Multimodal Data-Driven Interpretable Deep Modeling Approach of Dioxins Generation for Municipal Solid Waste Incineration Processes

Dioxins (DXN) is a persistent environmental pollutant that poses risks such as a weakened immune system, and teratogenic and carcinogenic effects. Municipal solid waste incineration (MSWI) plants are one of the major DXN generation sources. It is imperative to implement the monitoring and control. However, the harsh environment prevents the use of conventional equipment for detection, resulting in a lack of information on DXN generation concentration. This article presents an advanced tree-based interpretable deep modeling approach that utilizes a multimodal data-driven strategy. The available data types include two modalities: numerical and image data. To address the above issue and modeling, first, the time scale of the multimodal data is adjusted to match the sampling period of DXN based on the mechanism knowledge. Then, a novel adaptive deep forest regression algorithm based on cross-layer full connection (ADFR-clfc) is proposed for modeling process numerical data and recorded operational data. Furthermore, a convolutional neural network feature extraction method based on transfer learning combined with ADFR-clfc is employed for modeling image data. Finally, the DXN generation concentration is obtained by taking the arithmetic average of the former models. The proposed method is validated using approximately one year of data in an MSWI plant in Beijing. Experimental results show that the root mean square error (RMSE) of the concentration estimate is 0.0864 and the MAE is 0.0707, demonstrating the effectiveness of the proposed method.