University of Moratuwa

Torrefaction experiments of Rubberwood and Gliricidia were conducted at 250–300 °C for 30–60 min in a 3–9% oxygen environment to study the oxidative torrefaction behavior. The higher heating value of the torrefied Rubberwood increased from 18.9 to 24.68 MJ/kg and from 19.46 to 23.19 MJ/kg for Gliricidia under the most severe oxidative conditions. Effects of torrefaction conditions on the solid yield, VM removal, C enhancement, HHV enhancement, and energy yield were modeled using response surface methodology, and temperature and oxygen concentration mainly affected the torrefied biomass properties. Rubberwood recorded a significantly greater energy mass co-benefit index (EMCI) than Gliricidia. EMCI of oxidative torrefaction of Gliricidia showed no significant difference from that of inert conditions. A new severity factor was introduced for oxidative torrefaction, and the normalized severity factor showed a linear correlation with torrefied biomass properties, which could facilitate oxidative torrefaction modeling.

Sustainability is the ability to carry out required tasks and meet required needs without compromising the long-term availability of the required resources and the long-term condition of the environment. Sustainability recognizes the finite nature of available resources and requires that steps should be taken to ensure that products and processes do not adversely impact the environment, society and the economy. The textile industry is one industry that has been known to have an impact on the environment and as a result on society. This has been seen in different sectors of the industry, particularly the colouration and yarn manufacturing sectors. Chemicals, which are widely used for the colouration and finishing processes, can be harmful to the environment. The dwindling availability of raw materials warns against the indiscriminate use of textile materials. The substitution of synthetic materials in place of natural raw materials can cause problems about degradability. Testing plays a major role in ascertaining the suitability of materials and processes in terms of their sustainability and their impact on the environment. This chapter discusses various tests that can be carried out to ascertain the sustainability of textile materials and related processes. It also seeks to discuss the actual testing itself in terms of its own sustainability.

The textile and apparel industry is the second largest polluter in the world; hence, it is the primary export industry in Sri Lanka. Because of this high negative impact level of the industry, this chapter explains a study that investigated the impact of the export apparel industry in Sri Lanka on Sustainable Development Goal (SDG)12: ensure sustainable consumption and production patterns to concise possible regulatory guidelines and suggestions for the Sri Lankan apparel Industry. Also, ISO 14062:2002 standards introduced eight sustainable product design strategies that can be used to ensure the goals of SDG12. Sri Lankan Apparel exports mainly cater to mass-market retailers; therefore, this study was conducted with mass-market-oriented apparel exports to get a broader impact. The study employed a mixed method where qualitative and quantitative elements were examined. Thirty-five designers with 3–20 years of experience in this sector were questioned through an online questionnaire. The answers were analysed based on a quantitative approach to find patterns. Ten designers with more than ten years of experience were interviewed to clarify the generated patterns of the online questionnaire. The outcomes revealed that the design process in mass-market-oriented apparel exports is mainly based on profits. However, they have to pay deep concentration to sustainability due to the pressure from external policies such as governments and other non-government organisations. Furthermore, the export apparel designers in Sri Lanka use ISO 14062:2002 sustainable design strategies based on two perspectives: profit-oriented; and policy-oriented. Specific strategies still need to be considered thoroughly while designing with profit orientation. Also, the effective use of land is heavily neglected during the design process due to the lack of awareness. However, it can make an impact on both profit and policy orientations. Based on the findings, the study suggested that the export apparel designers need more awareness, training, and support from higher management to implement the design strategies. Also, it is found that ISO 14062:2002 related sustainable design strategies should be refined and eloborated to get more outcomes to achieve SDG12. Finally, interpreting sustainable design strategies from a profit-oriented perspective can attract the mass-market apparel industry to actual, sustainable initiatives to achieve SDG12 more realistically. Therefore, more explorations are needed to fulfil that requirement.

