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17th IFIP 8.1 Working conference on the Practice of Enterprise modeling, PoEM 2024

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.

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An Efficient Method for Generating a Super-Sized and Heterogeneous Pore-Throat Network Model of Rock

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APPLIED SCIENCES-BASEL 2025年第3期15卷

作者： Yu, Chunlei Chen, Wenbin Li, Junjian Wang, Shuoliang Sinopec Shengli Oilfield Branch Explorat & Dev Res Inst Dongying 257000 Peoples R China China Univ Geosci Beijing Sch Energy Resources Beijing 100083 Peoples R China China Univ Petr Sch Petr Engn Beijing 100100 Peoples R China

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.

关键词： pore-throat network quartet structure generation set random growth partition control

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Numerical modeling for analysis and improvement of hydrogen refueling process for heavy-duty vehicles

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APPLIED ENERGY 2025年 380卷

作者： Fragiacomo, Petronilla Martorelli, Michele Genovese, Matteo Univ Calabria Dept Mech Energy & Management Engn I-87036 Cosenza Italy

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.

关键词： Hydrogen mobility and infrastructure Hydrogen refueling station Refueling protocols Heavy-duty vehicle Energy and thermodynamic analysis

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Hybrid data-Driven and Mechanistic modeling Approaches for Gas Turbine CCPP control System Validation 1st

Hybrid Data-Driven and Mechanistic Modeling Approaches for G...

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1st Electrical Artificial Intelligence conference, EAIC 2024

作者： Wu, Ke Ji, Zheng Li, Jianliang Huang, Lei Chen, Zijing Guodian Nanjing Automation Co. Ltd. Nanjing210032 China Nanjing SAC Valmet Automation Co. Ltd. Nanjing210032 China China Huadian Corporation Ltd. Beijing100031 China

ISBN: (纸本)9789819640621

The utilization of heavy-duty gas turbines in power plant, coupled with a combined cycle system (CCPP), represents a significant alternative to conventional coal-fired boiler turbine units, primarily due to its swift load response capabilities and environmentally benign emissions profile. In China, the gas turbines control system (TCS) have been predominantly supplied and stringently regulated by international vendors. The domestically developed TCS needs extensive validation prior to its commercial deployment. The adequate fidelity model of the CCPP to represents both static and dynamic characteristics plays a critical role in this process. Hybrid modeling provides a promising option to tackle such complex problems. In hybrid modeling, the process-related principles are represented by mechanistic models, while the coefficients added to the first-principal models as adjustments to present unknown process characterizes, are described by data-driven models. This article highlights the significance of hybrid modeling in multiscale CCPP processes. The common design methodology is first introduced. The piece-by-piece devices modeling are then performed following the mass and energy flow. A novel control-oriented hardware-in-the-loop (HIL) simulator for TCS validation is introduced, which helps to reduce costs, mitigate the risk of equipment damage compared to comprehensive and dangerous on-site testing and expedites the commissioning process. Concluding remarks are provided, and future perspectives are pointed out at the end. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Combined cycle power plants

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AI-Assisted data modeling and High-Resolution Image Synthesis: An Interactive Framework with Latent Diffusion Model Visualization

AI-Assisted Data Modeling and High-Resolution Image Synthesi...

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2025 International conference on Intelligent control, Computing and Communications, IC3 2025

作者： Borah, Asha Rani Archana Rani, K. Pushpa Ziara, Sajjad Lakhanpal, Sorabh Vidyadhari, Chalasanvi New Horizon College of Engineering Department of Computer Science Engineering Bangalore India Galgotias university Department of Electrical Electronics & Communication Engineering Uttar Pradesh Gerater Noida203201 India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India College of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq Lovely Professional University Punjab Phagwara India Griet Department of Information Technology Bachupally Telangana Hyderabad India

ISBN: (纸本)9798331527495

In this research paper, we present the design and implementation of an AI assisted interactive framework for data modeling and high resolution image synthesis that leverages both state of the art latent diffusion models and visualisation of the real-time algorithm. The system proposed enables user to be able to better understand complex data modeling processes and creates high resolution images in the latent space using diffusion. The framework is both interactive, allowing modifications in parameters and real time visualization of change, making it a useful tool both for educational and for advanced AI research. In this paper, we also discuss the underpinning AI-assisted data modeling algorithms, and why they enable efficient data manipulation as well as high quality image synthesis then provide simulation results and practical applications to illustrate the capabilities of the framework of visualizing the latent diffusion process and generating high resolution images. © 2025 IEEE.

关键词： Flow visualization

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Inherently Interpretable Physics-Informed Neural Network for Battery modeling and Prognosis

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2025年第1期36卷 1145-1159页

作者： Wang, Fujin Zhi, Quanquan Zhao, Zhibin Zhai, Zhi Liu, Yingkai Xi, Huan Wang, Shibin Chen, Xuefeng Xi An Jiao Tong Univ Natl Key Lab Aerosp Power Syst & Plasma Technol Xian Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Peoples R China Beijing Aerosp Control Ctr Beijing 100094 Peoples R China Xi An Jiao Tong Univ Key Lab ThermoFluid Sci & Engn Minist Educ Sch Energy & Power Engn Xian 710049 Peoples R China

\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.

