Processes

Welding is an essential process across various industries; however, it exposes workers to dangerous fumes, extreme heat and physical stress, which pose considerable health and safety hazards. To tackle these issues, this article introduces the creation of a remote-controlled human–robot welding system aimed at safeguarding workers while ensuring the quality of the welds. The system monitors a welder’s torch movements through a stereoscopic sensor and accurately reproduces them with a robotic arm, facilitating real-time remote welding. Operated by a student, it effectively welded standardized sheet metals in overhead positions while adhering to critical quality standards. The weld geometry met ISO 5817 requirements, tensile strength surpassed the base material specifications, and bending and hardness assessments verified the durability and integrity of the welds. When utilized in hazardous settings, the system showcases its capability to produce high-quality welds while significantly enhancing worker safety, underscoring its potential for real-world industrial applications. Full article

During the development of metallurgical technology in the feudal period, the main ironmaking technology in the Benxi region was the crucible, reaching its peak period in the Ming Dynasty. By studying the Wangguan ironmaking site in Benxi, the historical details of the Ming Dynasty ironmaking process in the region were investigated, and a technical analysis was carried out. The results show that this historical site was the location of the Hundred-Household Iron Yard in the northeastern region during the Ming Dynasty. The unearthed slag, iron, and crucible samples indicate that a relatively complete ironmaking process chain had been formed at this time. The raw material used for the crucibles was high-alumina clay, which has been widely distributed in Benxi, Liaoning, China, since ancient times. The refractoriness of the crucibles exceeded 1700 °C, and the molar ratio of SiO₂ to Al₂O₃ was close to the upper limit for the optimal formation of mullite and thermal shock resistance. Slag was produced from a typical high-silica, high-alumina aluminosilicate system, and no fluxes, such as limestone and dolomite, were added during the smelting process. Moreover, coal resources have been widely used in ironmaking activities in the Benxi region at least since the Ming Dynasty, and craftsmen at that time had already mastered the technology of using coke as fuel and reductant to control the sulfur content in pig iron. Full article

Wellbore washing technology is a basic operation in wellbore maintenance. Problems such as low automation levels, long processing times, the fact that it is easy to cause downhole falling, and cleaning blind areas greatly affect the use and maintenance of traditional cleaning equipment. These problems usually come from design defects such as a complicated installation process, a lack of an anti-impact structure, and a fixed jet direction. To address the aforementioned issues, this paper proposes an efficient and integrated rapid-disassembly and -assembly automatic filtration rotary jet cleaning device. The device is divided into two main units and further subdivided into four modules. The quick-assembly unit comprises an elastic connection module and a downstroke quick-assembly module, which can automatically compensate for deviations in equipment position during the installation process, ensuring the reliability of the installation process and the sealing of the equipment and facilitating the rapid connection and separation of the tool string. The wellbore cleaning unit includes a hydraulic rotary washing module and a rotary filtration storage module. The wellbore is jet-flushed by hydraulic drive, and the solid particles are separated and filtered during the cleaning fluid circulation process to realize the purification and reuse of the cleaning fluid. The device reduces the installation operation time and labor cost, improves the reliability of equipment in the well, improves the flushing coverage area and the cleaning efficiency, realizes the reuse of the cleaning liquid in the wellbore, reduces the energy consumption of the flowback treatment, and comprehensively improves the cleaning efficiency and the energy utilization efficiency. Full article

The stability and uniformity of a liquid line formed by the sequential deposition of droplets are essential to the quality of products in many industry applications. In this work, a numerical model based on the front tracking method (FTM) is developed to investigate the displacement dynamics of sequential droplets on wetting confinement. We systematically examine the impact of wetting conditions and confinement width on the spreading length, morphology, and confined angle for a droplet. In addition, an analytical model is derived to predict the droplet displacement spacing for a uniform line. The analytical results align well with the numerical results, and the sequential droplets displaced with the predicted space achieve the minimum cross-section error and exhibit enhanced uniformity. Our numerical and analytical studies of droplet displacement within wetting confinement provide fundamental insights and a predictive framework for enhancing the uniformity and stability of liquid lines in precision manufacturing processes. Full article

