Estimating particle size of hydrocyclone underflow discharge using image analysis

Uahengo, Foibe Dimbulukwa Lawanifwa (2014-04)

Thesis (MScEng)--Stellenbosch University, 2014.

Thesis

ENGLISH ABSTRACT: Hydrocyclones are stationary separating machines that separate materials based on centrifugal separation and are widely used in chemical engineering and mineral processing industries. Their design and operation, compact structure, low running costs and versatility all contribute to their applications in liquid clarification, slurry thickening, solid washing and classification. With any of these operations, the overall profitability of the process relies on the effective control of the process equipment. However, in practice, hydrocyclones are difficult to monitor and control, owing to the complexity and difficulty in measuring internal flows in the equipment. Several studies have indicated that hydrocyclone underflow images can be used to monitor process conditions. The research described in this thesis considers the use of image analysis to monitor particle size and solids concentration in the underflow discharge of a hydrocyclone. The experimental work consisted of laboratory and industrial-based case studies. The laboratory cyclone used was a 76 mm general laboratory cyclone. A Canon EOS 400D digital camera was used for the underflow imaging. Image features such as pixel intensity values, underflow discharge width and grey level co-occurrence matrix (GLCM) were extracted from the images using MATLAB Toolbox software. Linear discriminant analysis (LDA) and neural network (NN) classification models were used to discriminate between different PGM ore types based on features extracted from the underflow of the hydrocyclone. Likewise, multiple linear regression and neural network models were used to estimate the underflow solids content and mean particle size in the hydrocyclone underflow. The LDA model could predict the PGM ore types with 61% reliability, while the NN model could do so with a statistically similar 62% reliability. The multiple linear regression models could explain 56% and 40% of variance in the mean particle size and solids content respectively. In contrast, the neural network model could explain 67% and 45% of the variance of the mean particle size and solids content respectively. For the industrial system, a 100% correct classification was achieved with all methods. However, these results are regarded as unreliable, owing to the insufficient data used in the models.

AFRIKAANSE OPSOMMING: Hidrosiklone is stasionêre skeidingsmasjiene wat materiale skei op grond van sentrifugale skeiding en word algemeen gebruik in die chemiese ingenieurswese en mineraalprosessering industrieë. Hul ontwerp en werking, kompakte struktuur, lae bedryfskoste en veelsydigheid dra by tot hul gebruik vir toepassings in vloeistofsuiwering, slykverdikking, vastestof wassing en klassifikasie. In enige van hierdie prosesse hang die oorhoofse winsgewendheid van die proses af van die effektiewe beheer van die prosestoerusting. In die praktyk is hidrosiklone egter moeilik om te monitor en beheer weens die kompleksiteit en moeilikheidsgraad daarvan om die interne vloei in die apparaat te meet. Verskeie studies het aangedui dat hidrosikloon ondervloeibeelde gebruik kan word om die proseskondisies te monitor. Die navorsing beskryf in hierdie tesis maak gebruik van beeldanalise moniteringstegnieke om die ertstipes en grootte- verspreidingsgebiede/ klasse van die ondervloei afvoerpartikels te bepaal. Sodoende word ‘n grondslag gelê vir verbeterde sikloon monitering en beheer. Die eksperimentele werk het bestaan uit beide laboratorium en industrieel-gebaseerde studies. Die laboratorium sikloon wat gebruik is, was ‘n 76 mm algemene laboratorium sikloon. ‘n Canon EOS 400D digitale kamera is gebruik om die hidrosikloon ondervloei beelde vas te vang. Beeldeienskappe soos beeldelement intensiteitswaardes, ondervloei afvoerwydte en grysvlak mede-voorkoms matriks is onttrek uit die beelde deur gebruik te maak van MATLAB Toolbox sagteware. Lineêre diskriminantanalise (LDA) en neural netwerk (NN) klassifikasiemodelle is gebou om te onderskei tussen die verskillende PGM ertse en gebaseer op veranderlikes wat afgelei is uit beelde van die ondervloei van die sikloon. Net so is daar ook gebruik gemaak van lineêre regressie- en neural netwerkmodelle om die vasestofkonsentrasie en gemiddelde partikelgrootte in die ondervloei van die sikloon te beraam. Die LDA model kon die PGM ertstipes met 61% betroubaarheid voorspel, terwyl die neural netwerkmodel dit kon doen met statisties dieselfde betroubaarheid van 62%. Die lineêre regressiemodelle kon onderskeidelik 56% en 40% van die variansie in die gemiddelde partikelgrootte en vastestofkonsentrasie verduidelik. In teenstelling iermee, kon die neurale netwerkmodel 67% en 45% van die variansie in die gemiddelde partikelgrootte en vastestofkonsentrasie verduidelik. In die nywerheidstelsel kon beide tipe modelle perfekte onderskeid tref tussen die partikelgroottes wat gemeet is op opeenvolgende dae van die bedryf van die siklone. Hierdie resultate is egter nie betroubaar nie, a.g.v. die beperkte hoeveelheid data wat beskikbaar was vir modellering.

Please refer to this item in SUNScholar by using the following persistent URL: http://hdl.handle.net/10019.1/86365
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