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2018 | OriginalPaper | Buchkapitel

4. Concept for an Integrated Planning of Heat Flows in Production Systems

verfasst von : Denis Kurle

Erschienen in: Integrated Planning of Heat Flows in Production Systems

Verlag: Springer International Publishing

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Abstract

The introduction of an integrated planning concept for heat flows is motivated by the identified discrepancy between existing approaches and defined requirements. To close this discrepancy and further elaborate on this issue, the chapter first formulates different objectives, requirements and potential stakeholders for an appropriate concept (Sect. 4.1). Based on those objectives and requirements, a system perspective is derived which is capable of including and reflecting all relevant system levels (Sect. 4.2). Those aspects are broad together in the developed model concept which is embedded into a problem solving process, provides various models as well as their interactions to perform diverse evaluations, and enhance an interdisciplinary system understanding. The models are described regarding both the conceptual logic as well as considerations for implementation (Sect. 4.3). In more detail, the existing heat flows in production systems demonstrate the necessity to use an integrated approach in order to gain a sound system understanding. Thus, it is described how the different system entities can be modeled (Sect. 4.4). Different analysis and visualization methods per system level further facilitate the understanding of individual system entity’s performance (Sect. 4.5) as well as their impact and repercussions on other system levels. Those methods interact with the system entity models. The chapter closes with an application procedure explaining how the concept can be used in practice (Sect. 4.6).

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Vorheriges Kapitel State of Research

Nächstes Kapitel Exemplary Application of Concept

Those interests represent only a few examples implying that the interests are not limited those and may comprise other additional interests.

Figure 4.3 states not necessarily an all-embracing perspective since it omits a few aspects on purpose for reasons of clarity.

At this point waste heat is not yet further classified according to media types.

In this regard the machine type denotes a different process type such as die casting, hot stamping, heat treatment etc.

For instance to remain within the customer tact (see Sect. 2.1.3 for more information about different material flows).

The concept is not limited to these models and could be further extended to other machine type models.

Such a signal is sent from product agents which are explained in Sect. 4.4.3.

To prevent unnecessary machine setups from happening as a result from the flexible PPC and product logic described in Sect. 4.4.3.2 this state further triggers a control loop checking if any other product types also require this specific machine setup as a next machine or generally the process this machine belongs to.

Section 4.4.3 explains the functionality of buffers and further process chain related aspects.

The Weibull functions allow to specify appropriate scale and shape parameters (Birolini 2010), but in general any other probability distribution could be applied as well.

This procedure may not always be valid, since some failures require a restart of the machine or a removal of a potentially damaged product or batch.

See May and Schimek (2015) for more information about TPM.

At this point it is assumed that media demands are measured with a sampling rate of 1 Hz. For more detailed information regarding media measurements it is referred to Posselt (2016).

For example by energy value stream maps (EVSM), see Sect. 4.5.2.

See Sect. 4.4.3 for more information on the behavior of product agents.

However, this could be easily amended for example by using Eq. 4.27 as a simplification.

The remarks in this work consider the influence of cutting fluid on the machining process. However, the machining process can also be executed with minimal quantity lubrication (MQL) or even as a dry process whereas both options entail different repercussions on the process chain regarding the overall set up and media demands. More information can be found in Madanchi et al. (2015).

Of course this information can also be used as further input for other heating, ventilation and air conditioning (HVAC) model, for example as described in Schönemann (2017), Hesselbach (2012).

See description of

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MC.

This assumption corresponds to the used mass during the calculations of heat flows of the WP, where the temporal changes of the mass are neglected and considered as a mean value.

This also applies for all other machine type agent classes.

Knowing that simulation models are numerical models entailing a certain numerical error as opposed to exact analytic solutions.

The approach can also be extended to multiple compressors involving different control strategies (e.g. cascading overlapping pressure ranges or shared pressure ranges) to provide the required capacity and flexibility for compressed air supply as further explained in Bierbaum and Hüttner (2004) and Ruppelt (2003).

