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检索条件"任意字段=Conference on Simulation-Driven Modeling and Optimization"

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Reinforcement Learning Based optimization Method for Comfort-energy Balance in Multi-zone HVAC System 14

Reinforcement Learning Based Optimization Method for Comfort...

引用

14th Asian Control conference (ASCC)

作者： Li, Xiaolong Shi, Rui Deng, Heng Zhang, Liguo Beijing Univ Technol Fac Informat Technol Beijing Peoples R China Minist Educ Engn Res Ctr Intelligence Percept & Autonomous Co Beijing Peoples R China

ISBN: (纸本)9798331540845;9789887581598

The operation of heating, ventilation, and air conditioning (HVAC) systems in modern buildings often suffers due to the limited adaptability and efficiency of conventional rule-based control mechanisms. Addressing the need for more rational and flexible operation, we introduce a novel multi-objective optimization approach leveraging Deep Q-Network (DQN) for open office environments. Our method aims to strike an optimal balance between minimizing energy consumption and maximizing occupant comfort within the air conditioning system. Unlike rule-based methods, our data-driven approach circumvents the need for extensive a priori knowledge of building-specific thermodynamics, thereby enhancing adaptability and ease of deployment. We developed a building simulation model for the target open office area, complemented by a custom-built simulation and testing platform integrated with a building modeling interface. This platform facilitates real-time data acquisition and parameter adjustment, enabling the DQN agent to efficiently control HVAC operations. By dynamically adjusting temperature setpoints across various thermal zones via variable air volume (VAV) actuators, the DQN agent skillfully navigates the energy-occupant comfort trade-off. Empirical evidence from extensive training iterations indicates that our DQN-based control method outperforms traditional rule-based systems, reducing energy consumption by up to 24% while maintaining optimal occupant comfort. The experimental results demonstrate the efficacy of the proposed method for energy optimization in building management.

关键词： deep reinforcement learning multi-objective optimization HVAC energy optimization energy simulation

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A digital twin modeling for the production line optimized management in the soft and deformable food sector 30

A digital twin modeling for the production line optimized ma...

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30th IEEE International conference on Engineering, Technology, and Innovation

作者： Croci, Stefano Mazzuto, Giovanni Ortenzi, Marco Ciarapica, Filippo Emanuele Bevilacqua, Maurizio Osler, Gilberto Univ Politecn Marche Dept Ind Engn & Math Sci Ancona Italy St Orsola Sca IT Manager ICT Localita Cire Di Pergine TN Italy

ISBN: (纸本)9798350362442;9798350362435

The Production Logistics system is generally a large-scale complex system with various operational phases and management levels that must integrate. In the specific context of soft and deformable food products, the core of AGILEHAND European project, this complexity increases further due to challenges related to the handling and movement of such items. Efficient coordination of production and logistics phases becomes crucial to ensure product quality, prevent losses, and optimize the entire process. In this article, focus will be placed on a data-driven framework for the automated generation of simulation models, serving as the foundation for digital twins in intelligent factories within the previously mentioned sector. The proposed framework represents a multi-layered data-driven system designed for real-time/near-real-time simulation, planning and synchronization of production and logistics systems during line reconfiguration. The digital model forms the basis for a digital twin with simulation and optimization capabilities, designed to facilitate decision-making at various management levels in the production and logistics process and control activities such as changes, maintenance, quality and safety. Exploiting information provided by the Enterprise Traceability system, the digital twin aims to establish a real-time/near-real-time information flow. This flow enables accurate capturing of dynamics occurring in the physical layer and effective assessment of their negative effects on the overall operational state of the system in the digital layer. In this context, the use of the digital twin is intended to simplify and expedite the reconfiguration of production and logistics systems. This is achieved through the early detection of system design or process sequence through cross-sectional simulation.

关键词： Digital Twin Data-driven Digital Modelling Food industry simulation Production Logistics Industry 4.0

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Proceedings - 2024 IEEE 31st International conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

Proceedings - 2024 IEEE 31st International Conference on Hig...

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31st IEEE International conference on High Performance Computing, Data, and Analytics Workshops, HiPCW 2024

ISBN: (纸本)9798331509118

The proceedings contain 79 papers. The topics discussed include: designing a card game for computer science instructors to evaluate students’ parallel and distributed computing knowledge;a hands-on approach to teaching parallel and heterogeneous computing;efficient feature extraction for vision transformer model using a custom CNN accelerator;object detection for autonomous vehicles in adverse weather and varying lighting conditions using a hybrid YOLO approach;predictive modeling of performance variability in HPC applications;simulation-driven design of large-scale systems architecture;performance optimization on cxl products using in-house modeling and simulation toolchain;and hide mastermind using an intermediate connection on social network

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Intelligent Multi-Well Placement optimization for CO2-EOR and Storage (CEORS) in Complex Reservoirs

Intelligent Multi-Well Placement Optimization for CO2-EOR an...

