การติดตามและปรับปรุงประสิทธิภาพเตาหลอมอลูมิเนียม Striko ด้วยปัญญาประดิษฐ์
คำสำคัญ:
AI, Aluminium Furnace, Energy Efficiency, Real-Time Monitoring, Optimizationบทคัดย่อ
This study focuses on optimizing the Striko Aluminium Melting Furnace by leveraging AI-driven predictive analytics to enhance operational efficiency and sustainability. Two machine learning models, Linear Regression (LR) and Radial Basis Function Network (RBFN), were developed to predict critical furnace parameters, including temperature, gas flow, and CO2 emissions. These models were integrated into a real-time monitoring framework featuring a dashboard for live data visualization and automated alerts to notify deviations from optimal conditions. Comparative analysis revealed the superior performance of the RBFN model, achieving higher prediction accuracy and contributing significantly to operational improvements.
The results demonstrated a 21% reduction in energy consumption, an 18% decrease in CO2 emissions, and a 4.35% increase in product yield. This study underscores the transformative potential of AI in driving energy-efficient, sustainable, and cost-effective industrial furnace operations.
เอกสารอ้างอิง
Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3), 1-58.
Chalapathy, R., & Chawla, S. (2019). Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407.
Fan, C., Chen, M., Wang, X., Wang, J., & Huang, B. (2021). A review on data preprocessing techniques toward efficient and reliable knowledge discovery from building operational data. Frontiers in energy research, 9, 652801.
Hastie, T., Tibshirani, R., Friedman, J., & Franklin, J. (2005). The elements of statistical learning: data mining, inference and prediction. The Mathematical Intelligencer, 27(2), 83-85.
Pan, Z., Wang, Y., Wang, K., Chen, H., Yang, C., & Gui, W. (2022). Imputation of missing values in time series using an adaptive-learned median-filled deep autoencoder. IEEE Transactions on Cybernetics, 53(2), 695-706.
Williams, C. K., & Rasmussen, C. E. (2006). Gaussian processes for machine Learning. (Vol. 2, No. 3, pp. 4). Cambridge, MA: MIT press.
Yang, W., & Zhang, S. (2014, August). A PDM System for Small and Medium Enterprises Based on. Net Framework. In 2014 Enterprise Systems Conference. (pp. 24- 27). IEEE.
ดาวน์โหลด
เผยแพร่แล้ว
ฉบับ
ประเภทบทความ
สัญญาอนุญาต
ลิขสิทธิ์ (c) 2025 วารสารวิชาการวิทยสารบูรณาการเทคโนโลยีอุตสาหกรรมและวิศวกรรมประยุกต์
อนุญาตภายใต้เงื่อนไข Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
