AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration

Numerous investigations have shown that the municipal solid waste incineration (MSWI) has become one of the major sources of dioxin (DXN) emissions. Currently, the primary issue that needs to be addressed for DXN emission reduction control is the online measurement of DXN. Data-driven AI algorithms enable real-time DXN concentration measurement, facilitating its control. However, researchers mainly focus on building models for DXN emissions at the stack. This approach does not allow for the construction of models that online measurement of DXN generation and absorption throughout the whole process. To achieve optimal pollution control, models that encompass the whole process are necessary, not just models focused on the stack. Therefore, this article focuses on modeling the whole process of DXN concentrations, including generation, adsorption, and emission. It uses machine learning techniques based on advanced tree-based data-driven deep and broad learning algorithms. The determination of data characteristics at different phases is grounded in the understanding of the DXN mechanism, offering a novel framework for DXN modeling. State-of-the-art tree-based models, including adaptive deep forest regression algorithm based on cross layer full connection, tree broad learning system, fuzzy forest regression, and aid modeling technologies, are applied to handle diverse data characteristics. These characteristics encompass high-dimensional small samples, low-dimensional ultra-small size samples, and medium-dimensional small samples across different phases related to DXN. The most interesting is the robust validation where the proposed a whole process tree-based model for DXN is validated using nearly one year of authentic data on DXN generation, adsorption, and emission phases in an MSWI plant of Beijing. The proposed modeling framework can be used to explore the mechanism characterization and support the pollution reduction optimal control.