COMPUTER-IMPLEMENTED METHOD FOR DIMENSIONING A PROCESS PLANT

Description

[0001] The invention relates to a method for dimensioning and constructing a process plant for carrying out a chemical, process engineering process, namely an air separation plant or a natural gas plant for natural gas liquefaction. Background of the invention

[0002] During a planning or design phase, a process plant is first dimensioned before the process plant is constructed or built at a suitable location according to the selected dimensioning and ultimately put into operation to carry out a chemical, process engineering process.

[0003] In the course of dimensioning, particular dimensions of the process plant can be selected, for example the dimensions or capacities of individual elements and components of the process plant or the specific type of various components, as well as process parameters according to which the process engineering process is ultimately to be carried out.

[0004] In conventional methods for planning, designing, and dimensioning process plants, the parameters to be dimensioned are usually first determined computationally based on a design case, typically full load. Subsequently, further load cases are recalculated or verified in relation to this design case. If it is not possible to operate all relevant operating cases with the design of the design case, the dimensioning parameters are usually adjusted so that the plant can be operated in all considered cases. This is usually done iteratively and manually. Often, lengthy iterations are necessary if the chosen design is not operational for all specified operating cases.An optimal design, especially for a large number of operating scenarios to be considered, is often not achievable with such an iterative method, since the chosen design may deviate from the optimal design for several operating scenarios.

[0005] For example, "Gade Pandu Rangaiah: 'Multi-Objective Optimization in Chemical Engineering: Developments and Applications', March 20, 2013," describes a so-called multi-objective optimization (MOO) for simulating chemical engineering processes. In such an MOO procedure, several optimizations of different functions are performed, while simultaneously satisfying multiple different conditions. This multi-objective optimization aims to optimize a multitude of functions or equations [f1(x), ..., fn(x)] concurrently, finding a solution F with all vectors x that simultaneously satisfy all predefined conditions. The goal is to find a set of values ​​for x that provides the best possible compromise for all functions.

[0006] "Painton et al.: 'Synthesizing optimal design configurations for a Brayton cycle power plant', September 27, 1993," describes, for example, the dimensioning of nuclear power plants. Using a sequential modular chemical process simulator, the operation of a nuclear power plant is simulated in order to dimension the plant optimally. Disclosure of the invention

[0007] Based on this prior art, a method according to claim 1 is proposed. Advantageous embodiments are the subject of the dependent claims and the following description.

[0008] The process plant is an air separation plant, or more generally a fluid separation plant, or a natural gas plant. The process engineering procedure is accordingly air separation or natural gas liquefaction.

[0009] In this context, dimensioning the process plant means determining specific values ​​for process plant sizes and / or process parameters, according to which the process plant is to be designed and built, and according to which the chemical process is to be carried out in this process plant.

[0010] Within the scope of the invention, a multitude of process simulations are performed simultaneously (hereinafter also referred to as parallel or concurrently) for a process plant, in the course of which the process in the process plant is simulated for a specific application in a computing unit or a computer. An application is characterized by values ​​of process plant parameters and / or values ​​of process parameters.

[0011] In this context, process plant parameters, or design parameters, are understood to be quantities that describe or characterize the process plant and, in particular, its individual components. For example, these process plant parameters can be dimensions of various components, such as diameter, length, height, width, etc. They can also describe the specific type of individual components or units, such as the specific pump used, its specific power output, or the specific pressure at which it applies a fluid.

[0012] Changes in the values ​​of these process plant parameters directly affect the process engineering process carried out in the plant. Different values ​​for the process plant parameters characterize different process topologies or process plant topologies, and define the specific design of the process plant. In this context, the term process topology should not only be understood as the way different process engineering apparatus are interconnected, but is also conveniently used here to describe identical interconnections despite variations in the design of a process engineering apparatus.

[0013] In this context, process parameters are understood to be quantities that directly describe the process engineering process.

[0014] These process parameters characterize the operating conditions according to which the process plant can be operated or according to which the process is carried out within the plant. Different values ​​of the process parameters characterize different process variants according to which the process engineering process can be carried out. For example, these process parameters can relate to feedstock and / or product specifications of the process engineering process.

