{'fraction_non_alphanumeric': 0.010655737704918032, 'fraction_numerical': 0.0, 'mean_word_length': 5.359375, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Evaluation of Kinetic Models for Industrial Acetic Fermentation: Proposal of a New Model Optimized by Genetic Algorithms

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.