EP0643271A1 - Process for controlling the temperature and the humidity of the air in rooms with an air conditioning installation - Google Patents

Abstract

In order to develop a method for controlling the temperature and the humidity of air in rooms by means of an air conditioning installation to the effect that universal optimisation is possible given various stipulated target functions, the process characteristic (process cycle) is determined such that a) a selected target function is fulfilled, it being optionally possible to fix the scale of evaluation differently for each individual unit, b) boundary conditions, which limit the states of the air upon exit from the individual units with respect to relevant quantities, and which restrict the use of units with respect to characteristic quantities, are prescribed, c) the technically physically realisable changes in state in the individual units are determined, d) the overall process characteristic is realised by juxtaposing the changes in state in the individual units, and after the target function has been reached the control results are fixed, by giving the parameters of state of the air relative to the respective unit, in such a way that e) thereafter the operation of each unit can be controlled or regulated individually or in a fashion combined into groups, in which latter case the optimised air exit parameters can be used as desired values. <IMAGE>

Description

The invention relates to a method for regulating the temperature and humidity of air in rooms by means of a ventilation system, which consists of a number of individual units for carrying out changes in air status and uses outside air and exhaust air as input streams, the control of the individual air conditioning units and the process control taking place in this way that the supply air brings about a predetermined room air condition, whereby a target function is fulfilled, namely that the target condition is achieved with a minimum of energy expenditure, and where boundary conditions are specified with regard to the air conditions that can be set by the individual units and the realizable state changes are determined, as described in DE 34 39 288 A1 is known.

Air conditioning in modern systems is carried out according to control strategies that include partial energy optimization. The various forms of energy are only calorically assessed, and approved areas of the indoor air condition can only be used insufficiently.

In order to specify a method for regulating temperature and humidity in rooms, with which an air treatment system containing several air conditioning units is operated for every state of the outside air with the lowest possible energy consumption, a method has already been specified for an air treatment system which as air conditioning units has at least one mixing chamber for mixing the outside air with exhaust air from the rooms, a heating unit, a cooling unit and a humidifier, and with the current air conditions Providing sensors for the exhaust air, the outside and the supply air, whereby a partial combination of air conditioning units is operated based on the stored ventilation and air conditioning operating parameters of the air conditioning units, the current state of the outside air and the exhaust air, the target state of the supply air and the minimum volume fraction of the outside air in the supply air , with which the target state of the supply air can be reached, whereby it is provided that, based on the stored ventilation and air conditioning operating parameters of the air conditioning units, the current state of the exhaust air, the target state of the supply air and the minimum volume fraction of the outside air in the supply air, limits of outside air status areas are calculated, which In each case, the most energetically advantageous sub-combination of the air conditioning units and their operating mode are assigned, with which the desired state of the supply air can be achieved, that the belonging of the prevailing outside air condition to an outside air condition area is averaged and that the partial combination assigned to this outside air status area, the air conditioning units and their operating mode are released. It can be provided that a separate control circuit is assigned to each air conditioning unit, the individual controllers being supplied with the current input variables and not depending on the respectively optimal operating mode, which results from the assignment of the outside air status point to one of the outside air status ranges required air conditioning units are switched off in a controlled manner (DE 34 39 288 A1).

It has already been proposed to consider the overall process course as a sequence of the individual state changes in the individual units, the control parameters for the individual units being able to be determined in order to achieve the most energy-saving operating mode (energy saving in ventilation and air conditioning systems with the aid of improved automation concepts by P. Bork in automation technology Praxis, 28. Jhg, issue 4/1986, p.184-190).

It is also already known to specify a status range for the indoor air instead of a fixed value (DD 295 706 A5).

• 1. Das Auflösen des Gesamtprozesses in einzelne Regelkreise bzw. bei Beachten der Sequenzen in Regelkreiskomplexe gestattet nur die Optimierung von Prozeßteilen (Fig.2). Eine Summe optimaler Teilprozesse führt aber nicht zwangsläufig zu einem Optimum des Gesamtprozesses.
• 2. Die Vorgabe der Regelstrategie ist vom investierten Zeitaufwand sowie von der Intelligenz des Planers abhängig; das Ergebnis - die Betriebskosten - somit stark subjektiv beeinflußt. Bei der Vielzahl der im Jahresgang zu erwartenden Luftzustandsvarianten muß die Strategie der Prozeßführung ohnehin von Vereinfachungen - z.B. von zusammengefaßten Zustandsbereichen ausgehen (Fig.3).
• 3. Üblicherweise können die behaglichen oder technologisch erforderlichen Raumluftzustände in relativ großen Bereichen schwanken. Dies betrifft die Raumlufttemperatur und in ganz besonderem Maße die Raumluftfeuchte. Soweit für diesen Fall bisher überhaupt Regelungsstrategien entwickelt wurden, gehen sie von sehr vereinfachten Vorstellungen aus. Eine typische, oft nicht zutreffende Annahme legt den "optimalen" Raumpunkt stets auf den Rand des zulässigen Bereiches (Fig.4).
• 4. Die Strategien gehen von kontinuierlichen Zielfunktionen - z.B. der Minimierung des Energieeinsatzes - aus. Eine aggregatespezifische Bewertung der Energie ist üblicherweise nicht möglich. Somit können Strategien, die sich lediglich auf Enthalpiedifferenzen gründen, die evtl. verfügbare Umweltenergie nicht bevorzugt nützen (Fig.5).

In the center of an air conditioning system, the air flow is subjected to a targeted change in state. The most general case is that the outside air AU from the state (tAU; xAU) and exhaust air from the room AB from the state (tAB; xAB) flows in and the supply air CL of the state (tZU; xZU) entering the room is to be processed ( Fig. 8). The technically possible changes in state are linked to the units used. A conventional air heater can only heat the air along an isohygren (x = const), an adiabatic scrubber can only humidify along the wet-bulb isotherms (tf = const) etc. The selection of the units used, their sequence, the intended control loops and their sequences determine the process control. The outside air condition AU changes due to the weather; often the room setpoint is also sliding to the outside air temperature tAU bound, so that the exhaust air state AB is also variable. This fact and the temporally changing space load (enthalpy and humidity increase) require an adapted and thus changeable supply air condition ZU. These ongoing variations of the air entry states into the central and the associated required air outlet states from the central cannot be realized by single-acting control loops. Corresponding links (sequences) that switch off aggregates and connect others in succession must be provided. The choice of units and their operating points determine the process control and thus the energy and financial expenses, ie the operating costs of the system over the course of the year. Numerous sophisticated control strategies have been developed and tested through computer simulations and through practical use. They work very well with careful planning. Nevertheless, these strategies still have inherent disadvantages:

• 1. The dissolution of the overall process into individual control loops or, if the sequences in control loop complexes are observed, only allows the optimization of process parts (Fig. 2). However, a sum of optimal sub-processes does not necessarily lead to an optimum of the overall process.
• 2. The specification of the control strategy depends on the amount of time invested and the intelligence of the planner; the result - the operating costs - is strongly subjectively influenced. In view of the large number of air condition variants to be expected in the course of the year, the strategy of the process control has to be simplified anyway - For example, start from summarized status areas (Fig. 3).
• 3. Usually, the comfortable or technologically required indoor air conditions can fluctuate in relatively large areas. This affects the room air temperature and particularly the room air humidity. As far as control strategies have been developed for this case, they are based on very simplified ideas. A typical assumption, which often does not apply, always places the "optimal" point on the edge of the permissible range (Fig. 4).
• 4. The strategies are based on continuous target functions - eg minimizing the use of energy. An aggregate-specific evaluation of the energy is usually not possible. This means that strategies based solely on enthalpy differences cannot give priority to the environmental energy that may be available (Fig. 5).

The invention has for its object to develop a method of the type mentioned in such a way that a universal optimization is possible by specifying various target functions.

This object is achieved by the features characterized in claim 1.
It is a universal optimization technique based on dynamic optimization.

The target functions are freely configurable and can, for example, energy expenditure, operating costs or Minimize environmental pollution. As a result, the optimal process control, the operating status and performance of the units as well as the optimal indoor air status in the approved area are specified. When this method is implemented using modern data processing, it can be used directly as an ideal comparison process, for summing up the target function over a predetermined period of use and for practical plant operation.

While known methods provided for each air conditioning unit to be assigned its own control loop, the individual controllers being supplied with the current input variables and not depending on the respectively optimal operating mode, which results from the assignment of the outside air status point to one of the outside air status ranges The required air conditioning units are switched off in a controlled manner, it was previously not intended to provide a procedure in such a way that an optimum overall result is achieved in that, on the one hand, the individual air conditioning units may not work optimally and, on the other hand, not a specific target point for the supply air condition, but a target area for the air condition is specified in the area where the room is located, which makes it possible to use it variably, taking into account the input variables recorded - possibly going to the peripheral area - which allows an optimum in operating mode.

The main idea for the method according to the invention is that it has been recognized that a technical strategy of process control is primarily required. This sets according to the target functions (minimal costs or minimal environmental impact or minimal energy expenditure or ..)
and variably agreed boundary conditions (all condition points must be in the unsaturated area, use of a heat recovery device, a mixing chamber, etc.) the optimal route - ie the use of the units - firmly. The final goal is an area that is defined, for example, according to DIN 1946 Part 2 depending on the outside temperature. If all technical advance calculations were accurate, the air conditioning system could be operated according to this strategy. If, for example, the room load deviates, the control must correct this.

• I. In vorgegebenen Zeitabständen wird der Ausgangspunkt (Außenluftzustand) abgefragt, das Zielgebiet darauf aufbauend neu festgelegt und die optimale Steuer-Strategie errechnet. Der optimale Weg gibt an, welche Aggregate zu betreiben sind. Sie werden eingeschaltet.
• II. Die betriebsbereiten Aggregate sind bezüglich ihrer Leistung so zu regeln, daß
  • die gewünschte, optimale Zustandsänderung erfolgt,
  • Abweichungen ausgeglichen werden,
  • der Betrieb schwingungsfrei realisiert wird.

It is clear that primarily the strategy determines the optimal route. Theoretically, one could only get by with strategy analysis if the strategy was immediately redetermined for every real discrepancy. For practical operation, however, it is considered more expedient to work with a two-level system intelligence:

• I. The starting point (outside air condition) is queried at predetermined intervals, the target area is redefined based on this, and the optimal tax strategy is calculated. The optimal way indicates which units are to be operated. You will be switched on.
• II. The operational units are to be regulated in terms of their performance so that
  • the desired, optimal change of state takes place,
  • Deviations are compensated,
  • operation is carried out without vibrations.

