Engineering Access

Exploring and Assessment of Small-Scale Hydropower Potentials in Vhembe District Municipality Using Geographical and Spatial Information

2024-05-03T09:22:56+07:00

Harnessing the power of water to generate electricity is one of the most cost-effective and sustainable methods of energy production. Small hydropower plants are gaining prominence due to their ability to generate electricity with lower hydraulic heads and smaller flow rates, making them suitable for a wider range of applications. However, the growing global population and its associated demands for water for drinking, agriculture, and industrial purposes are putting increasing pressure on rivers. It is crucial to strike a balance between utilizing river waters for hydropower generation and preserving its availability for essential human needs. The study explored and assessed the small-scale hydropower potentials in the Vhembe District Municipality. The study used geographical and spatial information to identify potential sites for small scale hydropower projects. The study also considered several factors, such as the availability of water, the topography of the area, and the environmental impact of hydropower development. The study identified significant potential for small-scale hydropower development within the Vhembe District Municipality. It pinpointed several promising sites with minimal environmental impact. Developing small-scale hydropower in this area is presented as a viable option that could contribute to the local economy and South Africa's green energy transition. The study recommends that the Vhembe District Municipality further explore this potential and develop a plan for sustainable small-scale hydropower development in the region. These findings can inform similar projects in the region and beyond, becoming a valuable contribution to the national understanding of small-scale hydropower potential in South Africa.

Optimal PV Sizing and Location Based on Volt – Var Control and UPFC Using Particle Swarm Optimization for Microgrid System

2024-08-27T09:19:25+07:00

This research article details the creation and execution of a photovoltaic (PV) control system augmented with Volt-VAR (VAR) and Universal Power Flow Control (UPFC) to boost power system efficiency. The research examines the IEEE 13 bus test system, assessing four principal control strategies for effective power demand management: PV control, PV-VAR control, PV-UPFC control, and their amalgamation with Particle Swarm Optimization (PSO) for load forecasting. The PSO method is utilized to enhance load forecasting precision, facilitating accurate synchronization of PV generation with 24-hour power demand variations. The research examines the mechanisms of photovoltaic regulation, with the objective of optimizing photovoltaic resource usage and ensuring smooth integration with the electrical grid. The PV-VAR control system is examined to improve reactive power management, voltage stability, and grid resilience. The integration of photovoltaic systems with unified power flow controllers is examined to attain accurate control over power flow dynamics in the distribution network. The Open Distribution System Simulator (OpenDSS) is employed to assess electricity flow and examine grid performance under diverse load scenarios. Simulation findings illustrate the efficacy of the proposed technology, indicating a direct linkage between the photovoltaic system and the power grid optimized by particle swarm optimization. The ideal dimensions and location of the PV system were determined to be 100 kV at Bus 680, underscoring the system's capacity to conform to grid demands. This study offers essential insights into the obstacles and opportunities associated with the integration of photovoltaic systems into contemporary electricity grids. The research enhances the creation of sustainable, efficient, and resilient distribution networks in the renewable energy sector through comprehensive simulations and analysis.

Perception Comparison between the Physical Object and Virtual Model in Residential Project using BIM-based Virtual Reality

2024-09-18T15:02:20+07:00

Traditional methods of presenting residential projects, such as show units and scale models, have undergone significant transformation. These methods typically required potential people to visit the site in person to fully understand the designs and visualize the proposed structures' spatial relationships and aesthetic impact. However, both of these traditional approaches can be time-consuming. This study aims to create a proposed system that integrates Building Information Modeling (BIM) and Virtual Reality (VR) to enhance the presentation process in the residential project and examine the difference between the perception of the virtual model and the physical object throughout the residential project. Thirty respondents participated in the experiment, which measured their perception scores regarding the dimensions of three types of bedrooms at the residential project named Icon Project 7 (I-Sport). The paired sample t-test was used to analyze the differences in perception scores between the virtual model and the physical object. The results indicated that perceptions of the virtual model were not significantly different from the physical object. In addition, the time required to implement the proposed system to create a virtual model of this case study is 4 hours, which is a significantly faster approach than the conventional method, reducing the lead time before the show unit can be showcased to prospective buyers. Additionally, implementing the proposed system involved an investment cost of 169,993.29 Baht and an operation cost of 20,000.00 Baht per month. The ability of these virtual models to effectively simulate physical objects enables residential projects to enhance presentations while reducing project time and expenses.

