1,720,968 research outputs found
Optimization of anaerobic co-digestion of sewage sludge using bio-chemical substrates
Full text linkSubmitted in fulfillment of the requirements of the degree of Masters of Engineering: Chemical Engineering, Durban University of Technology, Durban, South Africa, 2018.The anaerobic process is increasingly becoming a subject for many as it reduces greenhouse gas emissions and recovers carbon dioxide energy as methane. Even though these benefits are attainable, proper control and design of the process variables has to be done in order to optimize the system productivity and improve stability. The aim of this research was to optimize methane and biogas yields on the anaerobic co-digestion of sewage sludge using bio-chemical substrates as co-substrates. The first objective was to find the bio-chemical substrate that will generate the highest biogas and methane yields. The anaerobic digestion of these substrates was operated
using 6 L digesters at 37.5℃. The substrate which generated the highest biogas and methane
yield in the first batch experiment was then used for the second batch test. The objective was to
optimize the anaerobic conditions (substrate to inoculum ratio, co-substrate concentration and temperature) in-order to optimize the biogas and methane yields. The second batch test was achieved using the conventional One-Factor-At-A-Time (OFAT) and the Design of Experiment (DOE) methods.
Final analysis showed that the bio-chemical substrates could be substrates of interest to biogas generators. Amongst the substrates tested in the first batch experiment glycerol (Oleo-Chemical Product waste) generated the highest methane and biogas yields of 0.71 and 0.93 L. (g volatile solids added)-1, respectively. It was believed that glycerol contains significant amount of other organic substances such as lipids that have higher energy content than the other bio-chemical substrates, thus generating larger biogas and methane yields. Moreover, digestion of sewage sludge alone produced biogas yields of 0.19 L /g VS and 0.33 L/g COD, and methane yields of
0.16 L/g VS and 0.28 L/g COD. Generally, co-digestion yields were higher than digestion yields of sewage alone.
Using the OFAT method the results of the second batch test on glycerol demonstrated highest amounts of volatile solids (VS) reduction, chemical oxygen demand (COD) reduction, biogas yield and methane yield of 99.7%, 100%, 0.94 L (g VS added)-1 and 0.75 L (g VS added)-1 at a
temperature, substrate to inoculum ratio and glycerol volume of 50℃, 1 (on VS basis) and 10
mL, respectively. Above 22 mL and substrate to inoculum ratio of 1, the process was inhibited.
The DOE results suggested that the highest methane and biogas yields were 0.75 and 0.94 L (g VS added)-1, respectively. These results were similar to the OFAT results, thus the DOE software may be used to define the biogas and methane yields equations for glycerol.
In conclusion, anaerobic co-digestion of bio-chemical substrates as co-substrates on sewage sludge was successfully applied to optimize methane and biogas yields.
Microalgae growth in industrial wastewater for the production of hydrocarbons
No full textSubmitted in fulfilment of the requirements for the degree of Doctor of Engineering: Chemical Engineering, Durban University of Technology, Durban, South Africa, 2025.Microalgae have demonstrated unique abilities to photosynthesise the conversion of
biodegradable organic materials and inorganic carbon to value-added biomass because
dissolved nitrogen and reactive phosphate are present in the cultivation medium. The
absence of a breakthrough in biomass production that would enable it to meet and
exceed the existing fossil energy demand has elicited research into technologies and
protocols that would yield competing energy output. The financial and energy
implications associated with the technology employed for biomass harvesting would
significantly contribute to the overall cost of the process. Would the microalgae strains
that exhibit high growth rates and lipid content, as well as accommodate culture
conditions, enhance biomass and lipid productivity?