[0015] A process simulation for a given application typically comprises a multitude of equations describing the processes within the plant. Setting up such a system of equations for a single application and plant is known in the art. The innovation lies in solving such equations simultaneously for multiple different operating conditions, but for the same plant, by mapping them into a single, unified system of equations and then solving it. This overcomes the problems inherent in the prior art. An optimal design can be found for all considered operating conditions. This design is obtained by determining optimal values ​​for the plant parameters and / or process parameters based on the overall system of equations, such that at least one predefined condition is met, e.g., a target function is minimized.

[0016] Thus, a complete system of equations is solved, in which at least two different process simulations, namely different load situations or utilizations, are included.

[0017] Because the overall system of equations concerns a process plant, in addition to free values ​​of process plant variables or process parameters that can be optimized independently to fulfill at least one predefined condition, there are also dependent values ​​of process plant variables or process parameters, each of which depends on a free value. In this context, a dependent value of a process plant variable or process parameter in one process simulation depends on a free value of a (preferably the same) process plant variable or process parameter in another process simulation.

[0018] A gradient-based optimization method is used to optimally select the free values, based on the evaluation of an analytically or numerically generated Jacobian matrix. The resulting system of equations for dimensioning a process plant in large-scale industrial plant engineering comprises hundreds to tens of thousands of equations.

[0019] The dependent values ​​result from the free values ​​according to the available

[0020] Relationships. Dependent values ​​concern design parameters, especially the process plant dimensions. While the design parameter itself is variable and accessible to optimization as a free value, it must then be the same in all process simulations because the plant designs do not change for different operating conditions. Therefore, the value for the parameter in the other process simulations depends on the first value, in this case, it is identical.

[0021] These parameters are now also optimized across all process simulations under consideration. By simultaneously running multiple process simulations, the influence of such parameters on the result can be directly identified.

[0022] Each of the numerous process simulations is assigned a weighting factor that indicates the importance (e.g., the frequency or probability of occurrence) of a specific process simulation or its associated use case within the overall operation. This allows frequently occurring operating scenarios to be weighted more heavily than rarely occurring ones.

[0023] By weighting the process parameters or the values ​​of the process parameters themselves, it is possible to specify how relevant individual process parameters or individual values ​​of different process parameters are. For example, it can be specified that high product quality has a greater weighting than a high product quantity.

[0024] In the course of various simultaneous process simulations, the process plant or its individual components, as well as the chemical subprocesses of the process taking place within these components, are modeled and theoretically described. In particular, the individual components of the process plant and the physical and chemical subprocesses occurring within them are described using chemical, physical, and mathematical equations.

[0025] For example, balance equations for mass, species, energy and momentum, the second law of thermodynamics, transport equations for species, energy and momentum, material and chemical equilibrium relationships or reaction equations, as well as empirical and semi-empirical correlation relationships between the above aspects or logical connections between different process parameters serve as a basis for describing process engineering equipment.

[0026] For a detailed description of how process engineering processes can be represented using mathematical equations, reference is made, for example, to: " Kraume, Matthias: Transport Processes in Process Engineering: Fundamentals and Equipment Implementations. Springer Vieweg, Berlin [etc.], 2nd, revised edition, 2012. ISBN: 978-3-642-25148-1 " .

[0027] Flowcharts are primarily used to perform simultaneous process simulations. Such a flowchart allows the individual components of the process plant, as well as their fluid and process engineering connections, to be depicted. Corresponding chemical, physical, and mathematical equations characterizing the subprocesses or components are expediently used as the basis for these flowcharts.

[0028] A concrete process simulation or flowchart with specific values ​​for the individual process plant parameters thus represents a theoretical image or model of a specific process plant. Using such a concrete process simulation or flowchart, it is possible to simulate how the process engineering process theoretically unfolds in different ways, i.e., with different values ​​of the process parameters, within this specific process plant.

[0029] Simultaneous process simulations allow us to model, in particular, that the same process plant—that is, a plant with the same constant values ​​for the plant parameters and thus the same process topology—can be carried out using different process variants, i.e., with different values ​​for individual process parameters. This makes it possible to see, in particular, how changes to individual process parameters affect the process within the same plant.

[0030] Furthermore, during simultaneous process simulations, it is advantageous to examine a large number of different process topologies, i.e., a large number of different values ​​for the process plant dimensions, under varying operating conditions. This allows, in particular, the identification of how changes to individual process parameters affect the overall process in different process topologies.

