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Welcome to the NWU Repository, the open access Institutional Repository of the North-West University (NWU-IR). This is a digital archive that collects, preserves and distributes research material created by members of NWU. The aim of the NWU-IR is to increase the visibility, availability and impact of the research output of the North-West University through Open Access, search engine indexing and harvesting by several initiatives.

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Recent Submissions

A scientific contextualisation and methodology for analysing cascade generation failures in power systems
(North-West University, 2025) Viljoen, M; de Kock, JA
Electrical power systems form the key infrastructure that underpins the health, stability, and general prosperity of modern societies. However, the stability and reliability of such power systems are not absolute, and vulnerabilities expose them to disruptions that, in turn, threaten to disrupt the very societies they serve. Electrical power systems are among the largest physical systems created by mankind, consisting of various constituent parts like power lines, substations, municipal distribution systems, and end-user customers. One of the most important components of any electrical power system is power stations - vast assets comprising intricate systems and processes that function in a coordinated fashion to generate electricity. When a power station is compromised, it threatens the stability and integrity of the electrical power system in which it is embedded. Disruption of power generation facilities (like power stations) constitutes a specific subset of adverse events that require in-depth understanding to manage and mitigate effectively. Such an understanding needs to be derived by investigating and analysing the events that caused the disruption. As a distinctly separate subset of adverse events in the diverse suite of undesirable societal disruption events, power station disruption events internationally suffer from an absence of a uniform methodology for investigation and analysis. This is particularly surprising given the ever-increasing number of large-scale power system disruption events that affect millions of people every year across the globe. This study provides an overview of the engineering endeavour that is electricity generation, its evolution, and its disruption. It explores the inherent vice of all power stations, namely interdependencies and co-dependencies, to provide a context for the problem of power system disruption. This study provides an overview of the engineering endeavour that is electricity generation, its evolution, and its disruption. It explores the inherent vice of all power stations, namely interdependencies and co-dependencies, to provide a context for the problem of power system disruption. Ultimately, a methodology is developed whereby cascade disruption events of power generation facilities can be appraised for analysis, recording, ranking and evaluating such events. For the development to be undertaken, various factors are identified that erode the integrity of an electrical power system during a disruption. Metrics are developed to assess an event's impact and severity, using suitable integrity erosion factors as inputs. Finally, a framework for collating event data and information is provided to aid in systematically recording and archiving event information.
Sustainability of Lean healthcare in South Africa: A practitioner's perspective
(North-West University, 2025) Wagner, RS; Hattingh, TS; Meijer, H
South African public healthcare organisations have adopted lean as one of the initiatives to enhance service delivery. This aligns with the global trend of Lean applications in healthcare, which has been steadily increasing since 2000. Lean implementation has yielded some positive results. Despite these successes, the longterm rate of success remains fairly low. This is often attributed to many factors including a lack of well-planned and sustainable implementation and misapplication of Lean. Furthermore, the scarcity of Lean healthcare literature has created a knowledge gap, including reasons for failure, that needs to be addressed to improve Lean sustainability. This study aims to identify the factors that impact the sustainability of Lean healthcare in South Africa and draw lessons from these findings to improve Lean implementation sustainability. The scope of this study is limited to public hospitals because they represent a bigger portion of the public healthcare sector and the majority of literature on Lean implementation is on them. The study employs semistructured interviews to explore these factors. The interviews are limited to practitioners who have been involved in Lean implementations in South African public hospitals. Through these interviews, 44 factors were identified and grouped into 13 themes (Long-term philosophy, Lean alignment, Implementation, Leadership, Commitment, Training, Teamwork, Support, Motivation, Communication, Management, Empowerment, and Healthy competition). These themes were grouped into four sustainability pillars (Foundation, Concepts of Lean, People and Organisational culture. The study further employs a systematic literature review (SLR) to explore the factors that would be used to test or evaluate factors from interviews. The SLR identified 46 factors affecting Lean sustainability, that were grouped into 13 themes. The themes identified through both methods were found to be compatible with each other. The findings from the interviews were then used to draw lessons that can be applied to enhance the sustainability of Lean in South African public hospitals.
