Institutional-Repository, University of Moratuwa
Welcome to the University of Moratuwa Digital Repository, which houses postgraduate theses and dissertations, research articles presented at conferences by faculties and departments, university-published journal articles and research publications authored by academic staff. This online repository stores, preserves and distributes the University's scholarly work. This service allows University members to share their research with a larger audience.
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Recent Submissions
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Exploring the potential of textile industry combustion residues as sustainable adsorbents: a study on thermal treatment -induced stabilization and methylene blue removal efficiency
(IEEE, 2025) Herath, T; Nayanathara, H; Udayanga, C; Pathirana, S; Manathunga, J
The Sri Lankan textile industry, while economically vital, generates significant quantities of combustion residues-fly ash (FA), bottom ash (BA), and cyclone ash (CA)-through industrial boilers. These ashes contain high inorganic compounds, heavy metals, and trace organic substances. Current improper disposal methods create significant environmental hazards, while these contaminants also prevent direct ash reuse in secondary applications. This study investigates the feasibility of inert thermal treatment to stabilize and valorize these ashes as low-cost adsorbents for wastewater treatment, supporting circular economy initiatives. Ash samples from textile boilers were characterized through moisture and ash content analysis, the rmogravimetric analysis (TGA), Fourier Transform Infrared (FTIR) spectroscopy, and methylene blue adsorption experiments. Distinct physicochemical results showed that FA exhibited high surface reactivity and BA displayed structural stability. Treated samples showed improved thermal stability and enhanced dye adsorption performance, with removal efficiencies ranging from 55% to 83%. A strong positive correlation (r = 0.897) between ash content and treatment yield, and a negative correlation (r =-0.972) with moisture content, underscored the importance of initial ash characteristics. The findings support the reuse of textile boiler ash as an effective adsorbent, paving the way for a sustainable waste management solution and environmental protection in the textile sector.
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Redesigning supply chains for industry 5.0 in developing economies: a research agenda
(IEEE, 2025) Bandara, A; Thibbotuwawa, A; Perera, HN
Unlike the previous industry revolutions that were predominantly focused on enhancing the efficiency of industrial systems, Industry 5.0 (I5.0) aims at transforming global industries to become more sustainable, resilient, and human-centric. This paper brings a much-needed developing economy perspective to the debate on the interplay between I5.0 and supply chain (SC). A sample of 89 peer-reviewed journal articles was selected from SCOPUS and Web of Science databases through a systematic literature review. The network co-occurrence diagram revealed three prominent research clusters, namely: SC Process Improvement, SC Resilience, and SC Sustainability. Research gap analysis revealed several future research directions: Promoting the adoption of I5.0 in Developing Economies, Human-centered SCs, Emerging technology applications Strategy roadmaps to adopt I5.0, and I5.0 certification and compliance. These directions can be used by future researchers, industries and intergovernmental organizations to streamline their efforts to popularize I5.0 in SCs operating at developing economies.
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Engagement in professional tasks with generative AI versus cognitive skills: a case of IT industry in Sri Lanka
(IEEE, 2025) Ranatunga, S; Jayasundara, R; Jayasinghe, B
The cognitive skills such as problem-solving, critical thinking, analytical skills, attention to detail are critically expected from professionals in the IT industry due to its competitive environment. AI technologies rapidly become integrated across the IT sector as in various other sectors. This study focuses to investigate the impact on critical cognitive competencies by using generative AI in their professional assignments. 177 responses were collected through a structured web-based questionnaire which was developed focusing the IT industry. The research methodology encompassed selecting a representative sample, reliability analysis, factor analysis, and multiple linear regression. The results reveal that Generative AI significantly improves problem solving and analytical capabilities by automating routine tasks and providing sophisticated data insights. This automation allows IT professionals to focus on more complex cognitive tasks, thereby promoting innovation and efficiency. Nevertheless, the study also highlights potential drawbacks, such as an overreliance on AI for critical thinking, which may hinder the development of independent analytical abilities and decrease attention to detail. The study underscores the importance of continuous learning and adaptability for IT professionals to remain relevant in an AI-augmented work environment. It advocates for a balanced approach where AI technologies augment but do not replace essential human cognitive skills.
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A Smart load shedding system for appliances to improve power system stability in smaller isolated grids
(IEEE, 2025) Gunathilaka, S; Rajapaksha, Y; Akalpa, N; Damayanthi, T; Siyambalapitiya, T
This work presents the development and system integration of a Smart Load Shedding System to improve power system stability in small and isolated grids, which are in the range of 2000-3000 MW. In grids similar to Sri Lanka, without power interconnection with any other grid and with very narrow margins to operate with, load shedding schemes are essential for maintaining stability under exceptional conditions. The system encompasses an advanced load shedding algorithm refined from existing techniques to adapt to local operation characteristics with a modified IEEE 14 bus system simulation model. The model has been further confirmed by simulation in RSCAD and PSSE environments. Simulation was performed with RSCAD software along with a Real-Time Digital Simulator (RTDS) to emulate dynamic behavior in grids accurately. A dedicated module for the smart appliance interface has been developed to interface with home appliances to initiate controlled tripping. In-depth data made available by the Sri Lanka Sustainable Energy Authority (SLSEA) has been used to study patterns of daily consumption to make the load shedding plan technically sound and economically feasible. The experimental result shows that the proposed system effectively corrects the frequency fluctuations and hence improves the overall stability of the grid.
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Enhancing subgrade strength of rural roads constructed on soft soils using varied grades of Coir fiber
(IEEE, 2025) Uhanovitage Dona, U; Hettiarachchi, C
With the urbanization in Sri Lanka, shortage of land for pavement construction has become a major issue as Engineers are supposed to resort to constructing pavements in areas with problematic soils. These problematic soils are commonly clays and silts due to their characteristics of high swell-shrinkage and poor load-bearing ability. "Excavation and Replacement" is conventional for subgrade alteration, but is expensive and time-consuming. Currently, subgrade modification includes the application of natural and synthetic reinforcement. Glass fibers, nylon fibers, polyester fibers (PET), polypropylene fibers (PP), and polyethylene fibers (PE) are geosynthetics. Natural fibers like coir, sisal, palm and kenaf are increasingly popular as they are affordable and environmentally beneficial. Two types of coir fiber (Mattress fiber and Bristle fiber) of 0.25% to 0.75% percentages with 0.25% increments and lengths between 1cm and 3 cm was considered in this study. Potential for increasing the soil's load- bearing ability has been evaluated through laboratory analysis, such as Hydrometer analysis, Standard Proctor Compaction (SPC) test, and California Bearing Ratio (CBR) test. The findings demonstrate that weak soils can be stabilized by adding bristle fiber (0.75% of the soil's dry weight) to reach a CBR value of 2.61%, or 2.15 times that of the control sample.