Machine Learning Framework Predicts NOx Emissions in Cement Plants
A novel framework utilizing data analytics for controlling emissions in cement production employs machine learning techniques to anticipate and project NOx emissions. This research, available on arXiv, evaluates nine different architectures based on operational data from four global plants. The prediction error differs by a factor of 3-5 across the plants, influenced by the quality of data. By integrating short-term process history, the accuracy of NOx predictions nearly triples, indicating that NOx generation retains significant process memory, unlike CO and CO2. This system can predict NOx spikes up to nine minutes ahead, enabling timely operational modifications. Cement manufacturing releases approximately 3 Mt of NOx annually, with current selective non-catalytic reduction (SNCR) methods showing low NH3 utilization efficiency.
Key facts
- Cement production emits ~3 Mt NOx/year.
- Selective non-catalytic reduction (SNCR) has low NH3 utilization efficiency.
- Framework uses data from four cement plants worldwide.
- Nine machine learning architectures were benchmarked.
- Prediction error varies 3-5x across plants due to data richness.
- Short-term process history nearly triples NOx prediction accuracy.
- NOx formation has substantial process memory, unlike CO and CO2.
- Models forecast NOx overshoots up to nine minutes in advance.
Entities
Institutions
- arXiv