Doctoral Degrees (Civil Engineering)

Permanent URI for this collectionhttps://hdl.handle.net/10413/6843

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Investigation of the structural response of masonry systems using traditional and data-driven numerical techniques.
(2022) Motsa, Siphesihle Mpho.; Drosopoulos, Georgios A.
The understanding of the structural behaviour of masonry structures is of great importance for the preservation of their structural integrity and restoration. Masonry arches are among the oldest structural systems in the world. The failure of these structures can lead to loss of the architectural inheritance and therefore, a full understanding of their structural behaviour is of paramount importance. Over the years, several approaches have been developed for the investigation of failure of masonry structures. Emphasis is given in the heterogeneous nature of masonry (masonry blocks and mortar joints), which imposes a difficulty in simulating the response of this structural type. Continuum damage and discrete models can be adopted to simulate damage in masonry structures. Finite element analysis is one of the numerical tools, which are widely used for this task. In this thesis, a methodology is proposed for the structural evaluation of masonry systems, such as buildings and arches, using nonlinear finite element analysis. Traditional constitutive descriptions, including non-smooth contact mechanics, as well as damage mechanics, are adopted for the investigation of the ultimate, failure response of masonry structures. Within this framework, the existing interfaces between masonry blocks, standing for potential damage surfaces, are simulated using unilateral contact and friction. To capture the compressive damage mode on the blocks, damage plasticity laws are introduced. Compressive and tensile damage plasticity laws can also be used to simulate the failure response of complex masonry systems. A new approach is also provided in the thesis, relying on data-driven structural engineering using machine learning principles. According to this approach, artificial neural networks are adopted to replace time-consuming numerical simulations, providing a fast and computationally efficient evaluation of the failure response for masonry arches. Datasets are built for this purpose, using finite element analysis simulations. For the implementation of the parametric simulations, which are needed for the development of the datasets, programming codes in Python and Matlab are developed, in collaboration with commercial finite element models. The proposed concept can be adopted to predict the mechanical response, failure load and collapse mechanism of masonry arches and thus, it can be used for the structural health monitoring of these structures. To provide a holistic investigation of the structural response, the thesis focuses on the evaluation of both the static structural and the dynamic response of masonry buildings. Case studies in real structural systems are included, highlighting the applicability and efficiency of the proposed methodologies. In particular, the structural response of a three-span masonry arch bridge in Turkey, as well as the response of a seven-span shipyard building in Greece, has been investigated. Among the outcomes of this thesis, is the evaluation of the collapse mechanisms of multi-span masonry arches, as these compare to the collapse mechanisms of single-span arches. It is proved that a four-hinge failure mechanism arises when a vertical load is applied at the middle arch of a three-span masonry arch bridge, which is a typical response observed on single span masonry arches. It is also noted that a hinge-mechanism is the critical failure pattern for discrete models of multi-span masonry arches, under in-plane and out-of-plane loads. For the structural assessment of masonry buildings, it is proved in this thesis that finite element analysis can be used to explain real and possibly undocumented structural damages experienced by the buildings, due to static and/or dynamic actions. An effort is also made in the thesis, to propose an innovative data-driven methodology, aiming to capture the structural response and collapse mechanism of masonry arches. Thus, it is shown how machine learning can be integrated within structural analysis and used to solve the complex problem of the structural evaluation of circular masonry arches. The computational cost of this methodology is significantly reduced, comparing to conventional finite element simulations. The extension of this concept can be adopted for the structural health monitoring of masonry structures.
Development of an integrated model for urban sustainable resilience through smart city projects in the Southern African context.
(2021) Blanco-Montero, Antonio.; Trois, Cristina.; Tramontin, Vittorio.; Loggia, Claudia.
The construct Smart City has gone through a few phases in the last decades. Today there is still no consensus on an accurate definition of Smart City, even though a few concepts are now accepted by most stakeholders, establishing frameworks heading to enhance the quality of life of citizens, sustainable development and economic competitiveness, and, most importantly, the optimal balance between these. Starting from the framework of the Smart City model as conceptualized by the developed world, this research attempts to critically analyse the challenges and barriers to a transition and upgrade of such a model for implementation in developing countries, particularly in the Southern Africa. The mid-term future trends in the region create a huge expectation and concern internationally. Factors like the considerable demographic increase in the post-colonial Africa, the massive migration from the rural areas to cities and the shift from the manufacturing world pole in the East to the African continent predict a remarkable dynamic and vibrant scene in the near future. Stressing the ability of the region to respond to these challenges is starting to gain the attention of scholars and organizations internationally. However, it is important to say that most of the research studies point to both, the solution of dramatic situations related to poverty and underdevelopment, and secondly, the market prospect studies that research the economic potential of the region to foreign capital. Moreover, regarding urban systems, most African governments have scarce and unreliable data. Therefore, looking from a local perspective, it is fair to explore ways Africa and Africans are able to cope with the challenges to come. Not only to make the place attractive to outsider eyes but to increase the quality of life and opportunities for local people through selfmanagement. Africa has undergone through a long history of catastrophes in recent times, with horrendous impact on the population. Yet, a proved resilience makes room for hope in a better future, away from a patronizing management by external forces. Part of this research stresses the feasibility of tailor made solutions to cope with future challenges from a local perspective in the era of globalization. International agencies tend to rate performance in multiple fields based on worldwide standards. Taking into account the use of a series of indicators as a tool to rationalize (evaluate) the performance of any particular field of human action; the measurement of those indicators can vary from region to region. In such resilient environment as described above, the aim of this research is to identify sustainable ways for long-term implementation of up to date technologies in Southern African cities for an effective leapfrog that would bring Southern Africa up to nowadays standards without losing local references. A deep dive into the literature about current technologies and the African city represents the starting point of the methodological approach in order to understand localities and real challenges. The research looked at worldwide urban trends and aims to extract those parameters that are meaningful to Africa today. In order to validate the findings of the research, a case study focussed on specific urban challenges has been identified: the Umgeni River estuary in eThekwini municipality is representative of the confluence of multiple urban dynamics: environmental concerns, lack of municipal services, climate change vulnerability, ocean pollution, poverty, regional business, mining, commercial activities, informal settlements and formal planning. The waste sector in particular, typically undermined in the Global South, has been identified as a potential common thread across the aforementioned urban dynamics. The application to the case study of the lessons learnt through the study of the smart city and urban sustainable resilience highlights the readiness of the Southern Africa city and unlocks a discussion about sustainable urban growth. The results indicate a dual scenario, concerning yet optimistic: there are great disparities between the aspirations from city managers and policy makers, and the conflicted reality at ground level. The pressure due the competitive agenda to render Southern African cities appealing in order to gain foreign economic attention could fade as local communities improve their life condition and strength local markets: “Africa by Africans for Africans”. Two important factors can make this shift possible: one is the presence of strong academic institution with great number of strong collaborations with organizations of great reputation. The case study proves a great interest to assist with solutions to African matters by the international community, but probably not in the way city managers expect. The second one is the advantage that can be taken from the “already made” infrastructure fabric, re-programming the initially “colonial-conceived extractive economic vision” towards social gain.
Influence of rainfall on red-light running at signalised intersections and the service quality implications.
(2021) Oyaro, Janet Kemunto.; Ben-Edigbe, Johnnie Ebioye.
