Control performance assessment for a high pressure leaching process by means of fault database creation and simulation

Miskin, Jason John (2016-03)

Thesis (MEng)--Stellenbosch University, 2016.


ENGLISH ABSTRACT: Platinum group metal (PGM) producing companies typically extract PGMs from a nickel-copper ore through a combination of processes including comminution, flotation, smelting, converter treatment, and leaching (Dorfling, 2012; Lamya, 2007; Liddell et al., 1986). The latter processing step is a hydrometallurgical process which aims to dissolve base metals (i.e. copper and nickel sulphides) out of a converter matte and into the liquid phase by means of oxidative reaction, while limiting the dissolution of PGMs. Dorfling (2012) developed an open-loop dynamic process model within MATLAB™ comprising the second and third stage pressure leaching system and surrounding process units of Western Platinum base metal refinery (BMR). The dynamic process model was subsequently reprogrammed into Simulink™ by Haasbroek and Lindner (2015). The developed dynamic process model is a powerful tool which can be used to investigate and possibly improve several aspects of the Western Platinum BMR operation. This project aims to improve the dynamic process model to mimic the Western Platinum BMR operation, and to ultimately use the model to analyse the process performance during the occurrence of faults (i.e. abnormal events that potentially lead to failure or malfunction of equipment which causes significant process performance degradation). The updated dynamic process model will allow the possibility of developing and testing fault detection and diagnostic algorithms for Western Platinum BMR. The Simulink™ dynamic process model was firstly validated using an approach developed by Sargent (2005). This approach validates the entire model on four different levels namely conceptual model validation, computerised model verification, operational validation and data validation. A total of 34 dynamic process model issues divided into the four validation categories of Sargent (2005) were identified. It was concluded that the reaction kinetics used within the baseline dynamic process model might cause inaccurate leaching predictions. This is attributed to issues existing in both the rate expressions and the experimental data used to fit the kinetics. Most of the other issues which effect the model predictability were addressed. The dynamic process model is therefore valid for predicting general process behaviour, but invalid for exact leaching predictions. The affect which a variety of variable step-changes has on the direction of leaching behaviour is however as expected. Several control layers which exist at Western Platinum BMR were implemented on the Simulink™ open-loop dynamic process model. This includes regulatory control, supervisory control, alarm systems and safety interlock systems. The addition of control layers ensures that the dynamic process model mimics and acts in a similar manner than the actual process. A total of 35 sensors; 21 actuators; 30 regulatory controllers; 33 alarms systems; 37 safety interlocks; and 4 supervisory controllers was implemented into the open-loop dynamic process model. These control layers correspond to that which is used at Western Platinum BMR. The developed closed-loop dynamic process model is a useful tool which can be used to train operators and therefore assist in developing operator decision making. A fault database was developed which contains entries of faults which commonly occur at Western Platinum BMR. Valuable fault characteristics (Himmelblau, 1978; Isermann, 2005; Patton et al., 2013) such as transition rate, frequency of occurrence, fault type and symptoms were included for each fault present in the fault database. Faults were organised based on their point of origin (Venkatasubramanian et al., 2003). Several faults were modelled which ultimately served as a tool to perturb the process so as to assess the process performance during fault occurrences. A total of 17 faults with the necessary fault characteristics were gathered during a site visit (McCulloch et al., 2014) and composed into a fault database. This includes common faults such as valve wear, valve stiction, pump impeller wear, and controller misuse. A total of 12 faults were subsequently modelled. The fault database can serve as a means of information transfer between several Western Platinum BMR operators and personnel. The control performance was expressed in terms of control and operational key performance indicators which were calculated at several locations within the dynamic process model. The control and operational key performance indicators (Gerry, 2005; Marlin, 1995; McCulloch et al., 2014; Zevenbergen et al., 2006) include integral absolute error, maximum deviation, time not at set-point, valve reversals, valve saturation; and throughput, extent of base metal leaching, extent of PGM leaching, spillage; respectively. The process performance during the occurrence of faults was compared to a faultless baseline run. The control performance during the occurrence of 8 independent fault cases was investigated. The extent in which process performance degraded varied significantly between faults. Two faults namely pump impeller wear and solid build-up in cooling coils proved to be the faults which caused the largest process upset. This is attributed to significant autoclave pressure and temperature variations, and the activation of safety interlocks. These two faults also proved to have the least localised symptoms. This is attributed to the major effect they have early in the process which results in a propagation of symptoms. Two faults namely valve wear and level sensor blockage on the other hand caused minimal deviation in process performance while also propagating through only a few of the measured key performance indicators. These faults occur in the latter part of the process which explains their localised symptoms. The extent to which the process performance was degraded by the level sensor blockage corresponds with expert knowledge (McCulloch et al., 2014); while the model underpredicts the process performance degradation caused by valve wear. The updated closed-loop dynamic process model together with the modelled faults can be used to develop and test fault detection and diagnostic algorithms for Western Platinum BMR. Moreover, fault signatures produced in this this project could possibly be used as a baseline at Western platinum BMR in an attempt to detect and identify fault occurrences though expert interpretation.

