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Optimization of green building interior design based on virtual reality Technology: Thermal modeling analysis of air conditioning systems

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THERMAL SCIENCE AND ENGINEERING PROGRESS 2025年 57卷

作者： Chen, Jingjing Nanjing Audit Univ Jinshen Coll Nanjing 210046 Jiangsu Peoples R China

With the increasing global attention to sustainable development, this study aims to optimize the interior design of green buildings through virtual reality technology, with a particular focus on thermal modeling analysis of air conditioning systems. The study elucidates the importance of green buildings and their role in environmental protection, highlighting the importance of optimizing indoor environmental control to improve energy efficiency. Firstly, thermal modeling analysis is conducted on the indoor air conditioning system, followed by design optimization based on virtual reality technology. We investigated the characteristic parameters of airconditioned rooms in thermal modeling analysis, established indoor and outdoor temperature models, and used temperature control transfer learning models to simulate temperature changes. The indoor temperature and air volume were tested, and the results showed that the energy consumption of traditional air conditioning systems was higher than expected, indicating a huge room for optimization. In terms of virtual system design and optimization, a comprehensive process framework has been developed, detailing the modeling process of virtual reality technology and the coordinate system for virtual image modeling. Through server communication testing, the real-time and accurate data exchange has been ensured, providing technical support for subsequent optimization.

关键词： Virtual Reality Technology Green building Interior design optimization Air conditioning system

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Dynamic Graph Embedding PCA to Extract Spatio-Temporal Information for Fault Detection

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第2期21卷 1714-1723页

作者： Bao, De Wang, Yongjian Li, Shihua Southeast Univ Sch Automat Key Lab Measurement & Control Complex Syst Engn Minist Educ Nanjing 210096 Peoples R China Univ Duisburg Essen Inst Automat Control & Complex Syst D-47057 Duisburg Germany

The complexity of coupled multivariate data in industrial settings often limits the effectiveness of principal component analysis (PCA) in revealing patterns and structures in the data. In this article, we propose a novel fault detection framework for industrial process time series data with temporal and spatial correlation. First, by applying graph theory, the framework captures the complex network structures inherent in industrial processes, enabling the discovery of hidden data associations from a topological perspective. Then, the proposed method integrates temporal and spatial correlations in the modeling process, ensuring a comprehensive and integrated analysis. Specifically, the time series data are divided into sliding window intervals, and then the graph convolution is embedded within each window. After the modeling optimization objectives are defined, the overall solution is derived. Finally, these components, which contain spatio-temporal information, are used to construct dynamic and static statistics. Experiments on a chemical dataset show that the proposed method can significantly reduce the false alarm rate and improve the fault detection rate compared with the dynamic internal PCA without considering spatial factors. In addition, by applying it to the actual hot rolling process of strip, the superiority of the method is further verified, and its practical value and robustness are highlighted.

关键词： Feature extraction Principal component analysis Heuristic algorithms data mining Time series analysis Predictive models Industries Fault detection Vectors Optimization graph convolutional network (GCN) principal component analysis (PCA) spatio-temporal sequence analysis

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Surrogate modeling with Sensitivity-Based Experiment Design and process Optimization of Seeded Cooling Crystallization: A Case Study on β-Form LGA

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2025年第18期64卷 9272-9286页

作者： Song, Bo Liu, Tao Zhao, Mingyan Cui, Yan Chen, Junghui Nagy, Zoltan K. Findeisen, Rolf Dalian Univ Technol Minist Educ Key Lab Intelligent Control & Optimizat Ind Equipm Dalian 116024 Peoples R China Dalian Univ Technol Inst Adv Control Technol Dalian 116024 Peoples R China Chung Yuan Christian Univ Dept Chem Engn Taoyuan 32023 Taiwan Purdue Univ Davidson Sch Chem Engn W Lafayette IN 47907 USA Tech Univ Darmstadt Control & Cyberphys Syst Lab D-64283 Darmstadt Germany

To facilitate quality-by-design (QbD) of seeded cooling crystallization, a novel surrogate modeling and process optimization method is proposed in this paper, based on the design of experiments (DoE) with sensitivity analysis on the process operating conditions. To overcome the deficiency of the crystal growth kinetic model related to the population balance equation, which could not reflect the explicit relationship between the process operating conditions (e.g., initial solution supersaturation and cooling rate) and product crystal size distribution (CSD), a surrogate model is established by using the Gaussian process regression (GPR) approach, based on experimental data from a permitted range of operating conditions. Correspondingly, a swarm-based metaheuristic algorithm named beluga whale optimization (BWO) is adopted to determine proper hyperparameters in the surrogate model. By analyzing the global sensitivity analysis (GSA) of product CSD with respect to these operation conditions, a sensitivity-based DoE is developed to reduce the number of batch experiments required for implementation. Based on the established surrogate model, a comprehensive quality criterion is introduced to optimize these operating conditions, which takes into account the information entropy of product CSD together with the desired product yield and size range. The seeded cooling crystallization process of the beta-form l-glutamic acid is tested to verify the effectiveness and merits of the proposed modeling and optimization method.

