Method for Providing at Least One Design Configuration for a Compressed Air System - Patent application

Abstract

The present invention relates to a method for providing at least one design configuration of a compressed air system (1) comprising at least two compressors (11, 12) connected in parallel, wherein the method comprises the following steps: A computer receives component data (Dk), wherein the component data (Dk) comprises a compressor list (Lv) including a plurality of compressors (V1, V2, ..., Vn) of different types. The computer generates a branching data structure (B). The computer generates compressed air system configuration data (DKonf1, DKonf2) indicating a compressed air system configuration (Konf1, Konf2) in which two of the compressors (V1, V2, ..., Vn) from the compressor list are connected in parallel. The computer calculates at least one quality value for at least one of the compressed air system configurations (Konf1, Konf2) based on the compressed air system configurations (Konf1, Konf2) and on at least one technical parameter (Kt) of a compressor of the compressed air system configurations (Konf1, Konf2), where the at least one quality value indicates a quality of the compressed air system configuration (Konf1, Konf2) preferably relative to a quality criterion specified by a user. The computer provides at least one compressed air system configuration (Konf1, Konf2), which in each case has at least one assigned quality value.

Description

[Technical field]

[0001] The present invention relates to a method for providing at least one design configuration for a compressed air system comprising at least two compressors connected in parallel. [Background technology]

[0002] Compressed air systems often consist of a number of different components, i.e. components for the generation, preparation, storage and distribution of compressed air. In addition, modern compressed air systems are often characterized by a higher-level control of the components. The components for compressed air generation, i.e. one or more compressors, are often connected to one another in parallel and each define a compressed air path in the compressed air system. The components for compressed air preparation, storage and distribution are often connected to one another in series. Several parallel compressed air paths each originating from a compressor can be combined, in particular for compressed air preparation and / or compressed air storage, for example by providing a common compressed air tank to which some or all compressed air paths open and from which the compressed air is distributed to one or more consumers.

[0003] When designing a compressed air system, components and their interconnections are selected for the (real) compressed air system. Manufacturers of compressed air components often offer a product portfolio with many different series and models of compressed air components. Compressors can sometimes differ significantly in terms of their investment and operating costs, in terms of their technical parameters such as size, delivery pressure, delivery volume, as well as in terms of the number of operating conditions and parameters that can be set. Operators of compressed air systems are also frequently offered a wide range of different products for components other than the compressor. Thus, there are many different options for the design of a compressed air system, where the combination and assembly of the components to form a compressed air system is a complex task that requires precise knowledge of the components and extensive experience.

[0004] Typically, compressed air systems are designed to a large extent manually, i.e. not (fully) automated. When designing a compressed air system, the operator or supplier (user) mentally designs a possible configuration of the compressed air system, which is determined by the selected components and their interconnections. For this purpose, the user, based on given requirements and his own experience, selects components from a product portfolio and uses them to create a computer model of the configuration of the compressed air system (compressed air system configuration). Then, based on the simulation model, a computer simulation is performed for this compressed air system configuration. Based on the results of the simulation, this configuration is evaluated by the user. This configuration is evaluated on the basis of meeting the economic requirements, i.e. cost requirements, and technical requirements of the compressed air system. In an iterative process by repeating the above procedure, the user tries to gradually find a configuration that best meets the requirements. This process does not guarantee that the user will find an optimal solution for this configuration. The quality of the found compressed air configuration depends heavily on the available time and experience of the user. The creation of the configuration model on which the simulation is based is time consuming. The procedure is also sequential, such that simulations of different configurations are performed one after the other in time. Particularly elaborate simulation models to generate accurate simulation results are expensive in terms of computer resources. One such well-known manual procedure for designing a compressed air system is shown in Figure 2.

[0005] From EP 2 902 930 A2 a system and a method are known for making recommendations for adding components to an existing compressed air system in a partially automated manner. However, this solution requires interaction with a user, i.e. via an interface (graphical user interface (GUI)). The user may create a virtual model for the compressed air system via the GUI itself. Alternatively, the user may scan barcodes arranged on components of the existing compressed air system, in which case a virtual model of the existing compressed air system is automatically created based on this user activity. However, in both cases human involvement is required in generating the model in order for it to be simulated. This method somewhat relieves the user from the burden in creating the configuration model by scanning barcodes of existing components. However, finding a suitable solution still depends on the user's mental activity, with the disadvantages mentioned above in this regard.

[0006] In chapter 3.1 of the publication Atlas Copco: Compressed air manual, 8th edition, Wilrijk, Atlas Copco Airpower NV, 2015, ISBN 978-9-08153-580-9, the various operator requirements to be taken into account when designing a compression station for the production of compressed air are described. For the design of a compression station, a procedure is illustrated in figure 3.2, which shows as steps to be taken: data collection, evaluation, operational analysis, calculation and simulation of the considered compression station. An operational analysis to determine the compressed air demand is described as the basis for determining the optimal amount of compressed air to be produced. This operational analysis should include the measurement of operational data, for example over at least one week, and should preferably be supplemented by an analysis of similar existing compressed air stations. This measurement data then makes it possible to simulate different measures and changes in the compressor operation and to analyze their impact on the overall efficiency of the compression station. The described procedure is not automated and requires human involvement. Different compression stations (i.e., each with a specific combination of compressors in specific interconnections) are manually created based on operational analysis and then simulated one after the other. Finding the most cost-effective compression station is also based on human selection.

[0007] Regarding the control of compressed air systems, the publication Atlas Copco: Compressed air manual, 8th edition, Wilrijk: Atlas Copco Airpower NV, 2015, ISBN 978-9-08153-580-9 describes in chapters 2.5.6 and 2.5.7 higher-level or central control systems for compression stations. In addition to the conventional sequence switch for compression stations with 2-3 compressors, a further developed sequence switch with its own pressure sensor for the entire compression station with 2-7 compressors is described as higher-level control system. The central control system has the task of maintaining the pressure within a narrow pressure band while controlling the connected compressors as economically as possible. For operational efficiency, it is important that the central control system always selects the most efficient compressor or compressor combination when a compression station consists of compressors of different sizes. These higher-level and central control systems influence the start-up and stop-up of the individual compressors in existing compression stations during operation. Thus, individual compressors are selected for operation by the control system to the extent that they are switched on or off again relative to other existing compressors. The design of the compressed air system, i.e. the compressors actually present in the compressed air system and their interconnections, does not change.

[0008] WO 2010 / 072803 A1 describes a method for controlling or regulating compressed air stations and other compressed air technical devices having several interconnected compressors of different technical specifications. The method initiates a switching strategy in a control cycle in order to influence the available compressed air volume and, on the other hand, adaptively adjusts the available compressed air volume to the future operating conditions of the compressed air station relative to the withdrawal volume of compressed air. A switching strategy is understood to be a series of switching operations, i.e. discrete or continuous changes of manipulated variables, which cause a change in the operation of one or more components of the compressed air station. To initiate a switching strategy, different switching strategies are tested in a pre-simulation procedure based on a model of the compressed air station. Based on defined quality criteria, the relatively most advantageous switching strategy is selected and transferred to the system control of the compressed air station. This control procedure determines the operation of the existing compressed air system and takes into account different virtual operating states. The components actually present in the compressed air system and their interconnections remain unchanged. Summary of the Invention

[0009] Based on this prior art, the present invention aims to create a method for providing a compressed air system design configuration, which reduces the time required for the compressed air system design and preferably provides an optimal compressed air system configuration, optionally by taking into account the user's specifications. In particular, the method is intended to minimize the user's involvement required in the compressed air system design.

[0010] This object is solved by a method according to claim 1, a computer readable storage medium according to claim 25, a server according to claim 26 and a terminal according to claim 27.

[0011] This object is in particular solved by a method, in particular a computer-aided method, for providing at least one design configuration of a compressed air system with at least two compressors connected in parallel, which method comprises: - receiving, by the computer, component data indicative of components of the compressed air system and at least one technical parameter of each component, where the component data comprises at least one component list having a plurality of functionally identical components of different types and at least one technical parameter assigned to each component, where the component list is a compressor list comprising a plurality of compressors of different types; - generating, by the computer, a branching data structure comprising node data structures each assigned to one of at least two node levels, where each child node data structure at a lower node level is assigned to a parent node data structure at a higher node level, where generating the branching data structure comprises at least generating, in memory, a first compressor parent node data structure based on component data of the compressors in the compressor list; generating, in memory, a first compressor child node data structure based on component data of a compressor in the compressor list, where the first compressor child node data structure is assigned to the first compressor parent node data structure; generating, in the memory, a second compressor child node data structure based on component data of the compressor of the compressor list, where a compressor type of the second compressor child node data structure is different from a compressor type of the first compressor child node data structure, where the second compressor child node data structure is assigned to the first compressor parent node data structure; or generating, in memory, a second compressor parent node data structure based on compressor component data of the compressor list, where a compressor type of the second compressor parent node data structure is different from a compressor type of the first compressor parent node data structure; and generating, in memory, a second compressor child node data structure based on compressor component data of the compressor list, where the second compressor child node data structure is assigned to the second compressor parent node data structure; Equipped with - generating, by the computer, first compressed air system configuration data indicative of a first compressed air system configuration in which a compressor in the first compressor parent node data structure and a compressor in the first compressor child node data structure are connected in parallel; - generating, by the computer, second compressed air system configuration data indicative of a second compressed air system configuration in which a compressor of the first compressor parent node data structure or a compressor of the second compressor parent node data structure is connected in parallel with a compressor of the second compressor child node data structure; - calculating, by the computer, for at least one of the compressed air system configurations based on the compressed air system configuration data of the compressed air system configurations and on at least one technical parameter of a compressor of the respective compressed air system configuration, where the at least one quality value is indicative of a quality of the compressed air system configuration relative to a quality criterion, preferably specified by a user; - providing, by a computer, at least one compressed air system configuration, each with at least one assigned quality value; Equipped with.

[0012] The invention is based on the idea that a large number of conceivable combinatorial possibilities, preferably all combinatorial possibilities, of a set of available components of a compressed air system can be mapped in a (fully) automated manner by a computer using a branching data structure as different compressed air system configurations and evaluated, in particular systematically, with respect to a quality criterion using a computer. For a number of compressed air system configurations represented by the branching data structure, quality values ​​can be calculated in each case by the computer in order to evaluate the various compressed air configurations. Based on the evaluated compressed air system configurations, one or more suitable, preferably (only) optimal configuration(s) for the design of the compressed air system can be provided.

[0013] The branching data structure of the method according to the invention is generated in particular by an algorithm using the so-called branch-and-bound procedure. The branching data structure can be understood as an implementation of a tree representing a hierarchical structure, which in particular as a branching data structure that can be generated by a recursive loop is basically known from computer science. Starting from a root node, the tree data structure branches further over several levels of interconnected nodes (node ​​levels) and further to a terminal node. At least one node (child node) in a relatively lower node level is connected to a node (parent node) in a relatively higher node level. The set of nodes that are connected to a node in a higher node level, in a lower node level, up to the terminal node, is called a branch. The tree structure can be used to represent a discrete number of combination possibilities (combinations). The nodes of the tree define a solution space in which an (optimal) solution can be found. To find the optimal solution, the combinations mapped by the nodes can be traversed (breadth-first search or depth-first search). Sub-optimal combinations can be eliminated at an early stage by appropriate bounds: the size of the tree (number of nodes) and therefore the combinations to be traversed are thus effectively limited.

[0014] In the branching data structure generated according to the invention, each node data structure represents in particular a compressed air system configuration, which in each case comprises a particular compressor and, optionally, further components of the compressed air system. In particular, the compressor parent node data structure and the assigned compressor child node data structure represent, respectively, the parallel-connected compressors of the compressed air system configuration. In particular, (all) compressors of the compressed air system configuration that are connected in parallel to each other are represented in the branching data structure along a node path (in the tree depth direction) of the compressor node data structures that are assigned to each other (connected), starting from the top node level to the nodes at the lower node level. In this respect, a compressed air system configuration (uniquely) assigned to a particular compressor node data structure (compressor parent node data structure or compressor child node data structure) includes, i.e. has been generated in memory, in particular (all) compressors of those compressor node data structures that are present in each case (i.e. along the node path) from the next higher node level to the top node level, starting from this particular compressor node data structure (upwards in the node hierarchy) and following the (pairwise) assignment of branching data structures (child node-parent node pairs). In particular, a compressed air system configuration represented by an end node at the bottom node level includes all compressors along the (unique) node path up to the node at the top node level. The same compressor node data structure may be called compressor child node data structure for the higher node level and compressor parent node data structure for the lower node level. Two different compressor node data structures (with two different node paths) represent two different compressed air system configurations.

