Data-based Control of Process Feature Dynamics through Latent Behaviours

通过潜在行为对过程特征动态进行基于数据的控制

• 批准号: DP220100355
• 负责人: Prof Jie Bao
• 金额: $ 29.13万
• 依托单位: The University of New South Wales
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2022
• 资助国家: 澳大利亚
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP220100355
• 关键词: Data; based; Control; Process; Feature

This project aims to develop a novel data-based approach to control the feature dynamics of complex industrial processes. The dynamic features of desired process operations (leading to high energy and material efficiencies and good product quality) are often not directly measured but can be distilled from high-dimensional big process data. However, little effort has been made to develop process control approaches to achieve desired dynamic features. This project aims to develop such a data-based approach by controlling latent variable dynamics, using the behavioural systems framework integrated with big data analytics and artificial neural networks. The outcomes are expected to help build a cornerstone for future smart manufacturing.

该项目旨在开发一种基于数据的新方法来控制复杂工业过程的特征动态。通常不会直接测量所需过程操作的动态特征（导致高能量和材料效率和良好的产品质量），而是可以从高维大流程数据中提取的。但是，几乎没有努力开发过程控制方法以实现所需的动态功能。该项目旨在使用与大数据分析和人工神经网络集成的行为系统框架来控制潜在变量动力学，从而开发这种基于数据的方法。预计结果将有助于为未来的智能制造建立基石。