Adsorption and desorption kinetics of dry sorbents for CO2 capture
(North-West University, 2025) Williams, J; Everson, RC; Okolo, GN
This study focused on commercially available activated carbons' adsorption and desorption kinetics using a laboratory-scale fixed-bed reactor. The three activated carbons are CQ650, derived from coconut shells and activated with a combination of steam and KOH impregnation; CQ30P, derived from coal, also using a combination of steam and KOH impregnation. The third is CQ006, derived from coal and activated using steam and an acid wash. Ten continuous adsorption and desorption cycles were performed at 30 to 70 ℃ with 10 ℃ increments and two CO2 concentrations of 5 and 15 vol.%. The characterisation data of the samples show that CQ006 has the highest fixed carbon content while CQ650 has the highest BET surface area of 517.1±4.5 m2/g and the highest Dubinin-Radushkevich micropore surface area at 735.0±27 m2/g. A scanning electron microscopy analysis revealed that the activated carbon samples have well-developed pore structures over the entire surface. All the investigated sorbents performed well under cyclic operation with no significant difference in adsorption capacity between cycle 1 and cycle 10. The most significant difference is the completion time, with CQ650 taking 8600 seconds at 60 ℃ and 15 vol.% CO2 feed concentration, while the quickest sorbent, CQ006, took 5900 seconds under the same conditions. CQ650 havethe highest adsorption quantity overall at 5 and 15 vol.% CO2 feed concentration with 0.96 mmol/g and 1.67 mmol/g, respectively. CQ30P and CQ006 have near-identical adsorption capacities of 0.85 mmol/g and 0.83 mmol/g at 5 vol.% CO2 feed and 1.03 mmol/g for both at 15 vol.% CO2 feed concentration. The increased temperatures decreased the sorbents' saturation times and adsorption capacities, while the feed concentration also significantly affected the CO2 quantity adsorbed and the saturation times of the experiments. CQ006 has the best cyclic desorption efficiency, with the lowest efficiency achieved at 93.2%; CQ650 is second with the lowest cyclic efficiency at 90.1%, and CQ30P at 86.7%. The same trend is seen when looking at the overall desorption efficiency across the 5 temperatures (30, 40, 50, 60 and 70 ℃), where CQ006 averages at 98.3% and 99.1% for the 5 and 15 vol.% CO2 feed concentrations, 96.5% and 95.1% for CQ650 and 93.4% and 92% for CQ30P at 5 and 15 vol.% CO2 feed concentrations, respectively. Three kinetic models were tested, with Avrami being the most suitable with a quality of fit of (99.1%), followed by the pseudo-first-order (94.7%) and the pseudo-second-order kinetic model (87.6%). The desorption activation energy is higher than the adsorption activation energy for all conditions, indicating that desorption (reverse adsorption reaction) requires more energy to remove the molecules from the surface of the sorbent than the energy released by binding to the surface of the sorbent. The thermodynamic results indicate that the adsorption mechanism is physical adsorption with a change in enthalpy of (-10 to -20 kJ/mol). The negative change in enthalpy indicates that the reaction is exothermic, aligning with other reports from the open literature. The entropy in all conditions is also negative (-0.00198 kJ/mol to -0.00660 kJ/mol), implying that the molecules are slightly more orderly when adsorbed. A negative change in the Gibbs free energy (-18.37 kJ/mol to -22.57 kJ/mol) implies that the reactions occur spontaneously. The breakthrough analysis using the Avrami equation for adsorption and a modified Avrami equation for desorption provided satisfactory results. The Avrami is well suited to predict adsorption breakthrough behaviour, with the lowest QOF% achieved being 96.4%. In contrast, the modified Avrami equation does not adequately predict the desorption breakthrough behaviour of the sorbents, with the QOF% ranging from 63.2% to 87.9%.
