Browsing by Author "Perez Correa, J. Ricardo"

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    Modeling temperature gradients in wine fermentation tanks
    (ELSEVIER SCI LTD, 2010) Zenteno, M. Isabel; Perez Correa, J. Ricardo; Gelmi, Claudio A.; Agosin, Eduardo
    Extreme temperatures are common in large wine fermentation tanks. If not controlled properly, they can lead to problematic fermentations. Thus, efficient cooling and automatic control systems must be designed. However, it is rather difficult to design and implement effective cooling and control systems without adequate models able to reproduce the complex dynamic behavior observed in large fermentors. Therefore, we developed a compartmental dynamic mass-and-energy-balance model able to simulate temperature and concentration gradients in large wine fermentation tanks. This paper presents the model, along with all its parameters. It discusses simulations of temperature, alcohol content, density, biomass, and sugar concentrations. Finally, it presents a sensitivity analysis of the must temperature dynamics. The model reproduced reasonably well the values of the observed variables, including the most critical one: must temperature (with an absolute mean error of 1.4 degrees C). After proper calibration, it can be used to design control strategies for cooling in large wine fermentation tanks. Our research efforts will be directed in designing such control strategies. (C) 2010 Elsevier Ltd. All rights reserved.
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    Realistic model of a solid-substrate fermentation packed-bed pilot bioreactor
    (ELSEVIER SCI LTD, 2007) Fernandez Fernandez, Mario; Perez Correa, J. Ricardo
    For any given large-scale solid substrate fermentation (SSF) bioreactor, to assess how well a control system will work in practice requires the most realistic model possible. This model needs to account fully for complicated dynamic reactor behaviour and, in addition, has to include a specific noise model that is capable of reproducing the disturbances observed in SSF bioreactor measurements. In this work, noisy data collected historically from SSF pilot scale fermentations was used to develop such a model. Applying standard signal processing techniques, each measured variable was separated into deterministic and noise signals. Deterministic signals were used to calibrate a previously developed phenomenological model of the bioreactor. Noise signals were used to construct a realistic noise model for each measured variable in turn. Finally, the two models were combined to attain simulations that compared well with real measurements. This integrated model will provide realistic simulations that will prove useful in the design of effective control systems for intermittently mixed SSF bioreactors. (c) 2006 Elsevier Ltd. All rights reserved.
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    Soft-sensor for on-line estimation of ethanol concentrations in wine stills
    (ELSEVIER SCI LTD, 2008) Osorio, Daniel; Perez Correa, J. Ricardo; Agosin, Eduardo; Cabrera, Miguel
    Batch distillation is a traditional and widely-used technique to produce Pisco brandy, a young spirit made from Muscat wine. It is necessary to track a given ethanol composition in the distillate in order to obtain a reproducible spirit with a desired aromatic profile. The use of multiple ethanol sensors represents a considerable cost, which prevents many distilleries from adopting this technology. Aiming to provide practical and affordable industrial-scale distillation control technology, we developed a soft-sensor to estimate distillate ethanol concentration on-line based on four temperature measurements in the still. The soft-sensor, calibrated with laboratory and industrial experimental data, consisted of an Artificial Neural Network and involved simple data pre-processing procedures. Simplicity and good performance were the metrics adopted for testing different algorithms and network structures. Returning mean prediction errors of +/- 0.6% v/v with laboratory scale distillations and +/- 1.6% v/v in industrial trials, the resulting accuracy of the soft-sensor is sufficient to improve standard practice and reproducibility. (c) 2008 Elsevier Ltd. All rights reserved.
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    Ultrasound based measurements of sugar and ethanol concentrations in hydroalcoholic solutions
    (ELSEVIER SCI LTD, 2008) Sint Jan, Michael Van; Guarini, Marcelo; Guesalaga, Andrs; Perez Correa, J. Ricardo; Vargas, Yolanda
    Industrial automation is useful to reduce production costs and improve product quality. The wine industry has been increasingly adopting industrial automation seeking these objectives. In Chile, wineries have been lagging behind in embracing new technologies to control the fermentation process. In most cases, enologists decide control actions (cooling, heating, pumping over) based on off line periodic measurements. In this work we explore the applicability of ultrasound to measure the sugar and alcohol concentrations of hydroalcoholic solutions mimicking fermenting musts. Additionally, implementation issues such as the attenuation effect of bubbles and tank curvature are analyzed. We show that working at two sufficiently distinct frequencies, we can measure sugar and alcohol content simultaneously. Measurement resolution achieved for the wave time of travel translates to a better than 0.02% for both concentrations, depending on the container size. (c) 2007 Elsevier Ltd. All rights reserved,
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    Using data mining techniques to predict industrial wine problem fermentations
    (ELSEVIER SCI LTD, 2007) Urtubia, Alejandra; Perez Correa, J. Ricardo; Soto, Alvaro; Pszczolkowski, Philippo
    Winemakers currently lack the tools to identify early signs of undesirable fermentation behavior and so are unable to take possible mitigating actions. Data collected from tracking 24 industrial fermentations of Cabernet sauvignon were used in this study to explore how useful is data mining to detect anomalous behaviors in advance. A database held periodic measurements of 29 components that included sugar, alcohols, organic acids and amino acids. Owing to the scale of the problem, we used a two-stage classification procedure. First PCA was used to reduce system dimensionality while preserving metabolite interaction information. Cluster analysis (K-Means) was then performed on the lower-dimensioned system to group fermentations into clusters of similar behavior. Numerous classifications were explored depending on the data used. Initially data from just the first three days were assessed, and then the entire data set was used. Information from the first three days' fermentation behavior provides important clues about the final classification. We also found a strong association between problematic fermentations and specific patterns found by the data mining tools. In short, data from the first three days contain sufficient information to establish the likelihood of a fermentation finishing normally. Results from this study are most encouraging. Data from many more fermentations and of different varieties needs to be collected, however, to develop a reliable and more broadly applicable diagnostic tool. (c) 2006 Elsevier Ltd. All rights reserved.