Theses and dissertations (Engineering and Built Environment)

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Application of lean tools in food manufacturing industries to improve productivity
(2025) Nkosi, Sibusiso Wiseman; Olanrewaju, Oludolapo Akanni; Moso, Matshidiso
Lean manufacturing principles and instruments have been widely applied in food manufacturing companies during the recent past. Already a successful management philosophy. Lean tools were applied in food production process of selected product in this research to identify and implement improvements. The focus is to sanitize the equipment and optimize the food production process with 5S and Value Stream Mapping and enhance overall food manufacturing efficiency and have ongoing productivity by eliminating common bottlenecks. With a focus on the product selected, this study will examine current practices, utilize cutting-edge techniques and provide recommendations for improved overall rate of production. The main tools used in this study were Value Stream Mapping (VSM) and 5S. VSM was used for mapping accessible current state of the manufacturing process, review inefficiencies and plan for the future state. Meanwhile, 5S was used to keep the working space in order, improve efficiency and discipline of operations. The tools were embraced because they have a proven history in Lean Manufacturing and continuous improvement. The process was measured before the enhancements were implemented to establish a baseline. The process was re-measured after VSM and 5S had been implemented, in order to identify the impact of the enhancements. Results through support of lean tools enhanced productivity by 6.7% and lead time by 5 minutes. Significant benefit was achieved through the application of Value Stream Mapping together with 5S to improve manufacturing industries. Significant improvement in efficiency, organization and overall productivity was unveiled from the outcomes. The findings indicate the need for formal project management and strategic presentation of lean tools in food manufacturing industry. Improvements in the manufacture process of the selected product is a testimony that 5S and VSM could drive process enhancement. The research provides practical suggestions to manufacturing practitioners who hope to implement the same changes on their own process. The contribution and novelty of this research is in applying and comprehensive examination of Value Stream Mapping (VSM) and 5S framework to prove their combined effect on increasing efficiency, organization and productivity of the production process of the selected product.
Fracture behaviour analysis of AA7075 aluminium alloy by finite element and boundary element methods
(2025) Oloo, Tryphone Obuya; Adeosun, Samson Oluropo; Olanrewaju, Oludolapo Akanni
Fracture mechanics is a field of study that focuses on the behaviour of materials containing cracks under stress, particularly for high-strength aluminium alloys like AA7075 series that are used in aerospace as well as automobile industries because of their high strengthto-weight ratio. This study focuses on fracture test experiments and the analysis of the behaviour of AA7075 under various loading conditions through finite element analysis (FEA) and boundary element techniques (BET) to determine life estimation, stress distribution, stress intensity factors (SIF), and energy release rates (ERR) during fracture. The FEA model is developed for a standard edge-cracked specimen of dimensions w= 50 mm, thickness 5 mm, and initial crack length, a= 10 mm. Under tensile loading of 10 kN, the stress intensity factor is computed as approximately 18.2 MPa√m, which agrees well with experimental observations. The corresponding energy release rate obtained from the simulation is 82.5 J/m², indicating a predominantly brittle fracture mode due to the alloy’s T6 temper. Using BET analysis, crack propagation trajectories are simulated, showing a stable crack growth phase until the critical crack length ac= 18.4 mm, beyond which unstable fracture occurs. The predicted fracture toughness for the alloy is found to be 25.6 MPa√m, consistent with literature values for AA7075-T6, ranging between 24 - 28 MPa√m. The simulated fatigue crack growth rate follows the Paris law with constants, C = 1.2 x 10-10 and m = 3.5, producing a fatigue crack growth rate between 1×10⁻⁶ and 5×10⁻⁴ mm/cycle for ΔK values from 10 to 20 MPa√m. The meshing and loading properties of Ansys 2024R2 are utilized for the boundary conditions, while the FEA inbuilt functions in MATLAB incorporate experimental data to predict behaviour under Mode I fracture. From the analysis, it is determined that stress intensity factors depend on both crack length and specimen geometry. The energy release rates and stress distribution at the crack tip are largely influenced by the applied stress, with peak von Mises stresses reaching 460 MPa near the crack tip region. These analysed data aid engineers in determining life estimation and crack propagation limits in AA7075 components. The study concludes that components made from AA7075 should maintain service stresses below 70% of the yield strength (≈350 MPa) to prevent premature crack propagation, improving fatigue life and reducing economic and safety risks.
Optimizing renewable energy-powered seawater desalination treatment process for zero-waste and improved productivity
(2026) Emmanuel, Ojo Olufisayo; Olanrewaju, Oludolapo Akanni
Desalination has become essential for supplying freshwater in arid regions, yet it presents a significant environmental challenge due to the generation of concentrated brine waste. For every liter of freshwater produced by a typical seawater reverse osmosis (SWRO) plant, approximately 1.5 liters of hypersaline brine are produced. The water recovery rate is typically around 25% – 40% for conventional SWRO, meaning over half of the intake water exits as brine. This brine, about twice as salty as seawater, often contains harmful treatment chemicals and heavy metals. Its discharge into marine environments significantly increases local salinity, lowers oxygen levels, and adversely affects marine ecosystems. This thesis aims to optimize renewable energy-powered SWRO processes to achieve zero brine waste and improve water production efficiency, thus aligning closely with several United Nations Sustainable Development Goals (SDGs). Specifically, it supports SDG 6 (Clean Water and Sanitation) by ensuring sustainable freshwater supply, SDG 7 (Affordable and Clean Energy) by utilizing renewable energy sources, SDG 13 (Climate Action) through greenhouse gas (GHG) emissions reduction, and SDG 12 (Responsible Consumption and Production) by converting brine waste into valuable products, promoting a circular economy. The research adopts a broad sustainability approach integrating renewable energy sources, zero-waste principles, brine resource recovery, life cycle environmental impact assessment, and Artificial Neural Network (ANN)-based process simulation. Renewable energy sources such as solar photovoltaics and wind turbines completely offset the high energy demands of RO, drastically reducing operational GHG emissions. Case studies, such as the solar-powered SWRO facility in Al Khafji, Saudi Arabia, and Australia’s wind-powered Perth Seawater Desalination Plant, demonstrate the feasibility and benefits of renewable-powered desalination at scale. Zero-waste brine management is addressed through innovative recovery methods, turning brine into valuable resources. The concentrated brine, rich in sodium, magnesium, calcium, potassium, and trace elements like lithium and bromine, is processed through methods like evaporation ponds, electrodialysis, and chemical precipitation. This process significantly reduces environmental pollution while creating additional economic benefits. A thorough Life Cycle Assessment (LCA) is employed to evaluate the environmental impacts of the desalination system systematically. Metrics such as carbon footprint, energy intensity, and marine ecotoxicity are analyzed, confirming that renewable integration and brine recovery significantly enhance sustainability compared to conventional methods. An advanced ANN model is developed using operational data from real-world plants, such as the Victoria & Alfred Waterfront Desalination Plant in Cape Town. This model predicts freshwater production rates, energy consumption, and brine composition based on varying operational parameters, facilitating precise optimization and enhanced productivity. Real-world data validate the ANN model’s predictive accuracy, enabling precise, proactive adjustments in operational strategies. The thesis further examines global case studies to reinforce its strategies and demonstrate practical feasibility. Renewable-powered desalination projects and brine utilization initiatives globally provide actionable insights, ensuring the optimized approach is grounded in practical applications and scalable solutions. In conclusion, this research delivers significant improvements in SWRO performance, achieving intensely higher water recovery rates of 45.7%, minimal environmental discharge, valuable by-product generation, reduced energy consumption, and significantly lower carbon footprints. By presenting a detailed and validated framework, this thesis supports global water security efforts by proposing a sustainable and practical desalination solution, transforming an environmental challenge into an opportunity for economic and ecological resilience.
