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Optimisation of Sinter Quality using the Sinter Pot Pilot Facility / SULLAYMAN BUTT

Swansea University Author: SULLAYMAN BUTT

DOI (Published version): 10.23889/SUThesis.68772

Abstract

This investigation studies the complex area of sinter production, conducting an analysis of factors influencing thermodynamics, metallurgical testing, bed permeability, mineralogy formation, and blast furnace efficiency. Employing extensive experimentation and advanced data analysis, including lever...

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Published: Swansea University, Wales, UK 2024
Institution: Swansea University
Degree level: Doctoral
Degree name: EngD
Supervisor: Pleydell-Pearce, C. and Bevan, K.
URI: https://cronfa.swan.ac.uk/Record/cronfa68772
first_indexed 2025-01-31T12:40:14Z
last_indexed 2025-01-31T20:27:35Z
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recordtype RisThesis
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Employing extensive experimentation and advanced data analysis, including leveraging computational tools such as thermodynamic prediction software FactSage and deploying a computer vision application named Intellesis, a novel aspect of this research emerges in iron ore sintering. The quality indicators for the sinter were examined through assessments of cold strength, degradation properties, chemical composition, microscopy, and mineralogy. Additionally, meticulous monitoring of process parameters, encompassing sintering time, temperature, flow rates, and flame front characteristics, ensured precision and accuracy in drawing conclusions.This investigation delves into the influential domain of particle size of flux, unravelling their intricate roles in shaping sintering thermal profiles and enhancing quality. Operating within the 1mm to 3.15mm particle size fraction demonstrates improved stability and homogeneity in iron ore sinter. The influence of particle size on oxygen accessibility to coke particles unfolds profound effects on combustion rates and temperature profiles.Exploring basicity levels provides a pivotal perspective on sinter quality. Operating within the optimal range of basicity 1.4 to 2.0, indicated by lower reductiondegradation index (RDI) values and superior mineralogical features, emerges as a significant finding. Analysis of Intellesis data reveals a uniform distribution of renown favoured Silico Ferrite of Calcium and Aluminium (SFCA) among iron oxide phases and a correlation corroborated by X-ray fluorescence (XRF) analysis indicating that higher basicity values correlate with stronger sinter and optimal FeO content. Thefindings here suggest that they agree with the current industry practice which is at a basicity of 1.7.The study dissects sinter properties related to silica and magnesium oxide (MgO) levels, shedding light on their intricate roles in sinter yield, SFCA formation, and productivity. A 7.0% silica level yields substantial sinter, coupled with remarkable SFCA formation. Furthermore, an optimal 2.5% MgO level was found, supported by optimal RDI values and superior SFCA production, which emphasises the critical role of compositional changes.Incorporating these insights, this thesis explains the pivotal role of key factors influencing sinter production, aiding the advancement of knowledge for iron and steel manufacturing. By optimising basicity value, particle size of flux, and other compositional changes, this study propels knowledge enhancement for an efficient and productive sintering process. This research marks substantial progress toward an efficient and productive sintering process through the optimisation of basicity levels, particle size of flux, and chemistry. The innovative use of Intellesis to analyse basicity, flux, and various chemistry levels further validates the findings, aligning with other quality indicators.</abstract><type>E-Thesis</type><journal/><volume/><journalNumber/><paginationStart/><paginationEnd/><publisher/><placeOfPublication>Swansea University, Wales, UK</placeOfPublication><isbnPrint/><isbnElectronic/><issnPrint/><issnElectronic/><keywords>Iron ore sinter, Machine learning, Silico ferrite of calcium and aluminium (SFCA), Basicity, Flux</keywords><publishedDay>11</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2024</publishedYear><publishedDate>2024-12-11</publishedDate><doi>10.23889/SUThesis.68772</doi><url/><notes>A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information.</notes><college>COLLEGE NANME</college><CollegeCode>COLLEGE CODE</CollegeCode><institution>Swansea University</institution><supervisor>Pleydell-Pearce, C. and Bevan, K.