WO2015044082A1 - Computer-implemented aggregation method, and method, computer system, and computer program for monitoring the operation of chemical-industry devices - Google Patents

Abstract

The invention relates to a computer-implemented method for calculating and storing at least one aggregation for an input flow and/or an output flow of at least one mass flow and/or energy flow, which flows through a directed connection between two units of a chemical-industry device, for at least one unit of the chemical-industry device, and a further computer-implemented method building thereon for monitoring the operation of chemical-industry devices by means of at least one characteristic value, which method calculates at least one characteristic value for monitoring the operation of the chemical-industry device, on the basis of at least one stored characteristic-value determination rule from the one or more aggregations, which were calculated according to the method according to the invention mentioned first, for a particular input flow and/or the particular output flow of a particular mass flow and/or energy flow and/or information flow of a unit or of units of the chemical-industry device and stores the at least one characteristic value for further processing or display.

Description

 Title: Computer-implemented aggregation method and method, computer system and computer program for monitoring the operation of chemical industry equipment

The present invention relates to a computer-implemented aggregation method for forming and storing at least one aggregation for an access stream (also called input stream) and / or an outgoing stream (also called output stream) of at least one mass flow and / or Energy flow flowing through a directed connection between two units of a chemical industry facility for at least one unit of the chemical industry facility, and a computer-implemented process based thereon and a computer system also based thereon, respectively, for operation monitoring of chemical industry facilities. According to the prior art, there is no method and no system that allows to capture technical characteristics at different hierarchical levels of a configured as a composite chemical industry device, consistent to model and on this basis initially for monitoring the chemical industry equipment, but ultimately also for their control and / or optimization to provide. It is true that business administration systems are known to have hierarchical relationships and to map complex processes; however, because of their purely business objectives, they are intended for the monitoring, control or even optimization of a complex technical system, such as the complex composite device of the chemical industry. not suitable. Such a key performance indicator system, which is based solely on business requirements, is the so-called DuPont (Registered Trademark) system (See also: Staehle, W .; Kennahlen und Kennhaelensysteme as a means of organizing and managing enterprises, revised edition, Wiesbaden , 1973; Bausch, A. / Kaufmann, L .; Innovations in Controlling using the Example of the Development of Monetary Identification Systems, Controlling 2000, 122ff).

Another approach to modeling a prior art composite system can be found in Viere, Brünner and Hedemann (See Viere, T. et al., Material Flow Based Flanging and Optimization of Highl Integrated Froduction Systems, Chem. Eng Technol 2010, 33 (4), 582ff). Be here visualized material flows and energy flows of a production system in so-called Sankey diagrams with the aid of a program called Umberto. However, it is not optimized on the basis of specific technical parameters, but the composite as a whole is considered. This means that a consideration or even evaluation of individual elements of different hierarchy levels of a complex technical network system is not provided for. Moreover, there are doubts as to the suitability of the Umberto program for the existing problem, which is based on a test of this nature by the applicant in the past.

Another approach using an optimization algorithm based on so-called Petri meshes is presented by von Moeller, Prox, Lambrecht, and Schmidt (see Moeller, A. et al., Simulation and Optimization of Material and Energy Flow Systems in: Rossetti, MD et al. (Eds); Proceedings of the 2009 Winter Simulation Conference IEEE, Baltimore 2009, 1444ff.)

However, Moeller et al do not define parameters for technical hierarchy levels, but rather look at the respective composite as a whole, focusing primarily on the simulation, visualization and optimization of a compound system. Other publications on so-called manufacturing execution systems contain only general advice on the creation of such system, but not explicit descriptions of an actual realization (Cf., for example ZVEI - Central Association of Electrical Engineering and Electronics Industry eV, Association of Automation (ed.); Automation: Manufacturing Execution Systems -

and manufacturer-neutral description of Eösungen Frankfurt, 2010.) Although the DIN EN ISO 50001 also describes the introduction of so-called energy management systems (see DIN EN ISO 50001: Energy Management Systems - Requirements with instructions ur application), but is only very general and above all only industry-independent ie, without taking into account the specific needs of chemical industry equipment, such as chemical industry composite systems, for the introduction of such systems. An example of this is their presentation that meaningful energy indices (so-called En-PPs) must be formed, which are not themselves defined in the standard. The standard thus only describes the framework conditions for introducing such systems and not their explicit implementation.

Steinbach, Winkenbach and Ehmsen (cf Steinbach, A. et al., And Sustainability in Chemistry: Where do we stand today ?, Chemie Ingenieur Technik 2011, 295ff. pay for the determination of material efficiency by describing that the total parameters are composed of the parameters of the respective individual processes, that is not uniformly defined for each hierarchy level, nevertheless it is also said that, although there are data for the evaluation of parameters, However, in the chemical industry there are no uniform parameters for evaluating the processes.

Kumana and Sidhwa (See Kumana, J. et al., Meaningful Energy Efficiency Performance of My for the Process Industries, Proceedings of the Thirty-First Industrial Energy Technology Conference, New Orleans), are also used to characterize energy efficiency. Lousiana, 2009.). In addition, general requirements for such characteristics are defined there. For example, it is proposed that the key performance indicators (the so-called key performance indicators) be divided into four different categories, which are there

Plant equipment (so-called equipment),

Processing unit (so-called process unit), as well

Product and business unit (so-called product and business unit). Then, for example, key figures for the categories are discussed and, as an important aspect for the breakdown of process discrepancies, the division of the key figures into categories is given. However, the categories are not subject to a hierarchical selection, but are chosen freely and partly independently of each other, so that different key figures are defined in each category. Thus, here too, no generic characteristic system is shown over different hierarchical levels, which would permit the information technology modeling of a chemical industry installation operating as a composite system and thus of a monitoring system for such a system.

According to the state of the art known in this regard, the concrete use of parameters in a hierarchical compound system of the chemical industry is therefore never described to this day. Thus, the state of the art does not disclose whether and, if so, in what manner such parameters can be detected in a chemical industry device, in particular in such a composite system, or can be determined from detected, preferably automatically measured, primary quantities in order to initially set up the device. for example, to monitor certain values for such parameters and, as a result, possibly zesssteuerung and / or process optimization of the chemical industry equipment to make.

In the foreground, however, is the fundamental problem of monitoring the mode of operation of the chemical industry facility in order to be able to control it and / or optimize it, for example with regard to its resource efficiency.

Concerning the aforementioned resource efficiency and its realization, in 2011 three VDI guidelines were addressed (VDI 4597 Framework Directive)

- Fundamentals and evaluation methods, VDI 4598 and VDI 4599). To date, however, none of these guidelines has been completed or published, so that no reference can be made to them and their (potential) content has not yet been publicly known.

Furthermore, discussions of the inventor with software manufacturers for the process industry have led to the finding that even in the non-printed prior art no methods or systems exist to capture the technical characteristics at different hierarchical levels in a configured as a composite chemical industry device to consistently model and on use this basis to monitor the chemical industry equipment.

Against the background of this state of the art, it is therefore an object of the present invention to provide such a method and system for monitoring the chemical industry device which is capable of forming technical parameters relating to units of different hierarchy levels of a chemical industry device designed as a composite system and thus to monitor their resource efficiency on the basis of these technical parameters.

This object is first achieved by a computer-implemented method for forming and storing at least one aggregation for an access stream (also called input stream) and / or an outgoing stream (also called output stream) of at least one mass flow and / or energy giestroms, which flows through a directed connection between two units of a chemical industry device for at least one unit of the chemical industry device according to claim 1, by allowing this method to provide both (mass or energy, preferably also information) access streams, as well as (mass or energy, preferably also information) outgoing streams for a respective unit that is located at a particular hierarchical level of a composite chemical industry device so aggregate that the in the respective unit, only internally flowing mass or energy flows (preferably also information flows [also called information flows]) are masked out by only considering the currents relevant at the respective hierarchical level. On the one hand, these are the inflowing streams, ie the access streams, whose start is outside and whose end lies within the respective unit, including all of the subunits of the chemical industry facility belonging to this unit. On the other hand, these are also all outflowing streams, ie the outgoing streams whose beginning within the respective unit and whose end are outside of this unit, and in turn also including all of the subunits of the chemical industry facility belonging to this unit. This aggregation is made possible in the inventive computer-implemented method according to claim 1, characterized in that here on the one hand a data structure - namely the block data structure - is provided, which maps the hierarchy of the respective units of the chemical industry device, this hierarchy is preferably to the standard ANSI / ISA -95.00.01-2010 (IEC 62264-1 Mod) Part 1: Models and Terminology based on the upper level of the company, then on the next lower level the location, after which a lower hierarchical level of operation, then the Finally, on a lower hierarchical level, the subsystem (preferably with corresponding equipment at the lowest hierarchical level) is provided and, on the other hand, a data structure, namely the connection data structure, is provided which controls the directed connections between the real units of the chemical equipment (there often piping for mass flows or power lines such as for electric current or data lines for information streams) in this data structure and thus enables a computer to use these two data structures, which need not necessarily be separated from each other, but also overlap space-overlapping, to determine where the directional connections belonging to a unit start and where they end so as to be able to perform the above-described suppression of the currents of interest only internally within the respective unit. The storage of the data elements in the respective data structure can take place in any desired manner, be it explicitly or implicitly. It should be remembered that, for example, a graph can be stored in the memory of a computer by means of a large number of data structures, such as a two-dimensional connection matrix whose indices then represent an implicit storage of the nodes of the graph - in this case the block data elements , Also, about the hierarchy of the units of the chemical device in the block data structure can be represented by the fact that the respective parent block data element the respective parent block data element refers to the respective subordinate block data element or, conversely, the respective subordinate block data element to the it refers to respective superordinate block data element. Likewise, however, the superordinate block data element may also contain the respective subordinate block data element, for example if a set or list structure is used as the data structure for the block data elements.