The goal of this study was to provide microalgae with nutrients from industrial
wastewater while also producing hydrocarbon compounds that could have positive
social effects. A tailored airlift-raceway photobioreactor was utilised to grow
microalgae in industrial wastewater after the wastewater was characterised and the
optimum conditions for microalgae development were investigated. The resulting
production of hydrocarbon derivatives was optimised. Wastewater from the sugar
refinery, brewing industry, and dairy industry was characterised by its physical,
chemical, mineral, and biological properties using conventional methods. The different
industrial wastewater sources were tested for microalgal growth rate and biomass
output. The generated biomass was assayed for carbohydrates, lipids, and protein
contents of the microalgae strains, and the wastewater that gave the highest biomass
and lipid yields was used for advanced cultivation techniques. After careful
consideration, the brewery wastewater was found to be the most effective wastewater
for microalgae growth and was thus selected for this investigation. Using a novel
airlift-raceway photobioreactor system, Scenedesmus sp. biomass was produced in
brewery wastewater using optimised conditions. Also, the biomass of a microalgae
consortium, native to Durban, South Africa, was produced, leading to hydrocarbons
and hydrocarbon derivatives using nutrient-enriched brewery wastewater.
This study investigated these capabilities to sequester heavy metals and other
pollutants from brewery wastewater and sparged carbon dioxide gas. The light was
sourced from 40 W fluorescent tubes, which were powered by a 210 V supply and used
at different electromagnetic frequencies ranging from red to blue in a novel airlift raceway system for microalgae cultivation. The microalgal biomass, which was
harvested by filtration, was freeze-dried and the surface morphology was analysed
using the scanning electron microscope (SEM). The microalgal lipid was extracted
with a hexane-methanol solvent system by the soxhlet technique. The morphology of
the extracted biomass was analysed using SEM, and the composition of the microalgae
oil was analysed using gas chromatography-mass spectrometry (GC-MS).
Investigations revealed that the sugar wastewater (SWW) used did not support
microalgal growth. However, dairy wastewater (DWW) only supported microalgal
growth to some extent, while brewery wastewater (BWW) was best suited for the
growth of Scenedesmus sp. and the microalgae consortium. The BWW was nutrients
enriched through the oxidation pond, thus raising the influent NO3
-
-N (4.98±0.13
mg/L), PO4
3-
(13.34±0.48), BOD (35±19), and COD (3979±3) to NO3
-
-N
(15.98±0.91), PO4
3-
(39.93±1.83), BOD (279±10), and COD (5855±4), respectively.
GC-MS analysis of the oil extract of the microalgae biomass showed the presence of
saturated, monounsaturated (MU), and polyunsaturated (PU) fatty acids in both
Scenedesmus sp. and the microalgae consortium, and the presence of an isolated C4
iv
and C8-C38 hydrocarbons and hydrocarbon derivatives, mostly fatty acid esters, in the
microalgal oils.
Nutrient enrichment of the brewery wastewater enables microalgal growth sustainably,
thus encouraging lipid accumulation. Using the novel airlift-raceway photobioreactor
in this study changed the mass transfer dynamics due to the enhanced hydrodynamics
of the novel reactor. Because of this, it was simpler for light and nutrients to reach
every area equitably, which is what propels the formation of biomass. The dominance
of fatty acid esters in the microalgal oil demonstrates that the protocols adopted in this
study can serve to save on the cost of the transesterification step in the production of
biodiesel and other useful bio-products. This serves as a major contribution to the body
of knowledge on this subject.
