Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2010. / Thesis presented in partial fulfilment
of the requirements for the degree
MASTER OF SCIENCE IN ENGINEERING
(EXTRACTIVE METALLURGICAL ENGINEERING)
in the Department of Process Engineering
at the University of Stellenbosch / ENGLISH ABSTRACT: Flotation is an important processing step in the mineral processing industry wherein valuable minerals
are extracted. Flotation is a difficult process to control due to its complexity, meaning that the reversal of
series of changes will not necessarily bring the process back to its original state. Expert knowledge is
incorporated in flotation control through operator experience and intervention, which is subject to many
challenges, creating the need for improvement in control. The performance of a flotation cell is often
determined by evaluating froth appearance. The application of image analysis to capture, evaluate and
monitor froth appearance poses multiple benefits such as consistent and reliable froth appearance
evaluation.
The objective for this study was to conduct a laboratory study for the collection of froth images with the
purpose of evaluating the feasibility of using image information to predict platinum froth grade.
Laboratory test work was performed according to a fractional factorial experimental design. Six variables
were considered: air flowrate, pulp level and collector, activator, frother and depressant dosages. The
laboratory study results were quantified by assay analysis. Analysis of variance only revealed the
significant effect of pulp height and collector addition on flotation performance. Data pre-processing
revealed information regarding feature correlations and variance contributions. Data analysis from
captured images achieved reliable froth grade predictions using random forest classification and artificial
neural network (ANN) regression techniques. Random forest classification accuracies of 86.8% and 75.5%
were achieved for the following respective datasets: image data of each individual experiment (average of
all experiments) and all image data. The applied ANN models achieved R2 values 0.943 and 0.828 for the
same 2 datasets. An industrial case study was done wherein a series of step changes in air flowrate was
made on a specific flotation cell. The limited industrial case study results supported laboratory study
results. Multiple linear regression performed very well, reaching Rª values up to 0.964. Neural networks
achieved slightly better with R2 values of up to 0.997.
Based on the findings, the following main conclusions were drawn from this study:
- Reliable predictions using classification and regression models on image data were proved
possible in concept by the laboratory study, and supported by results from an industrial case
study on a narrow system.
The following main recommendations were made for further investigation:
- Research over a larger range of operating conditions is needed to find a more comprehensive
solution.
- Investigations should be conducted to determine hardware requirements and specifications in
terms of minimum resolution, lighting requirements, sampling frequency and data storage.
Software requirements, specifications and maintenance challenges should also be investigated for
implementation purposes once a more comprehensive solution has been found.
- Strategies in terms of camera placement and model building will need to follow, giving special
attention to a strategy to handle ore composition change. / AFRIKAANSE OPSOMMING: Flotasie is ‘n belangrike proses in die mineraal proseseringsbedryf vermoeid met die ontginning van
waardevolle minerale. Die proses is moelik om te beheer vanweë sy kompleksiteit, wat verwys na die
onvermoë om die proses terug te bring na sy oorspronklike toestand deur ‘n reeks veranderinge om te
keer. In die algemeen word spesialis kennis deel van prosesbeheer deur die toepassing van operateurs se
ervaring en ingryping, wat opsigself verskeie uitdagings bied wat die behoefte aan verbeterde
beheertoestelle en strategieë daarstel. Die werkverrigting van flotasieselle word gereeld beoordeel op
grond van die voorkoms van die skuim. Die gebruik van beeldverwerking om dié inligting vas te vang vir
monitering en evaluering doeleindes hou verskeie voordele in, bv. konsikwente en betroubare evaluasie
van die skuimvoorkoms.
Die doelwitte vir hierdie studie was om ‘n laboratorium studie te loods vir die opname van skuimbeelde,
met die doel om die bruikbaarheid van beeldinligting vir die voorspelling van die flotasieprodukkwaliteit,
te ondersoek.
Die laboratorium gevallestudie is uitgevoer aan die hand van ‘n fraksionele faktoriale eksperimentele
ontwerp. Ses veranderlikes was ondersoek naamlik, lugvloeitempo, pulphoogte en versamelaar
aktiveerder en depressant toevoeging. Die studie se resultate is gekwantifiseer deur die analise van die
skuim inhoud. ‘n Analise van variansie het slegs die invloed van pulphoogte en versamelaartoevoeging op
die flotasievertoning uitgelig. Data voorverwerking het inligting uitgelig rondom die veranderlikes se
verhouding met mekaar. Data analise metodes, naamlik lukrake klassifiseringswoude en neurale netwerk
regressie, is toegepas op die versamelde beelddata en het belowende resultate gelewer. Lukrake
klassifiseringswoude het klasse gedentifiseer met akkuraathede van 86.8% en 75.5% vir die volgende
onderskeie datastelle: individuele eksperimente se beeld data (gemiddeld oor alle eksperimentele lopies),
alle beelddata as een stel. Die neurale netwerke het Rª waardes van 0.943 rn 0.828 gelewer vir dieselfde 2
datastelle. Die beperkte nywerheidsgevallestudie het verandering in lugvloeitempo toegelaat vir ‘n enkele
flotasie sel. Die resultate het die bevindinge van die laboratorium gevallestudie gesteun. Veelvoudige
lineere regressie het Rª waardes van tot en met 0.964 gelewer. Neurale netwerke het daarop verbeter met
waardes tot en met 0.997.
Die volgende hoof gevolgtrekkinge was duidelik vanuit die resultate:
- Betroubare voorspellings was moontlik met die toepassing van klassifikasie en regressie modelle
op die laboratorium studie data. Die resultate is ondersteun deur soortgelyke resultate van die
beperkte nywerheidsgevallestudie.
Die volgende hoof aanbevelings was gemaak vir verdere navorsing:
- Navorsing oor ‘n wyer reeks proseskondisies is nodig om ‘n meer omvattende oplossing te vind.
- ‘n Ondersoek moet geloods word om die hardeware vereistes en spesifikasies in terme van die
minimum beeld resolusie, beligting vereistes, monsterneming tempo en die berging van data te
bepaal. Sagteware vereistes, spesifikasies en instandhouding uitdagings moet ook ondersoek
word vir implementasie doeleindes sodra ‘n meer omvattende oplossing gevind is.
- Strategieë in verband met die plasing van kamers en die ontwikkeling van modelle is nodig,
waarin spesiale aandag gegee moet word om die probleem van veranderende ertssamestelling op
te los.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:sun/oai:scholar.sun.ac.za:10019.1/5346 |
| Date | 12 1900 |
| Creators | Marais, Corne |
| Contributors | Aldrich, C., University of Stellenbosch. Faculty of Engineering. Dept. of Process Engineering. |
| Publisher | Stellenbosch : University of Stellenbosch |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | Unknown |
| Type | Thesis |
| Format | 80 p. : ill. |
| Rights | University of Stellenbosch |
Page generated in 0.0114 seconds