The known methods were characterized in that, based on the regulation, better and more extensive strategic objectives were integrated ("basic structures and tax algorithms for the economical operation of air conditioning systems and possibilities of process evaluation", section 4, diss. By U. Feder, TU Dresden, 1993 According to the considerations according to the invention, however, the order of precedence must be reversed.

. Den physikalischen und technischen Gegebenheiten Rechnung tragend, wird eine Aneinanderreihung von Zustandsänderungen in den verschiedenen Aggregaten erforderlich sein. Allgemein ist die Richtung der Zustandsänderung im Aggregat k durch (Δh/ΔX) k, darstellbar. Ist weiterhin die Änderung der Feuchte (Δx)k bekannt, dann folgt sofort die Enthalpieänderung zu

Damit ergeben sich formal für den Gesamtprozeß:

Die Gln. (2) und (3) stellen die Nebenbedingungen dar. Die Auswahl der einzusetzenden Aggregate k = 1 ... n und die dort vorzunehmende Feuchteänderung ((Δx)k hat so zu erfolgen, daß die Zielfunktion, die die Kosten, den Energieaufwand oder umweltrelevante Größen beinhaltet, zum Minimum wird:

Fig. 1

Zweidimensionales Abarbeitungsschema der dynamischen Optimierung für die Variablen t und x. Vom Außenluftpunkt AU ausgehende Zustandsänderungen und am Zuluftpunkt ZU ankommende Zustandsänderungen sind komplett dargestellt. Dazwischenliegende Zustandsänderungen sind der besseren Übersicht wegen nur beispielhaft eingetragen.

Fig. 2

Beispiel für eine bekannte Regelung einer Klimaanlage. Die Strategie hat das Ziel, optimale Teilprozesse zu führen.

Fig. 3

Außenluftanteile MA sind vorgegebenen Außenluftenthalpiebereichen zugeordnet. Der Versatz zwischen je zwei Isenthalpen berücksichtigt den gleitenden Raumsollwert in Abhängigkeit von der Außenlufttemperatur. Die Außenluftanteile MA können durch fixierte Klappenstellungen an der Mischkammer realisiert werden.

Fig. 4

Zulässiger Raumluftzustand (schraffierter Bereich) und zugehöriger Zuluftzustand (doppeltschraffierter Bereich) verknüpft durch die Raumzustandsänderung Δh/Δx. Der im Sinne der Zielfunktion optimale Prozeß endet nicht zwangsläufig auf einer Begrenzung des Bereiches. Der Zuluftpunkt ZU ist beispielsweise kostengünstiger zu erreichen als der Punkt ZU1. Damit ergibt sich der Raumpunkt R als Optimum.

Fig. 5

Gegenüberstellung einer zusammengesetzten Prozeßführung (Heizen und Befeuchten) vom Außenluftpunkt AU zum Zuluftpunkt ZU. Wird die Heizenergie von der Temperatur unabhängig (kalorisch) bewertet, so sind die Prozeßverläufe 1 und 2 völlig gleichwertig. Berücksichtigt man dagegen, daß bis zur Heiztemperaturgrenze tHG Umweltenergie (z.B. Abwärme) kostengünstiger oder sogar kostenlos anfällt, so ist der Prozeßverlauf 2 zu bevorzugen.

Fig. 6

Prinzipielles Abarbeitungsschema der dynamischen Optimierung. Die Temperaturerhöhung im Erhitzer ist von der zugeführten Leistung abhängig. Der Temperaturabfall im Wäscher wird durch die Temperaturdifferenz - Eintrittstemperatur minus Feuchtkugeltemperatur - und den Befeuchtungswirkungsgrad beeinflußt. Es sind unendlich viele Zustandsverläufe möglich.

Fig. 7

Abarbeitungsschema der dynamischen Optimierung nach Einteilung der Zielgebiete in feste Abschnitte Δt. Die Endpunkte werden jeweils auf die Mitte der Abschnitte gelegt.

Fig. 8

Prinzipschaltung einer bekannten Klimaanlage mit Kennzeichnung der signifikanten Luftzustände.

Fig. 9

Schematische Einteilung des h,x-Diagramms in Zustandsabschnitte (i;j). (Die Sättigungslinie entspricht nicht der Realität!).

Fig. 10

Unterprogramm FELD zur Bestimmung des Feldes (i*;j*) für die Parameterpaarung (t*;x*) und das Unterprogramm FELDINV zur Zuordnung der Zustandspaarung (t*; x*) aus dem Feld (i*;j*). Die Zustandskennung "*" wurde im Hinblick auf das spätere Gesamtprogramm gewählt. Sie kennzeichnet die jeweilige Übergabevariable zur Unterscheidung der im Hauptprogramm geltenden Variablen.

Fig. 11

Hauptprogramm zur Ermittlung der optimalen Luftzustandsänderungen im Klimaprozeß.

Fig. 12

Unterprogramm EIN zur Organisation der Dateneingabe.

Fig. 13

Unterprogramm AUS zur Organisation der Datenausgabe.

Fig. 14

Unterprogramm OPTI zur Ermittlung der Luftzustandsänderung in den Aggregaten, Überprüfung des Endpunktes auf Zulässigkeit und ggf. Aktualisierung der Speicherinhalte.

Fig. 15

Zielfunktionen der Etappenfelder e und e + 1 mit dazwischen angeordnetem Lufterhitzer. Ausgehend von drei Startpunkten im Etappenfeld e ergibt sich eine Auffüllung im Etappenfeld e + 1 durch unterschiedliche Heizintensitäten, die von z aus gesteuert werden.

Fig. 16

Unterprogramm ABLUFT zur Ermittlung des Luftzustandsverlaufes im Abluftweg, ausgehend vom Optimierungsergebnis (Etappenfeld $\text{e = eR}$

).

Fig. 17

Unterprogramm ZUSTANDG zur Berechnung aller relevanten Zustandsgrößen für feuchte Luft, ausgehend von t* und x* nach (1).

Fig. 18

Unterprogramm ZUSTAND zur Berechnung der Zustandsgrößen *, h* und t * für feuchte Luft, ausgehend von t* und x* nach (1).

Fig. 19

Unterprogramm ZUSTANDH zur Berechnung der Temperatur t* für feuchte Luft, ausgehend von h* und x* nach (1).

Fig. 20

Unterprogramms PS und TS zur Wiedergabe der Verdampfungs- und Sublimationskurve von Wasser für die Zusammenhänge PS (t*) und TS (p*) nach (1).

Fig. 21

Unterprogramme RAUMU und RAUMO zur Ermittlung von Luftzustandsänderungen im unteren und oberen Raumabschnitt.

Fig. 22

Unterprogramm ERHITZER zur Lufterwärmung.

Fig. 23

Unterprogramm WAESCHER zur adiabaten Luftbefeuchtung.

Fig. 24

Unterprogramm DAMPFBEF zur Luftbefeuchtung mit Dampf.

Fig. 25

Unterprogramm KUEHLER zur Luftkühlung und Entfeuchtung.

Fig. 26

Unterprogramme LUEFTER und ALUEFTER zur Druckerhöhung des Zu- bzw. Abluftstromes.

Fig. 27

Unterprogramme MISCHER und AMISCH zur Zusammenführung von zwei Luftströmen.

Fig. 28

Unterprogramme WRGT und AWRGT zur Wärmerückgewinnung aus dem Abluftstrom.

Fig. 29

Typische Zustandsänderung bei Wärmerückgewinnungseinrichtungen in Komfort-Klimaanlagen.

Fig. 30

Etappenfelder e = 1 bis e = 4, die die Werte der Zielfunktion in Abhängigkeit von ganzzahligen t- und x-Werten in einer dem h,x-Diagramm analogen Form für Beispiel 1 angeben.

Fig. 31

Optimaler Weg der Luftzustandsänderungen, der Kostenaufwendungen und der signifikanten Aktionen für Beispiel 1 (Rechnerausdruck).

Fig. 32

Optimaler Weg der Luftzustandsänderungen für Beispiel 1, dargestellt im h,x-Diagramm.

Fig. 33

Optimaler Weg der Luftzustandsänderungen für Beispiel 2, Variante 1, Winterbetrieb mit Wärmerückgewinner.

Fig. 34

Optimaler Weg der Luftzustandsänderungen für Beispiel 2, Variante 2, Winterbetrieb mit Mischer.

Fig. 35

Optimaler Weg der Luftzustandsänderungen für Beispiel 2, Variante 3, Winterbetrieb mit Wärmerückgewinner und Mischer.

Fig. 36

Optimaler Weg der Luftzustandsänderungen für Beispiel 2, Variante 1, Sommerbetrieb mit Wärmerückgewinner.

Fig. 37

Optimaler Weg der Luftzustandsänderungen für Beispiel 2, Variante 2, Sommerbetrieb mit Mischer.

Fig. 38

Optimaler Weg der Luftzustandsänderungen für Beispiel 2, Variante 3, Sommerbetrieb mit Wärmerückgewinner und Mischer.

Fig. 39

Optimaler Weg der Luftzustandsänderungen für Beispiel 3 bei einem sommerlichen Außenluftzustand tAU = 22^oC; xAU = 8 gW/kgtL. Der Wärmerückgewinner ist außer Betrieb. Der Mischer steht auf 100 % Außenluft. Der Wäscher arbeitet mit dem kleinsten Befeuchtungsgrad, so daß der Raumpunkt sich im zugelassenen Raumzustandsfeld einstellt.

Fig. 40

Optimaler Weg der Luftzustandsänderungen für Beispiel 3 bei einem sommerlichen Außenluftzustand tAU = 33^oC; xAU = 8 gW/kgtL. Bis auf Vor- und Nachwärmer werden alle Aggregate betrieben. Es wird nur Außenluft eingesetzt.

The required air condition change from the outside air point AU to the supply air point ZU is only in the rarest cases along a straight path $\text{Δh / Δx AU-ZU = const possible}$

. Taking into account the physical and technical conditions, a series of changes in state in the various units will be necessary. In general, the direction of the change in state in the aggregate k can be represented by (Δh / ΔX) k. If the change in humidity (Δx) k is also known, the enthalpy change follows immediately

This results formally for the overall process:

The Gln. (2) and (3) represent the secondary conditions. The selection of the units to be used k = 1 ... n and the moisture change to be carried out there ((Δx) k must take place in such a way that the objective function, which includes the costs, the energy expenditure or environmentally relevant variables, becomes the minimum:

Fig . 1

Two-dimensional processing scheme of dynamic optimization for the variables t and x. Changes in status from the outside air point AU and changes in status arriving at the supply air point CL are shown in full. Intermediate status changes are only entered as examples for the sake of clarity.