关键词： End-of-discharge (EOD) inherently interpretable lithium-ion battery physics-informed neural network (PINN) prognosis

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data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance

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DISCOVER APPLIED SCIENCES 2025年第4期7卷 1-18页

作者： Mhetre, Harshada Pande, Sagar Singla, Babita Hiremath, Pavan Sahu, Samriddh Sorte, Sarvesh Kotecha, Ketan Naik, Nithesh Bharati Vidyapeeth Dept Elect & Commun Engn Coll Engn Pune 411043 Maharashtra India Pimpri Chinchwad Univ Sch Engn & Technol Dept Comp Sci & Engn Pune 412106 Maharashtra India Chitkara Univ Chitkara Business Sch Chandigarh 140417 Punjab India Manipal Acad Higher Educ Manipal Inst Technol Dept Mech & Ind Engn Manipal 576104 Karnataka India Bharati Vidyapeeth Dept Elect & Telecommun Engn Coll Engn Pune 411043 Maharashtra India Symbiosis Int Symbiosis Ctr Appl Artificial Intelligence Pune 412115 Maharashtra India

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.

关键词： LSTM Multiple regression models TiO2 nanofibers Electrospinning Machine learning model

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Multi-fidelity Bayesian neural networks for aerodynamic data fusion with heterogeneous uncertainties

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COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2025年 435卷

作者： Xie, Fangfang Zhang, Xinshuai Wu, Shihao Ji, Tingwei Zheng, Yao Zhejiang Univ Sch Aeronaut & Astronaut Hangzhou 310027 Zhejiang Peoples R China Xian Modern Control Technol Res Inst Xian 710000 Shanxi Peoples R China

Aircraft design requires extensive aerodynamic data to characterize various flight conditions throughout the aircraft's flight envelope. Typically, the aerodynamic data is acquired through wind tunnel testing or numerical analysis, which are costly and inevitably entails multiple sources of uncertainty. In the present work, we propose a multi-fidelity Bayesian neural network (MFBNN) framework for multi-source aerodynamic data fusion with heterogeneous uncertainties. We first employ mean-field variational inference (VI) to maximize the evidence lower bound (ELBO), yielding informative priors for BNN hyperparameters. Then, the stochastic Hamiltonian Monte Carlo (HMC) method is adopted to estimate their posteriors. Notably, we introduce mini-batch learning to address a key constraint of traditional HMC methods, particularly in the aerodynamic modeling scenarios involving large sample sizes, where the computation of required gradients for simulation of the Hamiltonian dynamical system is impractical. The proposed MFBNN framework is applied in three cases, including the RAE2822 airfoil, the ONERA M6 wing and the NASA Common Research Model. The results demonstrate that the proposed MFBNN framework can remarkably improve accuracy and outperform the multi-fidelity Gaussian process regression model.

关键词： Mulit-fidelity Aerodynamic data fusion Uncertainty quantification Bayesian neural network

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Surrogate-based optimization framework for enhancing SMT process quality and productivity in electronics manufacturing services

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2025年第11-12期136卷 5103-5122页

作者： Lee, Ching-Hung Lin, Kai-Xiang Chou, Che-Wei Natl Yang Ming Chiao Tung Univ Inst Elect & Control Engn Hsinchu Taiwan Chung Yuan Christian Univ Dept Elect Engn Taoyuan Taiwan Feng Chia Univ Dept Ind Engn & Syst Management Taichung Taiwan

The quality of the surface mount technology (SMT) process directly impacts product efficiency and reliability. Solder paste printing and reflow soldering processes are vital for assembling high-quality electronic components. Effectively optimizing these process parameters to ensure product consistency and reliability has become a critical issue in the electronics manufacturing services industry. Motivated by realistic needs to enhance the quality of the SMT process, this study integrates a surrogate-based optimization framework combining neural networks and particle swarm optimization (PSO) techniques to minimize experiment counts in SMT processes. Furthermore, a defect detection system utilizing YOLOv4 achieves real-time solder joint quality classification, significantly reducing production downtime and costs. This study proposes a surrogate-based optimization framework to improve the quality and productivity of the SMT production line. It encompasses five stages: domain knowledge, design of experiment, data collection and analysis, modeling, and optimization. Statistical correlation analysis and experimental design are used to reduce experiment counts. Then, neural networks and optimization algorithms are utilized to identify the optimal process parameters in the solder paste printing process. Moreover, this study proposes transfer learning methods for cross-product and line parameter optimization, which not only reduces production changeover time but also offers valuable insights for developing the solder paste printing process. A heat transfer model derived from a single experiment is used to identify parameters for reflow soldering. The target function is then optimized to find the optimal reflow recipe. Additionally, a solder joint defect detection system is established using deep learning and image processing techniques, capable of real-time detection and classification of solder joint defects. To evaluate the validity of the proposed framework, the surrogate

关键词： Surface mount technology Surrogate model process parameter optimization Particle swarm optimization Neural network

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Surrogate modeling for control of Microbial Biopolymer Production process 11

Surrogate Modeling for Control of Microbial Biopolymer Produ...

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11th Vienna International conference on Mathematical Modelling, MATHMOD 2025

作者： Dürr, Robert Otto, Eric Kok, Rudolph L. Duvigneau, Stefanie Kienle, Achim Bück, Andreas Magdeburg-Stendal University of Applied Sciences Magdeburg39114 Germany Otto-von-Guericke University Magdeburg39106 Germany Max-Planck-Institute for Dynamics of Complex Technical Systems Magdeburg39106 Germany Friedrich-Alexander-University Erlangen-Nuremberg Erlangen39058 Germany

In this contribution, the Dynamic Mode Decomposition with control (DMDc) is used to derive a surrogate model of a continuous PHA biopolymer production process based on a recently published complex process model. Here, snapshot simulation data of the original model is processed to obtain a linear surrogate model formulation using delay coordinates. The quality of the surrogate is statistically validated within simulation studies. Additionally, the influence of the of the order of delay coordinates is investigated. It is shown, that the highly nonlinear dynamics of the PHA-manufacturing process can be approximated accurately by the DMD-based model even for large variations of initial conditions and control variables. This offers the opportunity to apply well-studied and established tools from robust and optimal control in future investigations. Copyright © 2025 The Authors.

关键词： process control

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