ANOMALY DETECTION OF SMOKE EMISSIONS BASED ON WORKING CONDITION DATA
Identifying the abnormal phenomenon of pollutant emission data caused by subjective tampering or abnormal equipment working conditions is of great significance for environmental pollution monitoring,remediation and management of key pollutant discharging units.Taking a steel enterprise in Hebei Province as an example,we developed a prediction model,TabNet,based on hourly working condition data and smoke concentration.We trained the model by using an improved loss function,MSECorrLoss.TabNet was compared with XGBoost,LightGBM and BiLSTM.We developed a K-error anomaly detection algorithm to identify the anomaly data of smoke emission.The results show that:1)the MAPE of TabNet model decreases from 15.33％to 15.10％and TabNet model converges faster after being trained by improved MSECorrLoss comparing with being trained by RMSELoss loss function.2)LightGBM and XGBoost have high training speed,but low prediction accuracy(RMSE=0.3201,MAPE=29.45％).The robustness and stability of XGBoost and BiLSTM models(RMSE:0.3403～0.3425,MAPE:13.58％～18.38％)is lower than TabNet(RMSE:0.2886～0.2934,MAPE:15.10％～15.33％).Although TabNet takes longer training time,it does not require manual feature selection,has low application restrictions,and has a better application performance in smoke prediction.3)The TabNet model constructed based on working condition data has high prediction accuracy and stability in pollutant discharge prediction.With K-error detection,the TabNet model overcomes the subjectivity brought by a threshold method.This method can detect the abnormal data of pollutant discharge quickly and support environmental management decision making.
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