[0015] The branching data structure generated according to the invention is generated (constructed) by repeated, preferably recursively repeated, generation of compressor node data structures, in particular until a termination criterion is met. The termination criterion takes into account in particular a quality value of the compressed air system configuration represented by the compressor node data structure. The new compressor node data structure is generated in a recursive loop, in particular until a termination criterion prevents further compressor node data structures from being generated. The new compressor node data structure may in particular be generated as a compressor child node data structure at a lower node level relative to the node level of the compressor parent node data structure, or as a further compressor parent node data structure on the same node level of an already generated compressor parent node data structure, in particular until a termination criterion is met. The generation of further compressor node data structures may depend on constraints. In particular, the maximum number of node levels generated in the branching data structure may preferably be limited by the maximum number of (parallel connected) compressors, as a constraint. The branching data structure comprises at least two, preferably at most three, more preferably at most five, more preferably at most ten, preferably at most 20 node levels. Thus, the compressed air system configuration to be designed may comprise two, preferably at most three, more preferably at most five, more preferably at most ten, more preferably at most 20 compressors, which are in particular connected in parallel with each other.

[0016] A compressed air system configuration specifies in particular a set of components of a compressed air system and their interconnections. A compressed air system configuration may be understood as a possible (virtual) arrangement of the components of a compressed air system, which is taken into account for the (optimal) design of a compressed air system. A compressed air system design includes in particular a new planning, modification and / or expansion of a compressed air system. In particular in case of a new design, the (real) initial configuration of the compressed air system is not available. Removal or replacement of a component of an existing compressed air system may be understood as a modification and addition of a component as an expansion of the compressed air system.

[0017] The designations of components refer in particular to components for compressed air generation (compressors), components for compressed air preparation (driers, filters, oil separators), components for compressed air storage (compressed air tanks) and components for compressed air distribution. Components performing the same function in a compressed air system are called functionally identical components. For example, all compressors have the same function of compressing (generating) compressed air, even if they are of different types. The type of a component is preferably indicated by a (unique) type identifier (model number) of this component in the product portfolio. A compressor type can thus be a specific model or a model variant of a compressor. Within the context of the present invention, blowers can also be understood as compressors.

[0018] The component data includes information about the components, in particular at least one technical parameter, preferably energy consumption, as well as preferably at least one (unique) functional identifier and one (unique) type identifier, in particular for the (unique) identification of the components in a product portfolio. The component data may additionally include economic parameters of the components, such as investment costs (price). The component data originates in particular from a product database. The component data comprises in particular one or more component list(s). The component list comprises several, preferably all functionally identical components from the product portfolio that may be considered for use in the compressed air system. A component list may be understood as a data structure that specifies the functionally identical components from the product catalog, each component being assigned information about its technical properties and preferably about its cost. A component list may be understood as a linked list of data structures, each containing component data relating to a particular component. The component data may be received sequentially by the computer. In particular, parts (sub-lists) of the component lists, in particular parts of the compressor list, may be received sequentially. The received component data may have been sent by different sending devices.

[0019] One of the one or more component list(s) is a compressor list. In addition to (at least) one compressor list, the component data may include, for example, a dryer list, a filter list, and / or a compressed air tank list. The component data may comprise multiple compressor lists, each compressor list including, among other things, a set of (similar) compressors of different models. The component data for a compressor may indicate information about whether a particular type of compressor is a variable speed compressor or not. The compressor list may have multiple entries (list positions), for example, for more than 5, preferably more than 10, more preferably more than 20, more preferably more than 30, more preferably more than 50, more preferably more than 100, more preferably up to 200 different compressors. The length of the compressor list (number of available compressors) and, in particular, the maximum number of compressors allowed in the compressed air system configuration (constraints) significantly determine the maximum size that the branching data structure can reach.

[0020] The quality value may in particular indicate an estimated or calculated cost, preferably an energy cost and / or an investment cost and / or a maintenance cost, of the compressed air system configuration. The quality value may in particular indicate an energy consumption of the compressed air system. The quality value may in particular be based on a predefined, in particular received, energy price and / or a CO2 emission price. The calculation of the quality value may be based on at least one technical parameter of one, some or all components of the compressed air system configuration, in particular a compressor. The calculation of the quality value may (additionally) be based on at least one economic parameter of one, some or all components of the compressed air system configuration, in particular a compressor. For the calculation of the quality value, the branching data structure may be scanned in depth and / or width to calculate an assigned quality value for the compressed air system configuration corresponding to the compressor node data structure. Preferably, the quality value is calculated for the compressed air system configuration already generated in the form of a compressor node data structure (simultaneously) and the branching data structure is (further) constructed, i.e. a further compressor node data structure is generated.

[0021] In particular, at least a first quality value is calculated based on the first compressed air system configuration data and at least one technical parameter of a compressor of the first compressed air system configuration, where the first quality value is indicative of a quality of the first compressed air system configuration relative to a quality criterion, preferably specified by a user. In particular, at least a second quality value is calculated based on the second compressed air system configuration data and at least one technical parameter of a compressor of the second compressed air system configuration, where the second quality value is indicative of a quality of the second compressed air system configuration relative to a quality criterion.

[0022] Providing the compressed air configuration may consist in storing the compressed air system configuration, in particular in a computer readable memory, in transmitting it, in particular via a (wireless) data connection, or in displaying it, in particular by visualizing it via a display device (screen).

[0023] The (computer-aided) method according to the invention has the advantage that possible configurations of a compressed air system that can be considered for the design can be generated and evaluated (fully) automatically, quickly, and in particular systematically. In particular, no user participation, in particular no intellectual participation of the user, in the creation of the possible configurations is required. This significantly reduces the time required for the design of a compressed air system. Furthermore, it can be guaranteed that an optimal design configuration is found.

[0024] In one embodiment, the method further comprises the steps of receiving constraint data by the computer, where the constraint data indicates at least one predefined constraint, preferably by a user, for the compressed air system configuration, and determining by the computer based on the constraint data and the first compressed air system configuration data whether the first compressed air system configuration satisfies the predefined constraint and / or based on the constraint data and the second compressed air system configuration data whether the second compressed air system configuration satisfies the predefined constraint. In particular, at least one quality value is calculated if the respective compressed air system configuration satisfies the predefined constraint. In particular, at least one first quality value is calculated if the first compressed air system configuration satisfies the predefined constraint. In particular, at least one second quality value is calculated if the second compressed air system configuration satisfies the predefined constraint. By checking the constraints, it can be ensured that only those compressed air system configurations that satisfy the predefined constraint are taken into account for the design. In particular, configurations that do not satisfy technical specifications for a new compressed air system to be planned, modified or expanded can thus be excluded as design configurations at an early stage. In particular, the calculation of the quality value is performed (only) if the constraints are satisfied, which may save time and computational resources, especially in cases where the calculation of the quality value is costly due to simulation runs.

[0025] In one embodiment, the at least one predetermined constraint for the compressed air system comprises a specification regarding the maximum number of compressors and / or the maximum number of different compressor types and / or whether a variable speed compressor may or must be included. The terms "may" and "must" can in each case be understood as a specification of a user, in particular an operator of the compressed air system. A variable speed compressor places increased demands on the control system, so some users may want to exclude such compressor types for the design of their compressed air system. On the other hand, a variable speed compressor may be explicitly desired by other users, for example, in particular to ensure a demand-oriented compressed air production.

[0026] In one embodiment, the at least one predetermined constraint for the compressed air system comprises a maximum footprint of the compressed air system and / or a required minimum pressure of the compressed air system and / or a required maximum pressure of the compressed air system, in particular the required minimum pressure being the main criterion for the design of the compressed air system.

[0027] In one embodiment, the at least one predefined constraint for the compressed air system comprises in particular a maximum investment budget for new projects, modifications or expansions of the compressed air system, which in particular refers to the entire compressed air system with all installed components, in particular the compressor, making it possible to exclude compressed air system configurations that are too expensive even if they could meet the technical specifications.

[0028] In one embodiment, at least one technical parameter of the component, in particular of the compressor, comprises a pressure-dependent characteristic curve of the energy consumption and / or power consumption and / or a delivery volumetric flow rate, in particular at maximum pressure, and / or a CO2 emission, in particular per compressed air volume. In particular, the quality value may be calculated based on the CO2 emission (of the compressor) per (produced) compressed air volume (delivery volume). The CO2 emission may be determined by the energy consumption of the component (of the compressor) and preferably a predefined (from the user) or received (from the power plant operator) (current) value indicating the emitted CO2 emission per unit of provided (electrical) energy (e.g. kWh). In this respect, in particular an additional indication regarding the CO2 emitted during the generation of the required drive power of the compressor may be required for the indication of the CO2 emission as a technical parameter. In particular, the quality value may be calculated based on the CO2 emission (of the compressor) per compressed air volume (e.g. m 3 The CO2 footprint per tonne (kW) can be another technical parameter that indicates how much CO2 is emitted during the production of a compressed air volume by a particular compressor.

[0029] In one embodiment, at least one economic parameter of the respective component is assigned to each component in addition to the at least one technical parameter, where the economic parameter is in particular representative of the investment and / or maintenance costs of the component, and where the at least one quality value is calculated in particular based on at least one economic parameter and at least one technical parameter of at least one compressor of the respective compressed air system configuration. Preferably, the quality value is calculated in particular based on at least one economic parameter of some (all) compressors of the respective compressed air system configuration and / or based on at least one technical parameter of some (all) compressors. In particular, a first quality value is calculated in particular based on at least one technical parameter and at least one economic parameter of compressors of a first compressed air system configuration and / or in particular a second quality value is calculated in particular based on at least one technical parameter and at least one economic parameter of compressors of a second compressed air system configuration.

[0030] In one embodiment, the compressor list includes at least one compressor of the existing compressed air system and at least one compressor that is not installed in the existing compressed air system. The (actual) already installed compressors can also be mapped in the branching data structure by adding one or more compressors of the (actual) existing compressed air system to the compressor list or by including them in the compressor list created based on the product portfolio. In this way, the existing compressors of the compressed air system become part of the solution space for the design configuration to be explored. In particular, it is possible that a compressed air system configuration that does not include one or more existing compressors is evaluated better than the existing compressed air system configuration based on the quality value. In this way, the method can perform a modification of the existing compressed air system, i.e., removal or replacement of an installed compressor. For example, the required compressed air delivery rate can be reduced and it may be economical to remove a compressor. Since the installation of the existing compressed air system, a more powerful and / or more efficient compressor may have in the meantime become available in the product portfolio, which improves the quality of the existing compressed air system configuration with regard to quality criteria and should therefore be installed in place of the existing compressor.

[0031] In one embodiment, generating the branching data structure further comprises generating, in the memory, a compressor parent node data structure at a top node level based on component data of at least one compressor of an existing compressed air system. In particular, the top node level is assigned to exactly one parent node data structure, which in particular corresponds to the origin node (root) of the branching data structure (tree). In this way, an existing compressed air system configuration can be mapped as an output configuration in the branching data structure, i.e. by a compressor parent node data structure at a top node level (root node) that includes (some or all) of the compressors of the output configuration. In this case, at least one compressor of the initial configuration will be included in each compressed air configuration considered. Thereby, the design of the compressed air system in the form of an extension by adding compressors can be easily implemented.

[0032] In a variant of this embodiment, where an existing compressed air system is mapped as an initial configuration in the branching data structure (by the root node), the compressor list may include compressors of the existing compressed air system whose assigned technical and / or economic parameters have a negative sign. Alternatively (or additionally), the compressors may be assigned a corresponding identifier (marker) to identify them as (real) compressors of the existing compressed air system. By having a negative sign or such an identifier, the addition of a compressor node data structure for this (real) existing compressor may represent the removal of this compressor. For example, a negative energy consumption will reduce the total energy consumption of the configuration, or a negative investment cost will represent the sales revenue for a compressor to be removed. In this way, a compressor that is present twice in the compressed air configuration, once with a positive sign of the parameter and once with a negative sign, may be ignored when calculating the quality value.