This chapter highlights that oral tradition holds the identity of indigenous cultural textiles, which shows that the quality that comes from tradition is the trend and luxury and gives insights towards sustainable development. The village of ‘Thalagune’ in the Central hills of Sri Lanka has earned a long reputation for identical hand-loom textiles since ancient times. Today, ‘Thalagune’ textile productions are known as one of the best commercial cultural products among locals and foreigners. They are a community with 8 families living in an interconnected society and selling their skills. The research found that indigenous weavers have improved their quality of life by understanding and realising their potential in the art and craft of traditional hand-loom weaving. The way of life contains the capacity for continuance, which is bound with the inheritance memory. Folk poems/verses, stories, ritual recites, and beliefs show their valuing system of life and occupation. Oral tradition reveals the whole product development procedure. From cultivation of raw materials, preparation of cotton yarn, dyeing, feeding to weaving machines to weaving patterns, traditional design and motifs and a variety of products were recorded in their oral tradition. Many factors cause inequalities in the textile industry, such as gender wage, trade liberation, technology, lack of competent labour, and erratic yarn supply. The inability to make a livelihood from one’s skill also causes inequality in the textile industry. Besides, Luxury brands look for identity and uniqueness. The process of design in traditional hand-loom weaving shows the potential to provide firm alternatives. Traditional weaving shows unique aesthetic expression as they have been inherited from generation to generation. To strengthen the livelihood from one’s skill, it is suggested to develop local perspectives, have the service of traditional artisans’ skills, and let them earn. When designers join the instinctive with the mechanical, the hand-crafted and the technological, the result can be magical. This chapter found its close relation with sustainable development goal 10, which reduced Inequalities: Getting a better life and well-being would help reduce inequalities in the textile industry.

This chapter aims to provide available data on banana varieties of Sri Lanka, potential opportunities for extracting fibre from common varieties, their physical, chemical, and morphological characteristics, and possible textile applications and related government interventions. Banana is the most widely grown fruit crop in Sri Lanka and generates massive organic waste. This waste, a rich source of cellulose fibre, can be used to extract sustainable natural fibres as an alternative in the textile industry. A limited number of studies have been conducted on Sri Lankan banana fibres’ physio-chemical and morphological characteristics and their applications in textiles. Present literature on banana fibres are country-specific factors; therefore, an overall analysis and their application in the Sri Lankan context will generate new market opportunities and open avenues for further research. The chapter will address SDGs 1 and 9.

Sustainable natural dyeing is a process that aims to minimize the environmental impact of banana fiber dyeing while promoting the use of eco-friendly and renewable resources. This approach involves the use of plant-based dyes. This study explores the application of natural dyes for banana fiber, a versatile and abundant natural material. This study explores the potential of natural dyes derived from locally sourced materials such as banana pseudostem sap (Musa acuminata) extracted juice, jamun fruit (Syzygium cumini), coconut shells powder (Cocos nucifera), Ceylon green spinach (Basella alba), and Tree Turmeri (Coscinium fenestratum) dyeing for banana fibers. Each of these natural dye sources offers a wide range of pastel color palette with diverse colors in the dyeing process. Some of these dye sources, such as banana pseudostem sap juice and jamun fruit juice, possess unique chemical properties that can create interesting visual effects. One of the key aspects explored in this study is the mordant-free dyeing techniques for banana fibers. Banana fibers in mordant-free dyeing techniques have the essential ability to provide long-lasting colors, color fastness, and dye absorption without the need for mordants. By exploring the potential of natural dye sources and mordant-free dyeing techniques for banana fibers, this chapter aims to promote sustainable and vibrant coloration options in the textile and fiber industry in Sri Lanka. The extraction processes, dyeing techniques, and color fastness properties of these natural dyes are examined to assess their viability in textile applications. By providing comprehensive information on selected natural dye sources of five (5) and their properties for dyeing banana fibers, this study aims to promote sustainable and environmentally conscious practices in the fiber and textile industry and economic opportunities for local communities in Sri Lanka. Understanding the potential of natural dyes offers opportunities for creating unique and eco-friendly textiles, enhancing the market appeal of banana fiber-based products, and contributing to a more sustainable future.