[0031] By weighting the process parameters or their values, it is particularly possible to assess how relevant the effects of changes in these parameters are on the process. It is advantageous to implement correlations between changes in process plant dimensions and process parameters in parallel or simultaneously within the individual process simulations.

[0032] In particular, correlations or dependencies between individual operating scenarios or individual process variants are implemented.

[0033] From this multitude of interdependent or correlated process simulations, an optimal set of values ​​for the process plant dimensions and / or process parameters is determined and selected within the framework of the procedure. The determination of these optimal values ​​is carried out in such a way that at least one predefined condition is fulfilled. These conditions can depend on various factors, particularly those describing the economic efficiency and effectiveness of the process plant. For example, this condition can be implemented as an objective function, whereby all process simulations are optimized in parallel and, in particular, simultaneously with respect to this objective function.

[0034] In particular, the procedure simultaneously determines an optimal set of values ​​for the process plant dimensions and an optimal set of values ​​for the process parameters, depending on each other. This expediently leads to the determination of an optimal topology for the process plant and an optimal process variant for this topology. The optimal values ​​for the process plant dimensions and the process parameters are determined in such a way that an optimized process plant can be designed and that the process can be carried out as efficiently and economically as possible within this plant, or according to the selected criteria.

[0035] The procedure is carried out before the process plant is designed or built, for example, during the planning phase. Specifically, at the beginning of the planning phase, it may only be specified which chemical process the process plant is to perform—namely, air separation or the production of liquefied natural gas—and at which location the process plant is to be built, i.e., under which environmental conditions the process is to be carried out. During the planning phase, a basic plant topology can initially be established based, for example, on empirical data. This topology is then refined, particularly within the context of this procedure, to ensure that the plant can be operated as effectively and economically as possible at its respective location, according to the criteria chosen in the specific case.

[0036] In contrast to the conventional methods described in the introduction, the invention involves the simultaneous execution of a multitude of different process simulations, which are interdependent and correlated. The effects of value changes in one process simulation are expediently considered in parallel and simultaneously in the other process simulations. Thus, no manual, time-consuming iterations are necessary. The invention allows for the determination of parameters to be dimensioned and the optimization of a complex process plant by simultaneously considering a multitude of operating conditions. Such complex process plants exhibit, in particular, high degrees of thermal and / or material feedback, as well as highly realistic material behavior with phase transitions (i.e., not ideal gas behavior), and especially deviations in the specifications of reactants and / or products and / or environmental conditions for different operating conditions.

[0037] The determined optimal values ​​for the process plant dimensions are expediently designed to best suit the selected operating conditions and their weighting. Furthermore, the optimal values ​​for the process plant dimensions and process parameters are expediently chosen so that the process plant achieves a broad efficiency range across multiple operating conditions. With an economic plant lifetime of 20 to 35 years, a wide range of load conditions is highly likely to be encountered in practice.

[0038] Advantageously, one or more of the following quantities are used as process parameters: product quantity, product quality, feedstock quantity, feedstock quality, environmental conditions, energy price, remuneration conditions (especially for the provision of energy system services), availability of process plant components, and structural or transport-related constraints on the process components. By changing the values ​​of such process parameters, particularly product quantity and quality as well as feedstock quantity and quality, a wide variety of product and feedstock conditions can be taken into account.

[0039] Preferably, at least one given condition depends on at least one cost parameter. Cost parameters are understood to be parameters with respect to which the operation of the plant is to be optimized, particularly to enable the highest possible efficiency and economy of the process plant.

[0040] At least one predefined condition is advantageously met if at least one cost parameter does not exceed or fall below a threshold and / or is as large or as small as possible. This allows for efficient and economical operation of the process plant. In particular, several such boundary conditions should be met simultaneously, resulting in multi-variable optimization.

[0041] Preferably, one or more of the following parameters are used as at least one cost parameter: the requirement for operating resources, the requirement for energy, the product requirement, the availability of raw materials, operating costs, investment costs, compliance with construction restrictions, product yields, quantities of input materials, the load on the process plant, and the service life of the process plant. Minimizing or maximizing these cost parameters, or ensuring they do not exceed or fall below a certain threshold, thus has a significant impact on the economic efficiency and effectiveness of the process plant.

