Kinetic modelling of acetic fermentation in an industrial process by genetic algorithms with a desirability function

Abstract

The basic tool to simulate the evolution of a bioprocess, such as the industrial process of acetic fermentation, is the kinetic model. The model must be simple and have a high predictive ability to give results capable of explaining the real behaviour. The difficulties in the kinetic modelling of biological processes are mainly related to the description of the bacterial growth. In this paper a genetic algorithm is designed to obtain a set of kinetic parameters for the specific growth rate that enables the model to explain the industrial fermentation. Only acetic concentration data from industrial fermentators are required. A four-factor desirability function works properly as the response to maximize. The model optimized can explain not only the behaviour of the industrial batch process studied, but also the experimental results obtained with a pilot fermentator working continuously with and without cell recycling.

Kinetics of Carbon Monoxide Oxidation over CuO Supported on Nanosized CeO2

“…GA, developed on Matlab 7.0 commercial software (The MathWorks, Natick, MA), was used for minimization of SSR, , with the following specifications: real-value coding for parameters, initial population of 100 individuals, 200 generations, 10 individuals guaranteed to survive to the next generation, mutation fraction of 0.1 (crossover fraction of 0.9), and proportional fitness scaling. Initial population, number of generations, and surviving individuals were optimized previously.…”

Modeling and optimization of acetic acid fermentation: A polynomial-based approach

“…Most such models have a phenomenological Abbreviations: (r A )est, estimated mean acetic acid formation rate (g acetic acid (100 mL h) -1 ); C, wine loading rate(L min −1 ); E, ethanol concentration remaining at the time the reactor is unloaded (% (v/v)); V, percent unloaded volume (%); (P A )est, estimated acetic acid production (g acetic acid h −1 ); (EtOHmean)est, estimated mean ethanol concentration (% (v/v)); (HAcmean)est, estimated mean acetic acid concentration (% (w/v)); ([Total cells]mean)est, estimated mean total cell concentration (cells mL −1 ); ([Viable cells]mean)est, estimated mean viable cell concentration (cells mL −1 ); (Vmean)est, estimated mean volume (L); HAc final , acetic acid concentration at the time the reactor is unloaded (% (w/v)); t total , total cycle duration (h); Vmean, mean volume (L). or unstructured first principles basis [11][12][13][14] and use differential equations to balance substrate and product concentrations, and kinetic equations to define the influence of the different variables [15,16]. This approach has the advantage of being valid over broad ranges of operating conditions by virtue of its relying on physico-chemical properties of the processes concerned.…”