用机器学习预测并控制水泥厂氮氧化物排放，省氨降碳。
A Multi-Plant Machine Learning Framework for Emission Prediction, Forecasting, and Control in Cement Manufacturing
- 基于四座水泥厂数据，构建多厂通用的排放预测框架。
- 提前九分钟预报氮氧化物超标，准确率提升近三倍。
- 无需改造设备，可降低34%-64%氮氧化物排放。
水泥生产是工业空气污染的主要来源之一，每年排放约300万吨氮氧化物（NOx）。行业标准减排技术选择性非催化还原（SNCR）存在氨利用效率低的问题，导致运行成本高。本文基于全球四座水泥厂的大规模运行数据，开发了一套数据驱动的排放控制框架。在九种机器学习模型中，不同工厂的预测误差相差3-5倍，反映数据丰富度差异。引入短期工艺历史数据后，NOx预测准确率几乎提升三倍，表明NOx生成具有显著过程记忆效应，该特性在CO和CO2中未观察到。进一步构建模型可提前9分钟预警NOx峰值，为操作调整提供缓冲时间。该框架从源头控制NOx生成，减少下游SNCR对氨的需求。代理模型预测显示，可实现34%-64%的NOx减排，同时保持熟料质量，年均减少约290吨NOx排放和约5.8万美元氨耗成本。本研究建立了一个可迁移的数据驱动减排框架，为无需结构改造或新增硬件的低碳运行提供路径，适用于钢铁、玻璃、石灰等难减排行业。
原文摘要 · Abstract (English)
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.
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