Zorig Melong | A Technical Journal of Science, Engineering and Technology
Zorig Melong is a peer-reviewed interdisciplinary journal published by the College of Science and Technology, Royal University of Bhutan. It serves as a platform for researchers, academicians, engineers, architects, and industry professionals to share and access the latest advancements in science, technology, engineering, and innovation. The journal encourages high-quality, original research that addresses real-world challenges while promoting sustainable and practical solutions. Aim and Scope The journal aims to be a leading national publication in the fields of science, technology, engineering, and innovation by fostering the dissemination of authentic research. Its scope includes a wide range of topics, from fundamental scientific discoveries to applied technological solutions, with an emphasis on feasibility, economic viability, and social acceptability. Zorig Melong seeks to bridge the gap between academia and industry, supporting researchers and professionals in staying updated with emerging trends and developments.
College of Science and Technology, Royal University of Bhutan
Evaluating the Seismic Risk of Reinforced Concrete Buildings of Colleges under Royal University of Bhutan using RVS Method
Bhutan is located in seismically active Eastern Himalayas and is highly vulnerable to future earthquake disasters. Moreover, most of the colleges under Royal University of Bhutan (RUB) have been upgraded from schools and training institutes. Therefore, an immediate assessment of the vulnerability of educational infrastructure is required to ensure user safety and continuity of education after a disaster. In this study, the Rapid Visual Screening (RVS) methodology of FEMA P-154 and IIT Bombay is used to evaluate all reinforced concrete (RC) buildings of colleges under RUB. A detailed field survey was carried out from late 2024 to early 2025 in all colleges. It shows that 40% of RC buildings are below the commonly accepted benchmark score (S>2) according to FEMA P-154. And similarly, around 49% have scores in the range (0.7 < S < 2.0) according to IIT Bombay’s methods, indicating "moderate risk", as this range will have a high possibility of Grade 3 damage and a very high chance of Grade 2 damages during an earthquake. These two different methods of RVS provide similar results due to inadequate seismic detailing, soft-storey irregularities, and ageing materials. The findings provide a classification of buildings based on potential seismic vulnerabilities and recommend priorities for detailed assessment, retrofitting, and mitigation measures for risk prevention and reduction. The study advocates that RVS is an effective preliminary tool for decision-makers to prioritize risk intervention strategies under limited time and resources.
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Predictive Analytics for Identifying Surface Water Sources for Domestic Water Supply in Phuentsholing, Bhutan
Surface water is a primary source of drinking water in Bhutan. To ensure a reliable supply that meets both quality and quantity requirements, it is crucial to identify suitable sources. This study presents an integrated approach using Geographic Information Systems (GIS) and machine learning to identify potential surface water sources. Satellite imagery from Landsat-8 and Sentinel-2 was utilized to generate geospatial datasets. Five key variables influencing the spatio-temporal presence of water—rainfall, temperature, soil type, Normalized Difference Vegetation Index (NDVI), and topography were analyzed within a GIS environment. The Random Forest (RF) algorithm, known for its robustness in handling nonlinear and high-dimensional data, was employed to predict potential water sources. Model outputs were validated through field surveys and spectral analysis using the Normalized Difference Water Index (NDWI). The study identified 50 viable water source locations situated above 450 meters in elevation. The model achieved an area under the curve (AUC) score of 0.99, indicating a strong correlation between predicted and actual water sources. These results confirm that integrating machine learning with remote sensing and GIS is an effective approach for surface water resource planning in Bhutan's hilly terrain.
Towards a Responsible AI Governance Framework-Lessons from Policy Implementation in Malaysia
The rapid integration of Artificial Intelligence (AI) technologies across public and private sectors has underscored the urgent need for governance frameworks that ensure ethical, transparent, and accountable deployment. AI's economic contribution is projected to reach RM 480 billion by 2030 in Malaysia. This study employed a qualitative analysis approach to capture diverse perspectives from government agencies, private sectors, and academia. The study examines the Malaysia’s AI governance readiness across five critical dimensions: country governance and regulation, education and research innovation, talent development, cultural sustainability, and facilities management. The McKinsey 7S framework was used to analyze the readiness of AI deployment in the Malaysia’s, highlighting the interconnectivity of 7 McKinsey elements which are strategy, structure, system, shared values, skill, style, and staff and additional elements which are stakeholders & ecosystem, all of which collectively influence the nation’s AI preparedness. Findings reveal that, in Malaysia, the regulatory ambiguity remains a major challenge leading to inconsistent enforcement and compliance gaps. Stakeholder engagement is further complicated by conflicting interests and a lack of shared understanding of AI ethics and risks. Ethical considerations are paramount throughout the research process. A significant strength in institutional readiness, including investment in education, talent pools initiatives and ICT related infrastructure was identified. However, weak AI deployment observed in heritage and cultural protection efforts and limited environmental protection initiatives remain barriers, requiring tailored capacity-building programs and cross-sector collaboration. The study calls for strengthened international cooperation, risk management practices, and inclusive stakeholder engagement to ensure ethical and accountable AI deployment in diverse national socio- economic sectors in Malaysia.
