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Surfactant-stabilized cyclopentane hydrate emulsions for removing tetramethylammonium hydroxide from semiconductor wastewater
No full textThis study presents an innovative hydrate-based wastewater treatment approach specifically designed for semiconductor wastewater containing tetramethylammonium hydroxide (TMAH), utilizing cyclopentane (CP)in-water emulsions stabilized by nonionic surfactants Span 80 and Vitamin E-TS. The stabilized emulsions significantly accelerated hydrate formation kinetics, reducing the induction time from 313.33 min to approximately 12.67 min (a 96 % decrease), thus greatly enhancing the process's energy efficiency. Structural analyses using powder X-ray diffraction (PXRD) and Raman spectroscopy confirmed that TMAH molecules at concentrations of 515 mg/L were effectively excluded from the hydrate structure, demonstrating no interference with hydrate crystalline structures or cage occupancy. Furthermore, incorporating Vitamin E-TS as an antiagglomerant promoted the formation of porous hydrate structures, increasing TMAH removal efficiency substantially from 45.51 % to 62.96 %, though reducing water recovery from 69.74 % to 61.64 %. Subsequent post-washing procedures further increased TMAH removal efficiency to 74.88 % but decreased water recovery to 49.24 % due to partial hydrate melting. These findings underscore the importance of balancing water recovery and contaminant removal efficiency, providing essential insights for optimizing hydrate-based wastewater treatment processes, and highlighting its potential as an energy-efficient and sustainable solution for semiconductor wastewater and other industrial wastewater containing persistent organic contaminants.
Enhancement of waste activated sludge hydrolysate digestion efficiency via promotion of direct interspecies electron transfer
No full textAnaerobic digestion (AD) of waste activated sludge (WAS) yields low methane (CH4) because extracellular polymeric substances hinder hydrolysis and limit its biodegradability. Pretreatment methods such as alkaline or mechanical disruption can enhance solubilization; however, the resulting hydrolysate often contains recalcitrant compounds that inhibit further degradation. In this study, a combination of alkali along with ultrasonication was applied to enhance the solubilization of WAS, followed by CH4 production under AD. Pretreated results showed alkaline (pH = 12) + ultrasonication (30 min) showed 300 % higher solubilization compared to ultrasonication (60 min) alone. Batch experiments (with and without Fe3O4) were conducted, and the results showed that pretreated hydrolysate supplemented Fe3O4 showed higher CH4 yield than their control counterparts (up to 85 %). To validate the batch results of pretreated hydrolysate, a continuous operation was conducted without (Control) and with an electric voltage reactor (EVR) at different organic loading rates (OLR) up to 4 g chemical oxygen demand (COD)/L/d. The results showed that EVR enhanced the CH4 production by 28 % and COD removal by 19 % at 4 g COD/L/d compared to the control. Microbial community analysis highlighted the dominance of Syntrophomonas zehnderi (a fatty acid oxidizer) in EVR, which increased by 27 %, suggesting stronger syntrophic partnerships with methanogens. Genetic profiling further supported these findings, showing a 25 % upregulation in Adenosine triphosphatease related genes and a striking 69 % increase in pili-associated genes, both critical for direct interspecies electron transfer. These results demonstrated that the combined pretreatment (alkali + ultrasonication) offers a promising alternative for enhanced AD of WAS.
A Multi-View Attention-Based Encoder-Decoder Framework for Clustered Traveling Salesman Problem
No full textMany autonomous mobile robot path planning scenarios require servicing grouped delivery points. Such clustered routing problems are naturally formulated as the clustered traveling salesman problem (CluTSP), which comprises two interdependent subproblems: global inter-cluster routing to determine the order of cluster visits and local intra-cluster routing to optimize paths within each cluster. Existing approaches often solve these subproblems separately, which leads to suboptimal solutions due to limited information sharing between global and local decisions and requires long computation times. To address these limitations, we propose a unified deep reinforcement learning framework to obtain a powerful and flexible CluTSP routing agent based on a novel multi-view attention-based encoder-decoder framework. Our graph neural network-based dual encoder structure effectively captures both global and local routing contexts, and the collaborative decoder generates the overall robot trajectory from a global perspective. Our novel and efficient architecture enables solving both subproblems via unified one-shot construction without addressing each problem separately. Extensive experiments demonstrate that our approach significantly outperforms existing decomposition-based and learning-based methods.
