Rainer Benning

Hybrid model of the fouling process in tubular heat exchangers for the dairy industry

A simulation model of the fouling behaviour of an arbitrary heat treatment device for milk, instancing tubular heat exchangers, is described. The final target is to examine new processing technologies in order to lengthen the processing time between two cleaning cycles whilst maintaining product quality. For this purpose a hybrid model of the fouling process in tubular heat exchangers was developed, combining deterministic differential equations with cognitive elements. The model allows the calculation of both processing and product behaviour throughout the whole heat exchanger. Pressure drop, temperature distribution and the chemical and biological effects of the heat treatment on the product can be calculated, so that the fouling behaviour and the expected product quality can be estimated. In order to validate the process model measured data from an industrial UHT plant were used. The calculated temperature profiles and pressure drops were in reasonable agreement with the experimental data. The deviation of calculated to measured values ranges between 10% and 20% for pressure, between 5% and 10% for temperature.

Initial studies of predicting flow fields with an ANN hybrid

Abstract Principal investigations of the application of a hybrid, consisting of an artificial neural network (ANN) and a conventional numerical method to predict an exemplary flow field are presented. Topics of the work were to show principle possibility of using ANN in fluid mechanics and to show the potential of integrating physical a priori knowledge into the training procedure. The flow fields for training and evaluation were generated by a numerical algorithm. Major result was that prediction of the flow field, including the existence of vortices in the bodies outflow at higher Reynolds numbers can be realized in much shorter times than necessary for numerical calculation. The results were obtained with training data, which represented totally different relations in their dynamical behavior, depending on the geometric location. Furthermore, physical a priori knowledge was included in the learning process with an obvious improvement of the hybrid models performance.

Basic aspects of phase changes under high pressure

Some substances of technological importance reveal phase change phenomena in the pressure and temperature range typically applied in biotechnology and food processing. For example, media with high molar volumes like edible oils and fats undergo liquid–solid phase transition at pressure increases up to several hundred megapascals. This article is concerned with theoretical considerations of the line of coexistence of solid and liquid phases in the pressure and temperature domain that corresponds to the phase boundary as a function of temperature and pressure. A universal model equation based on the equilibrium thermodynamics allowing prediction of the phase transition line of homogeneous media with high molar volume is presented. Approximate solutions of the model equation are discussed, which allow the phase boundary of substances with high molar volume to be described by a linear relation at least sequentially. The methods of experimental determination of the phase boundary under high pressure are presented and an attempt is made to validate the theoretical model with respect to the experimental data.

Towards system theory based adaptive strategies for high pressure bioprocesses

High pressure has been shown to offer unique possibilities for designing novel bioprocesses and generating new structures. However, high pressure processes have to be considered also as instantaneous but not homogeneous. As a consequence of this, thermofluiddynamical processes influence molecular and cellular mechanisms and kinetics. Thus, the urgent requirement for creating an adaptive process management, missing in the literature, is obvious. In this paper, a first attempt towards the development of adaptive process strategies is presented. It is shown that adaptive strategies have to include considerations regarding the interactions of all the components of the high-pressure system, variances due to the biological origin of the substances, and, particularly, methods for managing different data and knowledge sources. Furthermore, suggestions for the suitable design of adaptive process management strategies are presented and discussed. By means of a few examples it is elucidated that hybrid methods can contribute significantly in achieving the adaptability required. This paper is conceived as a brief composition to a plenary lecture presented at the XLIV EHPRG Meeting 2006 in Prague.

