融合CEEMD和TCN的受热面积灰预测研究

对锅炉受热面积灰程度的有效预测,可为锅炉提升生产效率和故障预警提供重要依据。 采用清洁因子来评估受热面的灰污沉积状况,针对其序列非线性、非平稳性的特点,提出一种基于互补集合经验模态分解与时间卷积网络的受热面积灰预测方法。 首先,通过互补集合经验模态分解将经过小波阈值去噪处理后的原始序列分解为一组子序列分量;然后,针对不同子序列分别构建基于时间卷积网络的时序预测模型,并优化网络超参数提升预测准确性;最后,将各 IMF 分量的预测结果叠加得出清洁因子的预测数值。 由实验结果可得,相较于其他两种模型,预测精度分别提高 62. 1%和 57. 1%,CEEMD-TCN 模型对受热面积灰状况预测精度最高,验证了该模型的准确性和可靠性。

Study on the prediction of heated area ash fused with CEEMD and TCN

Effective prediction of the degree of ash in the heated area of the boiler can provide an important basis for boiler productionefficiency and fault early warning. The cleaning factor is used to evaluate the ash deposition status of the heated surface, and accordingto the characteristics of nonlinearity and non-stationariness of the sequence, a method of predicting the heated area ash based on theempirical modal decomposition and time convolutional network of complementary sets is proposed. Firstly, the original sequence afterwavelet threshold denoising is decomposed into a set of sub-sequence components by complementary set empirical mode decomposition,then the time series prediction model based on the time convolutional network is constructed for different sub-sequences, and the networkhyperparameters are optimized to improve the prediction accuracy; finally, the prediction results of each IMF component aresuperimposed to obtain the prediction values of the cleaning factor. Compared with the other two models, the prediction accuracy isimproved by 62. 1% and 57. 1%, respectively, and the CEEMD-TCN model has the highest prediction accuracy for the ash condition ofthe heated area, which verifies the accuracy and reliability of the model.