Systematic evaluation of a full-scale textile wastewater treatment plant using the GPS-X and analytical measurements
(2026) Wondim, Tilik Tena; Dzwairo, Bloodless; Dagnachew, Aklog
Extensive quantities of water and chemicals are used in the textile industry processes. Therefore, the treatment of textile wastewater is vital to protect the environment, maintain the public health, and recover resources. However, due to inadequate quality data, inexperienced plant operators, and inconsistent measurements, a prediction on the effluent quality of a textile wastewater treatment plant is difficult. Thus, the aim of this study was to comprehensively evaluate the full scale textile wastewater treatment plant using the GPS-X software and analytical measurements to establish the operational strategies for the plant. It also aimed to develop the troubleshooting strategies in a Bahir Dar textile factory, Ethiopia. Based on the stated aim, the research had the following four specific objectives. The first specific objective of the research was to characterize the wastewater physicochemical properties and evaluate the performance of the wastewater treatment plant in the textile factory. In the inlet and outlet of the wastewater treatment plant (WWTP), samples were collected for six months and analyzed on-site and in a laboratory for parameters including dissolved oxygen, pH, temperature, total Kjeldhal nitrogen (TKN), chemical oxygen demand (COD), biochemical oxygen demand (BOD5), total suspended solids (TSS), total nitrogen (TN), total phosphorous (TP), nitrite, nitrate, and metallic compounds. These 22 parameters were classified into three functional groups: regulatory compliance (to assess legal discharge), nutrient dynamics (to monitor biological health), and model fractionation (to serve as specific mathematical inputs for the simulation). Statistical analyses were conducted using descriptive statistics and outlier analysis via SPSS to manage the stochastic nature of textile discharge; these methods conform to current environmental engineering trends, specifically identifying that effluent failures were operational rather than influent-driven. The results showed that the TSS, BOD5, COD, TP, nitrite, ammonia, and total chromium contents were above the discharge limit with the values of 73.2 mg/L, 48.45 mg/L, 144.08 mg/L, 7.9 mg/L, 1.36 mg/L, 1.96 mg/L, and 0.16 mg/L, respectively. The second objective was conducted to model, simulate, and optimize the operational process control parameters (DO setpoints, HRT, SRT, WAS, and RAS) under different scenarios. Scenario development is centered on a comparative analysis between the factory’s existing as-is physical layout and an optimized should-be digital model, with the primary criteria focused on aligning operations with scientifically accepted process flows through a highly regulated Conventional Activated Sludge (CAS) framework. By transitioning to this optimized state, the research establishes a performance benchmark designed to maximize pollutant removal efficiency while simultaneously achieving significant reductions in both energy consumption and overall operational costs. GPS-X was selected over similar software like BioWin or WEST due to its superior Carbon and Energy Footprinting modules and validated accuracy in full-scale industrial modelling. Two primary scenarios were developed based on a gap analysis approach: scenario I (Existing Layout), governed by the criterion of physical fidelity, and scenario II (Modified Process Flow), governed by scientific optimization. While limited to two structural layouts for comparative feasibility, true optimality was achieved within scenario II through thousands of digital iterations via dynamic sensitivity analysis on variables such as SRT and RAS. The model was calibrated using four-months’ measured data and validated and verified using two-months’ measured data. The results showed that the existing process model (scenario I) compared to the modified process model (scenario II) for TSS, COD, TP, and NO3, violated the compliance limit. However, the energy consumption and operation cost for scenario II were reduced by 52.9% and 56.98%, respectively. The third objective was conducted to evaluate the treatment plant performance using analytical measurement and GPS-X modelling software. While GPS-X supports dynamic simulation, this study utilized steady-state analysis to establish a robust operational baseline, as the lack of online sensors would have introduced significant synthetic noise into a dynamic model. The selection of steady-state modelling was a strategic decision necessitated by the absence of high-frequency historical data and the stochastic nature of textile batch-dyeing operations. In this regard, the pollutant removal efficiency results from analytical measurement and GPS-X model were 71% and 43%, respectively. The simulation results for scenario I showed that it was energy intensive, and indicated poor effluent quality, elevated operation costs, and high sludge production. However, scenario II was found to be the more efficient and a more effective treatment option compared to scenario I. The modelling-based performance evaluation technique was shown to be superior to the analytical measurements evaluation by identifying the parameter that violated the permissible limit, the duration of the violation, the mode of operation, and its location in the treatment plant. The fourth objective of this research was conducted to optimize key process control parameters to the observed operational challenges of existing processes, and to suggest an operational guide to the operators and decision makers to enhance the treatment performance in the GPS-X software. According to the formulated troubleshooting and decision support strategy, the optimization results of waste activated sludge in the primary and secondary clarifiers were within the range of 15 ± 5 m3/d and 83 ± 7 m3/d, respectively. In line with this, the recycled activated sludge flow was optimized to 150 ± 10 m3/d. The sludge retention time was found to be 5 ± 1d and 6.7 ± 0.5d in the secondary and primary clarifiers, respectively. In the GPS-X model, molasses addition represented as an increase in the readily biodegradable substrate fraction of the influent which contributed to save the mechanical energy for aeration by creating a layer of biochemical control instead.The addition of a carbon source, molasses resulted in a flow of 0.5 ± 0.05 m3/d, and the variation of influent was optimized to 600 ± 50 m3/d due to wastewater characteristics and rainfall. The optimum airflow into the aeration tank was 550 ± 5 m3/hr, which resulted in a 91.5% saving of energy in the optimized process. The solid mass flow production was reduced from 1087 kg/d (existing process) to 760 kg/d (optimized process), while the overall pollution load in the effluent was reduced from 260 kg/d (existing process) to 20 kg/d (optimized process). Consequently, the findings disclosed that the optimized process control parameters tested under different troubleshooting strategies reduced the energy consumption, increased the effluent quality, and reduced the pollution load compared to the existing process of the plant.