Process Design for Improved Fouling Behaviour in Dairy Heat Exchangers Using a Hybrid Modelling Approach

The main purpose of this study is the optimization of the heat exchange process in the dairy industry. A tool for simulating heat exchanger performance was fed with technical data of various heat exchanger confi gurations and process variables. Individual plant sections, like the preheater or ultra-high-temperature (UHT) heater, were simulated with modified variables, e.g. water temperatures or heat transfer areas. One modification, developed by simulation, was successfully implemented in a dairy plant. This proved the potential of the simulation software as a tool for design of new plants or the modification of existing plants. Performance rating was done by comparing pressure differences and overall heat transfer coefficients of the original setup and the modification. Improvements of simulated model running times for individual sections of up to 50% were achieved. Advantageous modifications were embedded in a simulation of the complete plant, additionally testing its effects upon milk quality. The main parameters for evaluation of plant performance were the pressure loss over the complete plant as well as the inlet temperature of the UHT heating water while milk quality primarily was rated by inactivation of thermophilic spores and loss of thiamine.

Principal Feasability Studies Using Neuro – Numerics for Prediction of Flow Fields

In this paper initial studies of the application of a hybrid model using artificial neural networks and conventional numerical methods to predict – as an example – twodimensional, isothermal, steady flow fields is presented. Main topics of the work were to show the principal possibility of using ANN in fluid mechanics and, additionally, to realize the potential of incorporation a priori knowledge of physical phenomena into the training procedure. For training, as well as for rating the prediction, flow fields, consisting of velocity, temperature and pressure, were generated by numerical simulation. Major result was that prediction of the flow and especially the existance of vortices in the bodies outflow depending on the Reynolds number can be realized with a much lesser time consumption than necessary for numerical calculation. Furthermore, a priori physical knowledge could be included in the learning process with an obvious improvement of the predicting ability of the hybrid model.

Mass transfer of organic substances in supercritical carbon dioxide

In this work special attention is paid on the direct visualization of the diffusion process of oil droplets in supercritical carbon dioxide as well as better characterization of the process by quantitatively evaluating the important parameter - the diffusion coefficients obtained with a shearing interferometer. Experiments are also to be carried out under microgravity to improve the experiment condition where the influence of gravity-driven convection that usually dominates the transport process is minimized.

Mass Transfer of Organic Substances in Supercritical Carbon Dioxide

In this work special attention is paid on the direct visualization of the diffusion process of oil droplets in supercritical carbon dioxide as well as a better characterization of the process by quantitative evaluation of the diffusion coefficients obtained with a shearing interferometer. Experiments are also to be carried out under microgravity in to improve the experiment condition where the influence of gravity-driven convection that usually dominates the transport process is minimized.

Optimierung von Prozessketten der Fleischverarbeitung auf der Grundlage von Referenz‐Petri‐Netzen

rithmus aus diesen Prozessschritten eine Fliesbildsuperstruktur. In der dritten Phase wird die Superstruktur mit einem hybriden evolutionaren Algorithmus durchsucht und die daraus generierten Alternativen im Bereich unsicherer Parameter untersucht. Alternativen mit den grosten Herstellungskosten werden aus den nachfolgenden Bewertungen ausgeschlossen. Bei Uberschneidung der Prozesskostenintervalle werden die signifikanten Einflussgrosen auf die Varianz der Kostenfunktion bestimmt. Basierend auf den signifikanten Unsicherheiten werden Versuchsplane erstellt, aus denen sich neue Modelle oder Datenpunkte ermitteln lassen, die bei Wiederholung der drei Phasen zu scharferen Grenzen der Kostenintervalle fuhren. So wird eine standige Wechselwirkung zwischen Experiment, Modell und Kostenbewertung erreicht.

Detection of Foreign Bodies in Closed Food Containers Supported With Fluid-Solid-Interaction Analysis

The unwanted inclusions in food and beverages pose a threat to both consumer health and the business, including producer image and liability. Detection of such inclusions, in particular metal and glass particles of millimetre size, is an important element of quality control in such industries. Specific solutions usually are limited in their detection range or are investment and space intensive. The presented project investigates a principally new detection method for foreign particles in fluid media of different densities and transparencies. Proof of concept is provided for the important case of glass containers, but the method is applicable to other materials.© 2009 ASME