Handloom textiles in the Southern Province are a testament to the cultural legacy and traditional knowledge passed down through generations. With the continued practice of traditional design methods and techniques, these textiles stand as a ‘Living Heritage.’ This term captures the intangible cultural heritage of handloom textiles, encompassing traditions, knowledge, and skills inherited over time. The Textile Department of the Southern Province consistently motivates and encourages the skill development of handloom textile weavers. As a result, these weavers are constantly engaged with ‘Living Skills,’ showing a readiness to learn at all times. Recognizing the industry’s interconnectedness with societal facets, such as culture, environment, and economic growth, a holistic approach to promoting and achieving sustainability in the handloom textile sector. The Textile Department of the Southern Province adheres to the Sustainable Goals by following a Strategic Plan. This Strategic plan seeks to balance the requirements of the various elements of Sustainable Goals. The aim of the study is to identify how handloom textile weaving heritage prevails as a living entity and living skills in the Southern Province of Sri Lanka toward advancing Sustainability Goals 01 and 04. Despite the challenges posed by the current economic situation and administrative shortcomings, the Department and its management recognize and value the weavers and the broader workforce for their significant economic contributions. These weavers and textile designers are valued as the vital human capital of the industry. This chapter emphasizes the alignment with SDG 01, which focuses on enhancing people’s skills for improved livelihoods, and SDG 04, centered on Learning and Education. It underscores the importance of collaborating with Universities to leverage their educational resources and training for the weavers. This partnership also facilitates the dissemination of information to the broader local community. This research employed qualitative research methods. The Primary data was collected through participant observation and semi-structured interviews. The researchers directly observed Departmental documents to glean further information. Administrative Officers, Weavers, Coordinating Officers of the Department, and Instructors were interviewed to gain insights into the experiences, perspectives, and viewpoints of SGD 01, which is vital to nurturing people’s skills to attain a more prosperous and enriched life. SGD 04 focused on learning and education in the textiles industry, which has held a pivotal role in societies over the ages, offering a wealth of opportunities for exploration. Universities focusing on textiles can significantly impact the sector by offering free educational tools, training, and sharing expertise within the local community. Policymakers, academics, and government authorities will benefit from this study.

This study examines how traditional crafts intersect with sustainable development, aiming to uncover factors that support the ongoing viability of rural artisanal livelihoods. The study centers on Dumbara weaving, Sri Lanka's ancient textile tradition, to explore its role in fostering long-term sustainability. For approximately six centuries, Dumbara weaving has been upheld by a few artisan families residing in the village of Talagune nestled in the central hills of Sri Lanka. The Dumbara artisan community, historically relegated to a low caste status, continues to endure marginalization and oppression to this day. The present study proposes that the endurance of Dumbara weaving over time is attributed to its integration into social institutions such as familial and kinship networks, alongside non-kin artisan-patron relationships. Artisans utilize both forms of social relationships to mitigate their marginalized status, perceiving their craft as more than just a means of livelihood. The central focus of this study is on how the Dumbara artisan community negotiate the complexities of tradition and innovation, authenticity and commercialization within a socio-economic structure where elite patrons exert substantial influence. By exploring this two-way relationship, the study deals with how Dumbara weaving contributes to the attainment of Sustainable Development Goals (SDGs), particularly emphasizing the sixteenth goal outlined by the United Nations. This goal underscores the significance of respecting and fostering cultural diversity, nurturing cross-cultural understanding and harmony, mitigating inequalities, and advocating for the rights of marginalized communities. Dumbara artisans’ commitment to sustainable practices in maintaining social relations through their craft have enabled them to ensure economic prosperity as well as attain upward social mobility by fostering communal values.

