Method and system for configuring an industrial gas plant complex powered by a renewable power source

Abstract

A method and system for selecting a design configuration for an industrial gas plant complex, the industrial gas plant complex comprising one or more industrial gas plants and powered by one or more renewable power sources.

Description

[0001] Cross Reference to Related Applications

[0002] This application claims priority to U.S. Non-Provisional Patent Application No. 17 / 975,707, filed October 28, 2022. TECHNICAL FIELD

[0003] The present invention relates to methods and systems for configuring an industrial gas production complex superstructure powered by a renewable power source. More specifically, the present invention relates to methods and systems for selecting a design configuration of an industrial gas production complex superstructure comprising one or more industrial gas production facilities and one or more renewable energy sources for powering the industrial gas production facilities. BACKGROUND

[0004] An industrial gas production complex comprises one or more industrial processing facilities that produce a gas or are involved in the production of a gas. In non-limiting examples, these gases can include industrial gases, commercial gases, medical gases, inorganic gases, organic gases, fuel gases, and green fuel gases in gaseous form, liquefied form, or compressed form.

[0005] There is considerable interest in methods and systems that utilize renewable energy sources for powering industrial gas production facilities and industrial gas production complexes. However, a significant drawback to using renewable energy sources, such as wind energy, solar energy, and tidal energy, is the naturally variable and transient nature of such energy sources.

[0006] Generally, a constant or substantially constant supply of power is preferred for an industrial gas production facility or an industrial gas production complex. Thus, the variable and intermittent nature of wind energy, solar energy, and / or tidal energy is problematic and makes it difficult to design an industrial gas production complex that can efficiently, safely, and cost-effectively utilize such power sources while operating with commercially viable capabilities.

[0007] An exemplary industrial gas is hydrogen. Hydrogen is generally produced by the electrolysis of water. Another exemplary industrial gas is ammonia. Ammonia is produced using hydrogen from water electrolysis and nitrogen separated from air. These gases are then fed into the Haber-Bosch process, in which hydrogen and nitrogen react together at high temperature and high pressure to produce ammonia.

[0008] There is considerable interest in the production of hydrogen and / or ammonia using renewable energy sources. These gases are referred to as green hydrogen and green ammonia. However, production of both hydrogen and ammonia can be sensitive to variable energy availability, and careful design of such production facilities is required in order for such production facilities to be efficient, safe, cost-effective, and economically viable. Designing a hydrogen production facility or an ammonia production facility that is operable to run on renewable energy is a complex and multi-factorial problem that presents a significant challenge to infrastructure designers and industrial enterprises.

[0009] Accordingly, there is a need for solutions to these technical problems to enable the efficient production of industrial gases from renewable power sources. SUMMARY

[0010] The following introduces some concepts in a simplified form to provide a basic understanding of some aspects of the present disclosure. The following is not an extensive overview of the present disclosure, and is not intended to identify key or critical elements of the present disclosure or to delineate the scope of the present disclosure. The following merely presents some concepts of the present disclosure in a simplified form, as a prelude to the more detailed description provided thereafter.

[0011] Disclosed herein are methods and systems for selecting a design configuration of an industrial gas facility complex (also referred to herein as “computer-implemented methods and systems”), the industrial gas facility complex comprising one or more industrial gas facilities and powered by one or more renewable power sources.

[0012] Several preferred aspects of the methods and systems according to the present invention are outlined as follows.