Fig . 2nd

Example of a known control of an air conditioning system. The strategy has the goal of leading optimal sub-processes.

Fig . 3rd

Outside air proportions MA are assigned to specified outside air enthalpy areas. The offset between two isenthalpics takes into account the sliding room setpoint depending on the outside air temperature. The outside air proportions MA can be realized by fixed flap positions on the mixing chamber.

Fig . 4th

Permitted room air condition (hatched area) and associated supply air condition (double hatched area) linked by the change in room state Δh / Δx. The optimal process in terms of the objective function does not necessarily end with a limitation of the area. For example, the supply air point ZU is cheaper to reach than the point ZU1. The spatial point R thus results as the optimum.

Fig . 5

Comparison of a combined process control (heating and humidification) from the outside air point AU to the supply air point CLOSE. If the heating energy is evaluated independently of the temperature (caloric), the process sequences 1 and 2 are completely equivalent. If, on the other hand, one takes into account that environmental energy (eg waste heat) is generated more cheaply or even free of charge up to the heating temperature limit tHG, process 2 should be preferred.

Fig . 6

Basic processing scheme of dynamic optimization. The temperature increase in the heater depends on the power supplied. The temperature drop in the scrubber is influenced by the temperature difference - inlet temperature minus wet bulb temperature - and the humidification efficiency. An infinite number of state profiles are possible.

Fig . 7

Processing scheme of dynamic optimization after dividing the target areas into fixed sections Δt. The end points are placed in the middle of the sections.

Fig . 8th

Basic circuit of a known air conditioning system with identification of the significant air conditions.

Fig . 9

Schematic division of the h, x diagram into state sections (i; j). (The saturation line does not correspond to reality!).

Fig . 10th

Sub-program FELD for determining the field (i *; j *) for the parameter pairing (t *; x *) and the sub-program FELDINV for assigning the state pairing (t *; x *) from the field (i *; j *). The status identifier "*" was chosen with a view to the later overall program. It identifies the respective transfer variable to differentiate between the variables that apply in the main program.

Fig . 11

Main program for determining the optimal changes in air condition in the climate process.

Fig . 12th

Subroutine ON for organizing data entry.

Fig . 13

Subroutine AUS for organizing the data output.

Fig . 14

Subroutine OPTI for determining the air condition change in the units, checking the end point for admissibility and, if necessary, updating the memory contents.

Fig . 15

Target functions of the stage fields e and e + 1 with air heater arranged in between. Starting from three starting points in stage field e, filling in stage field e + 1 results from different heating intensities that are controlled from z.

Fig . 16

EXHAUST subroutine to determine the course of the air condition in the exhaust air path, based on the optimization result (stage field $\text{e = eR}$

).

Fig . 17th

Sub-program STATUS for the calculation of all relevant state variables for moist air, starting from t * and x * according to (1).

Fig . 18th

CONDITION subroutine for calculating the state variables *, h * and t * for moist air, starting from t * and x * according to (1).

Fig . 19th

STATUS subroutine for calculating the Temperature t * for moist air, starting from h * and x * according to (1).

Fig . 20th

Subroutines PS and TS for reproducing the evaporation and sublimation curve of water for the relationships PS (t *) and TS (p *) according to (1).

Fig . 21

Subroutines RAUMU and RAUMO to determine changes in air condition in the lower and upper section of the room.

Fig . 22

HEATER subroutine for air heating.

Fig . 23

WAESCHER subroutine for adiabatic humidification.

Fig . 24th

DAMPFBEF subroutine for humidifying air with steam.

Fig . 25th

COOLER subroutine for air cooling and dehumidification.

Fig . 26

Subroutines LUEFTER and ALUEFTER for increasing the pressure of the supply and exhaust air flow.

Fig . 27

MIXER and AMISCH subroutines for merging two air flows.

Fig . 28

WRGT and AWRGT subroutines for heat recovery from the exhaust air flow.

Fig . 29

Typical change in condition of heat recovery systems in comfort air conditioning systems.

Fig . 30th

Stage fields e = 1 to e = 4, which indicate the values of the objective function as a function of integer t and x values in a form analogous to the h, x diagram for example 1.

Fig . 31

Optimal way of the air condition changes, the cost expenditures and the significant actions for example 1 (computer printout).

Fig . 32

Optimal path of air state changes for example 1, shown in the h, x diagram.

Fig . 33

Optimal way of air condition changes for example 2, variant 1, winter operation with heat recovery.

Fig . 34

Optimal way of air condition changes for example 2, variant 2, winter operation with mixer.

Fig . 35

Optimal way of changing the air condition for example 2, variant 3, winter operation with heat recovery and mixer.

Fig . 36

Optimal way of air condition changes for example 2, variant 1, summer operation with heat recovery.

Fig . 37

Optimal way of air condition changes for example 2, variant 2, summer operation with mixer.

Fig . 38

Optimal way of air condition changes for example 2, variant 3, summer operation with heat recovery and mixer.

Fig . 39

Optimal way of the air state changes for example 3 with a summer outside air state tAU = 22 ^o C; xAU = 8 gW / kgtL. The heat recovery device is out of order. The mixer is 100% outside air. The scrubber works with the smallest degree of humidification so that the point in the room is set in the permitted room condition field.

Fig . 40

Optimal way of the air condition changes for example 3 with a summer outside air condition tAU = 33 ^o C; xAU = 8 gW / kgtL. Except for the preheater and reheater, all units are operated. Only outside air is used.

The working principle of dynamic optimization is shown in FIG. 6, whereby initially only the variable t and three aggregates (heater, washer, heater) are considered. ZU can be reached from AU in an infinite number of ways. This theoretical amount of data cannot be processed.
Fig. 7 shows the possible way out; the "milestones" of the change of state are divided into sections of the size Δt and all state courses ending in this section are labeled with the average temperature of the section. So there are finally many ways between AU and ZU. The numerous physical and technical boundary conditions that limit temperatures at certain milestones, for example, are easy to take into account (see t _{B, limit} in FIG. 7).

• die bis dahin gültige Zielfunktionssumme und
• die Abschnittskennzeichnung, aus der die Zustandsänderung entsprang ("Quelle"),

zu vermerken. Dies ist notwendig, denn erst am Endpunkt ZU kann man an Hand der Zielfunktionssumme die letzte optimale Zustandsänderung lokalisieren. Die schrittweise Rückverfolgung über die jeweiligen Quellpunkte ermöglicht es, den gesamten optimalen Prozeßverlauf zu kennzeichnen.
In Wirklichkeit ist der dargestellte Lösungsweg aufwendiger, denn die dynamische Optimierung muß zweidimensional für die Variablen Temperatur t und Feuchte x geführt werden. Fig. 1 zeigt den schematischen Lösungsweg. Zwischen den aufeinanderfolgenden Zustandsverläufen wird jeweils ein Etappenfeld e angeordnet. In ihm können alle relevanten Daten, wie Zielfunktionssumme und Quelle der ankommenden Zustandsänderung vermerkt werden. Folgende Kennzeichnungen sind zu verwenden:
e Nummer des Etappenfeldes
i Nummer des Temperaturabschnittes
j Nummer des Feuchteabschnittes.In parallel to every technically and physically possible change in state, the additive of the target function K _{k is} determined and added to the previous target function sum. If several status changes end in the same section, the status change whose target function has the smallest value is always saved. To each section of a milestone are

• the target function sum valid until then and
• the section identification from which the change of state originated (" source "),

to note. This is necessary because only at the end point CLOSED can you locate the last optimal state change based on the target function sum. The step-by-step traceability over the respective source points enables the entire optimal process to be marked.
In reality, the solution presented is more complex because the dynamic optimization must be carried out two-dimensionally for the variables temperature t and humidity x. Fig. 1 shows the schematic approach. A stage field e is arranged between the successive state profiles . All relevant data, such as the target function sum and the source of the incoming change of state, can be noted in it. The following markings must be used:
e Number of the stage field
i Number of the temperature section
j Number of the moisture section .

These order identifiers prove to be particularly advantageous for later algorithmization and programming. On the other hand, the numbering of the aggregates k can be dispensed with if the aggregates are assigned to the stage fields e at the entrance.

In addition, there are numerous references to data flow plans for the process and individual process measures in order to represent and explain the actual technical processing.

Each stage field e shown in FIG. 1 can be thought of as a t, x diagram for moist air. For the sake of clarity, however, the universally introduced h, x diagram is used for the visual representation, but still remains with the variables t and x. The area of the ongoing climate process is divided into 60 temperature and 25 humidity sections, i.e. 1500 fields. The dimensions of a field are Δt = 1 K or Δx = 0.001 kg _W / kg _tL . The range - 19.5 ° C ≦ t <40.5 ° C; 0 ≦ x <0.0245 kg _W / kg _tL covered. The field _numbers with t in ° C and x in kg _W / kg _tL result in:

$\text{i = INT (t + 20.5); j = INT (1000 x + 1.5). (5)}$

Examples

t; x -19.5; 0 0; 0.005 40.4; 0.0244 i; j 1; 1 20; 6 60; 25th

The predefined state points t; x are represented by the assignment in state sections (i; j) with an error ≦ 0.5 K and ≦ 0.0005 kg _W / kg _tL . This is clear from Fig. 9.

The following applies to the inverse assignment:

${\text{t = i - 20 in ° C; x = (j - 1) / 1000 in kg}}_{\text{W}}{\text{/ kg}}_{\text{tL}}\text{. (6)}$

Examples

i; j 1; 1 20; 6 60; 25th t; x -19; 0 0; 0.005 40; 0.024

The subroutines FELD and FELDINV belonging to equations (5) and (6) are shown in Fig. 10.

K[e; i; j] = - 1

Zielfunktion (z. B. Kostenfunktion), die die Summe der Aufwendungen bis zum Zustandspunkt e; i; j erfaßt;

Qi[e; i; j] = 0

Quellfunktionen, die angeben von welchem

Qj[e; i; j] = 0

Feld (e - 1; Qi; Qj) die Zustandsänderung zum Feld (e; i; j) führt;

A[e; i; j] = 0

Aktion, die eine kennzeichnende Größe der Zustandsänderung von (e - 1; Qi; Qj) nach (e; i; j) vermerkt (z. B. Leistung eines Erhitzers; Stellung der Mischklappe usw.).

