{"id":{"repo_id":"stellenbosch","oai_identifier":"oai:scholar.sun.ac.za:10019.1/136209"},"canonical_url":"https://search.dev.ndltd.org/etd/stellenbosch/oai:scholar.sun.ac.za:10019.1/136209","repository":{"repo_id":"stellenbosch","name":"Stellenbosch University","base_url":"https://scholar.sun.ac.za/server/oai/request"},"display":{"title":"A Method for Automated System Integration and Enhanced Ore Transportation","abstract":"The mining industry faces increasing pressure to sustain or expand ore throughput as higher-grade ore bodies are depleted and operations tend to extend to greater depths. This challenge is particularly pronounced in deep-level mining, where infrastructure originally designed for lower capacities must now accommodate significantly higher volumes. This thesis investigated and developed a method for increasing ore throughput potential by implementing automation and making minor infrastructure adjustments. The research combined soft systems and case study research methodologies to develop a structured, step-by-step approach for identifying, modelling and implementing automation solutions in underground ore transportation. The method integrated operational diagnostics, throughput estimation, and system evaluation through discrete event simulation. The case study applies the method to a multi-level conveyor and ore pass network, incorporating real-world constraints such as feeder timing, sensor accuracy and equipment limitations. Simulation modelling demonstrated that optimised control logic, combined with instrumentation and sequencing, could increase throughput potential without the need for major capital upgrades. Specifically, the results indicated a potential to increase monthly ore-handling capacities to meet the demands of the case study by reducing the idle times of the conveyor and vibrating feeders. These findings highlight the economic advantage of leveraging automation to enhance existing infrastructure rather than pursuing costly physical upgrades. The study contributes to both theory and practice by offering a validated method for integrating automation into ore transportation systems and demonstrating its effectiveness in a deep-level mining context. The method framework produced a repeatable and adaptable approach for similar operations facing throughput constraints and bridges the gap between academic modelling and practical implementation in the mining sector.","abstract_html":"The mining industry faces increasing pressure to sustain or expand ore throughput as higher-grade ore bodies are depleted and operations tend to extend to greater depths. This challenge is particularly pronounced in deep-level mining, where infrastructure originally designed for lower capacities must now accommodate significantly higher volumes. This thesis investigated and developed a method for increasing ore throughput potential by implementing automation and making minor infrastructure adjustments. The research combined soft systems and case study research methodologies to develop a structured, step-by-step approach for identifying, modelling and implementing automation solutions in underground ore transportation. The method integrated operational diagnostics, throughput estimation, and system evaluation through discrete event simulation. The case study applies the method to a multi-level conveyor and ore pass network, incorporating real-world constraints such as feeder timing, sensor accuracy and equipment limitations. Simulation modelling demonstrated that optimised control logic, combined with instrumentation and sequencing, could increase throughput potential without the need for major capital upgrades. Specifically, the results indicated a potential to increase monthly ore-handling capacities to meet the demands of the case study by reducing the idle times of the conveyor and vibrating feeders. These findings highlight the economic advantage of leveraging automation to enhance existing infrastructure rather than pursuing costly physical upgrades. The study contributes to both theory and practice by offering a validated method for integrating automation into ore transportation systems and demonstrating its effectiveness in a deep-level mining context. The method framework produced a repeatable and adaptable approach for similar operations facing throughput constraints and bridges the gap between academic modelling and practical implementation in the mining sector.","abstract_has_math":false,"creators":["Labuschagne, Brandon Dylon"],"institution":"Stellenbosch : Stellenbosch University","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2026,"date_issued":"2026-03","date_published":"2026-03","updated_at":"2026-07-24T04:40:09Z","subjects":[],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholar.sun.ac.za/handle/10019.1/136209","outbound_label":"Repository record","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Labuschagne, Brandon Dylon"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-04-28T09:17:14Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-04-28T09:17:14Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-03"]},{"key":"dc:publisher","label":"Institution","values":["Stellenbosch : Stellenbosch University"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholar.sun.ac.za/handle/10019.1/136209"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Thesis (MEng)--Stellenbosch University, 2026."]