[0013] Aspect 1 : A method of configuring an industrial gas production superstructure, the industrial gas production superstructure comprising one or more equipment subsystems and being powered at least in part by one or more renewable power subsystems, the method being performed by at least one hardware processor and comprising: providing a model of the industrial gas production superstructure, the model having a plurality of selectable configurations representative of potential configurations of the industrial gas production superstructure; specifying a plurality of selectable modeled renewable power subsystems in the model, each modeled renewable power subsystem having predicted time series power profile data for a predetermined time period associated therewith; specifying a plurality of selectable modeled equipment subsystems in the model, each selectable modeled equipment subsystem having a plurality of selectable modeled components associated therewith; associating a plurality of operational parameters and a plurality of operational constraints with each of the plurality of modeled renewable power subsystems, each of the plurality of modeled equipment subsystems, and each of the plurality of selectable modeled components; selecting a plurality of configurations of the model by selecting, for each configuration: one or more modeled renewable power subsystems, one or more modeled equipment subsystems, and one or more components associated with the selected one or more modeled equipment subsystems; for each selected configuration, determining a predicted operation of the selected configuration of the industrial gas production superstructure over a predetermined time period to determine a maximum value of a predetermined operational output parameter for the selected configuration and for the predetermined time period, the predicted operation utilizing the power profile data associated with the one or more selected renewable power subsystems and the operational parameters and the operational constraints associated with the selected configuration; utilizing a surrogate model to identify one or more configurations of the industrial gas production superstructure operable to maximize a value of the operational output parameter while satisfying predefined operational constraints based on the operational output parameter data and the selected configuration data for each configuration; and generating one or more designs for the industrial gas production superstructure based on the identified one or more configurations.

[0014] Aspect 2: The method of aspect 1, wherein the plurality of selectable modeled renewable power subsystems are arranged in a group of wind farm subsystems, solar farm subsystems, tidal energy subsystems, and hydroelectric energy subsystems.

[0015] Aspect 3: The method of aspect 2, wherein within each of the groups, a plurality of selectable modeled renewable power subsystems are available for selection, each of the selectable modeled renewable power subsystems sharing the same profile of the predicted time series power profile data, but differing in magnitude of available maximum power.

[0016] Aspect 3A: The method of aspect 2, wherein the plurality of selectable modeled renewable power subsystems within each group differ in time-averaged maximum output power, the predicted time series power profile data being scaled according to the time-averaged maximum output power.

[0017] Aspect 3B: The method of aspect 2 or 3, wherein the plurality of selectable modeled renewable power subsystems are each associated with an operational constraint of available physical dimensions of the modeled subsystem, the magnitude of available maximum power being proportional to the available physical dimensions.

[0018] Aspect 3C: The method of aspect 3B, wherein within each group, the available physical dimensions differ for each subsystem within the group.

[0019] Aspect 4: The method of aspect 2 or 3, wherein the plurality of selectable modeled renewable power subsystems can be selected from at least two different groups.

[0020] Aspect 5: The method of any of aspects 1, 2, 3, or 4, wherein the plurality of selectable modeled device subsystems are arranged in groups of gas production device subsystems and gas storage subsystems.

[0021] Aspect 6: The method of aspect 5, wherein the gas production device subsystems include one or more of a hydrogen production device, an air separation unit, and an ammonia production device; and wherein the gas storage subsystems include one or more of a hydrogen gas storage, a hydrogen liquefier, a nitrogen storage, and an ammonia storage.

[0022] Aspect 7: The method of aspect 6, wherein at least one selected gas production device subsystem includes a hydrogen production device, and wherein the selectable modeled components of the hydrogen production device can be selected from one or more of: an electrolyzer type; an electrolyzer capacity; a compressor system; a purifier system.

[0023] Aspect 8: The method of any of aspects 1-7, wherein the operational output parameter includes an amount of gas produced.

[0024] Aspect 8A: The method of any of aspects 1-8, wherein the predefined operational constraint includes a predicted available power for the predetermined time period.

[0025] Aspect 8B: The method of Aspect 8A, wherein when the power consumption of the industrial gas complex superstructure exceeds the predicted available power, a maximum of the output parameter is achieved while minimizing an amount of time within the predetermined time period.

[0026] Aspect 8C: The method of Aspects 8A and 8B, wherein the predefined operational constraints include efficiency, safety, and regulatory constraints.

[0027] Aspect 9: The method of any one of Aspects 1 to 8, further comprising: designing the industrial gas production complex superstructure.

