A Conceptual Framework for AI-based Operational Digital Twin in Chemical Process Engineering

2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) Pub Date : 2020-06-01 DOI:10.1109/ICE/ITMC49519.2020.9198575

Evrim Örs, Robin Schmidt, Moein Mighani, Marwan Shalaby

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引用次数: 18

Abstract

As digitalization is becoming more and more an integral part of business in all sectors, the digital twin paradigm starts to play a more crucial role. This paper primarily aims at describing a generic framework for digital twin development in chemical process industry from an operational perspective. The main building blocks of the operational digital twin are presented, namely data management, process modeling, process optimization, production scheduling, and process control, as well as the deployment. Strong emphasis is put on the advanced process control hierarchy. Additionally, the role of artificial intelligence in the development and deployment of operational digital twin in process industry is presented, particularly regarding surrogate modeling, predictive modeling and AI supported optimization and control. Consequently the potential for new business models induced by digitalization is discussed, and an outlook for prospective research topics is provided.

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化工过程工程中基于人工智能的可操作数字孪生概念框架

随着数字化越来越成为各行业业务的重要组成部分，数字孪生范式开始发挥更重要的作用。本文主要旨在从操作的角度描述化工过程工业中数字孪生发展的通用框架。提出了可操作数字孪生的主要构建模块，即数据管理、过程建模、过程优化、生产调度和过程控制以及部署。重点介绍了先进的过程控制层次。此外，还介绍了人工智能在流程工业中可操作数字孪生的开发和部署中的作用，特别是在代理建模、预测建模和人工智能支持的优化和控制方面。在此基础上，讨论了数字化带来的新商业模式的潜力，并对未来的研究课题进行了展望。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2020 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)

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