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

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Data-driven Dynamic modeling and Fault Diagnosis of Dimethyl Oxalate Industrial Production Process 6

Data-driven Dynamic Modeling and Fault Diagnosis of Dimethyl...

引用

6th International conference on Data-driven optimization of Complex Systems (DOCS)

作者： Zhang, Jingxuan Yu, Guo Bo, Cuimei Li, Jun Nanjing Tech Univ Nanjing Peoples R China

ISBN: (纸本)9798350377859;9798350377842

Ethylene glycol (EG) is an indispensable substance in the chemical industry and polyester fiber supply chain. The synthesis of dimethyl oxalate (DMO) by carbon monoxide gas-phase catalytic coupling is a key step in the coal-based syngas to ethylene glycol process route. The strong coupling between the reaction unit and the feedstock regeneration unit, as well as the risk of feedstock gas explosion, poses a great challenge to the stability control and safety of the dimethyl oxalate production process. In this paper, the dimethyl oxalate production process is studied from three aspects: steady-state modeling, dynamic modeling and fault simulation. First, the dimethyl oxalate production process was comprehensively modeled using Aspen Plus software. Second, a dynamic model was constructed on the basis of the steady-state model to fit the actual production process. Finally, deep learning algorithms were combined with dynamic simulation techniques. Using the fault scenarios and data in the dynamic model, the T-DOAE algorithm is used to study the fault detection in the production process of dimethyl oxalate, which is of great significance to ensure the safe and stable operation of the gas-phase coupling process of dimethyl oxalate production in the process of coal chemical industry.

关键词： DMO process steady-state simulation fault detection T-DOAE deep learning

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Ship Equipment modeling Method Based on Neural Networks and Particle Swarm optimization 4

Ship Equipment Modeling Method Based on Neural Networks and ...

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4th International conference on Communication Technology and Information Technology, ICCTIT 2024

作者： Shao, Yanshan Lianyungang JARI Automation CO. LTD LNG Equipment Division Lianyungang China

ISBN: (纸本)9798331528973

To address the challenges in ship equipment control simulation and the complexity of building equipment mechanism models, a data-driven equipment modeling approach is proposed. This method aims to enhance model prediction accuracy and generalization capability through the construction of neural network models and optimization via Particle Swarm optimization (PSO). First, a data-driven neural network model is developed using real ship operational data. Then, the connection weights of the neural network are optimized using PSO to reduce prediction errors and improve model performance. Experimental results demonstrate that the proposed method significantly improves the prediction accuracy of critical ship equipment, reduces the Mean Absolute Error (MAE), and increases the Coefficient of Determination (R2). The proposed neural network and PSO-based modeling approach offers a novel perspective and effective tools for the simulation and optimization of ship equipment. © 2024 IEEE.

关键词： Neural network models

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8th International conference on Innovation in Artificial Intelligence, ICIAI 2024

8th International Conference on Innovation in Artificial Int...

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8th International conference on Innovation in Artificial Intelligence, ICIAI 2024

ISBN: (纸本)9798400709302

The proceedings contain 33 papers. The topics discussed include: driving energy efficiency at scale by mass deployment of ai-based chiller energy optimization: exploring a scalable framework for ai-driven chiller energy optimization: a case study on mass deployment in Hong Kong;AI-powered earth disaster management: a collaborative innovation platform;enhancing travel planning and experiences with multimodal ChatGPT 4.0;systems engineering enhanced by AI-driven multiphysics simulation: multiphysics modeling and simulation with artificial intelligence / multiphysics modeling and simulation for technology transfer using artificial intelligence;the knowledge training system based on machine learning technology;and leveraging automated POS tagging to decode parent-infant interactions in digital gameplays.

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Combined Physics-Data driven modeling for Design and Operation optimization of an Energy Concept 36

Combined Physics-Data Driven Modeling for Design and Operati...

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36th International conference on Efficiency, Cost, optimization, simulation and Environmental Impact of Energy Systems, ECOS 2023

作者： Kansara, Rushit Lockan, Michael German Aerospace Center Institute of Low-Carbon Industrial Processes Simulation and Virtual Design Walther-Pauer-Straße 5 Cottbus Germany