[0033] In one embodiment, the generation of the compressor parent node data structure and / or the compressor child node data structure is additionally based on component data of components for compressed air preparation, in particular a group of components for compressed air preparation. In particular, the component list is a list of components for compressed air preparation, preferably a list of groups of components. The components for compressed air preparation are preferably connected in series to the compressor. The group of components for compressed air preparation is in particular intended to be placed along the compressed air path between the compressed air outlet of the compressor and the compressed air reservoir, and comprises, for example, a dryer, a filter, an oil separator or other components. A group of several compressed air preparation components may be grouped (modeled) as a (single) alternative component. By associating additional component data indicating compressed air preparation components to the compressor node data structure, the series connection of these compressed air preparation components in the compressed air path defined by the compressor of the compressor node data structure may be mapped in the branching data structure.

[0034] In one embodiment, preferably as an alternative to the previous embodiment, the constraints for the compressed air system configuration specify a required minimum differential pressure to compensate for the pressure loss of at least one component for compressed air preparation, in particular of a group of components for compressed air preparation. The required minimum differential pressure may in particular be specified in addition to the required minimum pressure of the compressed air specification. The pressure loss caused by the compressed air preparation may be taken into account via a suitable constraint when finding a suitable compressed air system configuration. In this case, only those compressed air system configurations whose generated minimum pressure is high enough to compensate the pressure loss of the processing components, for example 0.3 bar, and still provide the minimum pressure required by the consumer may be considered as solutions.

[0035] In one embodiment, generating the branching data structure comprises generating, in memory, at least one further compressor node data structure at a node level based on the compressor component data of the compressor list, where preferably the compressor type of the compressor node data structure to be generated is different from the compressor type of the already generated compressor node data structure of this node level assigned to the same compressor parent node data structure. In this way, the branching data structure (tree) can be increased in width. Compressor child node data structures for the compressor parent node data structure from one or more (lower) node levels(s) are added, preferably in a recursive loop, in particular until a termination criterion is met. At the (upper) node level of the already shown compressor parent node data structure, further compressor parent node data structures are added, preferably in a recursive loop, in particular until a termination criterion is met. This is especially true if there is no single compressor parent node data structure at the top node level (root node). In particular, further compressor node data structures may be generated until no new compressor node data structures can be generated for a selection of compressors or for all compressors in the compressor list. Based on certain criteria, in particular checked based on a quality value, preferably based on a minimum branch cost value, the generation of certain compressor child node data structures or compressor parent node data structures may be prevented. In principle, component child node data structures based on components other than compressors may also be added to the branch data structure to represent their interconnections within the compressed air system.

[0036] In one embodiment, a compressor parent node data structure based on component data of the same compressor (i.e. of the same type) of the compressor list is generated multiple times at the node level, where for each of the multiple compressor parent node data structures, a component child node data structure is generated and assigned to one of the multiple compressor parent node data structures, where the component types of the component child node data structures are different from each other. In this way, a branching data structure (tree) can be generated in which different compressed air system configurations for the same compressor are mapped in series with different components, such as different variants of compressed air preparation components. For example, a particular compressor can be mapped in this way in the branching data structure (tree) in two different series circuits with different compressed air dryers in each case.

[0037] In one embodiment, the compressors in the compressor list are ordered according to a sorting criterion, where the component data of the sorted compressors in the compressor list at the same or lower list position as the compressors in the compressor parent node data structure are used to generate the compressor child node data structure to be assigned to the compressor parent node data structure. This helps to avoid permutations between the considered compressed air configurations, i.e. equivalent compressed air configurations where the same compressor types are connected in parallel to each other, but with different arrangements of the parallel compressed air paths relative to each other. The exchange of compressed air paths of two parallel connected compressors in the branching data structure represents the same compressed air configuration and therefore does not need to be considered more than once. Unnecessary expansion of the branching data structure can be avoided and calculation time can be saved.

[0038] In one embodiment, the quality criterion is a cost criterion, where at least one quality value is indicative of the energy cost and / or the investment cost and / or the maintenance cost of the compressed air system configuration. The energy cost and the maintenance cost may be related to a certain operation time, preferably specified by a user. The investment cost may include the installation cost for the compressed air system configuration in addition to the price of the components. In particular, the most advantageous compressed air system configuration, preferably the most advantageous (optimum) compressed air system configuration, may be developed based on the cost criterion.

[0039] In one embodiment, the method further comprises: comparing, by the computer, the two compressed air system configurations based on the assigned quality values; storing the compressed air system configuration data of the compressed air system configuration whose assigned quality value better meets the quality criterion as the current best compressed air system configuration, preferably storing the quality value assigned to the current best compressed air system configuration as the current best quality value. The current best quality value indicates, in particular, the current best cost, preferably the lowest cost. The current best compressed air system configuration may change continuously, in particular while the branching data structure is searched for a suitable compressed air system configuration. The variable of the current best compressed air system configuration is initialized, in particular with the first found compressed air system configuration or with the initial configuration. In particular, the found current best compressed air system configuration is temporarily stored in order to be compared with other compressed air system configurations that are considered to be even better in terms of the quality criterion. Preferably, the compressed air system configuration stored last, i.e. at the end of the process, is the optimal solution for the design of the compressed air system.

[0040] In one embodiment, calculating the at least one quality value comprises calculating, preferably based on an estimation, a minimum configuration cost value indicating a lower limit value for the cost of the compressed air system configuration. A lower limit value as a minimum configuration cost value may be understood as a (conservative) estimate of the cost that is sure to be lower than a (relatively accurately) calculated cost of the compressed air system configuration, preferably based on a heuristic method. The minimum configuration cost value is preferably based on energy costs and / or investment costs of at least one, preferably all, of the components of the compressed air system configuration.

[0041] The energy cost may be predefined as a technical parameter of the component (compressor), in particular as part of the received component data. The energy cost is preferably calculated based on the energy consumption of the compressor with the highest energy efficiency of the compressors included in the compressed air system configuration. The investment cost may be predefined as an economic parameter of the component (compressor), in particular as part of the received component data. Preferably, the investment cost is calculated as the sum of the investment costs of the compressors included in the compressed air system configuration. In particular, the minimum configuration cost value gives a conservative estimate of the cost of the compressed air system configuration. In particular, no (complex) simulation of the compressed air system configuration is performed to calculate the minimum configuration cost value. The calculation of the minimum configuration cost value is based on the idea that a more accurate, but expensive calculation performance in terms of computer power, can be omitted if the already conservative cost estimate - i.e. the estimated cost value which is in any case lower than the actual cost - is worse (higher) than the quality value of the currently best known compressed air configuration (i.e. the currently cheapest known cost value). In particular, if the minimum configuration cost value is worse than the current best quality value with respect to the quality criterion, cost-intensive computer simulation runs may be omitted.

[0042] In one embodiment, the calculation of at least one quality value of the compressed air system configuration comprises the calculation of a minimum branch cost value, which preferably specifies, based on an estimation, a lower limit value of the cost of those further compressed air system configurations, including a compressor of the compressed air system configuration, in particular not (yet) represented by a node data structure in the branch data structure. Those further compressed air system configurations, including a compressor of the (currently considered) compressed air system configuration, in particular those (all) further compressed air system configurations, not (yet) represented by a node data structure in the branch data structure, whose compressor node data structure is located at a lower node level relative to the node level of the compressor node data structure corresponding to the (currently considered) compressed air system configuration (i.e. compressed air system configurations not considered so far, but possible in principle). The lower limit value as a minimum branch cost value can be understood as a (conservative) estimate of the cost, which is sure to be lower than the (relatively accurate) calculated costs of all compressed air system configurations of the branches of the branch data structure, preferably based on a heuristic method. The minimum branch cost value is preferably based on the energy costs of the compressors of the compressor list and / or on the investment costs of at least one, preferably all, of the components of the compressed air system configuration. The energy cost is preferably calculated based on the energy consumption of the compressor with the highest energy efficiency among all compressors included in the compressor list (not only in the considered compressed air system configuration). The investment cost is preferably calculated as the sum of the investment costs of the compressors included in the compressed air system configuration. In particular, the minimum branch cost value gives a conservative estimate of the cost of those compressed air system configurations that belong to a branch of the branch data structure (tree) starting at the node of the currently considered compressed air system configuration. In particular, the minimum branch cost value gives a lower bound on the costs of all compressed air system configurations that are directly or indirectly assigned to a parent node that could potentially still be generated. In particular, no (time-consuming) simulation of the compressed air system configuration is performed to calculate the minimum configuration cost value.The calculation of the minimum branch cost value is based on the idea that further branches of the data structure may be omitted if the already conservative cost estimate - i.e. the estimated cost value which is in any case lower than the actual cost - is worse (higher) than the quality value of the currently best known compressed air configuration (i.e. the currently cheapest known cost value). This is because adding an additional node data structure representing a component which is actually added to the compressed air system would increase the cost of the corresponding compressed air system configuration. If the minimum branch cost value assigned to a parent node data structure is worse than the current best quality value with respect to the quality criterion, the generation of further child node data structures may be prevented at an early stage.

[0043] In one embodiment, based on a quality value assigned to a compressed air system configuration, preferably based on a minimum branch cost value, in particular if the quality value, preferably the minimum branch cost value, satisfies a worse quality criterion than the stored current best quality value assigned to the current best compressed air system configuration, the generation of further compressor child node data structures of the compressor parent node data structure is precluded or an already generated compressor child node data structure of the compressor parent node data structure is deleted. This procedure corresponds in particular to the so-called bounding of a branch-and-bound procedure. Further compressor child node data structures are not generated or are deleted if the minimum branch cost value is higher than the current best quality value. The current best quality value may be a simulated cost value calculated by simulation. As a result of the bounding, unnecessary expansion or branching of the branch data structure (tree) can be avoided. Thus, the number of compressed air configurations to be tested is limited. This saves computing capacity for compressed air configurations that are unnecessarily considered. Thus, the procedure is accelerated without precluding finding a better (optimal) solution.

[0044] In one embodiment, calculating the quality value comprises performing a computer simulation and calculating a simulated cost value based on the results of the computer simulation, where the simulated cost value is indicative of the cost of the compressed air system configuration over a certain (predetermined) operating period. In particular, the certain operating period is set by a user (simulation horizon). For example, the operating period comprises a simulated period of 7 days and can preferably be extrapolated to a period of one year. The computer simulation is based in particular on a simulation model of the compressed air system configuration, which in particular maps the dynamic behavior of the compressed air system configuration, where the simulation model preferably takes into account as boundary conditions a time-varying compressed air consumption profile (in the case of a configuration composed of compressors) or a counter pressure profile (in the case of a configuration composed of blowers), preferably specified by the user, and / or maps the dynamic operating behavior of at least one compressor of the compressed air system configuration and / or maps the control behavior of a central control system, in particular an integrated control system, of the compressed air system. The simulation model is based in particular on a set of several (time-dependent) differential equations, preferably partial differential equations, which are in particular solved (iteratively) by means of numerical integration methods and in particular integrated stepwise over time (by a predefined time step width). The compressed air consumption profile in particular indicates the amount of compressed air made available to the consumer over time (amount profile over time). The compressed air consumption profile is typically specified for the compressor. The counter pressure profile is typically specified for the blower, which in the sense of the present invention can also be understood as the compressor. An integrated control system is understood to mean the control methods described in WO 2010 / 072808 A2 and WO 2010 / 072803 A1, the descriptions of which are incorporated in the present application by reference. The computer simulation allows a very accurate calculation of the costs of the compressed air system configuration that will occur during the operation of the compressed air system.In particular, the simulated cost values ​​are indicative of the energy and / or maintenance costs of the compressed air system configuration. Computer simulations are relatively expensive in terms of computational capacity and time, in particular compared to the generation of branching data structures and the calculation of minimum configuration or branching cost values, which are determined by a (much) simpler calculation.

[0045] In one embodiment, the method comprises storing at least one compressed air system configuration in a simulation queue list that specifies compressed air system configurations intended for computer simulation. In particular, compressed air system configurations whose minimum configuration cost value is smaller than the current best quality value are temporarily stored in the simulation queue list. By performing computationally intensive computer simulations only for a limited number of compressed air system configurations in the simulation queue list, computational resources are used only for the most promising compressed air system configurations. As a result, a suitable, preferably optimal design configuration can be found in a relatively short time, even for complex and accurate simulation models.

[0046] In one embodiment, the computer simulations for different compressed air system configurations are performed in parallel, preferably at least partially simultaneously, in particular by different processors or by different groups of processors. Preferably, the compressed air system configurations held in the simulation waiting list for the simulation are simulated independently of each other by different processors. The compressed air system configurations found by the branching data structure can be simulated independently of each other, whereas the generation of the branching data structure (tree) cannot be parallelized or can be parallelized only with difficulty. The computer simulation generally imposes significantly higher demands on the computational capacity compared to the construction of the branching data structure. By parallelizing the computer simulation, the process can be significantly accelerated compared to performing the computer simulations sequentially for the suitable found compressed air system configurations.