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2024 European conference on the Mathematics of Geological Reservoirs, ECMOR 2024

作者： Mao, J. Ghahfarokhi, A. Jahanbani Norwegian University of Science and Technology Norway

ISBN: (纸本)9798331313319

The urgent need to mitigate greenhouse gas emissions, especially CO2, has prompted extensive research into sustainable strategies like Geological CO2 Storage (GCS). Utilizing depleted hydrocarbon reservoirs for CO2-enhanced oil recovery (CO2-EOR) and storage presents a multifaceted solution to address environmental, economic, and technological challenges. This paper will focus on the strategic placement of wells to co-optimize hydrocarbon recovery and CO2 storage in a partially depleted oil reservoir. Traditional approaches to well placement optimization face significant computational burdens due to the complexity of geological systems involved. To address this issue, this study leverages advancements in machine learning (ML) to develop data-driven models (DDMs) for dynamic reservoir modeling. Several numerical simulation runs are performed, applying constrained random sampling (RS) methods to generate training datasets for data-driven modeling. The input features are selected to be well coordinates, well block permeabilities, porosities, initial water saturations, pressure and well-to-well distances. The output targets comprise cumulative oil, water, and CO2 production over a fixed period. Various ML methods such as Artificial Neural Networks (ANN), Extreme Gradient Boosting method (XGBoost), and Deep Neural Networks (DNN) are explored, demonstrating efficient and accurate predictions with R2 correlation being greater than 0.96 between proxy predictions and reservoir simulation results. By integrating these DDMs into a genetic algorithm (GA) optimization framework, we manage to optimize the placement of injector and producer in a three-dimensional channelized reservoir of high heterogeneity, where physical well locations serve as decision variables and net cash flow (NCF) as the objective function. Lastly, the performance of this data-driven optimization strategy is compared against the traditional approach using numerical simulations, highlighting its efficiency a

关键词： Deep neural networks

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An Operational Chain optimization Model for Unmanned Swarm under Dynamic Task Scenarios driven by Resilience

An Operational Chain Optimization Model for Unmanned Swarm u...

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2024 IEEE International conference on Unmanned Systems, ICUS 2024

作者： Zhong, Yuanfu Cai, Yuepeng Zhuang, Xuebin School of Systems Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798350384185

In persistent battlefield confrontation, combat tasks present dynamic characteristics, i.e., new tasks continuously emerge as old tasks are ***, how to enhance the battlefield resilience of unmanned swarm (US) through the rational allocation of limited resources in this scenario has become the focus of *** response, this paper proposes an operational chain optimization model for the US, which provides modeling and resilience enhancement ideas for the dynamic nature of battlefield ***, the feasibility and effectiveness of the proposed model is demonstrated under simulation experiments in different attack scenarios. © 2024 IEEE.

关键词： Swarm intelligence

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Automatic spray shop monitoring system based on digital twin 4

Automatic spray shop monitoring system based on digital twin

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4th International conference on Internet of Things and Smart City, IoTSC 2024

作者： Ma, Hangxing Xu, Jiangmin School of Mechanical Engineering Jiangsu University of Science and Technology Zhenjiang212000 China Marine Equipment and Technology Institute Jiangsu University of Science and Technology Zhenjiang212000 China

ISBN: (纸本)9781510681767

A digital-based method was proposed to address challenges in large-scale segmented hull automated spraying workshops, such as the production of toxic gases and solid particles during operations, hindering real-time monitoring of spraying activities. The method focuses on the mismatch between site data and process system performance, leading to poor film quality. The intelligent monitoring method of twin technology establishes a system architecture for a digital twin five-dimensional model, exploring twin model establishment and data-driven technology. Specifically, a new digital twin modeling method for spray film thickness was studied and validated through experiment-simulation comparisons, showing high accuracy in restoring film thickness and uniformity. The system leverages the iterative nature of the spraying process to accumulate and process on-site data, allowing for iterative optimization of spraying process parameters to enhance efficiency and quality. © 2024 SPIE.

关键词： Film thickness Data modeling Mathematical modeling Associative arrays Coating 3D modeling Computer simulations Mathematical optimization modeling Virtual reality

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Advancing Pandemic Preparedness through a Data-driven Hybrid simulation Model

Advancing Pandemic Preparedness through a Data-Driven Hybrid...

