Monitoring, modelling and simulation of spiral concentrators
| dc.contributor.advisor | Auret, Lidia | en_ZA |
| dc.contributor.author | Nienaber, Ernst Carel | en_ZA |
| dc.contributor.other | Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering. | en_ZA |
| dc.date.accessioned | 2018-11-29T09:40:08Z | |
| dc.date.accessioned | 2018-12-07T07:00:09Z | |
| dc.date.available | 2018-11-29T09:40:08Z | |
| dc.date.available | 2018-12-07T07:00:09Z | |
| dc.date.issued | 2018-12 | |
| dc.description | Thesis (DEng)--Stellenbosch University, 2018. | en_ZA |
| dc.description.abstract | ENGLISH ABSTRACT: Spiral concentrators are robust gravity separation devices often compactly implemented in industry with large amounts of spirals per plant – organized in banks. Current automated monitoring strategies at spiral concentrator plants involve quantifying overall feed and product stream states. However, spiral unit monitoring is performed by manual operator inspection and control is mainly achieved by operators manually changing splitter settings of spirals across a plant. In large spiral plants, containing thousands of individual spiral concentrators, changing splitters can become tedious or is sometimes neglected. Automated monitoring and control of spirals can aid spiral plant operators in achieving optimal spiral plant performance. Computer vision orientated mineral interface detection have been proposed, in past studies, as a method to monitor spiral concentrators. This is due to the formation of different mineral bands within spiral troughs during heavy mineral separation. Particles differentiate based on density and size differences usually creating three, visually discernible, mineral bands (flowing down the spiral trough). These streams are known as the concentrate, middling and tailings streams. The concentrate band is often visually darker than the streams containing gangue and the mineral interfaces can serve as a useful cue for setting splitters. However, interface tracking on industrial slurries have not yet been demonstrated and due to the large number of spirals within spiral plants it is necessary to determine what sparse sensor implementation will look like (this is due to the lack of appropriate sensor placement algorithms for metallurgical plants). This text follows a framework that spans from sensor development to sensor implementation strategy within spiral concentration plants – exploring possible stumbling blocks along the way. A spiral interface sensor is proposed, as a spiral monitoring tool, and demonstrated with experimental work during which spiral modelling was also performed. Two image processing algorithms, CVI (edge detection based) and CVII (logistic regression based), were prepared to detect spiral interfaces. Experimental modelling of a Multotec SC21 spiral concentrator was performed by formulating and comparing response surface methodology (RSM) with a proposed extended Holland-Batt model. Two sensor placement strategies, SPI (state estimation based) and SPII (metallurgical performance based), were prepared to help determine important monitoring positions based on steady state spiral plant simulations. Optimal monitoring locations minimize sensor network financial cost while maximizing some proxy for monitoring benefit. Spiral concentrator and spiral plant modelling (including optimal sensor placement) is based on the case study of the Glencore Rowland spiral plant which treats slurry containing UG2 ores to upgrade chromite content. Algorithm CVII proved to be the superior interface detection approach and can identify chromite concentrate interfaces in slurry representative of industrial conditions. Spiral splitter control should be further investigated; however, spiral unit monitoring will still provide operators with useful information on process changes (should control be infeasible or unprofitable). RSM models were more precise than the extended Holland-Batt model; however, the latter showed superior extrapolation and plant simulation ability (emphasizing the need that modelling should be done with plant simulation in mind). SPI and SPII were used to rank different sensor configurations. Optimal sensor configurations determined by SPI were ultimately controlled by sensor financial cost. SPII is accepted as a superior sensor placement algorithm