Design, Fabrication, and Evaluation of A 3D-Printed Hand Amputees within a Kathmandu Innovation Ecosystem
In low-resource settings such as Nepal, patients with partial hand amputation face serious difficulties in obtaining prosthetic options both in terms of functionality and social acceptability. This paper presents the design of a customized 3D-printed cosmetic prosthesis through local innovation ecosystems to bridge these gaps. With a participatory, user-led process, the project combined the expertise of occupational therapists (OTs), prosthetists and orthotists (P&Os), and engineers to design, produce, and test by iterative cycles a prosthetic device tailored to the anatomical and psychosocial needs of a partial hand amputee. Occupational therapists played a key role in establishing user requirements, that the prosthesis supported legitimate everyday activities, whereas P&Os contributed clinical input on biomechanical alignment and prosthetic performance. Advanced techniques, including computer-aided design and 3D scanning, directed the evolution of anatomically correct prototypes manufactured from thermoplastic polyurethane (TPU), selected for its blend of flexibility and strength. Multiple iterations emphasized aesthetic realism, ergonomic fit, and task-specific function, based on quantitative biomechanical assessment and qualitative user scores. The resulting prototype demonstrated meaningful improvements in user-reported confidence and performance in daily activities, as well as enhanced social integration. Material sustainability, scalability in production, and the technical limits of 3D printing were critically interrogated, highlighting the imperative for context-matched innovation. The study recognizes the capability of locally situated, inter-disciplinary interventions to democratize access to assistive technology in low-resource settings as a replicable model for inclusive healthcare innovation.
Spatial and Temporal Analysis of Urban Heat Island Effects in College of Science and Technology
The phenomenon of Urban Heat Island (UHI) which is defined by increasing surface and air temperature in urban areas compared to surrounding areas is a growing concern due to several impacts. These includes heat stress, increase in energy consumption, degradation in environmental quality among others. Understanding the spatial and temporal variation of UHI will be useful in the planning of urban infrastructures. Therefore, the aim of the present study is to analyze the spatial and temporal variations of UHI in the College of Science and Technology (CST) from 2013 to 2023. The study analyzed the changes in Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-up Index (NDBI). Air temperature was recorded at multiple locations at 8 AM, 12 PM and 4 PM over four days to examine the variation in air temperature due to the type of ground surface using handheld thermometer. The findings of the study show that there was a 3.5% increase in built-up area and a 17.14% decrease in vegetation cover during the 10-year period. The lower and upper limit of LST decreased over the decade which may be attributed to different seasonal timing. Normalized LST (NLST) analysis revealed that UHI zones expanded from 46.7% in 2013 to 53.3% in 2023. A weakening negative correlation between NDVI and LST (from -0.65 to -0.52) and a strengthening positive correlation between NDBI and LST (from 0.58 to 0.60) were observed, highlighting the cooling effect of vegetation and the heat retention of built-up surfaces. Air temperature patterns showed consistently higher values over built-up areas and cooler, more stable temperatures over vegetated zones. The findings provide valuable insight into the extent of the UHI effect in CST.
Enhancing Flood Susceptibility Mapping Through Machine Learning and Multi-Criteria Decision-Making Techniques
Flooding has historically caused massive social, economic and environmental damage affecting lives and livelihood. There is a concerted effort to study the phenomenon of flooding, hazard assessment, mitigation and prevention of flood and associated risks. In the planning phase, Flood Susceptibility Mapping (FSM) to identify vulnerable areas are useful to provide critical data for preparedness, risk management, and sustainable land use planning. Most flood susceptibility maps are generated using multi-criteria Decision models (MCDM) and Machine Learning models. The reliability and accuracy of Flood Susceptibility Maps are increased by systematic evaluation of multiple flood related factors and their interrelationship which are more easily analyzed by machine learning models. Therefore, the aim of the study is to develop FSM for Toorsa River and the Pasakha River. using the two multi-criteria Decision models, the Shannon Entropy model and the Criteria Importance Through Intercriteria Correlation method. Further FSM were also developed using two machine learning algorithms of Random Forest and Support Vector Machine. Factors such as elevation, slope, soil type, and land use/land cover, rainfall, and Topographic Wetness Index were assigned weights based on the two MCDM techniques to develop the maps. Similarly, the same factors were used for training ML models and validating their performance in flood-prone area classification. The results of the study shows that Area Under Curve scores were high (0.98) in both the study area using Random Forest while the lowest score of 0.21 was obtained for Pasakha river using the Shannon Entropy Model. In general the Machine Learning models are found to be more accurate, which may be attributed to its ability to interpret data in a non-linear manner unlike the MCDM methods. The final Flood Susceptibility Maps of the study area were produced based on Random Forest models, as it provided the most accurate results.
Criteria Importance Through Intercriteria Correlation
Short-Term Load Forecasting In Thimphu and Phuentsholing Regions Using Machine Learning and Deep Learning Techniques
Accurate short-term load forecasting is essential for efficient power system operation, energy management, and electricity pricing. Traditional statistical methods, such as seasonal autoregressive integrated moving averages with exogenous variables (SARIMAX), often fail to capture the intricate and dynamic patterns of electricity demand. Addressing the knowledge gap in developing countries, particularly Bhutan, this research explores advanced machine learning and deep learning techniques to enhance short-term load forecasting (STLF) accuracy in the Thimphu and Phuentsholing regions of Bhutan, characterized by unique electricity demand patterns due to population growth, industrial and commercial activities, and supply constraints. We evaluated SARIMAX, Support Vector Regression (SVR), Long Short-Term Memory Networks (LSTM), Convolutional Neural Networks (CNN), and hybrid CNN-LSTM architectures. On single-step STLF, we analyzed day-ahead aggregated load forecasts and 1-hour-ahead load forecasts based on historical load data over five years (2018-2022) for both Thimphu and Phuentsholing regions. In day-ahead aggregated load forecasting, the hybrid CNN-LSTM outperformed all other models with Mean Absolute Percentage Error (MAPE) values 2.332 ± 0.075% for Thimphu and 3.216 ± 0.036% for Phuentsholing, while also achieving the best MSE, RMSE, and R2 metrics. For 1-hour- ahead forecasting, the CNN model achieved the lowest MAPE of 3.224 ± 0.06% in Thimphu and the hybrid CNN-LSTM model achieved a best MAPE of 3.687 ± 0.027% for Phuentsholing. Careful preprocessing, optimal feature engineering, and hyperparameter tuning were performed for all forecasting types. The findings demonstrate that data-driven approaches significantly enhance forecasting accuracy, providing valuable insights for energy planners to manage resources and maintain the power grid, preventing blackouts and other disruptions.