The data flow diagram of the main program is shown in Fig. 11. First, all field sizes are set to the initial state. These are:

K [e; i; j] = - 1

Objective function (e.g. cost function), which is the sum of the expenses up to the state point e; i; j detected;

Qi [e; i; j] = 0

Source functions that indicate which

Qj [e; i; j] = 0

Field (e - 1; Qi; Qj) the change of state leads to field (e; i; j);

A [e; i; j] = 0

Action that notes a characteristic quantity of the change in state from (e - 1; Qi; Qj) to (e; i; j) (e.g. output of a heater; position of the mixing flap, etc.).

$\text{(xmax[eR] + xmin[eR])/2.}$

The data is then entered with the ON subroutine. It will be introduced later.
Between the stage fields e; e + 1, etc., each has an aggregate which changes the air condition. With known entry conditions, the physical, technical description can be carried out well. The only problem is the mixer (MI) and heat recovery unit (WR), which provide feedback from the following via a second input Process progress received. This is particularly complicated if the desired spatial condition is not a fixed value, but if it is a target area. In this case, an iterative calculation must be made. The process is determined by the control variable SGG> 0. An initial value follows from averaging the limits for the indoor air condition:

$\text{(tmax [eR] + tmin [eR]) / 2}$

$\text{(xmax [eR] + xmin [eR]) / 2.}$

(Raumzustand) berechnet und bewertet (Einzelheiten im separaten Abschnitt).If a difference of> 1 K or> 1 g _W / kg _{tL is} found at the end of the calculation, a new iteration _calculation starts from the label (1).
The core of the main program is the OPTI subroutine, in which all technically possible changes in state in the aggregates between the stage fields e = 1 (outside air state) and $\text{e = eR}$

(Room condition) calculated and evaluated (details in separate section).

The optimal target is selected by the value with the smallest target function - e.g. B. the lowest cost - is determined. The optimal route is then traced back, with the significant stage values identified:
topt [e]; xopt [e]; Copt]; File].
With the sub-program EXHAUST, the air leaving the room area is tracked further.
After completion of the iterations, all parameters of interest are output with the subroutine AUS.

The subroutines for data input and output ON and OFF are shown in detail in FIGS. 12 and 13.

(Raumeintritt) und innerhalb dieser wiederum alle i = 1 ... 60 sowie j = 1 ... 25 abgearbeitet, wenn die Zielfunktion K[e; i; j] ≧ 0 ist. Zu Beginn stellt der Außenluftzustand einen Quellpunkt dar, da im UP EIN der Außenluftpunkt im Etappenfeld $\text{e = 1 mit K[1; i*; j*] = 0}$

besetzt wurde.
Mittels UP FELDINV folgen t und x. Die Zustandsänderungen in den zwischen e und e + 1 angeordneten Aggregaten kann in verschiedenen Stufen vorgenommen werden. So sind in einem Erhitzer beispielsweise Temperaturerhöhungen um 1 K, 2 K usw. bis zu einer Endtemperatur von z. B. 40°C möglich. Diese Intensitätsvariationen werden durch den Zähler z gesteuert. Ist die technisch mögliche Endstufe der Zustandsänderung erreicht, so wird die Steuergröße zmax = 1, ansonsten zmax = 0 gesetzt.
Als AGGREGAT[e] können Mischer, Erhitzer, Kühler, Dampfbefeuchter, Wäscher, Lüfter und thermische Wärmerückgewinner durch die gleichnamigen Unterprogramme aufgerufen werden. Gleichfalls wird die Aufenthaltszone des Raumes (Raum-unten) als Aggregat aufgefaßt. Mit diesem sei der Weg der zu optimierenden Zustandsänderungen abgeschlossen.
Die Enddaten der Luftzustandsänderung tn (neue Temperatur) und xn (neue Feuchte) sowie das Additiv der Zielfunktion ΔK werden berechnet. Ist im betrachteten Aggregat technisch keine Zustandsänderung durchführbar, so wird ΔK < 0 gesetzt.
Das Unterprogramm ZUSTAND liefert die zu tn und xn gehörigen Werte: relative Feuchte φn, spezifische Enthalpie hn und Taupunkttemperatur tτn.The optimization algorithm and the associated flow chart are illustrated in the OPTI subroutine (FIG. 14). All stage fields from e = 1 (outside air condition AU) to $\text{e = eR - 1}$

(Room entry) and within this all i = 1 ... 60 and j = 1 ... 25 are processed if the target function K [e; i; j] ≧ is 0. At the beginning, the outside air condition represents a source point, since in the UP ON the outside air point in the stage field $\text{e = 1 with K [1; i *; j *] = 0}$

was occupied.
Use UP FELDINV to follow t and x. The state changes in the units arranged between e and e + 1 can be carried out in different stages. For example, in a heater temperature increases of 1 K, 2 K etc. up to a final temperature of e.g. B. 40 ° C possible. These intensity variations are controlled by the counter z. If the technically possible final stage of the state change is reached, the control variable zmax = 1, otherwise zmax = 0 is set.
As AGGREGAT [e], mixers, heaters, coolers, steam humidifiers, scrubbers, fans and thermal heat recovery units can be called up using the subroutines of the same name. Likewise, the area where the room is located (room below) is understood as an aggregate. With this, the path of the state changes to be optimized was completed.
The final data of the change in air condition tn (new temperature) and xn (new humidity) as well as the additive of the target function ΔK are calculated. If no change of state is technically feasible in the unit under consideration, then ΔK <0 is set.
The CONDITION subroutine supplies the values associated with tn and xn: relative humidity φn, specific enthalpy hn and dew point temperature tτn.

$\text{xmin[e + 1] ≦ xn ≦ xmax[e + 1] (8)}$

$\text{φmin[e + 1] ≦ φn ≦ φmax[e + 1] (9)}$

$\text{hmin[e + 1] ≦ hn ≦ hmax[e + 1] (10)}$

$\text{tτmin[e + 1] ≦ tτn ≦ tτmax[e + 1] (11)}$

und gilt außerdem ΔK ≧ 0, dann wird die betrachtete Zustandsänderung akzeptiert (Im Zusammenwirken mit Gl.(5) ist es sinnvoll, die Grenzwerte für t mit ± 0,5 K und für x mit ± 0,0005 kg_W/kg_tL zu tolerieren.). Sie findet endgültig Aufnahme, wenn für die Zielfunktion des neuen Punktes in; jn

$\text{K[e + 1; in; jn] < 0 (12)}$

oder

$\text{K[e; i; j] + ΔK < K[e + 1; in; jn] (13)}$

gilt. Es folgen dann die Speicherbelegungen:

$\text{K[e + 1; in; jn] = K[e; i; j] + ΔK (14)}$

$\text{Qi[e + 1; in; jn] = i (15)}$

$\text{Qj[e + 1; in; jn] = j (16)}$

$\text{A[e + 1; in; jn] = Aktion. (17)}$

Letzteres stellt ein typisches Merkmal der durchgeführten Zustandsänderung dar.
Bild 15 zeigt zwei aufeinanderfolgende Etappenfelder; dazwischen befindet sich ein Lufterhitzer, der beispielsweise pro 1 K Temperaturerhöhung einen Kostensummanden von $\text{ΔK = 2 DM/h}$

verursacht.Are the new condition values in the permissible target area

$\text{tmin [e + 1] ≦ tn ≦ tmax [e + 1] (7)}$

$\text{xmin [e + 1] ≦ xn ≦ xmax [e + 1] (8)}$

$\text{φmin [e + 1] ≦ φn ≦ φmax [e + 1] (9)}$

$\text{hmin [e + 1] ≦ hn ≦ hmax [e + 1] (10)}$

$\text{tτmin [e + 1] ≦ tτn ≦ tτmax [e + 1] (11)}$

and if ΔK ≧ 0 also applies, then the considered change in state is accepted (in cooperation with Eq. (5), it makes sense to apply the limit values for t with ± 0.5 K and for x with ± 0.0005 kg _W / kg _tL tolerate.). It is finally included if for the objective function of the new point in; jn

$\text{K [e + 1; in; jn] <0 (12)}$

or

$\text{K [e; i; j] + ΔK <K [e + 1; in; jn] (13)}$

applies. The memory allocations then follow:

$\text{K [e + 1; in; jn] = K [e; i; j] + ΔK (14)}$

$\text{Qi [e + 1; in; jn] = i (15)}$

$\text{Qj [e + 1; in; jn] = j (16)}$

$\text{A [e + 1; in; jn] = action. (17)}$

The latter is a typical feature of the change of state carried out.
Figure 15 shows two successive stage fields; in between is an air heater, for example for every 1 K temperature increase a sum of costs of $\text{ΔK = 2 DM / h}$

caused.

) und den optimalen Raumzustandswerten ( $\text{t = topt[eR]; x = xopt[eR]}$

) werden bis zum Etappenfeld (emax - 1) die möglicherweise einsetzbaren AGGREGATE [e], wie z. B. Raum-oben, Ablüfter, Abzweig-Mischer und Abluft-Wärmerückgewinner, betrachtet. Die durch diese Aggregate bewirkten Zustandsänderungen sind Inhalt der entsprechenden Unterprogramme. Die Austrittsgrößen (tn; xn) werden dem nächsten Etappenfeld

$\text{topt[e + 1] = tn (18)}$

$\text{xopt[e + 1] = xn (19)}$

zugeordnet. Die Zielfunktion erfährt keine weitere Veränderung

$\text{Kopt[e + 1] = Kopt[eR]; (20)}$

die aggregattypische Handlung wird vermerkt

$\text{Akt[e + 1] = Aktion. (21)}$

Die im Abluftweg auftretenden Beeinflussungen der Zielfunktion - beispielsweise der Betriebskosten für den Ablüfter - sind bereits im Optimierungsalgorithmus mit zu berücksichtigen, da die eigentliche Optimierung am Etappenfeld $\text{e = eR}$

abgeschlossen wird. Dies ist leicht möglich, indem die Antriebskosten beim Zulüfter bereits mit zu erfassen sind (siehe UP: LUEFTER). Analog kann nötigenfalls auch bei der stets zweiteiligen Abwärmerückgewinnung vorgegangen werden.The EXHAUST subroutine (Fig. 16) shows the exhaust air tracking algorithm and the detailed procedure. Starting from the stage field ( $\text{e = eR}$

) and the optimal room condition values ( $\text{t = topt [eR]; x = xopt [eR]}$

) until the stage field (emax - 1) the possibly usable AGGREGATE [e], such as B. room-top, exhaust, branch mixer and exhaust air heat recovery, considered. The state changes caused by these aggregates are the content of the corresponding subroutines. The exit variables (tn; xn) become the next stage field

$\text{topt [e + 1] = tn (18)}$

$\text{xopt [e + 1] = xn (19)}$

assigned. The target function does not undergo any further change

$\text{Kopt [e + 1] = Kopt [eR]; (20)}$

the action typical of the unit is noted

$\text{Act [e + 1] = action. (21)}$

The influencing of the target function occurring in the exhaust air path - for example the operating costs for the exhaust fan - must already be taken into account in the optimization algorithm, since the actual optimization at the stage field $\text{e = eR}$

is completed. This is easily possible by already having to record the drive costs for the supply fan (see UP: LUEFTER). If necessary, the same procedure can also be used for the two-part waste heat recovery.

