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@inproceedings{faucris.117766484,
abstract = {In the production of cooked sausages a critical step for product quality is the cutting process, where the comminuting and mixing of meat, fat, ice and spices are carried out. These processes take usually place in bowl cutters, which main control parameters are the working time, knife geometry (shape and sharpness) and rotational velocities of the knives and the bowl. The choice of the geometry and sharpness of the knives influences not only the meat matrix properties (mechanical, rheological, etc.) and, as a consequence, the sensory value of the sausages (size of connective tissue particles, water binding, etc.), but also the energetic demand for the production. However, the cutting process proves to be understood only fragmentarily due to the complex colloid chemical and mechanical behavior of the product. This is documented on the one hand by numerous knife types on the market, extremely empirical approach during determination of geometry and process parameters in practice as well as, on the other hand, by contradictory statements and explanation approaches of observed phenomena present in literature. The present contribution applies numerical simulations to analyze thermo fluid mechanical phenomena, e.g. shear stresses, during the cutting process of the non-Newtonian meat matrix. Combining these results with selected experimental investigations from literature, e.g. sensory properties, knife geometry, velocity of the knife and bowl, improvements of the cutting and mixing process are proposed using cognitive algorithms (Artificial neural networks) aiming at an optimization regarding energy and time demand and product quality. © 2010 American Institute of Physics.},
author = {Diez Robles, Lucia and Rauh, Cornelia and Delgado, Antonio},
booktitle = {AIP Conference Proceedings},
date = {2010-09-19/2010-09-25},
doi = {10.1063/1.3498159},
faupublication = {yes},
keywords = {Meat Matrices; Numerical Simulation; Process Design},
note = {UnivIS-Import:2015-04-16:Pub.2010.tech.ITC.stmmec.analys{\_}8},
pages = {1672-1675},
peerreviewed = {unknown},
publisher = {American Institute of Physics},
title = {{Analysis} and optimization of the production process of cooked sausage meat matrices},
venue = {Rhodos, Griechenland},
volume = {1281},
year = {2010}
}
@incollection{faucris.215906699,
abstract = {
Artificial neural networks
(ANNs) represent a mathematical approach that permits to mimic the
learning process occurring in the brain of mammalians. The present
article presents a short overview on ANNs. The basic need of the
employment of ANNs in up-to-date food management systems, the theoretical background, and design of ANNs are described shortly. Examples of ANNs in food technology applications are also presented. The last section discusses current and future ANN methods in food technology.
},
author = {Rauh, Cornelia and Delgado, Antonio and Park, Jinyoung and Kim, You Jin and Groß, Frauke and Diez Robles, Lucia},
booktitle = {Reference Module in Food Science},
doi = {10.1016/B978-0-08-100596-5.03125-5},
faupublication = {yes},
keywords = {Artificial neural networks (ANNs); Food technology; Fuzzy logic (FL); Hybrid methods},
peerreviewed = {unknown},
publisher = {Elsevier Inc.},
title = {{Artificial} {Neural} {Networks}: {Applications} in {Food} {Processing}},
year = {2016}
}
@article{faucris.117743384,
abstract = {In the present contribution experimental and numerical investigations of multiphase flow in a sequencing batch reactor (SBR) are presented. In the bioreactor the formation and growth of granular activated sludge (GAS) with diameter up to 5 mm occurs. In order to experimentally analyse multiphase flow patterns in a mixture of water, air and granules in the SBR, optical in situ techniques are applied. Particle image velocimetry (PIV) and particle tracking velocimetry (PTV) are employed to observe the velocity fields of fluid and granules. For the three-dimensional numerical simulation of the flow problem the Euler-Euler approach is used. The comparison of experimental and numerical results shows a lot of similarities. The characteristic flow patterns can be observed in three zones of the SBR. It can be shown that effect of normal strain rate up to over(ε{lunate}, ̇) = 15 s- 1 and shear strain rate up to over(γ, ̇) = 15 s- 1, besides biochemical activities have a major influence on the formation, shape and size of the granules in the SBR under aerobic conditions. © 2007 Elsevier Ltd. All rights reserved.},
