Imperial College London
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DrAntonioDel Rio Chanona

Faculty of Engineering, Department of Chemical Engineering

Senior Lecturer
 
 
 
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Contact

 

a.del-rio-chanona Website

 
 
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Location

 

ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inbook{Petsagkourakis:2019:10.1016/B978-0-12-818634-3.50154-5,
author = {Petsagkourakis, P and Sandoval, IO and Bradford, E and Zhang, D and del, Rio-Chanona EA},
booktitle = {Computer Aided Chemical Engineering},
doi = {10.1016/B978-0-12-818634-3.50154-5},
pages = {919--924},
title = {Reinforcement Learning for Batch-to-Batch Bioprocess Optimisation},
url = {http://dx.doi.org/10.1016/B978-0-12-818634-3.50154-5},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CHAP
AB  - Bioprocesses have received great attention from the scientific community as an alternative to fossil-based products by microorganisms-synthesised counterparts. However, bioprocesses are generally operated at unsteady-state conditions and are stochastic from a macro-scale perspective, making their optimisation a challenging task. Furthermore, as biological systems are highly complex, plant-model mismatch is usually present. To address the aforementioned challenges, in this work, we propose a reinforcement learning based online optimisation strategy. We first use reinforcement learning to learn an optimal policy given a preliminary process model. This means that we compute diverse trajectories and feed them into a recurrent neural network, resulting in a policy network which takes the states as input and gives the next optimal control action as output. Through this procedure, we are able to capture the previously believed behaviour of the biosystem. Subsequently, we adopted this network as an initial policy for the “real” system (the plant) and apply a batch-to-batch reinforcement learning strategy to update the network's accuracy. This is computed by using a more complex process model (representing the real plant) embedded with adequate stochasticity to account for the perturbations in a real dynamic bioprocess. We demonstrate the effectiveness and advantages of the proposed approach in a case study by computing the optimal policy in a realistic number of batch runs.
AU  - Petsagkourakis,P
AU  - Sandoval,IO
AU  - Bradford,E
AU  - Zhang,D
AU  - del,Rio-Chanona EA
DO  - 10.1016/B978-0-12-818634-3.50154-5
EP  - 924
PY  - 2019///
SP  - 919
TI  - Reinforcement Learning for Batch-to-Batch Bioprocess Optimisation
T1  - Computer Aided Chemical Engineering
UR  - http://dx.doi.org/10.1016/B978-0-12-818634-3.50154-5
ER  -
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