Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework whi...

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Main Authors: Dongda Zhang, Ehecatl Antonio Del Rio‐Chanona, Panagiotis Petsagkourakis, Jonathan Wagner
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Published: 2019
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Online Access:https://hdl.handle.net/2134/9579641.v1
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spelling rr-article-95796412019-07-17T00:00:00Z Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation Dongda Zhang (3927530) Ehecatl Antonio Del Rio‐Chanona (7216406) Panagiotis Petsagkourakis (7216466) Jonathan Wagner (5214482) bioprocess optimization data recalibration fed-batch operation kinetic modeling machine learning Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application. 2019-07-17T00:00:00Z Text Journal contribution 2134/9579641.v1 https://figshare.com/articles/journal_contribution/Hybrid_physics_based_and_data_driven_modelling_for_bioprocess_online_simulation_and_optimisation/9579641 CC BY-NC-ND 4.0
institution Loughborough University
collection Figshare
topic bioprocess optimization
data recalibration
fed-batch operation
kinetic modeling
machine learning
spellingShingle bioprocess optimization
data recalibration
fed-batch operation
kinetic modeling
machine learning
Dongda Zhang
Ehecatl Antonio Del Rio‐Chanona
Panagiotis Petsagkourakis
Jonathan Wagner
Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation
description Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.
format Default
Article
author Dongda Zhang
Ehecatl Antonio Del Rio‐Chanona
Panagiotis Petsagkourakis
Jonathan Wagner
author_facet Dongda Zhang
Ehecatl Antonio Del Rio‐Chanona
Panagiotis Petsagkourakis
Jonathan Wagner
author_sort Dongda Zhang (3927530)
title Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation
title_short Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation
title_full Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation
title_fullStr Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation
title_full_unstemmed Hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation
title_sort hybrid physics‐based and data‐driven modelling for bioprocess online simulation and optimisation
publishDate 2019
url https://hdl.handle.net/2134/9579641.v1
_version_ 1799453700183293952