Comparing a linear transfer function-noise model and a neural network to model boiler bank fouling in a kraft recovery boiler

Abstract

Boiler bank fouling reduces heat transfer efficiency in kraft recovery boilers. Here, we model the relationships between boiler parameters and boiler bank pressure drop, an indicator of fouling, based on recovery boiler operating data. We compared two models: an autoregressive integrated exogenous (ARIX) model and a feedforward neural network. The ARIX model better simulates boiler bank pressure drop compared to the neural network (R2 of 0.64 vs. 0.58). Based on the ARIX model, we identified six boiler parameters that significantly influence boiler bank fouling and their relative contributions. Finally, we demonstrate how the models can simulate boiler bank pressure drop given artificial perturbations in boiler parameters.