The thermodynamic relationships form the basis for all process control calculations. The basics and summarized algorithms are known. They are required and are used in three subroutines. As the air pressure (total pressure) p, the input value for the outside air condition (subroutine ON) is generally used for all stages of the climate process.

STATUS subroutine (Fig. 17):
All relevant relationships (G indicates "global") are calculated based on the known parameters t * and x *. Available are: relative humidity φ * (φ * = 2 characterizes supersaturated area at t *> 0 ° C; φ * = 3 and the like at t * ≦ 0 ° C), specific volume v *, density ρ *, specific enthalpy h *, Wet bulb temperature tf *, dew point temperature tτ *, moisture content xW and xS, partial pressures pW, pL.

CONDITION subroutine (Fig. 18):
This is a shortened version of ZUSTANDG with the results φ *, h *, tτ *, xW.

STATUS subroutine (Fig. 19):
In the caloric calculations for moist air, the specific enthalpy h * and the humidity x * often appear as results. The temperature t * must be determined for further calculation. This is done with the present subroutine (H indicates the input variable h *).

In FIG. 20, the evaporation and sublimation curve in mutual parameter assignment pS * (t *) and tS * (p *), which are required at numerous points in the calculation, are formulated as subroutines or subroutines.

ΔHR W

Enthalpiestrom ("trockene und feuchte Wärme")

ΔWR kg_W

Feuchtestrom

zugeführt. Im oberen Raum erfolgt nur noch die Übergabe von

ΔHRO W

trockener Wärmestrom,

der beispielsweise aus der Leuchtenwärme resultiert.The change in state that the air experiences as it flows through the room is considered in two sections. In the lounge area (lower room)

ΔHR W

Enthalpy current ("dry and moist heat")

ΔWR kg _W

Moisture flow

fed. In the upper room only the transfer of

ΔHRO W

dry heat flow,

which results, for example, from the heat of the lights.

ṁ

kg/s Luftmassestrom.

Damit sind die beiden Unterprogramme RAUMU und RAUMO (Fig. 21) nutzbar, woraus die Parameter am jeweiligen Austritt (tn; xn) folgen. Als "Aktion" wird die Richtung der Zustandsänderung (ΔHR/ΔWR) im unteren Raumabschnitt bzw. die reine Temperaturerhöhung (tn-t) im oberen Raumabschnitt vermerkt. Die "Endparameter des unteren Raumes" müssen definitionsgemäß den geforderten Raumsollwerten entsprechen. Das bedeutet, daß damit der zu optimierende Klimaprozeß endet. Die Zustandsänderung im oberen Raumbereich wird inhaltlich bereits zum Abschnitt "Verfolgung der Abluft" gerechnet.The default is still

ṁ

kg / s air mass flow.

The two subroutines RAUMU and RAUMO (FIG. 21) can thus be used, from which the parameters at the respective outlet (tn; xn) follow. The direction of the change in state (ΔHR / ΔWR) in the lower section of the room or the pure temperature increase (tn-t) in the upper section of the room is noted as an “action”. By definition, the "end parameters of the lower room" must correspond to the required room setpoints. This means that the climate process to be optimized ends. The change in status in the upper area of the room is already included in the "Tracking exhaust air" section.

The calculation algorithms are documented in detail in data flow plans. They are therefore only briefly characterized.
In general, the following algorithms are only examples that can be changed at any time. In theory, even the exact aggregate calculations could be integrated, so that there are no limits to the approximation accuracy. Here, however, the correctness of the solution is primarily to be demonstrated. A cost function is used as the objective function.

Die absolute Feuchte bleibt bei der Erwärmung konstant

$\text{xn = x. (23)}$

Die übertragene Leistung beträgt

$\text{Δ = (1,01 + 1,86 x) (tn - t)}\dot{\text{m}}\text{in kW. (24)}$

Unterhalb der Grenztemperatur tn ≦ tHG wird mit spezifischen Kosten $\text{kx = k1H}$

, darüber mit $\text{kx = k2H - jeweils in DM/kWh - gerechnet}$

. Damit sind kostengünstige oder kostenlose Umweltenergieangebote optimal nutzbar. Die Aufwendungen betragen

$\text{ΔK = Δ kx in DM/h. (25)}$

Unter "Aktion" wird die Leistung Δ gespeichert.Heater: HEATER subroutine (Fig. 22)
Starting at the inlet temperature t, there is a temperature increase of 1 K in each case up to a final temperature tn = 40 ° C

$\text{tn = t + z. (22)}$

The absolute humidity remains constant during heating

$\text{xn = x. (23)}$

The transmitted power is

$\text{Δ = (1.01 + 1.86 x) (tn - t)}\dot{\text{m}}\text{in kW. (24)}$

Below the limit temperature tn ≦ tHG comes with specific costs $\text{kx = k1H}$

, about it with $\text{kx = k2H - calculated in DM / kWh each}$

. This means that inexpensive or free environmental energy offers can be optimally used. The expenses are

$\text{ΔK = Δ kx in DM / h. (25)}$

The power Δ is stored under "Action".

The WAESCHER subroutine is shown in FIG.

gehörige Sättigungsfeuchte xf. Vereinfachend werden die linearen Schrittweiten

bestimmt. Damit ergeben sich dann in z = 20 Schritten die Austrittsgrößen

$\text{tn = t - z Δtf (28)}$

$\text{xn = x + z Δxf (29)}$

und weiter der Befeuchtungsgrad

Liegt er im technisch möglichen Bereich - die Grenzwerte sind im UP EIN eingegeben worden -

$\text{ηBmin ≦ ηB ≦ ηBmax (31)}$

oder ist z = 0, dann werden die Kosten bestimmt. Ausgehend von der Beziehung

mit

ΔP_W

Differenzdruck der Sprühwasserpumpe

V̇_W

Wasserstrom

η_P

Pumpen- und Motorwirkungsgrad

P_e1

spezifischer Preis der Elektroenergie.

und dem erforderlichen Wassermassestrom

$${\dot{\text{m}}}_{\text{W}}\text{≈ -}\frac{\text{1}}{\text{C}}{\text{ln (1-η}}_{\text{B}}\text{)}\dot{\text{m}}\text{ (33)}$$

folgt

Mit Δp_w = 2 bar, ρ_W = 1000 kg/m³, C = 4 (Annahme für einen bestimmten Wäschertyp) und ${\text{kW = p}}_{\text{e1}}{\text{/η}}_{\text{p}}$

in DM/kWh ergibt sich in der Flußbildschreibweise

$\text{ΔK = - 0,05 ln(1 - ηB)}\dot{\text{m}}\text{kW in DM/h. (35)}$

Unter "Aktion" wird der Befeuchtungsgrad vermerkt.A regulated scrubber is used as a basis. The first time through the calculation (z = 0), the wet bulb temperature tf * belonging to t and x is determined and to $\text{t * = tf *}$

proper saturation moisture xf. The linear increments are simplified

certainly. This gives the exit variables in z = 20 steps

$\text{tn = t - z Δtf (28)}$

$\text{xn = x + z Δxf (29)}$

and further the degree of humidification

Is it in the technically possible range - the limit values were entered in the UP ON -

$\text{ηBmin ≦ ηB ≦ ηBmax (31)}$

or if z = 0, then the costs are determined. Based on the relationship

With

ΔP _W

Differential pressure of the spray water pump

V̇ _W

Water flow

η _P

Pump and motor efficiency

P _e1

specific price of electrical energy.

and the required water mass flow

$${\dot{\text{m}}}_{\text{W}}\text{≈ -}\frac{\text{1}}{\text{C.}}{\text{ln (1-η}}_{\text{B}}\text{)}\dot{\text{m}}\text{(33)}$$

follows

With Δp _w = 2 bar, ρ _W = 1000 kg / m³, C = 4 (assumption for a certain type of washer) and ${\text{kW = p}}_{\text{e1}}{\text{/ η}}_{\text{p}}$

in DM / kWh results in the flow chart notation

$\text{ΔK = - 0.05 ln (1 - ηB)}\dot{\text{m}}\text{kW in DM / h. (35)}$

The degree of humidification is noted under "Action".

The steam program is shown in FIG. 24.

Das UP ZUSTANDH liefert das zugehörige tn. Die Kosten ergeben sich zu

wobei die spezifischen Energiekosten kDB aus dem spezifischen Preis der Elektroenergie P_e1 und dem Wirkungsgrad der Dampferzeugung η_D nach ${\text{kDB = p}}_{\text{e1}}{\text{/η}}_{\text{D}}$

in DM/kWh zu bilden sind. Bei nichtelektrischer Dampferzeugung ist eine entsprechende Anpassung vorzunehmen.
Unter "Aktion" werde die Feuchtezunahme in g_W/kg_tL gespeichert.In the first calculation step (z = 0), the enthalpy of saturated steam ΔhD according to (1) and the enthalpy of the air inlet state (t; x) are determined with UP CONDITION according to the input value steam pressure pDB.
The humidification takes place in z = 10 steps each by 1 g _w / kg _tL . The enthalpy and moisture values increase accordingly:

The UP CONDITION provides the associated tn. The costs arise too

where the specific energy costs kDB from the specific price of electrical energy P _e1 and the efficiency of steam generation η _D after ${\text{kDB = p}}_{\text{e1}}{\text{/ η}}_{\text{D}}$

are to be formed in DM / kWh. A corresponding adjustment must be made for non-electric steam generation.
Under "Action" the increase in moisture is saved in g _W / kg _tL .

The COOLER subroutine is shown in FIG.

