Modelling the furnace temperature field of a municipal solid waste incinerator using the numerical simulation and the deep forest regression algorithm

Abstract

The furnace temperature field is key information to reflect the combustion conditions and is related to process safety, equipment failure rates, and pollution emissions in municipal solid waste incinerators. However, the measurement of the actual temperature distribution of the incinerator is difficult because of the limitations of physical sensors (thermocouples). A new furnace temperature field modeling strategy was proposed to address the above problem. It included five virtual temperature points, with the numerical simulation and the deep forest regression algorithm used. First, numerical models of different benchmark conditions were established via FLIC. Second, modeling data were obtained by orthogonal experiments. Third, deep forest regression based on the fully connected cross layer (DFR-clfc) was used to establish five virtual temperature models. The five models had high prediction performance (R2 = 0.707, 0.870, 0.918, 0.895, and 0.907) and characterized the distribution of the furnace temperature. The grate speed had the greatest influence on temperature distribution, and based line condition (2) was more suitable for a stable operation. The novelties of the work are as follows. (1) Furnace temperature field estimation based on FLIC simulation and data-driven model. (2) Multi-conditions design based on micro, composition, and operating parameters. (3) Multi-dimensional visual analysis of incinerator virtual temperature field. (4) Temperature field model by using deep ensemble forest with potential interpretability.