Effect of Microcrystalline Cellulose from Paper Pulp on Poly(Lactic Acid) Properties as Reinforced Fiber in Biodegradable-Material Application

2024-12-24T17:12:13+07:00

This study investigates the application of thermal processing techniques in packaging, focusing on the development and characterization of a composite material comprising poly(lactic acid) (PLA) and cellulose derived from paper pulp (PP). Cellulose sourced from the paper industry is treated with sodium hydroxide and sulfuric acid under controlled temperature conditions. Fourier-transform infrared (FT-IR) spectroscopy reveals structural similarities between the synthesized cellulose and standard cellulose. X-ray diffraction (XRD) analysis indicates a potentially high crystalline structure in the microcellulose, about 81% from raw PP at 63%. In comparison, the commercial microcrystalline cellulose (MCC) was about 82%, and peaks were similar to those of MCC. Scanning electron microscopy (SEM) analysis reveals a reduction in the size distribution of cellulose after processing. The morphology of the polymer composite shows heterogeneous dispersion of cellulose within the PLA matrix. Differential scanning calorimetry (DSC) results indicate that the crystallization and melting enthalpies in the composite material are comparable to those of its components. Thermal stability shows a lower degradation temperature, about 10°C, making degradation easier. Adding microcrystalline cellulose enhances the tensile strength (50 to 67 MPa) and Young's modulus (2424 to 2898 MPa) of the PLA/cellulose composites. However, stability issues arise at a cellulose content of 5 phr compared to neat PLA. Furthermore, UV-vis spectroscopy demonstrates that the PLA/cellulose composites exhibit improved UV-blocking ability. Lastly, the materials show suitable results for biodegradable-material applications.

Comparison of the Effectiveness of Detecting Variability between Parametric and nonparametric Moving Average Control Charts

2025-06-27T10:26:49+07:00

Production process variability is a problem that must be resolved promptly to reduce damage and costs. An important tool in statistical quality control is often the use of control charts as a tool to track process changes because they can show the trend of changes more clearly than other tools. The use of control charts can be both parametric and nonparametric. The use of control charts has both parametric and nonparametric types, each with its own advantages and disadvantages. Therefore, this research aims to study the efficiency in detecting process variation between parametric and nonparametric moving average control charts by using sign test. Using the Monte Carlo simulation technique to gather study results, it was discovered that in every scenario examined, the parametric control chart is able to identify changes more quickly than the nonparametric chart. Moreover, the tensile test results of both carbon fiber bundles and individual fibers, which comprised the experimental dataset, agreed with the simulation outcomes.

Modeling Future Water Deficit Trends under Varying Climate Change Projections in Huai-SamMor Basin, Thailand

2025-03-03T14:28:30+07:00

This study, two global climate change predictions (RCP4.5 and RCP8.5) are used to forecast severity of drought occurrence in Huai-SamMor basin. The study examines severity of future drought conditions using Generalized Monsoon Index (GMI) which is separated into four future periods: near future, middle future 1, middle future 2 and far future. The research finding indicated that for RCP8.5 scenario, CESM1_CAM5 and NorESM models were identified as most suitable. In contrast, for RCP4.5 scenario, CNRM and Miroc5 models were selected as most suitable. These selections were made using CMIP5 model as basis for evaluation. When considering GMI index and global climate change forecasts for both southwest and northeast monsoon scenarios, RCP8.5 projection indicate greater severity than RCP4.5. The findings suggest that frequency of drought occurrences increases by approximately 15–34% under RCP8.5 compared to RCP4.5, particularly during dry season and in far future. Additionally, in RCP8.5, dryness is significantly exacerbated by elevated temperatures and reduced precipitation at critical periods. In contrast, northeast monsoon scenario exhibits highest degree of drought severity in far future in both RCP4.5 and RCP8.5 when comparing different time periods. Examinations of spatial and temporal patterns during northeast monsoon reveal a progressive intensification of drought severity over time, with most pronounced effects anticipated in far future. The basin's western and upper sections emerge as areas of elevated risk, underscoring notable regional variations in drought vulnerability. These observations suggest that global warming expedites hydrological cycles, contributing to an increased frequency and intensity of droughts. However, no existing studies have specifically utilized Generalized Monsoon Index (GMI) to forecast drought-prone areas under future climate scenarios, leaving a critical gap in understanding severity of monsoon-induced droughts. This research addresses that gap and reveals a 15-34% increase in drought frequency under RCP8.5 relative to RCP4.5, with upper and western sections of Huai-SamMor basin facing the highest vulnerability.