Is the blanking of the irrelevant streams on the basis of the information of the block data elements stored in the block data structure and the connection data elements stored in the connection data structure, which model the real chemical industry device in terms of information technology (ie in terms of its interest in the present invention im portant properties in memory), the access and outflow streams remaining for the respective unit need only be aggregated in accordance with an aggregation rule, for example by summation, by averaging (not just in the narrower sense of an arithmetic mean, but in the narrower sense of the art also as a geometric mean or as a median or also - for example, if a formation takes place over time, as a sliding means -) or by extreme value formation (minimum or maximum) can happen, which may be particularly useful if, for example, a certain period for the Aggregated should be selected, which is preferably also possible according to the present invention. This aggregation of the respective streams or of the respective stream takes place in such a way that quantities-preferably measured variables-which are assigned to the respective stream (access or exit stream) are detected and aggregated in accordance with the aggregation rule, that is to say added up.

A summation over the hierarchy levels can be done, for example, in flow measurements or at cost, whereas at temperatures, pressures and compositions only the formation of an average makes sense. Currents of which a maximum is to be determined, for example, can be steam consumption.

Basically, there are two types of aggregation. On the one hand, an aggregation takes place via the considered block data element of a hierarchy level (according to various possible aggregation rules, which can be used for summation, averaging or extreme value formation) and aggregation over time, both of which are mentioned above species can also be combined with each other. A more detailed consideration of the abovementioned aggregation possibilities can be found in the further discussion in FIGS. 1 and 2, which also makes use of a formal specification.

The above computer-implemented method of forming and storing at least one aggregation for an access stream and / or a leaving stream of at least one of a mass flow and / or energy stream - preferably also an information stream - flowing through a directional connection between two units of a chemical industry facility , for at least one unit of the chemical industry device, thus solves the task of being able to form units based on a hierarchical adaptation of the mass and energy flows, preferably based on units of different hierarchical levels of a chemical industry device designed as a composite system also information streams - allows.

The above-described aggregation process of the invention is thus for the determination of parameters (so-called ^KPI's [Key Performance Indicators]) was used. Depending on which mass, energy or information flow is aggregated for KPI determination, one of the aforementioned general aggregation rules (summation, averaging or even extreme value formation) can be used. It should be noted that the aforementioned indicators ^(KPI's) not only technical parameters (including technical indicators called) but also economic indicators (also economic indicators called) can be, such as the monetary value of an inventory or about a stream of a, Werf represents, such as an information flow with the unit, ßuro '. In principle, of course, other, more aggregation rules than those mentioned (summation, averaging or extreme value) are possible in the context of the method according to the present invention. The aggregation method according to the invention thus provides the technical conditions for monitoring the operation of chemical industry facilities by means of at least one parameter (KPI ), wherein based on at least one stored characteristic determination rule from the one or more aggregations formed according to the above-described method of the present invention for a respective access stream and / or the respective outgoing stream of a respective mass flow and / or Energy flow from one unit or units of the chemical industry At least one - preferably - technical characteristic (KPI) for monitoring the operation of the chemical industry device is formed and stored for further processing or display.

 Thus, for each unit of the chemical industry equipment for which at least one, preferably technical, parameter is to be formed, one or more aggregations of the mass or energy streams (and / or information streams) must be carried out, which are necessary for the formation of the parameter. Then, the parameter itself can be determined from the aggregations thus formed.

In this way, as a technical parameter preferably an energy performance indicator (so-called Energy Performance Indicator, also called EnPI) for a relevant unit of the chemical industry device can be determined (or formed), the characteristic determination rule for this energy performance indicator is that the aggregated access energy flow of all energies to the respective unit of the chemical industry facility by accumulating over a selected period of time according to the method according to the method according to the invention by the summation mass flow of all products of aggregated by accumulation over the selected period according to the method of the present invention the unit of chemical industry equipment is divided so as to obtain the energy performance indicator for the respective unit of the chemical industry equipment as the technical characteristic.

A preferred embodiment of the computer-implemented method for monitoring the operation of chemical industry equipment according to the present invention is characterized in that at least one stock - preferably a stock or level - of a material belonging to a unit of the chemical industry equipment is detected and respectively as that unit of the chemical industry equipment is assigned stored, and that at least one of the technical parameters for monitoring the operation of the chemical industry device is determined at least one of these stocks of the respective material at least one of the respective units of the chemical industry.

Thus, other KPIs can be considered: turnover, yield and / or selectivity,

the mass balance factor and / or material efficiency, Product Loss Indicator (Product Loss Indicator), inventory turnaround frequency, inventory reach, and / or lead time.

The details of such characteristic determination are also below the discussion of Figs. 1 and 2 refer. Also preferably a computer system for monitoring the operation of chemical industry facilities is provided, wherein the computer system at least one computer and at least one processor and at least one computer system memory and at least one detection device for detecting mass flows and / or energy flows - preferably also information streams - by a respective directed connection between two units of the chemical industry device flow, and which according to the invention is characterized in that the computer system is arranged by means of a computer program for carrying out the method according to the invention, preferably so that the computer system, when set up as above in operation is set by at least one processor according to the method described above for forming and storing at least one aggregation for an access stream and / or a leaving stream a mass flow and / or energy flow flowing through a directed connection between two units of a chemical industry device, for at least one unit of the chemical industry device and / or the method according to the invention for operation monitoring of chemical industry facilities according to the present invention.

Preferably, the computer system as a detection device for detecting mass flows and / or energy flows - or information streams - at least one sensor, preferably a flow sensor, such as a mass flow meter or an energy meter for measuring the electrical energy or a power meter for measuring the electrical power , However, the detection can also take place, for example if a corresponding sensor for automatic detection is missing, by corresponding input of the data, for example by means of a keyboard.

In a further embodiment of the computer system according to the invention for monitoring the operation of chemical industry facilities, the latter also provides at least one detection device for detecting at least one stock, preferably a stock or a filling level, which belongs to a unit of the chemical industry equipment. Here, as a Device for detecting at least one of the stocks - preferably an inventory or a level - listening to a unit of the chemical industry device, a level sensor or a weight sensor may be provided. Nevertheless, the detection can also take place here-if, for example, there is still a corresponding sensor for automatic detection-by corresponding input of the data, for example by means of a keyboard.

 Also, the computer system according to the invention for monitoring the operation of chemical industry facilities in a particularly preferred embodiment according to the present invention, at least also have a display device for - preferably graphically illustrated - display the or according to the aggregation rule (s) for the respective access stream and / or serves the respective output current of the respective mass or energy flow of the unit or the units of the chemical industry device aggregation or aggregation and / or according to the characteristic Ermitflungsregel (n) determined characteristic ^) and on the corresponding sizes preferably - particularly preferably graphically - also presented. Finally, the implementation of the present inventive method also serves a computer program having instructions that are set up to carry out the method according to the present invention. In other words, a computer program constructed to execute the corresponding inventive method of the present invention encoded therein upon execution on a (suitable) computer, preferably on a computer system as hereinbefore described.

The same applies to a computer program product which has a computer-readable medium with computer program code means in which, after loading the computer program into a (suitable) computer (preferably into a computer system as described hereinbefore), the computer is executed by the program for performing a Method is set up according to the present invention. In other words, a computer program product with a computer program on about a disk as a computer-readable medium (ie about a memory stick, such as a USB memory stick or a compact disc (CD) or DVD or the like) is located, wherein the computer program is constructed so that it when executed on a (suitable) computer - preferably on a computer system as hereinbefore described described - according to the invention encoded therein according to the inventive method - preferably on at least one processor - performs.

Likewise, the present invention also relates to a computer program product which has a computer program on an electronic signal, in each case after loading the computer program in a (suitable) computer, the computer for carrying out a method according to the present invention is established, since today computer programs are often also for so-called download, so to download on an (electronic) signal, preferably a carrier signal offered or placed on the market.

In the following non-limiting exemplary embodiments will be discussed with reference to the drawings. In this shows:

1 is an illustration of a part of a formal model for modeling a hierarchically structured chemical industry device designed as a composite system in the form of so-called blocks leading to blocks that lead to higher hierarchical blocks and wherein the respective block in its information technology modeling as Storing a block data element in a block data structure in a memory of a computer and each representing a unit of the chemical industry device,

FIG. 2 is an illustration of a simple example of the IT modeling of a hierarchically structured chemical industry device as a composite system with associated blocks representing entities of the chemical industry equipment and associated access and exit streams; FIG.