Techno-economic analysis and life cycle assessment for production of biofuels from spent coffee grounds
No full textSubmitted in fulfillment of the requirements for the degree of Master of Engineering: Chemical Engineering, Durban University of Technology, Durban, South Africa, 2023.Spent Coffee Grounds (SCGs) are one of the most abundant agro-industrial residues generated from the coffee brewing industry and coffee espresso machines in restaurants, cafeterias, cafes and homes. It is believed that for every ton of coffee beans processed, 650 kg of SCG is left as solid residues. Coffee being the second traded commodity after petroleum, means that a lot of SCGs are generated annually and end up into landfills. Efforts are being made to turn this valuable waste into biofuels, however, most of these efforts end up at laboratory benches and few studies have focused on industrial scale production of biofuels from SCG. Six biomass-to-energy conversion technologies were compared from technical, economic and environmental perspectives: Fast pyrolysis, Hydrothermal Liquefaction (HTL), gasification, Anaerobic Digestion (AD), fermentation and biodiesel production. The processing technologies were selected because they are the most researched biomass-to-fuel conversion routes. Each of the processing routes was simulated in Aspen plus V11 using input data from literature. The mass and energy balances obtained from simulations were used to conduct Techno-Economic Analyses (TEAs) and Life Cycle Assessments (LCAs). TEA was conducted with help of Aspen Process Economic Analyzer (APEA) and Microsoft Excel spreadsheets whereas OpenLCA V1.11.0 software was employed for LCA. After the processing routes were successfully simulated, APEA was used to estimate the installed Cost of all Equipment (COE). The Capital Expenditure (CAPEX) required to build the biorefineries was then estimated basing on COE for each biorefinery. Then the Operating Expenses (OPEX) required for running the day-to-day operations of the plant were estimated as the sum of Variable Operating Expenses (VOC) and Fixed Operating Expenses (FOC). The revenues from the sales of finished products were estimated and used to calculate the gross profit. For the plant life of 25 years; using straight-line depreciation of 10% per year, discount rate of 12% and tax rate of 28%, the Discounted Cash Flow Analysis (DCFA) was used to calculate the economic indicators i.e. the Net Present Value (NPV), Profitability Index (PI), Internal Rate of Return (IRR) and Discounted Payback Period (DPBP). For LCA, the methodology outlined by the ISO 14040/44 framework was used. The method outlines four steps followed to conduct LCA i.e. goal and cope definition, Life Cycle Inventory (LCI), Life Cycle Impact Assessment (LCIA) and interpretation of results. The goal of this study was to identify the processing route with least environmental impacts and the cradle-to-gate system boundary was selected. LCI was conducted using the mass and energy balances obtained from Aspen plus simulation and the flows present in the Agribalyse Version 3 database, downloaded from OpenLCA nexus. LCIA was conducted using the ReCiPe 2016 Midpoint (H) and was also downloaded from OpenLCA nexus. Eight impact categories namely, global warming, fossil resource scarcity, particulate matter formation, terrestrial acidification, freshwater eutrophication, marine eutrophication, mineral resource scarcity and water consumption were selected. The results were analysed to identify the conversion route with less environmental effects. Results from the economic analysis showed that fast pyrolysis was the most economically profitable processing route with a NPV, PI, DPBP and IRR of 6.3 million USD, 1.85, 5.4 years and 37%, respectively. In the second position was biogas production with a NPV, PI, DPBP and IRR of 3.4 million USD, 1.65, 5.7 years and 34%, respectively. Gasification was in the third position with a NPV, PI, DPBP and IRR of 5.4 million USD, 1.48, 6.0 years and 32%, respectively. In the fourth position was biodiesel production with a NPV, PI, DPBP and IRR of 3.9 million USD, 0.86, 8.0 years and 24%, respectively. HTL was in the fifth position with a NPV, PI, DPBP and IRR of 0.68 million USD, 0.29, 13.0 years and 16%, respectively. Bioethanol production was not economically profitable as the revenues generated from sales of finished products were smaller than the operating expenses, thus no profit could be generated. Results from environmental impact assessment showed that fast pyrolysis was the most environmentally friendly processing route, followed by biogas production, biodiesel production, gasification, and bioethanol production, whereas HTL had the highest environmental impacts. Electricity consumption was the biggest contributor to the environmental impacts, making HTL, which was the highest electricity consuming processing route, to be the worst environmentally. However, biogas production was the least electricity consuming processing route but not the best environmentally due to large production of carbon dioxide and methane (biogas) from anaerobic digestion. The large production of carbon dioxide can be mitigated through using it to grow algae or in supercritical carbon dioxide extraction of lipids. However, the cost associated with additional unit processes can escalate the biogas production costs. These greenhouse gases were the biggest contributors of global warming, pushing biogas production to the second position after pyrolysis.Fast pyrolysis was proposed to be the best environmentally and economically feasible processing route for the production of biofuels from SCG.