ISBN: (纸本)9781713874928

The industrial sector accounts for a huge amount of energy- and process-related CO2 emissions. One decarbonization strategy is to build an energy concept which provides electricity and heat for industrial processes using combination of different renewable energy sources such as photovoltaic, wind turbine, and solar thermal collector system combined with energy conversion power-to-heat components such as heat pump, electric boiler etc. The challenge for the industries is the economic aspect of the decarbonization, as industries require a cost-efficient solution. The total cost for an industrial energy concept includes investment and operating costs. This complex problem of minimizing cost and emission requires two major tasks: (I) modeling of components and (II) multi-objective coupled design and operation optimization of the energy concept. The optimal design and capacity of the components and optimal system operation depend majorly on the modeling of the components. The modeling of the components is either physics-driven or data-driven. The corresponding multi-objective coupled optimization is a complex problem with a large number of variables and constrains involved. This paper shows different types of physics- and data-driven modeling of energy components for the multi-objective coupled optimization for minimizing cost and emission of an industrial process as a case study. The optimization problem is solved as single-level problem and bi-level problem with different combinations of physics- and data-driven models. Different modeling techniques and their influence on the optimization are compared in terms of computational effort, solution accuracy and optimal capacity of components. The results show that the combination of physics and data-driven models have computational time reduction up to 37% with high accuracy compared to complete physics-driven models for the considered case study. Specific combination of physics-driven and polynomial regression models show

关键词： Operating costs

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An Improved Data-driven modeling Method for Aircraft Based on Prediction and optimization 35

An Improved Data-Driven Modeling Method for Aircraft Based o...

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35th Chinese Control and Decision conference (CCDC)

作者： Su, Shihong Xiao, Bing Li, Lingwei Luo, Jinfeng Zhao, Hui Northwestern Polytech Univ Sch Automat Xian Peoples R China

ISBN: (纸本)9798350334722

This paper proposes a data-driven modeling method for aircraft by predicting model errors and optimizing model structures. Based on flight test data from the mechanism model, the aircraft data-driven model is established by several trained basic neural networks for fitting dynamics relationships of aircraft and recurrent neural networks for compensating for model errors. Compared to the traditional data-driven modeling method, this method can more effectively avoid and solve the problem of instability of data-driven models with disturbances at long running times. Finally, the proposed method's feasibility and the established model's credibility are verified by simulation experiments with complex disturbance and statistical analysis for model accuracy.

关键词： aircraft data-driven model prediction structure optimization long running time disturbance

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Data-driven modeling and Robust optimization of the Ammunition Ramming Mechanism Based on Artificial Neural Network 2nd

Data-Driven Modeling and Robust Optimization of the Ammuniti...

引用

2nd International conference of Mechanical System Dynamics, ICMSD 2023

作者： Zhang, Yuntian Chen, Longmiao Chen, Guangsong Liu, Taisu Nanjing University of Science and Technology Nanjing210094 China Nanjing Institute of Technology Nanjing211167 China

ISBN: (纸本)9789819980475

Aiming to address the challenges of strong nonlinearity and lengthy experimental and simulation prediction time in the ammunition ramming process of ammunition ramming mechanisms, this study proposes a data-driven modeling method based on artificial neural networks. To begin with, the composition of the ammunition ramming mechanism is analyzed, and a dynamic model of the ammunition ramming process is established. The model’s accuracy is verified through experiments. Subsequently, a data-driven model for the ammunition ramming process is developed using artificial neural networks and simulation data. The effectiveness and accuracy of this model are confirmed through numerical examples. Finally, based on the data-driven model, a robust optimization design is conducted for the ammunition ramming process. The obtained results demonstrate a significant improvement in efficiency and accuracy of both the data-driven model prediction and the optimization design without compromising accuracy. Consequently, this method offers valuable theoretical support for the rapid design of ammunition ramming mechanisms and other equipment. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Neural networks

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Radio-in-the-Loop simulation modeling for Energy-Efficient and Cognitive IoT in Smart Cities: A Cross-Layer optimization Case Study 18

Radio-in-the-Loop Simulation Modeling for Energy-Efficient a...

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18th Wireless On-Demand Network Systems and Services conference (WONS)

作者： Boehm, Sebastian Koenig, Hartmut ZITiS Communicat Dept Munich Germany

ISBN: (纸本)9783903176560

Wireless communication technologies and Internet of Things (IoT) applications are the main drivers of upcoming sustainable Smart City networks which require an effective resource management. The reduction of the transmission energy consumption and the efficient utilization of the available spectrum for wireless communication, for instance, have to be enabled by energy-efficient and cognitive IoT networks. These are implemented through optimized communication protocol stacks and algorithms that rely on actual physical layer and channel state information. The modeling and the prototype evaluation of protocol optimization approaches are mainly driven by pure simulation studies with abstracted physical layer and channel models. With the Radio-in-the-Loop (RIL) simulation [1] and modeling [2], we have created an evaluation approach that integrates real wireless hardware and radio environments into the simulation of protocol sequences and algorithms. In this paper, we demonstrate a cross-layer optimization case study for energy efficient modeling using software-defined radios alongside this basic methodology. We exemplary show the scenario of a receiver-sensitivity control to increase the energy efficiency of receiver-dominated IoT nodes in Smart City networks.

