An efficient model construction strategy to simulate microalgal lutein photo-production dynamic process
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2017-07-27
Date Acceptance
2017-06-30
Citation
Biotechnology and Bioengineering, 2017, 114 (11), pp.2518-2527
ISSN
1097-0290
Publisher
Wiley
Start Page
2518
End Page
2527
Journal / Book Title
Biotechnology and Bioengineering
Volume
114
Issue
11
Copyright Statement
© 2017 Wiley Periodicals, Inc. This is the accepted version of the following article, which has been published in final form at https://dx.doi.org/10.1002/bit.26373
Sponsor
Engineering & Physical Science Research Council (E
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000411699200010&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Grant Number
EP/L017393/1
Subjects
Science & Technology
Life Sciences & Biomedicine
Biotechnology & Applied Microbiology
artificial neural network
dynamic simulation
lutein production
real-time framework
fed-batch operation
bioprocess modeling
ARTIFICIAL NEURAL-NETWORK
C-PHYCOCYANIN PRODUCTION
TOLERANT DESMODESMUS SP
HAEMATOCOCCUS-PLUVIALIS
BIOHYDROGEN PRODUCTION
HYDROGEN-PRODUCTION
PREDICTIVE CONTROL
CO2 FIXATION
OPTIMIZATION
CULTIVATION
Publication Status
Published