[0028] Aspect 10: A system for configuring an industrial gas production superstructure, the industrial gas production superstructure comprising one or more equipment subsystems and being powered at least in part by one or more renewable power subsystems, the system comprising: at least one hardware processor; a subsystem module operable to: provide a model of the industrial gas production superstructure, the model having a plurality of selectable configurations representative of potential configurations of the industrial gas production superstructure; specify a plurality of selectable modeled renewable power subsystems, each modeled renewable power subsystem having predicted time series power profile data for a predetermined time period associated therewith; and specify a plurality of selectable modeled equipment subsystems, each selectable modeled equipment subsystem having a plurality of selectable modeled components associated therewith; a simulation module operable to: associate a plurality of operational parameters and a plurality of operational constraints with each of the plurality of modeled renewable power subsystems, each of the plurality of modeled equipment subsystems, and each of the plurality of selectable modeled components; select a plurality of configurations by selecting, for each configuration: one or more modeled renewable power subsystems, one or more modeled equipment subsystems, and one or more components associated with the selected one or more modeled equipment subsystems; and for each selected configuration, determine a predicted operation of the selected configuration of the industrial gas production superstructure over a predetermined time period to determine a maximum value of a predetermined operational output parameter for the selected configuration and for the predetermined time period, the predicted operation utilizing the power profile data associated with the one or more selected renewable power subsystems and the operational parameters and the operational constraints associated with the selected configuration; and an optimization module operable to: utilize a proxy model to identify one or more configurations of the industrial gas production superstructure operable to maximize a value of the operational output parameter while satisfying predefined operational constraints based on the operational output parameter data and the selected configuration data for each configuration; and generate one or more designs for the industrial gas production superstructure based on the identified one or more configurations.

[0029] Aspect 11 : The system of aspect 10, wherein the plurality of selectable modeled renewable power subsystems are arranged in a group of wind farm subsystems, solar farm subsystems, tidal energy subsystems, and hydroelectric energy subsystems.

[0030] Aspect 12: The system of aspect 11, wherein within each of the groups, a plurality of selectable modeled renewable power subsystems are available for selection, each of the selectable modeled renewable power subsystems sharing the same profile of the predicted time series power profile data, but differing in the magnitude of available maximum power.

[0031] Aspect 13: The system of aspect 11 or 12, wherein the plurality of selectable modeled renewable power subsystems can be selected from at least two different groups.

[0032] Aspect 14: The system of any one of aspects 10 to 13, wherein the plurality of selectable modeled equipment subsystems are arranged in groups of gas production equipment subsystems and gas storage subsystems.

[0033] Aspect 15: The system of any one of aspects 11 to 14, wherein the gas production equipment subsystems comprise one or more of a hydrogen production equipment, an air separation unit, and an ammonia production equipment, and wherein the gas storage subsystems comprise one or more of a hydrogen gas storage, a hydrogen liquefier, a nitrogen storage, and an ammonia storage.

[0034] Aspect 16: The system of aspect 15, wherein at least one selected gas production equipment subsystem comprises a hydrogen production equipment, and wherein the selectable modeled components of the hydrogen production equipment can be selected from one or more of: electrolyzer type; electrolyzer capacity; compressor system; purifier system.

[0035] Aspect 17: The system of any one of aspects 10 to 15, wherein the predetermined operational output parameter comprises an amount of gas produced over the predetermined time period.

[0036] Aspect 18: A computer-readable storage medium storing a program of instructions executable by a machine to perform a method of controlling an industrial gas production facility comprising one or more industrial gas plants powered by a power network comprising one or more renewable power sources, the method performed by at least one hardware processor, the method comprising: providing a model of the industrial gas production superstructure, the model having a plurality of selectable configurations representing potential configurations of the industrial gas production superstructure; specifying a plurality of selectable modeled renewable power subsystems in the model, each modeled renewable power subsystem having predicted time series power profile data for a predetermined time period associated therewith; specifying a plurality of selectable modeled plant subsystems in the model, each selectable modeled plant subsystem having a plurality of selectable modeled components associated therewith; associating a plurality of operational parameters and a plurality of operational constraints with each of the plurality of modeled renewable power subsystems, each of the plurality of modeled plant subsystems, and each of the plurality of selectable modeled components; selecting a plurality of configurations of the model by selecting, for each configuration: one or more modeled renewable power subsystems, one or more modeled plant subsystems, and one or more components associated with the selected one or more modeled plant subsystems; determining, for each selected configuration, a predicted operation of the selected configuration of the industrial gas production superstructure over a predetermined time period to determine a maximum value of a predetermined operational output parameter for the selected configuration and for the predetermined time period, the predicted operation utilizing the power profile data associated with the one or more selected renewable power subsystems and the operational parameters and the operational constraints associated with the selected configuration; utilizing a proxy model to identify one or more configurations of the industrial gas production superstructure operable to maximize a value of the operational output parameter while satisfying predefined operational constraints based on the operational output parameter data and the selected configuration data for each configuration; and generating one or more designs for the industrial gas production superstructure based on the identified one or more configurations.

