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Co-incineration of multiple inorganic solid wastes towards clean disposal: Heat and mass transfer modeling, pollutant generation, and machine learning based proportioning
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 simula...
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Published in: | Green Energy and Resources 2024-09, Vol.2 (3), p.100086, Article 100086 |
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creator | Chen, Guanyi Chen, Guandong Li, Jingwei Pan, Queyi Liang, Daolun Qiu, Jie Zhao, Xiqiang Wang, Xiaojia Li, Zhongshan Li, Xiangping Ma, Xiaoling Wu, Shuang Sun, Yunan |
description | 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.
[Display omitted]
•Modeling analysis of heat and mass transfer process of co-disposal solid waste.•NO generation was inhibited in blast furnace.•After thermal treatment, ZnFe2O4 becomes the main mineral.•TP347H is more resistant to flue gas corrosion than 20G.•The optimum processing conditions of solid waste disposal were calculated. |
doi_str_mv | 10.1016/j.gerr.2024.100086 |
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[Display omitted]
•Modeling analysis of heat and mass transfer process of co-disposal solid waste.•NO generation was inhibited in blast furnace.•After thermal treatment, ZnFe2O4 becomes the main mineral.•TP347H is more resistant to flue gas corrosion than 20G.•The optimum processing conditions of solid waste disposal were calculated.</description><identifier>ISSN: 2949-7205</identifier><identifier>EISSN: 2949-7205</identifier><identifier>DOI: 10.1016/j.gerr.2024.100086</identifier><language>eng</language><publisher>Elsevier B.V</publisher><subject>Blast furnace ; Co-disposal ; Heat and mass transfer ; Machine learning ; Pollution mechanism</subject><ispartof>Green Energy and Resources, 2024-09, Vol.2 (3), p.100086, Article 100086</ispartof><rights>2024 The Authors</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2756-72014e8a12ebd23c4680a231b5b4533af9d47df7ccb54dc91b4d389bb2d30ab33</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S2949720524000407$$EHTML$$P50$$Gelsevier$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,3549,27924,27925,45780</link.rule.ids></links><search><creatorcontrib>Chen, Guanyi</creatorcontrib><creatorcontrib>Chen, Guandong</creatorcontrib><creatorcontrib>Li, Jingwei</creatorcontrib><creatorcontrib>Pan, Queyi</creatorcontrib><creatorcontrib>Liang, Daolun</creatorcontrib><creatorcontrib>Qiu, Jie</creatorcontrib><creatorcontrib>Zhao, Xiqiang</creatorcontrib><creatorcontrib>Wang, Xiaojia</creatorcontrib><creatorcontrib>Li, Zhongshan</creatorcontrib><creatorcontrib>Li, Xiangping</creatorcontrib><creatorcontrib>Ma, Xiaoling</creatorcontrib><creatorcontrib>Wu, Shuang</creatorcontrib><creatorcontrib>Sun, Yunan</creatorcontrib><title>Co-incineration of multiple inorganic solid wastes towards clean disposal: Heat and mass transfer modeling, pollutant generation, and machine learning based proportioning</title><title>Green Energy and Resources</title><description>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.