When drivers approach a signalised intersection stop-line, they must decide whether to stop or proceed and clear the intersection before the end of a green phase. The driver behaviour at the intersection affects signalised intersections' performance especially in terms of safety (red-light violation) and efficiency (throughput and delay). Drivers are affected by the state of the traffic lights, prevailing traffic conditions, road conditions and prevailing yellow light laws. When rain falls driver behaviour is affected, and this could, in turn, affect the performance of signalised intersections. This study aims to determine the impact rainfall has on red-light violations and what implications that could have on intersection service quality. In Durban South Africa, four (4) signalised intersections were selected for a “dry” versus “rainy” study carried out using traffic, and rainfall data collected over eight weeks covering the rainfall season. The probability of red-light running (RLR) was found to decrease with an increase in rainfall intensity. The probability reduced from about 42% on average under dry conditions to 17% under light, 7% under moderate and 2.5% for heavy rainfall intensity. It implies that it becomes nearly impossible to violate a red light under rainfall conditions due to speed reduction and hence increase in travel time. The average time needed to safely cross the stop line at the onset of yellow time interval also increased from 3s during dry weather to 3.6s for light rain, 3.9s for moderate rain, and 4.5s for heavy rain. Thus, approaching vehicles cannot safely enter the signalised intersection and must wait at the stop line for a green signal. Therefore, it can be summarised that rainfall has a mitigating effect on red light violations especially under heavy rain where it is near impossible to run a red light. South Africa does not have a highway capacity manual (HCM) and relies on USA-HCM for signalised intersection assessment. HCM uses delay as the sole determiner of signalised intersection quality of service; this was found inadequate and not a complete reflection of driver perception of the level of service; this study proposed a criterion that incorporated degree of saturation in addition to the delay. With the developed criteria, analysis was done to determine the rainfall influence on signalised intersection performance. Through and right-turning traffic were considered separately in this study. For through traffic, saturation flow rate reduced by 3.9% under light rainfall, 8.68% under moderate and 10.88% under heavy. It led to a capacity loss of 4.25% under light, 9.18% under moderate and 11.5% under heavy. For right-turning traffic, saturation flow rates decreased by 7.07% under light rainfall intensity, 13.44% under moderate and 17.88% under heavy. The capacity loss was also recorded where light rainfall caused a 7.38% loss, moderate 14.5% and heavy 19.15%. For the degree of saturation, delay, and queue length, all three increased. The degree of saturation increased by 1.55% under light, 7.23% under moderate and 9.4% under heavy. The overall impact on service quality was mixed; for through traffic lanes, few instances where heavy rainfall caused a deterioration in SQ by one level were recorded. For right-turning traffic lanes, the results were more consistent with expectations. There was an increase in both degrees of saturation and delay. Overall, the SQ level deteriorated especially under heavy rainfall conditions. Right-tuning lanes showed higher SQ deterioration attributed to their higher saturation level.
Development of a universal water quality index and water quality variability model for South African river catchments.
(2020) Banda, Talent Diotrefe.; Kumarasamy, Muthukrishna Vellaisamy.
The assessment of water quality has turned to be an ultimate goal for most water resource and environmental stakeholders, with ever-increasing global consideration. Against this background, various tools and water quality guidelines have been adopted worldwide to govern water quality deterioration and institute the sustainable use of water resources. Water quality impairment is mainly associated with a sudden increase in population and related proceedings, which include urbanisation, industrialisation and agricultural production, among others. Such socio-economic activities accelerate water contamination and cause pollution stress to the aquatic environment. Scientifically based water quality index (WQI) models are then essentially important to measure the degree of contamination and advise whether specific water resources require restoration and to what extent. Such comprehensive evaluations reflect the integrated impact of adverse parameter concentrations and assist in the prioritisation of remedial actions. WQI is a simple, yet intelligible and systematically structured, indexing scale beneficial for communicating water quality data to non-technical individuals, policymakers and, more importantly, water scientists. The index number is typically presented as a relative scale ranging from zero (worst quality) to one hundred (best quality). WQIs simplify and streamline what would otherwise be impractical assignments, thus justifying the efforts of developing water quality indices (WQIs). Generally, WQIs are not designed for broad applications; they are customarily developed for specific watersheds and or regions unless different basins share similar attributes and test a comparable range of water quality parameters. Their design and formation is governed by their intended use together with the degree of accuracy required, and such technicalities ultimately define the application boundaries of WQIs. Such an academic gap is perhaps the most demanding scientific need; that is, to establish universally acceptable water quality indices, which can function in most, if not all the catchments in South Africa. In cognisance of such, the study suggests four water quality models that are not limited to specific application boundaries, and such contribution is significant, not only to the authors but to the entire nation. The first model, namely the universal water quality index (UWQI) developed based on conventional techniques using unequal weight coefficients and weighted arithmetic sum method. Model input parameters, relative weight coefficients and sub-index rating curves are established through an expert opinion by means of participatory based Rand Corporation’s Delphi Technique and extracts from literature. The second developed artificial intelligence (AI) in the form of artificial neural network (ANN) has three neuron layers parallel-distributed to accommodate feedforward sequence and backpropagation. The multi-layer perceptron model architecture includes nineteen highly interconnected neuro-nodes and seventy weighted synapses operating in a feedforward manner, from left to right. The study applied the Broyden-Fletcher-Goldfarb- Shanno (BFGS) algorithm to perform backpropagation training and optimising channel weights. The three-layered feedforward neural network indicated an increased performance registering an overall correlation coefficient of 0.985 and specific performance ratings of 0.987, 0.992 and 0.977 for training, testing and validation, respectively. The AI-based demonstrated an average target to an output error margin of ±0.242. Pointwise sensitivity analysis authenticated the robustness and computational aptitude of the suggested artificial neural network model. Both UWQI and ANN model functions with thirteen explanatory variables which are NH3, Ca, Cl, Chl-a, EC, F, CaCO3, Mg, Mn, NO3, pH, SO4 and turbidity (NTU). The third model entitled surrogate WQI works with four proxy water quality parameters comprising of chlorophyll-a, electrical conductivity, pondus Hydrogenium and turbidity. The proxy linear-based mathematical model is an abridged version of an outright WQI, purposefully stablished to substitute the UWQI and ANN model, thereby providing provisional index scores in the absence of extensive datasets. Water quality indices (WQIs) are customarily associated with massive data input demand, making them more rigorous and bulky. Such burdensome attributes are too taxing, time-consuming, and command a significant amount of resources to implement. Which discourages their application and directly influences water resource monitoring—making it increasingly indispensable to concentrate on developing compatible, more straightforward, and less-demanding WQI tools, but with equally matching computational ability. Surrogate models are the best fitting, conforming to the prescribed features and scope. Consequently, the study proposes an alternative water quality monitoring tool requiring fewer nputs, minimal effort, and marginal resources to function. Multivariate statistical techniques which include principal component analysis (PCA), hierarchical clustering analysis (HCA) and multiple linear regression (MLR) are applied primarily to identify four proxy variables and define relevant regression coefficients. Resulting in Chl-a, EC, pH and turbidity being the final four proxy variables retained. The selected input parameters are conformable with remote monitoring systems; which is a vital feature allowing the surrogate index model to be considered for remote monitoring programs. The fourth and final model suggested is a software-based water quality variability model (WQVM) established by integrating three distinctive water quality indices (WQIs) emerging from this study. The three WQIs are founded on different indexing methods, and they are documented as (a) universal water quality index, (b) artificial neural network, and (c) surrogate water quality index. Usually, most WQIs are presented as arithmetic formulas that are somewhat challenging to comprehend and apply in the real world. Therefore, the study attempts to address such research tendencies and set forth an excel-based hybrid water quality monitoring tool applicable at a national level. The WQVM enables the assessment of multiple water quality parameters, thereby solving practical water science problems. The proposed WQVM is earmarked for improving and promoting water quality monitoring programs, by providing a simple, convenient and userfriendly monitoring toolkit. Indeed, putting forward the WQVM has an increasing impact on water resource evaluation and optimising decision making amongst water scientists and professionals. Suggested models yield one-digit index values rending from zero to one hundred, where zero denotes poor water quality, and one hundred represents excellent water quality. Furthermore, the index scores are classified using a categorisation schema having five classes. Whereby “class one” with a possible maximum score of hundred designate the highest degree of purity and vice versa, “class five” signifies water quality of the lowest degree with index scores nearing or equal to zero. The WQIs and WQVM are developed and tested using water quality data from Umgeni Water Board (UWB) in KwaZulu-Natal Province, South Africa. From the original dataset, the current study retained 638 samples with 7,741 tests measured monthly over four years. The water quality records are from six sampling stations located within four different river basins identified as Umgeni, Umdloti, Nungwane and Umzinto/uMuziwezinto River catchments. The data samples are further curtailed to satisfy statistical requirements of each particular WQI model. All four models are considered robust and scientifically stable, with minor divergence from the ideal values. Better off, the prediction pattern matches the exemplary graph having comparable index scores and identical classification ranks, which signifies their readiness to appraise water quality status across South African watersheds. The established models symbolise a significant milestone with the prospects of promoting water resource monitoring and assisting in capturing spatial and temporal changes within river systems. The study intends to substantiate the methods used and document results achieved.