[0042] Advantageously, one or more parameters influencing fluid flow through the process plant are used as process plant parameters, in particular a flow cross-section and / or a diameter of a process plant element, e.g., pipelines and / or equipment. Pressure losses in pipelines and equipment depend, to a first approximation, primarily on the flow velocity. Flow cross-sections are determined as optimal values ​​for such process plant parameters, either freely or from catalogs of defined diameters.

[0043] To illustrate the distinction between free and dependent values, the determination of an optimal pipe diameter is used below. Generally, a pipe pressure loss Δp follows the following physical law: Δ p = ζ / 2 ⋅ ρ ⋅ v 2 with the fluid density ρ, the flow velocity v and the pipe friction resistance ζ, which is e.g. for the laminar case as ζ = 64 ⋅ η / ρ ⋅ v ⋅ D is defined. Here, η is the dynamic viscosity and D is the diameter of the pipeline.

[0044] When simulating n applications in parallel, the following n equations result for determining the respective pipeline pressure loss Δp n. Δp n = 32 ⋅ η n / D n ⋅ v n

[0045] To calculate the same system simultaneously, all diameters Dn must have the same value, which is subsequently referred to as D1 and is a free value for dimensioning the process system. Depending on the choice of this free value, fixed calculation rules for the pressure drop of the form Δpn = f(ηn, D1, vn) result for the other simultaneously calculated simulations. According to the introduced terminology, the values ​​D2 ... Dn correspond to dependent values.

[0046] Alternatively or additionally, one or more parameters characterizing a heat exchanger within the process plant are used as process plant parameters, in particular the total heat exchanger surface area and / or the distribution of heat exchanger surfaces between side outlets. To a first approximation, the heat transfer coefficient depends primarily on the flow velocity and thus the throughput, as well as the composition of the flows in the heat exchanger. The relative geometric position of side outlets is, in particular, constant. Free and n-1 dependent values ​​can also be defined for these process plant parameters.

[0047] Alternatively or additionally, one or more dimensions of the process plant are preferably used as process plant parameters, in particular a column diameter and / or a column height. Free and n-1 dependent values ​​can also be defined for these process plant parameters. Mass transfer efficiency depends in particular on the gas and liquid loading and composition, while pressure losses depend in particular on the flow velocity and the theoretical number of trays.

[0048] Alternatively or additionally, one or more process plant parameters influencing process efficiency are used, in particular a design point of the process plant and its components, preferably a mass flow rate, a suction pressure, a final pressure, an inlet temperature, etc. The efficiency depends in particular on the mass flow rate, the suction and final pressures, and the inlet temperature. For this purpose, interpolations are performed using dimensionless machine characteristic maps. Advantageously, boundary conditions for process plant components are specified. These boundary conditions are expediently considered during simultaneous process simulations and the determination of optimal values. In particular, these boundary conditions allow the operation of various process plant components to be defined within technically feasible ranges.Preferably, one or more of the following parameters are specified as such boundary conditions: thermal stresses in heat exchangers, hydraulic limits of different process engineering equipment, e.g., flooding points of column internals, pumping and load limits, and specific rotational speeds of machines.

[0049] The process plant is a fluid separation plant, specifically an air separation plant. The chemical process is therefore a fluid separation, namely air separation. An air separation plant primarily comprises distillation column systems, which can be configured as two-column systems, particularly classic Linde double-column systems, but also as three- or multi-column systems. In addition to distillation columns for the recovery of nitrogen and / or oxygen in liquid and / or gaseous states (e.g., liquid oxygen, LOX; gaseous oxygen, GOX; liquid nitrogen, LIN; and / or gaseous nitrogen, GAN), i.e., distillation columns for nitrogen-oxygen separation, distillation columns can be used to recover other air components, especially the noble gases krypton, xenon, and / or argon. Distillation columns are also referred to as distillation columns.Within the scope of this method, it is advantageous to dimension individual or, in particular, all such components of the air separation plant, as well as the subprocesses taking place therein. For example, the invention allows for the dimensioning of a distillation column in a fluid separation plant. A process plant parameter that can be considered includes, for example, a feed fluid flow rate, which can be varied, in particular, by controlling a compressor unit. Modeling of a distillation column can be carried out, in particular, according to the method described in application EP17020477.0 or DE102017010787.4.