Die untere Grenze beträgt tKW = 6°C.
Mit dem Rippenwirkungsgrad berechnet sich die mittlere Oberflächentemperatur zu

$\text{tK = t - ηK (t - tKW). (40)}$

Liegt diese über der Taupunkttemperatur der Luft tτ, so verläuft die Zustandsänderung auf einer Isohygren

$\text{xn = x, (41)}$

ansonsten werde die zu tK gehörige Sättigungsfeuchte xK bestimmt und mit Hilfe des Bypaßfaktors die Austrittsfeuchte der Luft

$\text{xn = x - By (x - xK) (42)}$

berechnet. Die Enthalpie an der Kühleroberfläche hK wird ermittelt. Eine "Mischung" dieses Luftzustandes mit der eintretenden Luft ergibt näherungsweise die Enthalpie am Austritt

$\text{hn = h - By (h - hK). (43)}$

Mit UP ZUSTANDH folgt tn. Liegt die mittlere Kaltwassertemperatur tKW über der Grenztemperatur tKG, so gelten die spezifischen Kosten $\text{kx = k1K}$

, sonst $\text{kx = k2K}$

.
Die übertragene Leistung berechnet sich zu

$\text{Δ = (h - hn)}\dot{\text{m}}\text{in kW, (44)}$

die erforderlichen Kosten ergeben

$\text{ΔK = Δ kx in DM/h. (45)}$

Unter "Aktion" werde die Leistung vermerkt.A simple model is chosen to determine the course of the state in the surface cooler, which works with the so-called fin efficiency of the cooler ηK and the bypass factor By. In the first calculation step (z = 0), the values h and tτ belonging to t and x are determined for the state of air entry. The average cold water temperature is gradually reduced by 1 K.

$\text{tKW = t - z. (39)}$

The lower limit is tKW = 6 ° C.
The average surface temperature is calculated using the rib efficiency

$\text{tK = t - ηK (t - tKW). (40)}$

If this is above the dew point temperature of the air tτ, the change of state takes place on an isohygren

$\text{xn = x, (41)}$

otherwise the saturation moisture xK belonging to tK is determined and with the help of the bypass factor the outlet moisture of the air

$\text{xn = x - By (x - xK) (42)}$

calculated. The enthalpy at the cooler surface hK is determined. A "mixture" of this air state with the incoming air gives approximately the enthalpy at the outlet

$\text{hn = h - By (h - hK). (43)}$

With UP STATE follows tn. If the mean cold water temperature tKW is above the limit temperature tKG, the specific costs apply $\text{kx = k1K}$

, otherwise $\text{kx = k2K}$

.
The transmitted power is calculated

$\text{Δ = (h - hn)}\dot{\text{m}}\text{in kW, (44)}$

result in the necessary costs

$\text{ΔK = Δ kx in DM / h. (45)}$

The performance is noted under "Action".

The subroutines LUEFTER and ALUEFTER are shown in Fig. 26.

Dabei bedeuten:

v* m³/kg

spezifisches Volumen

Δp Pa

Druckerhöhung (ΔpZ Zulüfter; ΔpA Ablüfter)

p Pa

Vordruck (näherungsweise gleich dem Außenluftdruck)

ηL

Wirkungsgrad (ηLZ Zulüfter; ηLA Ablüfter)

κ

Isentropenexponent für Luft (κ = 1,4).

Starting from the air inlet state (t; x) and the variables h * and v * determined from this, the enthalpy increase in the supply and exhaust air follows

Here mean:

v * m³ / kg

specific volume

Δp Pa

Pressure increase (ΔpZ supply fan; ΔpA exhaust valve)

p Pa

Form (approximately equal to the outside air pressure)

ηL

Efficiency (ηLZ supply fan; ηLA exhaust fan)

κ

Isentropic exponent for air (κ = 1.4).

woraus mit der neuermittelten Enthalpie unter Verwendung von UP ZUSTANDH die Endtemperatur tn folgt. Die Kosten für den Zu- und Ablüfter werden gemeinsam beim Zulüfter - der in der Optimierungsstrecke liegt - erfaßt:

Unter "Aktion" werde die Temperaturerhöhung gespeichert.The humidity remains constant

$\text{xn = x, (47)}$

from which the final temperature tn follows with the newly determined enthalpy using UP CONDITION. The costs for the supply and exhaust fans are recorded together with the supply fan - which is in the optimization section:

The temperature increase is saved under "Action".

Liegt er im zugelassenen Bereich

$\text{MAmin ≦ MA ≦ MAmax, (50)}$

folgen die Berechnungen

$\text{xn = MA x + (1 - MA) xMII (51)}$

$\text{hn = MA h + (1 - MA) hMII. (52)}$

Vorgegeben sind dabei die Luftzustände (x; t) und für den zweiten Zustrom (xMII; tMII), woraus mit UP ZUSTAND h und hMII ermittelt werden.
Aus xn und hn folgt mit UP ZUSTANDH endgültig tn. Als Kostenfunktion sei

$\text{ΔK =}\dot{\text{m}}\text{kM in DM/h (53)}$

verwendet. In der Regel wird jedoch kM = 0 gelten. Der charakteristische Außenluftanteil MA wurde unter "Aktion" vermerkt.The MIXER and AMISCH subroutines are shown in Fig. 27 The proportion of outside air MA (outside air mass flow / mixed air mass flow) that can be set by the flaps on the mixing chamber is increased in steps (z = 0 to z = 10)

$\text{MA = 1 - 0.1 z. (49)}$

Is it in the approved area

$\text{MAmin ≦ MA ≦ MAmax, (50)}$

follow the calculations

$\text{xn = MA x + (1 - MA) xMII (51)}$

$\text{hn = MA h + (1 - MA) hMII. (52)}$

The air conditions (x; t) and for the second inflow (xMII; tMII) are specified, from which the UP CONDITION h and hMII are determined.
From UP and STN tn finally follows from xn and hn. As a cost function

$\text{ΔK =}\dot{\text{m}}\text{kM in DM / h (53)}$

used. As a rule, however, kM = 0 will apply. The characteristic proportion of outside air MA was noted under "Action".

The UP AMISCH only looks at the branch in the airway that leads to the mixing chamber. Of course, the parameters of the exhaust air do not change.

$\text{xn = x. (55)}$

tWRGTII stellt die abluftseitige Lufteintrittstemperatur in den Wärmerückgewinner dar. Der übertragene Wärmestrom beträgt dann

$\text{ΔWRGT = (1,01 + 1,86 x) (tn - t)}\dot{\text{m}}\text{in kW, (56)}$

woraus aufbauend die Kostenfunktion

$\text{ΔK = ¦ ΔWRGT ¦ kWRGT in DM/h (57)}$

formuliert wird. Es kann vielfach auch sinnvoll sein,

$\text{ΔK = kWRGT oder ΔK =}\dot{\text{m}}\text{kWRGT in DM/h (58)}$

anzusetzen bzw. kWRGT = 0 zu verwenden. Die spezifischen Kosten sind im UP EIN entsprechend zu modifizieren. "Aktion" speichert den Betriebszustand des Wärmerückgewinners (0 außer Betrieb; 1 in Betrieb).
Fig.29 zeigt, daß bei Komfortklimaanlagen die Zuluft-Zustandsänderung üblicherweise längs einer Isohygren verläuft. Die Abluft-Zustandsänderung kann im Winterfall sowohl nach a, bei tieferen Temperaturen t aber auch nach b verlaufen. In der Regel interessiert der Abluft-Austrittszustand nicht mehr, denn es handelt sich meistens um Fortluft. Dennoch soll im UP AWRGT (Fig.28) der Austrittszustand qualitativ richtig erfaßt werden. Die Abfrage $\text{Akt[eWR+1]}$

gibt an, ob der Wärmerückgewinner betrieben wurde. Wenn nein, dann gelten

$\text{tn = t}$

$\text{xn = x,}$

ansonsten werden mit UP ZUSTAND die Enthalpie am Eintritt des Abluftstromes h* und die im Zuluftstrom übertragene Enthalpiedifferenz ΔWRGT bestimmt. Mit der Enthalpie am Austritt des Abluftstromes

$\text{hn = h* - ΔWRGT}$

und x folgen aus UP ZUSTANDH die Größen $\text{tn = t* und tτ*}$

. Liegt der Austrittszustand nahe an der Sättigungskurve, so wird

${\text{xn = xS - 0,001 kg}}_{\text{W}}{\text{/kg}}_{\text{tL}}$

gesetzt und mit UP ZUSTANDH erneut tn bestimmt. Unter "Aktion" werde wiederum der Betriebszustand des Wärmerückgewinners gespeichert.The subroutines WRGT and AWRGT are shown in FIG. 28. Because of the wide variety of heat recovery systems, a simple, but generally applicable model is used. It is based on the grade (operational characteristics) ΦWRGT. Starting from the input variables (t; x), the end values of the change in state follow:

$\text{tn = t + ΦWRGT (tWRGTII - t) (54)}$

$\text{xn = x. (55)}$

tWRGTII represents the air inlet temperature in the heat recovery unit on the exhaust air side. The heat flow transferred is then

$\text{ΔWRGT = (1.01 + 1.86 x) (tn - t)}\dot{\text{m}}\text{in kW, (56)}$

from which building the cost function

$\text{ΔK = ¦ ΔWRGT ¦ kWRGT in DM / h (57)}$

is formulated. In many cases it can also make sense

$\text{ΔK = kWRGT or ΔK =}\dot{\text{m}}\text{kWRGT in DM / h (58)}$

or to use kWRGT = 0. The specific costs must be modified accordingly in the UP EIN. "Action" saves the operating state of the heat recovery device (0 out of operation; 1 in operation).
Fig. 29 shows that in comfort air conditioning systems, the change in the supply air status usually runs along an isohygren. In the winter, the change in the exhaust air status can run both after a, but at lower temperatures t, and also after b. As a rule, the exhaust air outlet state is no longer of interest, since it is mostly exhaust air. Nevertheless, the exit condition should be recorded correctly in the UP AWRGT (Fig. 28). The query $\text{Act [eWR + 1]}$

indicates whether the heat recovery unit has been operated. If not, then apply

$\text{tn = t}$

$\text{xn = x,}$

Otherwise the UP CONDITION determines the enthalpy at the inlet of the exhaust air flow h * and the enthalpy difference ΔWRGT transmitted in the supply air flow. With the enthalpy at the outlet of the exhaust air flow

$\text{hn = h * - ΔWRGT}$

and x follow the sizes from UP CONDITION $\text{tn = t * and tτ *}$

. If the exit state is close to the saturation curve, then

${\text{xn = xS - 0.001 kg}}_{\text{W}}{\text{/ kg}}_{\text{tL}}$

set and determined again with UP CONDITION tn. The operating state of the heat recovery device is saved under "Action".

If heat recovery units and mixers are used in combination in systems, the algorithms with regard to the cost functions must be designed in such a way that the real or none at all mass flows are used.

