Search Results (16)

The mechanical performance of metallic components is intrinsically linked to their microstructural features. However, the manual quantification of microconstituents in metallographic images remains a time-consuming and subjective task, often requiring over 15 min per image by a trained expert. To address this limitation, this study proposes an automated approach for quantifying the microstructural constituents from low-carbon steel welded zone images using convolutional neural networks (CNNs). A dataset of 210 micrographs was expanded to 720 samples through data augmentation to improve model generalization. Two architectures (AlexNet and VGG16) were trained from scratch, while three pre-trained models (VGG19, InceptionV3, and Xception) were fine-tuned. Among these, VGG19 optimized with stochastic gradient descent (SGD) achieved the best predictive performance, with an $R^2$ of 0.838, MAE of 5.01%, and RMSE of 6.88%. The results confirm the effectiveness of CNNs for reliable and efficient microstructure quantification, offering a significant contribution to computational metallography. Full article

The global need for energy has risen sharply recently. A global shift to clean energy is urgently needed to avoid catastrophic climate impacts. Hydrogen (H₂) has emerged as a potential alternative energy source with near-net-zero emissions. In the African continent, for sustainable access to clean energy and the transition away from fossil fuels, this paper presents a new approach through which waste energy can produce green hydrogen from biomass. Bio-based hydrogen employing organic waste and biomass is recommended using biological (anaerobic digestion and fermentation) processes for scalable, cheaper, and low-carbon hydrogen. By reviewing all methods for producing green hydrogen, dark fermentation can be applied in developed and developing countries without putting pressure on natural resources such as freshwater and rare metals, the primary feedstocks used in producing green hydrogen by electrolysis. It can be expanded to produce medium- and long-term green hydrogen without relying heavily on energy sources or building expensive infrastructure. Implementing the dark fermentation process can support poor communities in producing green hydrogen as an energy source regardless of political and tribal conflicts, unlike other methods that require political stability. In addition, this approach does not require the approval of new legislation. Such processes can ensure the minimization of waste and greenhouse gases. To achieve cost reduction in hydrogen production by 2030, governments should develop a strategy to expand the use of dark fermentation reactors and utilize hot water from various industrial processes (waste energy recovery from hot wastewater). Full article

Novel quantum materials offer the opportunity to expand next-generation computers, high-precision sensors, and new energy technologies. Among the most important factors influencing the development of quantum materials research is the ability of inorganic and materials chemists to grow high-quality single crystals. Here, the synthesis, structure characterization and magnetic properties of Na₂Cu₃(SeO₃)₄ are reported. It exhibits a novel two-dimensional (2D) structure with isolated layers of Cu nets. Single crystals of Na₂Cu₃(SeO₃)₄ were grown using a low-temperature hydrothermal method. Single-crystal X-ray diffraction reveals that Na₂Cu₃(SeO₃)₄ crystallizes in the monoclinic crystal system and has space group symmetry of P2₁/n (No.14) with a unit cell of a = 8.1704(4) Å, b = 5.1659(2) Å, c = 14.7406(6) Å, β = 100.86(2), V = 611.01(5) ų and Z = 2. Na₂Cu₃(SeO₃)₄ comprises a 2D Cu-O-Cu lattice containing two unique copper sites, a CuO₆ octahedra and a CuO₅ square pyramid. The SeO₃ groups bridge the 2D Cu-O-Cu layers isolating the neighboring Cu-O-Cu layers, thereby enhancing their 2D nature. Magnetic properties were determined by measuring the magnetic susceptibility of an array of randomly oriented single crystals of Na₂Cu₃(SeO₃)₄. The temperature-dependent magnetic measurement shows an antiferromagnetic transition at T_N = 4 K. These results suggest the fruitfulness of hydrothermal synthesis in achieving novel quantum materials and encourage future work on the chemistry of transition metal selenite. Full article