Design and implementation of a portable machine vision system for real-time object detection and auditory feedback
(2024) Sivate, Themba Mthembane; Pillay, N; Singh, N
A primary challenge faced by individuals with visual impairments is the difficulty or inability to perform object identification. While conventional aids such as magnifying spectacles may assist with near-field vision, individuals with reduced or absent sight often rely extensively on tactile exploration for object recognition. This reliance presents significant navigational challenges, particularly in dynamic environments such as roadways, where the increased risk of accidents is a major concern. To address this, this research proposes the development of a portable machine vision system designed to provide real-time auditory feedback regarding detected proximal objects, thereby assisting individuals with visual impairments in navigation. The design of the system prioritizes portability, reliability, modularity, and unobtrusiveness during typical operation. The hardware implementation of the proposed system consists of three key elements: a Single Board Computer (SBC), a wireless camera, and a Bluetooth-enabled earpiece. The experimental results demonstrate that the proposed system is capable of delivering real- time audio feedback of detected objects to visually impaired individuals. The system was evaluated in a real-life environment in both acceptable and poor lighting conditions. The efficacy of the proposed system in well-lit environments resulted in an average detection rate of 87.65%. However, in low-light scenes an average detection rate of 51% was observed because of the low image resolution. The minimum observed delay was 4.9 seconds, while the maximum was 10.2 seconds. This latency encompasses the duration required for image capture, processing, and audio translation. The latency can be mitigated by incorporating an integrated circuit with a dedicated Graphical Processing Unit (GPU), which is more proficient in handling machine learning and video processing tasks.
Critical inquiry into ecologicalally responsive architecture : a case study of the Isimangaliso Wetland Park (KwaZulu-Natal)
(2026) Subkaran, Tasheel; Pretlove, Stephen; Mishghina, Belula Tecle; du Plessis,Louis
This dissertation explored the severe strain on South Africa's natural resource base, leading to ecosystem degradation. The expanding built environment poses a threat to the sustainability of the natural ecosystem, resulting in imbalances between the resource base and socio-economic development. In support of Sustainable Development Goal 11 (2023:1), “Take an active interest in the governance and management of your city. Advocate for the kind of city you believe you need,” preserving and conserving the environment was deemed crucial, prompting an urgent shift towards ecologised architecture1 . The research aimed to critically examine ecologically responsive architecture in the sensitive environment of iSimangaliso Wetland Park, Kwa-Zulu Natal, South Africa, using a case study approach incorporating a qualitative analysis of primary data, secondary data and visual observation. The focus was on analysing theories, concepts, and principles related to the relationship between the built environment, natural environment and humanity, as well as ecological architecture, conservation, traditional buildings, and precedents crucial for successful conservation initiatives. The study sought to understand the factors leading to the "world heritage site" designation, emphasise the importance of conserving the natural ecosystem in iSimangaliso Wetland Park, analyse the implementation of sustainable pro- ecological development for a new green economy, and explore how traditional methods in the built environment can be combined with modern sustainable technologies for an alternative ecological response to built form. The study found the historical significance of traditional shelters in the humanenvironment relationship, underscoring their vital role in achieving sustainability. The preservation of South African heritage sites, particularly iSimangaliso Wetland Park, was highlighted. The research delved into the multifaceted concept of conservation within iSimangaliso Wetland Park, defining conservation as the wise management of natural resources. It aligned the park's vision with the concept of conservation, aiming for a renowned World Heritage Park with sustainable practices. Recognition as a UNESCO World Heritage site underscored its exceptional value, meeting three criteria and emphasising the need to preserve the environment in the context of the built environment. The dissertation highlighted the significance of ecological architecture, addressing the gap in integrating traditional techniques with modern technologies. Yahya and Hassanpour (2022:3) proposed a Hybrid Model that combined these elements, specifically for iSimangaliso Wetland Park, to bridge this gap and enhance conservation efforts within heritage sites. Precedent studies showcased diverse approaches but revealed a lack of synergy, particularly in the park's existing architectural structures. Focusing on the park's cultural richness, the research advocated extracting principles from indigenous communities like the Thonga and Zulu. The case study revealed a diverse architectural language, lacking consistency and cultural identity. The park’s buildings suggested that traditional cultures could influence design principles, emphasising the use of locally sourced materials and encouraging sustainability. The study identified the relevance of a shift towards a new green economy, proposing the implementation of a design approach in iSimangaliso Wetland Park based on the Hybrid Model by Yahya and Hassanpour (2022). This model combines traditional architecture with ecological principles to address gaps, raise awareness, and contribute to ecosystem conservation.
Optimal energy management in decentralized systems
(2025) Mazibuko, Thokozile Fortunate; Moloi, Katleho; Akindeji, Kayode Timothy
South Africa is currently grappling with a significant energy crisis, marked by increasing electricity demand, deteriorating coal-based infrastructure, and frequent instances of load shedding. With coal accounting for more than 80% of electricity production, this dependency results in elevated carbon emissions, power outages, and economic instability. Shifting towards renewable energy offers a viable solution to these issues by harnessing South Africa’s plentiful solar and wind resources, thereby promoting sustainability and cost-effectiveness. Nevertheless, critical challenges such as energy intermittency, management of excess energy, and high infrastructure expenses must be overcome to facilitate a dependable and equitable energy transition. This research introduces a comprehensive and optimized energy-sharing framework aimed at addressing these challenges, in alignment with Sustainable Development Goal 7 (SDG7), which seeks to ensure affordable, reliable, sustainable, and modern energy for all. A hybrid renewable energy system is modeled using HOMER Pro, integrating solar, wind, and battery storage systems to achieve cost-effective energy generation. To enhance energy demand forecasting, a hybrid Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost) model is utilized, allowing for data-driven planning and minimizing supply-demand discrepancies. Thesizing of system componentsisfurther refined through a hybrid Genetic Algorithm-Particle Swarm Optimization (GA-PSO) method, aimed at reducing costs and enhancing performance. An energy-sharing model based on Linear Programming (LP) guarantees equitable energy distribution among consumers, with a focus on prioritizing surplus renewable energy. This framework is further strengthened by incorporating Game Theory (GT) to encourage cooperative energy trading, enhance equitable energy access, and maximize cost savings. Key performance indicators, such as reduced grid dependence, cost savings, integration of renewable energy, and decreased carbon emissions, are assessed to validate the framework’s effectiveness. The results indicate that this hybrid, data-driven strategy significantly improves energy efficiency, resilience, and sustainability.