3 shows the schematic representation of a fictitious composite system as an example of a chemical industry device in the same way as shown in FIG. 2, FIG.

4 shows the structure of a relational database for implementing the data model for the modeling of the notional interconnected system according to FIG. 3 at a glance,

5 shows a so-called Hierarchy-Vienf, a so-called low-view ⁱ and a so-called lock-view ⁱ on the structure of a relational database for implementing the data model for the modeling of the notional interconnected system according to FIG. 3 in a joint representation, Fig. 6 the so-called JrLierarchy-Vien? in Fig. 5 in isolated representation,

Fig. 7 the so-called J ^ low-Vien? in Fig. 5 in isolated representation,

 Fig. 8 the so-called Jttock-Vien? in Fig. 5 in isolated representation,

 Fig. 9 a so-called Model-Vien ^ ', and

Fig. 10 a so-called, KEM-View

FIG. 1 shows, as part of a formal model for modeling a hierarchically structured chemical industry device in the context of the present invention, the illustration of so-called blocks belonging to blocks, which lead to hierarchically higher blocks and wherein the respective block in its information technology modeling is stored as a block data element in a block data structure in a memory of a computer and each represents a unit of the chemical industry device and Fig. 2 shows the representation of a simple example of the information technology modeling a composite system designed as a hierarchically structured chemical industry device with associated blocks blocks, the units represent the chemical industry equipment and associated access and exit streams.

 On the basis of these two representations, the aggregation method according to the invention, but also the determination of parameters (KPIs) based thereon on the basis of various examples will now be explained in detail.

Here, however, a formal basis should first be laid on which these explanations are then based subsequently.

Basic components of the present invention and their related modeling constitute streams, namely mass flows, energy streams, or information streams and blocks, the blocks and their informational mapping as block data elements each corresponding to a unit of a chemical industry device, arranged in a hierarchy and the flows flow between aforementioned units of the chemical industry equipment represented by the blocks. In the following, a formal representation of this modeling will be given, for which hierarchical levels, streams and blocks are defined. This starts with the concept of hierarchical levels. As already stated, the hierarchy term used in the context of the present invention is based on the ISA 95 standard, where six different hierarchical levels are defined. These are in descending order:

 Companies,

 - Location,

 Business,

 Plant, and finally

 Unit, and as part of this,

 their equipment.

In this way, every part of a chemical industry facility or a company can be classified in this plant hierarchy. An example shows the following table:

 Designation of the German example

 Hierarchy level designation

 according to ISA 95 standard

1. Enterprise company Evonik

 2. Site location Marl

 3. Area operation butadiene operation

 4. Plant plant NMP plant

 5. Subplant unit Distillation

 6. Equipment Equipment Column 201

In the above-mentioned modeling in the context of the present invention, a stream F- denotes a connection between two blocks, ie a stream has a start and end point, start and end points representing blocks. It does not matter on which hierarchy level the start and end blocks are located. Accordingly, streams are defined across hierarchy levels. Modeling the flows quickly reveals the benefits of this definition. In reality, it often happens that a stream flows from a block of a higher hierarchical level, for example a port tank farm, to a lower one, eg a sub-installation. This is possible because of the definition. The start block and the end block must be different, ie a stream can not end in the same block in which it started. A division or branching of a stream is not possible. There is always only one stream with a defined beginning and end. If a division of a current must be mapped, then one must additional block is added, in which a current is received and two exit again. In summary, this leads to the following definitions.

For every stream:

SP (Fj) = B _k and EP (Fj) = B _z (1) comprising:

SP (Fj) Φ EP (Fj) (2)

 For the amount of all streams:

Ψ = [Fj, 7 = 1 ... /} (3)

A block B _k represents in the system an element of a particular hierarchy level of the chemical industry equipment, or may also represent a hierarchical level (eg Enterprise). There is no limit to the number of blocks on the respective hierarchy levels. Only the number of blocks at the highest hierarchical level, the enterprise level, is limited to three (external-in, external-out, and enterprise). Thus, blocks can stand for a company, a location, an enterprise, a plant, a unit or an equipment and are stored in the information-technical modeling according to the invention as block data elements in a block data structure in a memory. Blocks form the start and end points of streams, and this criterion does not have to be met by each block. A block of a hierarchy level, except the lowest, can also only be flown through so that a stream is input or output current for that block. Each block can be assigned any simple attributes, eg a hold-up, ie a fill level of a container. For each block, input and output streams can be defined. Thus, a stream for block B _{k may be} an output stream and for block B ₂ an input stream. The hierarchy level HE _m is directly dependent on a block and results in:

HE {B _k ) = m (4)

The variable m can take a value between 1 and 6, since it stands for a fixed depth in the given hierarchy. For example, a value of m-3 means that a block is considered at the third hierarchical level. For the quantity of all blocks:

Γ = {B _k , k = 1 ... K] (5)

In addition, only one unique parent block can be assigned to each block. A parent block, on the other hand, can contain several blocks. One exception is the highest Hierarchy level [m - 1). Blocks on this hierarchy level have no parent block. This structure is to be understood as a hierarchical tree structure. This leads to the definition for the parent block:

^{P Bk)} - {0, if m = l ⁽⁶⁾

 For the following definitions, however, the parent block definition must be extended. A consideration of the parent block parent block requires the double use of the parent block rule, which in the following notation is represented by a superscript two:

Ρ ² (ΒΠ = Ρ (Ρ (Βψ) (7)

 Using these definitions, a path of a block can be described. The path of a block contains all parent blocks that are unique to the block, including the top-level block. This leads to the following general definition of a path:

Ρ (ΒΠ = [P ^m _-Bk ) P ² (β _k ), P (β _k ), β 1} (8)

 Thus, for the blocks of a path of any block, the definition follows:

(B _k , if m = 1

Pf (B _k) = ^ p (p (ß _k)) if m> 1 ⁽⁹⁾

The example shown in FIG. 1 is used to explain what is meant by a path. There, blocks are arranged in a tree structure on the left side of the illustration. Based on the superscript m, the respective hierarchy level can be determined. On the right side is a table containing the respective path. This is explained on the basis of the block with the code number (in this case the sub-index of B) 5 of the hierarchy level 3 [m- 3]. To the path of block 5 thus belongs the parent block B ₂ and the parent block B _{j of} the block B ₃ :

 Considering the blocks subordinate to a block leads to the definition that all blocks are to be counted among these sub-blocks that have the considered block in their path:

0 (£? _K ) = {B _z e Γ IB _k e PF (β _z )} (10)

Accordingly, the blocks that are not subordinate to the block are all others and thus the complement of Θ. This can be written as follows: 0 (B _k ) = 1 - 0 (B _k ) (11)

 Entry and exit streams

There are access and exit streams (also called input and output streams) in each chemical group or production facility. In the model of the present invention, the streams always flow in or out of a particular block. In the illustration of Fig. 2 is such a structure consisting of the three basic elements, namely the elements

currents

Hierarchy levels and blocks to see.

As can be seen in Figure 2, a block is always associated with a particular hierarchical level. For example, block A may be assigned to hierarchy level A, block B to hierarchical level B and block C and D to hierarchical level C. In the representation hereby used in this way also elsewhere in the present description - the inner block is always at least one hierarchical level lower than the comprehensive block. Thus, for example, block B may be considered to be at hierarchy level B, whose hierarchical level is lower than that of block A, but at the same time higher than that of blocks C and D. If the currents are additionally considered, it is clear that Currents (called input currents) and outgoing currents (so-called output currents). A special feature is the current between block C and block D. This is for the block C, a outgoing stream (output stream), but for the block D, an access stream (input stream), a fact that in the modeling of the currents must be considered. In the following, access and outgoing flows (input and output streams) are defined in general and in terms of blocks, whereby the definition of the access streams (input streams) and then of the outgoing streams (output streams) takes place first.