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International Joint conference on Neural Networks (IJCNN)

作者： Shuvo, Shaon Bhatta Das, Jyoti Kobti, Ziad Kar, Narayan Univ Windsor Sch Comp Sci Windsor ON Canada Univ Alberta Dept Math & Stat Sci Edmonton AB Canada Univ Windsor Dept Elect & Comp Engn Windsor ON Canada

ISBN: (纸本)9798350359329;9798350359312

The rise of new disease variants, such as COVID-19, influenza, and others, highlights the critical need for advanced epidemiological modeling to guide early-stage outbreak management, especially when vaccine options are not available or reliable. This paper presents a novel, hybrid, data-driven model that integrates Agent-Based modeling (ABM) with an extended SEIHRD (Susceptible, Exposed, Infectious, Hospitalized, Recovered, and Dead) framework, enhanced by N-step Deep Q Reinforcement Learning (N-Step DQRL). This model merges ABM's behavioral insights with the SEIHRD model's progression dynamics, utilizing DQRL for adaptive, data-informed decision-making. It is particularly focused on enhancing non-pharmaceutical interventions, such as lockdown policies, which are crucial in managing outbreaks in the absence of vaccines. This approach strikes a balance between detailed analysis and scalability, vital for policymakers in responding to emerging disease variants. The model's efficacy, as evidenced by an analysis of recent COVID-19 data, highlights its potential to significantly improve global pandemic preparedness and response, merging behavioral analysis with disease progression trends through the use of advanced deep learning techniques.

关键词： agent-based modeling compartmental model deep learning reinforcement learning optimization

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Society of Petroleum Engineers - SPE Hydraulic Fracturing Technology conference and Exhibition, HFTC 2025

Society of Petroleum Engineers - SPE Hydraulic Fracturing Te...

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2025 SPE Hydraulic Fracturing Technology conference and Exhibition, HFTC 2025

ISBN: (纸本)9781959025573

The proceedings contain 63 papers. The topics discussed include: acid fracturing treatment design optimization in north sea chalk wells in Norway;simulation-driven operational value optimization within design constraints: a case study in applying dynamic stage lengths at execution pace;Permian ultra-long lateral drillout and intervention using hydraulic completion units;a new method to quantify the connections between wells based on pressure and strain responses measured in a monitoring well;recent developments and utilization of produced water in Bakken well stimulations;maximizing hydraulic fracturing performance and reducing emissions though acoustic friction analysis;and far-field drainage along hydraulic fractures: insights from integrated modeling studies in the Bakken and Permian Basin.

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Benchmarking Model Predictive Control Algorithms in Building optimization Testing Framework (BOPTEST) 18

Benchmarking Model Predictive Control Algorithms in Building...

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18th IBPSA conference on Building simulation, BS 2023

作者： Mostafavi, Saman Song, Chihyeon Sharma, Aayushman Goyal, Raman Brito, Alejandro E. Palo AltoCA United States Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Texas A&M University College StationTX United States

We present a data-driven modeling and control framework for physics-based building emulators. Our approach consists of: (a) Offline training of differentiable surrogate models that accelerate model evaluations, provide cost-effective gradients, and maintain good predictive accuracy for the receding horizon in Model Predictive Control (MPC), and (b) Formulating and solving nonlinear building HVAC MPC problems. We extensively evaluate the modeling and control performance using multiple surrogate models and optimization frameworks across various test cases available in the Building optimization Testing Framework (BOPTEST). Our framework is compatible with other modeling techniques and can be customized with different control formulations, making it adaptable and future-proof for test cases currently under development for BOPTEST. This modularity provides a path towards prototyping predictive controllers in large buildings, ensuring scalability and robustness in real-world applications. © 2023 *** rights reserved.

关键词： Model predictive control

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Adaptive responses of cancer cells to tumor microenvironment changes: an evolutionary model perspective 20

Adaptive responses of cancer cells to tumor microenvironment...

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IEEE 20th International conference on Automation Science and Engineering (CASE)

作者： Romano, Chiara Borri, Alessandro Di Benedetto, Maria Domenica Univ Aquila Ctr Excellence Res DEWS Dept Informat Engn Comp Sci & Math Laquila Italy Natl Res Council Italy CNR IASI Biomath Lab Rome Italy

ISBN: (纸本)9798350358513;9798350358520

Evolution has always driven species change. The most interesting manifestations of evolution arise from natural selection, that favors the settling of more suitable species at the expense of others. This also occurs in cancer, where the hostile environment drives cancer cells to evolve to acquire favorable traits, phenotypes, in order to enlarge their species. A therapy can render the environment hostile, thus the physician's role in limiting cell growth while considering their evolution is crucial. In this paper, we focus on the formalization and analysis of a novel eco-evolutionary model of cancer cells, enriched by biological considerations. simulations validate the model and show how the role of the treatment is crucial in driving cellular dynamics.

关键词： Modelling simulation and optimization in healthcare tumour modeling and treatment

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