Kinetics and Isotherms Study of Methylene Blue Dye Adsorption on Water Hyacinth Stem Powder Adsorbent

2025-06-11T09:53:40+07:00

This study aims to evaluate the adsorption efficiency of water hyacinth stem powder (WHSP) to remove methylene blue (MB) dye from aqueous solutions. Batch adsorption experiments were conducted to investigate the influence of contact time and initial dye concentration. The kinetic data were fitted to both pseudo-first-order and pseudo-second-order models, with the latter providing a better fit (R² = 0.993), indicating chemisorption. Isotherm modelling revealed that the Freundlich model best described the adsorption behavior (R² = 0.9706), suggesting multilayer adsorption on heterogeneous surfaces. Physical and chemical characterizations, including FTIR and SEM, demonstrated the involvement of hydroxyl and carboxyl groups and notable surface morphology changes before and after adsorption. WHSP exhibited a maximum adsorption capacity of 126.7 mg/g and a removal efficiency of 92.1%. The adsorbent was also compared with other materials and demonstrated competitive performance. Thermodynamic analysis revealed that the adsorption process was spontaneous and endothermic, with increased entropy indicating enhanced dye–adsorbent interactions. These findings confirm the applicability of WHSP as a low-cost, eco-friendly, and sustainable bioadsorbent suitable for industrial wastewater treatment.

Improving the Order Response Process in Online Mattress Topper Retail with Lean Principles and Business Intelligence

2025-02-27T11:38:39+07:00

Efficient order response processes and the adoption of information technology are critical for success in the competitive e-commerce market. This research analyzes the order response process of an online mattress topper store by applying Lean principles, particularly the pull strategy and the elimination of waiting waste, alongside the development of a Business Intelligence (BI) system using Power BI dashboards. The objective is to reduce non-value-added (NVA) time and improve operational efficiency. Primary data were collected through workflow analysis and time studies over 12 months. The initial process revealed a 40-minute waiting period as a major non-value-added activity, contributing to a total process time of 162 minutes. By coordinating proactive production with suppliers and leveraging sales insights from Power BI, the waiting time was eliminated, reducing the total process time to 122 minutes and increasing the Value-Added Ratio (VAR) from 40.12% to 53.28%. This study presents a novel integration of Lean methodologies with BI-powered analytics, utilizing the BI system to provide daily sales and inventory insights, enabling proactive inventory management and data-driven decision-making. The findings demonstrate that the synergistic application of Lean principles and BI-driven insights effectively streamlines order fulfillment processes, reduces lead times, and enhances agility and competitiveness in online retail operations.

The Adaptive Hybrid MCDA for Land Use Prioritization: Case Study Dry Port Size Analysis

2024-12-23T16:40:14+07:00

The development of Dry Ports serves as a crucial strategy in modern logistics, enhancing supply chain efficiency and resilience. The boundary area of Dry Ports necessitates the integration of diverse analytical approaches to ensure their effective area development, within 3 Main criteria’s and 16 Minor’s factors. The comprehensive framework that amalgamates Multi-Criteria Decision Analysis (MCDA) including SAW, TOPSIS and VIKOR, size analysis, and land use planning represented the alternative of effective boundaries. The result revealed the size of the Dry Port context. Considering conventional and TOPSIS land acquisition prioritization presents the pricing effectiveness. Additionally, sensitivity analysis indicates that the distance between the dry port and the conventional railway network identified as the most influential factor significantly impacts effectiveness once it exceeds 35%.