Significance of Density in Structural Timber Engineering and Density Profile of Bhutanese Softwood Timber
Given its favorable structural and environmental attributes, timber is witnessing a renaissance as a viable modern structural material. A vital property influencing the use of timber as a structural material is its density. Density not only dictates the self-weight of structures but is one of the key characterizing parameters for timber strength. Structural softwood species in Europe are graded into strength class ranging from C14 to C50 according to the requirement established in EN338. A C14 strength class, for instance, must have a characteristic density value of 290kg/m3 and a mean density value of 350kg/m3 at 12% moisture content. More significantly, the strength of fasteners used for connections in timber structures is greatly influenced by density. The embedment and withdrawal strength of fasteners which affect the connection’s ultimate capacity and behavior are very much a function of timber density. Further, the ease and cost of processing and fabrication and the charring rate of timber during fire are also dependent on its density. Timber, however, exhibits significant inter and intra species variability in its density. The Joint committee for Structural Safety (JCSS) recommends a probabilistic model for density variability for graded softwood in Europe with a normal distribution variation and a coefficient of variation (COV) of 0.1. Currently models to represent the density variability for native timber species in Bhutan is nonexistent. This paper discusses the importance of density in timber structures and presents the density profile recorded till date within the overall framework of developing the probabilistic model to represent the density variation in Bhutanese conifer timber species.
Distribution and Coefficient of Variation
Dynamics of Electricity Demand: Analysis and Forecasting for Bhutan
Bhutan is endowed with abundant renewable energy sources to generate electricity from hydro, solar, and wind. Bhutan achieved over 99% electrification in 2016, well ahead of its year 2020 target. This paper provides a comprehensive overview of Bhutan’s electricity demand, a country experiencing significant economic growth and accelerated energy demand growth. This study aims to assess current trends and project future electricity demand. Bhutan's electricity demand has been growing over the years, reaching 9,167 kWh per capita in 2024, with a 12% CAGR over two decades. The industrial sector dominates consumption, accounting for over 88% of total demand, while residential, institutional, and commercial consumers contribute just over 12%. Electricity demand follows seasonal patterns, peaking in winter due to heating needs. To forecast low voltage demand, the Holt-Winters method was used, a time series forecasting technique incorporating trend and seasonality. This model effectively captures demand fluctuations, which are influenced by economic growth and seasonal variations. The forecast for the existing and upcoming high voltage industries are based on the contract demand, demand factor and load factor of existing industries. Using the Holt-Winters forecasting method for low voltage and contract demand for industries, the study projects a 15.05% CAGR in electricity demand over the next five years, with 94% demand from energy-intensive loads. As demand outpaces supply, strategic planning, capacity expansion, and policy actions are critical for ensuring reliable energy to support Bhutan’s socio-economic development. The projections suggest that there is a need to enhance firm generation capacity to be able to ensure energy security and meet the increasing demand and eliminate winter power deficits.
Analysis of Bhutan Power Systems using PSS®E Software
Electricity demand in Bhutan has been increasing steadily ever since utility-scale power supply was introduced into the country in the 1960s and 1970s and Bhutan’s first large-scale hydropower project, 336 MW Chhukha Hydropower Plant was commissioned in the 1980s. In the past three years, the rise in the demand has accelerated due to rapid growth in the industrial development. This has led to increasing stress on the power system infrastructure. Further, the country’s grid is subjected to varying operating conditions brought about by seasonal variations in generation, peaking in summer, flexible operation and reaching its lowest level in winter. Additionally, the grid is connected to Indian systems via thirteen feeders at different voltage levels further increasing the risks of disturbances and influence from the larger Indian grid. Therefore, it is of paramount importance to carry out the system analysis at regular intervals to study and assess the behavior of the power systems, based on which the timely corrective measures can be implemented to ensure grid stability and efficiency. In this study, the existing power system model of Bhutan is developed in PSS®E software and simulation is carried out for summer and winter seasons. For summer, the data sets of 28th July 2024 at 20:00 hours is used while for winter, the data sets of 14th February 2024 at 15:00 hours is used corresponding to the highest generation and the lowest generation respectively. The simulation results show the overloading of Mangdechhu to Yurmo line, overloading of transformers at Gedu substations, and under voltage issues in a few substations in both the study periods. Therefore, to resolve the overloading issues, upgradation of transformers at Gedu substation and connection of a new line from Mangdechhu to Yurmo is recommended. Further, to resolve under-voltage issue, shunt capacitor of 12 MVAR is recommended in Damji substation, which has been determined based on the QV analysis.