F _j is an input stream for a block k (B _y of hierarchy level m (HE, _^ )) if F _j does not start in B _k of HE _m or a hierarchy level lower than HE _m within B _k and in B _k the HE _m or a lower hierarchical level HE _m ends within the B _k . According to the definition, an access stream (input stream) can therefore also be an access stream (input stream) for several hierarchy levels, but it always ends in a specific block. For the set Ψ ^{1 of} all access streams (input streams) of a block k of the hierarchical level m, the following applies:

Ψ Β _Η ) = [Fj G Ψ | SP (Fj) G Θ (B _K ) Λ EP {F _j ) G 0 (£ _k )} (12)

The following example again refers to FIG. 2: Fl F is an access stream (input stream) of the hierarchical level A and of block A (B ^, the hierarchical level B and of block B (B _B ) as well as the hierarchical level C and from block C (B _c ). Mathematically this means:

Ψ% Β _Α ) = F [; Ψ Β _Β ) = F [; Ψ Β ₀ ) = F

F _j is an output current for a block k (B ^ of the hierarchy level m (HEJ, if F _j starts in B _k of HE _m or in a block lower than HE _m within B _k and not in B _{k of} the HE _m or in a block lower of the HE _m within the B _k ends.A output stream (output stream) can therefore by definition also be a departure stream for several hierarchy levels, but it always starts in a particular block For the set Ψ ° of all outgoing currents of a block k of the hierarchical level m, we have: (B _K ) = [Fj G Ψ | SP (Fj) G Θ (B _K ) Λ EP {F _j ) G 0 (£? _k )} (13)

. Also in the following example, which in turn refers to FIG 2, stream ₃ f * f is considered: Ff ₃ is an outgoing current (output current) of the hierarchical level A and block A, the hierarchy level B and of block B , as well as the hierarchical level C and of block D. The mathematical formulation is therefore as follows:

Ψ ° (Β _Α ) = F ₃ °; Ψ ° (Β _Β ) = F ₃ °; Ψ ° (Β _Β ) = F ₃ °

Aggregation of streams Streams, such as access or exit streams, each of which can be in the form of a mass flow, an energy stream or an information stream, can be determined by certain measurable quantities, such as flows, temperatures, pressures, costs, etc. (in terms of information technology, this is referred to here also characterized by an attribute) (in terms of information technology, attributieri). Thus, certain (measuring) quantities are assigned to a current, which can also be detected by means of suitable measuring sensors. Each stream must be assigned at least one such (measurement) size be. Even a block, which represents a unit of the chemical industry equipment, may or may not be characterized by such a (measurement) size, since blocks are used in the model formation used according to the present invention mainly as start and end points for the streams (FIG. between them). Also, the blocks and the streams (possibly) differ in size (measurement) of their kind. Blocks can - at any given time in each case considered - be assigned static variables, such as stock sizes, such as a fill level [attributieri]. On the other hand, a stream can not be assigned such a static (measured) quantity as a fill level, and vice versa a block has no flow rate in liters per minute, but it is very well attributable to a corresponding stream as (measurement) size butteren) is.

In terms of the currents, this means that different attributes can be assigned to the access and outgoing streams (input and output streams) of (measured) quantities. Depending on the (measuring) variable, different aggregation rules are used for the respective (measured) quantities. For example, it can be aggregated by summing up (measurement) quantities. For others, it makes sense to aggregate via an average (be it the arithmetic mean, be it the geometric mean or even the median) of the respective (measurement) variable (s) or a corresponding extreme value (such as maximum or minimum) of the ( Meß) size (s) to form this. Thus, a summation takes place over the hierarchy levels, for example in the case of flow measurements or costs, whereas for assigned (measured) variables such as temperatures, pressures or compositions, the formation of an average value may be expedient. Determining a maximum of the (measuring) variable (s) may be useful, for example, for steam consumption.

Basically, as already explained, there are two different types of aggregation of the attributes. On the one hand there is an aggregation over the considered block of the hierarchy level and on the other hand an aggregation over the time, whereby both types can be combined with each other.

In the following, the aggregation is first defined via a respective block of the considered hierarchy level.

When aggregating the (measured) size of a stream (category A), the following aggregation rule is preferably applied across the block of the hierarchy level and without taking into account the time: alls F _j e Ψ ^) (14)

If the (measured) variable (s) are aggregated over the mean value (category B), the following aggregation rule is preferably used:

In the preceding formula, n stands for the number of streams which are considered in the sum.

Aggregations in which a maximum of the (measured) size (s) is determined (category C), are preferably carried out according to the following aggregation rule:

F ^c (B _k ) = max Jalls F _j e Ψ ^) (16) Consideration of the maximum value of the sum of currents is generally considered when a temporal aspect is included, otherwise only one of the sum is always included absolute value can be determined. As a result, the inclusion of time is the next step. (Measured) quantities which are aggregated by means of summation (category A) are aggregated in time over the sum of the (measured) variable (s). (Measured) quantities which are aggregated via averaging (category B) are aggregated in time via the corresponding mean value (for example the arithmetic mean) and (measured) variables which are aggregated via an extremal value formation (eg maximum value formation) temporally aggregated over the corresponding extreme value (eg maximum value) of the sum of the individual streams. The previous aggregation rules without the inclusion of time are extended below by the component of temporal aggregation (so-called complete aggregation). It is important that the temporal aggregation must first be formed. All measured values are previously multiplied by the sampling time t _A , which converts a time-related measured value (eg kg / h) into an absolute value (eg kg). The sampling time can be set individually for each individual measuring point in the data model.

The number of sampling points is denoted by n in the following definitions. Simplified applies: F (t ₀ + n - t _Aj ) (17)

 The variable N stands for the number of observation periods. For example, a period of observation may be a day, a month, or a year. For further definitions, the following simplified formulation applies:

F (t ₀ + N - t _B ) = F (N) _{(1 8)}

With the help of an additional factor κ the aggregation rules can be defined:

Ki = (19) Since different sampling times can be defined for different measuring points, the observation period must be greater than or equal to the sampling time. This leads to the following formulation: t _B > t _A. Year (20)

 For the complete aggregation of the flows for which (measurement) quantities are aggregated for aggregation (category A), the following applies:

F ^A (β _k ) OV) = ^ ^ F _j (n), if F _} 6 Ψ (β _k ) (21)

 j n = Kj (N-1) + l

The complete aggregation of the streams for which (measurement) quantities are averaged for aggregation (category B) holds: k W = j Fj (n) \, if F _j e Ψ (Β *) (22)

 Finally, complete aggregation of the streams for which aggregation maximum is formed (category C) is as follows:

F ^c (SS _k) (N) = max (23)

For the minimum as another extreme, the formula above applies accordingly.

The formation of the aggregation of two outgoing currents (output currents) by means of a maximum value is described below with reference to an example: If the measured values of a measurement A that have already been multiplied by the sampling time are assumed to be at different times (in this case t ₁₅ t ₂ , t ₃ and t ₄ ) with:

F ° F °: /, = 10; t ₂ = 9; t _} = / // /, = / /

and measured values of a measurement B with:

F ₂ ° i; /, = 12; t ₂ = 13; t ₃ = 10; t ₄ = 12

Then the maximum of the sum of the elements results in:

F ^e ' ^c = F / ^{' r} Ct ₄ ) + * ^C () F ° '= F °' ^c {t) + F ₂ ° '(t ₄ ) = / / + 12 = 23

In addition, it should be mentioned that not only streams by means of their (measurement) variables, but also the blocks corresponding to the units of the chemical industry equipment, have (possible) sizes associated with them with regard to these variables, such as stock levels, preferably by summation ( but possibly also by averaging or extreme value formation) - preferably over time - can be aggregated. The three exemplarily formulated aggregation rules (summation, averaging or extreme value formation) then form the basis for the determination of the below-described - preferably technical - parameters (KPFs). Depending on which stream needs to be aggregated by means of which (measured) variable to determine a respective parameter (KPI), for example, one of the aggregation rules described here above can be used. In the following some of these parameters (KPIs) are described in more detail:

Energy performance indicators as parameters

According to DIN-EN ISO 50001, any company planning to introduce an energy management system must use appropriate energy performance indicators. These are referred to below as Energy Performance Indicators (EnPFs). As an example, in the aforementioned standard, the energy used for the production of a certain amount of product called. The EnPI describes the energy expenditure related to a manufactured product for a particular block B _{k of} a particular hierarchical level m. The EnPI can be determined as follows from the supplied energies and the outbound products according to the following characteristic determination rule:

E PI (B) ^ supplied energy of a block kWh

 k Σ outgoing product streams of a block ^ product (24)

The supplied energy flows are composed for example of different steam streams of different pressure. The conversion of the steam flows into the target unit kWh takes place via the following formula:

Q = mh _v (25) The calculation rule for the EnPI is:

As can be seen from the formula, the EnPI depends on the block B _k and the time t. The aggregation over the time and the considered block of the hierarchy level takes place in each case by summation, that is to say the currents are aggregated over the sums by means of their associated (measured) quantities.

 Sales, yield and selectivity as parameters

The conversion X _a can be used to determine how much of a component defined in the input stream is still present in the output stream. Mathematically, this can be expressed by the ratio of the amount converted to the amount used. A turnover is defined for a chemical reaction. The conversion should therefore always be between 0-100%, with 100% representing the ideal case, because then the entire reactant was implemented.

Since in practice usually many separation sequences exist within a plant, the following characteristic determination rule for the turnover is also valid for separation sequences. This has the advantage that a turnover can be determined for almost every block in the hierarchy. For this purpose, analogously to the classical definition, a guide component x _{a is defined} in the inlet / outlet streams and the conversion is related to the residual content of the conductive component in the outlet streams. The aggregation takes place both via the block of the hierarchy level and over time by way of summation as an aggregation rule. Since the outgoing streams also include streams, such as exhaust gas, which should not be included in the analysis, sales may only be agreed exhaust gas streams, namely the product streams to be purchased. The calculation of the component mass flow by multiplying by x _a must take place before the aggregation steps, since the component mass flow must be aggregated. The characteristic determination rule for the revenue X _a is thus:

When applying this formula, it should be noted that the absolute values differ between separation sequences and reactions. In a reaction, a 100% conversion is ideal, whereas in the case of a separation sequence a value that approaches 0% would be ideal. This has the background that at a separation no substance is converted. That is, ideally, the component defined in the input stream should also be fully contained in the product stream. In a reaction, it is ideal if the corresponding component no longer exists, because then it is completely reacted. By means of an example, the resolution of the counter from the sales definition is explained: The calculation steps which appear in the further KPI definitions are to be solved analogously.