Extraction of caffeine from spent coffee grounds using ionic liquids
No full textSubmitted in fulfillment of the requirements for the degree of Master of Engineering: Chemical Engineering, Durban University of Technology, Durban, South Africa, 2022.Coffee is the most popular beverage consumed and the second-highest commodity in the
world, after crude oil. In 2018, a total of 9,5 million metric tons of coffee were produced
globally. This in turn generated 6 million tons of waste coffee grounds. In South Africa alone,
it is estimated that approximately 100 million cups of coffee are brewed a year, resulting in
3000 tonnes of waste produced, of which 93% ends up in landfill sites (Lombard, 2021). This
abundant waste source has shown promising potential for reusing, recycling, or converting
the waste into valuable products like biofuels, fertilizers, animal feed, high-value chemicals,
cosmetics and pharmaceutical products such as caffeine for medicinal purposes. Besides
coffee being one of the most important agricultural commodities in the world, coffee is also
one of the most valuable primary products in world trade. Coffee is also the central and
popular activity of many cultures. The most popular reason for the consumption of coffee is
its refreshing properties. Large quantities of this waste pose threats to the environment as it is
a source of severe contamination and serious health problems. To avoid this catastrophe of
the coffee waste, spent coffee grounds can be utilised to generate valuable products. The
long-term usage of fossil fuels depletes the finite supply and contributes to greenhouse gas
(GHG) and exhaust emissions. The global economic and environmental crisis related to the
usage of fossil fuels and the fast depletion of natural resources has raised much awareness
and need to find alternate strategies for cleaner and greener energy and chemical products
needed for recycling waste has risen drastically. The use of biomass and other lignocellulosic
material to produce bio-fuels and other high value products show promising results. Using
lignocellulosic material has attracted considerable amounts of attention due its renewable
nature and being abundantly available. Lignocellulosic material is used for sustainable
development in the world. In this study caffeine extraction is a promising solution for
sustainable development, where biomass is valorised. The characterisation of spent coffee
grounds (SCGs) using Technical Association of the Pulp and Paper (TAPPI) methods was
carried out. The effect of temperature, reaction time and solid-to-liquid loading ratio on the
yield of caffeine extracted from spent coffee grounds was investigated. Simultaneously, the
best extraction solvent between the (i) ionic liquid (IL) 1-ethyl-3-methylimidazodium
chloride (98%), (ii) dichloromethane and (iii) water was determined. Variation of the
parameters were established using the Box-Behnken design of experiment (DOE)
methodology which varied the (i) temperature (88-120 degrees Celsius), (ii) reaction time
(15-35 minutes) and (iii) solid-to-liquid loading ratio (20 g/10-25 mL). For the extraction process, both the conventional method and green method (IL and water) were investigated.
The conventional method includes using dichloromethane as the extraction solvent, whereas
the green method makes use of the ionic liquid 1-ethyl-3-methylimidazolim chloride and
water as the extraction solvents. Extraction was carried out in a Parr pressure reactor where
solid-liquid extraction occurs. High performance liquid chromatography (HPLC) was used to
quantify the yield of extracted caffeine. Recrystallization of the highest caffeine yield was
carried out and thereafter analysed using Scanning Electron Microscopy (SEM), Transition
Electron Microscopy (TEM), Energy Dispersive Spectroscopy (EDS) and Differential
Scanning Calorimetry (DSC). The maximum yield of caffeine was obtained at the optimum
conditions of 120 °C for 25 minutes using 25 mL volume of extracting solvent. The caffeine
extracted from 1-ethyl-3-methylimidazolium, water and dichloromethane was 726.22mg/L,
646.33mg/L and 566.12mg/L respectively. Alternatively stated as 1-ethyl-3-
methylimidazolium chloride, water and dichloromethane extracted 0.00363 g caffeine / 1 g
SCG, 0.00323 g caffeine / 1 g SCG and 0.00283 g caffeine / 1 g SCG respectively.
SEM images of the spent coffee grounds prior to extraction displayed a dense morphological
chain-like structure, with large lumps present. The structure was tightly bonded together and
appeared rough. After extraction using each solvent, the SEM micrographs were analysed.
Extractions done with the IL demonstrated full degradation. The structure was loose, multiple
open pores on the surface with a smooth and thin appearance. The water extractions appeared
almost same to that of the IL, but slightly thicker. Lastly, extractions using DCM appeared to
be unsuccessful as the SCG attempted to be broken but were still together. The surface had
no open pores, rather an oil coated layer covering the spent coffee grounds.