The general behavior can be expressed by the ideal gas equation (\(\frac{p\cdot V}{T}\) = constant) (Bierbaum and Hüttner 2004).

The leakage rates are computed according to the detailed explanations provided in Nayyar et al. (2000). As an estimation the average size and number of leakages per defined pipe length are specified. The overall distance between the compressor and the respective machines is then computed. The same applies for the influence of pipe-related pressure drops. The formulas for both aspects have been used in the model but are omitted at this point to streamline the CA model explanation.

See also the verification study provided by Thiede (2012) based on switching operations which has also been achieved by this model for the given example.

This yields \(\dot{W}_{ex,CA} = \left( 1-\frac{T_{amb}}{T_{CA,Compressor}}\right) \cdot {\eta _{Compressor}} \cdot \dot{Q}_{CA}\).

See Birolini (2010) for more information about the Weibull function.

An illustration of an exemplary steam system can be found in Spirax (2006), Thiede (2012).

An overview of different boiler types in industrial practice can be found in Effenberger (2000). Furthermore, superheaters may be used to further heat the steam up to achieve the required temperature and pressure of specific consumers. However, the proposed model only focuses on general modeling terms and omits the modeling of different specifications such as superheaters.

Here for heating as opposed to external cooling utilities for cooling via a cooling tower.

Similar to the compressor model, the approach can be further extended to involve multiple boilers with differing specifications.

Change in steam density is approximated by \(\rho _S = \frac{P_{BO,S}\cdot 10^5 \cdot M_W}{R\cdot T_{BO,S}}\) with \(T_{BO,S}\) stating the steam system pressure, \(M_W\) the molecular weight of water, R the ideal gas constant and \(T_{BO,S}\) the system steam temperature.

Similar approaches are proposed by Wischhusen (2005), Becker (2006), Leobner (2016).

The required heating power can be computed by using the time derivative of Eq. 2.1.

Steam is usually expended until it reaches the required temperature by using vapid coolers.

\(\dot{V}_{FW} = \dot{V}_{required} \cdot \frac{\left( T_{required} - T_{FW}\right) }{\left( T_{storage}-T_{FW}\right) }\).

The granularity of the model may be improved if necessary by adding more temperature segments in the same manner.

More information about that can be found in Sect. 4.4.7.

Literature generally differentiates between dry and wet cooling towers, whereas the later can be further subdivided into natural or mechanical draft (fan) cooling towers each involving different pros and cons. Furthermore, the cooling flow inside wet cooling towers can be categories into countercurrent or crossflow cooling. More detailed information can be found in Berliner (1975), Morvay and Gvozdenac (2008), Kröger (2004).

This transfer is being driven by the differences of the dry bulb temperatures and vapor pressures of the water contact area and unsaturated air (Morvay and Gvozdenac 2008; Lechner and Seume 2010).

The total pressure increase of a cooling tower also depends on further aspects such as the necking of the tower, the geometry of the fan rotor (Grundmann and Schönholtz 2013; Fitzner and Rakoczy 2008).

The additional curve at the bottom indicates the dynamic share of the total pressure increase (I) (Berliner 1975).

An increase or decrease in latent heat has no direct impact on the temperature (Fitzner and Rakoczy 2008).

A detailed description how to iteratively solve the involved differential equations can be found in Martin (2006).

\(\dot{m}_{E_{v}}\) states the evaporation loss and can be computed pursuant to Eq. 4.41. Due to the separation of elements and its associated energy and water flows, the equation for the evaporation loss is only described in the next paragraph but already used for this example to provide a reasoned justification for using Merkel’s equation from here on.

The amount of water losses depends on the design of the cooling tower as well as external influences such as wind speed and direction. More information and more detailed calculation methods can be found in Dahl and Luke (2013).

The thickening is defined as the ratio of the salinity of the cooling water and the salinity of the added fresh water. Typical values range between 3–4 since higher values lead to pollution problems of the system whereas lower values entail a higher fresh water supply (Fitzner and Rakoczy 2008).

The relation is derived according to the Richmannsche rules of mixtures to determine the temperature of two mixed media (Pfeifer and Schmiedel 1997).