关键词： Sensitivity analysis

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data-driven recursive multivariable modeling, operation, and control of active distribution networks with distributed generation

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COMPUTERS & ELECTRICAL ENGINEERING 2025年 123卷

作者： Feroldi, Diego Rullo, Pablo del Portal, Sair Rodriguez Braccia, Lautaro Luppi, Patricio Zumoffen, David CIFASIS CONICET UNR Grp Ingn Sistemas Proc GISP 27 Febrero 210 BisS2000EZP Rosario Argentina Univ Nacl Rosario UNR FCEIA Pellegrini 250S2000BTP Rosario Argentina Univ Tecnol Nacl UTN FRSN Colon 332B2900LWH San Nicolas Argentina Univ Tecnol Nacl UTN FRRo Zeballos 1341S2000BQA Rosario Argentina

This paper addresses the challenges arising from the increasing integration of distributed generation into active distribution networks (ADNs), focusing on their modeling, operation, and control. data-driven recursive multivariable modeling, capable of capturing both static and dynamic interactions in real time, has emerged as a promising solution. By utilizing the extensive data generated by modern grid infrastructure, this approach enhances network model accuracy and improves operational efficiency and control strategies. This paper strengthens the connection between process Systems Engineering (PSE) and power systems, traditionally underexplored in this domain. By integrating PSE principles, particularly data-driven and control allocation methodologies, into the modeling, operation, and control of ADNs, this work optimizes power system performance. Three Recursive Partial Least Squares (RPLS) methodologies-sample-wise, block-wise, and moving-window-are rigorously compared regarding estimation/prediction characteristics and convergence speed. This novel analysis challenges the assumption of instantaneous model adaptation, emphasizing the importance of carefully considering convergence periods for effective monitoring, control, and optimization. The paper proposes and analyzes three control structures integrated into an RPLS-based supervisory strategy for voltage regulation at ADN nodes: (1) decentralized control, (2) control allocation with measurement combination, and (3) optimization-based centralized control. Different integration formats are evaluated based on the controller technology used: (a) simple setpoint updates, (b) full ADN model adaptation to recalculate controller matrices, and (c) full model adaptation for updating the optimization formulation. Simulation results were obtained using the IEEE 33-bus test system. The results reveal a trade-off between the complexity and performance benefits of each control strategy. Although no strategy proves defini

关键词： Voltage control Distributed energy resources Active distribution networks process systems engineering Recursive partial least squares Model predictive control control allocation

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Subject-Oriented modeling Workflow control Patterns with PASS 15th

Subject-Oriented Modeling Workflow Control Patterns with PAS...

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15th International conference on Subject Oriented Business process Management

作者： Elstermann, Matthes Kuhn, Sanja Karlsruhe Inst Technol Inst Informat Management Engn Karlsruhe Germany

ISBN: (纸本)9783031720406;9783031720413

This work presents an analysis of the Workflow control Patterns from the viewpoint of subject-oriented modeling, utilizing the explicitly subject-oriented process modeling language. Workflow control Patterns, a set of Petri-Net-based descriptions of process fragments, were originally developed by van der Aalst and ter Hofstede 25 years ago. They aimed to holistically delineate the fundamental requirements that recurrently arise in business process modeling and to provide a conceptual basis for process technology. In our study, we have analyzed these patterns and translated all 43 existing control patterns into one or more adequate equivalent representations in PASS. The goal was to investigate the hypothesis and show that and how PASS is capable of representing all patterns, while also identifying the differences that emerge due to the paradigmatic gap between Petri Nets and PASS. The complete results are available in an online repository, the PASS Workflow control Pattern Library. This resource will enable future modelers to review the translations and utilize the patterns to enhance their skills in SO/PASS modeling.