In traditional in situ leaching (ISL) uranium mining, the injection volume depends on technicians’ on-site experience. Therefore, applying artificial intelligence technologies such as machine learning to analyze the relationship between injection volume and leaching rate in ISL uranium mining, thereby reducing human factor interference, holds significant guiding importance for production process control. This study proposes a novel uranium leaching rate prediction method based on a CNN–LSTM–LightGBM fusion model integrated with an attention mechanism. Ablation experiments demonstrate that the proposed fusion model outperforms its component models across three key metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE). Furthermore, comparative experiments reveal that this fusion model achieves superior performance on MAE, MAPE, and RMSE metrics compared to six extensively utilized machine learning methods, including Multi-Layer Perceptron, Support Vector Regression, and K-Nearest Neighbors. Specifically, the model achieves an MAE of 0.085%, an MAPE of 0.833%, and an RMSE of 0.201%. This attention-enhanced fusion model provides technical support for production control in ISL uranium mining and offers valuable references for informatization and intelligentization research in uranium mining operations. Full article

Timely and accurate fault diagnosis of ship oil purifiers is essential for maintaining the operational reliability of a degree-4 maritime autonomous surface ship (MASS). Conventional approaches rely on manual feature engineering or simple machine learning classifiers, limiting their robustness in dynamic maritime environments. This study proposes an adaptive latent space learning framework that couples a two-dimensional convolutional autoencoder (2D-CAE) with a component-aware triplet-loss regularizer. The loss term structures the latent space to reflect both the fault severity progression and component-specific distinctions, enabling severity-proportional distances among a latent vector learned directly from vibration signals even in a limited data environment. Using data collected on a dedicated ship oil purifier test bed, the method yields a latent vector that encodes the fault severity and physical provenance, enhancing the interpretability and diagnostic accuracy. Experiments demonstrate enhanced performance over state-of-the-art deep models, while offering clear insight into fault evolution and inter-component dependencies. The framework thus advances intelligent, condition-based maintenance for autonomous maritime systems. Full article

In large-scale warehouses, mobile robots often face energy shortages during inspection tasks, necessitating multiple charging points. Considering battery limits and multiple charging points makes path planning challenging. This paper presents a two-level solution: (i) local path planning via improved B-RRT* (adaptive Gaussian sampling + dynamic goal bias) to build a path-cost matrix, and (ii) global inspection and charging scheduling under multi-charging-point constraints. We evaluate planning time, total path length (as an energy proxy), and the number of sampling points. Experimental results demonstrate that the improved B-RRT* algorithm achieves an average reduction of 10–15% in path length, 20–30% in computation time, and 15–40% in the number of sampling points compared to the initial B-RRT* and RRT* algorithms across various warehouse environments. For global planning with up to 60 inspection targets and 3–5 charging points, a feasible charging schedule is obtained within 150–360 s on a standard desktop (Ryzen 7 5800H, 16 GB RAM), demonstrating strong practicality and scalability. Full article

Plastic injection molding is a widely used manufacturing process for producing plastic components. However, achieving optimal process stability and part quality remains a persistent challenge due to limited real-time feedback during production. The main objective of this study is to present a method to overcome this limitation by integrating in-mold cavity pressure sensors with an Iterative Learning Control (ILC) strategy to optimize key processing parameters autonomously. The ILC methodology established a closed-loop system; over successive production cycles, cavity pressure profiles were analyzed to automatically adjust the holding pressure, holding time, and switchover point. Each iteration refined the parameters based on sensor data, creating a learning-based optimization loop that accelerated the convergence to optimal settings. The methodology was validated by producing an automotive plastic component. The results demonstrate a 100% success rate in correcting ten critical dimensional errors, fulfilling all part tolerances. Additionally, the overall cycle time decreased by 8%, from 55.0 to 50.6 s. Other findings included updates to key process molding parameters, such as reducing holding pressure from 250 to 230 bar and holding time from 18 to 12 s, as well as increasing the switchover point from 41 to 72 mm. This research confirms that combining real-time cavity pressure monitoring with ILC offers a strong, data-driven framework for significantly improving quality, efficiency, and process stability in injection molding. Full article