</supervisor><degreelevel>Doctoral</degreelevel><degreename>EngD</degreename><degreesponsorsfunders>EPSRC doctoral training grant</degreesponsorsfunders><apcterm/><funders>EPSRC doctoral training grant</funders><projectreference/><lastEdited>2025-01-31T12:46:09.7737479</lastEdited><Created>2025-01-31T12:28:09.2565454</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Engineering and Applied Sciences - Materials Science and Engineering</level></path><authors><author><firstname>SULLAYMAN</firstname><surname>BUTT</surname><order>1</order></author></authors><documents><document><filename>68772__33460__ba4c6b4522f046f9a45efafc240d3dfe.pdf</filename><originalFilename>2024_Butt_S.final.68772.pdf</originalFilename><uploaded>2025-01-31T12:35:09.1931558</uploaded><type>Output</type><contentLength>7777198</contentLength><contentType>application/pdf</contentType><version>E-Thesis &#x2013; open access</version><cronfaStatus>true</cronfaStatus><documentNotes>Copyright: The Author, Sallayman Butt, 2024</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language></document></documents><OutputDurs/></rfc1807>
spelling 2025-01-31T12:46:09.7737479 v2 68772 2025-01-31 Optimisation of Sinter Quality using the Sinter Pot Pilot Facility 70b7cb809cf2198629c72f326a4e6871 SULLAYMAN BUTT SULLAYMAN BUTT true false 2025-01-31 This investigation studies the complex area of sinter production, conducting an analysis of factors influencing thermodynamics, metallurgical testing, bed permeability, mineralogy formation, and blast furnace efficiency. Employing extensive experimentation and advanced data analysis, including leveraging computational tools such as thermodynamic prediction software FactSage and deploying a computer vision application named Intellesis, a novel aspect of this research emerges in iron ore sintering. The quality indicators for the sinter were examined through assessments of cold strength, degradation properties, chemical composition, microscopy, and mineralogy. Additionally, meticulous monitoring of process parameters, encompassing sintering time, temperature, flow rates, and flame front characteristics, ensured precision and accuracy in drawing conclusions.This investigation delves into the influential domain of particle size of flux, unravelling their intricate roles in shaping sintering thermal profiles and enhancing quality. Operating within the 1mm to 3.15mm particle size fraction demonstrates improved stability and homogeneity in iron ore sinter. The influence of particle size on oxygen accessibility to coke particles unfolds profound effects on combustion rates and temperature profiles.Exploring basicity levels provides a pivotal perspective on sinter quality. Operating within the optimal range of basicity 1.4 to 2.0, indicated by lower reductiondegradation index (RDI) values and superior mineralogical features, emerges as a significant finding. Analysis of Intellesis data reveals a uniform distribution of renown favoured Silico Ferrite of Calcium and Aluminium (SFCA) among iron oxide phases and a correlation corroborated by X-ray fluorescence (XRF) analysis indicating that higher basicity values correlate with stronger sinter and optimal FeO content. Thefindings here suggest that they agree with the current industry practice which is at a basicity of 1.7.The study dissects sinter properties related to silica and magnesium oxide (MgO) levels, shedding light on their intricate roles in sinter yield, SFCA formation, and productivity. A 7.0% silica level yields substantial sinter, coupled with remarkable SFCA formation. Furthermore, an optimal 2.5% MgO level was found, supported by optimal RDI values and superior SFCA production, which emphasises the critical role of compositional changes.Incorporating these insights, this thesis explains the pivotal role of key factors influencing sinter production, aiding the advancement of knowledge for iron and steel manufacturing. By optimising basicity value, particle size of flux, and other compositional changes, this study propels knowledge enhancement for an efficient and productive sintering process. This research marks substantial progress toward an efficient and productive sintering process through the optimisation of basicity levels, particle size of flux, and chemistry. The innovative use of Intellesis to analyse basicity, flux, and various chemistry levels further validates the findings, aligning with other quality indicators. E-Thesis Swansea University, Wales, UK Iron ore sinter, Machine learning, Silico ferrite of calcium and aluminium (SFCA), Basicity, Flux 11 12 2024 2024-12-11 10.23889/SUThesis.68772 A selection of content is redacted or is partially redacted from this thesis to protect sensitive and personal information. COLLEGE NANME COLLEGE CODE Swansea University Pleydell-Pearce, C. and Bevan, K. Doctoral EngD EPSRC doctoral training grant EPSRC doctoral training grant 2025-01-31T12:46:09.7737479 2025-01-31T12:28:09.2565454 Faculty of Science and Engineering School of Engineering and Applied Sciences - Materials Science and Engineering SULLAYMAN BUTT 1 68772__33460__ba4c6b4522f046f9a45efafc240d3dfe.pdf 2024_Butt_S.final.68772.pdf 2025-01-31T12:35:09.1931558 Output 7777198 application/pdf E-Thesis – open access true Copyright: The Author, Sallayman Butt, 2024 true eng
