Mathematical modelling and its use for design of feeding strategies for L‐Sorbose fermentation

Abstract

Batch sorbitol to sorbose bioconversion by Acetobacter suboxydans using initial sorbitol concentration (S0 = 100 g/L) yielded a productivity of 10.11 g/L‐h and 98.6% conversion in 10 h time. The batch kinetics was then used to develop an unstructured mathematical model. Model parameters were identified using a nonlinear regression technique assisted by a computer program which minimized the deviation between the model predictions and actual batch experimental data. F test indicated 99% confidence on the prediction of model using optimized parameters. The batch model was eventually extrapolated to identify nutrient feeding strategies to maintain constant noninhibitory sorbitol supply and eliminate substrate limitation for fed‐batch fermentation in order to improve the sorbose productivity. The adequacy of the fed‐batch model was established by excellent agreement between experimental data and model simulation (except towards the end of fermentation).

Synthesis of l-Hexoses

“…There is considerable literature on the latter process as it is the first step of a two-step biotechnological process for preparation of vitamin C from 508 . − Many smart developments such as immobilized organisms, − thermoresistant strains, fed-batch methods, − continuous fermentation, computer-coupled substrate feeding, and using oxygen vectors have been reported. Yields are frequently over 90%, but byproducts such as d -fructose and d -mannitol can be formed, and rates can be limited …”

“…341−344 Many smart developments such as immobilized organisms, 345−349 thermoresistant strains, 350 fed-batch methods, 351−355 continuous fermentation, 356 computer-coupled substrate feeding, 357 and using oxygen vectors 358 have been reported. Yields are frequently over 90%, 359 but byproducts such as D-fructose and D-mannitol can be formed, 360 and rates can be limited. 361 Aldoses can also be made by oxidation of alditols.…”

Investigation of Power Characteristics of small Scale Stirred Tank Bioreactor

“…The sum of the square of the deviation in the observation groups Q 2 defines the influence of the Relative error. S 1 2 is the assessment of the dispersion from the influence of the factor and S 2 2 and it is the assessment of dispersion from the influence of the Relative error. The comparison of the Fisher function (Table 2) shows that the investigation factor is considerable (F Е >F Т ) and it will be included in the model for determination of the power input.…”