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Recycled polylactic acid (PLAr) was reinforced with treated nanocellulosic hemp fibers for biocomposite fabrication. Cellulosic fibers were extracted from hemp fibers chemically and treated enzymatically. Treated nanocellulosic fibers (NCF) were analyzed by Fourier-transform infrared spectroscopy, X-ray diffraction, and scanning electron microscopy. Biocomposite fabrication was done with PLAr and three concentrations of treated NCF (0.1%, 0.25%, and 1% (v/v)) and then studied for thermal stability and mechanical properties. Increased thermal stability was observed with increasing NCF concentrations. The highest value for Young’s modulus was for PLAr + 0.25% (v/v) NCF (250.28 ± 5.47 MPa), which was significantly increased compared to PLAr (p = 0.022). There was a significant decrease in the tensile stress at break point for PLAr + 0.25% (v/v) NCF and PLAr + 1% (v/v) NCF as compared to control (p = 0.006 and 0.002, respectively). No significant difference was observed between treatments for tensile stress at yield.

Global fossil fuel reserves are declining due to differential uses, especially for power generation. Everybody can help to do their bit for the environment by using solar energy. Geographically, Bangladesh is a potential zone for harnessing solar energy. In March 2021, the renewable generation capacity in Bangladesh amounted to 722.592 MW, including 67.6% from solar, 31.84% from hydro, and 0.55% from other energy sources, including wind, biogas, and biomass, where 488.662 MW of power originated from over 6 million installed solar power systems. Concurrently, over 42% of rural people still suffer from a lack of electricity, where solar energy can play a vital role. This paper highlights the present status of various forms of solar energy progress in Bangladesh, such as solar parks, solar rooftops, solar irrigation, solar charging stations, solar home systems, solar-powered telecoms, solar street lights, and solar drinking water, which can be viable alternative sources of energy. This review will help decision-makers and investors realize Bangladesh’s up-to-date solar energy scenario and plan better for the development of a sustainable society.

Energy efficiency is an increasingly important dimension of household appliances, which is why they are labeled to indicate their energy consumption. In 2020, the European Union countries changed the labeling system from the previous system: ranging from A+++ to D, to the new system: ranging from A to G, assuming it would be more transparent for the consumer. The aim of the study was to find out the extent to which consumers are aware of the new labeling system, and the impact that the new labels have (compared to the previous ones) on the perception of household appliances and consumer decision-making. For this purpose, the survey was conducted on a nationwide representative Polish sample (n = 1054). The research was partly experimental, where the same appliances were presented with new and previous energy labels. The results showed that currently most people do not identify the new energy classes. Furthermore, products with the new labels are perceived as being less energy efficient in comparison with products with the previous labels, which shows that there is some confusion among consumers in terms of the new energy efficiency labeling system.

Reservoirs in Taiwan often provide hydroelectric power, irrigation water, municipal water, and flood control for the whole year. Taiwan has the climatic characteristics of concentrated rainy seasons, instantaneous heavy rains due to typhoons and rainy seasons. In addition, steep rivers in mountainous areas flow fast and furiously. Under such circumstances, reservoirs have to face sudden heavy rainfall and surges in water levels within a short period of time, which often causes the water level to continue to rise to the full level even though hydroelectric units are operating at full capacity, and as reservoirs can only drain the flood water, this results in the waste of hydropower resources. In recent years, the impact of climate change has caused extreme weather events to occur more frequently, increasing the need for flood control, and the reservoir operation has faced severe challenges in order to fulfil its multipurpose requirements. Therefore, in order to avoid the waste of hydropower resources and improve the effectiveness of the reservoir operation, this paper proposes a real-time 48-h ahead water level forecasting system, based on fuzzy neural networks with multi-stage architecture. The proposed multi-stage architecture provides reservoir inflow estimation, 48-h ahead reservoir inflow forecasting, and 48-h ahead water level forecasting. The proposed method has been implemented at the Techi hydropower plant in Taiwan. Experimental results show that the proposed method can effectively increase energy efficiency and allow the reservoir water resources to be fully utilized. In addition, the proposed method can improve the effectiveness of the hydropower plant, especially when rain is heavy.

In recent years, prices for photovoltaics have fallen steadily and the demand for sustainable energy has increased. Consequentially, the assessment of roof surfaces in terms of their suitability for PV (Photovoltaic) installations has continuously gained in importance. Several types of assessment approaches have been established, ranging from sampling to complete census or aerial image analysis methodologies. Assessments of rooftop photovoltaic potential are multi-stage processes. The sub-task of examining the photovoltaic potential of individual rooftops is crucial for exact case study results. However, this step is often time-consuming and requires lots of computational effort especially when some form of intelligent classification algorithm needs to be trained. This often leads to the use of sampled rooftop utilization factors when investigating large-scale areas of interest, as data-driven approaches usually are not well-scalable. In this paper, a novel neighbourhood-based filtering approach is introduced that can analyse large amounts of irradiation data in a vectorised manner. It is tested in an application to the city of Giessen, Germany, and its surrounding area. The results show that it outperforms state-of-the-art image filtering techniques. The algorithm is able to process high-resolution data covering 1 km2 within roughly 2.5 s. It successfully classifies rooftop segments which are feasible for PV installations while omitting small, obstructed or insufficiently exposed segments. Apart from minor shortcomings, the approach presented in this work is capable of generating per-rooftop PV potential assessments at low computational cost and is well scalable to large scale areas.

The Galapagos Islands have been declared a World Heritage site due to their unique biodiversity, which makes them a living museum and a natural laboratory for humankind. However, to fulfill the energy needs of its habitants and foreign visitors, the islands have depended on fossil fuel energies that have produced levels of lead and chemical agents that are affecting the islands’ air quality, flora, and fauna. Therefore, zero-carbon initiatives have been created to protect the islands, wherein solar and wind power plants have been studied as reliable alternatives. In this way, Geographical Information Systems based on Multicriteria Decision Methods constitute a methodology that minimizes the destruction and disturbance of nature in order to assess the best location for the implementation of these alternative energy sources. Therefore, by exploring the geographical information along with the Analytical Hierarchical Processes and the Ordered Weighted Average methods, it was possible to identify the potential for solar power plants of 10 MW on each island; likewise, for wind power plants, it was found that the islands possess implementation potential that has been analyzed in the field, showing that the best location is on Baltra Island, but is not limited to it.

Thermomagnetic heat engines were designed, constructed, and tested, where numbers of gadolinium (Gd) blocks were used to exploit low temperature waste heat. Gadolinium is a rare earth material whose magnetic property changes with temperature, altering between ferromagnetic and paramagnetic. A motion develops in the thermomagnetic heat engine as Gd blocks are exposed to different temperatures causing changes in their magnetic property. A change in the magnetic property of any Gd block is directly related to the resultant torque driving the thermomagnetic heat engine for power production. Among heat engines studied to date, the cylindrical thermomagnetic heat engine was able to develop a maximum mechanical power of 1.1 W at a temperature difference of 45 °C between hot and cold thermal resources. Furthermore, depending on the effectiveness of an electromagnetic generator (EMG) combined with a triboelectric nanogenerator (TENG), the electric power output can be notably improved.