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LSTM based Time-series Prediction for Optimal Scheduling in the Foundry Industry

  • 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.

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Author:Alexander RoseORCiD, Martin GrotjahnORCiDGND
DOI original:https://doi.org/10.1109/IJCNN55064.2022.9892576
Parent Title (English):2022 International Joint Conference on Neural Networks (IJCNN)
Document Type:Conference Proceeding
Year of Completion:2022
Publishing Institution:Hochschule Hannover
Release Date:2023/03/08
Tag:LSTM; application; foundry; industrial production process; neural network model; optimal scheduling; prediction methods; time-series forecast
GND Keyword:Neuronales Netz; Zeitreihe; Optimale Kontrolle; Produktionsprozess; Gießerei
Page Number:8
Link to catalogue:1841688088
Institutes:Fakultät II - Maschinenbau und Bioverfahrenstechnik
DDC classes:620 Ingenieurwissenschaften und Maschinenbau
Licence (German):License LogoUrheberrechtlich geschützt