连续排放监测系统通常用于监测城市固体废物焚烧(MSWI)过程中的NOx排放。然而,它仍然面临定期维护和测量滞后的挑战。这些问题显著影响NOx排放的准确和稳定控制。因此,开发软NOx排放传感器来补充硬件监测变得势在必行。考虑到数据噪声,动态非线性,MSWI过程中的时间序列特征和波动性,本文介绍了利用完整集成经验模式分解自适应噪声(CEEMDAN)-小波阈值(WT)方法和双向长短期记忆(Bi-LSTM)的NOx排放预测软测量模型。首先,使用CEEMDAN将原始数据信号分解为一组固有模式函数(IMFs)。随后,WT处理噪声主导的高频IMF。然后,重构所有的IMF以获得去噪信号。最后,采用Bi-LSTM模型预测NOx排放。与传统的建模方法相比,本文提出的模型展示了最佳的预测性能。平均绝对百分比误差,模型测试集上的均方根误差和平均绝对误差为3.75%,m-3和m-3分别为5.34mg和4.34mg。该模型为NOx排放的软测量提供了一种新方法。该研究对MSWI工艺NOx排放的精确、稳定监测具有重要的实用价值,为关键工艺参数建模研究提供参考。
Continuous emission monitoring system is commonly employed to monitor
NOx emissions in municipal solid waste incineration (MSWI) processes. However, it still encounters the challenges of regular maintenance and measurement lag. These issues significantly impact the accurate and stable control of
NOx emissions. Therefore, developing a soft
NOx emission sensor to complement hardware monitoring becomes imperative. Considering data noise, dynamic nonlinearity, time series characteristics and volatility in the MSWI process, this article introduces a soft sensor model for NOx emission prediction utilizing the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN)-wavelet threshold (WT) method and bidirectional long short-term memory (Bi-LSTM). Firstly, the original data signal is decomposed into a group of intrinsic mode functions (IMFs) using the CEEMDAN. Subsequently, the WT processes the high-frequency IMFs that are noise-dominant. Then, all IMFs are reconstructed to obtain the denoized signal. Finally, the Bi-LSTM model is employed to predict
NOx emissions. Compared to conventional modelling approaches, the model proposed in this article demonstrates the best predictive performance. The mean absolute percentage error, root-mean-squared error and average absolute error on the test set of the proposed model are 3.75%, 5.34 mg m-3 and 4.34 mg m-3, respectively. The proposed model provides a new method to soft sensing
NOx emissions. It holds significant practical value for precise and stable monitoring of NOx emissions in MSWI processes and provides a reference for research on modelling key process parameters.