Numerical and experimental analysis of weathering effect in liquid air tank of liquid air energy storage system
No full textLiquid Air Energy Storage (LAES) is a promising large-scale energy storage technology that relies on the liquefaction and subsequent re-gasification of air to generate electricity. However, during long-term storage, the preferential evaporation of nitrogen due to heat ingress leads to an increase in oxygen concentration, known as the weathering effect. This phenomenon presents safety hazards and affects the thermodynamic performance of the system. This study quantitatively investigates the weathering effect in liquid air storage tanks through both experimental measurements and a non-equilibrium thermodynamic model. A laboratory-scale cryogenic storage system was developed to measure oxygen concentration changes over time, and a non-equilibrium model for mixture fluid was formulated based on mass and energy conservation principles. The model, validated against experimental data, accurately predicts oxygen concentration dynamics and boil-off gas (BOG) rates. The validated model was further used to determine the maximum allowable storage duration before oxygen concentration exceeds 23.5 vol%, a critical safety threshold set by industrial guidelines. The study also establishes a relationship between boil-off rate and insulation performance, enabling the optimization of storage tank design. The results indicate that oxygen concentration increases with time, with the rate of increase accelerating for higher initial oxygen compositions. The proposed model provides a robust tool for assessing long-term storage performance and contributes to the safe and efficient operation of LAES systems.
Feed-O-Meter: Investigating AI-generated mentee personas as interactive agents for scaffolding design feedback practice
No full textEffective feedback, including critique and evaluation, helps designers develop design concepts and refine their ideas, supporting informed decision-making throughout the iterative design process. However, in studio-based design courses, students often struggle to provide feedback due to a lack of confidence and fear of being judged, which limits their ability to develop essential feedback-giving skills. Recent advances in large language models (LLMs) suggest that role-playing with AI agents can allow learners to engage in multi-turn feedback without the anxiety of external judgment or the time constraints of real-world settings. Yet prior studies have raised concerns that LLMs struggle to behave like real people in role-play scenarios, diminishing the educational benefits of these interactions. Therefore, designing AI-based agents that effectively support learners in practicing and developing intellectual reasoning skills requires more than merely assigning the target persona’s personality and role to the agent. By addressing these issues, we present Feed-O-Meter, a novel system that employs carefully designed LLM-based agents to create an environment in which students can practice giving design feedback. The system enables users to role-play as mentors, providing feedback to an AI mentee and allowing them to reflect on how that feedback impacts the AI mentee’s idea development process. A user study (N=24) indicated that Feed-O-Meter increased participants’ engagement and motivation through role-switching and helped them adjust feedback to be more comprehensible for an AI mentee. Based on these findings, we discuss future directions for designing systems to foster feedback skills in design education.
Spatiotemporal-dependent reliability analysis with adaptive sampling physics-informed neural networks
No full textBalancing accuracy and computational efficiency remains a challenge for simulation-based time-dependent reliability analysis (TRA) under uncertainty. Traditional TRA methods often fall short in focusing solely on fixed hotspot(s) of complex engineering systems, neglecting the dynamic nature of potential failure regions, such as moving hotspots. To address these limitations, this paper proposes a spatiotemporal-dependent reliability analysis (STDRA) framework for engineering systems governed by partial differential equations (PDEs). Key contributions include: (1) resolving incompleteness by employing a physics-informed neural network (PINN) to calculate global performance across all spatiotemporal "spots" in the investigated PDE system; (2) enabling STDRA by deriving results through Monte Carlo simulations of the PINN-based framework, without the need for design of experiment (DoE) samples; and (3) enhancing accuracy and efficiency through a novel adaptive spatiotemporal sampling (ASTS) strategy, which optimally trains the PINN by focusing on critical spatial and temporal domains. The proposed ASTS-PINN-based STDRA framework is validated using a 2D isotropic elastic plate and a complex laser cladding process, showcasing its superiority over existing state-of-the-art TRA methods.
