A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing

2026-01-11 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Environmental Science & Earth Systems · Depth: Expert, extended

Summary

A data-driven framework has been developed for emission control in cement manufacturing, a sector contributing approximately 3 Mt NOx/year. This framework utilizes large-scale operational data from four global cement plants, benchmarking nine machine learning architectures. XGBoost consistently demonstrated the lowest Mean Absolute Percentage Error (MAPE) for NOx and CO prediction. The study found that incorporating short-term process history significantly improves NOx prediction accuracy by nearly threefold, indicating substantial process memory for NOx formation, a characteristic absent in CO and CO2. The framework also includes models that forecast NOx overshoots up to nine minutes in advance, enabling proactive operational adjustments. Surrogate model projections estimate a 34–64% reduction in NOx emissions while maintaining clinker quality, translating to an annual saving of ~$290 t NOx/year and ~$58,000 USD/year in ammonia consumption by reducing the load on downstream Selective Non-Catalytic Reduction (SNCR) systems.

Key takeaway

For AI Scientists and Machine Learning Engineers developing industrial control systems, this framework demonstrates that integrating historical process data and multi-step forecasting can yield substantial environmental and economic benefits. You should prioritize data richness and temporal context in your models to achieve robust emission reductions and operational efficiency. Consider adopting similar data-driven optimization strategies to enhance existing industrial processes without requiring capital-intensive retrofits, thereby accelerating progress towards sustainability goals.

Key insights

Data-driven ML models can significantly reduce cement plant NOx emissions and costs without hardware changes.

Principles

• Data richness is critical for industrial ML model accuracy.
• NOx formation exhibits significant process memory.
• Multi-step forecasting outperforms recursive single-step.

Method

The method involves benchmarking ML architectures, incorporating process history for emission prediction, developing multi-step forecasters for early warnings, and an optimization framework with KPI validation for NOx control.

In practice

• Use XGBoost for cement plant emission prediction.
• Integrate process history for accurate NOx modeling.
• Implement multi-step forecasters for proactive control.

Topics

• Machine Learning Framework
• NOx Emission Control
• Cement Manufacturing
• Emission Forecasting
• XGBoost

Best for: AI Engineer, AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.