Enhancing Middle Stream of Fluorocarbon Gas Management in Malaysia through AI-Driven Solutions
Fluorocarbon (FC) gases, widely used in refrigeration and air-conditioning (RAC) systems, pose significant environmental risks due to their ozone depletion potential (ODP) and global warming potential (GWP). In Malaysia, the midstream (usage) phase of FC gas management remains a critical hotspot for emissions, primarily due to undetected leaks and inefficient maintenance practices. Traditional reactive approaches to leak detection and maintenance are insufficient to address these challenges, necessitating a shift toward proactive, AI-driven solutions. This study explores the potential of digital technologies, including IoT-enabled sensors, predictive maintenance algorithms, and blockchain-based inventory systems, to enhance FC gas management in Malaysia. Using a mixed-methods approach, the research employs the DPSIR (Driving Forces, Pressures, State, Impacts, and Responses) framework and SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis to identify key challenges and opportunities in the current FC gas lifecycle. The findings reveal that AI-driven predictive maintenance and real-time leak detection can significantly reduce emissions, improve energy efficiency, and extend the lifespan of (refrigeration and air conditioning) RAC equipment. Furthermore, integrating blockchain technology can enhance transparency and compliance in FC gas inventory management. The study concludes that adopting these digital solutions, alongside structured training programs for technicians, can transform Malaysia’s midstream FC gas management, aligning it with global sustainability goals. This research contributes to the growing body of knowledge on AI-driven environmental management and offers actionable insights for policymakers and industry stakeholders.
Optimal Capacitor Placement for Minimizing Power Losses and Improve Voltage Profile on Distribution System of Dewathang Feeder
The strategic placement of capacitors in a distribution system is crucial for optimizing power quality, minimizing power losses, and ensuring voltage stability. Inadequate optimization of capacitor placement can result in increased power losses, voltage fluctuations, and suboptimal system performance. This paper focuses on determining the optimal location and size of capacitors in radial distribution systems to achieve minimal power loss and an improved voltage profile. A comprehensive analysis of metaheuristic algorithm techniques, such as Genetic Algorithms (GA) and Particle Swarm Optimization, is conducted to address the capacitor placement problem, with the goal of achieving the best solution. Detailed simulations using ETAP and MATLAB software demonstrate that the proposed method significantly improves voltage regulation. Additionally, the economic benefits of capacitor placement, including reduced energy costs and enhanced system reliability, are evaluated. Simulation studies on the outgoing feeders of the Nyellang substation (132/33/11 kV), particularly focusing on the Dewathang (33/11 kV) radial distribution network, show substantial reductions in power losses and notable improvements in voltage regulation. For electrical engineers, this research study provides a systematic and algorithm-driven methodology to manage power losses through optimizing the capacitor placement in transmission lines, enabling better decision-making in distribution network planning. Utility providers can also make use of these findings to achieve systems performance efficiency, minimize repair and maintenance cost, and reduce energy losses that enhance system reliability. It also assesses infrastructure upgradation and new energy efficient technology integration. The proposed approach offers a cost-effective and practical solution for optimizing the performance of distribution networks through optimal capacitor placement.
Comparative Analysis of Particle Swarm Optimization and Salp Swarm Algorithm in improving the Voltage Profile of IEEE 9 Bus System
Voltage stability is a critical aspect of modern power system operation, directly influencing system reliability, operational efficiency, and protection against grid collapse. Voltage instability often arises due to heavy reactive power demand, insufficient local reactive compensation, and weak transmission infrastructure, especially under high loading or fault conditions. To address these challenges, advanced optimization techniques are often employed for reactive power planning and voltage profile enhancement. Hence, a comparative analysis of two nature-inspired metaheuristic algorithms—Particle Swarm Optimization (PSO) and Salp Swarm Algorithm (SSA)—in mitigating voltage instability of the IEEE 9-Bus test system is done. The aim is to compute the optimal placement and sizing of reactive power compensation using shunt capacitors. The objective function is formulated to minimize overall voltage deviation from the nominal value (1 p.u.), and ensure computational efficiency of the algorithms. Both algorithms were evaluated on three main performance metrics: voltage loss minimization, convergence characteristics, and recommended capacitor sizes (MVAr ratings). PSO algorithm showed faster convergence towards near-optimal solutions, making it suitable for time-constrained applications. On the other hand, SSA showed superior performance in recommending more economical MVAr injection sizes. But SSA achieving comparable voltage profile improvements, resulted with slightly higher iteration counts. Original contribution of this work lies in the direct comparison of PSO and SSA for increasing voltage stability under the same system conditions, offering insights into their relative strengths. The significance of this study is its potential to guide power system engineers in selecting appropriate optimization methods based on computational and economic trade-offs. Future research can extend this approach to meshed and large-scale systems, integrating uncertainty in load demand and renewable generation.
ChatGPT: An Enabling Model for English Creative Writing in the Eleventh Grade of Phuentsholing Rigsar Higher Secondary School
In today’s digital era, education has undergone a significant transformation, particularly in English learning, where creativity plays a crucial role. However, Bhutan’s eleventh-grade curriculum primarily emphasizes grammar, comprehension, and writing skills, with minimal focus on creative expression. Similarly, at Phuentsholing Rigsar Higher Secondary School (PRHSS), creative writing is primarily confined to classroom assessments, which may inadvertently prioritise performance over student wellbeing by fostering stress and limiting intrinsic motivation. Moreover, while the school is equipped with Information and Communication Technology (ICT) facilities, it has yet to explore the potential of Artificial Intelligence (AI) tools to enhance creative writing. Thus, this action research explores the integration of ChatGPT, a generative AI tool, as a model to guide creative writing among 37 eleventh-grade students (18 females and 19 males) of varying English proficiency, selected through convenience sampling. A quantitative approach was employed, using pre- and post-intervention creative writing assessments. The intervention was grounded in Albert Bandura’s Observational Learning and the Modeling Process of attention, Retention, Reproduction, and Motivation (ARRM). Students were engaged in AI-assisted feedback, prompt-based writing, and collaborative poetry exercises over the study period, and the findings revealed a statistically significant improvement in writing proficiency, with mean scores increasing from 4.29 to 8.81 (p < .001). This suggests that ChatGPT can effectively enhance creative expression in writing. However, progress varied among individuals, indicating the need for differentiated support. Moreover, concerns regarding overreliance on AI and ethical use remained. Therefore, teacher intervention for balanced AI integration and
Electric Vehicle Charging Infrastructure in Bhutan: Performance Analysis, User Experiences, Challenges, and Future Prospects
Electric Vehicles (EVs) have emerged as a transformative technology in the global car market, offering improved performance and environmental benefits over traditional vehicles. However, despite their growing popularity, widespread adoption has been hindered by challenges such as inadequate charging infrastructure and high costs. This study examines the experiences of EV users in Bhutan, focusing on charging habits, battery performance, and user satisfaction. This is the first study in Bhutan to combine both user experience data and technical performance analysis of EV charging infrastructure, providing a complete understanding of the current EV system. The survey was conducted in four Dzongkhags namely Thimphu, Samdrup Jongkhar, Phuentsholing and Trashigang. In addition, this study also includes performance analysis, focusing on power quality measurement at charging station located at Samdrup Jongkhar. By incorporating on-site power quality measurements, this study assesses the impact of EV charging on the local electrical grid. Survey findings reveal significant concerns about charging station availability, limited driving range of EVs and battery degradation, which impact overall user experience. While respondents acknowledge the environmental and economic benefits of EVs, they emphasize the need for more charging stations, better maintenance of EV charging stations and affordable pricing. The findings can guide policymakers by highlighting the need for government action to achieve 70% EV sales by 2035. Future work should focus on optimizing charging station placement, ensuring sustainability through renewable energy integration, and addressing battery performance concerns to ensure a seamless transition to electric mobility.