[0047] In one embodiment, at least one step for generating the branching data structure and the computer simulation for the compressed air system configuration are performed at least partially simultaneously, in particular, an algorithm for generating the branching data structure and a computer simulation for the compressed air system configuration found by said algorithm are run in parallel.

[0048] In one embodiment, the method comprises pausing the step of generating the branching data structure if the number of compressed air system configurations in the simulation waiting list reaches a predetermined maximum number, in particular until the number of compressed air system configurations in the simulation waiting list reaches a value below the maximum number. This may prevent computer simulations from being performed for an unnecessarily large number of (relatively inferior) compressed air system configurations. By limiting the length of the simulation waiting list, a cost value simulated by the computer simulation, potentially indicative of a new currently best compressed air system configuration, may be taken into account when generating the branching data structure. In particular, by comparing the simulated cost value with the minimum branching cost value for the considered compressed air system configuration, the branches of the second data structure may optionally be truncated early, thereby preventing the inclusion of one or more worse compressed air system configurations in the simulation waiting list in a timely manner. Thus, the method saves computational resources and is accelerated.

[0049] In one embodiment, providing at least one compressed air system configuration preferably consists in outputting an optimal compressed air system configuration, in particular whose assigned quality value, preferably whose assigned simulated cost value, meets a quality criterion better than the quality values ​​of all other compressed air system configurations that may be generated based on the compressor list. In this respect, the method according to the invention solves an optimization problem.

[0050] The aforementioned object is further solved by a computer-readable storage medium comprising instructions for implementing at least some, preferably all steps of the method according to the invention, in particular when executed on at least one computing unit. The computer-readable storage medium according to the invention has similar advantages as those already explained in connection with the method according to the invention. In particular, the storage medium can be part of a notebook computer of a user, for example a field worker of the compressed air system provider, or a server of the compressed air system provider.

[0051] The aforementioned object is further solved, in particular, by a server comprising a computer-readable storage medium according to the invention and at least one computing unit for executing instructions.

[0052] The aforementioned object is further solved by a terminal device adapted in particular to send component data to a server, preferably a server according to claim 26, where the component data is indicative of components of the compressed air system and at least one technical parameter of each component, where the component data comprises at least one component list with a plurality of functionally identical components of different types and at least one technical parameter assigned to each component, where the component list is a compressor list comprising several compressors of different types, where the terminal device is further adapted in particular to send constraint data indicative of at least one constraint for the compressed air system, preferably a constraint according to the aforementioned embodiment of the invention, to the server, preferably a server according to the present invention, and / or to send in particular a desired compressed air consumption profile for the compressed air system to the server, preferably a server according to the present invention, and / or to display in particular at least one compressed air system configuration, preferably with an assigned quality value.

[0053] Exemplary embodiments of the invention are explained in more detail below with reference to the drawings. [Brief description of the drawings]

[0054] [Figure 1] FIG. 1 shows a schematic diagram of a compressed air system. [Diagram 2] FIG. 2 shows a prior art method for designing a compressed air system. [Diagram 3] FIG. 3 shows a general schematic diagram of the sequence of the method according to the invention. [Figure 4] FIG. 4 shows a schematic diagram of the solution space for compressed air system configuration according to the method of the present invention. [Diagram 5] FIG. 5 shows a schematic diagram of a branching data structure, component data, and compressed air system configuration data according to one embodiment of the method of the present invention. [Figure 6] FIG. 6 shows a schematic diagram of the sequence of the method according to the invention for finding the optimal compressed air system configuration. [Figure 7] FIG. 7 shows a schematic diagram of an exemplary branching data structure (tree) as part of the present invention. [Figure 8] FIG. 8 shows a schematic diagram of an exemplary sequence for generating a branching data structure as part of the present invention. [Figure 9a] FIG. 9a shows a first portion of a detailed schematic diagram of an exemplary branching data structure (tree) as part of the present invention. [Figure 9b] FIG. 9b shows a second part of the diagram of an exemplary branching data structure according to FIG. 9a. [Figure 10] FIG. 10 shows a simplified flow chart of the generation of branching data structures as part of the present invention. [Figure 11] FIG. 11 shows a schematic diagram of a simulation model of a compressed air system. [Figure 12] FIG. 12 shows a schematic diagram of a simulation as part of the method according to the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0055] In the following description of the invention, the same reference numbers are used for the same and similarly acting elements and method steps.

[0056] A compressed air system (compressed air station) is built by a number of different components, which are arranged and interconnected or connected to each other in a defined manner. These components are for compressed air generation, compressed air preparation, compressed air storage and compressed air distribution. Certain components have their own control systems. In many cases, compressed air systems have a higher-level control system for the whole plant.

[0057] 1 shows a schematic diagram of a compressed air system 1 with two parallel compressed air paths 2 and 3. The two parallel compressed air paths 2, 3 have a compressor 11, a dryer 21 and a filter 31, as well as a compressor 12, a dryer 22 and a filter 32. The dryers 21, 22 and the filters 31, 32 are located downstream of the compressors 11, 12, respectively. Both compressed air paths 2, 3 open into a common compressed air tank 40 serving to store the compressed air. From the compressed air tank 40, the compressed air is distributed via a compressed air network to one or more consumers 50. The compressors 11, 12 of the compressed air system 1 are regulated by a central control system 60.

[0058] A compressed air system configuration specifies a set of compressed air system components and their interconnections (the arrangement and connection of the components relative to each other). Whenever "configuration" is mentioned below or when a figure refers to "configuration", it always means the compressed air system configuration, i.e. the configuration of the compressed air system. In this regard, Figure 1 shows a particular actual compressed air system configuration that was selected when designing an actual existing compressed air system 1.

[0059] Compressed air systems have to be designed according to the needs of the user. For the design of a compressed air station, i.e. for the new planning, modification (removal or replacement of components) or expansion of a compressed air system, typically a large number of different components are taken into account, which are offered by one or various manufacturers in their product portfolio. The design has to meet certain quality criteria, e.g. from an economic point of view, the life cycle costs or the so-called "return on investment", and from a technical point of view, the compressed air consumption profile specified or envisaged by the user. The aim of the design is to find a compressed air configuration (design configuration) that meets the quality criteria.

[0060] The key influencing factors typically considered in quality standards are energy costs, maintenance costs and investment costs, where energy costs and maintenance costs differ from investment costs in that investment costs can only be determined by analyzing the configuration itself, whereas the dynamic behavior of the components in a particular configuration must always be determined to determine the energy costs and maintenance costs. Then, based on the dynamic behavior of the components in a specific configuration, the energy costs and maintenance costs can be inferred.

[0061] FIG. 2 shows a method for manually determining a compressed air station configuration of the prior art. The method according to FIG. 2 is partly computer-assisted in terms of simulation, but is essentially manual. A user or operator comes up with a configuration. For this, he uses a product portfolio of components, such as in a catalog, and takes into account any constraints that may exist. Based on the configuration that has been conceived, the user manually creates a simulation model with the aid of a simulation software. Then, based on the simulation model, a simulation is carried out. The execution of the simulation itself is carried out automatically. Once the simulation has been carried out, the configuration is evaluated based on the results of the simulation. In particular, the values ​​necessary for the evaluation are determined from the results of the simulation. If a configuration is interesting as a possible solution for the design, the user focuses on this configuration. The user then checks whether there is a sufficiently good solution among the already stored configurations. If not, the user checks whether he still has an idea for another configuration that could be a sufficiently good solution. If the user still has such an idea, the method proceeds to the next iteration, in which a concrete configuration is conceived. If the user does not have any ideas, the method ends. If a sufficiently good solution already exists among the stored configurations, the user can exit the method and use this configuration to design the compressed air system.

[0062] The method implemented according to Fig. 2 provides for the creation, simulation and evaluation of several configurations. In this context, human creation of configurations and manual creation of simulation models are limitations to find a suitable configuration. Creating a configuration according to the method of Fig. 2 is a creative process. In the creative process, the user relies on heuristics that he has learned, i.e., empirical knowledge, so the duration and outcome of this process depend heavily on the user's experience. An experienced user tends to determine a better configuration in less time. However, even an experienced user has no guarantee of finding the best possible configuration.

[0063] In addition, the devised configurations must be made available to the simulation software before the simulation is performed. This is because the quality of the configurations can only be reliably evaluated after the simulation is performed. Creating a configuration model for a simulation is time consuming. If you only have 3 minutes to input the configurations, you cannot test more than 20 configurations per hour. With limited time, you can only check a limited number of configurations. There may not be enough time to find the best possible configuration.

[0064] Using a calculation example, it is illustrated that the number of possible configurations grows exponentially, i.e. with the number of components in the compressed air system and with the number of components available in the product portfolio. For simplicity, only the compressor of the compressed air system is considered below. For example, the product portfolio includes various types of compressors. Assuming that a maximum of eight compressors can be installed in the compressed air system and ignoring permutations, the following number of possible combinations are possible:

[0065]

number

[0066] is obtained, i.e., the possible potential configurations with eight compressors connected in parallel in the compressed air system. With the prior art method shown in Figure 2, the problem is that due to the large number of possibilities, the entire solution space cannot be systematically searched for the best configuration. In addition, the prior art method does not ensure that an optimal configuration is actually found.

[0067] Starting from this problem, the invention is based on the realization that the design of a compressed air system is an optimization problem, the solution of which is the optimal configuration of the compressed air system under the available components and given conditions. Figure 3 shows a general schematic diagram of the flow of the inventive computer-aided method for finding an optimal solution for the configuration of a compressed air system. The optimization problem illustrated in Figure 3 consists of identifying and providing a suitable compressed air system configuration, in particular an optimal compressed air system configuration, on the basis of a given initial configuration of an existing compressed air system and with the components of the compressed air system available in the product portfolio, in particular compressors V1, V2, ... Vn (see Figures 2, 7, 8, 9a, 9b), for a given compressed air consumption curve, taking into account certain constraints based on a given quality criterion. The method according to the invention for providing at least one design configuration of a compressed air system solves the problem outlined in Figure 3.

[0068] In the method according to FIG. 3, an initial configuration is taken into account. It is possible to modify and / or expand an existing compressed air station, for example, since one or more components of the compressed air station have reached the end of their useful life and therefore need to be replaced. It is also possible to improve the efficiency of the compressed air station by replacing old components with newer, more efficient components. It is also conceivable that the compressed air consumption foreseeably increases to such an extent that the existing compressed air station can no longer reliably cover it. In such cases, the output configuration must be taken into account when designing a compressed air station. By modifying or expanding the output configuration, a new configuration is created that can be considered as an (optimal) configuration. It should be noted that if the output configuration is to be taken into account, no investment costs arise for components that are retained in the new configuration with respect to the output configuration. For components that are removed or replaced in the new configuration with respect to the initial configuration, there are essentially negative investment costs, since these components can be sold as used components and generate profit. In case of replanning, the initial configuration will correspond to an empty configuration, i.e. a configuration without components.

[0069] Figure 4 illustrates how the solution space of possible compressed air configurations can be restricted by the method. The total solution space is defined by all possible combinations of available components, in particular by the compressors contained in the compressor list Lv (see Figure 5). The solution space can be restricted by eliminating inappropriate configurations, which means that fewer configurations need to be considered as solutions.

[0070] Certain configurations may be implemented in a variety of ways depending on the constraints they specify (see Figs. 3 and 6), e.g. Maximum number of compressors, Maximum number of different types of compressors, A specification of whether a variable speed compressor may or must be included; Maximum compressed air system footprint, The minimum pressure required for the compressed air system, and / or The maximum pressure required for the compressed air system, Maximum investment budget Since it does not satisfy the above, it can be excluded.

[0071] The solution space can also be limited by upper and lower bounds of a so-called branch and bound procedure, which is used for the method according to the invention to build a branch data structure B (see Figs. 5, 7, 9a, 9b). By means of a lower bound, for example by a minimum branch cost value GBranchmin (see Figs. 8, 9a, 9b, 10), configurations that are definitely worse in terms of a quality criterion than the current best configuration Konfbest (see Figs. 6, 10) can be excluded at an early stage.

[0072] Such configurations that satisfy the constraints and are mapped by a branch-and-bound procedure in the branching data structure B are essentially considered for a simulation in order to calculate a quality value as accurately as possible for the quality criterion. The simulation may take into account energy costs, maintenance costs, investment costs and a given compressed air consumption profile. Other influencing factors such as heat recovery from the compressed air system may also be taken into account.

