Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes

Journal article (2018)

Authors

Eric Bradford Norwegian University of Science and Technology (NTNU)
A.M. Schweidtmann Rheinisch-Westfälische Technische Hochschule
Dongda Zhang Imperial College London
Keju Jing Xiamen University
Ehecatl Antonio del Rio-Chanona Imperial College London
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Published Date

2018

Language

English

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Abstract

Dynamic modeling is an important tool to gain better understanding of complex bioprocesses and to determine optimal operating conditions for process control. Currently, two modeling methodologies have been applied to biosystems: kinetic modeling, which necessitates deep mechanistic knowledge, and artificial neural networks (ANN), which in most cases cannot incorporate process uncertainty. The goal of this study is to introduce an alternative modeling strategy, namely Gaussian processes (GP), which incorporates uncertainty but does not require complicated kinetic information. To test the performance of this strategy, GPs were applied to model microalgae growth and lutein production based on existing experimental datasets and compared against the results of previous ANNs. Furthermore, a dynamic optimization under uncertainty is performed, avoiding over-optimistic optimization outside of the model's validity. The results show that GPs possess comparable prediction capabilities to ANNs for long-term dynamic bioprocess modeling, while accounting for model uncertainty. This strongly suggests their potential applications in bioprocess systems engineering.