Water saving and reuse strategies for a local South African fresh-produce bulk market
(2026) Mjoli, Nokubonga Sinalo; Tetteh, Kweinor Emmanuel; Rathilal, Sudesh
This research addresses water conservation and wastewater reuse strategies at the Clairwood fresh produce bulk market in Durban, South Africa. The research aims to optimise wastewater treatment processes through a comparative evaluation focusing on three technologies: electrocoagulation (EC), dissolved air flotation (DAF), and slow sand filtration (SSF). Effluents from three sources – Trader’s Hall, Distribution Centre, and Final Effluent were characterised based on physical, chemical, and biological parameters, including chemical oxygen demand (COD), turbidity, suspended solids, and microbial content. Results revealed high pollution levels, with COD concentrations ranging from 300 to 1200 mg/L, turbidity reaching 150 NTU, and suspended solids up to 500 mg/L. To address these challenges, wastewater samples were collected from the Trader’s Hall, Distribution centre, and Final Effluent and were analysed for key physical, chemical, and biological parameters. The samples were treated using EC, DAF, and SSF under controlled laboratory conditions. Optimisation of the processes was performed using Response Surface Methodology (RSM), considering operational parameters such as induced voltage, agitation speed, and retention time on treatment performance. The effectiveness of each technology was assessed based on pollutant removal efficiencies, including COD, turbidity, suspended solids, and microbial reduction. EC emerged as the most effective treatment, achieving 95% COD removal, 98% turbidity reduction, and significant decreases in conductivity. In comparison, DAF achieved 85% COD removal, while SSF demonstrated limited effectiveness, achieving only 60% COD removal. The optimised EC process showed scalability potential and was validated against the South African National Water Act and South African National Standards for applications such as cleaning the skip area, ablution systems, and process water. The study concluded that up to 27% of the market’s municipal water demand could be offset by reusing treated wastewater, based on a comparison between the volume of treated effluent meeting reuse standards and market’s recorded municipal water consumption. This research underscores the viability of EC as a cost-effective and environmentally sustainable technology, particularly for regions grappling with water shortages. Although a detailed economic analysis was not conducted, the high treatment efficiency and relatively low operational requirements suggest potential cost benefits. Beyond its technical contributions, the study highlights broader implications for optimising wastewater treatment and reuse processes in the fruit and vegetable process industry (FVPI). These findings align with global sustainability goals (in particular, #6 Clean water and sanitation), offering a replicable model for integrating wastewater reuse into industrial systems to mitigate water scarcity and promote environmental stewardship.
Valorisation of sewage sludge for sustainable bioenergy and bioresources recovery through hydrothermal treatment
(2025) Mutsvene, Boldwin; Chetty, Manimagalay; Pillai, Sheena Kumari Kuttan; Bux, Faizal
Amid rising global concerns over water and energy security, wastewater treatment plants (WWTPs) face a critical challenge in transforming sludge from an environmental burden into a valuable resource. The intricate composition of sludge, rich in organic matter and nutrients yet complex to harness, demands innovative solutions. This study developed an integrated hydrothermal treatment (HT)–based system to enhance energy recovery and nutrient valorisation from waste-activated sludge (WAS), thereby promoting sustainable wastewater management. Hydrothermal treatment promoted this transformation by breaking down complex organic matrices under high temperature and pressure, thereby making nutrients more bioavailable. At the optimised HT condition (220 °C, 20 min), soluble chemical oxygen demand (SCOD) increased more than tenfold (from 284 ± 15 mg/L to 3161 ± 45 mg/L), while phosphate and ammonium rose from 64.04 ± 12 mg/L and 10.3 ± 5.2 mg/L to 172.8 ± 21 mg/L and 151.6 ± 17 mg/L, respectively. However, this solubilisation also released inhibitory compounds, including phenolics (from 59.8 ± 1.2 to 119.4 ± 1.4 mg/L) and heavy metals, potentially constraining downstream recovery. Magnetic biochar (MBC) synthesised from sugarcane bagasse (550 °C) was applied for post-HT detoxification. Under the optimised RSMCCD conditions (pH 6.54, 5 g/ L, 35 min), removal efficiencies reached 37.9 % for phenolics, 60.4 % for As, and over 75 % for Cu, Ni, and Pb. Despite effective contaminant removal, nutrient co-adsorption resulted in 50.3±1.0 % phosphate and 47.2±1.5% ammonium loss. To mitigate these trade-offs, struvite precipitation was optimised (pH 9.24, MgCl2·H2O 10-25 mL/L sludge), yielding 79.5% phosphate and 32% ammonium recovery with 69.02% purity. Phosphorus fractionation revealed a 70% increase in water-soluble phosphorus, confirming enhanced bioavailability. Concurrently, HCl-extractable phosphorus (HCl-P) declined by 23% as HT effectively disrupts mineral-bound phosphorus complexes, facilitating the release of otherwise immobilised phosphorus. Four AD scenarios consisting of untreated (S1), hydrothermally treated (S2), adsorbed (S3), and struvite-precipitated (S4) sludges were subsequently evaluated for biomethane potential. HT-treated sludge achieved an over 270% increase in methane yield, with methane production escalating from 2.3 mL CH4/g VS to 72.7 mL CH4/g VS and reduced the lag phase from 3.5 to 1.2 days. Scenario 4 (HT + AD + adsorption + struvite) was the most economically efficient option, with a profitability improvement of over 200%, an ROI of 45.04 %, and an NPV of approximately US$2.5 billion over a 30-year lifespan, as determined by the technoeconomic evaluation. Sensitivity analysis has revealed that energy recovery efficiency, market value of struvite, and taxation are the most significant economic variables. A break-even point in a full-scale operation was estimated to be within five years. The life cycle assessment (LCA) reported decreases in global warming potential (GWP) of 21.3 % (S2), 37.4 % (S3), and 8.53 % (S4) compared to conventional sludge disposal (S1). These positive gains affirm the co-benefits to the environment and economy that accompany the integration of these processes. Despite the limitations of bench-scale HT-integrated sludge valorisation, including heat losses and the scarcity of LCA datasets, the study provides a validated framework for upscaling HT-integrated sludge valorisation systems. The results advance circular economy efforts as WWTPs are converted into bioresource recovery hubs that mitigate eutrophication, increase renewable energy production, and contribute to meeting SDGs 6 (Clean Water) and 13 (Climate Action). This work helps fill the gap between waste and resource recovery, ensuring the sustainability of next-generation wastewater treatment and representing a sustainable, energyefficient, and economical future for sludge management.
A comparative study of electrocoagulation and chitosan biosorption for the treatment of oil refinery wastewater
(2025) Ngcobo, Gugulethu Emmerencia; Rathilal, Sudesh; Bakare, Babatunde Femi
Water quality is being impacted by the rapid expansion in industrialization, particularly in the oil and gas industry. Improper treatment of industrial wastewater exacerbates the problem. Chemical coagulation has been the most widely used technology in treating oily wastewater; nevertheless, electrocoagulation (EC) has been employed effectively to some extent, with different metals serving as electrodes. On the other hand, valorising seafood waste through bioconversion into biosorbents offers the promise of an environmentally benign wastewater treatment method. This study compares the performance of electrocoagulation using aluminium electrodes with that of adsorption using chitosan derived from oyster shells as the adsorbent. It aims to address effluent from refineries while also tackling waste beneficiation. Biosorbent characterization involved analyzing functional groups, which was confirmed using Fourier-Transform Infrared (FTIR) spectroscopy. The crystal structure, surface morphology, and elemental composition of the produced chitosan were examined using Scanning Electron Microscopy (SEM) combined with Energy-Dispersive X-ray (EDX) analysis, respectively. The adsorption process study was conducted through the use of empirical adsorption models. The Langmuir isotherm was shown to have the greatest fit for equilibrium data, whereas the pseudo 2nd order model provided the best fit for kinetic data. When removing Chemical Oxygen Demand (COD), colour, and phenols, the biosorbent chitosan achieved removal rates of 88.2%, 87.5%, and 88.9%, respectively. The optimal conditions were found to be at the dosage of 15 g, a reaction time of 90 minutes, a speed of 250 rpm, and a settling time of 15 minutes. Double-blade aluminium electrodes have proven to be highly effective for the electrocoagulation process, achieving reductions of 91.41% in COD, 96.77% in colour, and 94.53% in phenols. This performance was attained by maintaining a configuration with double-blade electrodes, an electrolysis time of 80 minutes, and a magnetic stirrer speed of 250 rpm, resulting in excellent electrocoagulation results. The results of this research showed that the EC is the fastest and best technique, although the performance of the biosorbent chitosan is good and should be further evaluated.