[0037] Aspect 19: The computer-readable storage medium of aspect 18, wherein the plurality of selectable modeled renewable power subsystems are arranged in groups of wind farm subsystems, solar farm subsystems, tidal energy farm subsystems, and hydroelectric energy farm subsystems.

[0038] Aspect 20: The computer-readable storage medium of Aspect 19, wherein within each of the groups, a plurality of selectable modeled renewable power subsystems are available for selection, each of the selectable modeled renewable power subsystems sharing a same profile of the predicted time series power profile data, but differing in magnitude of available maximum power. BRIEF DESCRIPTION OF DRAWINGS

[0039] Embodiments of the present disclosure will now be described, by way of example only, with reference to the attached figures, wherein:

[0040] Figure 1 is a schematic of an industrial gas plant complex and control system;

[0041] Figure 2 is a schematic of a configuration system according to an embodiment;

[0042] Figure 3 is a graph showing a predicted wind power generation profile over a period of a year;

[0043] Figure 4 is a graph showing a predicted solar power generation profile over a period of a year; and

[0044] Figure 5 is a flowchart of a method according to an embodiment.

[0045] Embodiments of the present disclosure and their advantages are best understood by referring to the following detailed description along with the accompanying drawings. Identical reference numbers in the figures indicate identical elements, wherein the figures are not necessarily drawn to scale, and wherein the figures are for the purpose of illustrating one or more embodiments of the present disclosure and are not intended to limit the present disclosure. DETAILED DESCRIPTION

[0046] Various examples and embodiments of the present disclosure will now be described. The following description provides specific details for the purpose of providing a thorough understanding of and ability to describe these examples. One skilled in the relevant art, however, will recognize that one or more embodiments described herein can be practiced without one or more of the specific details set forth herein. Similarly, some features can be practiced without all being presented herein. Further, one skilled in the art will appreciate that one or more embodiments of the present disclosure can include other features and / or functions not specifically described herein. In addition, some well-known structures or functions can not be shown or described in detail below, to avoid unnecessarily obscuring the relevant description.

[0047] The present disclosure relates to methods and systems for selecting a design configuration of an industrial gas plant complex superstructure, the industrial gas plant complex superstructure comprising one or more industrial gas plants for producing one or more industrial gases and one or more renewable power sources for powering the industrial gas plant complex.

[0048] In non-limiting embodiments, the industrial gas plant complex can comprise a hydrogen production plant and / or an ammonia production plant powered by renewable energy sources. However, the present application has applicability to other types of industrial gas plant complexes. For example, the present application has applicability to air separation plants for producing nitrogen from the atmosphere.

[0049] Overall configuration of the industrial gas plant complex

[0050] Reference will now be made to Figure 1 The components of an exemplary industrial gas plant complex superstructure will now be described. The methods and systems of the present application are operable to design and / or configure the respective elements of the industrial gas plant complex to achieve an optimized and / or maximized configuration or design to achieve a desired capacity given the available power resources in a particular region and the desired operating characteristics of the industrial gas plant complex.

[0051] Figure 1 A schematic diagram of an exemplary industrial gas plant complex superstructure 10 that can be designed and / or configured in accordance with embodiments of the present application is shown.

