氮氧化物 (NOx) 是工业中最重要的有害空气污染物之一。我国每年工业废气中NOx排放量约为895.7万吨，而电厂仍是最大的人为NOx排放源，电厂NOx的精准控制至关重要。然而，由于燃煤电厂测量和管道的固有问题，NOx测量通常存在约3分钟的延迟，导致其控制与测量之间的不匹配。发电厂氮氧化物测量的延迟可能会导致过量氨喷射或无法满足氮氧化物排放环境标准。为了解决氮氧化物测量延迟的问题，本研究引入了适合现场实施的混合增压模型。该模型可以作为 SCR 控制中的前馈信号，补偿 NOx 测量延迟，并实现精确的氨喷射，以实现发电厂的精确脱硝。该模型结合了生成机制和数据驱动方法，通过将时间序列数据分类为线性、非线性和外生回归组件来提高其预测精度。在本研究中，提出了一种基于时间的方法来分析脱硝系统中的变量与NOx浓度之间的相关性。本研究还引入了新的评价指标R2的一部分（PR2），重点关注转折点的预测效果。最后，将所提出的模型应用于 330 MW 发电厂的实际数据，显示出出色的预测精度，尤其是一分钟预测。对于 3 分钟预测，与 ARIMA 的预测相比，R 平方 (R2) 和 PR2 分别增加了 3.6% 和 30。6%，平均绝对误差（MAE）和平均绝对百分比误差（MAPE）分别下降9.4%和9.1%。这些结果证实了集成模型作为发电厂3分钟高级预测软传感器现场实施的准确性和适用性。
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Development of a Real-Time NOx Prediction Soft Sensor Algorithm for Power Plants Based on a Hybrid Boost Integration Model
Nitrogen oxides (NOxs) are some of the most important hazardous air pollutants from industry. In China, the annual NOx emission in the waste gas of industrial sources is about 8.957 million tons, while power plants remain the largest anthropogenic source of NOx emissions, and the precise control of NOx in power plants is crucial. However, due to inherent issues with measurement and pipelines in coal-fired power plants, there is typically a delay of about three minutes in NOx measurements, bringing mismatch between its control and measurement. Measuring delays in NOx from power plants can lead to excessive ammonia injection or failure to meet environmental standards for NOx emissions. To address the issue of NOx measurement delays, this study introduced a hybrid boosting model suitable for on-site implementation. The model could serve as a feedforward signal in SCR control, compensating for NOx measurement delays and enabling precise ammonia injection for accurate denitrification in power plants. The model combines generation mechanism and data-driven approaches, enhancing its prediction accuracy through the categorization of time-series data into linear, nonlinear, and exogenous regression components. In this study, a time-based method was proposed for analyzing the correlations between variables in denitration systems and NOx concentrations. This study also introduced a new evaluation indicator, part of R2 (PR2), which focused on the prediction effect at turning points. Finally, the proposed model was applied to actual data from a 330 MW power plant, showing excellent predictive accuracy, particularly for one-minute forecasts. For 3 min prediction, compared to predictions made by ARIMA, the R-squared (R2) and PR2 were increased by 3.6% and 30.6%, respectively, and the mean absolute error (MAE) and mean absolute percentage error (MAPE) were decreased by 9.4% and 9.1%, respectively. These results confirmed the accuracy and applicability of the integrated model for on-site implementation as a 3 min advanced prediction soft sensor in power plants.
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