Design, Development and Performance Analysis of Solar-Electric Hybrid Dryer
Bhutan, like any other mountainous country, is facing serious issue in food preservation. Conventionally, open air sun drying is preferred for drying as it is environment friendly and cost- effective. Meanwhile, open-air drying has a few drawbacks, including dust contamination, hardening, discoloration of products and longer drying time. This, in turn, led Jigme Namgyel Engineering College, Bhutan, and Lund university, Sweden, to design and develop an indirect solar dryer. Using solar dryers has many advantages, such as lowering dependency on electricity and fossil fuels and creating a cost-effective and environmentally friendly food preservation solution. Numerous designs were developed every year and many upgrades were done to improve the efficiency of the dryer by which the main intended objective is to make the drying rate even throughout the drying chamber. In the most of dryer model developed, the prime drawback of the dryer was the nonuniform drying rate among the trays in drying chamber owing to many factors such as the number of trays, weather contingencies, tray placements and duration of the drying time. In this study, we present design, development, and performance analysis of a solar-electric hybrid dryer with an integrated heating source that is used to supplement the heated air in sections of the dryer where the drying rate is relatively slow. It also helps maintain the air temperature inside the dryer during cloudy weather conditions. We designed our hybrid solar dryer to be as simple and inexpensive as possible, and better than open sun drying while also shielding the food items from the environment. This work provides useful insights into optimizing airflow, improving component efficiency, and practical recommendations for future advancements, supporting the sustainable preservation of agricultural products in Bhutan and similar regions.
Basin-Scale Sediment Transport for Sustainable Sand Mining – A Case Study in Punatshangchhu Basin, Bhutan
This case study evaluated sediment dynamics in the Punatshangchhu basin in Bhutan. Sand mining operations in the basin commenced in 2007, with an average annual extraction rate of 0.3 × 106 m3 , accounting for approximately 65% of the country's sand demand. However, sand mining has been conducted without a comprehensive understanding of the basin's sediment budget. Therefore, this study examines the sediment budget at the basin scale using numerical modeling. Two numerical models were developed: the Rainfall-Runoff-Inundation (RRI) model simulated the 2017 flood event, while the Rainfall-Sediment-Runoff (RSR) model computed daily sediment yields during the monsoon season (May–September). The RSR model was simulated for two cases: Case 1 considered sediment supply from the river channel only, while Case 2 considered sediment supply from the river channel and slopes. The model results were evaluated against daily observed stream- flow data, suspended sediment load, and particle-size distribution. The model achieved NSE, PBAIS, and R2 values of 0.9, -0.12, and 0.9, respectively, in calibration. The validation achieved similar NSE and PBAIS values, with an improved R2 of 0.94. The total sediment yield in Case 2 was 34% higher than in Case 1. The model results showed balanced sediment inflow and outflow in the basin. However, sediment extraction at the current rate is unsustainable, with riverbed degradation at a rate of 0.26 m/year. Nevertheless, a downstream hydropower dam is expected to create an opportunity for sustainable mining.
Social Evaluation of an Improved Cookstove Intervention Program in Nepal - A Case Study
Transition to new technology is a gradual and challenging process in developing countries like Nepal. Shift to clean cooking in one of the prime focus, wherein national and international organizations are executing Improved Cookstoves (ICS) distribution programs. ICS are cleaner and more efficient options for cooking in rural areas of Nepal where electricity distribution is compromised. Despite technological advancements, due to limited consideration of social aspects, the beneficiaries are not using the distributed models as expected. This paper evaluates a case of an ICS dissemination program — particularly rocket and metallic stoves — conducted in 2019 through an international non-governmental organization based on social aspects. 163 out of 500 household beneficiaries were taken for the study from Baglung and Parbat districts of Nepal, wherein the concentration of distributed ICS were high. Survey and Key Informant Interviews were conducted to gather information on transition to clean cooking, infrastructural development, demography, health and psychology, institutional involvement, resource availability, political aspects and gender inclusion, community status, financial support and cultural aspect. The study found that 55.21% of the sampled households had the distributed ICS with them. Only 14.44% of them relied solely on ICS for cooking, and the rest depended majorly on Traditional Cookstoves (TCS) and Liquefied Petroleum Gas (LPG). 42.2% of ICS users had been using LPG as their alternative cooking solution. The distribution region lacked nearby ICS manufacturing facility. Some of the participants reported respiratory health issues and eye problems which could be due to use of TCS. Study also showed resistance among users for transition to ICS. Issues regarding fuelwood accessibility, operation and safety of ICS, food preference, and gender inclusion during distribution were minimal. It is concluded that authorities should conduct prior user study to refine their target households. Infrastructural development, institutional involvement and local stakeholder participation in decision making should be considered to promote the transition. This study recommends regular monitoring of grassroots level outcomes beyond distribution programs.