[Display omitted]
•Modeling analysis of heat and mass transfer process of co-disposal solid waste.•NO generation was inhibited in blast furnace.•After thermal treatment, ZnFe2O4 becomes the main mineral.•TP347H is more resistant to flue gas corrosion than 20G.•The optimum processing conditions of solid waste disposal were calculated.</description><subject>Blast furnace</subject><subject>Co-disposal</subject><subject>Heat and mass transfer</subject><subject>Machine learning</subject><subject>Pollution mechanism</subject><issn>2949-7205</issn><issn>2949-7205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9UUtuHCEUbFmJZMvxBbJ6B_BMaKB_ljfRKLEtWcomWaMHPCaMGGhBO1aulFOG9jhRVlmBSlVFFdU071u2bVnbfzhs95TzljMuK8DY2J81F3yS02bgrHvzz_28uSrlUCl84mIQ_KL5tUsbH42PlHHxKUJycHwKi58DgY8p7zF6AyUFb-EZy0IFlvSM2RYwgTCC9WVOBcMN3BMugNHCEUtlZYzFUYZjshR83F_DnEJ4WjAusKc_D16_Ksz3mgGqY46VCxoLWZhzmlNeaRV717x1GApdvZ6XzbfPn77u7jePX-4edh8fN4YPXb_2bCWN2HLSlgsj-5EhF63utOyEQDdZOVg3GKM7ac3UamnFOGnNrWCohbhsHk6-NuFBzdkfMf9UCb16AeqXKKyZantlue7YNHb0Yj6SRuH60TkSnex7XL34ycvkVEom99evZWodTx3UOp5ax1On8aro9iSi2vKHp6yK8RQNWZ_JLDWG_5_8N_gWp1o</recordid><startdate>202409</startdate><enddate>202409</enddate><creator>Chen, Guanyi</creator><creator>Chen, Guandong</creator><creator>Li, Jingwei</creator><creator>Pan, Queyi</creator><creator>Liang, Daolun</creator><creator>Qiu, Jie</creator><creator>Zhao, Xiqiang</creator><creator>Wang, Xiaojia</creator><creator>Li, Zhongshan</creator><creator>Li, Xiangping</creator><creator>Ma, Xiaoling</creator><creator>Wu, Shuang</creator><creator>Sun, Yunan</creator><general>Elsevier B.V</general><general>Elsevier</general><scope>6I.</scope><scope>AAFTH</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>DOA</scope></search><sort><creationdate>202409</creationdate><title>Co-incineration of multiple inorganic solid wastes towards clean disposal: Heat and mass transfer modeling, pollutant generation, and machine learning based proportioning</title><author>Chen, Guanyi ; Chen, Guandong ; Li, Jingwei ; Pan, Queyi ; Liang, Daolun ; Qiu, Jie ; Zhao, Xiqiang ; Wang, Xiaojia ; Li, Zhongshan ; Li, Xiangping ; Ma, Xiaoling ; Wu, Shuang ; Sun, Yunan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2756-72014e8a12ebd23c4680a231b5b4533af9d47df7ccb54dc91b4d389bb2d30ab33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Blast furnace</topic><topic>Co-disposal</topic><topic>Heat and mass transfer</topic><topic>Machine learning</topic><topic>Pollution mechanism</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Guanyi</creatorcontrib><creatorcontrib>Chen, Guandong</creatorcontrib><creatorcontrib>Li, Jingwei</creatorcontrib><creatorcontrib>Pan, Queyi</creatorcontrib><creatorcontrib>Liang, Daolun</creatorcontrib><creatorcontrib>Qiu, Jie</creatorcontrib><creatorcontrib>Zhao, Xiqiang</creatorcontrib><creatorcontrib>Wang, Xiaojia</creatorcontrib><creatorcontrib>Li, Zhongshan</creatorcontrib><creatorcontrib>Li, Xiangping</creatorcontrib><creatorcontrib>Ma, Xiaoling</creatorcontrib><creatorcontrib>Wu, Shuang</creatorcontrib><creatorcontrib>Sun, Yunan</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>CrossRef</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Green Energy and Resources</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Guanyi</au><au>Chen, Guandong</au><au>Li, Jingwei</au><au>Pan, Queyi</au><au>Liang, Daolun</au><au>Qiu, Jie</au><au>Zhao, Xiqiang</au><au>Wang, Xiaojia</au><au>Li, Zhongshan</au><au>Li, Xiangping</au><au>Ma, Xiaoling</au><au>Wu, Shuang</au><au>Sun, Yunan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Co-incineration of multiple inorganic solid wastes towards clean disposal: Heat and mass transfer modeling, pollutant generation, and machine learning based proportioning</atitle><jtitle>Green Energy and Resources</jtitle><date>2024-09</date><risdate>2024</risdate><volume>2</volume><issue>3</issue><spage>100086</spage><pages>100086-</pages><artnum>100086</artnum><issn>2949-7205</issn><eissn>2949-7205</eissn><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.
[Display omitted]
•Modeling analysis of heat and mass transfer process of co-disposal solid waste.•NO generation was inhibited in blast furnace.•After thermal treatment, ZnFe2O4 becomes the main mineral.•TP347H is more resistant to flue gas corrosion than 20G.•The optimum processing conditions of solid waste disposal were calculated.</abstract><pub>Elsevier B.V</pub><doi>10.1016/j.gerr.2024.100086</doi><oa>free_for_read</oa></addata></record> |
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title | Co-incineration of multiple inorganic solid wastes towards clean disposal: Heat and mass transfer modeling, pollutant generation, and machine learning based proportioning |
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