[0073] From the simulated configurations, one or more configurations are resulting as best solutions based on the determined quality value GsimCosts (see Fig. 8, Fig. 9a, Fig. 9b, Fig. 10, Fig. 12). The configuration with the best overall quality value GsimCosts among all the considered configurations is the optimal solution to the optimization problem, according to which the compressed air system should be designed.

[0074] The generation of the branching data structure B as part of the invention is explained with reference to Fig. 5. Exemplary data structures B are also shown in Figs. 7, 8, 9a and 9b. Fig. 10 contains a flow chart for the generation of the branching data structure B. The method according to the invention is based on the idea of ​​considering the available components from the product portfolio as a component list Lk of functionally identical components, which list elements can be combined with each other in an all-combination way to form a compressed air system configuration. The component list Lk includes technical and economic properties, in particular the technical parameters Kt and the economic parameters Kw of each component. In principle, the components of the component list Lk can be considered as available for the generation of a configuration as often as desired. Thus, a component from the component list Lk can be incorporated several times in a configuration. A systematic search of all possibilities would also take into account the optimum solution, but due to the large number of possibilities, this is not feasible in practice. Thus, the method according to the invention uses a branching data structure B in order to be able to systematically run through the configurations (number N) Konf1, Konf2, ..., KonfN that can in principle be considered. Such a branching data structure B is in principle known as a tree, which is created by an algorithm. According to the invention, a branch-and-bound procedure is used to generate the branching data structure B.

[0075] The components available in the supplier's product portfolio for the design of a compressed air system are available in the form of component data Dk, and functionally identical components are grouped together in a component list Lk. n different compressors V1, V2 to Vn are included in the compressor list Lv according to a sorting criterion. m different components KA1, KA2 to KAm for compressed air preparation and p different components KS1, KS2 to KSp for compressed air storage, respectively, are included in the component list Lk. Based on the component data Dk a branching data structure B is generated according to the invention, which shows the possible combinations of components.

[0076] Fig. 5 illustrates an embodiment of a branching data structure B as a component of the method according to the invention. As an example, three node levels B0, B1, B2 are assigned multiple node data structures, where the top node level B0 is assigned only a single compressor node data structure BEv0, the first node level B1 is assigned compressor parent node data structures BEv1, BEv2, . . . BEvn, and the second node level B2 is assigned compressor child node data structures BKv1, BKv2, ..., Bkvn. Each compressor child node data structure at a relatively lower node level Bt is assigned to a parent node data structure at a relatively higher node level Bh. For node level B1, B0 represents the higher node level Bh. For node level B2, B1 represents the higher node level Bh. Branching can occur from the higher node level Bh to the lower node level Bt, so that the number of compressor node data structures can be greater at the lower node level Bt. In this example, the compressor parent node data structure BEv0 corresponds to an empty output configuration of the compressed air system, and at this point is a node data structure that does not contain any compressors. If an output configuration exists, it may be represented by the compressor parent node data structure BEv0. The branch data structure B is constructed by a recursive loop that generates compressor node data structures until a termination criterion is met.

[0077] Each of the compressor parent node data structures BEv1, BEv2 to BEvn represents one of the compressors V1, V2 to Vn included in the compressor list Lv, which is one of the component lists Lk. The compressors V1, V2 to Vn each have a unique compressor type that is different from each other. At least one technical parameter Kt and one economic parameter Kw are assigned to each compressor V1, V2 to Vn, and the compressor list Lv may include further information regarding each compressor V1, V2 to Vn. In the branching data structure B, each compressor node data structure represents a particular compressed air system configuration. Each compressed air system configuration comprises compressors whose node data structures are located along a node path (see FIG. 7) from the respective node data structure, through the next higher node level, through all higher node levels, and upwards to the top node level B0.

[0078] In Fig. 5, the exemplary first compressed air system configuration Konf1 comprises a compressor parent node data structure BEv1 and an assigned compressor child node data structure BKv1, each representing a compressor of type V1. The exemplary second compressed air system configuration Konf2 comprises a compressor parent node data structure BEv1 and an assigned compressor child node data structure BKv2, each representing a compressor of type V1 and a compressor of type V2, respectively. The alternative exemplary second compressed air system configuration Konf2 in Fig. 5 is composed of a compressor parent node data structure BEv2 and an assigned compressor child node data structure BKv2, each representing a compressor of type V2. Thus, Fig. 5 illustrates both alternatives for the generation of the first compressed air system configuration Konf1 as well as the generation of the second compressed air system configuration Konf2 according to the invention. Of the compressed air system configurations 1 to N shown in the branch data structure B, it is arbitrary to designate specific compressed air system configurations as Konf1, Konf2, . . . , KonfN.

[0079] In the compressed air system configurations Konf1, Konf2 shown in Fig. 5, the compressors V1, V1 and V1, V2, respectively, are connected in parallel. The component KA1 for compressed air preparation, connected in series to each compressor, is the same in each case, as is the component KS1 for compressed air storage, to which two parallel compressed air paths are coupled. The compressed air system configuration data Dkonf1, Dkonf2 clearly show the components and their connections. The shown compressed air system configuration data Dkonf2 refers to the compressed air system configuration Konf2 "V1, V2" (left side of Fig. 5) with compressors V1 and V2. The compressed air system configuration data for the alternative compressed air system configuration Konf2 "V2, V2" (right side of Fig. 5) similarly comprises two compressors of type V2 connected in parallel.

[0080] By using the branching tree structure B, the entire solution space can in principle be searched in a computer-assisted, systematic and automatic manner: essentially all suitable configurations that satisfy the constraints, e.g. the best configurations that meet a given quality criterion well, and the (only) optimal configuration that best meets the quality criterion, are contained in the branching tree structure B and can be found.

[0081] The present invention provides a method for finding the best compressed air system configuration and calculating the assigned quality value in a fully automated manner. Compared to the prior art shown in FIG. 2, the method in particular automates the determination of new configurations, the creation of configurations for the evaluation of their quality, the verification of whether the configurations are suitable solutions and the recognition that an optimal configuration has been found, if one exists. In particular, the result of the method does not depend on the experience of the user. In addition, the computer-implemented method is relatively fast. By creating a branching data structure B according to the idea of ​​a branch-and-bound procedure, the solution space to be explored is limited, i.e. the branching tree structure is "pruned" in an appropriate way, so that the procedure quickly leads to the desired design configuration, even with limited computational capacity.

[0082] Fig. 6 shows the basic procedure of the method according to the invention for finding an optimal compressed air system configuration. Starting from an initial configuration based on the component data Dk of the available components, in particular the compressor list Lv, the given constraints and the given quality criterion, as already explained with reference to Fig. 3, suitable configurations (e.g. Konf1, Konf2) are determined using an algorithm for generating a branching data structure B (see, for example, Fig. 5), which are considered for simulation and stored in the simulation waiting list. A simulation considered by the branching data structure B is included in the simulation waiting list if, based on a comparison of the minimum configuration cost value GKonfmin with the quality value Gbest of the current best configuration Konfbest, GKonfmin satisfies the quality criterion better than Gbest. A computer simulation based on a simulation model of the compressed air system (see Fig. 12) determines a simulated cost value GsimCosts as a quality value of the simulated configuration, taking into account a given compressed air profile. Based on GsimCosts, the simulated configuration can be compared with the quality value Gbest of the current best configuration Konfbest. The current best configuration Konfbest, stored at the end of the process, whose quality value GsimCosts best meets the quality criterion, is the optimal configuration for designing the compressed air system.

[0083] One idea of ​​the invention is that the algorithm for constructing the branching data structure B and the computer simulation for the configurations identified based on the tree structure B are executed in parallel in time. Essentially each time a suitable configuration is identified, it is stored in a simulation waiting list of configurations to be simulated. The configurations in the simulation waiting list are simulated one after the other, preferably in parallel on different computers, and a simulated cost value GsimCosts is calculated as a quality value for this configuration. A simulation waiting list of length 1 results in all configurations suitable for simulation being simulated sequentially. The maximum length of the simulation waiting list can be used to control how many configurations can be generated by the data structure B without using the simulated cost value GsimCosts determined by simulation for a configuration already generated against the most efficient possible constraints of the data structure. If the simulated configuration is stored as the current best configuration Konfbest with an assigned quality value Gbest, the result of the simulation will be used in the generation of the branching data structure B. This is because the algorithm for generating data structure B considers whether a configuration even better than the current best configuration Konfbest can be generated at all by adding an additional compressor child node data structure to data structure B, by comparing the minimum branch cost value GBranchmin with Gbest, otherwise the corresponding compressor node data structure is not generated at all.

[0084] FIG. 7 shows a schematic diagram of an embodiment of the branching data structure B on three node levels B1, B2, B3, i.e. for three compressors connected in parallel. This example concerns a new planning of a compressed air system, where possible configurations for three parallel-connected compressors V1, V2, V3 are considered, without considering other components of the compressed air system. The root node of the branching data structure B at node level B0 is empty if no initial configuration is considered. The solution space, i.e. the set of all possible configurations (see FIG. 4), is created by generating compressor node data structures in a recursive loop. Each compressor node data structure represents compressed air system configurations Konf1 to Konf16, which are composed of compressors along the node path from the compressor node data structure to the root node, based on the component data Dk of one of the compressors V1, V2, V3. The depth of the tree, i.e. the number of node levels B1, B2 and B3, may be limited by the number of compressors allowed in the compressed air system as a constraint. In this case, the maximum number of compressors to be used is specified as three.

[0085] Typically, the arrangement sequence of compressors in a compressed air system does not matter as long as all compressors are arranged in parallel. This fact is taken into account in the branching data structure B by not inserting nodes into the tree that result in configurations that differ only in sequence from configurations already present in the tree (in the tree shown above, the configuration "V1, V2, V2" exists, but the configuration "V2, V1, V2" does not exist, because these two configurations are equivalent when sequence is ignored). By avoiding equivalent configurations, the branching data structure B (the tree), and therefore the solution space, is kept small.

[0086] The construction of a tree for the representation of the solution space can be most easily described using a recursive algorithm, where we also consider algorithms that proceed iteratively for the construction of the tree. For the description of the recursive algorithm, we assume that the product portfolio of components is sorted according to some criteria, for example by ascending investment cost, by ascending supply volume, by alphabetically ascending type designation, etc. The components can therefore be considered as an ordered component list Lk, where each component has a list position. The algorithm starts from the root at node level B0 and for each element from the compactor list adds a child node under the root at the next lower node level B1. Then the algorithm proceeds to each child node and adds a new element as a child node in each child node. Here, the rule for adding a new element as a child node is that only elements from the sorted list that are located at the same or a subsequent position in the sorted list can be added as a child node. To add a child node, constraints are also taken into account. An example of a conditional addition of a child node is as follows: - A child node is added only if adding the child node does not exceed the maximum allowed investment cost, - A child node is added only if doing so would not exceed the maximum number of compressors allowed, - Child nodes are added only if adding them would not exceed the maximum number allowed for the compressor type.

[0087] The process for recursively adding child nodes continues until there are no more child nodes that can be added at any point in the branching data structure B. The nodes of the branching data structure B, coded with their respective node paths from or to the root, now form a configuration that spans the solution space.

[0088] The branching data structure B shown as an example in FIG. 7 represents the following list of 16 configurations Konf1 to Konf16, in each of which two or three compressors are connected in parallel to each other: “V1, V1” (Konf1), “V1, V2” (Konf2), “V1, V3” (Konf3), “V1, V1, V1” (Konf4), “V1, V1, V2” (Konf5), “V1, V1, V3” (Konf6), “V1, V1, V3” (Konf7), “V1, V1, V4” (Konf8), “V1, V1, V5” (Konf9), “V1, V1, V6” (Konf10), “V1, V1, V7” (Konf11), “V1, V2” (Konf12), “V1, V1, V7” (Konf13), “V1, f6), "V1, V2, V2" (Konf7), "V1, V2, V3" (Konf8), "V1, V3, V3" (Konf9), "V2, V2" (Konf10), "V2, V3" (Konf11), "V3, V3" (Konf12), "V2, V2, V2" (Konf13), "V2, V2, V3" (Konf14), "V2, V3, V3" (Konf15), "V3, V3, V3" (Konf16). The compressor node data structures at the B1 level do not represent valid compressed air configurations as they each have only a single compressor.

