Co-incineration of multiple inorganic solid wastes towards clean disposal: Heat and mass transfer modeling, pollutant generation, and machine learning based proportioning

Abstract

The co-disposal of solid waste by industrial kilns is presently attracting increasing attention. In this study, we investigate the co-disposal of solid waste, i.e. converter ash (CA), sintered ash (SA), blast furnace bag ash (BA), and municipal solid waste incineration fly ash (MSWIFA), under simulated blast furnace ironmaking conditions. The results show that it is feasible to use blast furnace to treat MSWIFA, but the stability of temperature field should be controlled in the process of co-disposal. With the increase of temperature, the conversion rate of NO decreased to 16.4%, and ZnFe2O4 became the main mineral composition, accounting for 75.53%. Corresponding to the flue gas corrosion condition of solid waste treatment, it is found that the corrosion resistance of the furnace material TH347H is better than 20G. Finally, based on the experimental data, the nested optimization algorithm of machine learning model is established to achieve the reverse output of optimal conditions. Overall, the study provides theoretical support and methodology guidance for the co-disposal of solid waste in blast furnaces in providing support for the further development of co-disposal of solid waste in industrial kilns.

Keywords

• Blast furnace
• Co-disposal
• Heat and mass transfer
• Pollution mechanism
• Machine learning