Spray drying is a common a technique in process engineering, involving analysis tasks for large-scale multiphysics mechanisms, typically addressed using Computational Fluid Dynamics (CFD) software based on Finite Elem...
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The additive-manufacturing (AM) field necessitates a robust process-monitoring system for quality assurance and control. To meet this industrial requirement, quality-evaluation models have emerged as powerful tools fo...
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The additive-manufacturing (AM) field necessitates a robust process-monitoring system for quality assurance and control. To meet this industrial requirement, quality-evaluation models have emerged as powerful tools for providing quality feedback. Recently, convolutional-neural-network- (CNN)-based classification models have gained popularity in quality evaluation using image data. However, such models require sufficient image data for training, a requirement that is challenging to fulfill in the context of metallic AM due to the complexity of decomposition and analysis. This challenge is particularly pronounced in start-up or medium-sized metallic-AM enterprises. Moreover, many countries around the world have faced a decline in population and a shortage of labor in the engineering field. This growing shortage of workers to collect datasets exacerbates this challenge. In this study, experiments of directed-energy-deposition (DED) processes for single-line and single-track metallic deposition using AISI 316 L stainless-steel powders were conducted with two experimenters. After the process, a minimal amount of cross-sectional surface image data of the metallic deposition was binary-processed and analyzed across three quality states: normal state, surface burrs, and internal defects. To compensate for the lack of training data, multiple strategies are proposed, including image preprocessing and ResNet transfer learning. The selection of an optimization solver and layer depth for maximizing classification performance was discussed. The potential performance of ResNet dealing with binary images and performance standards with few training data was also identified by comparing with other higher-level architectures (Inception and Xcepition).
1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocesscontrol based on real-time information of feedstock and main products is cru...
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1,3-propanediol (1,3-PDO) is a significant product of fermentation, with glycerol serving as the primary substrate in most cases. Bioprocesscontrol based on real-time information of feedstock and main products is crucial for reducing the cost of production. However, rapid quantification of 1,3-PDO and glycerol remains challenging due to their highly similar molecular structures. In this study, the feasibility of near-infrared (NIR) spectroscopy to monitor 1,3-PDO, glycerol, acetate, and butyrate concentrations in the fermentation process using strain Clostridium pasteurianum was evaluated. NIR spectra were acquired through at-line measurement involving sampling and ex-situ analysis or on-line measurement with a fiber optic probe immersed in fermentation broth, integrated with Partial Least Squares (PLS) regression to establish calibration models on a laboratory-scale and pilot-scale. The best PLS regressions of 1,3-PDO, glycerol, acetate, and butyrate with two measurement approaches provided excellent performance, with the root-mean-squared errors of prediction (RMSEP) of 1.656 g/L, 1.502 g/L, 0.746 g/L, and 0.557 g/L in at-line measurement and 1.113 g/L, 1.581 g/L, 0.415 g/L, and 0.526 g/L in on-line measurement. The cross-scale application performance of at-line measurement was evaluated by an external fermentation trial and an acceptable result was achieved. At-line measurement technique represents a superior choice for the optimization of fermentation process since the robustness across varying fermentation scales and its applicability in multiple bioreactors. Thus, a calibration model developed for one bioreactor is likely to be used in other bioreactors, which enables the reduction of modeling costs. On-line measurement technique, owing to its automated operation and frequent data acquisition, enables real-time monitoring and precise control of the fermentation process, thereby reducing cost and improving production efficiency.
Hospitals currently face numerous challenges in managing their pharmacy operations efficiently. While Business process Reengineering (BPR) has been proposed as a solution, its implementation in healthcare is more comp...
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The proceedings contain 17 papers. The special focus in this conference is on Practice of Enterprise modeling. The topics include: Fostering Digital Progression of Society: Exploratory Case Studies of Third Place for ...
ISBN:
(纸本)9783031779077
The proceedings contain 17 papers. The special focus in this conference is on Practice of Enterprise modeling. The topics include: Fostering Digital Progression of Society: Exploratory Case Studies of Third Place for Services;using Enterprise modeling for Dealing with Complexity of Elderly Care in Sweden;evaluation of Categorization Patterns for Conceptual modeling of IoT Applications;SmartCML: A Visual modeling Language to Enhance the Comprehensibility of Smart Contract Implementations;assessing Model Quality Using Large Language Models;grass-Root Enterprise Modelling: How Large Language Models Can Help;investigating the Effectiveness of Feedback-Driven Exercises on Deadlock Detection Skills in Conceptual Modelling;knowledge Graphs as a Scholarly data Fabric: A data Silo Transformation Pipeline with Visualization Semantics;enriching Business process Event Logs with Multimodal Evidence;towards Timeline-Based Layout for process Mining;Conceptualisation and (Meta)modelling of Problem-Solution Chains in Early Business-IT Alignment and System Design;SymboleoAC: An Access control Model for Legal Contracts;Functional Security in Automation: The FAST Approach;Configuration of Software Product Lines Driven by the Softgoals: The TEAEM Approach;the Dual Nature of Organizational Policies.
Cavity pressure control can enhance the repeatability of injection molding processes. While extensive research has focused on thermoplastic cavity pressure control, there is a notable gap in models and control strateg...
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The super-sized pore-throat network model can reflect both microscopic pore characteristics and macroscopic heterogeneity and is excellent in describing cross-scale flow fields. At present, there is no algorithm that ...
