The modeling and optimization of industrial processes requires an intensive study of the factors involved. In this work, a continuous pilot system for studying the industrial process of acetic fermentation is developed. A Doehlert design is applied to the five variables involved in the pilot process. This experimental design allows reduction of the experimental burden and the maximum amount of information to be obtained, studying the factors at different levels depending on their significance. The experimental system provides a robust measure of the specific growth rate and the rates of substrates consumption and acetic acid production, related to the flow of effluent stream evaluated in the steady state. The results demonstrate the growth-associated kinetics of substrates and product, and the yield factors are calculated with low values of variances for the coefficients, i.e., within the range 1-11%. The specific growth rate suits the quadratic model proposed. The response surfaces generated by the model are applied to explain the behavior of the bacterial growth and, therefore, the effects of the process variables studied over the acetic acid production. Very low levels of ethanol or oxygen make the acetification rate decrease, and a saturation effect with high levels of ethanol or oxygen is also deduced. The effects of the aeration rate, agitation, and overpressure suggest a kind of inhibition of the acetic acid production caused by the oxygen that has not been practically studied before. The temperature strengthens the inhibitory effect of the ethanol and the oxygen. The conclusions of this work consolidate the structure of a hybrid model for the acetic fermentation.

No abstract

No abstract

No abstract

The most important kinetic models developed for acetic fermentation were evaluated to study their ability to explain the behavior of the industrial process of acetification. Each model was introduced into a simulation environment capable of replicating the conditions of the industrial plant. In this paper, it is proven that these models are not suitable to predict the evolution of the industrial fermentation by the comparison of the simulation results with an average sequence calculated from the industrial data. Therefore, a new kinetic model for the industrial acetic fermentation was developed. The kinetic parameters of the model were optimized by a specifically designed genetic algorithm. Only the representative sequence of industrial concentrations of acetic acid was required. The main novelty of the algorithm is the four-composed desirability function that works properly as the response to maximize. The new model developed is capable of explaining the behavior of the industrial process. The predictive ability of the model has been compared with that of the other models studied.