Investigating the upscaling of an anaerobic digester for biogas production from industrial wastewater
(2025) Ngema, Lindokuhle; Tetteh, Emmanuel Kweinor; Rathilal, Sudesh
South Africa faces challenges of water scarcity and carbon-based energy pollution, and is currently under pressure to explore and capitalize on alternative methods to produce green energy. In addressing these challenges, the application and optimization of anaerobic digestion (AD) technology presents a robust solution for the treatment of wastewater and bioenergy production if operated efficiently. Under the South African National Development Roadmap and the United Nations Sustainability Development Goals on clean water and sanitation (#6), climate action (#13), and clean and affordable energy (#7), this research addressed the water scarcity, energy, and environmental concerns in South Africa. The study was aimed at up-scaling and optimizing an anaerobic digester for biogas production from industrial wastewater. Sequentially, characterisation of the wastewater streams, evaluating and optimising the operational factors of the AD process as well as cost-benefit analysis were among the specific objectives carried out to achieve the overall aim of the study. The characterization results show that the sugar refinery and industrial sewage have the highest organic content with COD of 18770 mg/L and 4320 mg/L respectively. The sugar refinery stream had the highest concentration of volatile solids at 0.026 g/mL, as well as a high concentration of phosphates and nitrates, followed by industrial sewage, and the least concentration of volatile solids, phosphates and nitrates was observed from the oil refinery stream. The results of biochemical methane potential tests show the highest biogas production from the sugar refinery stream (148 mL/gTDS), followed by the industrial sewage (76 mL/gTDS), and the least production from the oil refinery (64 mL/gTDS) and municipal wastewater (45 mL/gTDS). Although the sugar refinery stream produces the highest biogas, it also shows difficulty in the removal of organic content with COD removal of 62.8%, indicating that a major fraction of organic content is not degraded and converted into biogas. Using the OFAT (one-factor-at-time) approach, the hydraulic retention time (HRT) and magnetite load were among the key operational factors evaluated. It was observed that, at the initial stage there was a poor response in biogas production and COD removal, followed by an exponential increase in biogas production between 9-18 days, a decline in biogas production between 19-22 days, and retardants or no production between 22-30 days. This suggested that there was a long lag phase due to poor microbial activity and an optimum HRT can be obtained between 18 – 22 days. The addition of magnetite reduced the lag phase significantly from 9 days to 3 days, indicating that the addition of magnetite improves the interspecies electron transfer and ultimately enhances biogas production. The addition of 0.4 – 0.6 g/L magnetite achieved high biogas production rates of 23 mL/d and 20 mL/d respectively between 9-12 days while the addition of 0.8 g/L resulted in catalyst overloading which inhibited the microbial activity and a rapid decline in biogas production to 2 mL/d after 9 days was observed. An increase in organic content from 4320 mg/L to 18770 mg/L resulted in a 30% increase in biogas production, however, a decline of over 40% in the removal of contaminants was noted due to a decrease in pH leading to the accumulation of long-chain fatty acids which are inhibitory to groups of microorganisms. At the upscale of 50 L AD, the operating conditions were optimised using the response surface methodology (RSM). The statistical analysis revealed that the quadratic models for the three responses studied (biogas yield, COD removal and colour removal) were significant with p-values below 0.05. The R2 values for the three responses were 0.99 with differences between predicted and adjusted R2 below 0.2, suggesting reasonable agreement with observational data points. The optimum operating conditions obtained were an HRT of 21 days, pH of 7.01 and magnetite load of 0.42 g/L with a desirability of 0.99. The optimum conditions were validated against two streams, industrial sewage (low organic content) and sugar refinery (high organic content) and achieved over 85% and over 60% in the removal of the contaminants, respectively. The addition of sensors to the up-scaled 50 L AD ensured accurate control and monitoring of operating conditions, thus enhancing methane content to 90%. Furthermore, a cost-benefit analysis of the optimized AD system with biogas production and its applicability as an alternative source of energy was conducted. The annual operating costs for the upscaled AD system exceeded the annual revenue, resulting in a net cash flow of -R8 506. The 50 L AD has a net present value of -R121 016, indicating that the system is not economically viable due to the high capital cost required for the design and commissioning of the system. Based on the cumulative cash flow, the 1 L, 5 L, 10 L, and 50 L systems will take 24.8, 21.9, 25.3, and 19.03 years to pay back the initial investment. Also, the benefit-cost ratio increases with an increase in AD size: 1 L (0.05), 5 L (0.12), 10 L (0.13), and 50 L (0.4), indicating that up-scaling has the potential to generate more revenue with a decrease in operating cost for the optimized and stabilized system. Therefore, upscaling of an anaerobic digester (AD) with the optimum conditions for treating industrial wastewater and biogas production was feasible. However, up-scaling beyond 50 L capacity is essential to increase biogas production, enhance revenue generation and improve the benefit-cost ratio to 1 and above. This would make the AD system economically viable for large-scale operations.
Reliability analysis of the distribution network due to the integration of distributed generations
(2025) Lunga, Zanele Zamalunga; Ojo, Evans Eshiemogie; Chetty,Nelson Dhanpal
Reliability analysis is critical to power system design and planning, ensuring that electrical networks operate efficiently under defined conditions over a specified period. The growing integration of Distributed Generation (DG) units, driven by advancements in renewable energy technologies such as solar photovoltaic (PV) and wind energy systems, has significantly impacted power distribution networks. DG units, which are small-scale power generation sources, can be connected at distribution substations or dispersed throughout the network. Their implementation influences voltage profiles and reduces power losses, but their increasing penetration levels also affect overall system operation. This study evaluates the reliability of a distribution network with and without DG integration. A numerical model is developed to analyse the impact of DG integration on network performance. The IEEE 30-bus system is the test network, incorporating solar PV and wind energy conversion systems as DG sources. Numeral Simulations are conducted, implemented on the MATLAB/Simulink software. The Newton-Raphson method is employed for load flow analysis, determining the network's voltage magnitudes and phase angles. Additionally, the Particle Swarm Optimization (PSO) algorithm is utilised to determine the optimal placement of DGs, aiming to minimise power losses, reduce operational costs, and improve voltage stability under various conditions. A reliability assessment is performed using Monte Carlo simulation, which calculates key reliability indices to evaluate system performance. The results confirm that the location and capacity of DG units significantly influence network reliability. The study establishes that integrating optimally placed DGs enhances power system reliability by improving voltage stability and reducing power losses. These findings highlight the potential benefits of renewable energy-based DGs in strengthening distribution networks and ensuring a more stable and resilient power supply.