关键词： Subject-Orientation PASS Petri-Nets Worflow Patterns

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A material handling system modeling framework: a data-driven approach for the generation of discrete-event simulation models

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2025年第1期37卷 67-96页

作者： Soufi, Zakarya Mestiri, Slaheddine David, Pierre Yahouni, Zakaria Fottner, Johannes Univ Grenoble Alpes CNRS Grenoble INP G SCOP F-38000 Grenoble France Univ Grenoble Alpes Inst Engn Grenoble France Tech Univ Munich Chair Mat Handling Mat Flow Logist Boltzmannstr 15 D-85748 Munich Germany

The design and reconfiguration of Material Handling Systems (MHSs) at the factory scale are known to be complex. Various design and reconfiguration alternatives have to be considered and evaluated through indicators such as: On Time Delivery (OTD) within the plant, number of material shortages or product waiting time, etc. Due to the dynamic behavior of MHS, simulation-based approaches play an essential role in such analysis. However, developing simulation models for MHS can be time-consuming (especially for modeling Large Scale Systems) and difficult to build (some skills and knowledge are required to use simulation software). To overcome these challenges, data-driven approaches have been proposed in the literature for the generation of MHS simulation models. Nevertheless, the available approaches focus on specific domains and may not always account for all the necessary data, including MHS control policies. Therefore, this paper aims to propose a framework that employs a data catalog regrouping five data categories (layout, product features, production process, material handling process, and MHS control methods) to support the generation of MHS simulation models using SIMIO. The article details the data structure used to gather MHS simulation data, the selection of a simulation tool, the modeling patterns integrated into the simulations, and the application of the transformation rules. The whole approach is implemented to form the generation framework. The framework is designed to assist decision-makers (who have basic simulation knowledge) in the evaluation of MHS design/reconfiguration alternatives. The paper finally presents a validation of the framework on two case studies.

关键词： Manufacturing systems Material handling systems Performance analysis data-driven approach Discrete event simulation

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Fault detection of multimode chemical processes using weighted density peak clustering and trend slow feature analysis

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process SAFETY AND ENVIRONMENTAL PROTECTION 2025年 196卷

作者： Deng, Xiaogang Wu, Meicong Yang, Wenjie Tang, Xiaoguang Cao, Yuping China Univ Petr East China Coll Control Sci & Engn Shandong Prov Engn Res Ctr Intelligent Sensing & M Qingdao 266580 Peoples R China

Modern chemical processes frequently operate under various modes due to the alterations of raw materials and market demands. For monitoring faults in multimode chemical processes more effectively, this paper presents a novel multiple modeling-based fault detection method integrating weighted density peak clustering and trend slow feature analysis (WDPC-TSFA). The proposed method consists of two blocks: mode clustering and statistical modeling. Considering that the traditional density peak clustering (DPC) algorithm often performs poorly when applied to high-dimensional process data, a weighted DPC method is designed which applies larger weights to the important variables contributing significantly to the mode difference, thereby enhancing the accuracy of mode classification. Subsequently, to capture the intrinsic slow features for statistical modeling, a trend slow feature analysis (TSFA) is proposed, which firstly employs the Hodrick-Prescott filter to extract trend components, and then builds an average first order derivative of the extracted trend component for measuring slowness. The TSFA allows for the computation of genuine trend slow features that accurately reflect changes in intrinsic characteristics, resulting in a more reasonable measure of slowness. Finally, the effectiveness of the proposed method is validated using a simulated continuous stirred tank reactor (CSTR) and an industrial hydrocracking reactor. The results demonstrate that the weighted density peak clustering method exhibits higher purity, enabling more accurate division of process mode data. Meanwhile, the TSFA method can extract slower slow features for more effective fault detection.

关键词： Fault detection Multimode chemical processes Slow feature analysis Density peak clustering

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data-Driven Distributed Robust Monitoring and control Optimization for Interconnected Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第2期21卷 1399-1408页

作者： Wang, Hao Luo, Hao Qiao, Xinyu Huo, Mingyi Xu, Xiaoyi Harbin Inst Technol Dept Control & Simulat Ctr Harbin 150001 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

This article proposes a projection-aided robust distributed monitoring and control optimization approach for interconnected systems with disturbances. The disturbances and state coupling between subsystems are a challenge in achieving accurate distributed process monitoring using data-driven techniques. To address the problems, a distributed adaptive residual generator uses the average consensus algorithm to perform data fusion on the subsystem residual generator to implement disturbance decoupling process monitoring. The key to implementing this process is to use input and output data disturbance in the perturbed orthogonal complementary space to drive the adaptive residual generator. Then, using the projection technique, the residual signal in the disturbance space drives the distributed learning of plug-and-play (PnP) controller parameters. The average consensus algorithm ensures that the subsystem PnP controller parameter gradient consistency converges to the centralized design. The feasibility and effectiveness of the proposed approach are verified and demonstrated through a simulation.

