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Kai Lui

Using recurrent neural networks to model fed-batch processes.

Email: k.liu5@ncl.ac.uk

Supervisors

Project description

Fed-batch processes are used to produce high value products in the chemical, biological, food, pharmaceutical and semiconductor industries. The general features of fed-batch biological processes include:

  • strong nonlinearity
  • no steady state operation
  • instinctive time variation
  • batch-to-batch variation
  • uncertainty caused by drifting of raw materials

These features complicate modelling and control.

Recurrent neural networks (RNNs) have a dynamic memory. They can process temporal context information. They are highly promising tools used for solving complex temporal, nonlinearity, time variation and uncertainty tasks.

In this project, I will use RNNs to model fed-batch processes. I will optimise them using a covariance matrix adaption evolutionary strategy.

Publications