A strategy to prioritise compressed air energysavings initiatives during production stoppages
(North-West University, 2025) Read, GP; Marais, JH
The cost of electricity in South Africa has increased significantly since 2007. Electricity tariffs increased by 165% over the past decade alone. This negatively affects the profitability of mines with electricity costs forming a large portion of the operational costs. The compressed air system, which is widely regarded as inefficient, is the largest consumer of electricity at a mine. Energy-saving initiatives are a proven way of reducing electricity costs on the compressed air system of a mine. However, mines face challenges that affect the implementation of energysaving initiatives. One such challenge is production stoppages. Mines experiencing production stoppages may have limited time, capital and resources available to implement these initiatives. Previous studies have primarily focused on prioritising and implementing energy-saving initiatives during a typical production period and did not consider production stoppages. The need exists for a strategy that assists mines during a production stoppage by prioritising and filtering energysaving initiatives. To address the aforementioned need, a pre-existing prioritisation method was customised specifically for this scenario. By integrating it with a curated checklist of proven compressed air energy-saving initiatives from industry research, a user-friendly tool was created to streamline decision-making. Alongside this, a straightforward four-step energy-saving strategy was developed that integrates the prioritisation method to enable mines to effectively reduce compressed air demand and decrease electricity costs during production stoppages. The developed strategy was applied to two case study production stoppages. The first case study tested the accuracy and benefit of the prioritisation method and compared it with industry knowledge and experience. This was done by comparing the results of the prioritisation method with the actual initiatives that were implemented, without a guided strategy, during the case study period. The prioritisation method generated a list of four feasible energy-saving initiatives. These generated initiatives aligned with four out of the six initiatives that were implemented in reality. Unforeseen events resulted in the implementation of two initiatives which were not identified by the strategy. Therefore, the prioritisation method was found to be accurate in determining feasible energy-saving initiatives as well as prioritising these initiatives, except when faced with unforeseen events. Additionally, results showed that an investigation period of five weeks could have been avoided if the prioritisation method had been used at the start of the case study production stoppage instead of relying on industry knowledge and experience. Usage of the method would have resulted in compressor set-point adjustments being made sooner and a potential saving of 500 MWh (14.3 MWh/day) could have been achieved. This is equivalent to a cost-saving potential of R410 000 with a daily saving of R14 000/day in the summer and R22 300/day in the winter. The second case study tested the effectiveness of the developed strategy when fully implemented during a production stoppage. Four energy-saving initiatives were identified and successfully implemented according to priority as soon as the production stoppage began. Initiatives were fully reversed on the last day of the production stoppage and production restarted without delays. A total energy saving of 250 MWh (22.7 MWh/day) was achieved during eleven days, equivalent to a cost saving of R200 000 with a daily saving of R22 350/day in the summer and R35 400/day in the winter. The overall strategy has proven to be an effective tool that may assist mines in reducing costs and saving time during production stoppages by prioritising and filtering energy-saving initiatives.
An artificial neural network model for predicting deep-level mine refrigeration plant performance
(North-West University, 2025) Pretorius, EW; Marais, JH; van Laar, JH
Deep-level mining is common in South Africa due to the depletion of shallow resource deposits. To maintain worker productivity in hot underground conditions, mining at depth demands the use of cooling systems that are reliant on the production of chilled water at centralised refrigeration plants. These plants are large consumers of energy and, coupled with persistent and acute increases in electricity prices, place a heavy financial burden on the mining industry. Improved performance of the refrigeration plant leads to improved energy efficiency. To achieve this this, the creation of a model to establish the relationship between the operational parameters of a refrigeration system and its performance is needed. Artificial neural networks (ANNs) can establish such relationships. The multi-layer perceptron (MLP) is a type of ANN that exhibits superior performance when working with noisy data typical of energy systems and is used to avoid the difficulty of using traditional methods on an ageing system where design specifications are outdated. Despite this, an MLP has not yet been used to model the performance of a deeplevel mine refrigeration plant. An MLP was therefore chosen to develop the model. The aim of this study is to develop a new method that uses multi-layer perceptron theory to create a model of a vapour-compression refrigeration plant on a South African deep-level mine that accounts for changing operational conditions. The input parameters for the model were chosen based on successful implementations of ANNs in studies on refrigeration systems and the availability of sensor data at the case study plant. The raw data was collected, filtered, and randomly distributed into four independent datasets for training, validation, testing, and verification purposes, respectively. Models with varying architectures and training algorithms were trained where it was found that the best-performing network consisted of two hidden layers containing 37 hidden neurons in its first hidden layer and 21 hidden neurons in its second hidden layer. It was found that the Levenberg-Marquardt algorithm outperformed the scaled conjugate gradient (SCG) algorithm in convergence speed and accuracy. Coefficients of determination (R2) for the training (0.9999), validation (0.9998), and test (0.9998) subsets show that the model possesses good generalisation capabilities and can accurately predict the COP of the case study refrigeration plant. The relationship between the input parameters and COP were extracted from the ANN using the fundamental input-output equations of the ANN. Evaluation of these relationships showed that higher relative water flow rates to the evaporator and condenser led to an average increase in COP of 0.52. The improved system performance resulted in a reduction in compressor power of 130 kW, which amounts to R1.8 million in annual savings. To verify the model, it was implemented on the test subset to evaluate its prediction accuracy on an independent and unseen data set. The model achieved an R2 of 0.9983, RMSE of 0.0098, and a MAPE of 2.55% which reaffirms is robustness and accuracy in predicting the performance of a deep-level mine refrigeration plant.