The integrated optimization of stope design and underground mine production scheduling is an approach that has been shown to effectively leverage the synergies among these two underground mine planning components to generate truly optimal stope layouts and extraction sequences. The existing stochastic integrated methods, however, do not include several elements of a mining complex, such as stockpiles, due to the computational complexity and non-linearity that they might add to the optimization of the long-term mine plan. Additionally, sequential simulation methods that rely on two-point statistics and Gaussian distribution assumptions are commonly used to generate the input realizations of the mineral deposit. These methods, however, are not able to properly characterize complex spatial geometries or the high-grade connectivity of non-Gaussian and non-linear natural phenomena. The present work proposes an extension of previous developments on the integrated stope design and underground mine scheduling optimization through an expanded stochastic integer programming formulation that incorporates long-term stockpiling decisions. An application of the proposed method at an operating underground copper mine compares the cases in which the geological simulated orebody models are based on high-order and Gaussian sequential simulation methods. The extraction sequence and related final stope design are shown to be physically different. It is seen that the optimization process takes advantage of the better representation of high-grade connectivity when high-order sequential simulations are used, by targeting the areas with grades that follow the mill’s blending requirements and by making less use of the stockpiles. Overall, a 4% higher copper metal production and a resultant 6% higher net present value are observed when high-order sequential simulations are used. Full article

Graphene oxide (GO), as a novel carbon-based nanomaterial (CBN), has been widely applied to every respect of social life due to its unique composite properties. The widespread use of GO inevitably promotes its interaction with heavy metal cadmium (Cd), and influences its functional behavior. However, little information is available on the effects of GO on greening hyperaccumulators under co-occurring Cd. In this study, we chose a typical greening hyperaccumulator (Lonicera japonica Thunb.) to show the effect of GO on Cd accumulation, growth, net photosynthesis rate (P_n), carbon sequestration and oxygen release functions of the plant under Cd stress. The different GO-Cd treatments were set up by (0, 10, 50 and 100 mg L⁻¹) GO and (0, 5 and 25 mg L⁻¹) Cd in solution culture. The maximum rate of Cd accumulation in the roots and shoots of the plant were increased by 10 mg L⁻¹ GO (exposed to 5 mg L⁻¹ Cd), indicating that low-concentration GO (10 mg L⁻¹) combined with low-concentration Cd (5 mg L⁻¹) might stimulate the absorption of Cd by L. japonica. Under GO treatments without Cd, the dry weight of root and shoot biomass, P_n value, carbon sequestration per unit leaf area and oxygen release per unit leaf area all increased in various degrees, especially under 10 mg L⁻¹ GO, were 20.67%, 12.04%, 35% and 28.73% higher than the control. Under GO-Cd treatments, it is observed that the cooperation of low-concentration GO (10 mg L⁻¹) and low-concentration Cd (5 mg L⁻¹) could significantly stimulate Cd accumulation, growth, photosynthesis, carbon sequestration and oxygen release functions of the plant. These results indicated that suitable concentrations of GO could significantly alleviate the effects of Cd on L. japonica, which is helpful for expanding the phytoremediation application of greening hyperaccumulators faced with coexistence with environment of nanomaterials and heavy metals. Full article

A startling shift in waste composition has been brought on by a dramatic change in lifestyle, the quick expansion of consumerism brought on by fierce competition among producers of consumer goods, and revolutionary advances in the packaging sector. The overflow or overspill of garbage from the bins causes poison to the soil, and the total obliteration of waste generated in the area or city is unknown. It is challenging to pinpoint with accuracy the specific sort of garbage waste; predictive image classification is lagging, and the existing approach takes longer to identify the specific garbage. To overcome this problem, image classification is carried out using a modified ResNeXt model. By adding a new block known as the “horizontal and vertical block,” the proposed ResNeXt architecture expands on the ResNet architecture. Each parallel branch of the block has its own unique collection of convolutional layers. Before moving on to the next layer, these branches are concatenated together. The block’s main goal is to expand the network’s capacity without considerably raising the number of parameters. ResNeXt is able to capture a wider variety of features in the input image by using parallel branches with various filter sizes, which improves performance on image classification. Some extra dense and dropout layers have been added to the standard ResNeXt model to improve performance. In order to increase the effectiveness of the network connections and decrease the total size of the model, the model is pruned to make it smaller. The overall architecture is trained and tested using garbage images. The convolution neural Network is connected with a modified ResNeXt that is trained using images of metal, trash, and biodegradable, and ResNet 50 is trained using images of non-biodegradable, glass, and hazardous images in a parallel way. An input image is fed to the architecture, and the image classification is achieved simultaneously to identify the exact garbage within a short time with an accuracy of 98%. The achieved results of the suggested method are demonstrated to be superior to those of the deep learning models already in use when compared to a variety of existing deep learning models. The proposed model is implemented into the hardware by designing a three-component smart bin system. It has three separate bins; it collects biodegradable, non-biodegradable, and hazardous waste separately. The smart bin has an ultrasonic sensor to detect the level of the bin, a poisonous gas sensor, a stepper motor to open the lid of the bin, a solar panel for battery storage, a Raspberry Pi camera, and a Raspberry Pi board. The levels of the bin are maintained in a centralized system for future analysis processes. The architecture used in the proposed smart bin properly disposes of the mixed garbage waste in an eco-friendly manner and recovers as much wealth as possible. It also reduces manpower, saves time, ensures proper collection of garbage from the bins, and helps attain a clean environment. The model boosts performance to predict waste generation and classify it with an increased 98.9% accuracy, which is more than the existing system. Full article

