Parameter Identification and Nonlinear Analysis of the Continuous Bio-dissimilation Process of Glycerol

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

“…The parameters in the original GMA system are then identified by the two-stage method. Compared with the error results of existing literature [5,6,10,20,22,25], smaller error values have been achieved. This shows that our proposed GMA system can better describe the microbial continuous fermentation process.…”

“…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.…”

“…Liu et al [11] established a two-stage fractional order dynamic system and used the penalty method and parallel particle swarm optimization algorithm to find the parameter values. Han [6] conducted research on the parameter identification of the nonlinear excess dynamics system and S-system for the continuous bio-dissimilation process of glycerol, and obtained the smaller concentration error values. Wang et al [20] identified the parameters of the fractional order model of continuous fermentation and obtained better identification value than the integer order, but its average relative error is still large.…”

“…Although the mathematical modeling or parameter identification methods for the microbial continuous fermentation of glycerol to 1,3-propanediol have been addressed in the literature [5,6,10,20,22,25], a comparison study suggested that the steady-states computed by these literatures significantly violate the experimental data (See Tables 2-3 of Section 5). This concludes that the models given in the literature [5,6,10,20,22,25] cannot satisfactorily represent the microbial continuous fermentation. Thus, we must propose the new mathematical modeling or parameter identification approaches to describe the microbial continuous fermentation.…”

“…In order to identify the parameter values of the GMA system, a two-stage method is proposed in Section 4. The optimal values of the parameters are presented in Section 5, which results in the smaller concentration error values compared to the existing literature [5,6,10,20,22,25]. In Section 6, a corresponding steady-state optimization model was established by optimizing the maximum volume yield of 1,3-propanediol.…”