Browsing by Author "Chetty, Manimagalay."

Now showing 1 - 3 of 3

Feasibility of implementing the balanced scorecard in a higher education institution : a case study of the Faculty of Engineering at Durban University of Technology.
(2013) Chetty, Manimagalay.; Kader, Abdulla Dawood.
Higher education institutions are being challenged to reform and restructure to offer top quality education, while at the same time produce highly skilled graduates for the workforce. In order to be competitive and sustainable Higher Education Institutions (HEIs) have to make changes in their management, operations, recruitment of students and staff, curriculum offerings and in all areas of the organisation. The aim of this research was to investigate the current management performance systems in the Faculty of Engineering at Durban University of Technology (DUT) and propose a framework for a performance management system based on the balanced scorecard. The readiness of the institution for a performance management system, its culture fit with performance management systems and the link between individual and organisation performance was also surveyed. It has been noted from the surveys that individual performance impacts on the organisational performance. The institution has procedures, policies and measures in place for quality of the academic programs, research outputs and student success rates. The integrated electronic database systems can ensure updating and reporting of performance indicators. Performance indicators can be linked to the financial, student, internal processes and organisational learning perspective. The program quality, student success rates and research outputs from individuals in academic departments do impact on the organisational performance. Outputs from individuals are collated in the department into faculty and institutional data which is then used for the Department of Higher Education and Training (DOHET) institution subsidy. Even though there are numerous reports generated at various sub levels in the areas of management, facilities, research, teaching and external links, this information still exists in a dispersed format. The establishment of a performance measurement tool like the balanced scorecard would not only serve as a single source of data and information on the institution’s progress but would also highlight that DUTs objectives have been met. The balanced scorecard framework will allow for a central location of data, provide specific information on research and student success rates and track expenditure while linking individual goals to organisational goals. It can also be used to predict long term sustainability of the university. The outcome of this research will benefit students, the community, employers, academic and support staff of the university. The adoption of the balanced scorecard will favour effectiveness and efficiency within all sectors of the institution.
Neural network models for leukaemia.
(2009) Chetty, Manimagalay.; Carsky, Milan.; Kormuth, Emil.
Artificial neural networks (ANN) can detect complex non-linear relationships between independent and dependent variables. Properly trained ANNs have repeatedly demonstrated superior predictive accuracy to other predictive technologies when applied to non-linear systems. Currently there are no studies that have been carried out on predicting survival of leukaemia patients at all. The neural network prediction method adopted in this study aims to provide a robust and accurate method for predicting survival of leukaemia patients for both censored and uncensored patient data. The aim of this research was also to find out the effectiveness of neural networks in modelling leukaemia prognosis and to determine the factors that have the most influence. There is ongoing research into finding ways and means of extending the life span of diseased patients. There is great interest in identifying factors that will yield better predictions of survival for terminally ill leukaemia patients. Prognostic factors generally differ with the treatment of leukaemia. Clinicians face the problem of how to choose the appropriate treatment regime, therefore an analysis of prognostic factors that predict success or failure may identify patients who require an alternative approach of specialist or targeted treatment. Being able to predict an individual patient’s prognosis will enable clinicians to categorise them into the relevant high and low risk treatment groups for conventional treatment or allow for the patients to be incorporated into specialised treatment schedules and clinical trials if available. In this study there is believed to be relationship that exists between the results gained on diagnosis and the period of survival. A patient’s health status is dependent on various symptoms and the complexity of the medical condition is dependent on an individual’s biological system. This complexity allows for the application of artificial neural networks (ANN) in predicting outcomes in medical application, especially in prognosis prediction and survival rate. This thesis contains contributions to the development of neural network models for survival analysis of leukaemia patients. The feed forward back propagation algorithm (BPA) modified to the gradient descent BPA was identified for the training and building of the neural network for predicting survival of leukaemia patients. The prognostic factors that affect survival have also been determined by the neural networks. The comparisons of models were based on using combined groups of leukaemia patients and comparing them with individual groups of the sub-types of leukaemia, i.e. acute lymphoid leukaemia (ALL), acute myeloid leukaemia (AML), chronic myeloid leukaemia (CLL) and chronic myeloid leukaemia (CML). A combination of 38 variables was used in the development of the neural networks. The variables were age, race, sex, gender, and results of full blood counts, differential tests and flow cytometry. The survival period of patients was based on the diagnosis date and the date of treatment. Those patients who status of mortality was known as of October 2008 were considered to be uncensored and were used for the 2-year and 3-year case studies. The patients with unknown mortality were considered as censored patients and used for the censored case study. The patient data was processed into a coded system and used to build the neural networks for each data set. The choice of patient groups used for the model building was prompted by the availability of uncensored data for analysis. For the group of combined leukaemia patients and the sub-group CML-CLL, it is recommended that the 2-year neural network model be used. The main prognostic factors affecting leukaemia survival were found to be the patient’s age, the mean haemoglobin concentration, % neutrophils and the markers CD13, CD20 and CD56. The race group, platelet count, % monocytes and the markers CD3, CD4, CD34 and LC lambda were found to significantly affect the CML-CLL group of patients. For the ALL and AML groups the 3-year neural network models were favoured. Prognostic factors for the survival of ALL patients were their age, the mean corpuscular haemoglobin concentration, % blasts and the markers CD8 and CD22. For the AML group the important prognostic factors were the patient’s age, the mean corpuscular haemoglobin concentration, the % neutrophils, % lymphocytes, and the markers CD7 and CD34.
