Description
础颈尘:听
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Provide fundamentals on automation in materials manufacturing.听听
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Provide practical understanding and tools for the development of data-driven models and digital twins for material manufacturing.听听
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To bridge the gap between hardware and software development in material synthesis by using data-driven models and digital twins for process simulation, monitoring and optimization听听
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To train the students on the use of a range of practical computational tools for online data analysis, process monitoring and optimization.听听
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To train the students on effective team working with others to deliver a process design project on automated materials manufacturing.听
Synopsis:
In this computational module, students will learn how to apply machine learning techniques and statistical methods to develop data-driven models and 鈥渄igital twins鈥 (i.e. in-silico surrogates of selected material manufacturing processes related to material synthesis in flow or batch. This will be done through a project where students will learn:
- fundamentals of I/O digital communication in automated flow synthesis processes;
- how to develop and use data-driven models and digital twins to simulate, monitor and/or optimise material synthesis at the lab scale;
- how to assess the potential scalability of synthesis processes using statistical techniques.
The module will be delivered through face-to-face lectures, seminars from industrial experts and computer tutorials aiming to bridge the gap between data-driven modelling and experimentation in chemical manufacturing.
Learning Outcomes:
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Automate processes suitable for the synthesis of materials in industrial applications .听
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Develop computational tools for data visualisation and analysis in automated material manufacturing.听
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Make informed decisions and propose new solutions aided by data acquisition, processing and analysis.听
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Practically assess the viability of material synthesis solutions for the manufacturing of materials at the larger scale using information from automated lab scale experiments.听
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Proficiently develop data-driven models and digital twins for process simulation, monitoring and optimization.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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