Radiative equilibrium boundary condition and correlation analysis on catalytic surfaces in DSMC
No full textThis study integrates radiative equilibrium boundary conditions on a catalytic surface within the Direct Simulation Monte Carlo (DSMC) method. The radiative equilibrium boundary condition is based on the principle of energy conservation at each surface element, enabling the accurate capture of spatially varying surface temperatures and heat fluxes encountered during atmospheric re-entry. The surface catalycity is represented through the finite-rate surface chemistry (FRSC) model, specifically focusing on the heterogeneous recombination of atomic oxygen on silica surfaces. Both the FRSC model and the radiative equilibrium boundary conditions within the DSMC framework are validated through comparison to analytical solutions. Numerical simulations are conducted for rarefied hypersonic flow around a two-dimensional cylinder under representative re-entry conditions for both non-catalytic and catalytic surfaces. The results demonstrate significant discrepancies in computed surface properties between the radiative equilibrium and conventional isothermal boundary conditions. Furthermore, linear interpolation between results from two independent isothermal boundary conditions is shown to be inadequate for accurately predicting surface heat flux, particularly when surface reactions are considered. The observed discrepancies originate from a non-linear correlation between surface temperature and heat flux, influenced by factors such as surface catalycity and local geometric variations along the cylinder. These findings highlight the necessity of implementing radiative equilibrium boundary conditions within DSMC to ensure physically accurate aerothermodynamic computations.
Laser ultrasonic inspection of wire welds in cylindrical lithium-ion battery pack
No full textAs the demand for lithium-ion batteries continues to increase, quality control and safety assurance become increasingly critical in the battery manufacturing industry. This study presents a laser ultrasonic inspection technique to evaluate the quality of the welding between a wire and a busbar in a cylindrical lithium-ion battery pack. Ultrasonic waves are generated using a neodymium-doped yttrium aluminum garnet (Nd:YAG) laser at the wire, and the corresponding ultrasonic responses are measured at both the wire and busbar using a laser Doppler vibrometer (LDV). The proposed technique is based on the principle that inadequate welding leads to poor transmission of ultrasonic waves from the wire to the busbar. Based on this concept, an ultrasonic energy ratio is defined as the ratio of the ultrasonic wave energy measured at the wire to that measured at the busbar. Numerical simulations and experimental tests demonstrate a strong correlation (correlation coefficient R2 = 0.8899) between the welding condition and the energy ratio. The proposed laser ultrasonic inspection technique enables non-contact and non-destructive evaluation of welds, offering potential for in-situ and in-line inspection of cylindrical lithium-ion-battery packs.
Machine learning-driven optimization of the cure cycles of self-polymerizing epoxy molding compounds for semiconductor packaging applications
No full textA significant obstacle to both the performance and production efficiency of semiconductor packages is warpage, which is generated by the coefficient of thermal expansion (CTE) mismatch caused by high-temperature bonding between the epoxy molding compound (EMC) and the substrate. In this study, we propose an autoencoder-based machine learning model for predicting the curvature and cure cycle required to obtain the curvature of a semiconductor package based on the curvature data with respect to the cure cycle involving ramp, rapid cooling, and reheating. The curing reaction was measured according to the steps of the cure cycle using differential scanning calorimetry, and was analyzed to investigate the effect of heating rate on curvature and self-polymerization during the rapid cooling step. Furthermore, the bonding point was determined based on the curvature of the semiconductor package and the degree of cure. Using these data, the cure cycle for the target curvature was predicted using the machine learning model. The predicted values by the trained machine learning model were validated through experiments.
Flame quenching and oscillatory behaviors of premixed H2/CH4/C3H8 flames in a narrow-gap disk burner
No full textThe effects of adding hydrogen to premixed methane and propane flames were investigated using a narrow-gap disk burner of constant volume (NGDB-CV) at elevated pressures. Flame behaviors during quenching and oscillation were investigated for various disk gaps and gas compositions. The quenching distances were measured and converted to quenching Peclet numbers, which were normalized with the corresponding flame thicknesses. Although the quenching Peclet number has been discussed in relation to the Lewis number depending on the gas compositions, an additional effect of the flame thickness was found. Flame oscillation behaviors were also investigated in terms of frequencies and amplitudes. Local extinction can significantly disturb flame propagation behaviors. Without local extinction, the flame oscillation frequencies remained largely consistent, regardless of the disk gap, although they varied depending on the flame properties and pressure. In contrast, the amplitudes of the flame oscillation decreased as the disk gap increased, and their variation could be explained based on the ratio between the heat loss from the burned gas to the wall and the heat release from the flame. The critical condition of flame oscillation was affected by the initial flame propagation velocity. Conclusively, continuous and distinct flame oscillation can occur when an appropriate balance exists between heat loss and heat release. Finally, an improved mechanism of flame oscillation is suggested.