One of the worst pollutants in the world is the fashion industry. Landfills receive enormous amounts of post-consumer trash each year. However, as current customers’ awareness of environmental issues grows, they are increasingly drawn to wearing used clothing. However, the concept of secondhand fashion consumption clearly lacks awareness in the Sri Lankan context. This aligns with the 12th SDG sustainable development goals (SDGs)-Ensure sustainable consumption and production patterns. The qualitative study incorporates the theoretical notion of the means-end chain model. It demonstrates five consideration values of secondhand consumers: price-consciousness, emotional bond consciousness, comfortability consciousness, quality and brand consciousness, and social and environmental consciousness. Additionally, we demonstrate non-secondhand fashion consumers’ consideration values of social status consciousness, hygienic consciousness, comfortability consciousness, quality and brand consciousness, and self-expressive consciousness. Ten hypotheses were developed based on the findings. Hypotheses were investigated using a survey questionnaire in study two. The data were analysed using multiple regression analysis in SPSS. The findings indicated that consumer purchase and word-of-mouth intentions grew as emotional bonds increased. So, consumers with high emotional bonds have the highest purchase and word-of-mouth intentions. Further, consumer purchase intention increased as hygienic factors grew. This research sheds some light on the growing knowledge of secondhand consumption by identifying the impact of Sri Lankan consumer values on their purchase intention and word-of-mouth intention of secondhand fashion products. This research also has practical implications by highlighting a few opportunities and constraints related to the secondhand fashion retail sector in Sri Lankan fashion retail.

The Sri Lankan textile and apparel industry is a significant contributor to the country's economy, but it also faces challenges related to sustainable development. This chapter explores sustainable development approaches that the industry can adopt to reduce environmental impact and promote social responsibility. The study begins by examining the current state of the textile and apparel industry in Sri Lanka and identifying its sustainability challenges, including water scarcity, carbon emissions, and labour practices. The chapter then discusses sustainable development approaches such as circular economy, eco-design, and green supply chain management, which can help address these challenges. The circular economy model emphasizes resource efficiency and waste reduction, eco-design incorporates sustainability into the design process, and green supply chain management promotes sustainability throughout the entire supply chain. These approaches can enable the textile and apparel industry in Sri Lanka to become more environmentally and socially sustainable, while also improving economic performance. Overall, the adoption of sustainable development approaches is critical for the long-term success of the Sri Lankan textile and apparel industry.

The growing urban population and traffic congestion underline the importance of building pedestrian-friendly environments to encourage walking as a preferred mode of transportation. However, a major challenge remains, which is the absence of such pedestrian-friendly walking environments. Identifying locations and routes with high pedestrian concentration is critical for improving pedestrian-friendly walking environments. This paper presents a quantitative method to map pedestrian walking behavior by utilizing real-time data from mobile phone sensors, focusing on the University of Moratuwa, Sri Lanka, as a case study. This holistic method integrates new urban data, such as location-based service (LBS) positioning data, and data clustering with unsupervised machine learning techniques. This study focused on the following three criteria for quantifying walking behavior: walking speed, walking time, and walking direction inside the experimental research context. A novel signal processing method has been used to evaluate speed signals, resulting in the identification of 622 speed clusters using K-means clustering techniques during specific morning and evening hours. This project uses mobile GPS signals and machine learning algorithms to track and classify pedestrian walking activity in crucial sites and routes, potentially improving urban walking through mapping.

The combination of deep-learning and IoT plays a significant role in modern smart solutions, providing the capability of handling task-specific real-time offline operations with improved accuracy and minimised resource consumption. This study provides a novel hardware-aware neural architecture search approach called ESC-NAS, to design and develop deep convolutional neural network architectures specifically tailored for handling raw audio inputs in environmental sound classification applications under limited computational resources. The ESC-NAS process consists of a novel cell-based neural architecture search space built with 2D convolution, batch normalization, and max pooling layers, and capable of extracting features from raw audio. A black-box Bayesian optimization search strategy explores the search space and the resulting model architectures are evaluated through hardware simulation. The models obtained from the ESC-NAS process achieved the optimal trade-off between model performance and resource consumption compared to the existing literature. The ESC-NAS models achieved accuracies of 85.78%, 81.25%, 96.25%, and 81.0% for the FSC22, UrbanSound8K, ESC-10, and ESC-50 datasets, respectively, with optimal model sizes and parameter counts for edge deployment.