This study developed an unattended electric weeder (UEW) to control floor weeds in an orchard greenhouse. The UEW was a motor-driven dolly equipped with a spark exposer. The spark exposer was constructed by applying an alternating voltage (10 kV) to a conductor net (expanded metal net). The charged conductor net (C-CN) discharged into the surrounding space. Wild oat and white clover were used as test weed species. Weed seedlings growing on the floor were grounded by the biological conductor and were subjected to a spark from the C-CN when they reached the discharge space. The spark-exposed seedlings were singed and shrunk instantaneously. In the present experiment, the UEW was remotely controlled to move on the soil-cover metal nets, which were laid on the floor to make a flat surface, in a stop-and-go manner, and to eject a spark to the weed seedlings that emerged from the floor. All of the mono- and dicotyledonous weed seedlings, which had been artificially sown on the floor, were completely eradicated using this method. Thus, this study provides an experimental basis for developing an unattended technique for controlling floor weeds in an orchard greenhouse. Full article

An electrostatic technique was developed to generate a simple physical method to eradicate weeds in crop fields. The proposed apparatus consisted of double-expanded metal nets connected to a pulse-charging type negative voltage generator and a grounded line. The two metal nets were arranged in parallel at an interval (6 mm) that caused no arc (spark) discharge between the negatively charged metal net (NC-MN) and the grounded metal net (G-MN). The paired nets were used as a soil cover to zap weed seedlings emerging from the ground. As plant seedlings are biological conductors, the seedling was subjected to an arc discharge from the upper metal net (NC-MN) when it emerged from the soil and passed through the lower net (G-MN). The discharge was strong enough to destroy the seedling with a single exposure. The arc treatment was highly effective for eradicating successively emerging mono- and dicotyledonous weed seedlings, regardless of the number of coexisting weeds or the area of the netted field. Thus, the present study provides a simple and reliable weed eradication method that could be integrated into a sustainable crop production system. Full article

A simple electrostatic apparatus that generates an arc discharge was devised to control adult houseflies emerging from a soil bed in a greenhouse. Adult houseflies emerging from a soil bed in a greenhouse are a potential vector of pathogenic Escherichia coli O157, carried by animal manure used for soil fertilization. A simple electrostatic apparatus that generates an arc discharge was devised to control these houseflies. The apparatus consisted of two identical metal nets; one was linked to a negative-voltage generator to create a negatively charged metal net (NC-MN), and the other was linked to a grounded line to create a grounded metal net (G-MN). A square insulator frame was placed between the two nets, separating them by 6 mm, and a plastic grating with multiple cells was placed beneath the G-MN to provide a climbing path (54 mm in height) to the arcing sites of the apparatus for adult houseflies emerging on the soil surface. Houseflies that climbed up the wall of the grating and reached the arcing zone were subjected to arc-discharge exposure from the NC-MN and thrown down onto the soil by the impact of the arcing. The impact was destructive enough to kill the houseflies. The structure of this apparatus is very safe and simple, enabling ordinary greenhouse workers to fabricate or improve it according to their own requirements. This study developed a simple and safe tool that provides a physical method to manage houseflies. Full article

