Thesis (MScEng (Process Engineering))--University of Stellenbosch, 2010. / Thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF SCIENCE IN ENGINEERING (MINERAL PROCESSING) in the Department of Processing Engineering at the University of Stellenbosch / ENGLISH ABSTRACT: This work evaluates the use of inverse least squares (ILS) and classical least squares (CLS) models for calibration of a diffuse reflectance spectrophotometer for on-line monitoring of the aqueous phase in a flotation cells. Both models use a Beer's law for the quantification of the metals. The formulated statistical models are compared to a proprietary Blue Cube model in terms of prediction ability to determine the potential applicability of the models. A diffuse reflectance spectrophotometry was used for simultaneous analysis of copper (Cu), cobalt (Co) and zinc (Zn) in the solutions. The laboratory set-up of Blue Cube instrument was used for the experimental analysis. The concentrations and matrix compositions of the samples are simulated according to Skorpion zinc mine plant conditions. The calibration samples were prepared using a simplex-centroid mixture design with the triplicates of the centroid run. The unknown or test samples were prepared randomly within the same concentration of the calibration samples. The effects of temperature and nickel concentration on absorption of the metals were evaluated in the following range, 20 - 80 °C and 125 - 400 ppm, respectively. The statistical models (ILS and CLS) were calibrated from visible and near infrared (VNIR) spectra data of the calibration samples. A modified Beer's method was used as a preprocessing technique to convert the raw data into absorbance values. The manual wavelength selection procedure was used to select the wavelengths to be used in both models. The quality of the models was evaluated based on Rª and % root mean squared error (RMSE) values with 0.90 and 10% used as the guideline for the respective statistical parameters. Both ILS and CLS models showed good results for all three metals (Cu, Co and Zn) during their calibration steps. It was further shown that both models give worse predictions for Zn as compared to other metals due to its low relative intensity in the mixture. The derivative orders of absorbance spectra that were used to enhance the prediction results of Zn had no positive effect but they rather lowered accuracy of predictions. An increase in temperature was found to increase the intensities of the absorption spectra of all the metals while an increase in nickel concentration decreases the prediction ability of model. The developed statistical models were compared to a Blue Cube model in terms of prediction ability using analysis of variance (ANOVA) test. The ANOVA results revealed that there is no statistical difference between the developed models and Blue Cube model since the F-values for all the metals were below the critical F-value. Furthermore, the partial least squares (PLS) model shows an increased accuracy results for prediction of zinc metal as compared to both the ILS and CLS models. Finally, good comparisons of the statistical models results with atomic absorption spectroscopy (AAS) analyses were establish for the unknown samples. The study demonstrates that chemometric models (ILS and CLS) developed here can be used for quantification of several metals in real hydrometallurgical solutions as samples were simulated according to a plant conditions. However, in order to have confidence in the results of the models, a factorial-mixture design must be used to study the effect of temperature and nickel concentration. Moreover the models must be further tested and validated on the real samples from a plant. / AFRIKAANSE OPSOMMING: Hierdie werkstuk evalueer die gebruik van inverse kleinste kwadraatmetodes (IKK) en klassieke kleinste kwadraatmetodes (KKK) vir die kalibrasie van 'n diffuse reflektansiespektrofotometer vir die aanlyn monitering van die waterige fase in flottasieselle. Beer se wet word vir die kwantifisering van metale vir albei modelle gebruik. Die omskrewe data-gebaseerde modelle is op grond van voorspellingsvermoë vergelyk met'n. Blue Cube model, sodat die moontlike toepaslikheid van hierdie modelle bepaal kan word. 'n Diffuse reflectantie spektrofotometrie is ingespan vir die gelyktydige analise van koper (Cu), kobalt (Co) en sink (Zn) in oplossing. Eksperimentele analises is met behulp van 'n laboratoriumopstelling met 'n Blue Cube instrument uitgevoer. Die konsentrasies en matriks-samestellings van monsters is gesimuleer om Skorpion sinkmyn aanlegkondisies na te boots. Kalibrasie monsters is voorberei volgens . simpleks-sentroïed mengselontwerp met drievoudige sentroïede lopies. Onbekende (toets) monsters is ewekansig voorberei binne dieselfde konsentrasie spesifikasies as die kalibrasie monsters. Die invloed van temperatuur en nikkelkonsenstrasie op die absorpsie van die metale is in die bestek van 20 - 80 °C en 125 - 400 dpm, onderskeidelik, bepaal. Die data-gebaseerde modelle (IKK en KKK) is met sigbare en naby infrarooi (SNIR) spektra data van die kalibrasie monsters gekalibreer. 'n Gewysigde Beer metode is vir data voorbereiding benut om rou data na absorbansie waardes om te skakel. Die handgolflengte-seleksieprosedure is vir beide modelle gebruik om die golflengtes te kies. Die kwaliteit van die modelle is op grond van Rª en % wortel gemiddelde kwadratiese fout (WGKF) geevalueer, met waardes van 0.90 en 10% (onderskeidelik) as riglyne vir hierdie statistiese parameters. Beide IKK en KKK modelle het vir hul kalibrasie stappe vir al drie metale (Cu, Co en Zn) goeie resultate getoon. Dit is verder getoon dat albei modelle die slegste voorspellings lewer vir Zn (vergeleke met die ander metale) as gevolg van Zn se lae relatiewe intensiteit in die mengsel. Afgeleide ordes van absorbansie spektra is gebruik om die Zn voorspellings te versterk, maar het geen positiewe effek gehad nie; inteendeel, voorspellingakkuraatheid is verlaag. ʼn Verhoging in temperatuur het die intensiteite van die absorpsie spektra van alle metale verhoog, terwyl ʼn verhoging in nikkelkonsentrasie die voorspellingakkuraatheid van die modelle verlaag het. Die ontwikkelde data-gebaseerde modelle is met ʼn Blue Cube model vergelyk in terme van voorspellingsvermoë met behulp van variansie-analise (ANOVA). Die ANOVA resultate toon dat daar geen statistiese verskil tussen die ontwikkelde modelle en die Blue Cube model is nie, aangesien die F-waardes vir al die metale onder die kritiese F-waarde is. Die gedeeltelike kleinste kwadraatmodel (GKK) toon verder verhoogde voorspellingakkuraat-heid vir sinkmetaal tenoor beide die IKK en KKK modelle. Ten slotte, goeie ooreenstemming van die data-gebaseerde modelresultate met atoomabsorpsie spektroskopie (AAS) analise is vir die onbekende monsters gevind. Hierdie werkstuk toon dat die chemometriese modelle (IKK en KKK) wat hier ontwikkel is, gebruik kan word vir die kwantifisering van verskeie metale in werklike hidrometallurgiese oplossings, aangesien monsters gesimuleer is volgens aanlegkondisies. Om egter verdere vertroue te hê in die modelresultate, sal ʼn faktoriaal-mengselontwerp toegepas moet word om die effek van temperatuur en nikkelkonsentrasie te ondersoek. Voorts moet die modelle verder getoets en gevalideer word op werklike monsters van ʼn aanleg.