This research work reports the influence of 3-μm-sized Palm Sprout Shell Ash (PSSA) reinforcement on the mechanical and tribological behavior of the Al-Cu-Mg alloy. Composites of varying weight percentages of reinforcement ranging from 1 to 6 at intervals of 1 Wt.% were produced using the ultrasonic-assisted bottom-poured stir casting technique. Microstructural studies, mechanical testing, and wear properties analysis were performed on the alloy and the synthesized composites. The microstructure of the obtained samples was examined using Scanning Electron Microscope, Energy Dispersive Spectroscopy (SEM/EDS), and X- Ray Diffraction (XRD). The XRD patterns provided confirmation of the presence of PSSA (SiO2 and Al2O3) particles. The addition of PSSA reinforcement has significantly improved the hardness, tensile strength, and compression strength of composites. The hardness, ultimate tensile strength, and compression strength were improved by 13.89%, 24.04%, and 32.93%, respectively, with the 6 Wt.% PSSA-reinforced composite. However, the incorporation of reinforcement has resulted in a decrease in the ductility of the Al-Cu-Mg alloy composite; the maximum decrement of 42.87% was with the 6% PSSA-reinforced composite. Tests were conducted at different loads and speeds to evaluate the wear behavior of the prepared samples. Superior wear resistance was observed in the composites. The fracture and wear mechanisms of reinforced and unreinforced were observed using SEM.

This study investigated the impact of a cylindrical obstacle on convection in an inclined square cavity filled with water-Al2O3 nanofluid. Using the finite volume method, the problem was resolved by having the inner cylinder rotate adiabatically while other walls were thermally insulated. Additionally, the bottom wall was hotter than the top. The study examined the effects of cylindrical obstacle radius (0.1 ≤ R ≤ 0.2), rotation speed (-500 ≤ Ω ≤ 500), Richardson number (0.01 ≤ Ri ≤ 100), volumetric nanoparticle fraction (0.02), and Grashof number (Gr=104) on heat transfer rate or Nusselt number. The results were compared with previous literature, and the influence of the cylindrical obstacle rotational speed on convection flow was evaluated. An increase in the counterclockwise angular rotating speed resulted in higher nanofluid flux. The heat transmission coefficient increased as the Richardson number decreased. The use of nanofluid in the enclosure increased the coefficient of heat flow through mixed convection. Finally, the study showed that the convection heat exchange is enhanced with the increase in the radius. Moreover, an enhancement of the Nusselt number around 46% was reported for the cylinder, under Gr=10000, ∅=0.02, γ=45° and Ri=10.

The objectives of this study are to evaluate the technical and cost implications of retrofitting post-combustion Carbon Capture and Storage (CCS) in existing coal-fired power plants in Thailand, with a special focus on the Mae Moh plant managed by the Electricity Generating Authority of Thailand (EGAT). We undertake a detailed analysis using AspenPlus simulation models to determine the optimum capture cost per ton of CO2 and to examine the effects of various flue gas loads on CO2 capture performance and cost-effectiveness. The research reveals a key operational insight: as the flow rate of flue gas increases, the cost to capture a ton of CO2 decreases, indicating economies of scale in CCS operations. Furthermore, the study explores the potential for integrating solar photovoltaic (PV) technology as a renewable energy source, which shows promise in lowering Thailand’s power sector emissions and operational costs. By comparing the levelized cost of electricity (LCOE) for solar PV against conventional coal-fired power generation and considering the country’s favorable geographic and climatic conditions, solar PV emerges as an economically viable and environmentally sustainable alternative. The findings of this research aim to inform strategic energy policy decisions in Thailand, advocating for a transition to more sustainable energy systems and emphasizing the balance between environmental responsibility and economic feasibility.

Machine Learning (ML) is gaining attention in civil engineering especially within operational phase of building life cycle. This phase is crucial for managing every energy aspect while ensuring occupant comfort. Previous ML experiments have explored occupant behavior, occupancy estimation, load prediction, defect detection, and Heating, Ventilation, and Air Conditioning (HVAC) system diagnostics. However, challenges such as ML transferability and limited literature on ML components for the operational phase hinder broader industry adoption. This critical review aims to assess the potential of ML in building operations, focusing on energy consumption, big data control, reinforcement learning, and thermal comfort modeling. By identifying knowledge gaps, the study recommends further research to leverage ML for sustainable energy consumption and occupant comfort. It highlights ML’s promising role in striking a balance between energy efficiency and occupant wellbeing.

The enhancement of the thermal conductivity of dielectric oils has a positive effect on the performance of electrical equipment that uses these oils as a cooling medium. Nanofluids (NFs) have inspired high-voltage engineers to use them as alternative fluids in power transformers due to their impressive heat transfer and insulation compared to traditional dielectric oils. The present study is a numerical simulation by COMSOL Multiphysics of the thermal conductivity of NFs based on dielectric oils used in power transformers, to identify the effect of temperature, the concentration of nanoparticles (NPs), type of insulating fluid and NPs on thermal conductivity. The NFs were modeled inside a cube using the finite element method (FEM) by applying a temperature gradient. Several types of NPs were used (SiC, ZnO, TiO2 , and Al2O3) in addition to several volume concentrations (0%, 0.001%, 0.002%, 0.01%, and 0.02%). The results showed a significant improvement in the thermal conductivity of the NFs with increasing concentration since the best results were recorded at an estimated volume concentration of 0.02%, while the lowest results were obtained for samples using a volume concentration estimated at 0.001%. The base fluid (BF) type and NPs play a dominant role in the thermal performance of the NFs, as the vegetable oil-based nanofluid provided the highest thermal conductivity values and silicon carbides (SiC) was the best NPs used in this study. However, a decrease in thermal transfer capacities was observed for all samples with increasing temperature.