A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing
📰 ArXiv cs.AI
Learn how to apply machine learning for emission prediction, forecasting, and control in cement manufacturing using a multi-plant framework
Action Steps
- Collect large-scale operational data from multiple cement plants
- Develop and train machine learning models for emission prediction and forecasting
- Implement a data-driven framework for emission control using the trained models
- Evaluate the performance of the framework using benchmarking metrics
- Optimize the framework for improved emission reduction and operational efficiency
Who Needs to Know This
Data scientists and machine learning engineers working in the cement industry can benefit from this framework to improve emission control and reduce operational costs. The framework can be applied by plant operators and environmental managers to optimize emission mitigation strategies.
Key Insight
💡 A multi-plant machine learning framework can improve emission prediction, forecasting, and control in cement manufacturing
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🌎 Reduce cement industry emissions with machine learning! 🤖
Key Takeaways
Learn how to apply machine learning for emission prediction, forecasting, and control in cement manufacturing using a multi-plant framework
Full Article
Title: A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing
Abstract:
arXiv:2604.19903v1 Announce Type: cross Abstract: Cement production is among the largest contributors to industrial air pollution, emitting ~3 Mt NOx/year. The industry-standard mitigation approach, selective non-catalytic reduction (SNCR), exhibits low NH3 utilization efficiency, resulting in operational inefficiencies and increased reagent costs. Here, we develop a data-driven framework for emission control using large-scale operational data from four cement plants worldwide. Benchmarking nine
Abstract:
arXiv:2604.19903v1 Announce Type: cross Abstract: Cement production is among the largest contributors to industrial air pollution, emitting ~3 Mt NOx/year. The industry-standard mitigation approach, selective non-catalytic reduction (SNCR), exhibits low NH3 utilization efficiency, resulting in operational inefficiencies and increased reagent costs. Here, we develop a data-driven framework for emission control using large-scale operational data from four cement plants worldwide. Benchmarking nine
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