author = {Zima-Kulisiewicz, Bogumila Ewelina and Diez Robles, Lucia and Kowalczyk, Wojciech and Hartmann, Christoph and Delgado, Antonio},
doi = {10.1016/j.ces.2007.09.048},
faupublication = {yes},
journal = {Chemical Engineering Science},
keywords = {Fluid mechanics; Multiphase reactors; Particle image velocimetry (PIV); Particle tracking velocimetry (PTV); Simulation; Visualilsation},
note = {UnivIS-Import:2015-04-14:Pub.2008.tech.ITC.stmmec.bioflu},
pages = {599-608},
peerreviewed = {Yes},
title = {{Biofluid} mechanical investigations in {Sequencing} {Batch} {Reactor} ({SBR})},
url = {http://www.sciencedirect.com/science/article/pii/S0009250907007695},
volume = {63},
year = {2008}
}
@incollection{faucris.122761584,
address = {Bonn},
author = {Delgado, Antonio and Rauh, Cornelia and Gladbach, Katharina and Anderl, Daniela and Diez Robles, Lucia and Rüde, Ulrich and Bauer, Martin},
booktitle = {Proteinschäume in der Lebensmittelproduktion: Mechanismenaufklärung, Modellierung und Simulation},
faupublication = {yes},
isbn = {978-3-925032-53-0},
note = {UnivIS-Import:2015-04-20:Pub.2014.tech.IMMD.lsinfs.experi},
pages = {103-121},
peerreviewed = {unknown},
publisher = {FEI},
title = {{Experimentell} validierte {Simulation} strömungsinduzierter {Effekte} auf {Proteinschäume} mittels {Lattice}-{Boltzmann}-{Methoden}},
url = {http://www.fei-bonn.de/download/abschlusspublikation-proteinschaeume.pdf},
year = {2014}
}
@article{faucris.117703564,
abstract = {Numerical simulations of three-phase flows are facing the challenge that their mathematical models include a lot of assumptions and the equation systems often deliver controversial solutions. The object of this study is the improvement of numerical simulations of a three-phase (solid, gas, liquid) flow according to the four-way coupling Eulerian-Eulerian frame. Following the strategy of incorporating a priori knowledge in a system, initial velocity information achieved by several experimental and numerical techniques is implemented in the numerical simulations. Particle image velocimetry (PIV) data are employed in a numeroexperimental hybrid and artificial neural network (ANN) data in numeroneuronal and neuroexperimental hybrids, where the ANNs are trained with numerical or PIV data, respectively. The employment of the three presented hybrid methods affords better convergence of the numerical simulations, delivers more accurate numerical results and enables saving of computational time, thus, more precise information about the behaviour of the fluid mechanical system is faster achieved. © 2011 The Society of Powder Technology Japan.},
author = {Diez Robles, Lucia and Rauh, Cornelia and Delgado, Antonio},
doi = {10.1016/j.apt.2011.02.007},
faupublication = {yes},
journal = {Advanced Powder Technology},
keywords = {Hybrid method; Mathematical modelling; Numerical simulation; Three-phase flow},
note = {UnivIS-Import:2015-03-09:Pub.2011.tech.ITC.stmmec.improv{\_}4},
pages = {277-283},
peerreviewed = {Yes},
title = {{Improvement} of three-phase flow numerical simulations by means of novel hybrid methods},
url = {http://www.sciencedirect.com/science/article/pii/S0921883111000252},
volume = {22},
year = {2011}
}
@article{faucris.117738984,