title Optimisation of Sinter Quality using the Sinter Pot Pilot Facility
spellingShingle Optimisation of Sinter Quality using the Sinter Pot Pilot Facility
SULLAYMAN BUTT
title_short Optimisation of Sinter Quality using the Sinter Pot Pilot Facility
title_full Optimisation of Sinter Quality using the Sinter Pot Pilot Facility
title_fullStr Optimisation of Sinter Quality using the Sinter Pot Pilot Facility
title_full_unstemmed Optimisation of Sinter Quality using the Sinter Pot Pilot Facility
title_sort Optimisation of Sinter Quality using the Sinter Pot Pilot Facility
author_id_str_mv 70b7cb809cf2198629c72f326a4e6871
author_id_fullname_str_mv 70b7cb809cf2198629c72f326a4e6871_***_SULLAYMAN BUTT
author SULLAYMAN BUTT
author2 SULLAYMAN BUTT
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publishDate 2024
institution Swansea University
doi_str_mv 10.23889/SUThesis.68772
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Materials Science and Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Materials Science and Engineering
document_store_str 1
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description This investigation studies the complex area of sinter production, conducting an analysis of factors influencing thermodynamics, metallurgical testing, bed permeability, mineralogy formation, and blast furnace efficiency. Employing extensive experimentation and advanced data analysis, including leveraging computational tools such as thermodynamic prediction software FactSage and deploying a computer vision application named Intellesis, a novel aspect of this research emerges in iron ore sintering. The quality indicators for the sinter were examined through assessments of cold strength, degradation properties, chemical composition, microscopy, and mineralogy. Additionally, meticulous monitoring of process parameters, encompassing sintering time, temperature, flow rates, and flame front characteristics, ensured precision and accuracy in drawing conclusions.This investigation delves into the influential domain of particle size of flux, unravelling their intricate roles in shaping sintering thermal profiles and enhancing quality. Operating within the 1mm to 3.15mm particle size fraction demonstrates improved stability and homogeneity in iron ore sinter. The influence of particle size on oxygen accessibility to coke particles unfolds profound effects on combustion rates and temperature profiles.Exploring basicity levels provides a pivotal perspective on sinter quality. Operating within the optimal range of basicity 1.4 to 2.0, indicated by lower reductiondegradation index (RDI) values and superior mineralogical features, emerges as a significant finding. Analysis of Intellesis data reveals a uniform distribution of renown favoured Silico Ferrite of Calcium and Aluminium (SFCA) among iron oxide phases and a correlation corroborated by X-ray fluorescence (XRF) analysis indicating that higher basicity values correlate with stronger sinter and optimal FeO content. Thefindings here suggest that they agree with the current industry practice which is at a basicity of 1.7.The study dissects sinter properties related to silica and magnesium oxide (MgO) levels, shedding light on their intricate roles in sinter yield, SFCA formation, and productivity. A 7.0% silica level yields substantial sinter, coupled with remarkable SFCA formation. Furthermore, an optimal 2.5% MgO level was found, supported by optimal RDI values and superior SFCA production, which emphasises the critical role of compositional changes.Incorporating these insights, this thesis explains the pivotal role of key factors influencing sinter production, aiding the advancement of knowledge for iron and steel manufacturing. By optimising basicity value, particle size of flux, and other compositional changes, this study propels knowledge enhancement for an efficient and productive sintering process. This research marks substantial progress toward an efficient and productive sintering process through the optimisation of basicity levels, particle size of flux, and chemistry. The innovative use of Intellesis to analyse basicity, flux, and various chemistry levels further validates the findings, aligning with other quality indicators.
published_date 2024-12-11T08:18:48Z
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score 11.055436

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