Modeling and Identification of ABE Fermentation Processes

[Resumen] Se investigan el modelado y la identificación de procesos de fermentación Acetona-Butanol-Etanol (ABE). El enfoque tradicional intenta ajustar los parametros del modelo mediante una optimización para que las simulacines coincidan con los datos experimentales. Estas optimizaciones a menudo tardan mucho y no siempre consiguen un buen ajuste por la necesidad de imponer una estructura de modelo fijo y por la no-convexidad de la función de coste resultante. Se presenta un enfoque que divide el problema en unos sub-problemas que se pueden resolver de forma más efectiva y que elige el modelo del crecimiento de celulas libremente usando ALAMO (Automatic Learning of Algebraic MOdels). Finalmente, se implementa el algoritmo y realiza una identificación con datos reales y se com- prueban los resultados mediante una validación.[Abstract] The modeling and parameter identification of an Acetone-Butanol-Ethanol (ABE) fermentation process is investigated. The traditional approach tries to adjust the model parameters by means of an optimization such that the simulations fit the experimental data. These optimizations often take a lot of time and do not achieve good fits due to the necessity of imposing a xed model structure and due to the non-convexity of the resulting cost function. A different approach, which divides the big problem into smaller and more efficiently solvable subproblems, is presented. Its advantage is that it determines a model of the cellular growth term on the go using ALAMO (Automatic Learning of Algebraic MOdels) and thus others more degrees of freedom. Lastly, the algorithm is implemented, tested and validated with real data.Agradecemos los datos experimentales al departamento de Ingeniería química de la Universidad de Valladolid y al proyecto DPI2015-70975-P (MINECO/FEDER)https://doi.org/10.17979/spudc.978849749808

Possibilistic calculus as a conservative counterpart to probabilistic calculus

In this contribution, we revisit Zadeh's Extension Principle in the context of imprecise probabilities and present two simple modifications to obtain meaningful results when using possibilistic calculus to propagate credal sets of probability distributions through models. It is demonstrated how these results facilitate the possibilistic solution of two benchmark problems in uncertainty quantification

On data-based estimation of possibility distributions

In this paper, we show how a possibilistic description of uncertainty arises very naturally in statistical data analysis. In combination with recent results in inverse uncertainty propagation and the consistent aggregation of marginal possibility distributions, this estimation procedure enables a very general approach to possibilistic identification problems in the framework of imprecise probabilities, i.e. the non-parametric estimation of possibility distributions of uncertain variables from data with a clear interpretation

On the solution of forward and inverse problems in possibilistic uncertainty quantification for dynamical systems

In this contribution, we adress an apparent lack of methods for the robust analysis of dynamical systems when neither a precise statistical nor an entirely epistemic description of the present uncertainties is possible. Relying on recent results of possibilistic calculus, we revisit standard prediction and filtering problems and show how these may be solved in a numerically exact way

9th Vienna International Conference on Mathematical Modelling (MATHMOD 2018)

Producción CientíficaThis paper presents a methodology for developing grey models of process systems, that is, models that, being based on fundamental principles and laws of nature, combine them with sub-models obtained from experimental data. The method follows two steps: the first one takes advantage of what is known, while the second uses the data and mixed-integer optimization algorithms to identify the structure and parameters of the remaining parts of the model. The method is illustrated in a challenging biotechnological process: the Acetone-Butanol-Ethanol (ABE) fermentation process.MINECO/FEDER Grant DPI2015-70975 (INOPTCON)EU H2020-SPIRE Grant Agreement nº 723575 (CoPro

Very high energy gamma-ray observation of the peculiar transient event Swift J1644+57 with the MAGIC telescopes and AGILE

Context. On March 28, 2011, the BAT instrument on board the Swift satellite detected a new transient event that in the very beginning was classified as a gamma ray burst (GRB). However, the unusual X-ray flaring activity observed from a few hours up to days after the onset of the event made a different nature seem to be more likely. The long-lasting activity in the X-ray band, followed by a delayed brightening of the source in infrared and radio activity, suggested that it is better interpreted as a tidal disruption event that triggered a dormant black hole in the nucleus of the host galaxy and generated an outflowing jet of relativistic matter. Aims. Detecting a very high energy emission component from such a peculiar object would be enable us to constrain the dynamic of the emission processes and the jet model by providing information on the Doppler factor of the relativistic ejecta. Methods. The MAGIC telescopes observed the peculiar source Swift J1644+57 during the flaring phase, searching for gamma-ray emission at very-high energy (VHE, E > 100 GeV), starting observations nearly 2.5 days after the trigger time. MAGIC collected a total of 28 h of data during 12 nights. The source was observed in wobble mode during dark time at a mean zenith angle of 35 degrees. Data were reduced using a new image-cleaning algorithm, the so-called sum-cleaning, which guarantees a better noise suppression and a lower energy threshold than the standard analysis procedure. Results. No clear evidence for emission above the energy threshold of 100 GeV was found. MAGIC observations permit one to constrain the emission from the source down to 100 GeV, which favors models that explain the observed lower energy variable emission. Data analysis of simultaneous observations from AGILE, Fermi and VERITAS also provide negative detection, which additionally constrain the self-Compton emission component