Assuming that the access flow measurements shown in the table below are as follows for three different times:

Time Current F _t Component A Current F ₂ Component A

 in weight% in weight% t, 10 50 20 50

t ₂ 12 10 25 20

t ₃ 11 50 20 20

First, the respective component mass flows for the respective time are formed and aggregated over the sum. The results of this solution step are summarized in the following table below: Time F _j × weight% F ₂ × weight%

 [Mass / h] [mass / h]

 t, 5 10

t ₂ 1,2 5

 5.5 4

 ! O Σ V,; i, .AA ^ 11,7 19

 Xa (t)

The next step is then the resolution of the total over the hierarchy levels, summarized in another, again following table:

Time Aggregation Comp. Stream ¥ _x Comp. Stream F ₂ ^"1

That is, the counter in this example returns the result 30.7. The denominator resolution could be done in the same way. Since the calculation rules of the other parameters (KPPs) contain similar mathematical formulations, this example is exemplary for the following parameters.

2 0 The yield Y _p relates the actual yield of a product with the maximum possible yield according to the stoichiometry. The starting material, which reacts most quickly, has a limiting effect on the yield. This is mainly due to the secondary and subsequent reactions. For this reason, the yield and the conversion may also differ, with the yield usually being lower.

In a simplified way, when defining the yield for the model, it is assumed that the stoichiometry plays no role in reactions and the yield can also be determined for separation processes. For this, the same components are allowed in the numerator and denominator. This means that for separation processes, the amount of a component in the educt is considered and the amount of the same component in the product. Nevertheless, between the definition of separations or Storage containers and reactions distinguished. First, the characteristic determination rule of yield for separation sequences and storage containers follows:

F ° ^ - * _a (B _k ) (t)

r _P (B _k ) (t) = (28)

F ⁴ - * _a (B _k ) (£)

The yield depends on the block and the time. The x _a used in the formula stands for the component part. As with sales, only the product streams are included in the calculation.

The characteristic determination rule for the yield of a reaction, neglecting the stoichiometry, is then as follows:

The difference to the first characteristic determination rule H lies in the counter. Due to the chemical reaction, the component in the starting material (y is not identical to the component in the product (x, since a chemical reaction of the educt takes place.) The calculation instructions are also identically structured.

 Finally, the characteristic determination rule for selectivity follows. The selectivity describes how much of the reacted educt was actually converted into the desired product. Due to secondary or subsequent reactions, it may be that undesirable products have arisen. Therefore, the highest possible selectivity is desired since this means that much of the starting material has been converted into the desired product.

There is a connection between the conversion, the yield and the selectivity. This has the advantage that the selectivity can be calculated with the aid of the previously determined yield and the conversion.

The relationship is reflected in the following selectivity characteristic determination rule:

(30) It is not necessary to integrate the aggregation rules to determine the selectivity. This is because the aggregations have already occurred in the determination of the sales and yield. At the same time, however, this means that in order to determine the selectivity it is always necessary first of all to determine the conversion and the yield. Mass balance factor and material efficiency as parameters

Then the parameters Mass Balance Factor and Material Efficiency should be considered. The mass balance factor M _B refers to all material flows in the system. The mass balance factor can be used to check whether the mass balance of a system is correct, ie whether the mass of the access flows correlates with that of the outgoing flows. If no notable losses are recorded in a process, the mass balance should be absorbed. Since a process chain can be very long and storage containers may be included at various points, the mass balance will in most cases (depending on the period under consideration) not be fully absorbed. In order to determine on which systems or subsystems this is the priority, the model used here in the context of the present invention is ideal. The mass balance factor is composed of the total access flows and the total outgoing flows, resulting in the following characteristic determination rule:

The aggregation takes place both in terms of time and over the block of the hierarchy level by summation. If the solution of the mass balance factor M _B = 1, then the mass balance rises, ie the same mass flows into and out of the system during the considered period of time. If the M _{B is} less than 1 (M _B <1), then the mass flow leaving the system is less than the mass flow flowing into the system over the period under consideration.

To make a statement about the effectiveness of the material use, the key figure material efficiency M _{E is} used. This includes all starting materials that are relevant for the production of the product. Material efficiency in other contexts also means material productivity. According to the German Materials Efficiency Agency, material efficiency is understood to mean the ratio of the amount of material in the produced products of the amount of material used in their production.) In the chemical industry and in the following definition, the simplification is meant that the ratio of the main product quantity to the amount of starting material is meant. The material efficiency M _E characteristic determination _rule can therefore be given as follows:

F ° ^ (B _k ) (t)

M _£ (B _k ) (t) =

F ^d (B _k ) (t) (32)

The aggregation of the streams takes place by adding up their respective (measured) quantities both in time and over the block of the hierarchy levels. The material efficiency can be calculated for each block of the model so that a variation of material efficiency found on a high hierarchical level on the lower level blocks can be examined. The optimization can be done selectively depending on the modeling level.

 Product waste indicator as a parameter

In a process, not only is the amount of product compared to the educt amount used interesting, but also how much product is discharged via the waste gas or waste streams. The more effective a separation, the less product is lost through exhaust or waste streams. However, a high product yield at a separation also means a high energy consumption. This means that a process control can be operated with the aid of the developed parameters (KPFs) and the assignment of different priorities. The Product Waste Indicator (PWI) describes product loss, i. Product components that are discharged via waste gas / waste streams. The goal to be achieved is to suffer the least possible product loss. This can be at odds with the parameters (KPIs) that are related to energies. For the PWI, the following characteristic determination rule can be specified:

F ° ^ - * _a (B _k ) (t)

PW / (B _k ) (£) =

F ⁴ -x _a (B _k ) (£) (33) The PWI depends on the considered block and the time. Both over time and over the block of the hierarchy level are aggregated via the corresponding aggregation rule by means of the summation. The goal is to achieve the lowest possible PWI because this means that little product will leave the system via the exhaust / waste streams. The PWI should therefore be optimized against a value of zero, taking into account other relevant parameters (KPPs).

Inventory turnover rate, warehouse reach and lead time as characteristics

 In the following, stock-related parameters (KPPs) are treated. Key figures in this area mostly come from the manufacturing industry and have been developed for discontinuous processes. Nevertheless, under certain assumptions, these parameters (KPPs) can also be transferred to the chemical industry and there especially to continuous processes. For parameters in this area, the inventory and the duration of storage must be kept as short or as short as possible. As a result, there is little capital tied up in the form of products or reactants in the modeled composite system. In the following, three storage-related parameters (KPPs) are presented.

 The inventory turnover rate LU indicates how often an average inventory (here a tank level) is completely emptied and replaced. This characteristic (KPI) is assembled from the warehouse outflows or the total outgoing flows and the average inventory, which corresponds to the average level La. The inventory turnover frequency can be formed by means of the following characteristic determination rule:

Bearing outflows F ° (B _k ) (t)

t _/ (B _k ) (t) (34)

0 Stock Level 0 L _a (B _k ) (t)

 The level is aggregated over time by means of an aggregation rule for averaging. That is, the levels are averaged over time and then summed over the considered block of the hierarchy level. Here, therefore, not only a collection and aggregation of (measurement) sizes of streams, but also the detection (and aggregation) of a belonging to a block inventory, namely a stock.

With the help of the warehouse reach LR, a statement can be made as to how long a stock of goods is sufficient to supply customers. In a continuous operation of the chemical industry can be made above all statements, when the production increased because there is often a direct dependency in the interconnected system. This means that if the production plant fails, it is possible to make a statement about the range of supply of the following production plants. The characteristic determination rule of the warehouse area LR is defined as follows:

^Ä kJ) - (jjagerabgang ^~ ₀ F ° (B _k ) (t) ⁽³⁵⁾

The warehouse reach is determined using the quotient of the average warehouse stock and the average goods issue. The aggregation over time is due to averaging, that is, the mean over time is considered. Subsequently, the results are aggregated by summing them over the considered block of the hierarchy level. The inventory range in the chemical industry plays an important role for the interdependent companies of the network. With a sufficiently high storage range, it is ensured that the subsequent productions can continue to be supplied in the network.

Finally, the parameter of the cycle time (DLZ) should be considered. The lead time is formed from the quotient of current stock (here assumed as stock) and feedstock throughput. The goal is to achieve the lowest possible turnaround time, i. to have a low circulation stock and to drive a high throughput. The characteristic determination rule is as follows:

 (36)

Circulation = inventory) L _a (ß _k ) (t)

k ^{W ~} educt - flow rate ^~ F ^ ^d (B _k ) (t)

In the chemical industry, the stock in circulation can be understood as the level of the respective container, which must be aggregated in time over the mean and over the block of the hierarchy level over the total. The throughput is reduced to the educt throughput, which usually consists of the main starting material. With the help of the lead time, for example, periods for defective productions can be determined. If, in a measurement before the first production step, an analysis reports that a limit has been exceeded with consequences for the product quality, the plant can not be shut down immediately in a continuous operation of the chemical industry. Taking the throughput time into account, it can be determined when this quantity has passed through the plant. For this period, the product of lower quality can, for example, be placed in a separate container and after normalization of the product Measurements leave the operation in the normal way. This prevents a customer from receiving a product of reduced quality.