Performance Evaluation of Packed Bed Energy Storage System: A Simulation Study
Packed bed energy storage (PBES) is an alternative system for thermal energy storage through a sensible heat storage mechanism. With relatively local construction, PBES is suitable for various purposes for both low and high temperature applications. It is packed with materials that do not undergo phase change over the temperature range during the storage process. The materials have good thermal storage capacity and effective thermal conductivity. The outer tank, insulation, inlet and outlet pipelines, heat transfer fluid and energy source make up the system. This study aims to simulate the system, and analyse its thermal energy behaviour including, absorption and heat retention. For this study, the materials for the tank, insulation, and heat transfer fluid have been selected using the Pugh Matrix Method. Steatite, Basalt rock and Sand are chosen as packing materials due to their relatively desirable cost, availability and thermal properties. The simulation is conducted in TRNSYS with the components: pump, boiler, tank and segmented rock beds. Heat retention and the temperature change of the packed bed system are studied across different layers and arrangements. It is observed through the simulation that the layer of basalt, followed by steatite, and then sand showed better heat retention for identical ambient conditions and processes.
Examining The Techno-Economic Viability of Institutional Energy Generation Through Rooftop Solar Photovoltaic - A Case Study
Use of institutional rooftop solar photovoltaics (PV) is considered as a potential alternative to address energy deficit in low middle-income countries. This study aims to conduct techno-economic feasibility assessment of rooftop solar PV taking a sample case of a school in Kathmandu valley. Comprehensive questionnaire survey and field measurement are done to determine load demand and energy usage habits of the institution. PVSyst is used to develop an optimized rooftop solar PV system with a total capacity of 72.6 kWp. Technical feasibility study is done through performance ratio and loss diagrams, and economic feasibility study is done through determination of payback period. Performance ratio of 85% was achieved, and design losses are within acceptable range. The highest losses are seen in soiling and temperature derating which was below 4%. The designed system has an annual yield of 121.56 MWh, payback period of 6.7 years and return on investment of 211.4% after the payback period. For validation, the simulated results are compared with data logged in a comparable institution with a net metering system. The simulation for this institution was carried out using an identical regime. The payback period of 11.5 years was empirically calculated through the logged generation/usage data whereas the simulation result showed a payback period of 11.8 years, which is in acceptable range. This study shows that institutional rooftop solar PV is both technically and economically viable in Nepal.
Review on Contemporary Heat Transfer Enhancement Technologies in Conventional Heat Exchangers
This review examines modern approaches for improving heat transfer in heat exchangers, focusing on findings from 52 recent research papers. The strategies presented include nanofluids, surface modification techniques, the use of extended surfaces, and novel design combinations. Nanofluids, which contain nanoparticles such as Al2O3, CuO, and TiO2, have higher thermal characteristics and greatly improve heat transfer efficiency over ordinary fluids. Surface changes, such as laser texturing, electrochemical deposition, and hydrophobic coatings, significantly reduce fouling while increasing boiling heat transfer. Extended surfaces with a variety of fin designs, including louvered, wavy, and helical fins, improve turbulence and thermal performance. Machine learning algorithms, genetic algorithms, and multi-objective optimization frameworks are examples of emerging technologies that improve heat exchanger performance under a variety of scenarios. Phase change and piezoelectric materials present innovative thermal management strategies. The existing review paper lacks individual enhancement methods, making it difficult to compare knowledge across categories. Moreover, recent innovations like hybrid nanofluids, fractal/biomorphic designs, surface modification techniques were missing in earlier reviews. This review highlights the significance of combining material science, surface engineering, and computational methodologies to improve heat exchanger efficiency and operational effectiveness.
Finite Element Method Based Technical Investigation of ICEV Chassis with EV Conversion Module
Nepal is shifting towards cleaner and sustainable means of transportation through import of electric vehicles and conversion of conventional vehicles into electric. The conversion process requires static and dynamic analysis of vehicle chassis to ensure structural integrity. This study aims to assess static structural performance of a conventional car model that is the brink of 20-year age limit. CAD modelling is done using SolidWorks and Finite Element Analysis is done in ANSYS on the chassis of a Maruti 800 model with electric retrofitting components. The study carries out on-field measurements and literature review to gather vehicle specifications, and defines simulation variants related to chassis material and configuration of battery and motor. The materials considered are carbon fiber, high-strength steel, low alloy normalized steel 4140, and aluminum alloy wrought 6061. A total load of 5,405.36 N was applied on different placement configurations of motor and battery: front, trunk, motor in front, battery in front, motor in trunk, battery in trunk, motor in front with battery at the bottom, and motor in trunk with battery in front. The ultimate stresses for the varied materials employed in this study for the chassis falls within the range of 3-12 MPa, all well below the allowable stress range of 104-1470 MPa. It can be inferred that the selected materials exhibit a high degree of safety for the structural integrity of the vehicle's chassis. Based on the simulation results, the optimal placement for the motor and battery is found to be at the front of the chassis. Additionally, low alloy steel stands out as the safest material choice for the chassis. The results stand out by addressing Nepal specific transportation needs and offering a comparative material performance analysis tailored for EV retrofitting in low-to-mid-income economies. The study underscores the need for identifying minimal stress conditions for maximum load capacity, and can be used as an important component to form guidelines for vehicle conversion in Nepal.
