Prediction of dioxin emission from municipal solid waste incineration based on expansion, interpolation, and selection for small samples

Dioxin (DXN), which is named a century poison, is emitted from municipal solid waste incineration (MSWI). The first step to effectively control and reduce DXN emissions is the application of soft sensors by utilizing easyto-detect process data. However, DXN samples for data-driven modeling are extremely lacking because of the high cost and long period of measurement. To address the above issue, this work proposes a DXN emission prediction method based on expansion, interpolation, and selection for small-sample modeling, i.e., EIS-SSM, involving three main steps: domain expansion, hybrid interpolation, and virtual sample selection. First, the domain of samples is determined by domain extension, a great number of virtual samples in this domain are generated through hybrid interpolation, and the optimal virtual samples are chosen for virtual sample selection. Afterward, a prediction model for DXN emission is constructed using the optimal samples and raw small samples. Two cases, that is, a benchmark dataset and a DXN dataset from an actual MSWI plant, are applied to implement the proposed method. Results showed that compared with the non-expansion and existing expansion methods, the proposed method exhibits an improved performance by 48.22% and 13.68%, respectively, in the benchmark experiment and by 72.44% and 34.67%, respectively, in the DXN emission prediction experiment. Therefore, the proposed method can substantially improve the prediction of DXN emission from MSWI.

Automated summary

This study proposes a method for predicting small-sample DXN emissions through expansion, interpolation, and selection, and demonstrates its effectiveness in improving emission predictions.