This review article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cut...
详细信息
This review article explores how emerging generative artificial intelligence (GenAI) models, such as large language models (LLMs), can enhance solution methodologies within process systems engineering (PSE). These cutting-edge GenAI models, particularly foundation models (FMs), which are pre-trained on extensive, generalpurpose datasets, offer versatile adaptability for a broad range of tasks, including responding to queries, image generation, and complex decision-making. Given the close relationship between advancements in PSE and developments in computing and systems technologies, exploring the synergy between GenAI and PSE is essential. We begin our discussion with a compact overview of both classic and emerging GenAI models, including FMs, and then dive into their applications within key PSE domains: synthesis and design, optimization and integration, and process monitoring and control. In each domain, we explore how GenAI models could potentially advance PSE methodologies, providing insights and prospects for each area. Furthermore, the article identifies and discusses potential challenges in fully leveraging GenAI within PSE, including multiscale modeling, data requirements, evaluation metrics and benchmarks, and trust and safety, thereby deepening the discourse on effective GenAI integration into systems analysis, design, optimization, operations, monitoring, and control. This paper provides a guide for future research focused on the applications of emerging GenAI in PSE.
A coupled multidisciplinary system is a complex system involving the integration and interaction of multiple disciplines. In such systems, the disciplines are interconnected and influence each other, and each contains...
详细信息
A coupled multidisciplinary system is a complex system involving the integration and interaction of multiple disciplines. In such systems, the disciplines are interconnected and influence each other, and each contains some sources of uncertainty, leading to a complicated and uncertain behavior of the overall system. Accurate modeling of the individual disciplines is crucial in the design, control, and analysis of coupled multidisciplinary systems. However, acquiring data for these disciplines through experiments or computational simulations is costly. To mitigate this challenge, we present an efficient framework aimed at acquiring the least number of informative data from each discipline, ensuring accurate estimation of the joint distribution of coupling variables. Our approach constructs a surrogate model for each discipline and focuses on acquiring a selected subset of data by prioritizing uncertainty reduction in regions critical for estimating the stationary behavior of the coupled multidisciplinary system. The efficacy of the proposed framework is demonstrated in numerical experiments using a coupled aerodynamics-structures system. Copyright (c) 2024 The Authors.
Efficient monitoring of production performance is crucial for ensuring safe operations and enhancing the economic benefits of the Iron and Steel Corporation. Although basic modeling algorithms and visualization diagra...
详细信息
Efficient monitoring of production performance is crucial for ensuring safe operations and enhancing the economic benefits of the Iron and Steel Corporation. Although basic modeling algorithms and visualization diagrams are available in many scientific platforms and industrial applications, there is still a lack of customized research in production performance monitoring. Therefore, this article proposes an interactive visual analytics approach for monitoring the heavy-plate production process (iHPPPVis). Specifically, a multicategory aggregated monitoring framework is proposed to facilitate production performance monitoring under varying working conditions. In addition, A set of visualizations and interactions are designed to enhance analysts' analysis, identification, and perception of the abnormal production performance in heavy-plate production data. Ultimately, the efficacy and practicality of iHPPPVis are demonstrated through multiple evaluations.
The need for mobile communications networks to provide fast data rates and maintain a high level of quality of service (QoS) is crucial. This requires low latency and low power consumption. In the realm of 6G mobile n...
详细信息
This study explores the complex process of sentiment analysis in reviews of international student rentals, a challenge made even more difficult by cultural, linguistic, and administrative barriers. The study utilized ...
详细信息
ISBN:
(纸本)9798350366457;9798350366440
This study explores the complex process of sentiment analysis in reviews of international student rentals, a challenge made even more difficult by cultural, linguistic, and administrative barriers. The study utilized Python crawlers to collect and analyze 15,537 reviews from 30 popular study abroad cities. Neural networks and sentiment analysis techniques were employed to determine the sentiments of international students. To enhance the effectiveness of sentiment analysis, a Transformer-based model was implemented. After manual labeling and data cleaning, this model was developed and optimized through hyperparameter tuning, achieving high accuracy and F1 scores. Finally, we applied LDA topic modeling to extract nine topics and analyze each one by identifying its keywords, providing valuable insights into the rental experiences of international students in various urban environments.