Microgrids driven by distributed energy resources are gaining prominence as decentralized power systems offering advantages in energy sustainability and resilience. However, optimizing microgrid operation faces challenges from the intermittent nature of renewable sources, dynamic energy demand, and varying grid electricity prices. This paper presents an AI-driven day-ahead optimal scheduling approach for a grid-connected AC microgrid with a solar panel and a battery energy storage system. Genetic Algorithm generates demand response strategies and optimizes battery dispatch, while LightGBM forecasts solar power generation and building load consumption. The approach aims to minimize operational costs and ensure microgrid sustainability, using a battery degradation cost function to extend its lifespan. Simulation results in that are conducted in the University of Moratuwa microgrid show a significant 14.22% decrease in electricity costs under Sri Lanka's current tariff structure, attributed to intelligent energy dispatch scheduling. Proactive demand response management has the potential to minimize costs further. This research contributes to microgrid optimization knowledge, promoting the adoption of intelligent and sustainable energy systems.

In this paper, we design a resource block (RB) oriented power pool (PP) for semi-grant-free non-orthogonal multiple access (SGF-NOMA) in the presence of residual errors resulting from imperfect successive interference cancellation (SIC). In the proposed method, the BS allocates one orthogonal RB to each grant-based (GB) user, and determines the acceptable received power from grant-free (GF) users and calculates a threshold against this RB for broadcasting. Each GF user as an agent, tries to find the optimal transmit power and RB without affecting the quality-of-service (QoS) and ongoing transmission of the GB user. To this end, we formulate the transmit power and RB allocation problem as a stochastic Markov game to design the desired PPs and maximize the long-term system throughput. The problem is then solved using multi-agent (MA) deep reinforcement learning algorithms, such as double deep Q networks (DDQN) and Dueling DDQN due to their enhanced capabilities in value estimation and policy learning, with the latter performing optimally in environments characterized by extensive states and action spaces. The agents (GF users) undertake actions, specifically adjusting power levels and selecting RBs, in pursuit of maximizing cumulative rewards (throughput). Simulation results indicate computational scalability and minimal signaling overhead of the proposed algorithm with notable gains in system throughput compared to existing SGF-NOMA systems. We examine the effect of SIC error levels on sum rate and user transmit power, revealing a decrease in sum rate and an increase in user transmit power as QoS requirements and error variance escalate. We demonstrate that PPs can benefit new (untrained) users joining the network and outperform conventional SGF-NOMA without PPs in spectral efficiency.

Microalgae are renewable biological resources that play a major role in the bioeconomy for sustainable development of diversified bioproducts and biomaterials. Microalgae have a tremendous potential in producing a variety of bioactive compounds, namely lipids, proteins, polysaccharides, photosynthetic pigments, vitamins, and minerals, as a single feedstock. Furthermore, these bioactive compounds can be processed to develop bioproducts and biomaterials, such as food/feed, nutraceuticals, pigments, biofuels, bioplastics, biofertilizers, and nanoparticles. Microalgae-based multiproduct biorefining is a widely used concept with the aim of enhancing economic feasibility as well as the overall sustainability of the production process. The sustainability of microalgae cultivation is further reinforced via utilization of waste streams, such as wastewater and CO2-enriched flue gas, for nutrient recovery and carbon sequestration, with simultaneous bioremediation. Consequently, a net-zero emission carbon-neutral production process is facilitated in the development of microalgal bioproduct and biomaterial, promoting a sustainable circular bioeconomy. In this context, the present chapter focuses on the key role of microalgae in maintaining a sustainable circular bioeconomy while scrutinizing its potential within a multiproduct biorefining model. In addition, technoeconomic challenges associated with sustainable processing of microalgae for bioproducts and biomaterials, potential strategies to overcome the drawbacks, and future prospects will be discussed.

