casting process of a foundry. With our approach, we contribute to qualify LSTM and its combination with feedback-corrected optimal scheduling for industrial processes.

The sand is produced in an energy intensive mixing process which is controlled by optimal scheduling. The optimal scheduling is solved for a fixed prediction horizon. One major influencing factor is the sand demand, which is highly disturbed, for example due to production interruptions. The causes of production interruptions are in general physically unknown. We assume that information about the future behavior of the sand demand is included in current and past process data. Therefore, we choose LSTM networks for predicting the time-series of the sand demand.

The sand demand prediction is performed by our multi model approach. This approach outperforms the currently used naive estimation, even when predicting far into the future. Our LSTM based prediction approach can forecast the sand demand with a conformity up to 38 % and a mean value accuracy of approximately 99%. Simulating the optimal scheduling with sand demand prediction leads to an improvement in energy savings of approximately 1.1% compared to the naive estimation. The application of our novel approach at the real production plant of a foundry proves the simulation results and verifies the capability of our approach.
KW  - neural network model
KW  - LSTM
KW  - prediction methods
KW  - time-series forecast
KW  - optimal scheduling
KW  - industrial production process
KW  - application
KW  - foundry
KW  - Neuronales Netz
KW  - Zeitreihe
KW  - Optimale Kontrolle
KW  - Produktionsprozess
KW  - Gießerei
Y1  - 2022
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-24717
SN  - 2161-4407
SS  - 2161-4407
SN  - 978-1-7281-8671-9
SB  - 978-1-7281-8671-9
U6  - https://doi.org/10.25968/opus-2471
DO  - https://doi.org/10.25968/opus-2471
SP  - 8
S1  - 8
PB  - IEEE
ER  -