 Fig. 3 shows the schematic representation of a fictitious composite system as an example of a chemical industry device in the same manner as shown in Fig. 2. Based on the comments on the preceding FIGS. 1 and 2, a modeling rule for a data model will be explained here by way of example with reference to the illustration. The goal here is to model the relationship between the currents, blocks and hierarchical levels in such a way that a characteristic system (KPI's) can be developed that allows monitoring or even control and / or optimization of the composite system shown here. The implementation of the data model takes place here in the form of a relational database, the structure of which can be seen in an overview in FIG. 4 and whose individual components are discussed in greater detail in the course of the discussion of the modeling of the fictitious compound system according to FIG.

In the illustration in FIG. 3, different blocks can be seen on different hierarchical levels. Overall, this example consists of five different hierarchy levels. The levels are graded inwards. This means that the outer block (company) 1 is located at the highest hierarchical level, the so-called enterprise level, and the example is modeled down to the unit levels, represented by the innermost blocks. Each stream in the illustration is identified by a reference numeral. The drawing shows a block called "Split" Appendix D. This has been introduced by way of example, since the current 16 flows into the systems E and F, but it is assumed that there is only one input measurement for this current. the current is split across the split into the currents 17 and 18. This has to be done this way since the definition states that each stream can only have one destination point .Together, in this example, twenty-two blocks are listed, each with a reference number The name of the hierarchy level is shown briefly on the left above the block, for example Plant A at Block 7 or Location Munich at Block 3. The blocks are connected by the streams the External-In-Block, which starts all streams that flow into the company from outside, and the other side is the Externa on the right l-out block that includes all flows leaving the company. The currents were chosen so that as many vari- are listed by way of example. Flows flow between blocks on the same hierarchical level, but also flows between different hierarchy levels. The external in- and the exterior-out-block are assigned to the highest level, the enterprise level. Block 5 represents another special feature, a tank farm. In a tank farm there are only storage tanks and as a rule the entry and exit quantities are identical over time, ie there are no reactions or separations. The relationship between the currents and the blocks will be recorded later in tabular form.

The first table is called HierarchyDef and can also be found in the overview of FIG. 4, where a 1: N relation is represented by the symbol and an N: 1 relation by the symbol.

The information entered in this table describes the hierarchy of the blocks. Using the codes or IDs that were previously assigned to each block, each block can be assigned a so-called parent block.

The following table shows a section of the content for the example presented here:

ID_Block BlockName ID_ParentBlock

 External_In 0

 External_Out 0

 Company 0

 Location Cologne 1

 Location Munich 1

 HierarchyDef- Table

The above table is composed of three columns, ID_B / ock, BlockName, and ID_ParentBlock. The primary key in this table is the ID block. This column assigns a unique ID to the respective block. As previously defined, External-In and Out, as well as the company, are at the highest hierarchical level and are therefore subordinated to the parent block with ID 0. In the case of the locations, ie location Cologne and location Munich, the ID-Block's are 2 (location Cologne) and 3 (location Munich). Both are subordinate to the enterprise (with ID_Block = 1), which is at the enterprise level. On In this way, the entire hierarchy can be captured in a table and each block can be uniquely placed in the hierarchy.

Then, based on this structuring, an image of the currents that flow between the blocks is possible. This is done in the so-called FlowDef-Tabelk. The following table summarizes a small part of the FlowDef-Tabelk, which of course can also be seen in the relational database overview in Fig. 4.

Below you can see this smaller section of this FlowDef table, but its function is clear from the example here:

ID FlowDescription ID ID ID ID

 Flow Material Category SourceBlock SinkBlock

1 power a 3 1 23 13

 2 steam b 2 2 23 15

 3 Electricity 3 1 13 14

FlowDef table The primary key of this table is in the first column under ID_Flon>. Each column is given a designation such as steam or the substance name of the stream, eg C4 mixture, via the Flow Oescription column. Two foreign keys, that is, a reference to a primary key of another table, are listed in this table by ID_Matenal and ID_Category. The relevant tables will be discussed later. Each stream can be assigned a material and a category. For example, one material would be 3bar steam and the associated category would be energy in this case. This information is needed to filter for materials or categories later when calculating key figures. Thus, a clear differentiation of the currents over two levels is possible. The IDs of the last two columns, ID_SourceBlock and ID_SinkBlock, both refer to the primary key ID_B / ock of the hierarchyDef table. The \ D_SourceBlock forms the starting point and the ID_SinkBlock the endpoint of a stream. The stream with the ID_Flow 1 has, for example, the starting point, or the start block External_In, characterized by the ID_SourceBlock 23 and the end block of the subsystem A, characterized by the ID_SinkBlock 13. Analogously, the remaining 22 streams can be defined. Due to the recursive definition of the individual blocks in the HierarchyDef table, the respective hierarchy level of the start and end blocks of a stream can be determined. This aspect is important for the later characteristic determination (determination of the KPIs), because thereby be determined can determine for which blocks of the various hierarchy levels the stream is an access or an outgoing stream (input or output stream). For example, stream 1 is an access stream for all hierarchical levels of the respective blocks, whereas stream 16 is an access stream only for certain blocks down to the plant level. In a so-called MaterialDef table (see also the database structure overview in Fig. 4), all materials used in the composite according to the embodiment discussed here are recorded. This information is shown approximately in the table below.

ID material material

 Material A

 Material B

 Material C

 Material D

 Material E

 MaterialDef table

The IDs of the left-hand column of the table, labeled ID_Matmal, are the primary key for the material. The column Material contains the simple name. Materials may be, for example, crack C4, 3 bar steam, cost, etc.

A higher level is the CategoryDef table (compare also the database structure overview in Fig. 4). This summarizes the various materials into one of three categories:

ID_Category Category

 1 energy

 2 fabric

 3 information

Ca tegoryDef table The primary key in this table is the ID_Category. The plain name is in the Category column. In total there are three categories, energy, substance and information. For example, in the category of energy, a steam or an electric current may be taken. The category Substance subordinates all streams or blocks that contain information on chemical substances. The category information comprises pure information streams, such as alarm rates. Each stream and certain blocks have different (measurement) quantities, which are also referred to as attributes in the data model. These can include quantities, pressures, temperatures, etc.

In order to provide these quantities or attributes in the data structure, another table, the A.ttributeDef table (cf., again, the database structure overview of FIG.

 ID_Attribute AttributeName ID_AttributeEngUnit

 1 quantity 1

 2 pressure 3

 3 temperature 2

 4 power 6

 5 component A 4

 6 component B 4

 7 component C 4

 8 component D 4

 9 component H 4

 10 component J 4

 11 costs 5

 12 hold-up 7

 AttributeDef-Tabeüe

In this table, each attribute is assigned an ID, the primary key, by the ID_Attribute column. The name of the respective attribute is noted in the AttributeName column, eg quantity or temperature. A foreign key is the ID_AttributeEngUnit, which references the AttributeEngUnitDef table, which will be described later. As attributes, individual components of a composite stream are considered. This brings with it the further advantage of dealing with attributes that are still subdivided. A temperature or a pressure are absolute values, whereas the attribute composition has to be divided into the subdivided components. By presenting each component as a stand-alone attribute with a separate primary key, a composition can be easily mapped. The differentiation of the MaterialDef table is important here. For example, it is possible for a stream to be fed 1,3 butadiene under the material. However, this does not mean that the stream is composed only of the component 1,3 butadiene, unless it is a pure stream, then the composition and material can be identical. An example in which the differentiation and need for material ordering becomes more apparent is a stream passing under the Crack-C4 material. The Crack-C4 consists of various substances components, which can only be depicted by dividing the attributes into different components in the model.

 Since the retrieval of the units can not be done exclusively automatically via a computer system, the input of the unit of an attribute in the model usually has to be done initially by hand. This has the advantage that when the unit is changed in the system a faster comparison can be made with the units originally entered manually. The characteristic size determination, which is based on this model later, partly uses fixed rules for the conversion of units. This means that when a unit changes, this is very likely reflected in the parameters (KPIs or KPIs). The error can be determined and remedied faster in this way. In the AttributeEngUnitDef table (see also in Fig. 4), the information about the unit of the corresponding attribute is stored:

 ID_AttributeEngUnit AttributeEngUnit

 ° C

 bar

 wt%

 €

 kWh

 T

 AttributeEngUnitDef table

The primary key in this table is the ID that is in the ID_lttributeEngUnit column. The corresponding unit can be read in the -AttributeEngUnit column. This is just an example list of attributes. The table can be expanded as required.