关键词： Monitoring Interconnected systems Generators Optimization Observers Aerospace electronics Accuracy Distributed databases Computational modeling Principal component analysis data-driven (DD) distributed monitoring disturbance rejection control interconnected systems

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Dynamic modeling of Multioperating Conditions for a Three-Converter Gas Holder System in Steel Industry by Using Time-Varying Dynamic Bayesian Networks

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Chen, Long Yan, Yaliang Zhao, Jun Wang, Wei Dalian Univ Technol Key Lab Intelligent Control & Optimizat Ind Equipm Minist Educ Dalian 116024 Peoples R China Dalian Univ Technol Sch Control Sci & Engn Dalian 116024 Peoples R China

The multiple joint Linz-Donawitz converter gas (LDG) holder systems are usually employed to alleviate the LDG fluctuation in steel enterprises. A dynamic modeling method based on time-varying dynamic Bayesian network (TVDBN) is proposed for a three-converter gas holder system (TCGHS). Considering the connectivity, the difference in pressure, and the interlocking rules between different gas holders, the operating conditions are defined in this article by physical mechanism analysis of the operation of this gas holder system. Based on them, the TVDBN's structural model with a mixture of continuous and discrete variables is constructed to describe their transition process through the change of the network structures and the uncertainty relationship of the process variables. The Gaussian regression network is designed for depicting the quantitative uncertain relationship between the gas holder level and gas generation as well as consumption flow rate under each operating condition. Furthermore, as several operating conditions occur rarely due to the operation mechanism, an online parameter learning method is proposed to learn these rare operating conditions according to new coming data. The simulation experiments are performed for a TCGHS of a steel plant in China based on actual data. The results show that the proposed method can not only accurately identify the joint operating conditions of the gas holders and their conversion process but also obtain higher prediction accuracy of gas holder levels compared with the state-of-the-art comparative methods, providing sound support for LDG scheduling.

关键词： Valves Predictive models Accuracy data models Brain modeling Steel Steel industry Learning systems Bayes methods Analytical models Dynamic modeling Linz-Donawitz converter gas (LDG) multiple operating conditions time-varying dynamic Bayesian networks (DBNs)

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data-driven modeling of transonic nonlinear flutter via convolutional neural network autoencoder

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NONLINEAR DYNAMICS 2025年第13期113卷 15741-15760页

作者： Peng, Zhijie Yao, Xiangjie Liu, Haojie Huang, Rui Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Aerosp Struct Nanjing 210016 Peoples R China China Ship Sci Res Ctr Natl Key Lab Ship Vibrat & Noise Wuxi 214082 Peoples R China

This paper proposes a novel data-driven aeroelastic modeling method based on the autoencoder (AE) and the nonlinear state-space identification. This method allows high-dimensional flow field nonlinear features to be characterized with high accuracy by lower-dimensional latent vectors, which facilitates the identification process and the generation of concise temporal models. The data-driven modeling initially employs a convolutional neural network autoencoder (CNN-AE) to reduce the dimensionality of pressure snapshots of unsteady flow fields obtained through high-fidelity numerical simulations. This process contributes to mapping the high-dimensional flow field to a low-dimensional latent space. Secondly, a temporal dynamics model of latent vectors is constructed using the state-space nonlinear identification. Subsequently, the temporal dynamics model is integrated with the decoder part of AE to reconstruct the temporal evolution of the unsteady transonic flow fields and aerodynamic forces. Finally, a nonlinear aeroelastic analysis is carried out by coupling the data-driven aerodynamic model and the structural model. In this paper, the effectiveness of this method in constructing a transonic data-driven model (DDM) is validated by the aerodynamic-structural coupling numerical example of the NACA0012 airfoil in a transonic regime. The results show that, in comparison to the linear flow modal decomposition method, the CNN-AE with nonlinear activation can utilize lower-dimensional latent vectors to represent the spatial structure of the flow field. Moreover, the resulting data-driven-based surrogate model is efficient and accurate in predicting the transonic flutter and limit-cycle oscillations (LCOs), proving a powerful tool for nonlinear aeroelastic analysis.

关键词： Transonic aeroelastic data-driven model Autoencoder Nonlinear state-space Unsteady flow

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