Developing and Prioritizing Lean Supply Chain Performance Indicators in the Thai Industrial Context

2025-03-19T13:57:01+07:00

his research aims to systematically develop and prioritize performance indicators for Lean Supply Chain (LSC) practices that have not been widely adopted in Thai industries. The study included a comprehensive literature review of national and international industry publications. The literature review identified Lean Supply Chain (LSC) performance indicators align with the Thai industrial context. The three dimensions of the industry are (1) organizational profile, (2) finance and operations, and (3) business results. Different prioritizations and weights within each dimension are assigned to reflect their relative significance. These variations distinguish the Thai industry from industries in other countries. The research achieve its objective by applying the Fuzzy-AHP technique. This technique undertook expert evaluations from the Thai industry to assign weights to the significant indicators for LSC. This study demonstrates the development and prioritization of indicators for LSC tailored explicitly to the captivating context of the Thai industry. The indicators ranking across various dimensions can provide valuable guidance for business owners to comprehend their advantages and implement them within their organizations. Additionally, these findings can serve as a fundamental basis for future research and development, enabling the establishment of performance indicators for LSC within other countries' industries. However, further research development using techniques such as AHP, SAW, and TOPSIS is prioritized.

Optimal Allocation of DERs Considering Existing Distribution Infrastructure Using Mountain Gazelle Optimizer: Practical Case Study

2025-02-24T16:01:46+07:00

The inclusion of distributed energy resources (DERs) in the power distribution network (DN) encountered rapid growth across the countries due to technological and environmental advantages. Moreover, this inclusion not only enhances diversity in resources but can improve the quality of service to users as well. However, the unplanned integration of DERs and their deployment in non-optimal locations can adversely affect the performance of DN. Hence, optimal positioning and sizing of DERs is very important aspects. Further, few studies have focused on DER and shunt capacitors (SCs) allocation in combination with the presence or absence of OLTC infrastructure. Therefore, in this paper, the recently developed Mountain Gazelle Optimizer (MGO) algorithm suitable for solving complex problem and addressing global optimization issues, is applied for optimal positioning of the DERs along with existent distribution infrastructure. In this work, considered objective is decreasing the cost of annual energy loss (CAEL). In order to showcase the usefulness of MGO algorithm in solving DER allocation problem this has been implemented on IEEE 33 bus and Indian 108 bus radial DN (real-life practical DN). The comparative analysis between MGO and other applied methods in the literature on same problem has also been presented. The obtained results indicate that simultaneous consideration of DERs, SCs and existing OLTC not only offers improved utilization of existing DN infrastructure but also minimizes the overall cost. The considerable improvement in results pertaining to CAEL for different scenarios, for an example around 10.6 % better compared to best reported results (IEEE-33 bus system, scenario-5), confirm the suitability of MGO algorithm.

Grading Invasive Ductal Carcinoma from Whole-Slide Histological Images using Deep Learning-Based Feature Encoding Techniques

2025-03-17T10:59:54+07:00

Invasive ductal carcinoma (IDC) grading is crucial for determining treatment and prognosis. However, the process of manual grading of whole-slide histological images (WSIs) is time-consuming and prone to variability. In this study, we propose a deep learning-based method aimed at automating the grading of breast cancer from WSIs. Unlike conventional approaches that directly process entire WSIs, our method divides them into smaller patches and employs an unsupervised autoencoder to extract pathological features from each patch. These features are then integrated into a comprehensive representation of the WSI. A classification model is subsequently utilized to assign one of three grades. The proposed approach effectively captures local pathological features while preserving spatial relationships between patches. This technique uniquely balances feature preservation with computational efficiency, addressing the challenges associated with the high resolution of WSIs. Experimental results on a breast cancer histological image dataset demonstrate that our method achieves an average accuracy of 71.43% while reducing training time by 50–67%. This performance outperforms the best results obtained using traditional feature extraction techniques. This highlights the robustness and reliability of our approach in reducing pathologists' workload and improving diagnostic consistency.

