Optimization of biotechnological processes. The acetic acid fermentation. Part III: Dynamic optimization

Metaproteomics of microbiota involved in submerged culture production of alcohol wine vinegar: A first approach

Acetic acid bacteria form a complex microbiota that plays a fundamental role in the industrial production of vinegar through the incomplete oxidation reaction from ethanol to acetic acid. The organoleptic properties and the quality of vinegar are influenced by many factors, especially by the raw material used as acetification substrate, the microbial diversity and the technical methods employed in its production. The metaproteomics has been considered, among the new methods employed for the investigation of microbial communities, since it may provide information about the microbial biodiversity and behaviour by means of a protein content analysis. In this work, alcohol wine vinegar was produced through a submerged culture of acetic acid bacteria using a pilot acetator, operated in a semi-continuous mode, where the main system variables were monitored and the cycle profile throughout the acetification was obtained. Through a first approach, at qualitative level, of a metaproteomic analysis performed at relevant moments of the acetification cycle (end of fast and discontinuous loading phases and just prior to unloading phase), it is aimed to investigate the microbiota existent in alcohol wine vinegar as well as its changes during the cycle; to our knowledge, this is the first metaproteomics report carried out in this way on this system. A total of 1723 proteins from 30 different genera were identified; 1615 out of 1723 proteins (93.73%) belonged to the four most frequent (%) genera: Acetobacter, Gluconacetobacter, Gluconobacter and Komagataeibacter. Around 80% of identified proteins belonged to the species Komagataeibacter europaeus. In addition, GO Term enrichment analysis highlighted the important role of catalytic activity, organic cyclic compound binding, metabolic and biosynthesis processes throughout acetic acid fermentation. These findings provide the first step to obtain an AAB profile at omics level related to the environmental changes produced during the typical semi-continuous cycles used in this process and it would contribute to the optimization of operating conditions and improving the industrial production of vinegar.

Modelling Acetification with Artificial Neural Networks and Comparison with Alternative Procedures

Modelling techniques allow certain processes to be characterized and optimized without the need for experimentation. One of the crucial steps in vinegar production is the biotransformation of ethanol into acetic acid by acetic bacteria. This step has been extensively studied by using two predictive models: first-principles models and black-box models. The fact that first-principles models are less accurate than black-box models under extreme bacterial growth conditions suggests that the kinetic equations used by the former, and hence their goodness of fit, can be further improved. By contrast, black-box models predict acetic acid production accurately enough under virtually any operating conditions. In this work, we trained black-box models based on Artificial Neural Networks (ANNs) of the multilayer perceptron (MLP) type and containing a single hidden layer to model acetification. The small number of data typically available for a bioprocess makes it rather difficult to identify the most suitable type of ANN architecture in terms of indices such as the mean square error (MSE). This places ANN methodology at a disadvantage against alternative techniques and, especially, polynomial modelling.

Quadratic Interpolation Based Simultaneous Heat Transfer Search Algorithm and Its Application to Chemical Dynamic System Optimization

Dynamic optimization problems (DOPs) are widely encountered in complex chemical engineering processes. However, due to the existence of highly constrained, nonlinear, and nonsmooth environment in chemical processes, which usually causes nonconvexity, multimodality and discontinuity, handling DOPs is not a straightforward task. Heat transfer search (HTS) algorithm is a relative novel metaheuristic approach inspired by the natural law of thermodynamics and heat transfer. In order to solve DOPs efficiently, a new variant of HTS algorithm named quadratic interpolation based simultaneous heat transfer search (QISHTS) algorithm is proposed in this paper. The QISHTS algorithm introduces three modifications into the original HTS algorithm, namely the effect of simultaneous heat transfer search, quadratic interpolation method, and population regeneration mechanism. These three modifications are employed to provide lower computational complexity, as well as to enhance the exploration and exploitation capabilities. Therefore, the ensemble of these modifications can provide a more efficient optimization algorithm with well-balanced exploration and exploitation capabilities. The proposed variant is firstly investigated by well-defined benchmark problems and then applied to solve four chemical DOPs. Moreover, it is compared with different well-established methods existing in the literature. The results demonstrate that QISHTS algorithm has the greatest robustness and precision than other competitors.