When numerically processing the examples and the associated presentation of the results of the process sequences in the h, x diagram, it should be noted that the division into sections of 1 K and 1 g _W / kg _tL (see FIG. 9) results in errors of half the section _size can. In addition, the permissible error limits for the main program iteration loop are also set at 1 K and 1 g _W / kg _tL .

example 1

A simple partial air conditioning system - consisting of preheater, scrubber, reheater - should _treat outside air (- 14 ° C, 1 g _W / kg _tL ) so that the final state in the range t = (20 ... 24) ° C; φ = (0.30 ... 0.65) or x <11.5 g _W / kg _tL . There are no limits for the intermediate states. The specific costs are: Thermal energy up to t _{air outlet} of 25 ° C: k1H = 0.05 DM / kWh Thermal energy over t _{air outlet} of 25 ° C: k2H = 0.15 DM / kWh Operating energy for humidifier pump including efficiency: kW = 0.35 DM / kWh.

The scrubber can be regulated in the range: ηBmin = 0.50; ηBmax = 0.95. Ṁ = 8.8 kg / s (approx. 27500 m³ / h) air must be processed. The course of the process is to be determined in such a way that minimal costs arise.

The program system presented is used to optimize plant operation.
In order to make the solution understandable, the individual stage fields, which are based on an h, x diagram and are filled with the objective function "costs in DM / h", are printed out in FIG. 30. The interim results are briefly characterized:
Stage field e = 1
Only the outside air condition (- 14 ° C; 1 g _W / kg _tL ) is documented with the costs 0 DM / h.

Stage field e = 2
The heating takes place in 1 K steps, the humidity remains constant. The costs are noted at the possible endpoints. The higher specific energy costs at temperatures> 25 ° C are evident from the cost jump shown.

Stage field e = 3
The scrubber is installed between e = 2 and e = 3. If it is not operated, the previous values are retained (see row x = 1 g _W / kg _tL ). When it goes into operation, air states with x> 1 g _W / kg _{tL follow} , whereby only the technically possible end points (ηB = 0.5 ... 0.95 and tf = const) are documented. Minor deviations and defects in the closed area result from the transition from t; x on i; j, which is done with UP FELD.

• technisch gleiche Endpunkte stets überspeichert werden mit den kostengünstigsten Zustandsänderungen;
• nur die im zugelassenen Zielgebiet liegenden Luftzustände eingetragen werden.

Stage field e = 4
Each section occupied in e = 3 is warmed up in 1 K steps. The results are noted according to the applicable cost function. It is particularly clear that

• technically identical endpoints are always overstored with the cheapest changes of state;
• only the air conditions in the approved target area are entered.

After selecting the lowest costs in the target area and tracing the previous source in each case, FIG. 31 summarizes the optimal way of the state changes with indication of the respectively significant "action". 32 shows the course in the h, x diagram.

Example 2

Außenluftzustand Winter:

tAU = - 12°C; xAU = 1 g_W/kg_tL

Sommer:

tAU = 32°C; xAU = 19 g_W/kg_tL

Luftdruck:

p = 101300 Pa

Raumluftbereich Winter:

t = 20°C..23°C; φ = 0,30..0,65;
x < 11,5 g_W/kg_tL

Sommer:

t = 24°C..27°C; φ = 0,30..0,65;
x < 11,5 g_W/kg_tL

Zwischenbereiche:

φ ≦ 1

Aggregate- und Raumdaten:

Wärmerückgewinner:

ΦWRGT = 0,6; kWRGT = 0 DM/kWh

Mischer:

MAmin = 0,5; MAmax = 1;
kM = 0 DM/h/kg s

Erhitzer (Vorwärmer):

k1H = 0,05 DM/kWh für t ≦ 25 °C,

sonst

k2H = 0,15 DM/kWh

Kühler:

ηK = 0,8; By = 0,75
k1K = 0,20 DM/kWh für t ≧ 16 °C,

sonst

k2K = 0,50 DM/kWh

Wäscher:

ηBmin = 0,50; ηBmax = 0,95;
kW = 0,35 DM/kWh

Erhitzer (Nachwärmer):

k1H = 0,05 DM/kWh für t ≦ 25 °C,

sonst

k2H = 0,15 DM/kWh

Zulüfter:

ηLZ = 0,70; ΔpZ = 700 Pa;
kL = 0,25 DM/kWh

Raum (unten) Winter (vollbesetzt):

ΔHR = 38 kW
ΔWR = 0,0055 kg_W/s

Sommer (vollbesetzt):

ΔHR = 75 kW
ΔWR = 0,0096 kg_W/s

Raum (oben) Winter und Sommer:

ΔHRO = 9 kW

Ablüfter: ηLA = 0,83;

ΔpA = 600 Pa

Die aufgeführte Anordnung der Aggregate und des Raumes entspricht der Richtung des Luftdurchganges. Es sind für beide Betriebsfälle folgende Varianten zu untersuchen:

Variante 1:

Wärmerückgewinner; kein Mischer

Variante 2:

Mischer; kein Wärmerückgewinner

Variante 3:

Wärmerückgewinner und Mischer.

Der Luftmassestrom beträgt idealerweise stets ṁ = 8,8 kg/s.The optimal process flow for auditorium air conditioning in winter and summer is to be determined. There are:

Outside air condition winter:

tAU = - 12 ° C; xAU = 1 g _W / kg _tL

Summer:

tAU = 32 ° C; xAU = 19 g _W / kg _tL

Air pressure:

p = 101300 Pa

Indoor air range winter:

t = 20 ° C. 23 ° C; φ = 0.30 ... 0.65;
x <11.5 g _W / kg _tL

Summer:

t = 24 ° C. 27 ° C; φ = 0.30 ... 0.65;
x <11.5 g _W / kg _tL

Intermediate areas:

φ ≦ 1

Aggregate and room data:

Heat recovery:

ΦWRGT = 0.6; kWRGT = 0 DM / kWh

Mixer:

MAmin = 0.5; MAmax = 1;
kM = 0 DM / h / kg s

Heater (preheater):

k1H = 0.05 DM / kWh for t ≦ 25 ° C,

otherwise

k2H = 0.15 DM / kWh

Cooler:

ηK = 0.8; By = 0.75
k1K = 0.20 DM / kWh for t ≧ 16 ° C,

otherwise

k2K = 0.50 DM / kWh

Washer:

ηBmin = 0.50; ηBmax = 0.95;
kW = 0.35 DM / kWh

Heater (reheater):

k1H = 0.05 DM / kWh for t ≦ 25 ° C,

otherwise

k2H = 0.15 DM / kWh

Inlet fan:

ηLZ = 0.70; ΔpZ = 700 Pa;
kL = 0.25 DM / kWh

Room (below) winter (fully occupied):

ΔHR = 38 kW
ΔWR = 0.0055 kg _W / s

Summer (full):

ΔHR = 75 kW
ΔWR = 0.0096 kg _W / s

Room (above) winter and summer:

ΔHRO = 9 kW

Exhaust vent: ηLA = 0.83;

ΔpA = 600 Pa

The arrangement of the units and the room listed corresponds to the direction of the air passage. The following variants must be examined for both operating cases:

Version 1:

Heat recovery; no mixer

Variant 2:

Mixer; no heat recovery

Variant 3:

Heat recovery and mixer.

The air mass flow is ideally always ṁ = 8.8 kg / s.

The possibilities of the optimization process are fully used. Both the heat recovery unit and the mixer are connected to the exhaust air flow, so that iterative calculations are necessary. Despite the enormous computing effort, the PC processing times are only about 1 minute. The optimal results are shown in FIGS. 33 to 38. The tabular summary shows the clear advantages of using the mixing chamber (MI) and the additional coupling with a heat recovery device (WR): Costs in DM / h WR MI WR + MI Winter fall 9.47 9.05 5.48 Summer fall 163.77 112.42 108.86

Example 3

The summer room load, the permissible room air range as well as the assembly of units with heat recovery and mixer apply as described in example 2. Only the summer outside air condition is varied. The results of the optimal process sequences are presented in FIGS. 39 and 40 and characterized in the description of the figures.

An automated determination of the permissible spatial area is advantageously possible.

Associated with the outside air temperature, the permissible indoor air status ranges can be determined with very little effort. B. according to DIN 1946 Part 2. This simplifies the entry. Furthermore, the addition of an external processing loop makes it possible to automatically calculate a sequence of outside air conditions.

The determination of target function interval totals is also possible.

Frequently, the energy and / or cost expenditures for the operation of an air conditioning system are of interest for a predetermined period, for example for a year. The outside air conditions can be specified according to temperature, humidity and the corresponding frequency in accordance with DIN 4710, or they are described using the test reference year. The summation of the target function is possible without any problems. Since the examined changes in state each represent optimal operating processes, the Objective function to the minimum. The result thus represents an optimal comparison process.

The optimization of the mass flow can also be carried out with the method according to the invention.

• Berechnung der Zielfunktion Z (z. B.: KSUM) und der Temperaturanderung der Zuluft im Raum ( ${\text{Δt}}_{\text{ZU}}\text{= topt[eR] - topt [eR - 1] für ein vorgegebenes}\dot{\text{m}}$
  
  ;
• Liegt ṁ über dem zulässigen Minimalwert und Δt_ZU im zulässigen Bereich wird solange eine Luftstromreduzierung (z. B.: ṁ₁ = 0,95 ṁ; ṁ₂ = 0,95 ṁ₁ usw.) und eine Neuberechnung des optimalen Weges durchgeführt, bis einer der Grenzwerte erreicht ist. Sind die Leistungen der Bauteile vom Luftwechsel abhängig, so muß dies in den entsprechenden Algorithmen berücksichtigt werden. Die Randbedingungen bedürfen gegebenenfalls auch der Anpassung, so beispielsweise der zulässige Außenluftanteil des Mischers MAmin. Die zu den ṁ₁; ṁ₂ usw. gehörigen Zielfunktionsgrößen Z₁; Z₂ usw. werden vermerkt und das Minimum bestimmt. Damit ist dann der optimale Luftdurchsatz determiniert.
  Bei speziellen Zielfunktionen wäre es auch denkbar, daß über dem Erstwert ṁ liegende Luftdurchsätze das Ergebnis positiv beeinflussen. Technisch sinnvolle, maximale Luftdurchsätze sind zu postulieren.
• Zum Auffinden des optimalen Luftstromes sollten bei größerer Variationsbreite die bekannten mathematischen Lösungsverfahren angewendet werden.

