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Biochemical and metabolic interpretation of microbial growth is an important topic in bioreactor design. We intend to address valuable information about the relation of critical operation variables and the simulation of bioprocesses with unstructured and structured kinetic models. Process parameters such as nutrient supply, pH, dissolved oxygen, and metabolic end-products directly impact the physiology and metabolism of microorganisms. Changes in the membrane as well as cell viability are of interest since protein expression and maturation in prokaryota are directly related to membrane integrity. This chapter intends to deliver an insight of different alternatives in kinetic modeling.

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A cluster-based artificial neural network model called CLASO (Classification-Assemblage-Association) has been proposed to predict the maximum of the 24-h moving average of PM 10 concentration on the next day in the three largest metropolitan areas of Mexico. The model is a self-organised, real-time learning neural network, which builds its topology via a process of pattern classification by using an historical database. This process is based on a supervised clustering technique, assigning a class to each centroid of the hidden layer, employing the Euclidean distance as a hierarchical criterion. A set of ARIMA models was compared with CLASO model in the forecast performance of the 24-h average PM 10 concentration on the next day. In general, CLASO model produced more accurate predictions of the maximum of the 24-h moving average of PM 10 concentration than the ARIMA models, although the latter showed a minor tendency to underpredict the results. The CLASO model solely requires to be built a historical database of the air quality parameter, an initial radius of classification and the learning factor. CLASO has demonstrated acceptable predictions of 24-h average PM 10 concentration by using exclusively regressive PM 10 concentrations. The forecasting capabilities of the model were found to be satisfactory compared to the classical models, demonstrating its potential application to the other major pollutants used in the Mexican air quality index.

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Modelado de la biodegradación en biorreactores de lodos de hidrocarburos totales del petróleo intemperizados en suelos y sedimentos (Biodegradation modeling of sludge bioreactors of total petroleum hydrocarbons weathering in soil and sediments)

The efficient characterization of the nonlinear dynamical response of kinetic molecular simulations is discussed. Following ideas originally proposed by Kevrekidis et al. [1, 2], one can empower molecular simulations as model-free equations and use them as a reference to perform bifurcation detection. Such a procedure requires the use of trajectories from the molecular simulation to generate low-order models (e.g. polynomial) that allow one to infer the location of a bifurcation. If such identification step can be performed robustly, a feedback control policy that drives the molecular simulation to the bifurcation point can be constructed. In previous work, the identification of the low-order model has been singled out as the key element in handling noise trajectories, such as those generated by low-resolution molecular simulations. Here, a procedure motivated by the use of Kalman Filter observers is proposed as a means to give robustness to the identification procedure. The potential of the technique to characterize the dynamical response of kinetic molecular simulations is illustrated using examples related to the CO oxidation over a catalytic surface.