[0089] In detail, the construction of the data structure B (tree) shown in Fig. 7 proceeds, for example, as follows: in a first step, component data Dk with a compressor list Lv containing compressors V1, V2, V3 in a given order is received by the computer. Then, a first compressor data node structure indicating a compressor of type V1 is added to the data structure B, starting from the root node at node level B1. At node level B2 below this compressor parent data node structure, another compressor child node data structure of compressor V1 is generated, corresponding to configuration Konf1. At node level B3 below this, another three compressor child node data structures are generated with contents V1, V2, and V3, respectively, corresponding to configurations Konf4, Konf5, and Konf6. More compressor child node data node structures at lower node levels are not added, as this would violate the constraint of a maximum of three compressors allowed in a configuration. At node level B2, a compressor node data structure for a compressor of type V2 is then generated, which corresponds to configuration Konf2. Based on this, at node level B3, further compressor child node data structures are generated for compressors V2 and V3, corresponding to configurations Konf7 and Konf8, respectively, but no compressor child node data structure is generated for compressor V1, since such a configuration is equivalent to configuration Konf5, which is merely a permutation of the compressor combination "V1, V1, V2". For the same reason, a compressor child node data structure for compressor V3, corresponding to configurations Konf3 and Konf9, respectively, is then generated at node levels B2 and B3, but no further compressor node data structures are generated. The remaining compressor node data structures corresponding to configurations Konf10, Konf11, Konf12, Konf13, Konf14, Konf15, and Konf16 are generated in a similar manner.

[0090] In principle, it is useful to keep the tree, and therefore the solution space to be explored, as small as possible. The user may specify constraints (see Fig. 3 and Fig. 6) to exclude certain solutions. For example, it may already be known, based on heuristics, that a configuration that meets these requirements should have a certain compressor type. For example, a constraint may specify that at least one variable speed drive compressor must be included in the configuration, or conversely, that the configuration must not have a variable speed drive compressor. Further restriction of the solution space is possible by predefining part of the solutions, in particular by not leaving the root node of the data structure empty, but by assigning a root node to the set of compressors that each configuration should contain. In this case, all nodes under the compressor parent data structure corresponding to the root node at node level B0 extend this already predefined compressor combination.

[0091] In principle, for the implementation of the method according to the invention, it is conceivable that in a first step of the method, a complete branching data structure B (tree) for the representation of the solution space is first created, and then the best configurations are searched for in the solution space. A preferred embodiment of the method provides for building and searching the solution space in parallel to performing the simulation and evaluating the configurations. This allows the solution space to be bounded by blocking the generation of the node data structure in time, i.e. blocking branches of the tree that have not yet been created. It is also possible to delete the node data structure later, i.e. cutting branches of the tree that have already been created but whose configurations have preferably not yet been simulated. This procedure is illustrated in Fig. 8. The method considers that the best configuration found so far, i.e. currently Konfbest, specifies a quality value Gbest, which is a value that the quality value of a potential configuration in a branch of the data structure B must at least reach in order to be a more suitable, i.e. even better, solution. If it can be ruled out that there exists a configuration in the branch that is better than the current best configuration Konfbest, the branch can be cut without running the risk of not finding a better solution any more. Cutting a branch is symbolically represented by scissors in Fig. 8. By cutting a branch, the solution space can be significantly reduced. This speeds up the method significantly and allows it to be implemented in a practical way at all in the case of very large solution spaces.

[0092] As a criterion for the restriction of the data structure B, a conservative estimate is used, which specifies how good a configuration can be from this branch of the data structure (tree). For this purpose, the minimum branch cost value GBranchmin is calculated as a quality value. If all configurations potentially found in this branch cannot undercut the current best configuration (GBranchmin>Gbest), further branching is prevented.

[0093] According to a first approach to estimating the quality of a configuration, the energy cost is used as the minimum branch cost value GBranchmin. For the estimation of the lowest possible energy cost in a branch, it is assumed that all compressed air for covering a given compressed air consumption profile is generated with the energy efficiency of the most efficient compressor potentially possible in this branch, i.e. by the compressor with the highest energy efficiency of all compressors in the compressor list.

[0094] According to a second approach, the sum of the energy costs and the investment costs can be formed as a minimum branch cost value GBranchmin, where a "break-even node" optimal in terms of costs can be identified, from which the addition of one or more more efficient compressors can no longer reduce the energy costs to the same extent that the investment costs increase the profits. At this node, the branch is cut off.

[0095] According to a further developed embodiment of the first approach, first an attempt is made to cover the amount of compressed air produced over the entire period under consideration with the most efficient compressor in the branch. For the remaining balance, the second most efficient compressor in the branch is used. If there is still a remainder, the third most efficient compressor in the branch is used for this, and so on.

[0096] In particular, the aforementioned minimum configuration cost value GKonfmin, used to decide whether or not to perform computer simulations for a configuration, is calculated based on a conservative estimate of the same cost as the minimum branch cost value GBranchmin.

[0097] The bounding of the data structures is explained in more detail below with reference to Figure 8. For the bounding, a cost estimate GBranchmin is calculated for each compressor node data structure, i.e. for each node, for the entire branch that will result from the branch point. The cost estimate GBranchmin indicates the minimum expected cost of the best compressed air system configuration that can be included from that branch point, i.e. in that branch.

[0098] To ensure that the optimal solution is not rejected by the constraints, the cost estimate GBranchmin must be conservative, i.e. GBranchmin must be in each case lower than the cost that can be determined by simulation as GsimCosts. If the sum of the investment cost and the energy cost is used as the quality value GBranchmin, it makes sense in each case to make an estimate of the lowest possible investment cost for GBranchmin and an estimate of the lowest possible energy cost. In this case, the sum of these two estimates is surely a conservative estimate of the lowest possible value of the cost.

[0099] To estimate the lowest possible investment cost in a branch, it is assumed that the investment cost of all nodes in the branch is equal to the investment cost of the node where the branch starts. Therefore, it is assumed that the investment cost does not increase for additional child nodes. This is an incorrect assumption, but in any case, this estimation is conservative. It is certain that it does not discard the optimal solution.

[0100] For the estimation of the lowest possible energy costs in a branch, it is assumed that the compressed air consumption is exclusively covered by the most efficient compressors contained in the compressor list Lv and thus potentially contained in the nodes of the branch.

[0101] If GBranchmin exceeds the current best quality value Gbest corresponding to the simulated costs GsimCosts of the current best configuration Konfbest, the data structure may be constrained without discarding the optimal solution.

[0102] The procedure for branching and bounding data structures is described below using an example with the following details illustrated in FIG. 8: Only two types of compressors V1 and V2 are available, namely: ○ Type V1, Investment cost: 22,000 euros, Delivery volume: 5.5m 3 / min, · Specific output: 6.7kWmin / m 3 , ○ and type V2, Investment cost: 40,000 euros; Delivery volume flow rate: 10m 3 / min, · Specific output: 6.5kWmin / m 3 , The compressed air system has at least one V1 type compressor. The required air volume over the life of the compressed air system is 21,000,000 m 3 It is. Electricity price is 0.15 euros / kWh. · There is no limit to the number of compressors in a compressed air system. A branching data structure B is built depth-wise first, and only then width-wise.

[0103] Starting from an initial configuration (*) of a compressed air system with at least one compressor, in step 1 a compressed air system is created that includes only one additional compressor of type V1. In this case, at least one compressor is already included in the root node (*). For this configuration, a minimum configuration cost value GKonfmin is generated for the compressed air system itself (€373,350) and a minimum cost estimate GBranchmin is generated for the entire branch (€363,250). The fact that the cost estimate GBranchmin for the branch is lower than that for the configuration itself GKonfmin is due to the fact that the configuration itself only has the inefficient compressor type V1, while compressed air systems at node levels below this configuration may use the more efficient compressor type V2.

[0104] Since no simulation has been performed yet, in step 2 this configuration is simulated and simulated costs GsimCosts of 387,300 euros are determined for this configuration. These are stored as the current best quality value Gbest, which indicates the current best (lowest) cost of the configuration.

[0105] In step 3, a new branch is created by adding a compressor of type V1, and the cost for the configuration GKonfmin and the cost for the branch GBranchmin are determined. Since the minimum cost of the configuration GKonfmin is higher than the current best cost Gbest, no simulation is performed for this configuration, thus saving computational effort for simulation.

[0106] Since the minimum cost GBranchmin of the just created branch is less than Gbest, in step 4, a new branch is created by adding another V1 type compressor, and the cost of the configuration GKonfmin and the cost of the branch GBranchmin are determined. Since the minimum cost of the configuration GKonfmin is higher than Gbest, no simulation is performed for this configuration. Thus, the amount of computation for simulation is saved again.

[0107] Since the minimum cost of the branch, GBranchmin, also exceeds Gbest, the just generated branch is truncated in step 5 and therefore is not pursued further.

[0108] In step 6, a new branch is created by adding a compressor of type V2 to a configuration that already contains two compressors of type V1, and the minimum cost of the configuration, GKonfmin, and the minimum cost of the newly created branch, GBranchmin, are determined. Due to the high minimum cost of the configuration, GKonfmin, no simulation is performed here either.

[0109] The minimum cost of the newly created branch, GBranchmin, is also higher than Gbest, so the newly created branch is discarded in step 7.

[0110] In step 8, a new branch is created by adding a compressor of type V2 to the configuration that already contains compressor V1, and the minimum cost GKonfmin for the configuration itself and the cost GBranchmin for the newly created branch are determined. No simulation is performed here due to the minimum configuration cost value GKonfmin being too high.

[0111] The minimum cost of the newly created branch, GBranchmin, is also higher than Gbest, so the newly created branch is discarded in step 9.

[0112] Further configurations and branches are not created since they cannot reliably represent the current best configuration Konfbest with respect to the quality criterion. Thus, the method ends and the best solution is found, i.e. a compressed air system consisting of only one additional V1 type compressor in addition to the compressors of the initial configuration.

[0113] This example clearly shows how a potentially infinitely large solution space can be reduced to a manageable level by this method: in total, only five configurations were generated and only one simulation was performed.

[0114] The method according to the invention will now be explained with reference to a further example of the construction of a branching data structure B which is split between FIG. 9a and FIG. 9b.

[0115] As a constraint, the following four compressors V1, V2, V3, V4 are selected from the product portfolio, to each of which the compressed air capacity and the electricity consumption are assigned as technical parameters Kt and the investment volume is assigned as economic parameter Kw:

[0116] [Table 1]

[0117] A sorting sequence is selected for the compressors: in this case, the compressors are sorted in descending order of compressed air capacity, so that the compressor with the highest compressed air capacity is always selected first, then the compressor with the next lowest compressed air capacity, and so on.

[0118] In a further step, constraints are defined, i.e. - 1 million m of air volume over the compressed air station's lifetime 3 Let us say, - Assume that the price of electricity is 0.15 euros / kWh, - The maximum number of compressors is 3, - The maximum number of compressor types used is two, - Peak air volume required is 19m 3 / min, - The maximum investment budget is 36,000 euros.

[0119] For the generation of the branched data structure B according to Figures 9a, 9b, a depth-first search up to the fourth node level B4 is used for the branch-and-bound procedure. Here, the steps for creating the nodes are given as an example. Further steps for creating the data structure B can be derived from the information in Figures 9a, 9b.

[0120] In Fig. 9a, 9b, in addition to the compressor type V1, V2, V3 or V4, further information is given in the first section of the compressor node data structure (also called "node" for simplicity). In the second section, the number of compressors, the number of different compressor types, the investment amount and the compressed air capacity are given. In the third section, if there is one, there is a minimum configuration cost value GKonfmin for the configuration corresponding to the node and a minimum branch cost value GBranchmin for the branches belonging to this node. In the third section, if there is one, a parameter validOption is given, indicating whether each is a valid solution that satisfies the constraints, i.e. a suitable configuration. In addition, simulated costs GsimCosts are given, which are obtained from a simulation of the configuration.

[0121] In the first step, the first compressor V1 is selected. This is a 13.7 m 3 / min, which is a compressed air capacity of only 19m 3 Since this choice does not yet represent a valid configuration, GKonfmin is estimated to be €35,975, and the minimum cost of the entire branch, GBranchmin, is also estimated to be €35,957. No simulations are performed in this case.