[0050] The process plant is a natural gas plant and the chemical, process engineering process is natural gas liquefaction or natural gas separation.

[0051] The procedure can, for example, take into account particularly varying specifications of products, especially quantities and compositions, and starting materials, especially quantities, compositions and conditions of entry.

[0052] In particular, the design and / or optimization of combined natural gas plants can be carried out within the framework of this procedure. Such combined natural gas plants combine at least two process steps. One such process step could be, for example, the separation of heavy elements, especially C²⁺ or C³⁺, to produce LPG (Liquefied Petroleum Gas). Another such process step could be the separation of nitrogen and / or helium, for example, by means of a so-called 'Nitrogen Rejection Unit' (NRU), which can reduce the nitrogen content in the natural gas to below one percent. A further process step could be the liquefaction of a natural gas stream to produce liquefied natural gas (LNG).

[0053] Further advantages and embodiments of the invention will become apparent from the description and the accompanying drawings.

[0054] The invention is schematically illustrated in the drawing with reference to exemplary embodiments and is described below with reference to the drawing. Brief description of the drawings Figure 1 schematically shows a process simulation of a process plant as it can be created in a preferred embodiment of a method according to the invention. Detailed description of the drawings

[0055] In a preferred embodiment of a method according to the invention, a process plant, namely an air separation plant, is to be dimensioned.

[0056] In this process, a large number of process simulations are carried out simultaneously in a computing unit or computer, in the course of which the process of air separation in the air separation plant is simulated for each application case, which is characterized by values ​​of process plant sizes and values ​​of process parameters.

[0057] In each of these process simulations, a process flow diagram ("flow sheet") is used to model the air separation plant. The air separation plant, its individual components, and the subprocesses of air separation taking place within these components are theoretically described by chemical, physical, and mathematical relationships in the form of equations. The fundamental topology to be used can, for example, be derived from empirical data.

[0058] An example of such a process simulation, as can be created in a preferred embodiment of the method according to the invention, is shown in Figure 1 schematically represented.

[0059] The air separation plant simulated in the flow diagram includes, among other things, a main air compressor 1, a pre-cooling unit 2, a cleaning system 3, a secondary compressor assembly 4, a main heat exchanger 5, an expansion turbine 6, a throttling device 7, a pump 8, and a distillation column system 10. In the example shown, the distillation column system 10 comprises a classic double-column arrangement consisting of a high-pressure column 11 and a low-pressure column 12, as well as a crude argon column 13 and a pure argon column 14.

[0060] In the simulated air separation plant, a feed air stream is drawn in and compressed by the main air compressor 1 through an unlabeled filter. The compressed feed air stream is then fed to the water-cooled pre-cooling unit 2. The pre-cooled feed air stream is purified in the cleaning system 3. In the cleaning system 3, which typically comprises a pair of adsorber tanks operating alternately, the pre-cooled feed air stream is largely freed of water and carbon dioxide.

[0061] Downstream of the cleaning system 3, the intake airflow is split into two partial flows. One of the partial flows is completely cooled to the pressure level of the intake airflow in the main heat exchanger 5. The other partial flow is recompressed in the secondary compressor assembly 4 and also cooled in the main heat exchanger 5, but only to an intermediate temperature level. This so-called turbine flow, after cooling to the intermediate temperature level, is expanded by means of the expansion turbine 6 to the pressure level of the completely cooled partial flow, merged with it, and fed into the high-pressure column 11.

[0062] In the high-pressure column 11, an oxygen-enriched liquid bottom fraction and a nitrogen-enriched gaseous top fraction are formed. The oxygen-enriched liquid bottom fraction is drawn off from the high-pressure column 11, partially used as a heating medium in a bottom evaporator of the pure argon column 14, and fed in defined proportions into a top condenser of the pure argon column 14, a top condenser of the crude argon column 13, and the low-pressure column 12. Fluid evaporating in the evaporation chambers of the top condensers of the crude argon column 13 and the pure argon column 14 is also transferred to the low-pressure column 12.

[0063] The gaseous nitrogen-rich overhead product is withdrawn from the top of the high-pressure column 11, liquefied in a main condenser which establishes a heat-exchanging connection between the high-pressure column 11 and the low-pressure column 12, and fed in portions as reflux back to the high-pressure column 11 and expanded into the low-pressure column 12.

