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Hybrid physics‐based and data‐driven modeling for bioprocess online simulation and optimization
Author(s) -
Zhang Dongda,
Del RioChaa Ehecatl Antonio,
Petsagkourakis Panagiotis,
Wagner Jonathan
Publication year - 2019
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.27120
Subject(s) - bioprocess , computer science , process (computing) , model predictive control , visualization , sensitivity (control systems) , filter (signal processing) , biochemical engineering , data mining , engineering , control (management) , artificial intelligence , chemical engineering , electronic engineering , computer vision , operating system
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.