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The super-sized pore-throat network model can reflect both microscopic pore characteristics and macroscopic heterogeneity and is excellent in describing cross-scale flow fields. At present, there is no algorithm that can generate a micro pore-throat network model at a macro reservoir scale. This study examines algorithms for super-sized pore-throat network reconstruction using actual core sample data. It conducts a random simulation of mineral growth and dissolution under the constraints of four microscopic pore structure parameters: porosity, coordination number, pore radius, and throat radius. This approach achieves high-precision, super-sized, and regional pore-throat network modeling. Comparative analysis shows that these four parameters effectively guide the random growth process of super-sized pore-throat networks. The overall similarity between the generated pore-throat network model and real core samples is 88.7% on average. In addition, the algorithm can partition and control the generation of pore-throat networks according to sedimentary facies. The 100-megapixel model with 85,000 pores was generated in 455.9 s. This method can generate cross-scale models and provides a basis for cross-scale modeling in physical simulation experiments and numerical simulations.
This paper presents the development, validation, and application of a numerical model to simulate the process of refueling hydrogen-powerd heavy-duty vehicles, with a cascade hydrohen refueling station design. The mod...
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This paper presents the development, validation, and application of a numerical model to simulate the process of refueling hydrogen-powerd heavy-duty vehicles, with a cascade hydrohen refueling station design. The model is implemented and validated using experimental data from SAE J2601. The link between the average pressure ramp (APRR) and flow rate, which is responsible for the dynamic evolution of the refueling process, was analyzed. Various simulations were conducted, with a vehicle tank of 230 L and nominal pressure of 35 MPa typical of tanks adopted in heavy-duty vehicles, varying the ambient temperature between 0 and 40 degrees C, the cooling temperature of the hydrogen by the system cooling between -40 and 0 degrees C and the APRR between 2 and 14 MPa/min. The study found that if the ambient temperature does not exceed 30 degrees C, rapid refueling can be carried out with not very low pre-cooling temperatures, e.g. -20 degrees C or - 10 degrees C, guaranteeing greater savings in station management. Cooling system thermal power has been investigated, through the analyses in several scenarios, with values as high as 38.2 kW under the most challenging conditions. For those conditions, it was shown that energy savings could reach as much as 90 %. Furthermore, the refueling process was analyzed taking into account SAE J2061/2 limitations and an update was proposed. An alternative strategy was proposed such that the settings allow a higher flow rate to be associated with a given standard pressure ramp. This approach was designed to ensure that the maximum allowable pressure downstream of the pressure control valve, as specified by the refueling protocol, is reached exactly at the end of the refueling process. It has been observed that the adoption of this strategy has significant advantages. In the case of refueling with higher APPR, refueling is about 20 s faster with a single tank, with limited increases in temperature and pressure within it.
\Lithium-ion batteries are widely used in modern society. Accurate modeling and prognosis are fundamental to achieving reliable operation of lithium-ion batteries. Accurately predicting the end-of-discharge (EOD) is c...
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\Lithium-ion batteries are widely used in modern society. Accurate modeling and prognosis are fundamental to achieving reliable operation of lithium-ion batteries. Accurately predicting the end-of-discharge (EOD) is critical for operations and decision-making when they are deployed to critical missions. Existing data-driven methods have large model parameters, which require a large amount of labeled data and the models are not interpretable. Model-based methods need to know many parameters related to battery design, and the models are difficult to solve. To bridge these gaps, this study proposes a physics-informed neural network (PINN), called battery neural network (BattNN), for battery modeling and prognosis. Specifically, we propose to design the structure of BattNN based on the equivalent circuit model (ECM). Therefore, the entire BattNN is completely constrained by physics. Its forward propagation process follows the physical laws, and the model is inherently interpretable. To validate the proposed method, we conduct the discharge experiments under random loading profiles and develop our dataset. analysis and experiments show that the proposed BattNN only needs approximately 30 samples for training, and the average required training time is 21.5 s. Experimental results on three datasets show that our method can achieve high prediction accuracy with only a few learnable parameters. Compared with other neural networks, the prediction MAEs of our BattNN are reduced by 77.1%, 67.4%, and 75.0% on three datasets, respectively. Our data and code will be available at: https://***/wang-fujin/BattNN.
The prospective utilization of electrospun nanofibers across diverse fields has elicited substantial scientific attention. Nevertheless, managing their diameter remains problematic due to the intricate interactions am...
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The prospective utilization of electrospun nanofibers across diverse fields has elicited substantial scientific attention. Nevertheless, managing their diameter remains problematic due to the intricate interactions among electrospinning variables. This research explores the application of Long Short-Term Memory (LSTM) networks and multiple regression models to forecast the diameters of Titanium Dioxide (TiO2) and Polyvinyl pyrrolidone (PVP) nanofibers, facilitating improved process regulation and enhancement. TiO2 + PVP nanofibers were fabricated under diverse conditions, including changes in applied voltage, solution concentration, and distance between tip to collector. The acquired data underwent analysis using LSTM and regression models to assess their predictive capabilities. The outcomes revealed that both approaches effectively estimated nanofiber diameters;however, the regression model surpassed LSTM with a lower error rate of 0.077% compared to 0.305%. This indicates that while LSTM captures nonlinear relationships, conventional regression models yield more precise predictions in this scenario. These findings underscore the potential of machine learning in advancing electrospinning technology by minimizing trial-and-error experiments and boosting nanofiber production efficiency. The incorporation of artificial intelligence-driven modeling into the electrospinning process sets the stage for more accurate control over fiber morphology, resulting in enhanced material properties and expanded applications in biomedical, environmental, and energy sectors.
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