This study investigates the extraction kinetics of luteolin, a bioactive flavonoid with recognized antioxidant and health-promoting properties, from the aerial parts of Reseda luteola (dyer’s weld), with emphasis on its industrial potential. A comparative analysis with peanut shells (Arachis hypogea) identified R. luteola as a superior source, containing 14 ± 3 mg of LUT/g of material, approximately eight times higher than the amount in peanut shells. Luteolin occurred predominantly as luteolin-7-O-glycoside (57%) and the aglycone (35%). Methanolic semi-batch extraction at 25 °C yielded 9.6 mg LUT/g (70%) within 60 min at a solid-to-liquid ratio of 1:9, demonstrating significantly greater solvent efficiency than conventional Soxhlet or maceration techniques. Kinetic modeling, based on Fick’s second law, revealed a biphasic process with a low rate constant ratio (3:1) between the two stages, indicating the need for process optimization. These results establish R. luteola as a cost-effective and sustainable source of luteolin for dietary supplements and functional foods, while indicating the need to explore alternative solvents and advanced extraction methods to further optimize yield and efficiency. Full article

During deepwater drilling operations, when influx gas invades the wellbore, gas hydrates may form through the combination of the gas with free water in the drilling fluid under favorable temperature and pressure conditions. This process can alter the physical properties and flow behavior of the wellbore fluid, potentially leading to safety incidents. To prevent natural gas hydrate formation, mitigate wellbore blockages caused by hydrates, and address the associated safety hazards, this study conducted laboratory experiments to investigate hydrate formation and remediation under multiple deepwater drilling conditions. The hydrate formation boundaries for four different drilling fluid systems—seawater-based bentonite mud, seawater polymer mud, Plus/KCl mud, and HEM mud—were determined for varying well depths and pressure–temperature conditions, and corresponding trend lines were fitted. Key results reveal that a higher carbon content promotes hydrate formation, and the phase equilibrium curves also reveal significant differences among the four drilling fluids. The hydrate aggregation states and blockage processes were clarified for three typical drilling scenarios: drilling, well killing, and drilling suspension. Hydrate formation risk is negligible during normal circulation but increases dramatically during well-killing operations, significantly shrinking the safe operational window. A comparative analysis identified that adding 1% P(M-VCL), a kinetic hydrate inhibitor, to the drilling fluid was the most effective solution, demonstrating superior performance in delaying hydrate nucleation and preventing agglomeration. The study established a complete formation–inhibition–remediation approach for hydrate management in deepwater drilling, thereby enhancing operational safety and efficiency. Full article

Evaluating green data centers is a multi-attribute decision problem. To enhance the rigor and precision of green data center assessment, this study verifies the weighting of the national green data center evaluation index system using the Analytic Hierarchy Process (AHP) with the participation of 19 domain experts from various data center sectors. The aim is to gain an in-depth understanding of the perspectives and priorities of different types of institutions regarding evaluation indicators and to investigate the underlying reasons for these perspectives and priorities. Through an analysis of expert sample distribution, this paper reveals the preferences of financial, internet, research, and design, as well as technical consulting service institutions, regarding indicators such as energy-efficient utilization, computational resource utilization, green low-carbon development, scientific layout, and intensive construction. Specifically, financial institutions tend to place a relatively lower emphasis on energy efficiency due to their focus on transaction speed and security. In contrast, internet companies prioritize efficient utilization of computational resources. Research and design institutions consider scientific layout and intensive construction more crucial, while technical consulting service institutions emphasize green and low-carbon development. Meanwhile, we identified substantial discrepancies among experts in determining the weights of specific indicators, suggesting a lack of consensus within the industry about the correlation between these indicators and green data centers. To propel the sustainable development of green data centers, future assessments should refine evaluation dimensions, consider disparities such as data center types and embrace regional differences, actively adopt novel technologies and innovative practices, and establish mechanisms for long-term monitoring and evaluation. Full article