The optimal way of the air state changes in the sense of the target function applies to the given mass flow. As a rule, energy and cost expenditures also decrease as the mass flow decreases. The minimum air flow is determined by hygienic, sometimes also by building physics requirements. The temperature difference between the room temperature and the supply air temperature is directly linked to the air flow. Limits related to comfort and air flow apply to them, which must be observed.
In general, the mass flow (or volume flow) can be optimized as follows:

• Calculation of the target function Z (e.g. KSUM) and the temperature change of the supply air in the room ( ${\text{Δt}}_{\text{TO}}\text{= topt [eR] - topt [eR - 1] for a given one}\dot{\text{m}}$
  
  ;
• If ṁ is above the permissible minimum value and Δt _ZU in the permissible range, an air flow reduction (e.g.: ṁ₁ = 0.95 ṁ; ṁ₂ = 0.95 ṁ₁ etc.) and a new calculation of the optimal path are carried out until one of the limit values is reached. If the performance of the components depends on the air exchange, this must be taken into account in the corresponding algorithms. The boundary conditions may also need to be adapted, for example the admissible proportion of outside air in the mixer MAmin. The to the ṁ₁; ṁ₂ etc. associated target function variables Z₁; Z₂ etc. are noted and the minimum determined. The optimal air flow rate is then determined.
  With special target functions, it would also be conceivable that air throughputs above the initial value Erst have a positive effect on the result. Technically reasonable, maximum air flow rates are to be postulated.
• In order to find the optimal air flow, the known mathematical solution methods should be used with a larger range of variation.

If the target function - for example the operating costs - is summed up over a period of one year for various configurations of the clinical system, the optimal assembly equipment can be determined. Typical examples are the comparison of steam humidifiers, controllable scrubbers and non-controllable scrubbers or checking the efficiency of heat recovery units, etc.
It would also be conceivable to provide a steam humidifier and a washer in the air conditioning system at the same time and to check which unit is selected under which conditions during the optimization. If necessary, the operating times would have to be determined over a year and then the selection made.

The operational operation of the ventilation system is shown below.

• Wärmerückgewinner in Betrieb setzen;
• Wäscherpumpe einschalten;
• Klappen am Mischer in die berechnete Stellung fahren;
• Kälte- oder Wärmebereitstellung, falls nicht ständig verfügbar, in Betrieb nehmen.

Problematischer verhält es sich mit der Regelung zur Leistungsanpassung der Aggregate. Es gibt dazu zwei Möglichkeiten:

• Jedes Aggregat erhält eine Einzelregelung mit dem errechneten Luftaustrittszustand als Sollwert.
• Mehrere Aggregate - z. B. Vorwärmer, Kühler, Nachwärmer (vgl. Fig.2 ) - werden mit einer Regelung bei entsprechenden Verknüpfungen und Sequenzen betrieben. Dies stellt im Sinne der Zielfunktion keinen "Rückschritt" dar, wenn Optimierungs- und Regelungsstrategie übereinstimmen oder wenn die Regelung nur in einem engbegrenzten Bereich "eigenmächtig" operieren darf.

The optimization of the air treatment process provides information about the most favorable room air condition in terms of the target function and the operating states of the individual units. This optimal strategy is an absolutely new result of great importance for the control and regulation of HVAC systems. The selection of the components to be operated and their performance are available. Appropriate controls can be made on this basis, e.g. B .:

• Put the heat recovery device into operation;
• Switch on the washer pump;
• Move the flaps on the mixer to the calculated position;
• Commission cold or heat supply, if not always available.

It is more problematic with the regulation for adjusting the power of the units. There are two ways to do this:

• Each unit receives an individual control with the calculated air outlet status as the setpoint.
• Multiple units - e.g. B. preheater, cooler, reheater (see Fig. 2) - are operated with a control with appropriate links and sequences. In the sense of the objective function, this does not represent a "backward step" if the optimization and control strategy match or if the control only may operate "independently" in a limited area.

Which of the two variants should be selected is to be determined primarily from a practical point of view - for example, stability considerations and the use of hardware.

• Die Zustandsänderung wird durch Ermittlung von Δh sowie Δx direkt bestimmt. Die Programmeingaben erfahren eine entsprechende Korrektur. Dieses Vorgehen bietet sich zur Erfassung der realen Raumlasten an. Es ist aber auch denkbar, dieses Verfahren auf Aggregate der RLT-Anlage anzuwenden und eine Zuordnung beispielsweise zu den Luft- und Wassereintrittstemperaturen herzustellen.
• Die im Optimierungsalgorithmus verwendeten Ersatzmodelle werden durch die realen Zustandsänderungen korrigiert. So sind beispielsweise aus dem Eintritts- und Austrittstemperaturen der Luft und des Kaltwassers der Rippenwirkungsgrad und der Bypaßfaktor des Oberflächenkühlers für den vorgegebenen Einsatzbereich realistisch bestimmbar.
• Die Modelle zur Nachbildung der Aggregatecharakteristiken können durch Auswertung stochastischer Zusammenhänge über längere Zeiträume und wechselnden Betriebsbedingungen bedeutend verfeinert werden.

The calculation of the optimal process control must be repeated at rhythmic intervals. The real situation should be adapted. This refers to the performance characteristics of the components and the changes in state in the room. Assuming that the corresponding entry and exit parameters are known, there are several options for the adaptation :

• The change in state is determined directly by determining Δh and Δx. The program entries are corrected accordingly. This procedure is suitable for recording the real room loads. However, it is also conceivable to apply this method to units in the HVAC system and to establish an association, for example, with the air and water inlet temperatures.
• The replacement models used in the optimization algorithm are corrected by the real state changes. For example, the rib efficiency and the bypass factor of the surface cooler can be realistically determined from the inlet and outlet temperatures of the air and cold water for the specified area of application.
• The models for simulating the aggregate characteristics can be significantly refined by evaluating stochastic relationships over longer periods of time and changing operating conditions.

Claims (5)

Method for regulating the temperature and humidity of air in rooms using a ventilation and air conditioning system that consists of a number of individual units for carrying out changes in air status and uses outside air and exhaust air as input streams, whereby the control of the individual air conditioning units and the process control takes place so that the supply air is one causes a predetermined room air condition, whereby a target function is fulfilled, namely that the target condition is achieved with a minimum of energy expenditure, and boundary conditions are specified with regard to the air conditions that can be set by the individual units and the realizable state changes are determined,
characterized,
that the process run is determined so a) that the operating parameters of the system that fulfill the target function are determined computationally at specific, selectable time intervals by discretizing the state field of the air conducted through the individual units by dividing them into temperature and humidity sections (delta t and delta x), so that starting From the state of the outside air there is a finite number (considering the boundary conditions) of theoretically possible routes to the condition of the indoor air or supply air and each of these routes is followed step by step, with the evaluations of the air treatment by the individual units for each route be summed up with regard to the target function, and finally the parameters of the route to the operation of the individual units are selected, the sum total of which fulfills the target function, b) that the "minimization of the exergetic, environmentally relevant, caloric or cost-assessed energy expenditure" or the "minimization of directly used natural resources" is specified as the objective function, the assessment scale also being able to be defined differently for each individual unit, c) that the air conditions at the outlet of the individual units with regard to temperature, absolute humidity, specific enthalpy and dew point temperature - based on a constant air pressure, which can be different for each individual unit - or quantities derived therefrom, as well as limitations on the use of the units, are used as boundary conditions with regard to the air throughput, the heating or cooling medium-side flow parameters, the performance, the state variables of the air to be treated or aggregate-specific characteristics, d) that the technically and physically realizable changes in state in the individual units - based on the incoming air states discriminated according to temperature and humidity - in parameter steps characteristic of the unit (temperature increases, moisture increases, Cooling water inlet temperature reductions etc.) are examined, whereby for uncontrolled units the investigation consists of only one step, with the first result of the air leakage state, which in turn is available in a discretized form after applying a rounding algorithm, and the second result of a summand for the overall evaluation in the sense of Objective function, whereby in the event that several parameter steps result in the same discretized air outlet state, the step which achieves the cheapest (smallest or largest) summand in the sense of the object function is included in the evaluation and e) that the operation of each unit can then be controlled or regulated individually or in groups, in which case the optimized air outlet parameters can be used as setpoints. Method according to claim 1,
characterized,
that instead of the specified room air condition, a room air condition defined by the parameters of the inlet air flows or a defined room air condition range with regard to temperature and humidity, the limits of which depend both on the internal heat and moisture loads - can be determined from the air conditions for the exhaust and supply air measured during system operation (delta h / Delta x, room) the number of people in the room and / or in the room electrical devices in operation - as well as the external weather conditions and the time of use, whereby the discretized temperature and humidity sections (delta t and delta x), whose surface centers lie within the defined range, are equivalent to one another. The method of claim 1 or 2,
characterized,
that the optimal process control also includes an optimization of the air throughput, with hygienic, air flow technology and / or building physics-related limitations being specifiable. Method according to one of claims 1 to 3,
where the ventilation system contains a number of individual units, namely at least one heating unit (EH), one cooling unit (KU) and one humidification unit (WA) and the current air conditions (pressure (p), temperature (t), humidity (x) and / or other air condition variables) supplying sensors for the outside air, which has exhaust air and supply air, whereby due to the optimization results determined in the sense of the target function taking into account the air conditioning and aggregate-specific boundary conditions, all aggregates or partial combinations of aggregates are operated with which the target room condition range is operated (t SHOULD min ... t SHOULD max, x SHOULD min ... x SHOULD max, phi SHOULD min ... phi SHOULD max, thaw SHOULD min ... thaw SHOULD max) can be reached,
characterized, a) that, taking into account the specified, stored boundary conditions, all possible temperature and / or humidity changes in the i temperature sections delta t ≦ 1 K and in j humidity sections delta x ≦ 1 g / kg are considered and the target function sum K is determined, whereby the starting point is the outside air condition (t AU, x AU) with K = 0, b) that all technically possible air states after each aggregate in a t, x-stage field e are characterized by the minimum effort (e, i, j) required up to this state point in the sense of the objective function, c) that only the Kmin (e, i, j) ≧ 0 can be the starting points for the change of state in the next unit, d) that the change in state in the room, preferably divided into the lounge area and an upper area, is considered from a technical and physical point of view as the change in state in an aggregate, but no additive is added to the target function sum, e) that the state points occupied by Kmin (e, i, j) ≧ 0 in the room-stay area, t (e, i), x (e, j), which lie within the desired room area, taking into account all boundary conditions, the technical represent possible indoor air conditions that can be achieved with the climate process, f) that the optimal ventilation process in the sense of the objective function in the room-stay area leads through the state point with the smallest, occupied Kmin (e, i, j) value, g) that the optimization of the ventilation process is repeated at regular intervals, this being done for predetermined time intervals or depending on the measured, preferably time-related, changes in the air status of the input variables (outside, exhaust and / or supply air). Method according to one of claims 1 to 4,
characterized,
that the aggregate-specific boundary conditions and technically possible changes in state, based on the default values using the actually measured change in air state, are automatically changed and stored according to a predetermined approximation function.

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