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2018 | OriginalPaper | Chapter

2. Heat Flows in Production Systems and its Modeling and Simulation

Author : Denis Kurle

Published in: Integrated Planning of Heat Flows in Production Systems

Publisher: Springer International Publishing

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Abstract

This chapter introduces the theoretical foundation of the book by describing relevant terms and concepts of production systems in general and heat flows in particular (Sect. 2.1). It further presents two modeling methods to capture the behavior of heat flows in production systems and to provide design related options for improvements (Sect. 2.2). The chapter ends with a summary of preliminary findings concerning heat flows in production systems and its modeling methods (Sect. 2.3).

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Deutsches Institut für Normung (DIN).

More information on PM can be found in Westkämper et al. (2006), Herrmann (2010), Schuh and Schmidt (2014), Wiendahl et al. (2015).

More information on informational flows can be found for example in Posselt (2016).

For example due to carry-off of cutting fluid and evaporation extracted through the exhaust air filtration system.

Comprehensive explanations about the different energy terms and their relation to each other can be found in Baehr and Kabelac (2012) and Weigand et al. (2013).

For more information on energy conversions in factories it is referred to Posselt (2016) providing a comprehensive overview on this topic.

Baehr and Kabelac (2012) provide comprehensive explanations regarding the term exergy.

One example of a variation of these mechanisms is known as heat transition. Heat transition applies for example when two fluids are separated from each other by a solid material. In this case, heat transition sums up the convection from the fluid 1 to the wall, the heat conduction through the wall and the subsequent convection from the wall to fluid 2 which are described individually in this work. More information on heat conduction can be found in Böckh and Wetzel (2015).

It can be distinguished between free and forced convection. Free convection considers flows caused by changes in temperature and thus also density in the fluid whereas forced convection often results from changes in pressure (Marek and Nitsche 2011; Böckh and Wetzel 2015).

A detailed explanation of \(\alpha _{C}\) can be found in Cerbe and Wilhelms (2011).

With \(\sigma = 5.67 \cdot 10^{-8}\,[\frac{W}{m^2 K^4}]\). More information regarding different influencing factors on Eq. 2.5 can be found in Marek and Nitsche (2011).

In that regard, heat often only depends on the type of phase change but not on the temperature, which is why this type of (waste) heat is also referred to as latent heat.

\(\varDelta H_e = \kappa _1 + \kappa _2 \cdot T_{fluid}\).

\(\kappa _1 = 2502000\,[\frac{J}{kg}]\).

\(\kappa _2 = -2430\,[\frac{J}{kg\cdot K}]\).

See Sect. 2.1.2 for more information.

In Monte Carlo experiments the simulation model is run repeatedly with changed variables in each run, which are often generated randomly.

The terms multi-level and multi-scale can be understood as synonymous, whereas this work uses the term multi-level due to its stronger emphasis on the spatial aspects in terms of simulation modeling.

A comprehensive overview of the different ares of applications can be found in Klemeš (2013).

Although in thermodynamics the enthalpy is a function dependent on temperature, pressure and the composition of the process flow, HI simplifies this relation by assuming a consistent composition of the process and mass flow as well as constant pressure and heat capacity.

In case the composition of a process flow changes (e.g. through condensation or evaporation) and/or an inconstant CP exists, the process flow can be piece-wise linearized by defining new, additional process flows often referred to as process flow segments (Kemp 2011; Klemeš 2013).

A more detailed analysis of the economical effect can be found in Shenoy (1995), Kemp (2011) or Ludwig (2012).

The gray area indicates only parts of the heat recovery potential which is relevant for the usage of external utilities and their temperature level. The entire potential for heat recovery is shown by the CC, for example in Figure 2.10.

Further types of formal models including also non-linearities as well as comprehensive explanations of them can be found in Domschke et al. (2015), Grossmann et al. (1999).

The Branch-and-Bound method has its name because it sets up decision trees with various branches leading to new sub-problems which are solved and evaluated based on the bounds (Grossmann et al. 1999).

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Title: Heat Flows in Production Systems and its Modeling and Simulation
Author: Denis Kurle
Publisher: Springer International Publishing
Book: Integrated Planning of Heat Flows in Production Systems
Print ISBN: 978-3-319-70439-5

Electronic ISBN: 978-3-319-70440-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-319-70440-1_2

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