abstract = {An analysis of a sequencing batch reactor (SBR) from a fluid mechanical point of view, used in the cultivation of granular activated sludge (GAS), is carried out by experimental, computational fluid dynamics (CFD) and artificial neuronal networks (ANN) methods. Due to the complexity of the three-phase problem, new hybrid methods are developed in order to shorten the calculation time and to improve the numerical prediction. In the numeroexperimental hybrid, experimentally obtained velocities from particle image velocimetry (PIV) method are implemented as initial conditions for the numerical simulation. This operation causes improvement of multiphase flow results and save CPU time of about 40% in comparison with the standard calculation. In the neuronumerical hybrid, numerically obtained results and process parameters are employed for training of an ANN. With the trained ANN, several geometrical and physical entities are calculated for a range of SBRs. In this case, the acceleration, in the prediction of several process parameters, reaches the factor 1.0 E + 05. © 2007 Elsevier Ltd. All rights reserved.},
author = {Diez Robles, Lucia and Zima-Kulisiewicz, Bogumila Ewelina and Kowalczyk, Wojciech and Delgado, Antonio},
doi = {10.1016/j.ces.2006.12.005},
faupublication = {yes},
journal = {Chemical Engineering Science},
keywords = {Artificial neuronal network (ANN); Bioreactors; Computational fluid dynamics (CFD); Multiphase flow; Simulation; Turbulence},
note = {UnivIS-Import:2015-04-14:Pub.2007.tech.ITC.stmmec.invest{\_}2},
pages = {1803-1813},
peerreviewed = {Yes},
title = {{Investigation} of multiphase flow in sequencing batch reactor ({SBR}) by means of hybrid methods},
url = {http://www.sciencedirect.com/science/article/pii/S0009250906007810},
volume = {62},
year = {2007}
}
@article{faucris.115735444,
abstract = {Fluid dynamic investigations of multiphase flow (fluid, air, granules) in a sequencing batch reactor (SBR) are presented. SBR can be considered as an attractive technology for cultivation of granular activated sludge (GAS). Granulation is a complicated process and its mechanism is not fully understood yet. Many factors influence the formation and structure of aerobic granular sludge in a bioreactor. Extracellular polymer substances (EPS) and superficial gas velocity (SGV) play a crucial role for granules formation. Additionally, it is supposed that EPS production is stimulated by mechanical forces. It is also assumed that hydrodynamic effects have a major influence on the formation, shape and size of GAS in SBR under aerobic condition. However, the influence of stress on granulation is poorly investigated. Thus, in the present paper, fluid dynamic investigations of multiphase flow in a SBR, particularly effect of normal and shear strain, are reported. In order to analyse multiphase flow in the SBR, optical in-situ techniques with particle image velocimetry (PIV) and particle tracking velocimetry (PTV) are implemented. Obtained results show a characteristic flow pattern in a SBR. It is pointed out that additional effects like particle-wall collisions, inter particle collisions, erosion can also affect significantly granules formation. © IWA Publishing 2007.},
author = {Zima-Kulisiewicz, Bogumila Ewelina and Diez Robles, Lucia and Kowalczyk, Wojciech and Delgado, Antonio},
doi = {10.2166/wst.2007.253},
faupublication = {yes},
journal = {Water Science and Technology},
keywords = {Granular activated sludge; Mechanical forces; Particle image velocimetry; Particle tracking velocimetry; Sequencing batch reactor},
note = {UnivIS-Import:2015-04-14:Pub.2007.tech.ITC.stmmec.sequen},
pages = {151-158},
peerreviewed = {Yes},
title = {{Sequencing} batch reactor ({SBR}) as optimal method for production of granular activated sludge ({GAS}) - fluid dynamic investigations},
url = {http://www.iwaponline.com/wst/05508/wst055080151.htm},
volume = {55},
year = {2007}
}
@article{faucris.117238704,
author = {Diez Robles, Lucia and Rauh, Cornelia and Delgado, Antonio},
doi = {10.1016/j.apt.2011.02.007},
faupublication = {yes},
journal = {Advanced Powder Technology},
note = {UnivIS-Import:2015-03-09:Pub.2010.tech.ITC.stmmec.threep},
pages = {-},
peerreviewed = {Yes},
title = {{Three}-phase flow numerical prediction by means of novel hybrid methods},
volume = {-},
year = {2011}
}