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

CRediT authorship contribution statement

Guanyi Chen: Writing - review & editing, Supervision, Project administration. Guandong Chen: Writing - review & editing, Writing - original draft, Visualization. Jingwei Li: Writing - original draft, Formal analysis, Data curation. Queyi Pan: Writing - original draft, Visualization, Methodology. Daolun Liang: Writing - original draft, Supervision, Data curation. Jie Qiu: Writing - original draft, Visualization, Data curation. Xiqiang Zhao: Writing - original draft, Visualization, Data curation. Xiaojia Wang: Validation, Supervision, Methodology. Zhongshan Li: Writing - review & editing, Supervision, Conceptualization. Xiangping Li: Validation, Supervision, Methodology. Xiaoling Ma: Visualization, Supervision, Methodology. Shuang Wu: Writing - review & editing, Supervision, Formal analysis. Yunan Sun: Writing - review & editing, Methodology, Formal analysis.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgement

This research was supported by the National Key R&D Program of China (No. 2020YFC1910000).

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.gerr.2024.100086.

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