Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. (4th October 2018)

• Record Type: Journal Article
• Authors: Bradford, Eric; Schweidtmann, Artur M.; Zhang, Dongda; Jing, Keju; del Rio-Chanona, Ehecatl Antonio
• Is Part Of: Computers & chemical engineering. Volume 118(2018)
• Page Start: 143
• Page End: 158
• Publication Date: 2018-10-04
• Subjects: Optimization under uncertainty -- Gaussian process -- Artificial neural network -- Machine learning -- Dynamic bioprocess; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2018.07.015
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A Gaussian process based model is constructed to simulate a complex biological system. Model accuracy and predictive capability is compared against the neural network model. Dynamic optimisation under uncertainty is conducted to maximise algal lutein production. A framework to build bioprocess models with Gaussian Processes is presented. 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.