To achieve rapid and accurate prediction of NO_x emissions in Municipal Solid Waste Incineration (MSWI) processes,this paper proposes a method based on a Mixed Regularization Block incremental Stochastic Configuration Network (MR-BSCN).After completing the model training with BSCN,redundant nodes are pruned by incorporating a momentum term into the convex optimization-approximated L₀ regularization.Meanwhile,to ensure the accuracy of the pruned model,L₂ regularization is applied to fine-tune the output weights.Finally,verification using real-world data from an MSWI plant in Beijing demonstrates that the proposed method achieves accurate NO_x concentration prediction with a more compact model structure,building upon the BSCN's fast modeling capability.This work lays a foundation for the optimal control of NO_x emissions.