Efficacy Study of a Pilot Installation of an On-Line Partial Discharge Measurement Sensor at KHP Unit 2 Generator
Partial Discharge (PD) is a slow-progressing phenomenon in electrical equipment which deteriorates the insulation system over time. Monitoring the PD activities has become a vital method to prevent major failures of the electrical equipment (Yue et al., 2006). This paper presents a comprehensive study of the installation & efficacy of an on-line PD coupling capacitors on the unit 2 generator of the Kurichhu Hydropower Plant (KHP), which had been in operation since August 15, 2001, without any form of monitoring or measurement system to assess the insulation system of the stators. A comprehensive site assessment identified the necessity of three PD coupling capacitors (CCs) and associated accessories suitable for real-time monitoring of the generator stator, under electrical, thermal, ambient, and mechanical stresses. Following installation, a commissioning test was conducted, and initial PD measurements were recorded as baseline data. To evaluate the system’s effectiveness, a trend analysis was performed on four sets of measurements taken from 2022 to 2024, also covering seasonal variations in 2024. Key parameters such as PD magnitude, NQS values, and level counts were analyzed along with Phase-Resolved Partial Discharge (PRPD) patterns to assess PD activity and severity. This study has done originally for the installed PD measurement system, which can be used for the similar study for the same measurement system on the generators. The results demonstrated that the online PD monitoring system effectively detected the integrity of the insulation system, with insulation resistance tests serving as a complementary validation method. The corresponding recommendations were made to KHP based on the findings.
Case Study on the Failure of 400kv XLPE Cables Termination of Tala Hydropower Plant through Electromagnetic Transient Analysis Using Mipower Software
This paper presents an investigation into the failure of 400kV XLPE (Crossed-Link Polyethylene) cable terminations at the Tala Hydropower Plant (THP) using Electromagnetic Transient (EMT) analysis in MiPower software. Four XLPE cables connect the underground Gas Insulated Switchgears (GIS) to the outdoor switchyard, from where twin moose transmission lines link to the Indian grid via Siliguri Substation. There have been frequent issues with cable terminations. Noteworthy incidents include a flashover in the outdoor cable terminations of Y phase feeder 1 on June 4, 2021 and another flashover in the indoor cable termination of B phase feeder 2 on March 17, 2022. Despite these recurring failures, the root causes remain inconclusive. EMT analysis was employed to examine the Temporary Over-Voltages (TOV), during various operations. The THP- Siliguri network was modelled in MiPower, and two case studies were analyzed, considering the impact of lightning arrestors (LAs). In the first case, a three-phase to earth fault was simulated at a distance of 0.5km from Tala on transmission line 1. In the second case, a simulation was conducted to replicate conditions during the energization of feeder 2 when circuit breakers (CBs) were closed sequentially at Siliguri and THP ends. Both cases indicated that TOVs occurred during fault but were effectively suppressed by LAs. The study confirms that the LAs at THP are functioning reliably, as simulations show TOVs suppression at cable terminations. With LAs in place, TOV levels remain within safe operational limits, validating their effectiveness. With TOV eliminated, the study suggests other degradations such as thermal aging, improper termination practices, or moisture ingress as potential contributors to the failure. As the first known EMT based study of such failures at THP, the findings offer broader insights for similar HV cable termination of hydropower plants in Bhutan with comparable configurations.
Analysis of Bhutan’s Long-Term Energy Demand Projection Using the End-Use Methodology
This paper addresses the pressing need to project Bhutan's long-term energy demand by considering crucial factors like socioeconomic dynamics, demographic trends, technological advancements, and emerging policies, which are essential for informed policymaking. It employs two scenarios: Business as Usual (BAU) which assumes a normal economic growth rate and follows historical trends in energy demand, while the Policy Motivated Scenario (PMS) scenario takes into account the potential impact of the EV (electric vehicle) Road Map 2035 policy, which aims to promote the use of electric vehicles in Bhutan. The Model for Analysis of Energy Demand (MAED_D) was utilized and the results projection shows that by 2040, the total energy of Bhutan will increase by about 121% (2,194.703 ktoe) under BAU and about 173% under PMS with respect to base year of this study (2017). Under BAU, by 2040, the industrial sector remains the largest consumer (59%), followed by households (21%), transportation (13%) and service (7%). The demand analysis by energy form projects a stable biomass demand at 533.9 ktoe and substantial electricity growth at 747.8 ktoe by 2040. Fossil fuel is anticipated to account 29% of the total energy in 2040 from 12 % in 2017. The motor fuel is expected to reach 286.9 ktoe by 2040. Under the PMS scenario, the electricity consumption will increase consistently reaching 9,866.19 GWh by 2040, which accounts to 44% share of the total fuel mix. Notably the electricity as a transportation fuel is anticipated to grow significantly to 39.42 ktoe by 2040 to represents 19.33% share of the fuel mix in the transportation sector. Furthermore the population and GDP are projected to reach 0.86 million and 5.789 billion US dollars, respectively, by 2040. Per capita GDP is expected to increase from 3477.547 US dollars in 2017 to 6732.48 US dollars by 2040, while final energy per capita and per GDP will account for 22.36 toe/cap and 0.069 kgoe/US$, respectively, by 2040, indicating the evolving energy landscape in Bhutan
Model for Analysis of Energy Demand (MAED)
Impact Assessment of Renewable Energy Integration: A Case Study on Sephu Solar PV Plant Integration
This paper examines the impact of integrating renewable energy resources into the Bhutan power grid, focusing specific on integration of 18 MW Sephu Solar Photovoltaic (PV) plant. The study evaluates grid stability and performance under different penetration levels of solar power. Dynamic simulation were carried using Power System Simulator for Engineering (PSSE) software to analyze two critical scenarios: the sudden disconnection of the PV plant and the occurrence of a three-phase fault at the point of coupling (POC). Further the study examines the effects of these scenarios on key system parameters such as bus voltage, system frequency, and the power output of conventional generator. The results show that the sudden disconnection of the PV plant causes voltage and frequency drops, with the severity increasing as the penetration level of solar power increases. Similarly, a three-phase fault at the PV connected bus results in substantial voltage fluctuations, including a temporary drop to zero, followed by oscillations that stabilize over time. These effects become severe with higher solar power penetration levels, underscoring the importance of addressing potential stability concerns. The study highlights the necessity of implementing proper under-voltage and under-frequency load-shedding relay settings at key substation to prevent unintended disconnection of the PV plant. Additionally, upgrading transformers at the 66/33 kV Gewathang substation is essential to accommodate reverse power flow resulting from the solar PV integration at Sephu.