EDS results from 99% pure caffeine standard was compared against the caffeine extracted by
all three extraction solvents. Pure caffeine appeared clean, properly formed, big separate
particles and distinctive shapes. The caffeine extracted using IL was similar to the structure,
crystallinity and appearance of the pure caffeine. Caffeine extracted by water were in long
shards, but not fully individual/separated. The caffeine extracted by DCM appeared less
crystalline, much smaller in size and more compact. DSC compared the melting points of the
pure caffeine standard to those caffeine samples extracted by different solvents, thus
providing the purity of the extracted caffeine. The standard caffeine sample had a melting
point of 233. 55 ºC equalling 99 % pure. The melting points of 226. 52 ºC; 212. 28 ºC and
200 ºC were obtained for IL, water and DCM respectively. Purity obtained were 96 %, 90 %
and 85 % per respective extraction solvent.
Techno-economic assessment of algae conversion to biofuels.
Full text linkMasters Degree. University of KwaZulu-Natal, Durban.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
Neural network models for leukaemia.
No full textThesis (Ph.D.)-University of KwaZulu-Natal, Durban, 2009.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
Feasibility of implementing the balanced scorecard in a higher education institution : a case study of the Faculty of Engineering at Durban University of Technology.
Full text linkMBA University of KwaZulu-Natal, Durban 2013.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
Minimisation of waste via the valorisation of spent coffee grounds into high-value products
No full textSpent coffee grounds (SCG) valorisation can produce high-value products to supply cosmetics, petroleum and pharmaceutical industries among others. An overview of the various products achievable from spent coffee grounds valorisation are established, while the effect of temperature, reaction time and solid-to-liquid loading ratio on the yield of caffeine extracted from SCG was investigated. The best extraction solvent between (i) dichloromethane, (ii) 1-ethyl-3-methylimidazolium chloride (IL) and (iii) water was established. Characterisation of SCG using Technical Association of the Pulp and Paper (TAPPI) methods was carried out. Variations of parameters were established using the Box-Behnken design of experiment (DOE) which varied the investigated parameters; (i) temperature (88 − 120 °C), (ii) reaction time (15 - 35 min) and (iii) solid-to-liquid loading ratio (5 g SCG per 10 -25 mL). The conventional extraction method used dichloromethane as the extraction solvent, whereas the green method used the ionic liquid and water in a Parr pressure reactor. High performance liquid chromatography (HPLC) quantified the yield of extracted caffeine. Recrystallised caffeine is analysed using scanning electron microscopy (SEM), transition electron microscopy (TEM) and energy dispersive spectroscopy (EDS) for its structural properties, crystalline structure and physical analysis, while differential scanning calorimetry (DSC) established the purity of extracted caffeine achieved from each extraction solvent. The expected yield of caffeine is between 4.67 and 8.0 mg/g SCG. According to this experimental methodology, at 120 °C, 25 min reaction time and 25 mL solvent volume the extraction yield ranged from 2.83 to 3.67 mg/g S
Degree accreditation report auto-generation by logic encoding and processing
No full textMaintaining the accreditation profile of an academic programme is a key activity in so-called
professional degrees such engineering, commerce and law. The complexity of the accrediting criteria
tends to rise over time as accrediting bodies require quantitative evidence of competence of increasingly
specific graduate attributes. Evaluation of graduate attributes may therefore require complex logic
processing which challenges the human capacity. This has the negative side effect of discouraging
curriculum revision not for pedagogic reasons but simply due to the complexity of evaluating complex
logic patterns against a data set whose structure is shifting. These challenges can be overcome through
the application of logic encoding and processing. A computing system is better suited to such processing
tasks since logic processing is fundamental and well-established to such systems. On the other hand,
the efficient representation of a complex accreditation logic rule base then becomes the challenge. This
paper describes the representation of the accreditation logic of eight engineering academic programmes
at the Durban University of Technology through the AutoScholar Advisor System in preparation for
evaluation by the Engineering Council of South Africa. It is shown that the system generates accurate
reports even with deeply nested logic structures and with changes in curriculum over time
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