This study investigates the properties of biopolymer films derived from semolina and farina, focusing on the effect of varying concentrations of glycerol as a plasticizer. The research fills a gap in the study of grains such as semolina and farina, which have the potential to expand the range of biodegradable materials. Mechanical tests revealed significant differences between the two film types. Farina-based films were notably more ductile, exhibiting an elongation at break of up to two times their original length, but with a low tensile strength of only 1–2 MPa. In contrast, semolina-based films were significantly stiffer, with a maximum elongation at break of 10%. A notable exception was the semolina film with a 25% glycerol concentration, which displayed an exceptionally high tensile strength of 17 MPa. This is a significant improvement over the typical potato starch-based film tested, which breaks at 5 MPa under static tearing. Furthermore, the study examined the films’ morphology, color, SFE, and surface roughness. Free surface energy ranged from 40 to 60 mJ/m² in the tests, where the influence of the plasticizer was significant. Color tests clearly show yellow discoloration. Full article

Due to the regulated nature and purity standards of the bioprocess and biotechnology industries, the sector has seen comparatively less sustainable practices than other chemical industries have. The achievement of sustainability in microbial fermenter design requires that quantitative tools with links between process parameters and end-environmental outcomes are employed. This review begins with environmentally friendly metrics such as process mass intensity, water and energy intensity, and related indicators that act as a template for resource usage and waste generation assessment. The objective of this paper is to highlight the primary focus on computational fluid dynamics (CFD) applied to bioprocesses in aerated stirred bioreactors using Escherichia coli (E. coli). Second, the objective of this paper is to explore state-of-the-art CFD models and methods documented in the existing literature, providing a fundamental foundation for researchers to incorporate CFD modelling into biotechnological process development, while making these concepts accessible to non-specialists and addressing the research gap of linking CFD outputs with sustainability metrics and life cycle assessment techniques. Impeller rotational models such as sliding mesh are an accurate and commonly used method of modelling the rotation of stirring. Multiple different turbulence models are applied for the purpose of stirred bioreactors, with the family of k-ε models being the most used. Multiphase models such as Euler-Euler models in combination with population balance models and gas dispersion models to model bubble size distribution and bubble characteristics are typically used. Full article

Understanding the fatigue behavior of materials is essential for designing components capable of enduring prolonged use under varying stress conditions. This study investigates the high-cycle fatigue and fatigue crack growth characteristics of X17CrNi15-2 stainless steel. Very high-cycle fatigue (VHCF) and fatigue crack growth tests were conducted on conventional fatigue and compact tension (CT) specimens fabricated from X17CrNi15-2 stainless steel. The fatigue crack growth behavior of the CT specimens was analyzed using Paris’ law. A revised version of Paris’ law was suggested based on the fatigue crack growth rate plotted against the stress intensity factor range, expanding on prior research utilizing three-point single-edge notch bend specimens. Scanning electron microscopy (SEM) was employed to examine the fracture mechanisms of both fatigue specimen types. The results indicated that the fatigue specimens failed in the VHCF regime under stress amplitudes ranging from 100 to 450 MPa. A power law correlation between stress amplitude and fatigue life was established, with material constants of 7670.3954 and −0.1663. These findings offer valuable insights into the material’s performance and are crucial for enhancing its suitability in engineering applications where high-cycle fatigue is a critical factor. Full article

