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Multiscale Modeling Using GAN and Deep Forest Regression With Application to Dioxin Emission Soft Sensor
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DOI：10.1109/TIM.2023.3309389.png)
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Abstract
En 中文
Dioxin (DXN) emission concentration is a key environmental index in the process of municipal solid waste incineration (MSWI). Realizing an intelligent optimization operation depends on building a soft sensor model of DXN emission. However, data on the MSWI process have missing and abnormal values, which lead to incomplete data used for modeling. In addition, soft sensor modeling of difficult-to-measure parameters with large lag characteristics has multiscale problems. Therefore, DXN emission soft sensor modeling has to tackle problems, such as missing data, data scale mismatch, and small samples. This study proposes a multiscale missing data modeling using improved generative adversarial network (IGAN) and deep forest regression (DFR) to address the abovementioned problems. First, the short time-scale samples and features are divided according to the missing data. Second, the missing data are filled by GAN, in which the mean square error (MSE) constraint term ensures the filling effect and the overfitting problem is alleviated by using the model complexity penalty term. Third, a certain range of the short time-scale input data is averaged to match the long time-scale output data. Finally, each layer of the improved DFR (IDFR) model using matching data fully connects the parallel and cascade forest algorithms to improve diversity and accuracy. The effectiveness and rationality of the proposed method are verified on the real DXN dataset. The root MSE (RMSE) of the proposed method is 17.4%, 14.0%, and 13.1% higher than those of the no filling, linear filling, and variational autoencoder (VAE) filling methods, respectively.
Keywords:
Index Terms- Adversarial generative data filling
dioxin (DXN) emission
improved deep forest regression (DFR)
missing data
soft sensor
Journal
IF:
5.9
Papers:
1.9W
Citations:
5.8W