Effect of rainfall on function service quality deterioration of dark roadways and its implication for stopping sight distance.
(2019) Makinde, Opeyemi Oluyemisi.; Ben-Edigbe, Johnnie Ebioye.
Driving in the rain at night is challenging and more so if the roadway has no light. This study aims to ascertain whether rainy dark roadways would have significant influence on functional service quality reduction, and also the associated stopping sight distance implications for road users. Dark roadways are prevalent in Nigeria mainly because of poor energy management and the absence of long term sustained-energy strategic plans. The objectives are to determine the functional service quality in the presence of rainy dark roadways and compare with that taken on dry dark roadways. To that effect a rainy dark roadway impact study was carried out at four (4) selected sites in Nigeria for a period of eight (8) weeks. Based on the circumstances prevalent at the time of the survey, the study assumed that density was a result of speed and flow hence not directly affected by rainfall. This implies that functional service quality was fully the result of speed and travel time changes. Functional service quality describes the assessment of service delivery of roadways based on both road provider (travel speed) and user (travel time) perceptions. Vehicle types, volumes, speeds and rainfall were collected continuously at each surveyed road section for eight weeks and the results analysed. Traffic volume was converted into flow using modified passenger car equivalents values. The results of the analysis show reduction in travel speed with ensuing increase in travel time. Results show that the average travel time increased by 27.1 percent on dark roadways due to night rainfall. Results show that the average travel speed decreased by 18.7 percent on dark roadways due to night rainfall. The results from the analysis were used to establish the stopping sight distance implications of rainy dark roadways for motorists. Results show that on dark roadways the average stopping sight distance (SSD) increased by 25.8 percent due to rainy night. Results from the predicted travel time loss confirm the established evidence that travel time is a significant guide for measuring road effectiveness. Finally, since there is the potential to improve functional service quality output based on efficient and appropriate energy on one hand; effective management of resources on the other, the study concluded that in the presence of rainfall, dark roadways have a significant impact on the functional quality of service and stopping sight distance.
Anaerobic digestion of energy crop (cassava)
(2019) Sawyerr, Nathaniel Olugbenga.; Trois, Cristina.; Workneh, Tilahun Seyoum.; Okudoh, Vincent Ifeanyi.
Global energy demand is on the rise due to continuous increases in population, economic growth, and energy usage. Methane production through anaerobic digestion of organic materials provides a resourceful carrier of renewable energy, as methane can be used instead of fossil fuels for both heat and power generation and also as vehicle fuel, thus cutting down the emissions of greenhouse gases and hence contribution in the slowing down climate change. Several studies have been done on biogas, but in South Africa, these are biased towards industrial wastewater. Therefore, there is need to explore other alternatives for biogas generation. Furthermore, the sustainability of anaerobic digestion processes depends on the availability and the identification of the optimal substrate. The use of cassava in South Africa provides a great potential for the production of bioenergy especially biogas, due to its suitable chemical composition. Cassava codigested with other feedstock could be an alternative substrate for various communities for the production of biogas in South Africa. Since cassava is yet to be listed as a staple food crop in South Arica, its peels and other by-products from its processing can be suitable for renewable energy production for small medium enterprises (SMEs). This study’s overall objective was that of establishing the suitability of cassava tubers as an alternative source of biomass feedstock for biogas production in South Africa. The specific objectives of the study were: 1) Comparing the yield and rate of biogas production of cassava peels inoculated with cattle manure using a batch digester under anaerobic digestion conditions addressed in chapter four and five of the thesis; 2) Investigate the biogas yield and rate of different co-digestion ratios of cassava with vegetable and fruit waste using batch digestion under anaerobic digestion conditions presented in chapter six; 3) Optimize the production of biogas through process optimization by maintaining the optimum temperature during fermentation and compare inexperiments subjected to different treatment or treatment combinations and, 4) While chapter seven addresses the objective of using the experimental results to design an upscale system using baseline data information from experiment. Several feedstocks (i.e. cassava tuber, cassava peels, vegetable and fruit waste and cattle manure) were identified and analysed using the American Standard Methods for examination of Water and Wastewater (ASTM). Cassava was selected as it has several advantages compared to other crops, including the ability to grow on degraded land and where soil fertility is low. It also has the highest yield of carbohydrate per hectare (4.742 kg/carb) apart from sugarcane and sugar beet, which makes it suitable for bioenergy (biogas) generation. In the first instance, a batch experiment of were cassava peels were digested anaerobically with and without cattle manure to determine whether cassava peels (CP) in combination with cattle manure (CM) at different ratios shows better biogas yield. The following ratio combinations of mixture were used 100:0, 0:100, 80:20 and 20:80 (CM:CP). A theoretical methane production was conducted using elemental composition and the results were compared with the experimental ones. The test of biogas yield was conducted using an anaerobic digester of 600 ml at mesophilic (35 ± 1 °C) temperature. In the second experiment a 50 litres anaerobic digester was used to investigate the biogas yield of peeled cassava tuber compared to unpeeled cassava tuber that yield biogas of 635.23 L/kg VS and 460.41 L/kg VS respectively. This was based on the finding of the first experiments of biogas yield from cassava peels. The biogas yield with and without inoculum was measured and the biogas yield were modelled using two different models namely modified Gompertz and cone model. Finally, in parallel with the previous batch experiments another set of batch experiments were carried out under anaerobic conditions at mesophilic (35 ± 1 °C) temperature in a 600 ml digester, this experiments was conducted by co-digesting cassava (CB) with vegetable and fruit waste (CB:VF) at different ratios (100:0, 60:40, 40:60 and 50:50). The cumulative biogas yield were modelled for kinetics using modified Gompertz model. Based on the results obtained from the experimental study cassava co-digested with vegetable and fruits at a ratio of 40:60 which was found to produce the maximum yield, a mathematical design (upscale system) was designed. This designed biogas plant could be located in several communities especially those close to the landfills to reduce the cost of transportation from source. The study’s results revealed that: • co-digestion influenced biogas production and methane yield. The final cumulative methane yields by the co-digestion of CM and CP at the CM:CP mixing ratios of 80:20 and 20:80 were 738.76 mL and 838.70 mL respectively. The corresponding average daily methane yields were 18.42 mL/day and 20.97 mL/day. This indicates that CP enhanced the production of methane in the co-digestion process with the 20:80 CM:CP ratio. • the feedstock of peeled cassava with inoculum, produced 28.75% more biogas yield when compared to peeled cassava without inoculum. This results highlights the important of inoculum in the anaerobic digester. • peeling the cassava tuber increase the biogas yield by 38% compared to the unpeeled tuber • cassava biomass co-digested with vegetable and fruit waste increased the methane yield compared to the mono-digestion with the highest methane production was achieved from the co-digestion of cassava biomass with vegetable & fruit waste at 40:60 ratio (CB: VF) Although several challenges hampering the smooth implementation of biogas generation in South Africa, this study concludes that cassava (peeled and unpeeled) co-digested with fruit and vegetables waste has potential to generate biogas thereby presenting a substantial opportunity to promote bioenergy production from cassava considering in many rural areas the needs for fuel and electricity are not satisfied fully. Finally, cassava anaerobic digestion facility at different scales could enhance additional benefits like the integration of nutrients and residual carbon into the land as fertilizer.