Literature Review on Modeling of Integrated T&D System using Dynamic Phasor Technique
With the advent of transitioning from conventional to the modern structure of electrical power systems, the systems are prone to numerous electrical dynamics that no longer have the luxury to be neglected. As a result , the existing time domain simulation software, such as Electromagnetic Transient (EMT) and Root Mean Square (RMS) programs, are becoming inefficient for larger and complex systems. Moreover, the need for integrated Transmission and Distribution (iT&D) research is becoming an obvious trend with more distributed energy resources connecting to the system. The previous researches in this area of modeling and simulation use different techniques but more assumptions and oversimplification have resulted in less accuracy. On the other hand, there is the Dynamic Phasor (DP) concept developed for the analysis of dynamics in power electronics components. With more power electronics components infusing into the power systems, this technique can become a powerful method to analyze the behaviors of the system. Therefore, this literature survey paper extends the idea to implement the DP technique in modeling iT&D systems owing to its multiple advantages over the other methods.
Bhutanese currency recognition using Convolutional Neural Network
Currency recognition, or digitization, refers to the process of converting physical currency, such as banknotes and coins, into digital formats. This transformation enhances convenience, security, accessibility, and cost efficiency in financial transactions, which are essential in the modern economy. This paper introduces a Convolutional Neural Network (CNN) model designed for the recognition of Bhutanese paper currency. The model was trained on a dataset comprising various currency types and denominations, achieving a training accuracy of 91 percent and a testing accuracy of 80.5 percent. The architecture consists of three convolutional layers, followed by a dense layer for classification. The findings suggest that CNNs are effective for currency recognition, with the potential for improved accuracy through the expansion of the training dataset.
Detection of Bird Species Found in Bhutan Using Vision Transformer-based Transfer Learning
Birdwatching is an emerging recreational activity in Bhutan, attracting both local enthusiasts and international tourists due to the country's rich avian biodiversity. This growing interest contributes to local tourism and economic development. However, accurate bird identification remains a challenge due to variations in size, shape, and coloration, compounded by inconsistencies in English and Dzongkha nomenclature. Traditional identification methods, which rely on field guides and expert observations, are often prone to errors and disagreements. To address this limitation, we developed a bird detection and recognition system utilizing image processing and machine learning techniques. Bird images were collected from birdwatchers in Paro, Thimphu, and Trongsa, as well as from the Kaggle dataset. These images underwent preprocessing and augmentation to construct a comprehensive dataset. The study considered 23 bird species, and the model was fine-tuned using Google’s pre-trained transformer encoder for image recognition, operating at a resolution of 244×244 with 16×16 patches. The model was trained on a dataset of 3,595 images, leading to a significant reduction in training and validation losses, from 2.8491 and 1.2231 to 0.0030 and 0.0529, respectively. The results indicate the effectiveness of the proposed approach in enhancing bird species identification, offering a valuable tool for birdwatchers and conservation efforts in Bhutan.
AI-based Animal Intrusion Detection System for Human-Wildlife Conflicts in Bhutan
This paper presents the proposed prototype of an Animal Intrusion Detection System powered by Artificial Intelligence of Things (AIoT) technology to address growing challenges of human-wildlife conflicts (HWC) in Bhutan. The major incursions of wildlife in the agriculture fields possess a major threat to sustainable food security and farmer livelihoods in the country. While the government has implemented various mitigation measures like electric and chain-link fencing, and animal repellent system, these solutions have notable limitations. Therefore, our AI-based system aims to provide as an alternative smart agriTech solution to address HWC. The system utilizes a Raspberry Pi 4, a night vision-based camera, an ultrasonic sensor and YOLOv8 deep learning algorithm for real-time animal detection and classification. The YOLO model was trained on a dataset of 30,800 images featuring seven local wildlife species which are common in raiding the crop in Bhutan. The system, upon detecting an intrusion on farmland, will automatically transmits an alert notification to farmers via a mobile app over a cellular network, enabling timely intervention to mitigate the crop damage. When the internet connection is down, the system will notify the farmers through SMS and Dial. In a controlled laboratory environment, the prototype achieved a detection accuracy of 95.7%. These finding indicates a promising alternative innovative agriTech solution for mitigating crop losses, enhancing food security and enhancing farmer livelihoods. However, the prototype requires field validation and further AI model training with a more extensive real animal dataset collected through its pilot implementation to evaluate the system's performance and robustness under real-world conditions of the agriculture field.
Artificial Intelligence of Things (AIoT)