[0122] In a further step, a first compressor V1 is added to the configuration "V1" at the second node level B2. Since for the configuration of this node with 40,000 euros the predefined maximum allowable investment amount of 36,000 has already been exceeded, this configuration and all configurations below it are discarded (in Fig. 9a and Fig. 9b, shown with a cross "X" under each discarded configuration). The branch belonging to this node is therefore cut. In a further step, a second compressor V2 is added to the configuration "V1" at the second node level B2. It is determined that this is a valid solution that satisfies the constraints (validOption=yes). The configuration is then simulated and a simulated cost GsimCosts of 55,000 euros is determined for it, which is stored as the best cost so far Gbest.

[0123] According to this example, the depth search is now postponed for the time being and other nodes in the second level B2 are determined first. To do this, other nodes under the first node "V1" of the first node level B1 are scanned sequentially. The same procedure is repeated for further nodes of the first node level B1 (see continuation of Fig. 9b). This procedure has the advantage that the simulation results GsimCosts of the valid nodes added to the second node level B2 can already be used to determine whether further nodes should be added to the third and fourth node levels B3, B4.

[0124] In this case, at the third node level B3, the configurations "V1, V3, V3", "V1, V3, V4", "V2, V2, V2", "V2, V2, V3", "V2, V2, V4", "V2, V3, V3" and "V2, V3, V4" are discarded because the predefined maximum investment amount or the maximum number of different compressor types is exceeded. In this case, the respective assigned branches are also cut off, since the investment amount or the number of compressor types used is exceeded, and these values ​​at least remain the same or even increase by adding compressors.

[0125] In contrast, at the third node level B3, it is determined that for the configurations "V1, V4, V4", "V2, V4, V4", "V3, V3, V3", "V3, V4, V4", "V4, V4, V4", the supplied compressed air volume is still insufficient. In the case of the configuration "V1, V4, V4", the simulation determines that the simulated cost GsimCosts of 51,975 already exceeds the best cost Gbest of 50,000 euros, determined at the second node level B2. The branch is therefore now cut off, since adding another compressor can only increase the total cost. In the other case, the current best cost Gbest of 50,000 is not yet exceeded and an additional node is added.

[0126] However, in the present case the constraints show that by using four compressors the maximum number of compressors is already exceeded. Alternatively, it is possible to omit the creation of a node at the fourth node level B4, since in this case the maximum permissible number of compressors would be exceeded in any case. In addition, in this example it results that for the configurations "V3, V3, V3", "V3, V4, V4", the investment volume is exceeded, while for the configurations "V3, V4, V4", "V4, V4, V4", the compressed air volume supplied is still not sufficient.

[0127] Overall, in this example, after the complete construction of the branching data structure B, the configuration “V2, V2” consisting of two compressors results to be the best solution, i.e., the best configuration Konfbest, which simultaneously satisfies the constraints and minimizes the cost.

[0128] Fig. 10 shows a flow chart for constructing the tree structure of the branching data structure B. In a first step, an initial configuration is used as a basis or a new plan is started. In a subsequent so-called "branching step", components are inserted, in this case compressors. In a further step, the minimum configuration cost value GKonfmin and the minimum branch cost value GBranchmin for the currently considered configuration Konf are determined for the branches belonging to the nodes of the currently considered configuration Konf. The currently considered configuration Konf corresponds to the currently considered node of the branching data structure B.

[0129] In a decision step it is checked whether the minimum configuration cost value GKonfmin for the currently selected configuration Konf is lower than the current best cost Gbest for the current best configuration Konfbest. The current best configuration Konfbest is the one that satisfies the constraints and has the best quality value with respect to the quality criterion. If so, a further decision step checks whether the currently selected configuration Konf violates the constraints. If not, in a further step a simulation is performed for the currently selected configuration Konf. The simulation determines the simulated cost GsimCosts of the configuration and, if necessary, other important quantities such as pressure curves or volumetric flow characteristics.

[0130] After the simulation, a further decision step tests whether the simulated costs GsimCosts are better than the costs Gbest of the current best configuration Konfbest. If so, the current configuration Konf is saved as the current best configuration Konfbest and the best costs Gbest are set to the simulated costs GsimCosts. If not, the previously determined values ​​for the best configuration Konfbest and the best quality value or best costs Gbest remain.

[0131] Subsequently, it is checked whether further configurations can be generated. For this purpose, those configurations that satisfy the constraints and have not already been previously excluded by the limitations of data structure B are taken into account. If no further configurations can be generated, the method ends and the best (optimal) configuration is equal to the stored current best configuration Konfbest. If not, the method jumps back to the branching step described above, where a node is inserted based on further components for the compressed air system configuration. Further configurations can in particular be found after a depth or breadth search in the branching data structure B, where the method can also be switched between a breadth search and a depth search.

[0132] If in the above decision step it is determined that the minimum configuration cost value GKonfmin for the currently considered configuration Konf is less than that of the current best cost Gbest of the current best configuration Konfbest, a further decision step checks whether the minimum branch cost value GBranchmin for the branch exceeds the cost Gbest for the current best configuration Konfbest. If so, the data structure B is bounded, i.e. the branch is pruned. If not, it is checked whether further configurations can be generated. If so, the method jumps back to the above branch step. If not, the method ends.

[0133] If in the above step it is determined that the configuration Konf under consideration violates at least one of the constraints and therefore does not represent a valid configuration, a further decision step tests whether configurations branching from it, located at a lower node level, can still satisfy the constraints. This is not the case, for example, if the technical or economic characteristics of the currently considered configuration Konf do not satisfy the constraints and this characteristic can only worsen with an increasing number of compressors. If the configuration at the lower node level cannot satisfy the constraints, the branch is pruned. If not, a check is made to see if any more configurations can be generated. If so, the method jumps to the above branching step. If not, the method ends.

[0134] Also, after the branch trimming step, a check is made to see if more configurations can be created. If so, the method loops back to the branching step above. If not, the method ends.

[0135] 11 shows a schematic representation of an automatically generated simulation model of the compressed air system 1, corresponding to a configuration in which compressors V1, V2 and V3 are connected in parallel (i.e. a "V1, V2, V3" configuration) and are centrally controlled by a model of an integrated control system 60'. The compressed air reserve component is modelled by an alternative component KA2, where the differential pressure due to the pressure drop across the compressed air reserve is 0.3 bar. This differential pressure may be specified as a minimum differential pressure in the form of a constraint. Here, the compressed air reservoir is 10 m 3 The consumer 50' is modelled via a compressed air consumption profile.

[0136] For configurations that are well adapted with respect to the quality criterion, a computer simulation is carried out so that these configurations can be better evaluated based on the results of the simulation. Simulation is in principle intended to mean that the behavior over time, i.e. the dynamic operating behavior, of the components of the compressed air station is mapped via a computer model. In a preferred variant of the method according to the invention, a set of differential equations is used for this purpose. In a further developed variant thereof, the set of differential equations is implemented in such a way that the structure-variant behavior of the components, i.e. the different behavior in discretely distinguishable operating states, is taken into account.

[0137] FIG. 12 illustrates a computer simulation carried out to calculate as accurately as possible a quality value of the simulated costs GsimCosts for a compressed air system configuration. Such a simulation model takes into account the dynamic operating behavior of the compressors of the simulated compressed air system configuration. The simulation model is based in particular on a set of several time-dependent differential equations, preferably partial differential equations, which are solved in particular by numerical integration methods. The simulation model can be generated automatically based on models for the individual components of each configuration (component-based approach). Alternatively, a generic simulation model for compressed air systems can be used, which can be adapted to a specific configuration by parameterization (monolithic approach), where components not present in the configuration can be "eliminated" by appropriate parameter selection.

[0138] For sufficiently accurate simulation results, the control algorithms running in the components, in particular in the compressors, and also in the central integrated control system are taken into account, which are modeled for this purpose and represented by a simulation model. Preferably, the control algorithms are adapted in terms of their parameterization to the respective configuration and the constraints to be met. For example, in the case of a certain type of integrated control, the parameters "demand pressure" and "pressure clearance limit" must be set so that it is possible to meet the constraints on the required minimum pressure and the maximum allowable pressure of the compressed air system. In the case of compressed air systems without an integrated control system, the parameters of the pressure regulator in the compressor are preferably set so that it is possible to meet the constraints on the required minimum pressure and the maximum allowable pressure of the compressed air system and a realistic switching behavior of the compressor results. An incorrectly set pressure regulator in the simulation model will result in too frequent switching of the compressor and therefore in an unrealistically poor result in the energy efficiency of the considered compressed air system configuration compared to a compressor in a real compressed air system with an accurately set pressure regulator.

[0139] Since the generation of the branching data structure B is usually much faster than a complex computer simulation, it may be useful to limit the length of the simulation queue list (see FIG. 6) to, for example, a maximum of 10 or 100 configurations. If the simulation queue list is full, the algorithm for generating the data structure B may pause until the simulation queue list can accommodate configurations again. This ensures that an unnecessarily large number of configurations do not accumulate in the simulation queue list, even though they do not actually (anymore) need to be simulated, because in the meantime a better current best configuration may have been found and the branch to which it belongs may actually have been blocked by limiting the data structure B. Since the individual simulations of the configurations to be made can be performed independently of each other, the execution of the computer simulation may be fully parallelized. This may speed up the entire method.

[0140] It should now be noted that all the aspects of the invention described above, considered separately and in any combination, and in particular the details shown in the drawings, are claimed as belonging to the invention, modifications of which are well known to those skilled in the art.

[0141] [List of reference numbers] 1 Compressed Air System 2, 3 Compressed air path 11, 12, 13 Compressor of compressed air system 21, 22 Dryer 32, 32 Filter 40 Compressed Air Tank 50 consumer 50' Modeled consumer 60 Plant Control 60' Integrated control system model V1, V2, ..., Vn Compressors 1 to n in the compressor list KA1, KA2, ..., KAm Components for compressed air preparation 1 to m KS1, KS2, ..., KSp Components 1 to p for compressed air storage Konf Compressed Air System Configuration Konf, Konf2, ..., KonfN Compressed air system configurations 1 to N Konfbest: The best compressed air system configuration today Dk Component Data Dn Constraint Data Dkonf1, Dkonf2 Compressed air system configuration data Lk Component List Lv Compressor List Kt Technical parameters Kw Economic parameters B Branch Data Structure B0 Top Node Level B1 Upper Node Level B2 Lower Node Level BEv0 Compressor Parent Node Data Structure Bev1, BEv2, ..., BEvn Compressor parent node data structures BKv1, BKv2, ..., BKvn compressor child node data structures GKonfmin Quality value, i.e., minimum configuration cost value GBranchmin quality value, i.e., minimum branch cost value GsimCosts Quality values, i.e. simulated cost values Gbest Current best quality value GKonfbest Current best configuration cost value

Claims

1. A method for providing at least one design configuration of a compressed air system (1) with at least two compressors (11, 12) connected in parallel, said method comprising: receiving, by the computer, component data (Dk), said component data (Dk) describing the components of the compressed air system and at least one technical parameter (Kt) of each component; wherein said component data (Dk) comprises at least one component list (Lk) having a plurality of functionally identical components of different types and at least one technical parameter (Kt) assigned to each said component, wherein the component list (Lk) is a compressor list (Lv) including a plurality of compressors (V1, V2, . . . , Vn) of different types; generating, by said computer, a branching data structure (B), said branching data structure (B) comprising node data structures each assigned to one of at least two node levels (Bt, Bh, B0, B1, B2, B3), where each child node data structure at a lower node level (Bt) is assigned to a parent node data structure at a higher node level (Bh), where said generating of said branching data structure (B) comprises at least generating, in memory, a first compressor parent node data structure (BEv1) based on component data (Dk) of compressors in said compressor list (Lv); generating, in said memory, a first compressor child node data structure (BKv1) based on compressor component data (Dk) of said compressor list (Lv), wherein said first compressor child node data structure (BKv1) is assigned to said first compressor parent node data structure (BEv1); generating, in said memory, a second compressor child node data structure (BKv2) based on compressor component data (Dk) of said compressor list (Lv), wherein a compressor type of said second compressor child node data structure (BKv2) is different from a compressor type of said first compressor child node data structure (BKv1), and wherein said second compressor child node data structure (BKv2) is assigned to said first compressor parent node data structure (BEv1); or generating, in said memory, a second compressor parent node data structure (BEv2) based on compressor component data (Dk) of said compressor list (Lv), wherein a compressor type of said second compressor parent node data structure (BEv2) is different from a compressor type of said first compressor parent node data structure (BEv1); generating, in said memory, a second compressor child node data structure (BKv2) based on compressor component data of said compressor list (Lv), wherein said second compressor child node data structure (BKv2) is assigned to said second compressor parent node data structure (BEv2); Equipped with generating, by a computer, first compressed air system configuration data (DKonf1) indicative of a first compressed air system configuration (Konf1) in which the compressor of the first compressor parent node data structure (BEv1) and the compressor of the first compressor child node data structure (BKv1) are connected in parallel; generating, by a computer, second compressed air system configuration data (DKonf2) indicative of a second compressed air system configuration (Konf2) in which the compressor of the first compressor parent node data structure (BEv1) or the compressor of the second compressor parent node data structure (BEv2) and the compressor of the second compressor child node data structure (BKv2) are connected in parallel; - calculating by said computer at least one quality value (GKonfmin, GBranchmin, GsimCosts) for at least one of said compressed air system configurations (Konf1, Konf2) based on said compressed air system configuration data (Dkonf1, Dkonf2) of said compressed air system configurations (Konf1, Konf2) and on at least one technical parameter (Kt) of said compressors of said compressed air system configurations (Konf1, Konf2), wherein said at least one quality value (GKonfmin, GBranchmin, GsimCosts) is indicative of a quality of said compressed air system configurations (Konf1, Konf2) with respect to a quality criterion, preferably specified by a user; providing, by said computer, at least one compressed air system configuration (Konf1, Konf2), each having at least one assigned quality value (GKonfmin, GBranchmin, GsimCosts); A method comprising:

2. The method further comprising: receiving, by said computer, constraint data (Dn), wherein said constraint data (Dn) indicate at least one predetermined constraint, preferably by a user, on the compressed air system configuration; determining, by said computer, on the basis of said constraint data (Dn) and said first compressed air system configuration data (DKonf1) whether said first compressed air system configuration (Konf1) satisfies said predetermined constraints and / or on the basis of said constraint data (Dn) and said second compressed air system configuration data (DKonf2) whether said second compressed air system configuration (Konf2) satisfies said predetermined constraints; Further comprising: wherein, in particular, the at least one quality value (GKonfmin, GBranchmin, GsimCosts) is calculated if the respective compressed air system configuration (Konf1, Konf2) satisfies the predetermined constraints.

2. The method according to claim 1 .

3. The at least one predetermined constraint may comprise, for the compressed air system: - the maximum number of compressors, and / or - the maximum number of different compressor types, and / or - Specifications as to whether a variable speed compressor may or must be included. The method of claim 2 , comprising:

4. The at least one predetermined constraint may comprise, for the compressed air system: the maximum footprint of said compressed air system, and / or the minimum required pressure of said compressed air system, and / or - the maximum pressure required for said compressed air system; 4. The method according to claim 2 or 3, characterized in that it comprises:

5. 3. The method according to claim 2, characterized in that the at least one predetermined constraint for the compressed air system comprises in particular a maximum investment budget for new projects, modifications or expansions of the compressed air system.

6. said at least one technical parameter (Kt) of a component, in particular of a compressor (V1, V2, . . ., Vn), - energy consumption, and / or - pressure-dependent characteristic curve of the power consumption, and / or the volumetric flow rate delivered, especially at maximum pressure, and / or - in particular CO per compressed air volume ２ emissions The method of claim 1 , comprising:

7. 2. The method according to claim 1, characterized in that at least one economic parameter (Kw) of the respective component is assigned to each component in addition to the at least one technical parameter (Kt), wherein the economic parameter (Kw) is in particular indicative of the investment costs and / or maintenance costs of the component, and wherein the at least one quality value (GKonfmin, GBranchmin, GsimCosts) is calculated based in particular on the at least one economic parameter (Kw) and on the at least one technical parameter (Kt) of at least one compressor of the respective compressed air system configuration (Konf1, Konf2).

8. 2. The method according to claim 1, characterized in that the compressor list (Lv) comprises at least one compressor of an existing compressed air system (1) and at least one compressor not installed in the existing compressed air system (1).

9. 2. The method according to claim 1, wherein generating the branching data structure (B) further comprises generating, in a memory, a compressor parent node data structure (BEv0) at a top node level (B0) based on component data (Dk) of at least one compressor of an existing compressed air system, wherein the compressor parent node data structure (BEv0) at the top node level (B0) is based in particular on component data (Dk) of compressors of an existing compressed air system (1) and preferably represents an initial configuration of a compressed air system.

10. 2. The method according to claim 1, characterized in that the generation of the compressor parent node data structures (BEv1, BEv2) and / or the compressor child node data structures (BKv1, BKv2) is additionally based on component data (Dk) of components for compressed air preparation (KA1, KA2, ..., KAm), in particular of groups of components for compressed air preparation, wherein in particular the component list (Lk) is a list of components for compressed air preparation, preferably a list of component groups.

11. 3. The method according to claim 2, characterized in that the constraints on the compressed air system configuration (Konf1, Konf2) specify a required minimum differential pressure for compensating for pressure losses of at least one component (KA1, KA2, ..., Kam) for compressed air preparation, in particular of a group of components for compressed air preparation.

12. 2. The method of claim 1, wherein the generating of the branch data structure (B) comprises generating in memory at least one further compressor child node data structure (BKv3, ..., BKvn) at a node level (Bt, B1, B2, B3) based on compressor component data (Dk) of the compressor list (Lv), wherein preferably the compressor type of the compressor child node data structure (BKv3, ..., BKvn) to be generated is different from the compressor type of an already generated compressor node data structure of this node level (Bt, B1, B2, B3) assigned to the same compressor parent node data structure (BEv0, BEv1, ..., BEvn).

13. 2. The method of claim 1, wherein the compressors (V1, V2, ..., Vn) of the compressor list (Lv) are ordered according to a sorting criterion, and wherein the component data (Dk) of the sorted compressors in the compressor list (Lv) are used at the same or lower list position as the compressors of the compressor parent node data structure (BEv1, ..., BEvn) to generate compressor child node data structures (BKv1, ..., BKvn) that are assigned to the compressor parent node data structures (BEv1, ..., BEvn).

14. 2. The method according to claim 1, characterized in that the quality criterion is a cost criterion, wherein the at least one quality value (GKonfmin, GBranchmin, GsimCosts) is indicative of the energy costs and / or investment costs and / or maintenance costs of a compressed air system configuration (Konf1, Konf2).

15. - comparing two compressed air system configurations (Konf1, Konf2) based on the quality values ​​(GKonfmin, GBranchmin, GsimCosts) assigned by a computer; - storing the compressed air system configuration data (Dkonf1, Dkonf2) of the compressed air system configuration (Konf1, Konf2) whose assigned quality value (GKonfmin, GBranchmin, GsimCosts) better meets the quality criterion as the current best compressed air system configuration (Konfbest), preferably storing the quality value (GKonfmin, GBranchmin, GsimCosts) assigned to the current best compressed air system configuration (Konfbest) as the current best quality value (Gbest); The method according to claim 1 , characterized in that

16. Calculating the at least one quality value (GKonfmin, GBranchmin, GsimCosts) comprises calculating a minimum configuration cost value (GKonfmin), which indicates a lower limit of the costs of the compressed air system configurations (Konf1, Konf2), preferably based on an estimation; wherein the minimum configuration cost value (GKonfmin) is preferably based on energy costs and / or investment costs of at least one of the components of the compressed air system configuration (Konf1, Konf2), wherein the energy cost is preferably calculated based on the energy consumption of the compressor having the highest energy efficiency of the compressors included in the compressed air system configurations (Konf1, Konf2), Here, the investment cost is preferably calculated as the sum of the investment costs of the compressors included in the compressed air system configuration (Konf1, Konf2).

2. The method according to claim 1 .

17. Calculating the at least one quality value (GKonfmin, GBranchmin, GsimCosts) of the compressed air system configurations (Konf1, Konf2) comprises calculating a minimum branch cost value (GBranchmin), which specifies, preferably based on an estimation, a lower limit value of the costs of those further compressed air system configurations which are not represented in the branch data structure, in particular by a node data structure, including the compressors of the compressed air system configurations (Konf1, Konf2); wherein said minimum branch cost value (GBranchmin) is preferably based on the energy costs of the compressors of said compressor list (Lv) and / or on the investment costs of at least one of said components of said compressed air system configuration (Konf1, Konf2), wherein the energy cost is preferably calculated based on the energy consumption of the compressor having the highest energy efficiency among all compressors included in the compressor list (Lv); wherein the investment cost is preferably calculated as the sum of the investment costs of the compressors included in the compressed air system configuration (Konf1, Konf2).

2. The method according to claim 1 .

18. Based on the quality values ​​(GKonfmin, GBranchmin, GsimCosts) assigned to the compressed air system configurations (Konf1, Konf2), preferably based on the minimum branch cost value (GBranchmin), in particular, based on the quality values ​​(GKonfmin, GBranchmin, GsimCosts), preferably based on the minimum branch cost value (GBranchmin), the current best compressed air system configuration (Konfbest) is assigned.

2. The method of claim 1, characterized in that the generation of further compressor child node data structures (BKv2, ..., BKvn) of a compressor parent node data structure (BEv0, ..., BEvn) is precluded or already generated compressor child node data structures (BKv2, ..., BKvn) of a compressor parent node data structure (BEv0, ..., BEvn) are deleted if the quality criterion is worse than a current best quality value (Gbest) stored in the memory.

19. said calculating of quality values ​​(GKonfmin, GBranchmin, GsimCosts) comprises performing a computer simulation and calculating simulated cost values ​​(GsimCosts) based on the results of said computer simulation, said simulated cost values ​​(GsimCosts) being indicative of the costs of said compressed air system configurations (Konf1, Konf2) over a certain operating time, said computer simulation being based in particular on a simulation model of said compressed air system configurations (Konf1, Konf2) which represents a dynamic behavior of said compressed air system configurations (Konf1, Konf2), Here, the simulation model preferably comprises: taking into account, as boundary conditions, a predefined, time-varying compressed air consumption profile or back pressure profile, preferably specified by the user, and / or - mapping the dynamic operating behavior of at least one compressor of said compressed air system configuration (Konf1, Konf2), and / or - mapping the control behavior of the central control system, in particular the integrated control system, of said compressed air system; 2. The method according to claim 1 .

20. 2. The method according to claim 1, characterized in that at least one compressed air system configuration (Konf1, Konf2) is stored in a simulation waiting list specifying the compressed air system configuration intended for carrying out the computer simulation.

21. 21. The method according to claim 19 or 20, characterized in that the computer simulations for different compressed air system configurations (Konf1, Konf2) are performed in parallel, preferably at least partially simultaneously, in particular by different processors or by different groups of processors.

22. 20. The method according to claim 19, characterized in that at least one step for generating the branching data structure (B) and the computer simulation for the compressed air system configuration (Konf1, Konf2) are performed at least partly simultaneously.

23. 21. The method according to claim 20, characterized in that if the number of compressed air system configurations (Konf1, Konf2) in the simulation waiting list reaches a predetermined maximum number, the step of generating the branching data structure is paused, in particular until the number of compressed air system configurations (Konf1, Konf2) in the simulation waiting list reaches a value below the maximum number.

24. 2. The method according to claim 1, characterized in that providing at least one compressed air system configuration (Konf1, Konf2, Konfbest), preferably an optimal compressed air system configuration (Konfbest), is output, in particular whose assigned quality value (GKonfmin, GsimCosts), preferably whose assigned simulated cost value (GsimCosts), meets the quality criterion better than the quality values ​​(GKonfmin, GsimCosts) of all other compressed air system configurations which may be generated based on the compressor list (Lv).

25. A computer-readable storage medium having instructions which, when executed on at least one computing device, perform at least some, and preferably all, of the steps of the method of claim 1.

26. 26. A server comprising the computer-readable storage medium of claim 25 and at least one computing unit for executing the instructions.

27. - transmitting component data (Dk) to a server, preferably a server according to claim 26, said component data (Dk) describing the components of the compressed air system and at least one technical parameter (Kt) of each component, said component data (Dk) comprising at least one component list (Lk) with a plurality of functionally identical components of different types and at least one technical parameter (Kt) assigned to said respective component, said component list (Lk) being a compressor list (Lv) comprising a plurality of compressors (V1, V2, ..., Vn) of different types, A terminal device designed for the purpose of: Wherein, the terminal device, in particular, - transmitting constraint data indicative of at least one constraint for the compressed air system, preferably a constraint as claimed in claim 3, to a server, preferably a server as claimed in claim 26; and / or - transmitting in particular a desired compressed air consumption profile for the compressed air system to a server, preferably a server according to claim 26; and / or In particular, the indication of at least one compressed air system configuration, preferably with an assigned quality value. The terminal device further configured for:

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