Techno-economic assessment of algae conversion to biofuels.
(2023) Duma, Ndumiso Sweet-man.; Mohammadi, Amir Hossein.; Chetty, Manimagalay.
One of the most promising biomasses for the production of biofuels is microalgae. This is because microalgae have a high growth rate and a highiCO2icapture ability when compared to other biomasses. Furthermore, biofuels produced from microalgae are deemed eco-friendly due to their low sulphur content, superior lubricating efficiency, and non-toxicity nature. As opposed to carbon-based fuels, biofuels are viable alternatives with the potential to meet the increasing demand for energy (Jafari & Zilouei, 2016). Because of its potential of being inexhaustible and a low-cost renewable energy carrier, biofuels research has increased (Akobi et al., 2016). This research investigated the thermochemical and biochemical conversions for producing algal biofuels on a technical, economic, and environmental basis. The primary feed considered was wet algal biomass with a 20 wt%. Each investigated process was simulated on Aspen Plus ® v12. The processing units considered for the thermochemical conversion on Aspen Plus were hydrothermal liquefaction (HTL) for depolymerization, hydrotreating for removing contaminants by using H2, and hydrocracking for removing contaminants by using a high-activity catalyst and H2. The primary processing units considered for the biochemical conversion simulation included pre-treatment where dilute sulphuric acid is fed, conditioning with the assistance of dilute ammonia, fermentation with the aid of S. cerevisiae, purification, and finally, anaerobicidigestion of the production of biogas. The process properties for the investigated conversion methods wereiobtainedithroughimass and energy balance calculations. The thermochemical conversion had a mass ratio of 0,39 and an energy efficiency of 47,45%. The biochemical conversion had a mass ratio of 0,98 and an energy efficiency of 73,11%. The processes were both optimized using the Aspen Energy Analyzer (AEA). The thermochemical simulation had a 23,56% energy savings and a 17,3% carbon emissions reduction. The base case simulation for the biochemical conversion had no design alternatives to improve the heat exchanger network (HEN). The fixed capital investment (FCI) for the thermochemical conversion was 18,3% lower than for the biochemical conversion. The internalirateiofireturn (IRR) for the thermochemical method was 27,36% and 29,61% for the biochemical conversion. The economic evaluation was completed using the discounted cash flow analysis. Both the thermochemical and biochemical processes were profitable. The thermochemical method had a discounted payback period of 7,2 years (break-even point at seven years five months) and seven years (break-even point at six years ten months) for the biochemical method. The environmental impacts of both processes were evaluated using OpenLCA. Typically, OpenLCA employs the cradle-to-gate approach. The assessment used the Agribalyse v3.0.1 database, and the LCIA method used was the ReCiPe 2016 midpoint (H) method and the CML-IA baseline method. The thermochemical method was the more sustainable method. The global warming impact was 42,25% less, the human toxicity was 41,46% less, and the freshwater aquatic ecotoxicity was 38,3% lower than the biochemical method. The investigation is summarized in the Table 0-1 below: Table 0-1 : Summary of the processes studied.