Development of a sustainable evolutionary-inspired artificial intelligent system for municipal water demand modelling.
(2020) Oyebode, Oluwaseun Kunle.; Saha, Akshay Kumar.; Modi, Albert Thembinkosi.
This study presents the development of a differential evolution (DE)-inspired artificial neural network (ANN) that incorporates climate and socioeconomic information for a more accurate and reliable water demand forecasting. The study also addresses the limitations of ANN. Multiple feature selection techniques were employed to identify the minimal subset of features for optimal learning. The performance of the feature selection techniques was validated and compared to a baseline scenario comprising a full set of data covering potential casual variables including weather, socio-economic and historical water consumption data. The performance of the models was evaluated based on accuracy. Results show that all the feature selection techniques outperformed the baseline scenario. More importantly, the subset of features obtained from the Pearson correlation technique produced the most superior model in terms of model accuracy. Findings from the study suggests that inclusion of weather and socioeconomic variables in water demand modelling could enhance the accuracy of forecasts and cater for the impacts of climate and socioeconomic variations in water demand planning and management. The performance of the optimal DE-inspired model was thereafter compared to those developed via conventionally-used multiple linear regression and standard time series technique – exponential smoothing as well as other prominent soft computing techniques, namely support vector machines (SVM) and conjugate-gradient (CG)-trained multilayer perceptron (MLP). Results show that the DE-inspired ANN model was superior to the four other techniques for both the baseline scenario and optimal subset of features. DE showcased robustness in fine-tuning algorithm parameter values thereby producing good performance in terms of the solution efficiency and quality. Generally, this study demonstrates that water demand models can account for the impacts of weather and socioeconomic variations by incorporating explanatory variables based on weather and socioeconomic factors. This study also suggests that the synergetic use of feature selection techniques, DE algorithm and an early stopping criterion could be used in addressing the limitations of ANN and developing an improved and more reliable water demand forecasting model. This work goes further to propose for a novel and more comprehensive integrated water demand and management modelling framework (IWDMMF) that is capable of syncing conventional evolutionary computation techniques and social aspects of society. The methodologies, principles and techniques behind this study fosters sustainable development and thus could be adopted in planning and management of water resources.
A mathematical model development for simulating in-stream processes of non-point source pollutants.
(2018) Adu, Joy Tuoyo.; Kumarasamy, Muthukrishna Vellaisamy.
In coming years, chronic water stress is inevitable owing to the unavailability of fresh water. This situation is occasioned by rapid urbanisation, climate change, rising food demand, and production. The increasing rate of water scarcity associated with water pollution problems, makes water quality management an issue of great concern. Rivers owe their existence to the relationship of rainfalls, soil properties and land use within a catchment. The entire hydrological processes that occur in the catchment area has a direct effect on occurrences and quality of the rivers there-in. A principal part of the hydrological cycle is runoff generation. Runoff characterises soil erosion, sediment transport, pollutants and chemicals all otherwise referred to as non-point source pollutants and released into water bodies. Most non-point source pollutants are generated from agricultural fields, informal settlements, mining fields, industrial areas, and roads. These sources produce increased nutrient concentrates (sewage effluent from informal settlements and fertilisers from agricultural fields) and toxic substances which alter the water quality in uncertain quantities. This affects aquatic biota and ultimately human health negatively. Non-point source pollution is a major source of water quality degradation globally and is the single most significant threat to subsurface and surface sources of usable water. Developed countries, unlike many developing countries, have long sought ways to stop the release of non-point source pollution directly into natural rivers through the establishment of best management practices but unfortunately with little success in actual practice. Numerous non-point source models exist which are basically watershed based and are limited to simulate the in-stream processes of non-point source pollution in water channels. Most existing non-point source models are site-specific, cumbersome to manipulate, need high-level operational skills and extensive data sets. Consequently, these models are difficult to use in areas apart from where they were developed and with limited data sets, as is the case with developing countries. Hence, to develop a non-point source pollution model that would adequately and effectively, simulate non-point source pollution in water bodies, towards restoring good river health is needed. This is required to enhance the proper monitoring and remediation of water sources affected by Non-Point Source Pollution especially in areas that have scarce data. Using the concept of the Hybrid Cells in Series model in this study, a hydrodynamic riverine Non-point source pollution model is conceptualized to simulate conservative pollutants in natural rivers. The Hybrid Cells in Series model was conceptualized to address the limitations identified in the classical advection dispersion model which is the foundation for all water quality modelling. The proposed model is a three-parameter model made up of three zones, which describes pure advection through time delay in a plug zone, and advection and dispersion occurring in two other thoroughly mixed zones linked in sequence. The model considers lateral inflow and pollutant loading along the river reach in addition to the point source pollutant entry and flow from upstream stations. The model equation for water quality along with hydrodynamic equation has been solved analytically using Laplace Transform. The derived mathematical formulation is appropriately coded, using FORTRAN programming language. Other components such as hyporheic exchange process and first order kinetic reaction simulations are incorporated to the proposed model. The response of these models matches the numerical solution of the classical Advection Dispersion Equation model satisfactorily when compared. The potential of the proposed model is tested using field data obtained from verifiable existing literature. A performance evaluation at 95 percent confidence is carried out. The correlation results of the observed and simulated data are seen to be in good agreement. The breakthrough curves obtained from the proposed model shows its capability to simulate Non-point source pollution transport in natural rivers effectively. The simplicity of the Hybrid Cells in Series model makes it a viable model for simulating contaminant transport from non-point sources. As the model has been validated using recorded data collected from the field for a specific tracer injection event, it is imperative to carry out investigation on changes in model parameters before, during and after storm events. However, this study adequately addressed and attempted to develop, validate new model components for simulating non-point source pollutant transport processes in stream.