On account of the long-term dependence on energy trade and the phenomenon of embodied carbon emissions in interprovincial trade (ECEs-IPT), energy supply bases (ESBs) in the economic integration regions (EIRs) are under unprecedented dual pressure of achieving carbon emissions (CEs) reduction targets and ensuring security and stability of the energy supply. This problem has attracted more and more attention and research by experts and scholars. This paper took Anhui, the coal ESB of the Yangtze River Economic Belt (YREB), as an example and took the key stage of rapid development of regional economic integration (REI) and accelerated the realization of CEs reduction targets in YREB from 2007 to 2017 as the study period. From the perspectives of regions and industry sectors, we calculated the transfer amount of ECEs-IPT in Anhui among the YREB, analyzed the spatial–temporal evolution pattern of ECEs-IPT, and revealed the industrial characteristics of ECEs-IPT. Then, we classified the industry sectors and proposed the direction of industrial improvement measures. The results showed that, during the decade, the amount of provinces undertaking the net ECEs-IPT outflow from Anhui increased significantly and spatially expanded from only Jiangxi Province to almost all of the YREB. In addition, 39.77% of the net ECEs-IPT outflow of Anhui was concentrated in petroleum processing, coking, and nuclear fuel processing (RefPetraol), metal smelting and rolling processing (MetalSmelt), and electricity and heat production and supply (ElectpowerProd) that trade with Shanghai, Jiangsu, Zhejiang, and Jiangxi. The analytical model and results will provide a useful reference for the global similar coal ESBs, especially the coal ESBs within the EIRs, to formulate improvement measures for regions or even the world to ensure stability of the energy supply and achieve regional CEs reduction targets. Full article

Energy supply bases (ESBs) are vital regions, intended to satisfy global energy demands and secure global energy supplies, which provide large amounts of energy products to their host countries (and even the world through trade). However, due to long-term dependency on energy trade, ESBs are facing the dual pressure of reaching emission reduction targets and securing energy supplies and have become one of the main obstacles for host countries trying to reach emission reduction targets. (1) Methods: We used the EEBT model, SDA model, and CR model to explore the spatio-temporal features and mechanisms of embodied carbon emissions in inter-provincial trade (ECEs-PT) in Shanxi. (2) Results: The spatio-temporal development characteristic of net ECEs-PT outflow in Shanxi is “from expanded coverage to enhanced agglomeration”. A total of 98% of the net ECEs-PT is highly concentrated in coal mining and washing (Coalmin), metal smelting and rolling processing (MetalSmelt) and petroleum processing, coking, and nuclear fuel processing (RefPetraol). Moreover, the ECEs-PT driving forces were technology, structure, and scale. While trade expands, the pressure of CEs reduction is increasing. We discussed optimization for different types of sectors. The results could provide scientific support for similar ESBs to reduce carbon emissions more efficiently with less disturbance to energy supply stability. Full article

The efficient recovery of low temperature waste heat, representing from 25% up to 55% of the energy losses in industrial processes, still remains a challenge and even Organic Rankine Cycles (ORCs) experience a strong efficiency decay in such a low temperature operating range (T < 150 °C). In similar heat transfer processes, several nanofluids have been proposed as a solution for increasing heat transfer efficiency, but they produced only moderate enhancements of the heat transfer efficiency in comparison with pure fluids. This paper aims at numerically assessing the potential gain in efficiency deriving from the application of an unconventional type of nanoparticles, the metal-organic heat carriers (MOHCs), in the ORC field. In comparison with standard nanoparticles, these MOHCs make it possible to extract additional heat from the endothermic enthalpy of desorption, with a theoretically high potential for boosting the heat transfer capacity of ORC systems. In this paper a numerical model was developed and customized for considering the adsorption/desorption processes of the pure fluid R245fa (pentafluoropropane) combined with a crystal structure for porous chromium terephthalate (MIL101). The R245fa/MIL101 nanofluid behavior was experimentally characterized, defining proper semi-emipirical correlations. Then, an optimization procedure was developed, combining the numerical model with a PSO algorithm, to optimize the thermodynamic conditions in the ORC so as to maximize the contribution of desorption/absorption processes. The results confirm the increase in net power output (+2.9% for 100 °C) and in expander efficiency (+2.4% for 100 °C) at very low heat source temperature. The relevance of tuning the operating cycle and the nanofluid properties is also demonstrated. Full article