Water scarcity is a problem affecting millions of people in the world, including northern Chile, with several cities declared under water scarcity by the Chilean government. This paper numerically evaluates an atmospheric water generator based on a single vapor compression refrigeration system using R410A. The monthly water harvesting rate and specific energy consumption are calculated for nine cities distributed throughout northern Chile. Every component is modeled in a modular way, using semi-empirical models, and integrated into an overall model. For the nine cities considered in this study, the monthly water harvesting varies between a maximum of 5518 L, obtained for Huasco during January, and a minimum of 0 L, in Combarbalá and Vicuña in some months during winter. In the case of the specific energy consumption, it varies between 0.355 and 1.146 kWh/L. By taking the period between December and April, the system can collect an average of 3868 L/month, with an average specific energy consumption of 0.533 kWh/L. The working domain of the system is strongly limited by the Chilean climate conditions, which is mainly influenced by the Humboldt Current. This restricts the operational efficiency of the AWGs, especially during the colder and drier months. Nonetheless, the modular modeling approach allows for flexible adaptation and optimization of the system across different geographic locations. Full article

This study reports the synthesis of high cis-1,4 polybutadiene with a narrow molecular weight distribution (Đ < 2.0) by coordinative chain transfer polymerization (CCTP) using a homogeneous ternary NdV₃/diisobutyl aluminum hydride (DIBAH)/dimethyldichlorosilane (Me₂SiCl₂) Ziegler–Natta catalyst system. The polymerization parameters, notably the monomer-to-initiator ratio ([M]/[Nd]) and the halogen-to-initiator ratio ([Cl]/[Nd]), were systematically varied to define the CCTP operational window. It was found that CCTP conditions are achieved only at low [M]/[Nd] ratios (<2500) and intermediate [Cl]/[Nd] ratios between 1.0 and 2.0, facilitating the production of polymers with molecular weights below 32 kDa and narrow dispersity. Increasing these ratios beyond these thresholds potentially induces the formation of insoluble, hyper-halogenated catalytic species and increases medium viscosity, which significantly broadens the molecular weight distribution (Đ > 4.0) and impairs CCTP control. These findings challenge previous assumptions that higher halogen concentrations are necessary for CCTP, thereby providing important mechanistic insights for tuning active species and achieving improved polymer architecture. The work demonstrates a viable pathway to control polymer microstructure and molecular weight in neodymium-based CCTP, which is critical for design of high-performance elastomeric materials. Full article

Pre-extraction gas technology is commonly used in coal mines to extract gas from single coal seams, initial protective layers, and both unprotected and protected coal seams. With the development of drilling equipment, directional long drilling, pre-extraction, coal seam gas technology has been widely applied, and negative pressure extraction is one of the key factors affecting the effectiveness of directional long drilling gas extraction. In order to determine the reasonable length of directional long boreholes, studying the negative pressure distribution and time-varying rules within such boreholes is of great significance for guiding later borehole layout and gas extraction. The COMSOL Multiphysics software v.5.3. was used to couple and solve the dynamic model of temperature, stress, and seepage in coal-containing gas, as well as the mathematical model of negative pressure attenuation in directional long boreholes. The gas pressure distribution in the coal surrounding the directional long borehole and the distribution and time-varying law of negative pressure in the borehole were studied. Then, the distribution and time-varying law of negative pressure in directional long borehole extraction were tested on site. Research has shown that the negative pressure attenuation during directional long drilling has a relatively small impact on the effectiveness of coal gas extraction, while the negative pressure at the hole opening is the key factor affecting the effectiveness of gas extraction. In the early stage of extraction, as the drilling depth increases, the pressure loss inside the hole increases and the negative pressure inside the hole decreases. As the extraction time becomes longer, the pressure loss inside the borehole decreases and the negative pressure inside the borehole gradually returns to the negative pressure value at the orifice. The gas flow velocity inside the extraction borehole gradually increases from the bottom of the hole to the hole opening, and the flow velocity at the bottom of the hole remains basically constant. The gas flow velocity inside the hole gradually decreases with the extension of extraction time, and the smaller the distance from the extraction hole opening, the greater the flow attenuation. The collapse of drilling holes during extraction affects the attenuation of negative pressure inside the hole in the short term. As the extraction time increases, the impact of the collapse on the negative pressure inside the hole is limited. The temperature of coal can significantly affect the negative pressure and gas flow distribution inside the pores. Considering the temperature effect, the gas flow velocity inside the pores is higher and the pressure loss is lower in the short term. On-site tests have determined that the depth of ultra-long directional drilling holes is shallower than 327 m, and the negative pressure changes inside the borehole are not significantly different from the negative pressure at the hole opening. The negative pressure stabilization speed near the hole opening and bottom is fast, usually reaching its peak within 3–10 min. The negative pressure stabilization process from the borehole opening to the hole bottom shows a “fast slow fast” trend. When using double-sided extraction, the time for negative pressure to reach stability is significantly shortened compared to single-sided extraction, and double-sided extraction is beneficial for improving the effectiveness of coalbed methane extraction. Full article

