University of Technology and Business

This study addresses the challenge of optimally planning distributed energy resources in grid-connected distribution systems under significant uncertainty in renewable generation and load demand. As high penetration of wind, solar PV, and battery energy storage systems (BESS) increases variability and operational risks, developing a robust long-term planning framework is essential for ensuring economic efficiency, technical reliability, and environmental sustainability. To tackle this, a stochastic mixed-integer nonlinear programming (MINLP) model is proposed, incorporating probabilistic representations of wind speed, solar irradiance, load profiles, and market energy prices using Weibull, lognormal, and normal distributions. Monte Carlo Simulation combined with a Backward Reduction Algorithm is used to generate representative scenarios. A hybrid evolutionary optimisation approach—integrating Genetic Algorithm (GA), Particle Swarm Optimisation (PSO), and Differential Evolution (DE)—is developed to solve the multi-objective problem. The framework simultaneously minimises cost, emissions, power losses, voltage deviation, and enhances reliability and voltage stability. Application to the IEEE 33-bus and 118-bus systems demonstrates substantial improvements: cost reductions up to 19.3%, emission reduction up to 80.9%, and significant improvements in network losses, voltage profiles, and reliability indices. The results confirm that coordinated planning of dispatchable DGs, renewable DGs, and BESS yields a resilient, economical, and sustainable solution for future smart distribution networks.

Enhancing energy efficiency and reducing fossil fuel consumption have become critical goals in the global pursuit of sustainable energy systems. Latent heat thermal energy storage systems using phase change materials (PCMs) provide an effective means of storing and reusing thermal energy, particularly for residential and solar-based applications. These systems enable the storage of excess heat during charging periods and its release during demand peaks. Here, a novel shell-and-tube thermal energy storage (ST-TES) configuration was introduced. The design incorporates a PCM-filled shell with embedded heat transfer fluid (HTF) tubes, complemented by geometric modifications to improve heat conduction. However, the low thermal conductivity of PCMs remains a major obstacle to achieving rapid charging and discharging rates in TESs. To overcome this limitation, a novel sub-fin geometry was introduced to extend the effective heat transfer area and enhance conduction within the PCM domain. Two advanced configurations were developed: one combining main fins and secondary sub-fins to extend the conductive pathways, and another utilizing only main fins. These are benchmarked against a base design with HTF tubes alone. Furthermore, a hybrid framework combining numerical simulation, artificial neural network (ANN) prediction, and genetic algorithm (GA) optimization was developed to efficiently identify the most effective sub-fin configuration with reduced manufacturing cost. Finally, the solidification process was examined to confirm the superior thermal charging and discharging capabilities of the ST-TES designs. A comparison was carried out between the optimal design, MF-design (which only has main fins), and the base design (which only has HTF tubes). By the end of the 5-hour simulation, the optimal sub-fin design absorbed approximately 18,451 kJ of energy, representing a 19.2 % improvement over the base design and a 4.46 % enhancement compared to the MF-design. During discharging, the energy released by the optimal configuration was 56.3 % higher than the base case and 40.9 % higher than the MF-design. The economic analysis revealed a payback period of less than one year (approximate to 264 days), confirming its practical viability. Overall, this study introduces a hybrid numerical-AI framework that not only accelerates the optimization of complex fin geometries but also demonstrates substantial thermal and economic benefits. The proposed design strategy offers practical potential for next-generation solar-assisted and low-carbon thermal storage systems, bridging the gap between computational innovation and sustainable energy applications.

Copper-graphene nanocomposites have emerged as promising materials in tribology owing to their unique combination of high mechanical strength, excellent thermal conductivity (K), and self-lubricating behavior. Incorporation of graphene derivatives such as graphene nanoplatelets (GNPs) and carbon nanotubes (CNTs) has been shown to reduce the coefficient of friction (COF) by up to 60

In this era of high competition and pandemic, the return to work is challenging because it requires organizations to balance operational demands with individualized support for employees recovering from psychological, physical, and/or personal setbacks. Additionally, managerial expectations, misalignment between workplace culture, and employee readiness can hinder effective reintegration, leading to reduced relapse or productivity. This study aimed to examine the influence of key organizational and psychological factors on employees' return-to-work. A stratified sampling approach was employed to collect data from 370 employees who had successfully returned to work after experiencing work-related injuries and diseases. Eight hypotheses were formulated and validated using structural equation modeling with SmartPLS. Findings from the present study reveal that perceived organizational support has an insignificant impact on the return to work; however, it has a significant impact on strategic human resource management and psychological empowerment. In addition, the present study confirmed a significant impact of organizational hierarchy culture on return-to-work and psychological empowerment. Thus, strategic human resource management had a significant impact on return-to-work rates and psychological empowerment. Finally, psychological empowerment had a significant impact on the return to work. The study highlights the importance of culturally aligned human resource strategies and psychological factors in fostering successful employee reintegration after work absence. These findings have practical implications for human resource professionals and policymakers aiming to design effective, inclusive, and sustainable return-to-work programs tailored to hierarchical organizational environments.

In Additive Manufacturing (AM), optimizing process parameters-like laser power and scanning speed are essential for controlling temperature distribution and melt-pool size. To efficiently guide process parameter selection in Laser Powder Bed Fusion (LPBF), this study develops and validates an accelerated numerical model for AlSi10Mg and Ti6Al4V, focusing on predicting thermal behavior and melt pool morphology. The model analyzes a comprehensive range of laser powers (100–400 W) and scanning speeds (500–1500 mm/s), employing a full factorial design of experiments to investigate their influence on temperature evolution, melt pool width, and melt pool depth. Results demonstrate a stronger correlation between laser power and peak temperatures, with increased power leading to higher thermal gradients and larger melt pools. Conversely, higher scanning speeds diminish peak temperatures and produce narrower, shallower melt pools due to reduced laser-material interaction time. The study identifies a laser power of 100 W combined with a scanning speed of 500 mm/s as optimal for both AlSi10Mg and Ti6Al4V. This parameter combination achieves a balance between sufficient energy input for proper melting and minimized heat accumulation, mitigating potential defects like porosity and residual stresses. The developed model provides a computationally efficient approach to pre-screen process parameters, significantly reducing the time and resources required for optimization compared to traditional experimental methods. This accelerated approach allows for rapid exploration of the parameter space and informs subsequent, more detailed Multiphysics simulations. By efficiently predicting thermal behavior, this model contributes to faster development cycles, improved part quality, and reduced material waste in additive manufacturing. The study’s findings are extendable to other materials and AM processes, promoting innovation and scalability within the field. Ultimately, this work advances LPBF technology by providing a robust and efficient framework for thermal analysis.