关键词： Hardware-in-the-loop radio-in-the-loop simulation emulation wireless sensor networks cross-layer optimization cognitive radio cognitive iot

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Towards User-driven Exoskeletal Assistive Control using Deep Reinforcement Learning 15

Towards User-Driven Exoskeletal Assistive Control using Deep...

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15th Ubiquitous Computing Electronics and Mobile Communication conference

作者： Goyal, Chinmayi Yorktown High Sch Yorktown Hts NY 10598 USA

ISBN: (纸本)9798331540913;9798331540906

Movement disorders such as sarcopenia or age-related muscle weakness affect nearly one in seven adults, transforming routine tasks like carrying objects and mobility into formidable challenges. Recent research has shown promise in robotic interventions for assisting and restoring movement in individuals with such disabilities. However, dynamic control of robotic exoskeletons is an open problem. In this paper, we present a user-driven exoskeletal assistance system developed using deep reinforcement learning (RL), specifically Proximal Policy optimization (PPO). This system leverages the observed body's pose, including joint positions, velocities and measured electromyography signals (EMG) or muscle activations, to provide exoskeletal assistance and precisely compensate for muscle weakness. We used a human musculoskeletal model acting in a high-dimensional, physics-based simulation environment to train and evaluate the exoskeleton control system. The designed exoskeleton parameters and policies achieved 99.4% accuracy in compensating for the loss of muscle function. Overall, this system serves as a step towards developing a task-agnostic, user-driven exoskeletal assistance system that could significantly enhance daily functional capabilities for sarcopenia patients.

关键词： deep reinforcement learning musculoskeletal modeling bio-mechanical simulation proximal policy optimization sarcopenia user-driven exoskeleton assistance

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Optimizing Cooling Load in a Central Chiller Plant: A Data-driven Approach 4

Optimizing Cooling Load in a Central Chiller Plant: A Data-D...

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4th modeling, Estimation, and Control conference (MECC)

作者： Souza, Diego de M. Stockar, Stephanie Ohio State Univ Dept Mech & Aerosp Engn Columbus OH 43212 USA

This work presents an advanced optimization strategy for the operation of the individual chillers in a plant that reduces the power demand while meeting the cooling load. This is achieved by first developing a hybrid model combining energy-based and data-driven methods to describe the energy demand of a central chiller plant for a given cooling load and environmental conditions. The model is calibrated and validated on The Ohio State University operation data. The validated model is then used in an optimization algorithm based on particle swarm optimization to find the optimal load of each chiller for different weather and operation conditions. The optimized strategy is tested in simulation considering one year of operation in Central Ohio and compared against the baseline strategy. The new strategy achieves, on average, a 4% reduction in daily peak power consumption for four mild weather months in the year, with instances of up to 12% reduction. Copyright (c) 2024 The Authors.

关键词： central chiller plant data-driven modeling particle swarm optimization Long-short term memory

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optimization of Dual-Drive modeling for Large-Scale Offshore Wind Farms 7

Optimization of Dual-Drive Modeling for Large-Scale Offshore...

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7th IEEE International Electrical and Energy conference, CIEEC 2024

作者： Zhang, Junyang Guo, Xiaojiang Yao, Zhongyuan Li, Zheng Zhao, Jianjian Chen, Yijing China Huaneng Clean Energy Research Institute Clean energy integration & electrical dept Beijing China China Huaneng Clean Energy Research Institute China Huaneng Group co. Limited Beijing China Huaneng Jiangsu Clean Energy Branch Production operation and maintenance dept Nanjing China

ISBN: (纸本)9798350359558

This paper introduced the new achievement from the research on optimization of dual-drive modelling technology for large-scale offshore wind farms. The dual-drive model consisted of data driven and knowledge driven respectively, and the iterative optimization is carried out by multi-parameter optimization neural network algorithm. Virtual twin model is a real-time simulation system formed by dual-drive simulation optimization algorithm, which takes multi-dimensional and multi-body dynamics modelling and multi-time scale simulation calculation as the basis, integrates multiple disciplines simulation and artificial intelligence algorithms, and combines renewable energy generation and integration as a significant result. The large-scale offshore wind farm real-time simulation model has improved the architecture of traditional electromagnetic transient digital simulation of power system. © 2024 IEEE.

关键词： Offshore wind farms

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