The investigation into the potential of ceramic waste powder as a pozzolanic material and inert cement filler in concrete
(2025) Adedeji, Peace Opeyemi; Ikotun, Jacob Olumuyiwa; Babafemi, Adewumi John
The construction industry is increasingly seeking sustainable alternatives to Portland cement (PC) due to its high carbon footprint. Ceramic waste powder (CWP) offers potential as either a supplementary cementitious material or an inert filler. However, its role in cement hydration is uncertain due to conflicting reports on its pozzolanic reactivity. This study investigates the potential of CWP as a partial replacement for both pozzolanic material (Ground Granulated Blast Furnace Slag, GGBS) and inert filler (limestone powder, LSP) in concrete. The CWP was incorporated into Portland-slag and Portland-limestone cement blends at varying levels. Its chemical compositions were analysed using X-ray fluorescence (XRF). Scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS), and X-ray diffraction (XRD) were use to better understand its microstructure. Workability (slump), strength, and durability were evaluated at water-binder ratios of 0.45, 0.50, and 0.55. Compressive and splitting tensile strength tests were conducted at 7, 28, 56, and 90 days. Durability performance was determined using the Oxygen Permeability Index (OPI), Water Sorptivity Index (WSI), and Chloride Conductivity Index (CCI). Additionally, SEM analysis was used to examine hydration products, pore distribution, and interfacial transition zones (ITZ) in concrete microstructures. Results indicated that CWP has limited pozzolanic reactivity, leading to lower early-age (7 days) strength compared to GGBS. However, it outperformed LSP in both early-age and long-term strength, as well as in durability. CWP reduced the slump in GGBS mixes, requiring more superplasticizers, but improved the slump in LSP-based mixes. Durability tests confirmed that CWP enhances resistance to permeability, moisture ingress, and chloride penetration. Microstructural analysis revealed improved densification, reduced porosity, and better hydration over time. Compared to LSP, CWP demonstrated similar filler effects but also generated minor secondary hydration products, suggesting partial pozzolanic activity. However, its hydration rate, as indicated by strength development over time, was lower than that of GGBS. Overall, CWP primarily acts as an inert filler with limited pozzolanic reactivity.
Application of lean and agile manufacturing process in new product development
(2024) Ezeani, Gregory Ifesinachi; Olanrewaju, Oludolapo Akanni
As technology evolves globally, it becomes difficult for a single production improvement tool to support the maximum productivity, flexibility, and competitiveness necessary for a firm to continuously increase its value chain to fulfill customer-changing requirements. The global market of today continues to be more competitive and the demand for highly functional products, highquality service, shorter delivery lead-time, and environmental friendliness continues to grow to fit customers` changing demand requirements. This research focused on tackling the problem of underperformance of a firm`s products that cannot be properly covered by a single productivity improvement process such as lean or Agile. The unsatisfactory factor of using either lean or agile makes it difficult for a firm to continuously increase its productivity, flexibility, and competitiveness to support a firm ability to fit values that satisfy customer-changing requirements in both stable and turbulent market environments. The research study focused on the successful integration of lean and agile manufacturing processes in new product development to improve productivity, flexibility, and competitiveness to fit customer-changing requirements in a global market environment. Moreover, the effect of disturbances such as new entrants, technological evolution, globalization, and customerchanging requirements deprives a single tool such as lean or agile the ability to satisfy changing customer requirements. This effect of technological evolvement, globalization, etc., has led to redundancy, obsolescence, abandonment, and extinction which creates financial losses for firms. This research utilized synergies (robustness and smartification) of lean and agile tools to improve new products to fit customer-changing requirements in stable and turbulent market environments. The study used Taguchi's design of experiment to determine productivity differences in using a single tool of lean or single tool of agile or a combined tool of lean and agile in the management of new product development. The study performance outcome in terms of process performance (CP & CPK), Performance distribution, waste variation reduction, and loss function financial evaluation revealed that using a combination of lean and agile performed significantly best than using either a single tool of lean or a single tool of Agile. The study used the Taguchi design of the experiment to support a robust improvement integration process. The result showed that the integration of lean and agile methodology offers better efficiency and effectiveness than using either a single lean or agile methodology. The study concluded that using a combination of lean and agile tools improves the underperformance of new product development to fit customer market-changing requirements compared to using either a single lean tool or agile tool alone.
Techno-economic assessment of biofuels production from sugarcane bagasse
(2026) Buthelezi, Ayanda Siphumelele; Chetty, M; Mohammadi, A H
In tropical countries such as South Africa, sugarcane bagasse is one of the most abundant lignocellulosic renewable feedstocks for biofuel production. The adoption of integrated processing routes for utilising by-products is a promising approach for achieving near-complete conversion of organic biomass while reducing waste generation. This study investigates a second-generation sugarcane bagasse biorefinery annexed to a sugar mill. A techno-economic assessment approach was adopted to determine the feasibility of each investigated process route. Five sugarcane bagasse-to-biofuel conversion routes were evaluated, namely the bioethanol fermentation process, the anaerobic digestion process, the dark fermentation process, the gasification process, and the fast pyrolysis process. Each process route was simulated individually in Aspen Plus V11 using input data obtained from literature sources. The simulations generated mass and energy balances, which were subsequently utilised for techno-economic analyses (TEA) and life cycle assessments (LCA). The TEA was performed using Aspen Process Economic Analyzer (APEA) and Microsoft Excel spreadsheets, while the LCA was conducted using SimaPro software.The study considered a typical sugar mill located at Sezela in KwaZulu-Natal, South Africa, with a cane crushing capacity of 300 tonnes of sugarcane per hour, producing approximately 81 tonnes of sugarcane bagasse per hour. It was assumed that 36% of the bagasse is utilised in boilers for steam generation. The remaining portion (51.84 tonnes per hour) was allocated to the biorefinery, which was assumed to be annexed to the sugar mill. This configuration eliminates transportation costs and minimises feedstock costs. Following the completion of process simulations in Aspen Plus, Aspen Process Economic Analyzer was used to estimate the installed equipment costs for each investigated process route. The installed equipment costs were escalated from a 2019 base year, consistent with Aspen V11 cost correlations, to 2024 values using the Chemical Engineering Plant Cost Index (CEPCI). Capital expenditure (CAPEX) for each process route was estimated using the installed equipment costs. Variable operating costs (VOC) and fixed operating costs (FOC) were combined to determine the operating expenditure (OPEX) of each biorefinery. It was assumed that the plant operates for 5000 hours per year. Total sales revenue was calculated based on the sales of valuable final products, and selling prices were obtained from literature sources and online market prices. Discounted cash flow analysis was used to evaluate theeconomic performance of each process route, assuming a tax rate of 28%, a discount rate of 12%, and straight-line depreciation over 5 years at 20% per year. The plant lifetime was assumed to be 25 years. Four economic indicators were calculated, namely net present value (NPV), payback period (PBP), return on investment (ROI), and profitability index (PI). Life cycle assessment (LCA) was conducted to evaluate the environmental impacts of the investigated process routes. SimaPro software was used for the LCA, following ISO 14040 and ISO 14044 methodology. The ISO framework includes four main stages: goal and scope definition, life cycle inventory (LCI), life cycle impact assessment (LCIA), and interpretation of results. The goal and scope of the LCA were to determine the least environmentally impactful processing route using a cradle-to-gate system boundary. The LCI was based on mass and energy balances obtained from Aspen Plus simulations, and the Ecoinvent database in SimaPro was utilised. The LCIA was performed using two