[0064] In the low-pressure column 12, an oxygen-rich liquid bottom fraction and a nitrogen-rich gaseous top fraction are formed. The former is partially pressurized in liquid form in pump 8, heated in the main heat exchanger 5, and supplied as a product. A liquid nitrogen-rich stream is drawn off from a liquid retention device at the top of the low-pressure column 12 and discharged as liquid nitrogen product from the air separation unit. A gaseous nitrogen-rich stream drawn off from the top of the low-pressure column 12 is passed through the main heat exchanger 5 and supplied as nitrogen product at the pressure of the low-pressure column 12. Furthermore, a stream is drawn off from an upper section of the low-pressure column 12 and, after heating in the main heat exchanger 5, is used as so-called impure nitrogen in the pre-cooling unit 2 or, after heating by means of an electric heater, in the purification system 3.

[0065] In the simulated air separation process, air (AIR) is supplied to the column or low-pressure column 12, and at least oxygen and nitrogen are removed in gaseous (GOX, GAN) and / or liquid (LOX, LIN) form. Furthermore, impure nitrogen (UN2) is typically removed, and, if desired, noble gases such as argon, etc.

[0066] As explained above, the procedure involves the simultaneous execution of a large number of such process simulations, each for different use cases.

[0067] To provide a wide range of process simulations, the values ​​of various process parameters are changed, allowing for the simulation of different process variants. For example, the quantity and quality of the feedstock are modified as such process parameters. To change the quantity of feedstock, the volumetric or mass flow rate of the feed air drawn in by the main air compressor 1 can be altered. The quality of the feedstock can be modified, for example, by changing the temperature of the air or by altering the concentration of impurities in the air.

[0068] Furthermore, process parameters such as product quantities and product qualities are changed, for example quantities and compositions of the liquid nitrogen and liquid oxygen withdrawn from the low-pressure column 12.

[0069] The product quantities and qualities are assigned specific weights for each simulation. The higher the weighting, the more important this process parameter becomes.

[0070] Furthermore, for the numerous simulations, the total heat exchanger surface area and its distribution between the side outlets of the main heat exchanger 5 are varied as values ​​for the process plant size. The heat transfer coefficient of the main heat exchanger 5 is, to a first approximation, dependent on the flow velocity and the composition of the flows. The relative geometric position of the side outlets and the size of the heat exchanger used are the same in all simulations (dependent values).

[0071] Another process plant parameter that is changed is the flow cross-section, for example the flow cross-sections of the partial flows downstream of the cleaning system. 3. Pressure losses in pipelines and equipment are, to a first approximation, dependent on the flow velocity.

[0072] Furthermore, the height and diameter of the column or low-pressure column 12 are changed as process plant parameters. The efficiency of the mass transfer depends on the gas and liquid load, and the pressure drop on the flow velocity, the type of tray, and the theoretical number of trays.

[0073] The efficiency of the air separation plant depends in particular on the optimal operating points, the mass flow rates, the suction and discharge pressures, and the inlet and intermediate cooling temperatures in the compressors used. These process plant parameters, or rather the design point of the compressors, which is usually the optimal operating point, are also modified during the simulations.

[0074] Furthermore, boundary conditions can be specified for the simulations, for example thermal stresses in the main heat exchanger 5, hydraulic limits e.g. of the flooding points of column internals, as well as pumping and load limits of the main air compressor 1, the secondary compressor 4, the throttling device 7 and the pump 8.

[0075] By changing the corresponding process plant dimensions, different configurations or process topologies of the air separation plant are simulated. By changing the corresponding process parameters, different process variants or operating conditions are simulated. In particular, through the numerous simulations, every process variant is simulated for each of the different process topologies of the air separation plant.

[0076] In particular, the interdependencies between the various operating scenarios are implemented simultaneously in the simulations. Thus, for example, correlations between changes in the values ​​of the process plant parameters and the process parameters are implemented in parallel or simultaneously in the individual process simulations. Specifically, correlations and dependencies between individual operating scenarios or individual process variants are implemented.