However, the general approach is not limited to four CTs and can be extended to more CTs. Four CTs have been chosen in this work because during the course of several industry projects it became apparent that production system often include four or less connected CTs. If the plant comprises more CTs, they are often hooked up to an individual CT system which could be modeled separately in the same manner.

Other control strategies involving different hysteresis can be applied as well.

This work introduces four exemplary machine type models to generally demonstrate the differences in machine modeling (see Fig. 4.14), which is why \(K=4\) (see Sect. 4.4.1.2–A.1) in this case. Generally, the concept is not limited to four machine type models and could be further extended by other machine type models.

Processes (\(PR_j\)) explained in the first step denote the entirety of all existing processes in the production system. However, single production processes (\(PP_{l,n}\)) from a product specific production process order (\(PP_l\)) may only include the selected processes from \(PR_j\) to produce the specific product. Furthermore, process parameters may vary between different products.

The order of production processes for one product \(P_l\) can be expressed by \(PP_{l,n} \forall n \in \left\{ 1,\dots ,N_l\right\} \).

Therefore, the notation of the production processes per product (\(PP_l\)) can also be flexibly enhanced to \(PP_{p,l}\) for the entire order or to \(PP_{p,l,n}\) for single production processes from the order.

This work exemplarily integrates \(R=4\) different TBS modules. The approach however is not limited to that and can incorporate more TBS modules with multiple stations.

Machine, machine type as well as TBS agents and their corresponding classes have already been introduced in Sects. 4.4.1 and 4.4.2.

Because of the flexible agent structure in combination with the data input, all machines and TBS technologies can be placed based on a user specified MS Excel Userform. Thus, the system layout can be set up via MS Excel.

Further explanation regarding different types of product flows can be found in Sect. 2.1.3.

This means that jobs with later production starts are also created at a later point in time.

See Sect. 4.4.1.1 for more detailed information about the generic machine model.

See Kurle et al. (2016a) for a detailed explanation of the terms and objective functions.

Such cases depend on the selected objective functions of the jobs and its priorities. In addition to that, such re-allocation may only be suitable for rush or express jobs since it may involve a new product related set-up of the machine. Whereas other jobs following for example an ‘energy-efficient’ PPC strategy may not be influenced by longer waiting times unless the machine idle demand is not allocated to them.

It is a significant difference in simulation run time whether only 10-50 product agents are constantly being updated or a couple of thousands.

Those designs are often referred to as so called ‘superstructures’ which can be differentiated in regards to their level of abstraction ranging from aggregated models (e.g. the transhipment model for energy and mass balances (Papoulias and Grossmann 1983; Grossmann and Guillén-Gosálbez 2010)) over detailed superstructures (e.g. involving often non-linear cost optimization including invests (Yee 1990; Ciric and Floudas 1991; Kocis and Grossmann 1989) to very detailed sub-superstructures (e.g. for refined process internal modeling Smith and Pantelides 1995)).

Solving the models in a sequential order reduces the computational effort since the overall design synthesis is broken down into several sub-problems to be solved and fed to the next model. This implies that the different models are connected to each other (Furman and Sahinidis 2002).

Simultaneous mathematical models solve the trade-off between heat reuse, number and surface area of heat exchangers as well as costs and invest concurrently. This leads to mixed integer non linear programming (MINLP) as proposed by Yee (1990), Ciric and Floudas (1991). However, due to their structure (non-linear, non-convex, discrete) MINLP models take too long to be solved for industrial applications. In addition to that, Furman and Sahinidis (2001) proved that this type of problem is strictly NP hard indicating that the existence of a limited polynomial algorithm to exactly solve the problem is unlikely. This in return has increased the interest in a sequential solving order again (Grundersen 2000; Anantharaman et al. 2010).

Sometimes a third step to minimize the invests related to the size of the heat exchanger surface area and its impacts on the design is further used. This step involves non-linear behavior due to cost degression effects stating a non-convex problem which may include local optima. However, since chiefly operating costs and not primarily invest decisions are in the focus of this work, this third step has been omitted. Invest decisions are only considered indirectly since finding a design with minimum heat exchange units (results of step 2) will likely entail fewer associated costs as well. More information on this third step can be found in Floudas et al. (1986) and Ludwig (2012).