The applicability of C&D waste material in South Africa road construction.
(2015) Zedda, Valentina.; Trois, Cristina.; Everitt, Philip Robert.
In contrast to developed countries, South Africa is starting to adopt the practice of using recycled rubble from civil engineering demolition works in road construction. This change is due to environmental considerations, but somewhat unique nature of the South African road construction techniques requires a careful classification of these alternative materials and a rigorous study. In this study the possibility of reclaiming the Construction and Demolition (C&D) waste produced in the Durban Metropolitan Area was investigated. A lack of policies for the reuse of waste produced by the civil industry caused the storage of large quantities of C&D waste in the metropolitan landfill sites. In order to decrease this volume the rubble can be reused as construction material for road works. Without alteration in collection process, the C&D waste should be kept sorted according to its nature in landfill site to keep its characteristics as homogeneous as possible. Moreover, specific tests performed in this thesis confirmed the viable construction properties of this material such as non-plasticity and bearing capacity that conform to the South African construction standard. The suitability of the C&D materials in civil works is further demonstrated by the analysis of the rubble generated by the deconstruction of the Natal Command, an ex-military area, in Durban. The demolition of the study area has followed a rigorous procedure of deconstruction. This approach allowed the principal components of the waste (Concrete and Masonry) to be kept separated. Laboratory tests accurately assessed the geotechnical properties of concrete, masonry and of a blend of them which were previously identified as suitable aggregates for unbound road base or sub-base layers. These materials are considered as A1a in AASHTO classification and as G4 in COLTO classification. In addition to the standard test of characterisation, the risk of polluting the environment was assessed. An instrumented embankment was built in accordance with South African road compaction standards using blend material. This enabled the verification of the behaviour of C&D material in real-word working conditions. The instrumentation recorded the stresses, strains and moisture at three depths of the embankment during the passage of trucks at different predetermined speeds. The results of the monitoring were analysed and correlated to the results coming from the full performance material characterization carried out in laboratory. This study will thus enhance knowledge of the behaviour of C&D material and also provide a useful tool to the designer in the planning stages as well as information for contractors involved in C&D road construction application.
Circular economy design visioning: exploring industrial and urban symbiosis in South African cities.
(2017) Govender, Kruschen Deenadayalan.; Trois, Cristina.
Cities of tomorrow will be at the coalface of the complex challenges posed by climate change, e.g. resource scarcity. Climate change adaptation strategies will include circular economy (CE) practices (e.g. industrial and urban symbiosis) to increase the rate of recycling technical nutrients, in turn improving the resource efficiency of cities. The study investigates industrial and urban symbiosis in South Africa. In doing so, exploring technology enabled (i.e. cyber-physical-social ecosystems) CE solutions to designing out waste in South African cities. One of the key contributions of the research is the comprehensive synthesis and testing of an iterative problem structuring, theory building and design visioning (problem-theory-design) continuum to inform CE experimentation. A mixed methods design visioning approach is developed through an experiential and iterative design practice nested in a network of interdisciplinary theoretical constructs: 1) philosophical construct – Ecological Literacy (systems thinking), 2) techno-economic construct – Third Industrial Revolution (internet-of-things enabled general purpose technology platform), and Circular Economy (industrial and urban symbiosis), and 3) design construct – properties of Ecodesign derived from the dynamic renewable design of natural ecosystems. The research argues that to construct a meaningful CE transition experiment, a logical starting point is to distil key findings from a theoretically embedded case study to inform the design of a virtual experiment and simulation sketch. Through an embedded multiple case study approach the research investigates complex resource recovery dynamics in two key waste economy sub-sectors; industrial waste management and urban informal recycling sectors in the province of KwaZulu-Natal (KZN). The case studies provide an integrated method (i.e. synthesising quantitative and qualitative knowledge) for holistic and high-resolution problem structuring. From a systems thinking perspective, key leverage points (i.e. data, information sharing and infrastructure) are identified for potential policy and technology intervention. Learnings from the case studies inform policy recommendations and CE innovation. The findings from the industrial symbiosis (IS) case study illustrate that firms and supply chain networks recognise the environmental importance of improving industrial waste management practices, however they are locked-in to end-of-pipe solutions. Firms highlighted regulation, price sensitivity, customer pressure and top management as key drivers of pro-environmental behaviour change (e.g. waste beneficiation). The findings highlight the unrealised IS potential in the South vi Durban Basin. In addition, revealing significant barriers to IS, i.e. lack of information sharing between firms and a weak regulatory environment. To increase the detection, matching and emergence of IS relationships will command the dynamic co-production of codified resource flow data; herein a big data analytics approach can be employed to construct open source platforms for interfirm information (e.g. residual resource flows) sharing and knowledge production – an industrial commons internet. The urban symbiosis case study explores the informal recycling sector in KZN analysing the instrumental role of waste pickers as primary looping agents in recovering recyclable materials from post-consumer waste and increasing the supply of recyclable materials (e.g. cardboard, paper, plastic and metal) in the secondary resources economy. Waste pickers are an important link in recycling value chains; sorting, gathering and manually transporting recyclable materials to buy-back-centres and informal collection pick up points. The case study investigates how their efficiency can be improved to stimulate greater positive environmental impacts, create decent employment opportunities, and reduce waste management costs for municipalities. The findings from the case study on waste pickers are extrapolated in a CE design visioning exercise. From a systems level perspective, the research culminates in the sketch of a virtual circular city experiment; a cyber-physical social ecosystem (CPSE) designed to increase recycling rates in cities by addressing the infrastructural needs of waste pickers. The hardware, software and social ecosystem is built out of an internet-of-things (IoT) platform. Firstly, the IoT enabled infrastructural system improves material recovery efficiencies (of post-consumer recyclable materials) by increasing connectivity between waste pickers and waste collectors. Increased connectivity allows for looping and aggregating material stock and flow data. Secondly, the integrated hardware and software infrastructure provides an automated, digitised and decentralised buy-back-transfer service – delivered through connected and solar-powered collection nodes strategically distributed throughout the city in a mesh network configuration. Thirdly, the digital platform aggregates big data and employs advanced analytics to generate actionable residual resource intelligence, consequently enabling evidence-based decision making by key stakeholders, e.g. government agencies, industry associations, recyclers and material reprocessors. To further the research agenda, the next step is structuring a real-world transition experiment based on the virtual circular city design experiment, defined as, the internet-of-waste pickers (IoWP).
Influence of rainfall on quality of service at multilane roundabouts and its time headway implications.
(2018) Ibijola, Stephen Olukayode.; Ben-Edigbe, Johnnie Ebioye.