[0077] The numerous process simulations are performed simultaneously by solving the underlying equations of these simulations as a single system of equations. This process determines optimal values ​​for the process plant dimensions and process parameters to meet predefined conditions. To this end, the individual process simulations are optimized simultaneously using a common objective function. This objective function comprises criteria such as resource requirements, operating costs, and investment costs.

[0078] By simultaneously optimizing the individual process simulations using this common objective function, optimal values ​​for the process plant sizes and process parameters are determined, so that the predefined conditions are met that the resource requirements, operating costs and investment costs are each as small as possible.

[0079] Based on the optimal values ​​determined in this way for the corresponding process plant sizes and process parameters, a dimensioning is found according to which the air separation plant is built and operated.

[0080] Thus, a suitable topology is selected by simultaneously optimizing a large number of possible process variants under precisely the same boundary conditions. Furthermore, the need for resources, operating costs, and investment costs is minimized by considering a multitude of possible operating scenarios for one and the same process topology.

Claims

1. A method for dimensioning and erecting a process plant for carrying out a chemical engineering process, namely an air separation plant or a natural gas plant for natural gas liquefaction, comprising a computer-implemented method for dimensioning the process plant, wherein a multiplicity of process simulations is carried out in parallel, in the course of each of which the process in the process plant is in each case simulated for a particular application case, wherein each application case is characterized by values of process plant variables and / or values of process parameters, wherein different values for the process plant variables characterize different process plant topologies and specific embodiment of the process plant, wherein the process parameters characterize operating cases in accordance with which the process plant can be run or in accordance with which the process is carried out in the process plant, wherein the multiplicity of process simulations is performed in parallel by solving equations underlying the multiplicity of process simulations as a common equation system in which at least two different load situations are contained, wherein, in the multiplicity of process simulations, values for the process plant variables and for the process parameters are determined in such a way that at least one predefined condition is met, wherein free values for process plant variables and / or process parameters are determined, and wherein dependent values for process plant variables and / or process parameters are determined from the free values for process plant variables and / or process parameters, wherein a dependent value of a process plant variable or of a process parameter in one of the multiplicity of process simulations depends upon a free value of a process plant variable or of a process parameter in another of the multiplicity of process simulations, wherein dependent values relate to constructive parameters, namely process plant variables, which are accessible to the optimization once as a free value, but are the same in all process simulations and therefore dependent upon the first value in the other process simulations, namely are identical to it, wherein the multiplicity of process simulations is each assigned a weighting factor which characterizes the weight of a certain load situation in the overall operation, wherein a gradient-based optimization method is used for the optimal selection of the free values, which optimization method is based upon the evaluation of an analytical or numerically generated Jacobian matrix, wherein the process plant is erected and put into operation at a location in accordance with the selected dimensioning.

2. The method according to claim 1, wherein one or more of the following variables are used as process parameters for the simulations: - a product quantity, - a product quality, - an educt quantity, - an educt quality, - an environmental condition, - an energy price, - a remuneration condition, - an availability of components of the process plant, - structural or transport-related boundary conditions on process engineering components of the process plant.

3. The method according to any one of the preceding claims, wherein the at least one predefined condition depends upon at least one cost variable.

4. The method according to claim 3, wherein the at least one predefined condition is met if the at least one cost variable does not exceed or fall below a threshold and / or is as large as possible or as small as possible.

5. The method according to claim 3 or 4, wherein one or more of the following variables are used as the at least one cost variable: - a need for resources, - a need for energy, - a product need, - an educt availability, - operating costs, - investment costs, - compliance with design constraints, - product yields, - input material quantities, - a process plant load, - a process plant service life.

6. The method according to any one of the preceding claims, wherein one or more variables characterizing a heat exchanger (5) of the process plant are used as process plant variables, in particular a total heat exchanger surface area and / or a division of heat exchanger surface areas between side offtakes.

7. The method according to any one of the preceding claims, wherein one or more variables influencing a fluid flow through the process plant are used as process plant variables, in particular a flow cross-section and / or a diameter of an element of the process plant.

8. The method according to any one of the preceding claims, wherein one or more dimensions of the process plant are used as process plant variables, in particular a tower diameter and / or a tower height.

9. The method according to any one of the preceding claims, wherein one or more variables influencing an efficiency of the process are used as process plant variables, in particular a design point of the process plant and its components.

10. The method according to any one of the preceding claims, wherein boundary conditions for components of the process plant are predefined.