Hospitals often use diatrizioic acid (DTZA), an iodinated radiocontrast agent, which is poorly biodegradable and persistent in aqueous media. Therefore, the objective of this work is to remove DTZA from water using an advanced separation process, namely liquid surfactant membrane (LSM) or emulsion liquid membrane. The LSM system is composed of Aliquat 336 as extractant, Span 80 as emulsifier, kerosene as diluent, and KCl as internal stripping phase. The impacts of experimental parameters impacting the extraction of DTZA from water by LSM, namely surfactant concentration, initial pH of the contaminated solution, extractant dosage, nature of base in the contaminated solution, concentration of the internal stripping phase, nature of stripping solution, emulsion/external solution volume ratio, internal solution/organic phase volume ratio, mixing rate, nature of diluent, emulsification time, emulsification rate, and initial DTZA concentration, were investigated. A highly stable emulsion with a good degree of removal of 90.8% of DTZA in water was obtained for an emulsifier dosage of 3% (w/w), an extractant dosage of 1.0% (w/w), a pH of the contaminated solution of 10 using NH₄OH, a concentration of the inner phase of 0.3 N KCl, an internal solution/organic phase volume ratio of 1/1, an emulsion/external solution volume ratio of 20/250, a mixing speed of 250 rpm, an emulsification time of 4 min, and an emulsification speed of 20,000 rpm. Additionally, the extraction of DTZA from various natural water matrices (natural mineral water, tap water and seawater) was examined. The developed LSM method offers a fascinating enhanced separation method for the elimination of DTZA in waters with low chloride ion concentrations. Full article

Renewable energy technologies play a key role in mitigating climate change and advancing sustainable development. Among these, photovoltaic (PV) systems have experienced significant growth in recent years. However, shading, one of the most common faults in PV modules, can drastically degrade their performance. This study investigates the application of convolutional neural networks (CNNs) for the automated detection and classification of shading faults, including multiple severity levels, using current–voltage (I–V) curves. Four scenarios were simulated in Simulink: a healthy module and three levels of shading severity (light, moderate, and severe). The resulting I–V curves were transformed into grayscale images and used to train and evaluate several custom-designed CNN architectures. The goal is to assess the capability of CNN-based models to accurately identify shading faults and discriminate between severity levels. Multiple network configurations were tested, varying image resolution, network depth, and filter parameters, to explore their impact on classification accuracy. Furthermore, robustness was evaluated by introducing Gaussian noise at different levels. The best-performing models achieved classification accuracies of 99.5% under noiseless conditions and 90.1% under a 10 dB noise condition, demonstrating that CNN-based approaches can be both effective and computationally lightweight. These results underscore the potential of this methodology for integration into automated diagnostic tools for PV systems, particularly in applications requiring fast and reliable fault detection. Full article