See Furman and Sahinidis (2002) for a comprehensive literature overview about HEN synthesis.

According to LfU (2003) \(\varDelta T_{min} = 10^\circ {\text {C}}\) states a reasonable minimum temperature difference to ensure a heat flow between the process flows and utilities.

An example of this procedure can be found in Ludwig (2012).

The heat integration literature is in some cases inconsistent concerning the use of the term heat flow, heat content and heat load. This work uses the term heat flow marked by \(\dot{Q}\) (Kemp 2011; Ludwig 2012).

See Sect. 2.2.4 for explanations regarding pinch points.

Although this model formulation applies only for one sub design, it can also be used for the overall design provided no further subdivisions or splittings of the design are intended.

The match restriction should comply with the previously set match restriction in the extended LP model to ensure consistent results.

Using an i7-processor with 2.7 GHz and 8GB memory.

Chen et al. (2014) further examine different possibilities to reduce the solving time for instance by using different upper bounds (\(U_{i,j}^{l}\)) as suggested by Gundersen et al. (1997).

This value can either directly be inserted or also calculated based on Eq. 2.1.

However, in case this assumption does not hold due to non-linear behavior of the process flow, it is further possible to linearize piecewise the process flow into several process flow segments, each with a different heat capacity flow (see Figure 2.9). Those process flow segments can also be specified in the data input.

A ‘1’ in this context sets the variable \(Q_{i,j,k}\) in the MILP model to ‘0’ implying no heat transfer.

The information of the LP model might still be interesting for example for very large problems involving more than 10 hot and cold process flows each, although the MILP might not be able to find a solution in a reasonable amount of time.

A direct calling of Matlab ^® through MS Excel may cause it to freeze until Matlab ^® has finished running the model(s). This also applies for the simple functions such as minimizing the MS Excel window.

Similar aspects have been addressed by Zahlan and Asfour (2015) in the context of compressed air systems to find favorable positions to reduce compressed air losses incurred by leakages.

Where \(z \in \{1,\ldots , N\}\) applies.

Another alternative flow could also be the material flow itself for example in case of liquid metal such as aluminum for die casting processes.

Thus, the temperature level inside a heat storage unit is supposed to be higher than the required temperature level. Otherwise additional heating for instance from a boiler is needed.

The differentiation of the temperature boundaries for example which is the minimum incoming and maximum outgoing temperature is controlled via the heat storage unit model itself and can be parametrized for diverse scenarios.

Since the cooling down between the different pipe parts is not linear (first parts are hotter and therefore entail higher heat losses than subsequent ones), more detailed heat loss calculation may further approximate the lost heat by computing the changes in temperature between the individual pipe parts. However, since the pipes are only a means for the method, a more detailed calculation has been waived in this work.

More information on determining optimal invest decision for technical installations can be found in Geldermann (2014).

For the sake of clarity and conciseness the implementation of this method is displayed on the main level but in a different section than for example Figure 4.42.

Factory segment count starts at ‘0’.

Another example can be found in Figure A.4 for the hard chrome plating machine model in the Appendix A.1.

This yields \(EVSM_{p,l,n} = PP_{p,l,n} \;\forall n \in \{1,\ldots , N_l\}\), see Sect. 4.4.3.1.

See Figure 5.12 for more information regarding the different influencing factors on a cooling tower system.

The generation of the German electricity mix in 2014 emitted 0.609

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See for example Sect. 4.5.2 for more information on this topic.

Visual Basic Application (VBA) represents an integrated script language in MS Excel.

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Titel: Concept for an Integrated Planning of Heat Flows in Production Systems
verfasst von: Denis Kurle
Verlag: Springer International Publishing
Buch: Integrated Planning of Heat Flows in Production Systems
Print ISBN: 978-3-319-70439-5

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

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-70440-1_4

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