Roundabouts, or traffic circles as they are often called in South Africa, are priority intersections with a unique yield rule. Drivers approaching the roundabout must give way to those that are already circulating the central island. The fixed features and yield rule do not change relative to rainfall; however, vehicular flow rate and driver behaviour are often affected by ambient conditions like rainfall among others. Consequently, in this the study the influence of rainfall on the quality of service delivery at multilane roundabouts and their implications for time headways have been investigated. Based on the hypothesis that rainfall, irrespective of intensity, has adverse effects on the quality of service delivery and time headway at roundabouts, an impact study was carried out in Durban, South Africa. Entry, circulating traffic flow rate and rainfall data were collected at four selected sites in Durban, South Africa. Over one million traffic volume data was collected during the August 2016 to February 2017 rainy season. The key selection criterion is proximity to an active rain gauge. Empirical data were collected continuously for six weeks on each selected roundabout. Rainfall data were collected from surface rain gauge stations with a distance range of 0.75km – 1.18km from the selected sites. Three classes of rain precipitation intensity (i) (light rain, i < 2.5mm; moderate rain, 2.5mm < i ≤10mm; and heavy rain 10mm < i ≤ 50mm) were considered. Very heavy rain, with an intensity greater than 50mm/h, was not considered because of associated drag force and aquaplaning which might be difficult to separate from the rainfall effect. Daylight data were separated into peak and off-peak traffic periods. Peak period data were used to develop a quality of service criteria table and the off-peak data were used to determine traffic flow rate performance. Passenger car equivalent (PCE) values used to convert vehicles per hour to pce per hour was investigated for analytical suitability given rainy conditions. Entry flow rate was used as a function of circulating flow rate to model entry capacity and, hence, determine the reserve capacity. Initially, both linear and exponential models were used, in turn, to test for analytical suitability. Linear model was the preferred after exponential function failed empirical tests. Linear function was used to model the relationships between entry and circulating traffic flow rates. The ensuing entry capacity was also used in conjunction with headway and degree of saturation to estimate entry delay under dry, light, moderate and heavy rainy conditions. The impact study reasons that quality of service is not the same as level of service, hence, the criteria table cannot be the same. This is a clear departure from Highway Capacity Manual (HCM) prescription for roundabout level of service criteria table. The novel quality of service criteria table prescribed in this thesis, has delay and reserve capacity as the xxxi key determinants of service grade. It is also referred to as Functional Quality of service (FQS) in the thesis. FQS criteria table was developed for each study site and used to assess their service delivery. The criteria table was divided into six classes (A to F), where A is the best grade and F is the worst. In any case, traffic performances were analysed and results show that; i) there is no significant difference between South Africa passenger car equivalent values and those estimated in the study; ii) the novel criteria table developed in the study is an effective determinant of FQS delivery at roundabouts; iii) entry traffic flow rate rates decreased because of rainfall and by extension induced a reduction in quality of service delivery at all surveyed sites; iv) entry delay and attendant queue increased during rainfall; v) time headway increased and entry reserve capacity decreased because of rainfall. It has been concluded that rainfall has an adverse effect on the FQS and also, that heavy rainfall has the most significant impact on FQS at roundabouts. It is proposed that in future research, on roundabout entry capacity estimation based on polynomial quadratic function where the single-variable quadratic polynomial would have density as the independent variable and flow rate as the dependent be considered.
The applicability of C&D waste material in South Africa road construction.
(2018) Zedda, Valentina.; Trois, Cristina.; Everitt, Philip Robert.
In contrast to developed countries, South Africa is starting to adopt the practice of using recycled rubble from civil engineering demolition works in road construction. This change is due to environmental considerations, but somewhat unique nature of the South African road construction techniques requires a careful classification of these alternative materials and a rigorous study. In this study the possibility of reclaiming the Construction and Demolition (C&D) waste produced in the Durban Metropolitan Area was investigated. A lack of policies for the reuse of waste produced by the civil industry caused the storage of large quantities of C&D waste in the metropolitan landfill sites. In order to decrease this volume the rubble can be reused as construction material for road works. Without alteration in collection process, the C&D waste should be kept sorted according to its nature in landfill site to keep its characteristics as homogeneous as possible. Moreover, specific tests performed in this thesis confirmed the viable construction properties of this material such as non-plasticity and bearing capacity that conform to the South African construction standard. The suitability of the C&D materials in civil works is further demonstrated by the analysis of the rubble generated by the deconstruction of the Natal Command, an ex-military area, in Durban. The demolition of the study area has followed a rigorous procedure of deconstruction. This approach allowed the principal components of the waste (Concrete and Masonry) to be kept separated. Laboratory tests accurately assessed the geotechnical properties of concrete, masonry and of a blend of them which were previously identified as suitable aggregates for unbound road base or sub-base layers. These materials are considered as A1a in AASHTO classification and as G4 in COLTO classification. In addition to the standard test of characterisation, the risk of polluting the environment was assessed. An instrumented embankment was built in accordance with South African road compaction standards using blend material. This enabled the verification of the behaviour of C&D material in real-word working conditions. The instrumentation recorded the stresses, strains and moisture at three depths of the embankment during the passage of trucks at different predetermined speeds. The results of the monitoring were analysed and correlated to the results coming from the full performance material characterization carried out in laboratory. This study will thus enhance knowledge of the behaviour of C&D material and also provide a useful tool to the designer in the planning stages as well as information for contractors involved in C&D road construction application.
Development of a mathematical model considering nutrients kinetics for assessing uMgeni river water quality.
(2018) Olowe, Kayode; Kumarasamy, Muthukrishna Vellaisamy.
Surface water is an essential component of the natural environment which needs to be sheltered from all sources of contamination because human and aquatic animals depend on it for their survival. However, the discharge of organic wastes into water bodies has deteriorated its quality and placed economic restrictions on water use. Anthropogenic inputs of nutrients into the water column has become one of the vast water quality problems around the world. This result in the reduction of dissolved oxygen level and promotes algae growth in the water body. Therefore, there is a need for the development of an effective nutrient management strategy which is essential in protecting the water body. Standard practice is to use water quality models as an important part of environmental valuation tools for modeling and controlling the surface water pollution. Water quality models have been a useful tool in maintaining the water quality status and evaluating the fate of pollutants in different water bodies. Numerous methods are available for solving solute transport in natural streams and rivers. However, existing methods are flawed either because of their limitation in application to a natural water body due to their inaccurate in estimating model parameters or their failure to simulate the advection component of solute transport. The Hybrid cell in series (HCIS) model which is a conceptualized model serves as an alternative method to solve solute transport in a natural river. It overcomes the difficulties associated with the existing approaches such as Fickian based models by converting its second � order partial differential equation to a first - order ordinary differential equation which could be solved analytically. Additionally, the conceptual hybrid model was able to include advection component to its process which overcomes the difficulty associated with mixing cells model. In this study, additional components to the conceptualized hybrid cells in series model have developed for the first order kinetic reaction of Ammonia (NH3), Nitrite (NO2) and Nitrate (NO3) along with advection and dispersion processes using mass balance concept. A basic hybrid model which consist of a plug flow cell and two different thoroughly mixed cells all connected in series is developed to predict nutrients solute transport in a river from a point source of pollution. Analytical solutions of the HCIS model along with the nutrients kinetics were obtained using Laplace transformation. A C-Sharp programming language was then used to implement the analytical solutions obtained for these models where a user-friendly software package was developed. The developed models were used to predict the temporal and spatial variation of the nutrients concentration in the water body v The potential of the developed model has been tested using hypothetical data and a field data obtained from uMgeni River to predict the effect of ammonia, nitrite, and nitrate nutrients concentration along the selected river reach. The data collected from several sampling locations along the study area from January 2014 to December 2014 were used to verify the model�s efficiency. The prediction of ammonia, nitrite and nitrate concentration using the developed HCIS models have shown excellent agreement with field data of the uMgeni River, South Africa. Thus, the analytical solutions obtained can accurately predict the nutrients solute transport in uMgeni River. Further, the study has shown that the response of hybrid models matched satisfactorily with the numerical solution of Fickian based Advection-Dispersion Equation model which was solved with explicit finite difference method. The performance of the model was validated using statistical tools based on the coefficient of determination (R2) which was carried out at a 95 percent level of confidence between the observed and simulated data. It was observed from the correlation that the observed and simulated values of the nutrients concentration in the river demonstrated a high correlation coefficient (R2) and the standard error (SE) was low for all components of the model (NH3, NO2, and NO3). Hence, the results show that the developed model has demonstrated high accuracy and provide a novel tool for predicting ammonia, nitrite, and nitrate concentration distributions in the River. This research work presents the development and application of HCIS model for predicting the concentration of nutrients, i.e., NH3, NO2, and NO3 in water bodies. Based on the study, the hybrid model is effective in predicting the spatiotemporal concentration of ammonia, nitrite, and nitrate nutrients in the natural water body. However, the influence of high rainfall event significantly increases the nutrient concentrations of the river which was not considered in the current model. Thus, this gives a prospect for the consideration of non�point source pollution component in the hybrid model formulation.
