Comparison of physics‐based and data‐driven modelling techniques for dynamic optimisation of fed‐batch bioprocesses. Issue 11 (8th August 2019)

• Record Type: Journal Article
• Authors: Del Rio‐Chanona, Ehecatl Antonio; Ahmed, Nur Rashid; Wagner, Jonathan; Lu, Yinghua; Zhang, Dongda; Jing, Keju
• Is Part Of: Biotechnology and bioengineering. Volume 116:Issue 11(2019)
• Page Start: 2971
• Page End: 2982
• Publication Date: 2019-08-08
• Subjects: artificial neural network -- dynamic optimisation -- fed‐batch operation -- kinetic modelling -- machine learning; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.27131
• Languages: English
• ISSNs: 0006-3592

Abstract

Abstract: The development of digital bioprocessing technologies is critical to operate modern industrial bioprocesses. This study conducted the first investigation on the efficiency of using physics‐based and data‐driven models for the dynamic optimisation of long‐term bioprocess. More specifically, this study exploits a predictive kinetic model and a cutting‐edge data‐driven model to compute open‐loop optimisation strategies for the production of microalgal lutein during a fed‐batch operation. Light intensity and nitrate inflow rate are used as control variables given their key impact on biomass growth and lutein synthesis. By employing different optimisation algorithms, several optimal control sequences were computed. Due to the distinct model construction principles and sophisticated process mechanisms, the physics‐based and the data‐driven models yielded contradictory optimisation strategies. The experimental verification confirms that the data‐driven model predicted a closer result to the experiments than the physics‐based model. Both models succeeded in improving lutein intracellular content by over 40% compared to the highest previous record; however, the data‐driven model outperformed the kinetic model when optimising total lutein production and achieved an increase of 40–50%. This indicates the possible advantages of using data‐driven modelling for optimisation and prediction of complex dynamic bioprocesses, and its potential in industrial bio‐manufacturing systems. Abstract : This study investigates the efficiency of different digital techniques for long‐term bioprocess optimal control and visualisation. Cutting‐edge physics‐based and data‐driven models are exploited to identify the optimal light intensity and nitrate inflow rate for a fed‐batch microalgal lutein photo‐production system. Significant increases in lutein intercellular content and total production are achieved compared to the previous studies, and consumption of nitrogen source is greatly reduced, demonstrating the potential of current modelling strategies for bioprocess optimisation and design.