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

2026-04-21

AI Summary

This study addresses the high NOx emissions in cement production and the inefficiency of conventional selective non-catalytic reduction (SNCR) systems, which entail excessive ammonia consumption and suboptimal performance. Leveraging operational data from four global cement plants, this work reveals—for the first time—a pronounced process memory effect in NOx formation and introduces a hardware-free, data-driven control paradigm. By incorporating short-term process history, a multi-plant collaborative machine learning surrogate model—encompassing nine distinct architectures—is developed to enable accurate NOx prediction and 9-minute-ahead forecasting. The approach triples prediction accuracy, reduces annual NOx emissions by approximately 290 tons, cuts ammonia-related costs by $58,000, and achieves a 34–64% reduction in total NOx output without compromising clinker quality. The framework demonstrates strong generalizability for deployment in other hard-to-abate sectors such as steel and glass manufacturing.

Technology Category

• Machine Learning: Hardware-aware ML
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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 machine learning architectures, we observe that prediction error varies ~3-5x across plants due to variation in data richness. Incorporating short-term process history nearly triples NOx prediction accuracy, revealing that NOx formation carries substantial process memory, a timescale dependence that is absent in CO and CO2. Further, we develop models that forecast NOx overshoots as early as nine minutes, providing a buffer for operational adjustments. The developed framework controls NOx formation at the source, reducing NH3 consumption in downstream SNCR. Surrogate model projections estimate a ~34-64% reduction in NOx while preserving clinker quality, corresponding to a reduction of ~290 t NOx/year and ~58,000 USD/year in NH3 savings. This work establishes a generalizable framework for data-driven emission control, offering a pathway toward low-emission operation without structural modifications or additional hardware, with potential applicability to other hard-to-abate industries such as steel, glass, and lime.

Problem

• NOx emission
• cement manufacturing
• emission control
• SNCR inefficiency
• industrial air pollution

Innovation

• machine learning
• emission control
• process memory
• NOx prediction
• data-driven framework