approaches. The first was damage assessment, which applied the ReCiPe 2016 Endpoint (H) method and evaluated three damage categories, namely human health, ecosystems, and resources. The second approach was characterisation, which applied the ReCiPe 2016 Midpoint (H) method. Under the midpoint approach, eight impact categories were evaluated, including global warming, fine particulatematter formation, terrestrial acidification, freshwater eutrophication, marine eutrophication, mineral resource scarcity, fossil resource scarcity, and water consumption. The fast pyrolysis process was identified as the most profitable process route, achieving an NPV of 199.44 million USD, a payback period of 1 year, an ROI of 10.27, and a PI of 46.64. The dark fermentation process ranked second, with an NPV of 67.41 million USD, a payback period of 3.3 years, an ROI of 1.51, and a PI of 7.95. The gasification process ranked third, with an NPV of 23.21 million USD, a payback period of 3.6 years, an ROI of 1.40, and a PI of 7.31. The anaerobic digestion process ranked fourth, with an NPV of 37.57 million USD, a payback period of 4.4 years, an ROI of 1.16, and a PI of 5.85. Under the conditions assumed in this study, the bioethanol fermentation process was economically unfeasible, as it produced a negative NPV, indicating that the project would not recover its initial investment by the end of the plant lifetime.The life cycle assessment results showed that the dark fermentation process was the most environmentally favourable process route, followed by the fast pyrolysis process, anaerobic digestion process, gasification process, and bioethanol fermentation process, respectively. The viiibioethanol fermentation process showed the highest environmental impacts across all evaluated impact categories, making it the least environmentally favourable processing route. Electricity was used as the energy source in the LCA, and energy consumption significantly influenced environmental impacts. Since South African electricity is largely generated from coal, process routes with high energy demand exhibited higher environmental impacts and were therefore less environmentally sustainable.
Mask R-CNN for real-time parts classification on an enamel paint coating line
(2026-06) Citlak, Tarik; Pillay, Nelendran; Singh, N.
An enamel paint coating line has relied heavily on human intervention and observation to determine the total number of parts produced in one shift. This was done by manually counting the parts as they were taken off the conveyor line which has led to inaccurate or lately received production performance data. With a manufacturing process that has a changing number of classes and a variation of the part’s physical orientation within the process, the importance of investigating an object detection model was realized. The rising demand to attain live production data has added more importance to monitoring and reporting within the industrial automation sector. Real-time parts screening requiring human intervention for data input may not be a feasible solution within a fast-moving consumer goods manufacturing facility. This has led to the proposed study using Mask Region-based Convolutional Neural Network (R-CNN) to detect objects within an image. The state of research surrounding object detection has increased rapidly over the past decade with several models being developed and adapted for industrial automation applications. However, there has been limited research regarding models that can perform instance segmentation to provide production performance data. This has prompted the study within this field. The objective of the study is to classify and accurately report on manufactured parts identified on an enamel paint-coating conveyor line. At any given instant, the parts may not be in the exact coordinates within the desired area of interest and the classes of objects may vary based on changing production requirements. To mitigate these challenges, this study proposes the use of a trained Mask R-CNN model to detect the objects and their associated class. Images are acquired using a high-definition camera fixed to a position next to the enamel coating line. This study compared the average precision based on changes made to the learning rate and the intersection over union (IoU) thresholds of the model. The outcome was analysed using the precision-recall curve and the confusion matrix to determine the highest average precision and overall accuracy. The highest achieved average precision obtained from the model was 98.27% with an overall accuracy of 98.24% using real-time captured images of the manufactured parts. The results satisfied the acceptable standard for the average precision of 97.5% set by the plant production quality engineers. Future research would include the use of pixel-wise segmentation generated from the mask branch in determining the objects’ exact location, this would aid in efficient robotic spray positioning.
The development of an optimization technique for transmission expansion planning with renewable energy integration
(2026-06) Ndlela, Nomihla W.; Moloi, Katleho; Kabeya, Musasa
The demand for electrical energy is rapidly increasing due to various socioeconomic factors, including industrialization, population growth, urbanization, and the advancement of modern technologies within the context of the Fourth Industrial Revolution. The rapid increase in energy demand poses a significant challenge to the power system. The desire for sustainability is driving significant changes in the global energy sector. Keeping the global average temperature within bounds is a critical concern, prompting countries to take tangible steps to reduce energy system dependence on fossil fuels. Recently, transmission network expansion planning (TNEP) was studied. Power network planning requires TNEP to determine the locations, timing, and quantity of additional transmission lines while ensuring grid stability to guarantee all equipment performs within limitations. Renewable energy (RE) is used in grid connectivity, small businesses, and photovoltaic (PV) solar systems in homes. This research proposes adding RE resources to transmission line expansion to enhance capacity. Studies have examined RE inclusion into power grid expansion plans. South Africa's electricity system uses solar and wind. However, the integration of RE in power systems can cause instability as most of the renewable energy has an intermittent nature. Therefore, this problem is discussed to provide an effective solution for generation and TEP. The first part of this study is the comprehensive analysis of Approaches for TNEP, which includes various approaches, methodologies, and technologies utilized in the expansion process, highlighting their advantages, limitations, potential implications, and reliability in transmission expansion planning (TEP), distributed generation, electrical markets, insecurity, line congestion, and reactive power planning (RPP). It also analyzes innovative transmission expansion planning models that integrate renewable energy sources (RES) utilizing improved optimization methods. The second case study shows the importance of TEP and is divided into sub-sections. The first, Tie Open Point Optimisation (TOPO) techniques in conjunction with Genetic Algorithm (GA), denote switchable connections among network segments, allowing system operators to reorganize a network for improved reliability, efficiency, minimal losses, and cost-effectiveness. After that, hosting capacity development and RE integration evaluate and upgrade the power grid's capacity to accommodate distributed energy resources (DERs) such as solar and wind while maintaining reliability to add more RE without strengthening the network. Reliability assessment with contingency is proposed, which examines the network response to any possible faults. The short and long-term TEP with load and generator forecasts predicts gridbehavior in different seasons over a year and over many years, when load growth increases with RE uncertainty, such as wind farms and solar PV plants. To assess network performance over time, quasi-dynamic simulation uses load flow computations at specified times. Finally, probabilistic analysis in conjunction with Quasi-Monte Carlo simulation (QMCS) is suggested to evaluate system performance under uncertainty, determine the best location for additional lines to maintain grid stability, and analyze system behavior, demand growth, generation availability, and network restrictions. It helps decision-makers evaluate expansion possibilities and mitigate power system development risks throughout time. A power park analysis tool evaluates wind farm profitability, losses, and energy. Basic energy analysis and probabilistic analysis using the QMCS are employed. The network with hybrid renewable plant integration was successfully constructed through both short-term and long-term transmission planning amidst various uncertainties. The optimal techniques employed for transmission expansion planning effectively maintained system stability over 15 years, as shown in Figure 6.38 and Table 6.10 / different conditions, as shown in Figure 6.23 and Table 6.9 with minimal losses under reliability assessment.