Once the streams and attributes have been defined and maintained in the model, further basic information and links can follow. The connection of the attributes and the flows is established in the FlowAttnbutesDef table (see again Fig. 4). The basic structure of this table is as follows:

 ID_ID ID Aspen AspenTag AsID ID Start End

Flow-Flow AtTag Description pen Source SamDate Date

Meas triName Tag ple

 ure bute tight time

 Ment Unit

 Day

 1 Fl 0001 Amount of starting material for TA A t / h 1 01.01.2013 30.06.2013

 GC1001 Comp. A of Edukt Gew 1 01.01.2013 30.06.2013

 %

F20002 Amount starting material for TA C t / h 1 01.01.2013 30.06.2013 P20002 Pressure educt before TA C bar 1 01.01.2013 30.06.2013 T20002 Temp. Educt before TA C ° C 1 01.01.2013 30.06.2013

 FlowAttributesDef table

The primary key in this table is the ID, ID_FlowMeasuremenfTag. Each individual ID 5 is assigned a measuring point. For example, the first line with the ID 1 is assigned to the measuring point with the AspenTagName Fl 0001. The two foreign keys ID_Flow and ID_Attrib are referenced to the respective table in which this ID is a primary key. In the first line this means that the stream with the ID 1 is assigned the attribute with the ID 1. In that case, that would be a flow. The information from the columns AspenTagName, -Aspen- IQ TagDescription and AspenTagEngUnit all come from the system and are automatically generated when querying the measuring point and designate measuring points of a widely used data acquisition system of the company Aspen (protected trademark), which is also used for data acquisition in the context of present invention. At this point, the information about the unit thus comes automatically from the system and can later be adjusted with the manually entered unit. The AspenTagDescription describes the measuring point in more detail, which can be helpful with the local, local allocation of the measuring point. As you can see in the table, a stream appears several times (see stream with ID_Flow 1 or 2). A stream can be assigned multiple attributes in this table. In the case of the current with the ID_Flow 2 these are a flow, a temperature and a pressure. If a composition of a stream is required, 2 0 appears several times the AspenTagName GC * in the table under the same ID of the stream. Another great advantage of modeling in this way is that a stream of any number of components can be put together, ie it does not matter if it consists of three or five components. In the last two columns, Start and EndDate, can be noted for which period the respective measuring point should be queried. In the column named ID_Source, the ID is stored, which indicates on which server the stored data for the measuring point are located. The associated SourceDef table (see Fig. 4) with the primary key is shown below.

ID_Source Source

 1 server 1

 2 servers 2

 SourceDef table

In the table, the ID is assigned to the corresponding server (under the column Source), as can be seen in this case with server 1 or server 2. Another piece of information that contains the VlowAttributesDef table is the ID_SampleTime. The table in which these ^ID's are stored, the SampleTimeDef table (see again FIG. 4.):

ID_SampleTime timestamp

 1 day

 2 hours

 3 minutes

 SampleTimeDef table

Each ID is assigned a specific time stamp, so that an individual time interval is possible for querying the measuring points. This may be interesting, for example, for measurements where a maximum value based on a quarter-hourly clocking is relevant for billing.

Since blocks can also have attributes (inventory sizes), the collection of data for the blocks must be done in a similar way to the streams. The difference to the streams is that a block, depending on the modeling level, can only be characterized by a limited number of attributes. The most important attribute is the so-called hold-up, ie the level of storage tanks or containers. The summary information for the blocks and their attributes are stored in the BlockAttributesDef table (see again Fig. 4), which is shown below:

 ID ID ID ID ID Aspen AspenTag As-ID StartDate EndDate

Block Bio Attrib- MatCa- Source Tag Description pen Sam

Measure Trib- erial Te- Name Day ple

 ure-ute gory Eng Time

 ment unit

 Day

 1 19 12 2 2 2 L10001 Level Container A 7 2 01.01.2013 30.06.2013

2 20 12 3 2 2 L20002 Level Container B 7 2 01.01.2013 30.06.2013

 BlockAttributesDef table

This table is structured almost analogously to the FlowDef table, with the exception that it refers to ID_B / ock and not to ID_Flow. Furthermore, the category and the material are defined here. As described at the beginning there are several possibilities of data structuring. To avoid unnecessary waste of space, a differentiation between stream and block attributes was preferred here. The other columns in the table are identical to those of the FlowAttnbutesDef table. In order to make the possibilities of further use of the data flexible, another table containing geographic information of certain blocks is used in the example of the local data model. This is denoted by BlockDef (see also Fig. 4):

ID Latitude Longitude ISO Country State Town Zipcode Block

 2. ... .. DE Germany NRW Cologne 50667

 3. ... .. DE Germany Bayern Munich 80331

 BlockDef table

The ID_Block is referenced in this table. For later presentation on maps, for example for the graphic evaluation of two locations, all necessary data are entered. These are Latitude and Longitude, ISO, Country, State, Town and Zipcode. It only makes limited sense to store this information for each block. Modeling down to the farm level is conceivable, for example, if it is possible to differentiate between the different farms on the basis of the exact latitude and longitude at one site. A modeling of the plant or plant level is less useful here. An entire composite can now be mapped in this way, meaning that the data model is complete. It is possible to model any chemical composite with continuous operations in this way. The last missing element, which is required for the calculation of the parameters (also called KPIs or KPIs), are the concrete measured values that have not yet been considered in the model.

The connection of the measured values to the modeled data model takes place via the MeasurementValues table (see again in the overview of the relational database structure in Fig. 4) and as reproduced below for the example here:

 ID_MeasurementTag TS Value

 1 01.01.2013 00:00 60.5

 1 01.01.2013 01:00 62

 1 01.01.2013 02:00 61.5

 1 01.01.2013 03:00 64

 1 01.01.2013 04:00 63

MeasurementValues table

The TD _MeasuremenfTag is an ID that will be introduced in the course of data processing when creating so-called views, which will be discussed below. Ultimately, this ID is the reference to the TlowMeasurement_TD or the lockMeasurement_ID. This means that the measuring points are assigned the values. As in this example, these can be values from the past or live values when interfacing with an interface to the live process data. The abbreviation TS from the table indicates the so-called TimeStamp, ie the exact time at which the corresponding value was recorded. In this example, the first, hourly measured values of a flow measurement are recorded.

The data model presented here for the implementation of the present invention is flexible and transferable to all, continuous production networks. The model can be easily and easily extended or changed, so that a conversion or new construction of equipment is no problem for the adaptation of the model.

FIG. 5 shows a so-called, Hierarchy-Vienf, a so-called low-Vienf and a so-called, block-view ⁱ on the structure of a relational database for implementing the data mode. 3 for the modeling of the fictitious composite system according to FIG. 3, whereas FIG. 6 shows the so-called "hierarchy view" and FIG. 7 the so-called "flow view" and FIG. 8 the so-called Jttock-Vien? to show 5 in isolated representation.

A so-called view is a view of a database structure, which is also called a virtual table. Such a virtual database table is defined via a query stored in the database (so-called query). Such a view can be queried by the database user like a normal actual table. This makes it possible to simplify access to the database by providing suitable views. In the present case, such views are preferred for the hierarchical hierarchy of the illustrated chemical industry hierarchy, for which the flows between the units of the chemical industry device (ie the flows between the blocks) are concerned (flow-view) and for which the units of the This is shown in the database structure representations of FIGS. 5, 6, 7 and 8 which, just like the overall representation according to FIG. 4, which manages without views, one each Select a representation of the relations where a 1: N relation is represented by the symbol C ^, and an N: l relation by the symbol.

9 shows a so-called VLodel view. This model yiew is composed of the previously generated hierarchy view, flow view and block view. In the ModelView, BlockDescription and FlowOescription are renamed to "Desaiption" and each block is now differentiated via Input / Ouput / State (ie Access, Outflow and Status) .Input or Access means that the stream with the associated metering point is in the block flows in the block ends, output or output means that the current flows out of the block, ie starts in the block and state or status, that it is an attribute of the block.Furthermore, the FlowMeasurementTag and the BlockMeasurementTag in, JMeasuremenfTag " renamed. This has the advantage that in the SEM view described below, only a so-called "value table" is necessary in order to read in the measured data.

10 shows a so-called, REM view The REM view is generated from the model view according to FIG. 9 and from the MeasurementValues table. In doing so, the measured values with all the information tion data, such as their respective hierarchical classification, their geographical classification and the characteristics of the respective measuring point. By looking at the individual blocks and their entrances and outputs, each measurement point of the flows is doubled, but the filtering in the tableau for access and outgoing flows (in and out flows) of the blocks is simplified. In addition, this set-up has the advantage that any attributes of the blocks (ie stocks) can be easily merged with attributes of the streams (ie, [measurement] quantities associated with the respective streams).

