Browsing by Author "Miskin, Jason John"
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- ItemControl performance assessment for a high pressure leaching process by means of fault database creation and simulation(Stellenbosch : Stellenbosch University, 2016-03) Miskin, Jason John; Auret, Lidia; Dorfling, C.; Bradshaw, S. M.; Stellenbosch University. Faculty of Engineering. Dept. of Process Engineering.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.