since sensor cost and metallurgical performance benefit were weighted in a way similar to a return on investment problem (suggesting a new perspective for this inherent multi-objective problem). | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING: Spiraalkonsentreerders is robuuste gravitasie skeidingsinstrumente wat dikwels in ‘n kompakte wyse geimplementeer word op aanlegte. Sensors word huidiglik net gebruik om hoof voer en produk strome se vloeitempos en digthede te benader. Monitering van spiraal eenhede word met die hand deur operateurs gedoen, en beheer word hoofsaaklik bewerkstellig deur operateurs wat met die hand die verdelerstellings van die spirale regoor die aanleg moet verander. In groot spiraalaanlegte, wat duisende individuele spiraalkonsentreerders bevat, kan die verstelling van verdelers vermoeiend raak of soms afgeskeep word. Geoutomatiseerde monitering en beheer van spirale kan spiraalaanlegoperateurs help om optimale werkverrigting van die spiraalaanleg te bereik. Spiraal mineraalflodder-tussenvlak deteksie is al in die verlede aangewys as ‘n moontlike spiraal moniterings strategie. Dit is as gevolg van dat konsentrasiebande vorm tydens die skeiding van swaar minerale (deur middel van spirale). Partikels skei van mekaar as gevolg van verskille in digtheid en groottes en neig om drie visueel onderskeidelike konsentrasiebande te vorm. Operateurs wil ideaal hierdie strome op deel in konsentraat, tussenskot- en uitskotstrome. Die konsentraatband is baiemaal visueel donkerder as die strome wat gangerts bevat en die mineraaltussenvlak dien dikwels as ’n nuttige aanwysing om skeidingstoestelle te plaas. Die teks stel ‘n raamwerk voor wat sensor ontwikkelling en die plasing van sensors, binne ‘n spiraalaanleg, insluit (struikel blokke met betrekking tot die projek se verskillende stappe word ook geidentifiseer). Die werking van spiraal tussenvlak sensors is gedemonstreer tydens eksperimentele werk wat ook gedien het vir spiraal modellering. Twee beeldverwerking algoritmes, genoem CVI (rand-deteksie gebaseer) en CVII (logistiese regressie gebaseer), is ontwikkel om spiraal tussenvlak deteksie te verrig. Eksperimentele modellering van ’n Multotec SC21 spiraalkonsentreerder is voltooi deur formulering en vergelyking van respons oppervlak (RSM) en voorgestelde uitgebreide Holland-Batt modelle. Ontwikkeling van twee sensor plasings algoritmes, SPI (toestand beraming gebaseer) en SPII (metallurgiese werkverrigting gebaseer), is ook voltooi sodat optimale plasing punte, gebaseer op sensor koste en metings of produksie werkverrigting benaderings, bepaal kon word. Spiraalkonsentreerder en spiraalaanleg modellering (insluitend optimale sensor plasing) is gebaseer op die gevallestudie van die Glencore Rowland spiraalaanleg wat UG2-erts bevattende flodder behandel om chromiet inhoud op te gradeer. Algoritme CVII het beter tussenvlak deteksie gedemonstreer op mineraalflodder verteenwoordigend van industriële kondisies. Spiraal konsetreeder beheer moet verder ondersoek word, maar monitering sal steeds aanleg operateurs help om proses veranderinge op te spoor (sou dit wees dat spiraal beheer nie moontlik of winsgewend is nie). RSM spiraal modelle was meer presies met die opleidingdatastel; die uitgebreide Holland-Batt model wys beter bevestiging en aanleg simulasie uitslae (dit beklemtoon dat spiraal modellering gedoen moet word in ‘n mate wat daaropvolgende spiraalaanleg simulasie in ag neem). SPI en SPII was suksesvol gebruik om sensor plasing ranglyste te vorm. Optimale sensor plasing wat deur SPI gevind is, was hoofsaaklik gedryf deur sensor uitgawes. SPII is aanvaar as die gepaste sensor plasings algoritme omdat optimale plasings besluite gebaseer is op ‘n verbeterde doel funksie wat plekhouers vir inkomestes (verbeterde metallurgiese werkverrigting) en uitgawes (sensor koste) vergelyk. | en_ZA |
| dc.format.extent | 254 pages : illustrations | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/105114 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Plant simulation | en_ZA |
| dc.subject | UCTD | en_ZA |
| dc.subject | Spiral concentrators | en_ZA |
| dc.subject | Separators (Machines) | en_ZA |
| dc.subject | Spiral plant -- Monitoring | en_ZA |
| dc.title | Monitoring, modelling and simulation of spiral concentrators | en_ZA |
| dc.type | Thesis | en_ZA |