Abstract
References
- Zermane, H., Madjour, H., & Bouzghaya, M. B. (2022). Prediction of the amount of raw material in an Algerian cement factory. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 19, 41-46.
Abstract
Factories are currently confronted with multifaceted challenges created by rapid technological Many technologies have recently appeared and evolved, including Cyber-Physical Systems, the Internet of Things, Big Data, and Artificial Intelligence. Companies established various innovative and operational strategies, there is increasing competitiveness among them and increasing companies’ value. A smart factory has emerged as a new industrialization concept that exploits these new technologies to improve the performance, quality, controllability, and transparency of manufacturing processes. Artificial intelligence and Deep Learning techniques are revolutionizing several industrial and research fields like computer vision, autonomous driving, predicting failures, etc. The idea of this work is the development of a predictive model to predict the amount of raw material in a workshop in a cement factory based on the Deep Learning technique Long Short-Term Memory (LSTM). The excellent experimental results achieved on the LSTM model showed the merits of this implementation in the production performance, ensuring predictive maintenance, and avoid wasting energy.
References
- Zermane, H., Madjour, H., & Bouzghaya, M. B. (2022). Prediction of the amount of raw material in an Algerian cement factory. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics (EPSTEM), 19, 41-46.
Details
| Primary Language | English |
|---|---|
| Subjects | Engineering |
| Journal Section | Articles |
| Authors | |
| Publication Date | December 14, 2022 |
| Published in Issue | Year 2022 |