Perovskite solar cells (PSCs) have emerged as promising candidates in PV research due to their exceptional photovoltaic properties. However, the toxicity and environmental concerns associated with lead‐based perovskites have led to the exploration of lead‐free alternatives. In this study, the performance of lead‐free bismuth PSCs, specifically PSCs based on Cesium Bismuth Iodide (Cs3Bi2I9) and Methyl Ammonium Bismuth Iodide (MA3Bi2I9), is investigated. Numerical simulations using the solar cell capacitance simulator (SCAPS) are conducted to comprehensively analyze the influence of key parameters, including the band‐to‐band radiative recombination rate, the absorber layer defect density, the absorber layer/electron transport layer (ETL) interface defect density, the absorber layer/hole transport layer interface (HTL) defect density, operating temperatures, and series and shunt resistances, and to optimize the proposed PSCs accordingly. The simulation results demonstrate a significant impact of these key parameters on the performance of Cs3Bi2I9 and MA3Bi2I9 PSCs. The optimized Cs3Bi2I9‐based PSC exhibited notable performance characteristics, including a PCE of 13.81%, a Voc of 1.16 V, a Jsc of 18.14 mA cm⁻², and FF of 65.51%. In contrast, the MA3Bi2I9‐based PSC demonstrated relatively lower performance, with a PCE of 11.82%, Voc of 1.14 V, Jsc of 18.06 mA cm⁻², and FF of 57.33%. The study on the defect density at the interfaces revealed the performance of PSCs is predominantly influenced by defects at the front interface (ETL/Absorber) rather than the rear interface (HTL/Absorber). These results provide valuable insights for the experimental design and optimization of lead‐free bismuth PSCs.

Identifying autism spectrum disorder (ASD) symptoms accurately is a challenging task. The traditional subjective diagnostic process of ASD relies on time‐consuming behavioural and psychological observations. In this study, we introduce an ensemble learning‐based classification model using an open‐access database focusing on functional magnetic resonance imaging (fMRI). We propose a novel multi‐model ensemble classifier (MMEC) and multisite ensemble classifier (MSEC) with transfer learning (TL) for ASD classification to improve the prediction accuracy. The MMEC utilizes four base classifiers, Inception V3, ResNet50, MobileNet, and DenseNet to boost the performance of the individual convolutional neural network (CNN) models. The MSEC combined the base classifiers trained from different data sites. We evaluate the two models with ensemble averaging, weighted averaging, and stacking methods. The proposed MMEC with stacking shows the state of art performance compared to MSEC, improving the prediction accuracy by 3.25%. The obtained results have shown an accuracy of 97.82%, 97.82%, and 97.78% for ensemble averaging, weighted averaging, and stacking methods, respectively, on multi‐site datasets. The ensemble classifier MMEC performed better than a single classifier on the multi‐site dataset. The proposed MMEC opens a new paradigm to design a universal ASD classification framework.

Recently, MXene has been identified as a novel reinforcing material for elastomers. Also, the free‐radical scavenging ability of MXene can be used to improve the antiaging properties of unsaturated elastomers, such as natural rubber (NR). Herein, Ti3C2Tx MXene is incorporated into NR at 0–0.8 wt% to investigate the mechanical and antiaging properties of NR/MXene nanocomposites (NRMX). The resultant nanocomposites with increasing MXene content show improved tensile properties and storage modulus due to the homogeneous distribution of MXene in the nanocomposite, interfacial interaction between MXene and NR molecules, and immobilization of NR polymer chains by MXene. Also, the reduced permanent deformation and hysteresis indicate a lowered heat build‐up with increasing MXene concentration. Finally, the improved mechanical properties retention, increased glass transition temperature, reduced free radical, and –OH and CO formation upon accelerated aging confirm the enhancement of the antiaging properties of NRMX nanocomposites.