An integrated environmental sustainability model for the real estate industry
(2025) Adjarko, Harold; Aiyetan, Ayodeji Olatunji; Anugwo, Iruka Chijindu
The real estate sector is a significant contributor to global carbon emissions, waste production, and natural resource consumption. While the industry's environmental performance has been studied in other sectors in Ghana, there remains a gap in understanding the specific roles of key stakeholders—lenders, developers, investors, occupiers, and valuers—in achieving environmental sustainability. This study addresses this gap by developing an integrated environmental sustainability model for the Ghanaian real estate industry. A Sequential Explanatory mixed methods approach was employed, beginning with semi-structured interviews (n=20) to gather contextual insights, followed by a Modified Delphi process with 15 purposively selected experts to refine sustainability constructs. A structured questionnaire survey (n=275; response rate: 91.7%) was then conducted with stakeholders drawn from GREDA, ARC, IET, and GHIE. Survey data were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM) to validate the proposed model. The validated model comprises five key constructs: energy efficiency, water conservation, waste management, green building materials, and air quality and pollution control. Reliability and validity were confirmed through high Composite Reliability scores (CR = 0.88 0.97), Average Variance Extracted (AVE = 0.61–0.82), and discriminant validity via HTMT ratios (<0.85). Model fit was acceptable (SRMR = 0.062), with predictive relevance confirmed (Q² = 0.31; R² = 0.42). Among the constructs, energy efficiency and stakeholder collaboration showed the strongest positive effects on sustainability outcomes, while green building materials had a weaker but still significant impact. No non-significant paths were observed, affirming the robustness of the model. This research expands existing knowledge and offers a validated, context-sensitive framework for promoting environmental sustainability in the real estate industry, particularly in Ghana and other developing economies.
Application of DMAIC technique to improve supply chain efficiency
(2025) Mthembu, Bongakonke Thandwayinkosi; Olanrewaju, Oludolapo Akanni; Mncwango, Bongumenzi T.
This study presents a detailed analysis of the use of Lean manufacturing approaches to improve operational efficiency and minimize waste in the production processes of a sugar packaging company situated in South Africa. Faced with obstacles in fulfilling waste reduction targets and improving operational efficiency, the study attempted to carefully identify underlying sources of waste and implement Lean concepts, notably the DMAIC technique. The study used a range of Lean problem-solving approaches, beginning with an ABC analysis to identify Stock Keeping Units (SKU) with the greatest waste levels, followed by a mix of the 5- Whys probing methodology and Ishikawa diagrams to go deeper into waste reduction initiatives. Matrix prioritization was then used to prioritize actions and implementations that address identified inefficiencies and issues, leading to the creation and execution of an implementation strategy. During the improvement phase, waste was significantly reduced, notably in the 500g stock-keeping unit. Despite encountering obstacles associated with 1 kg SKUs, due to variances in the Bill of Materials (BOM), significant progress was accomplished. The DMAIC framework offered an organized method that included problem identification, process evaluation, data analysis, improvement implementation, and control installation. The study indicated significant waste levels, which were above weekly targets, resulting in a 70% production efficiency, (or 30% inefficiency), emphasizing the need for process improvements. Among other recommendations, the study suggests improving supervisor handover methods and introducing non-conformance reports (NCRs) to increase supplier responsibility and raw material quality. Supplier participation in performance reviews emerged as a crucial driver in dramatically decreasing waste and increasing production efficiency, resulting in a remarkable 20% improvement in production efficiency, thus raising the production efficiency levels to 90%. In essence, this study sheds light on the efficacy of the DMAIC methodology within the sugar company, offering practical insights into enhancing supply chain efficiency and minimizing waste. By targeting significant process inefficiencies, the research contributes to enhancing sugar production operations, benefiting stakeholders, and bolstering industry competitiveness. The results advocate for the adoption of Lean methodologies to optimize production processes and enhance profitability.
Experimental investigation of epoxy-based foam composites for buoyancy applications
(2025) Ajayi, Ayodele Abraham; Mohan, Turup Pandurangan; Kanny, Krishnan
Selection of appropriate materials for composite design is very crucial in critical engineering applications such as aerospace, marine and automobile industries. This study focused on developing lightweight hybrid-filled foam composite panels with enhanced mechanical and thermal properties. Hollow glass microspheres (HGM) and nanoclay were the fillers used in the foam core. The HGM content was varied from 1wt.% to 3wt.% in foam composites panel while nanoclay content was varied from 1wt.% to 5wt.% in each of the HGM-filled series of foam composites panel, these foam composite panels were fabricated using a conventional resin casting method. These hybrid-filled foam panels were also reinforced with banana fibres as facesheet in the sandwich composites. Comprehensive characterization was carried out on the foam composite panels, this involve investigating their physical properties. The results obtained showed that tensile and flexural strength improved by 12% and 23.1% respectively with the infusion of hybrid fillers content of 3%wt.HGM+1%wt.clay and 1%wt.HGM+1%wt.clay into the epoxy when compared to neat epoxy. Thermal strength was optimum with infusion of 1%wt.HGM+5%wt.clay into the epoxy while the buoyancy results revealed that the sample with 3%wt. hollow glass microspheres concentration has the highest buoyancy due to the low density of the HGM used which is 0.19 g/cm3 and because sample 3%wt.HGM has the highest concentration of HGM with the respect to series of samples considered in this study. Similar trend of improvement in mechanical properties and physical properties was observed when the fabricated hybrid-filled foam panels was used as core in the sandwich composites developed which resulted to 22.11% and 29.53% improvement as flexural strength and tensile strength while there was 32.26% improvement in the impact energy. Also, there was 8.61% reduction in the water uptake. Furthermore, the tensile and flexural results was validated numerically by using finite element method and abaqus® 6.13 software and this revealed that most of the modelled samples are stronger than the experimental tested samples with up to 9% increase from experimental values obtained because of limitation in some parameter estimation of the numerical model such as the thermal properties, perfect contact and linear failure criteria. Since the improvement in mechanical and thermal properties has been established, the composite panels developed are suitable for applications in manufacturing ship propellers. Future studies aims to improve the fire retardation of sandwich composites for marine applications

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