Flood estimation in developing countries with case studies in Ethiopia.
(2017) Rabba, Zeinu Ahmed.; Stretch, Derek Dewey.
Extreme flood events have become more destructive in some parts of Ethiopia. Thus, accurate estimates of flood frequencies are vital for effective flood risk management. Yet, estimation of the peak flood is exceptionally complex requiring a wide range of methodologies. One of the approaches is the statistical (traditional) method, which determines the frequency of a flood value from the annual maximum discharge data. However, when such records are too short for flood frequency analysis, empirical formulae can be the option for peak flood estimation. But, most of these formulae are regional formulae based upon the statistical correlation of the recorded peak flood and one or two physical catchment characteristics, and they are unlikely to give reliable results of peak flood for other regions than those for which they were developed. On the other hand, when there are no streamflow observations at the site of interest, hydrological models such as Py- TOPKAPI are another option for modelling stream flows for flood frequency analysis. Thus, the main component of this study involves statistical data analysis and hydrological modeling aimed at finding out an appropriate method of flood frequency analysis for Ethiopian rivers. In this study, a broad overview of practical design-flood-estimation methods in Ethiopia along with international practices was carried out. The results revealed very large gaps in knowledge and in current design flood practices. The application of the PyTOPKAPI model in numerous catchments of the world was likewise reviewed including how the model has been used for flood prediction, forecasting of hydrological responses, etc. In this study, it was implemented in Ethiopia on Gilgel Ghibe and Mojo catchments, and promising results were obtained. This model was also combined with remotely sensed precipitation products for simulating stream flows which showed that the general streamflow patterns were well reproduced. Most importantly, the PyTOPKAPI model was applied in ungauged Ethiopian catchments using the Schreiber runoff ratio in an alternative model calibration approach. This shows how the PyTOPKAPI model can be used to predict runoff responses in ungauged catchments for water resources applications and flood predictions in developing countries. In addition, various flood frequency methodologies were evaluated on two Ethiopian rivers (Awash and Gilgel Ghibe). The aim was to find the most approprite method that best represents the statistical characteristics of the streamflow observations. In this case, the annual maximum discharge data from 14 stations of the two rivers (6 in Gilgel Ghibe and 8 in Awash) with 23 to 54 years of records were used. Seven flood frequency methodologies (TSPT, LN, LPIII, EVI, Chow’s, Stochastic and Weibull’s plotting position formula) were fitted to those data. Comparison of the results were made based upon probability plot correlation coefficient, normalized root mean square deviation and Nash-Sutcliffe fitting coefficient. The results showed that the TSPT technique was the best fit followed by Weibull’s Plotting Position formula, Chow’s, LPIII, EVI and Stochastic methods, in descending order of performance. Therefore, the TSPT method can be used for flood frequency analysis in Ethiopia. Moreover, flood frequency analysis was carried out based on the PyTOPKAPI modelled daily stream flows from the two case study catchments. The results were then compared with those of the traditional ones. It was found that simulation-based flood frequency analysis showed very good agreement with those from the traditional methods for both the case study catchments. It was thus concluded that PyTOPKAPI model-based flood frequency analysis could also be one of the appropriate methods of flood frequency analysis and peak flood estimation for Ethiopian rivers.
An investigation into the reduction of greenhouse gases associated with the disposal of municipal solid waste for the development of an institutional framework in developing countries.
(2015) Kelly, Thavamoney Murugan.; Trois, Cristina.
Municipal solid waste in landfills releases the greenhouse gas (GHG), methane. This study aimed to develop an institutional framework that could assist municipalities in developing countries to adopt an integrated waste management strategy to maximise the reduction of GHG emissions using appropriate technologies. The results of key informant interviews and a systematic literature review informed the selection of the case studies. The case studies involved a waste stream analysis in two developing countries in order to determine the level of the waste diverted from landfills and the most appropriate treatment technologies. These included a waste stream analysis of the Deonar landfill site in the Municipal Corporation of Greater Mumbai (MCGM) which receives waste volumes of 6 800 tonnes per day and the Newcastle landfill site, a medium-sized landfill in South Africa. The findings of the case study in Newcastle Municipality provide the basis for recommendations to municipal managers on potential alternatives processes for polyethylene terephthalate (PET) diverted from municipal solid waste. It focuses on the importance of Separation at Source including the effect of zero PET into landfills and their contribution to GHG reductions for the production of hollow woven fibre. Finally, an integrated waste management system is presented which sets out an institutional framework that illustrates the interrelationship between waste and energy, best practices and bottlenecks to guide municipalities in their efforts to utilize appropriate technologies. South Africa is challenged to find sustainable solutions that are aligned with government objectives in identifying appropriate technologies for prevailing waste streams. The institutional framework is based on the planning process, risks and learning curves associated with the uncertainty of landfill gas to energy technologies. The reduction of GHG emissions in municipal solid waste is of concern due to the pressure of non-renewable energy. GHG emitted due to waste management in developing countries’ cities creates problems in accounting and reporting these gases. Reducing the volumes of waste landfilled will also reduce methane emissions and other environmental impacts associated with landfills that will in turn contribute positively to climate impacts and the national carbon footprint.
On weather and waves : applications to coastal engineering.
(2015) Pringle, Justin James.; Stretch, Derek Dewey.
Shoreline erosion in response to extreme wave events can be severe. The reduction in beach width leaves development within the hinterland exposed and vulnerable to future wave attack. Wave climates are a fundamental driver of coastal erosion and changes to wave height, direction and period can severely impact a coastline. These changes are directly linked to changes within the principle drivers of wave climates namely synoptic scale atmospheric circulation. The links are complex and if they can be clarified they can be used to provide insight into wave climates and improve the evaluation of future climate scenarios. The coupling between atmospheric circulation and wave climates provides a tool for risk assessment that is strongly based on fundamental physical processes. This study is focused on exploring this relationship and its effect on coastal vulnerability. A statistical classification algorithm is utilized to explore the relationship between synoptic scale circulation patterns and regional wave climates. The algorithm is fully automated and discrete atmospheric patterns are derived through an optimization procedure. It is driven to an optimal solution through statistical links between regional wave climates and atmospheric circulation patterns (CPs). The classification is based on the concept of fuzzy sets and differs from standard classification techniques. It employs a "bottom–up" approach as the classes (or CPs) are derived through a procedure that is guided by the wave climate. In contrast existing classification techniques first explore the atmospheric pressure space while links to the variable of interest are only made post classification. The east coast of South Africa was used as a case study. Wave data off the Durban coastline were utilized to evaluate the drivers of the wave climate. A few dominant patterns are shown to drive extreme wave events. Their persistence and strong high– low coupling drive winds toward the coastline and result in extreme wave events. The sensitivity of the algorithm to key input parameters such as the number of CP classes and temporal resolution of the data was evaluated. The Shannon entropy is introduced to measure the performance of the algorithm. This method benefits from incorporating the link between atmospheric CPs and the wave climate. A new stochastic wave simulation technique was developed that is fundamentally based on the CPs. This technique improves the realism of stochastic models while retaining their simplicity and parsimony relative to process-based models. The simplicity of the technique provides the framework to evaluate coastal vulnerability at site specific locations. Furthermore the technique was extended to evaluate changes in wave behaviour due to climate change effects.