},{"key":"dc:description.abstract","label":"Abstract","values":["The mining industry faces increasing pressure to sustain or expand ore throughput as higher-grade ore bodies are depleted and operations tend to extend to greater depths. This challenge is particularly pronounced in deep-level mining, where infrastructure originally designed for lower capacities must now accommodate significantly higher volumes. This thesis investigated and developed a method for increasing ore throughput potential by implementing automation and making minor infrastructure adjustments. The research combined soft systems and case study research methodologies to develop a structured, step-by-step approach for identifying, modelling and implementing automation solutions in underground ore transportation. The method integrated operational diagnostics, throughput estimation, and system evaluation through discrete event simulation. The case study applies the method to a multi-level conveyor and ore pass network, incorporating real-world constraints such as feeder timing, sensor accuracy and equipment limitations. Simulation modelling demonstrated that optimised control logic, combined with instrumentation and sequencing, could increase throughput potential without the need for major capital upgrades. Specifically, the results indicated a potential to increase monthly ore-handling capacities to meet the demands of the case study by reducing the idle times of the conveyor and vibrating feeders. These findings highlight the economic advantage of leveraging automation to enhance existing infrastructure rather than pursuing costly physical upgrades. The study contributes to both theory and practice by offering a validated method for integrating automation into ore transportation systems and demonstrating its effectiveness in a deep-level mining context. The method framework produced a repeatable and adaptable approach for similar operations facing throughput constraints and bridges the gap between academic modelling and practical implementation in the mining sector."]},{"key":"dc:title","label":"Title","values":["A Method for Automated System Integration and Enhanced Ore Transportation"]}]}],"canonical_facts":{"dc:creator":["Labuschagne, Brandon Dylon"],"dc:date.accessioned":["2026-04-28T09:17:14Z"],"dc:date.available":["2026-04-28T09:17:14Z"],"dc:date.issued":["2026-03"],"dc:description":["Thesis (MEng)--Stellenbosch University, 2026."],"dc:description.abstract":["The mining industry faces increasing pressure to sustain or expand ore throughput as higher-grade ore bodies are depleted and operations tend to extend to greater depths. This challenge is particularly pronounced in deep-level mining, where infrastructure originally designed for lower capacities must now accommodate significantly higher volumes. This thesis investigated and developed a method for increasing ore throughput potential by implementing automation and making minor infrastructure adjustments. The research combined soft systems and case study research methodologies to develop a structured, step-by-step approach for identifying, modelling and implementing automation solutions in underground ore transportation. The method integrated operational diagnostics, throughput estimation, and system evaluation through discrete event simulation. The case study applies the method to a multi-level conveyor and ore pass network, incorporating real-world constraints such as feeder timing, sensor accuracy and equipment limitations. Simulation modelling demonstrated that optimised control logic, combined with instrumentation and sequencing, could increase throughput potential without the need for major capital upgrades. Specifically, the results indicated a potential to increase monthly ore-handling capacities to meet the demands of the case study by reducing the idle times of the conveyor and vibrating feeders. These findings highlight the economic advantage of leveraging automation to enhance existing infrastructure rather than pursuing costly physical upgrades. The study contributes to both theory and practice by offering a validated method for integrating automation into ore transportation systems and demonstrating its effectiveness in a deep-level mining context. The method framework produced a repeatable and adaptable approach for similar operations facing throughput constraints and bridges the gap between academic modelling and practical implementation in the mining sector."],"dc:identifier.uri":["https://scholar.sun.ac.za/handle/10019.1/136209"],"dc:language.iso":["en"],"dc:publisher":["Stellenbosch : Stellenbosch University"],"dc:title":["A Method for Automated System Integration and Enhanced Ore Transportation"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T04:40:09Z"}