Applied Ensemble Technique for Road Damage Detection based on YOLOv8

2025-05-29T10:58:26+07:00

The presence of road damage poses significant risks to pedestrians and traffic. Although deep learning-based object detection is widely used, the effectiveness of various detections shows considerable variation, and there remains substantial potential for enhancement. This paper proposes an ensemble technique using YOLOv8 for road damage detection. A comparison of object detection models, including YOLOv5, YOLOv8, Faster R-CNN, and SSD, is conducted to determine the best baseline. The study focuses on single-class detection to enhance accuracy in identifying specific types of road damage. Each model predicts class probabilities and bounding box locations. The predictions are then ensembled, with Non-Maximum Suppression applied to filter out overlapping detection boxes. The ensemble YOLOv8 model outperforms the standard one, especially in detecting alligator cracks and potholes, with detection accuracy improved by up to 3%. The method balances precision and recall effectively, suitable for complex road environments.

Two-Step Textual Similarity-Based Approach for Predicting Suitable Production Line for a Newly Designed Product

2025-05-20T09:35:20+07:00

During industry 4.0, digital technology has been integrated with manufacturing processes to improve operational efficiency and hence to enhance organization competitiveness. To this end, computerized methods have been rapidly developed to tackle various production and delivery issues. Production planning and scheduling often demand substantial resources, especially in terms of manpower and time. Consequently, minimizing the time dedicated to both planning and scheduling can hasten product delivery. This paper proposes a novel algorithm that analyzes an unseen process and then predicts a production line by using two-step similarity measures, used in ensemble within the process. Provided with an unseen product model, it identifies the most suitable line, based on the availability of machinery required by the process. In the experiments, eight similarity measures were assessed, based on a realistic production plant. The results revealed that Jaccard similarity and Dice similarity coefficients gave the most accurate predictions. The proposed method is thus believed to be applicable in dynamic production scenarios. Moreover, the developed system also supports incremental production lines.

Optimization-Based Tuning of a Cascaded PDN–PI Controller for Frequency Regulation in Solar PV–Powered Thermal Systems

2025-08-20T14:09:06+07:00

In modern power systems, Load Frequency Control (LFC) is crucial for maintaining system stability, especially in grids with substantial integration of variable renewable energy sources. This paper presents an innovative cascaded PDN–PI controller that hierarchically integrates a Proportional–Derivative with Filter (PDN) stage to mitigate rapid oscillations and a Proportional–Integral (PI) stage to eradicate steady-state faults. The suggested structure utilizes derivative filtering to reduce noise and preserve long-term accuracy, hence improving robustness in hybrid photovoltaic (PV)–thermal power systems, in contrast to traditional cascaded controllers. Five controller gains were optimized for optimal parameter tuning using three metaheuristic algorithms: Water Cycle Algorithm (WCA), Catch Fish Optimization Algorithm (CFOA), and Grey Wolf Optimizer (GWO), with the Integral of Time-weighted Absolute Error (ITAE) as the objective function. A two-area load frequency control (LFC) system was modeled, comprising a photovoltaic (PV) generation system and a reheat thermal power plant interconnected via a tie-line. The simulation findings for two load disturbance scenarios indicated that the suggested PDN–PI controller markedly surpassed conventional PI control, exhibiting enhanced damping, less overshoot, and expedited settling times. Among the optimization strategies, GWO demonstrated superior convergence and resilience, producing the lowest ITAE values and maintaining constant stability among parameter fluctuations. This study's contributions include (i) the development of an innovative cascaded PDN–PI controller specifically designed for renewable-integrated LFC systems, (ii) comparative metaheuristic optimization of its parameters, and (iii) robustness evaluation via sensitivity analysis. These findings offer novel insights into controller design for hybrid power systems and underscore prospective avenues for real-time and hardware-in-the-loop validation