Metals created by melting basic metal and welding rods in welding operations are referred to as weld beads. The weld bead shape allows the observation of pores and defects such as cracks in the weld zone. Radiographic testing images are used to determine the quality of the weld zone. The extraction of only the weld bead to determine the generative pattern of the bead can help efficiently locate defects in the weld zone. However, manual extraction of the weld bead from weld images is not time and cost-effective. Efficient and rapid welding quality inspection can be conducted by automating weld bead extraction through deep learning. As a result, objectivity can be secured in the quality inspection and determination of the weld zone in the shipbuilding and offshore plant industry. This study presents a method for detecting the weld bead shape and location from the weld zone image using image preprocessing and deep learning models, and extracting the weld bead through image post-processing. In addition, to diversify the data and improve the deep learning performance, data augmentation was performed to artificially expand the image data. Contrast limited adaptive histogram equalization (CLAHE) is used as an image preprocessing method, and the bead is extracted using U-Net, a pixel-based deep learning model. Consequently, the mean intersection over union (mIoU) values are found to be 90.58% and 85.44% in the train and test experiments, respectively. Successful extraction of the bead from the radiographic testing image through post-processing is achieved. Full article

Strategic control and eradication programs for wild pigs (Sus scrofa) are being developed to help curtail the expanding populations of this invasive, alien species. Drop nets and corral traps have a long history of capturing a multitude of wildlife species, so we evaluated the effectiveness and efficiency of these traps for controlling wild pigs in southern Oklahoma. We also developed and evaluated a suspended metal trap that provided real-time monitoring and deployment to capture animals. Effectiveness of each trap type was estimated as the proportion of pigs removed from the total population, whereas efficiency was calculated based on catch per unit effort (CPUE) (i.e., the number of person hours per pig removal). During 3 years of study (2010–2012), we removed 601 pigs, 296 using drop nets, 60 using corral traps, and 245 using suspended traps. Suspended traps removed 88.1% of the estimated population, whereas drop nets removed 85.7% and corral traps removed 48.5%. CPUE was 0.64 person hours/pig using suspended traps followed by 1.9 person hours/pig for drop nets and 2.3 person hours/pig for corral traps. Drop nets and suspended traps were more effective at removing a large proportion of the population (>85%), mainly through whole sounder removal, but the suspended trap with real-time notifications was the most efficient trap type, requiring fewer person hours to operate. Full article

The industrial sector is a major contributor to the economic growth of the Philippines. However, it is also one of the top consumers of energy, which is produced mainly from fossil fuels. The Philippine industrial sector must therefore be supported economically while minimizing the emissions associated with energy consumption. A potential strategy for minimizing costs and emissions is the installation of solar photovoltaic (PV) modules on the rooftops of industrial facilities, but this approach is hindered by existing energy policies in the country. In this work, we performed a techno-economic assessment on the implementation of rooftop solar PV in Philippine industrial facilities under different policy scenarios. Our study considered 139 randomly sampled industrial plants under MERALCO franchise area in the Philippines. Under the current net metering policy, 132 of the evaluated facilities were economically viable for the integration of rooftop solar PV. This corresponds to an additional 1035 MW_p of solar PV capacity and the avoidance of 8.4 million tons of CO₂ emissions with minimal financial risk. In comparison, an expanded net metering policy supports the deployment of 4653 MW_p of solar PV and the avoidance of 38 million tons of CO₂. By enabling an enhanced net metering policy, the widespread application of rooftop solar PV may present considerable savings and emission reduction for energy-intensive industries (electrical and semiconductors, cement and concrete, steel and metals, and textile and garments) and lower generation costs for less energy intensive industries (construction and construction materials, transportation and logistics, and food and beverages). Full article