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The future of sustainable chemistry and process: convergence of artificial intelligence, data and hardware

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posted on 2021-01-20, 15:24 authored by Xin Tai, Hao Zhang, Zhiqiang NiuZhiqiang Niu, Steven ChristieSteven Christie, Jin Xuan
Sustainable chemistry for renewable energy generation and green synthesis is a timely research topic with the vision to provide present needs without compromising future generations. In the era of Industry 4.0, sustainable chemistry and process are undergoing a drastic transformation from continuous flow system toward the next level of operations, such as cooperating and coordinating machine, self-decision-making system, autonomous and automatic problem solver by integrating artificial intelligence, data and hardware in the cyber-physical systems. Due to the lack of convergence between the physical and cyber spaces, the open-loop systems are facing challenges such as data isolation, slow cycle time, and insufficient resources management. Emerging researches have been devoted to accelerating these cycles, reducing the time between multistep processes and real-time characterization via additive manufacturing, in-/on-line monitoring, and artificial intelligence. The final goal is to concurrently propose process recipes, flow synthesis, and molecules characterization in sustainable chemical processes, with each step transmitting and receiving data simultaneously. This process is known as ‘closing the loop’, which will potentially create a future lab with highly integrated systems, and generate a service-orientated platform for end-to-end synchronization and self-evolving, inverse molecular design, and automatic science discovery. This perspective provides a methodical approach for understanding cyber and physical systems individually, enabled by artificial intelligence and additive manufacturing, respectively, in combination with in-/on-line monitoring. Moreover, the future perspective and key challenges for the development of the closed-loop system in sustainable chemistry and process are discussed.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering
  • Science

Department

  • Chemistry
  • Chemical Engineering

Published in

Energy and AI

Volume

2

Publisher

Elsevier BV

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by/4.0/

Acceptance date

2020-10-23

Publication date

2020-10-31

Copyright date

2020

ISSN

2666-5468

Language

  • en

Depositor

Prof Jin Xuan. Deposit date: 18 January 2021

Article number

100036

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