AFRIKAANSE OPSOMMING: Platinum groep metale (PGM) word tipies vanuit ‘n nikkel-koper erts ontgin deur middel van ‘n verskeidenheid prosesse insluitend komminusie, flotasie, smelting, mat behandeling en loging (Dorfling, 2012; Lamya, 2007; Liddell et al., 1986). Die laasgenoemde stap is ‘n hidrometallurgiese proses met die doelwit om basis metale (koper en nikkel sulfiede) uit ‘n mat op te los deur middel van oksidatiewe reaksies en ter selfde tyd die oplossing van PGMe te beperk. Dorfling (2012) het ‘n oop-lus dinamiese proses model in MATLAB™ ontwikkel wat die tweede en derde hoë druk logingstelsel en omliggende proseseenhede by Western Platinum basis metaal raffinadery (BMR) bevat. Die dinamiese proses model is binne Simulink™ deur Haasbroek en Lindner (2015) herprogrammeer. Die dinamiese proses model is ‘n kragtige hulpmiddel wat veskeie aspekte van Western Platinum BMR se bedryf moontlik kan verbeter. Die projek mik om eerstens die dinamiese proses model te verbeter tot ‘n mate dat dit die Western Platinum BMR bedryf beter naboots. Die projek se finale uitkomste is om die prosesverrigting tydens die voorkoms van foute (d.w.s. abnormale aktiwiteite wat potensieël die mislukking of wanfunksionering van toerusting veroorsaak en lei tot beduidende prosesverrigting agteruitgang) te voorspel. Die verbeterde toe-lus dinamiese proses model sal moontlik help om fout opsporing en diagnose algoritmes te ontwikkel wat ten einde op die Western Platinum BMR toegepas kan word. Die geldigheid van die Simulink™ dinamiese proses model is eerstens verklaar met behulp van ‘n benadering getoon in Sargent (2005). Hierdie benadering kyk na die geldigheid van die hele model op vier verskillende vlakke; naamlik konseptuele model validering, gerekenariseerde model verifiëring, operasionele model validering en data validering. ‘n Totaal van 34 dinamiese proses model kwessies wat in die vier validasie vlakke van Sargent (2005) verdeeld is, is geïdentifiseer. Die model validasie is afgesluit deur uit te wys dat die reaksie kinetika wat in die aanvangsmeting dinamiese proses model gebruik word moontlik onakkurate loging voorspellings veroorsaak. Hierdie word toegeskryf aan kwessies wat bestaan in beide die tempo-uitdrukkings en eksperimentele data wat gebruik is om kinetika te pas. Meeste van die kwessies wat die model voorspelling affekteer is aangespreek. Die model, vir die rede, voorspel algemene proses gedrag akkuraat, maar voorspel die presiese loginggedrag onakuraat. Die rigting van loging verander egter in die verwagte rigting wanneer ‘n verskeidenheid insetveranderlikes stapagtig verander word. Verskeie beheerlae wat bestaan by Western Platinum BMR is op die Simulink™ dinamiese proses model geïmplimenteer. Hierdie sluit regulatoriese beheer, toesighoudende beheer, alarmstelsels en veligheidsbindingstelsels in. Die addisionele beheerlae verseker dat die dinamiese proses model die werklike proses naboots. ʼn Totaal van 35 sensors, 21 aandrywers, 30 regulatoriese beheerders, 33 alarmstelsels, 37 veiligheidsbindingstelsels en 4 toesighoudende beheerders is in die oop-lus dinamiese proses model geïmplimenteer. Hierdie beheerlae stem ooreen met die wat tans by Western Platinum BMR bestaan. Die ontwikkelde toe-lus dinamiese proses model is ‘n nuttige hulpmiddel wat kan help om operateurs