The study of dynamic systems often assumes building a model based on numerical data describing the system's behavior, i.e., its identification. This problem is relevant, since it is impossible to study properties ...
详细信息
With the increasingly close coupling between the cyber system and the physical system, cyber attacks have a significant impact on the physical system by attacking the cyber system. As a cyber-physical system with mult...
详细信息
With the increasingly close coupling between the cyber system and the physical system, cyber attacks have a significant impact on the physical system by attacking the cyber system. As a cyber-physical system with multiple energy sources, integrated energy cyber-physical system (IECPS) is facing the risk of cyber attacks. However, the research on IECPS is still in the direction of operation optimization at the physical level, which cannot meet the requirements of system security and stability analysis under cyber cooperative attacks. Firstly, this paper analyzes the interaction mechanism between the energy network composed of electricity, heat and gas and the cyber network in IECPS, and put forward a layered modeling method of IECPS to analyze the influence of abnormal information flow caused by cyber attacks on energy flow. Secondly, based on the energy circuit method (ECM), a hybrid calculation method of IECPS energy-information flow is proposed to solve the model. Then, the load shedding state of IECPS and the tampering process of system control commands after the fake data injection attack are analyzed by using the proposed calculation method. Finally, the simulation analysis is carried out in an integrated energy system composed of IEEE 39-node power grid, 6-node heating network and 7-node gas network. The results show that, compared with the traditional Newton-Raphson method, the proposed IECPS hybrid calculation method can better obtain the change results of energy flow in the steady-state scenario and the cyber cooperative attack scenario, and identify the high-risk branches in the system, which is of great significance to the safe and stable operation of the energy system.
In process CPS systems, highly accurate modeling of the production system is essential for optimal control, fault diagnosis, and flexible manufacturing. Traditional data-driven modeling of industrial processes focuses...
详细信息
This work develops a transfer learning (TL) framework for modeling nonlinear dynamic systems using recurrent neural networks (RNNs). The TL-based RNN models are then incorporated into the design of model predictive co...
详细信息
ISBN:
(纸本)9798350328066
This work develops a transfer learning (TL) framework for modeling nonlinear dynamic systems using recurrent neural networks (RNNs). The TL-based RNN models are then incorporated into the design of model predictive control (MPC) systems. Specifically, transfer learning uses a pre-trained model developed based on a source domain as the starting point, and adapts the model to a target domain with similar data distribution. The generalization error for TL-based RNNs (TL-RNNs) that depends on model capacity and discrepancy between source and target domains is first derived to demonstrate the generalization capability on target process. Subsequently, the TL-RNN model is utilized as the prediction model in MPC for the target process. Finally, a chemical process example is used to demonstrate the benefits of transfer learning.
The proceedings contain 121 papers. The topics discussed include: DMobileNet: a novel MobileNet with dendritic learning for brain tumor detection;data-driven modeling and working condition prediction in process indust...
ISBN:
(纸本)9798350365221
The proceedings contain 121 papers. The topics discussed include: DMobileNet: a novel MobileNet with dendritic learning for brain tumor detection;data-driven modeling and working condition prediction in process industry production;dynamic gaussian mutation particle swarm optimization for accurate adaptive latent factor analysis;a GAN-based hybrid sampling method for transaction fraud detection;output feedback controls of a flexible wing under unknown constraint references;classification of reachable markings for automated manufacturing systems with multiple unreliable resources;a PID-incorporated second-order latent factor analysis model;an improved safe braking model of virtually coupled trains for closer tracking;and convolutional neural network with a novel attention mechanism for skin cancer recognition.
暂无评论