Abstract
This paper deals with the parameter identification of a microbial batch process of glycerol to 1,3-propanediol (1,3-PD). We first present a parameter identification model for the excess kinetics of a microbial batch process of glycerol to 1,3-PD. This model is a nonlinear dynamic optimization problem that minimizes the sum of the least-square and slope errors of biomass, glycerol, 1,3-PD, acetic acid, and ethanol. Then, a two-stage method is proposed to efficiently solve the presented dynamic optimization problem. In this method, two nonlinear programming problems are required to be solved by a genetic algorithm. To calculate the slope of the experimental concentration data, an integral equation of the first kind is solved by using the Tikhonov regularization. The proposed two-stage method could not only optimally identify the model parameters of the biological process, but could also yield a smaller error between the measured and computed concentrations than the single-stage method could, with a decrease of about 52.79%. A comparative study showed that the proposed two-stage method could obtain better identification results than the single-stage method could.
Highlights
There are widespread applications for 1,3-propanediol (1,3-PD) [1]
Vivek et al [9] carried out a comparative evaluation of the metabolite fluxes in 1,3-PD production of cell recycling, simple batch, and continuous fermentation processes by using the Lactobacillus brevis N1E9.3.3 strain
Hirokawa et al [13] used the engineered cyanobacterium Synechococcus elongatus to improve the production of 1,3-PD by optimizing the gene
Summary
There are widespread applications for 1,3-propanediol (1,3-PD) [1]. In the microbial production of 1,3-PD, the bio-dissimilation of glycerol to 1,3-PD has attracted the interest of researchers since the. Xu and Li [16] proposed a multilevel programming method to infer the common metabolic objective function for glycerol bio-dissimilation to 1,3-PD by Klebsiella pneumoniae. Xu and Wang [17] presented three parameter identification models of a microbial batch process of glycerol to 1,3-PD to identify the parameter values of the nonlinear biological system by considering three different error criteria of biomass, glycerol, 1,3-PD, acetic acid, and ethanol These parameter identification models are dynamic optimization problems. The transformed nonlinear programming problems are difficult to solve for global optimality To deal with this difficulty, in this study, a two-stage method is proposed to efficiently handle the parameter identification of the microbial batch process of glycerol to 1,3-PD.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