Claims

Patent claims

1. Computer-implemented method for forming and storing at least one aggregation for an input stream and/or an output stream of at least one mass flow and/or energy stream that flows through a directed connection between two units of a chemical industry facility, for at least one unit of the Chemical industry facility, wherein the computer-implemented method works with a memory content in at least one memory in which the chemical industry facility is modeled in terms of information technology in that the memory content has at least one block data structure for storing at least one block data element, each of which is a unit of the chemical industry facility represented in the data structure, where

a hierarchy of the units of the chemical device is represented in the block data structure in that at least one block data element is provided, which is a higher-level block data element, which in turn has at least one subordinate block data element, the respective higher-level block data element in each case a higher-level unit of the chemical industry facility, and the respective lower-level block data element each represents a lower-level sub-unit of the chemical industry facility contained in the higher-level unit in the data structure, and that the memory content

has at least one connection data structure for storing at least one connection data element, which corresponds to a directed connection between two mutually different block data elements, which corresponds to a respective associated directed connection between the respective two corresponding units of the chemical industry facility represented in the block data structure represents, and wherein at least one mass flow and / or an energy flow, which flows through the respective directed connection between two units of the chemical industry facility, is recorded with at least one respective (measured) variable and is each stored as this directed connection, and based on a respective stored connection Aggregation rule at least one aggregation for an input stream and/or an output stream of at least one of the aforementioned mass flows and/or energy flows is carried out for at least one unit of the chemical industry facility in such a way that the aggregation of the input stream associated with it is carried out for the respective unit of the chemical industry facility or the access streams associated with it or the output stream associated with it or the output streams associated with it of the at least one aforementioned mass flow or energy flow based on the block data elements stored in the block data structure and based on the connection data stored in the connection data structure. Data elements is made by

each such mass flow or energy flow of the respective unit of the chemical industry facility is used for the respective aggregation of the associated access stream, the associated directed connection of which begins outside the respective unit of the chemical industry facility, including all of its hierarchically subordinate sub-units and contained therein, and within the respective unit of the chemical industry facility including all of its hierarchically subordinate sub-units and those contained therein, and furthermore each such mass flow or energy flow of the respective unit of the chemical industry facility is used for the respective aggregation of the associated outlet stream, the associated directional connection within the respective unit of the chemical industry facility including all of its hierarchical subordinate and sub-units contained within it begins and outside the respective unit of the chemical industry rieeinrichtung including all of its hierarchically subordinate sub-units and contained therein ends, and the respective aggregation is formed and used in accordance with the aggregation rule for the respective incoming stream and/or the respective outgoing stream of the at least one aforementioned mass flow or energy stream of the respective unit of the chemical industry facility Further processing or display is saved.

Computer-implemented method according to claim 1, characterized in that the method also serves to form and store the aggregation of the incoming stream and/or the outgoing stream of at least one information stream, with at least one information stream passing through the respective directed connection flows between two units of the chemical industry facility, is recorded with at least one respective (measured) variable and is stored as assigned to this connection, and at least one aggregation for the incoming stream and / or the outgoing stream based on the aggregation rule stored for this purpose an aforementioned information stream for at least one unit of the chemical industry facility is carried out in such a way that

for the respective unit of the chemical industry facility, the aggregation of the access stream or streams associated with it or the output stream or streams associated with it of the at least one aforementioned information stream based on the block stored in the block data structure - Data elements and based on the connection data elements stored in the connection data structure by

each such information stream of the respective unit of the chemical industry facility is used for the respective aggregation of the associated access stream, the associated directed connection of which is outside the respective unit of the chemical industry facility. direction, including all of its hierarchically subordinate sub-units and contained within it, and ends within the respective unit of the chemical industry facility, including all of its hierarchically subordinated sub-units and contained within it, and further

Each such information stream of the respective unit of the chemical industry facility is used for the respective aggregation of the associated outlet stream, the associated directed connection of which begins within the respective unit of the chemical industry facility, including all of its hierarchically subordinate sub-units and contained therein, and outside the respective unit of the chemical industry facility including all of its hierarchically subordinate sub-units and those contained within it,

and wherein the respective aggregation is formed in accordance with the aggregation rule for the respective incoming stream and/or the respective outgoing stream of the at least one aforementioned information stream of the respective unit of the chemical industry facility and is stored for further processing or display.

Computer-implemented method according to claim 1 or 2, characterized in that the hierarchy of the units of the chemical device in the block data structure is represented in that the respective higher-level block data element has the block data element subordinate to it in such a way that the The respective higher-level block data element refers to the respective lower-level block data element or, conversely, the respective lower-level block data element refers to the respective higher-level block data element.

Computer-implemented method according to claim 1, 2 or 3, characterized in that the hierarchy of the units of the chemical device is represented in the block data structure in that the respective higher-level block data element has the block data element subordinate to it in such a way that that the respective higher-level block data element contains the block data element subordinate to it.

Computer-implemented method according to one of claims 1, 2, 3 or 4, characterized in that as an aggregation rule for aggregating the incoming stream and / or the outgoing stream of the respective mass flow and / or energy flow and / or information stream of the respective unit Chemical industry facility a summation of the respectively recorded (measured) size of the mass flows and / or energy flows and / or information flows to be used for aggregation is provided.

Computer-implemented method according to one of claims 1 to 5, characterized in that as an aggregation rule for aggregating the incoming stream and / or the outgoing stream of the respective mass flow and / or energy flow and / or information stream of the respective unit of the chemical industry facility - preferably the formation of the arithmetic or geometric mean or the median - the respectively recorded (measured) quantity (s) of the mass flow or mass flows to be used for aggregation and / or the energy flow or energy flows and / or the information stream or the Information streams are provided.

Computer-implemented method according to one of claims 1 to 6, characterized in that as an aggregation rule for aggregating the incoming stream and / or the outgoing stream of the respective mass flow and / or energy flow and / or information stream of the respective unit of the chemical industry facility - preferably a maximum or minimum determination of the respectively recorded (measured) quantity(s) of the mass flow or mass flows and/or the energy flow or energy flows and/or the information stream or information streams to be used for aggregation is provided.

Computer-implemented method according to one of claims 1 to 7, characterized in that the aggregation of the incoming stream and / or the outgoing stream of the respective mass flow and / or energy flow and / or information stream of the respective unit of the chemical industry facility via a to be selected period takes place.

Computer-implemented method for monitoring the operation of chemical industry facilities by means of at least one parameter, characterized in that based on at least a stored parameter determination rule from the aggregation (s) formed according to the method according to one of claims 1 to 8 for a respective incoming stream and / or the respective outgoing stream of a respective mass flow and / or energy stream and / or Information stream from a unit or units of the chemical industry facility at least one parameter for operational monitoring of the chemical industry facility is formed and stored for further processing or display.

10. Computer-implemented method for operational monitoring of chemical industry facilities according to claim 9, characterized in that a technical parameter, namely at least one energy performance indicator for the unit or units of the chemical industry facility, is determined as the parameter, the parameter determination rule being that the by summation over a selected period of time according to the method according to claims 8 and 5 aggregated input energy flow of all energies to the respective unit of the chemical industry facility through the output mass flow of all products from the respective one aggregated by summing over the selected period of time according to the method according to claims 8 and 5 Unit of the chemical industry facility is divided away in order to obtain the energy performance indicator for the respective unit of the chemical industry facility as the further technical parameter.

11. Computer-implemented method for operational monitoring of chemical industry facilities according to claim 9 or 10, characterized in that at least one inventory - preferably a stock or fill level - of a material that belongs to a unit of the chemical industry facility is recorded and assigned as this unit of the chemical industry facility is stored, and that at least one of the parameters for operational monitoring of the chemical industry facility is determined at least based on one of these stocks of the respective material of at least one of the respective units of the chemical industry facility.

12. Computer system for monitoring the operation of chemical industry facilities, wherein the computer system has at least one computer and at least one processor and at least one computer system memory as well as at least one detection device for detecting mass flows and / or energy flows and / or information flows that are transmitted through a respective directed connection between two units of the chemical industry facility flow, characterized in that the computer system is set up by means of a computer program to carry out the method according to one of claims 1 to 11.

13. Computer system for operational monitoring of chemical industry facilities according to claim 12, characterized in that as a detection device for detecting mass flows and / or energy flows at least one sensor, preferably a flow sensor, such as a mass flow meter or an energy meter for measuring the electrical energy or power measuring device for measuring the electrical power is used.

14. Computer system for operational monitoring of chemical industry facilities according to claim 12 or 13, characterized in that the computer system also has at least one detection device for recording at least one inventory - preferably a stock or a fill level - which belongs to a unit of the chemical industry facility, and the computer system by means of a computer program is set up to carry out the method according to claim 11.

15. Computer system for operational monitoring of chemical industry facilities according to claim 14, characterized in that a level sensor or a weight sensor serves as the detection device for recording at least one of the stocks - preferably a stock or a fill level - which belongs to a unit of the chemical industry facility.

16. Computer system for operational monitoring of chemical industry facilities according to one of claims 12 to 15, characterized in that it also has at least one display device for - preferably graphically illustrated - display of the one or more corresponding to the aggregation rule(s) for the respective access stream and/or or the respective outgoing flow of the respective mass or energy flow or information flow of the unit or units of the chemical industry facility determined aggregation or aggregations and / or in accordance with the parameter determination rule (s) determined ^), on which the one or more the aforementioned aggregation(s) and/or the or the Parameter(s) of the chemical industry facility - preferably graphically illustrated - can be displayed.

17. Computer program that has instructions that are set up to carry out the method according to one of claims 1 to 11.

18. Computer program product which has a computer-readable medium with computer program code means, in which, after the computer program has been loaded into a computer, the computer is set up by the program to carry out the method according to one of claims 1 to 11.

19. Computer program product which has a computer program on an electronic signal, in which the computer is set up to carry out the method according to one of claims 1 to 11 after the computer program has been loaded into a computer.