Prediction and optimization of emissions in cement manufacturing plant under uncertainty by using artificial intelligence-based surrogate modeling
Author:
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s10668-024-05068-5.pdf
Reference59 articles.
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2. Ali, M. B., Saidur, R., & Hossain, M. S. (2011). A review on emission analysis in cement industries. Renewable and Sustainable Energy Reviews, 15(5), 2252–2261. https://doi.org/10.1016/j.rser.2011.02.014
3. Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2020). Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects. Sensors (switzerland), 20(1), 1–34. https://doi.org/10.3390/s20010109
4. Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing : A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 114598. https://doi.org/10.1016/j.eswa.2021.114598
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