op te lei en dus help met die ontwikkeling van operateur besluitneming. ‘n Fout databasis met inskrywings van algemene foute wat by Western Platinum BMR teëgekom word, is ontwikkel. Waardevolle fouteienskappe (Himmelblau, 1978; Isermann, 2005; Patton et al., 2013) insluitend oorgangstempo, voorkomsfrekwensie, fout tipe en simptome is in die fout databasis ingesluit. Foute is volgens die punt van oorsprong georganiseer (Venkatasubramanian et al., 2003). Verskeie foute is gemodelleer en gebruik as ʼn hulpmiddel om die proses te ontstel met die einddoel om prosesverrigting te bepaal. ‘n Totaal van 17 foute met die nodige fouteienskappe is tydens ‘n aanlegbesoek versamel (McCulloch et al., 2014) en in ‘n fout databasis saamgestel. Hierdie sluit algemene foute soos klepverwering, klepwrywing, stuwerverwering, en misbruik van beheeders, in. ‘n Totaal van 12 foute is hierna gemodelleer. Die fout databasis kan optree as ‘n metode van inligting oordrag tussen verskeie Western Platinum BMR operateurs en personeel. Die prosesverrigting is in terme van beheer en operasionele kern verrigting-aanwysers uitgedruk. Die kern verrigting-aanwysers is by verskeie plekke binne die dinamiese proses model bereken. Die beheer en operasionele kern verrigting-aanwysers (Gerry, 2005; Marlin, 1995; McCulloch et al., 2014; Zevenbergen et al., 2006) sluit die integraal absolute fout, maksimum afwyking, tyd nie by stel-punt, klepomkerings, klepversadiging en deurset, mate van basis metaal loging, mate van PGM loging, en morsing onderskeidelik in. Die prosesverrigting tydens verskeie fout voorkomste is vergelyk met die van ‘n foutlose aanvangsmeting lopie. Die prosesverrigting tydens die voorkoms van 8 onafhanklike foutgevalle is ondersoek. Die mate van prosesverrigting agteruitgang verskil grootliks tussen foute. Twee foute naamlik stuwerverwering en vastestof-opbou in verkoelingspoele het die grootste prosesversteuring veroorsaak. Hierdie word aan die beduidende outoklaaf druk en temperatuur versteurings wat tydens die fout voorkomste ontaard het asook die aktivering van veiligheidsbindings toegeskryf. Hierdie foute het ook die minste gelokaliseerde simptome gehad. Hierdie word aan die beduidende groot simptome wat vroeg in die proses onstaan het toegeskryf. Die vroeë simptome het verder deur die proses gepropageer. Twee foute naamlik klepverwering en vlak sensorverstopping het aan die ander kant minimale afwyking in prosesverrigting veroorsaak en het ook net deur net ʼn paar gemete kern verrigting-aanwysers gepropageer. Die gelokaliseerde simptome word toegeken aan die foute wat plaasvind in die laaste deel van die proses. Die mate van prosesverrigting versteuring wat veroorsaak was deur vlak sensorverstopping stem ooreen met deskundige kennis (McCulloch et al., 2014); terwyl die prosesverrigting versteuring ondervoorspel word deur die model in die geval van klepverwering. Die verbeterde toe-lus dinamiese proses model saam met die gemodelleerde foute kan help om fout opsporing en diagnose algoritmes te ontwikkel wat ten einde op die Western Platinum BMR gebruik kan word. Verder kan die fouthandtekeninge van die model moontlik as ‘n aanvangsmeting by Western Platinum BMR gebruik word om foute deur deskundige interpretasie te identifiseer.

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