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Jackson, Cameron (2021) A self-optimising continuous-flow hydrothermal reactor for nanomaterial synthesis. PhD thesis, University of Nottingham.

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Abstract

Nanomaterials have emerged as an exciting class of materials with tuneable chemical and physical properties and often enhanced performance when compared to their bulk counterpart. However, the synthesis of nanomaterials remains complex, with a large number of process variables (temperature, reaction time, stoichiometry, pH etc.) having a significant effect on process outcome. Furthermore, traditional optimisation strategies are typically inefficient and often fail to identify factor interactions.

Recent advances in automation, machine learning and optimisation have given rise to the concept of “self-optimisation” in continuous-flow reactors. These integrated cyber-physical reactor systems combine online process analytical technologies with robotics and advanced optimisation algorithms, enabling closed-loop control of reaction outcome and the ability to rapidly optimise a chemical process.

The work presented in this thesis aims to demonstrate the feasibility of self-optimisation in the continuous-flow hydrothermal synthesis of nanomaterials. This Industry 4.0 approach to research and development aims to reduce the timescale necessary for the development of new materials to the point of reliable manufacture. A key objective in this work is the ability to transfer knowledge obtained from bench scale optimisation to pilot and industrial scale production.

A bespoke autonomous reactor platform is presented; capable of generating, analysing and executing experiments without the need for user intervention. Integrating online analytics with process control and machine learning ensures that the system can learn from and predict experiment outcome in real time, continually increasing in confidence over successive iterations.

Following development of the reactor platform, the system was demonstrated across various nanomaterial examples and objectives, including targeted particle size in metal oxides and maximising the surface area in metal-organic frameworks. This work represents the first reported example of self-optimisation in the continuous-flow hydrothermal synthesis of nanomaterials.

Item Type:	Thesis (University of Nottingham only) (PhD)
Supervisors:	Lester, Edward Robertson, Karen
Keywords:	Optimisation; Self-Optimisation; Nanoparticles; Hydrothermal; Continuous-Flow; Flow; Machine Learning; Automation
Subjects:	Q Science > Q Science (General) T Technology > TP Chemical technology
Faculties/Schools:	UK Campuses > Faculty of Engineering
Item ID:	66883
Depositing User:	Jackson, Cameron
Date Deposited:	26 Sep 2024 10:23
Last Modified:	26 Sep 2024 10:32
URI:	https://eprints.nottingham.ac.uk/id/eprint/66883

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