ENBIS Webinar: Bayesian Adaptive Design with Chemical Processes

Shirley Coleman (ISRU, Newcastle University)

Webinar: Bayesian Adaptive Design with Chemical Processes

Speaker: Liam Fleming, Newcastle University
New date: 26th October 2021, at 12:30-13:30 CEST

Short Bio:

Liam Fleming is a 3rd year PhD student studying statistics with a focus on stochastic computation for engineering problems. His research work is in collaboration with the Alan Turing Institute whilst based at Newcastle University. The webinar is based on an industrial placement applying statistical and machine learning techniques to data from simulation of complex chemical processes. He has a BEng first class honours degree in chemical engineering. His technical interests focus on using data and statistics to improve the quality and efficiency of numerical simulations.

    • 12:30 13:30
      Webinar: Bayesian Adaptive Design with Chemical Processes 1h

      Chemical processes are subject to considerable uncertainty arising from inherent stochasticity and process noise. On-plant experimentation is costly and time-consuming so approximating “digital twins” are constructed using simulation. A designed experiment is the starting point to providing responses from which to build a model to emulate the process.
      Gaussian Process (GP) regression provides a highly flexible model class to capture non-linearities in the process data and develop a predictive model. The webinar describes how Bayesian sequential design is used to maximise information gain for each experimental trial. A process simulator, modelled in Aspen Plus is used as a surrogate for a real chemical process, to demonstrate the capabilities of the methodology.

      Speaker: Liam Fleming (Newcastle University)