果冻影院

础颈尘:听

• Provide fundamentals on automation in materials manufacturing.听听

• Provide practical understanding and tools for the development of data-driven models and digital twins for material manufacturing.听听

• 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

• To train the students on the use of a range of practical computational tools for online data analysis, process monitoring and optimization.听听

• 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:

• Automate processes suitable for the synthesis of materials in industrial applications .听

• Develop computational tools for data visualisation and analysis in automated material manufacturing.听

• Make informed decisions and propose new solutions aided by data acquisition, processing and analysis.听

• 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

• Proficiently develop data-driven models and digital twins for process simulation, monitoring and optimization.