A Two-Stage Method for Parameter Identification of a Nonlinear System in a Microbial Batch Process

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.

Sequential Geometric Programming Method for Parameter Estimation of a Nonlinear System in Microbial Continuous Fermentation

“…Xu and Li [14] presented the mathematical optimization approach to optimize the metabolic objective for glycerol metabolism into 1,3-PDO production. Xu et al [15] proposed a two-stage approach to efciently solve the parameter identifcation problem of the microbial batch process of glycerol. Pröschle et al [16] designed the advanced controller to control the fed-batch fermenter of glycerol to 1,3-PDO.…”

Parameter identification and steady-state optimization of microbial continuous fermentation based on two-stage and interior point methods

“…In recent years, scholars have conducted extensive research on parameter identification, process optimization, and control in microbial fermentation production processes, and have achieved a series of research results [1,[4][5][6][10][11][12][13][14]17,[20][21][22][23][24][25][26][27]29]. For example, Abdullah et al [1] solved the parameter identification problem of biochemical systems based on hybrid optimization algorithms, resulting in higher accuracy and shorter time for the calculated parameters.…”

“…Teijeiro et al [17] applied the differential evolution algorithm in meta heuristic algorithms to estimate the parameters of biochemical systems, reducing computational time. Xu et al [26] studied the parameter identification of glycerol production process for 1,3-propanediol based on a two-stage method. Mosayebi et al [12] solved the parameter identification model of the biochemical system based on the improved particle swarm optimization, and obtained more accurate parameter values of the nonlinear biochemical system model.…”