An efficient model construction strategy to simulate microalgal lutein photo-production dynamic process

5 years ago

An efficient model construction strategy to simulate microalgal lutein photo-production dynamic process

Nilay Shah, Keju Jing, Nur R. Ahmed, Fabio Fiorelli, Ehecatl A. del Rio-Chanona, Dongda Zhang

Lutein is a high-value bioproduct synthesized by microalga Desmodesmus sp. It has great potential for the food, cosmetics, and pharmaceutical industries. However, in order to enhance its productivity and to fulfil its ever-increasing global market demand, it is vital to construct accurate models capable of simulating the entire behavior of the complicated dynamics of the underlying biosystem. To this aim, in this study two highly robust artificial neural networks (ANNs) are designed for the first time. Contrary to conventional ANNs, these networks model the rate of change of the dynamic system, which makes them highly relevant in practice. Different strategies are incorporated into the current research to guarantee the accuracy of the constructed models, which include determining the optimal network structure through a hyper-parameter selection framework, generating significant amounts of artificial data sets by embedding random noise of appropriate size, and rescaling model inputs through standardization. Based on experimental verification, the high accuracy and great predictive power of the current models for long-term dynamic bioprocess simulation in both real-time and offline frameworks are thoroughly demonstrated. This research, therefore, paves the way to significantly facilitate the future investigation of lutein bioproduction process control and optimization. In addition, the model construction strategy developed in this research has great potential to be directly applied to other bioprocesses. Biotechnol. Bioeng. 2017;9999: 1–10. © 2017 Wiley Periodicals, Inc. Two robust artificial neural networks were constructed to simulate the dynamic behavior of microalgae growth and lutein production; different advanced strategies were incorporated to guarantee the accuracy of the constructed models, including determining the optimal network structure through a hyper-parameter selection framework, generating artificial data sets by embedding appropriate random noise, and rescaling model inputs through standardization; the accuracy and predictive power of the models for long-term dynamic bioprocess simulation in real-time and offline frameworks were demonstrated and verified experimentally.

Publisher URL: http://onlinelibrary.wiley.com/resolve/doi

DOI: 10.1002/bit.26373