The use of commercial garden refuse at different degrees of maturity as an organic carbon source to bio-denitrify treated MSW landfill leachate.
(2014) Plüg, Björn.; Trois, Cristina.
Abstract available in PDF file.
Maximising the photobiological production of hydrogen using leachate, while monitoring algal photosynthesis using pam fluorometry.
(2014) White, Sarah Anne.; Trois, Cristina.; Anandraj, Akash.
Hydrogen is universally known as the most efficient renewable energy source capable of meeting global energy demands. Chlamydomonas reinhardtii has the ability to produce biohydrogen during the metabolic engineering of the photosynthetic pathways. The aim of this study was to 1) use leachate as a feedstock to enhance microalgal biomass and subsequent hydrogen production, 2) use Pulse Amplitude Modulated (PAM) Fluorometry to elucidate the role of photosystem one during hydrogen production, 3) use Nicotinamide Adenine Dinucleotide Phosphate (NADPH) fluorescence as an indicator of hydrogen production and 4) design a modular pilot scale biohydrogen bioprocessing system implementing experimental findings into a conceptual model. This resulted in a cost effective source of renewable hydrogen produced from waste. The use of 16% landfill leachate was found to increase biomass production by 26% as compared to using Tris- Acetate Phosphate (TAP) media alone. Hydrogen induction resulted in an increased gas synthesis of 37% as well as an increased production period of 8 days compared to the normal 5 days. Landfill leachate further reduced the costs as it acted as a free nutrient source with the added ecological advantage of leachate treatment. Hydrogen production was induced by sulphur depletion and physiological parameters were measured using PAM Fluorometry. Photosystem I was found to be dominant during hydrogen production while photosystem II was down-regulated due to the sulphur depletion and damaged D1 proteins. NADPH fluorescence was significantly correlated to hydrogen yields allowing for NADPH to be utilised as a molecular indicator for hydrogen synthesis. The overall functionality of this bioprocessing system relies on the optimum physiological functioning of cells. The above findings were implemented into a pilot scale design, maximising the physiological performance during hydrogen production. This study has contributed knowledge regarding the production of hydrogen gas from leachate, the physiological changes of photosystem I during hydrogen production and the use of NADPH fluorescence as an indicator. The fundamental theories of bioprocessing incorporate a firm understanding of cellular and biochemical processes. The use of molecular indicators determined from physiological studies can be used at pilot scale to improve overall efficiency of hydrogen production.
Effective HIV and AIDS management : a South African construction sector model.
(2013) Harinarain, Nishani.; Haupt, Theodore Conrad.
Abstract available on PDF file.
An investigation into the emissions of greenhouse gases associated with the disposal of solid waste in the eThekwini Municipality.
(2013) Friedrich, Elena.; Trois, Cristina.
The amount of greenhouse gases (GHG) emitted due to waste management in the cities of developing countries is predicted to rise considerably in the near future; however, these countries have a series of problems in accounting and reporting these gases. This study investigated GHG emissions from the municipal waste sector in South Africa. In particular, the eThekwini Municipality is researched in detail and current emissions as well as further projections have been calculated. This research has to be placed in the wider context where developing countries (including South Africa) do not have binding emission reduction targets, but many of them publish different greenhouse gas emissions data which have been accounted and reported in different ways. Results from the first stages of this research showed that for South Africa, inventories at national and municipal level are the most important tools in the process of accounting and reporting greenhouse gases from waste. However, discrepancies in the methodology used are a concern. This is a challenging issue for developing countries, especially African ones, since higher accuracy methods are more data intensive. Therefore, the development of local emission factors for the different waste management processes is important as it encourages a common, unified approach. In the accounting of GHG from waste at municipal level, emission factors, based on a life cycle approach, are used with increased frequency. However, these factors have been calculated for many developed countries of the Northern Hemisphere and are generally lacking for developing countries. The second part of this research showed how such factors have been developed for waste processes used in this country. For the collection and transport of municipal waste in South Africa, the average diesel consumption is around 5 dm3 (litres) per tonne of wet waste and the associated GHG emissions are about 15 kg CO2 equivalents (CO2 e). Depending on the type of landfill, the GHG emissions from the landfilling of waste have been calculated to range from -145 to 1 016 kg CO2 e per tonne of wet waste, when taking into account carbon storage, and from 441 to 2 532 kg CO2 e per tonne of wet waste, when carbon storage is left out. The highest emission factor per unit of wet waste is for landfill sites without landfill gas collection and these are the dominant waste disposal facilities in South Africa. The emission factors developed for the different recyclables in the country showed savings varying from -290 kg CO2 e (glass) to – 19 111 kg CO2 e (metals - Al) per tonne of recyclable. They also illustrated that there is variability, with energy intensive materials like metals having higher GHG savings in South Africa as compared to other countries. This study also showed that composting of garden waste is a net GHG emitter, releasing 172 and 186 kg CO2 e per tonne of wet garden waste for aerated dome composting and turned windrow composting, respectively. By using the emission factors developed, the GHG emissions from municipal waste in the eThekwini Municipality were calculated and showed that for the year 2012 net savings of -161 780 tonnes CO2 e were achieved. This is mainly due to the landfill gas to electricity clean development mechanism (CDM) projects and due to recycling in the municipality. In the absence of landfill gas (LFG) collection and utilisation systems, which is typical for the majority of South African landfills, important GHG emission from the anaerobic degradation of waste are recorded. In the near future (year 2014) the closure of one of the three local landfill sites and the re-directioning of the majority of waste to another landfill sites which does not have LFG collection and utilisation, will cause an increase of GHG emissions to 294 670 tonnes CO2 e. An increase in recycling and the introduction of anaerobic digestion and composting has the potential to reduce these emissions as shown for the year 2020. However, only the introduction of a LFG to electricity system will result in the highest possible overall GHG savings from waste management in the municipality. In the absence of the Clean Development Mechanism and the associated financial arrangements, these systems have to be financed locally and might present a financial challenge to the municipality. Therefore, the second intervention which will make a difference by lowering GHG emissions from waste management would be to increase recycling in general and in particular the recycling of paper and metals. Since there is no direct competition for carbon, in addition to recycling, anaerobic digestion can be introduced and this combination will achieve increased savings in the future. If anaerobic digestion is not possible, composting in addition to recycling will also lead to savings, albeit not as high as with anaerobic digestion. The results presented in this study show that life cycle based GHG emission factors for waste and their use can support a unified approach to accounting of GHG and better decision-making for municipalities in the local context. They can give valuable input for the planning and development of future waste management strategies and they can help optimise current municipal solid waste management.

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