[0052] In this embodiment, the industrial gas plant complex comprises an ammonia plant complex 10. However, this is to be considered exemplary and not limiting. Other types of industrial gas plant complex superstructures can be designed and / or configured with the disclosed embodiments of the present application; for example, a hydrogen production plant, a nitrogen production plant or other industrial gas production facility.

[0053] The industrial gas plant complex 10 comprises a hydrogen production plant 20, a hydrogen storage unit 30, a hydrogen liquefier 32, an air separation unit (ASU) 40, an ammonia synthesis plant 50 and an ammonia storage unit 60. The hydrogen liquefier 32 is connected to an external supply chain SI for onward distribution of liquid hydrogen. The ammonia storage unit 60 is connected to an external supply chain S2 for onward distribution of ammonia.

[0054] The industrial gas plant complex superstructure 10 further comprises power resources in the form of a main bus 70, renewable power sources 72, 74 and energy storage resources 76. The industrial gas production components of the industrial gas plant complex 10 will now be described in detail.

[0055] Hydrogen production plant 20

[0056] The hydrogen production plant 20 is operable to electrolyze water to form hydrogen and oxygen. Any suitable source of water can be used. However, in embodiments where seawater is used to produce water for electrolysis, the apparatus can further comprise at least one desalination and demineralization plant for processing the seawater.

[0057] The hydrogen production plant 20 includes a plurality of electrolysis units 22a, 22b...22n or electrolysis cells. Each unit or cell can be referred to as an "electrolyzer" 22a, 22b...22n. Any number of electrolyzers can be provided. In an embodiment, about 100 can be provided. The electrolyzers can give the hydrogen production plant 20 a total capacity of about 1 GW. In an embodiment, the capacity can exceed 2 GW; for example, 2.2 GW. However, the ultimate capacity of the hydrogen production plant 20 is limited only by practical considerations such as power supply. Any suitable capacity can be used depending on design requirements.

[0058] Any suitable type of electrolyzer can be used. In an embodiment, the plurality of electrolyzers are generally composed of a plurality of individual cells combined into "modules" that also include process equipment such as pumps, coolers, and / or separators. Hundreds of cells can be used, and the hundreds of cells can be grouped in separate buildings. Each module generally has a maximum capacity of greater than 10 MW, although this is not intended to be limiting.

[0059] Any suitable type of electrolyzer can be used. Generally, three conventional types of electrolyzers are utilized—alkaline electrolyzers; PEM electrolyzers; and solid oxide electrolyzers. Any of these types can be used in the present invention.

[0060] Alkaline electrolyzers transport hydroxide ions (OH - ) from the cathode to the anode through an electrolyte, where hydrogen is generated on the cathode side. Typically, a liquid alkaline solution of sodium or potassium hydroxide is used as the electrolyte.

[0061] PEM electrolyzers utilize a solid plastic material as the electrolyte, and water reacts at the anode to form oxygen and positively charged hydrogen ions. Electrons flow through an external circuit, and the hydrogen ions selectively move through the PEM to the cathode. At the cathode, the hydrogen ions combine with electrons from the external circuit to form hydrogen gas.

[0062] Solid oxide electrolyzers use a solid ceramic material as the electrolyte, which selectively conducts negatively charged oxygen ions (O 2- ) at elevated temperatures. Water at the cathode combines with electrons from an external circuit to form hydrogen gas and negatively charged oxygen ions. The oxygen ions pass through a solid ceramic membrane and react at the anode to form oxygen gas and generate electrons for the external circuit.

[0063] The electrolyzers can be arranged in any suitable configuration. For example, they can be arranged in parallel.

[0064] Hydrogen is produced by the hydrogen production plant 20 at approximately atmospheric pressure. The hydrogen stream so produced is removed from the electrolyzer at a slightly elevated pressure. However, this need not be the case, and hydrogen can be produced at much higher pressures as desired. In embodiments, this can eliminate the need for some or all of the downstream compressor system.

[0065] In embodiments, the hydrogen production plant 20 further includes a hydrogen compression stage and a purification stage.

[0066] In embodiments, the compression stage includes a multi-stage compression system having two sections 24, 26. The first section 24 comprises a low pressure (LP) section in which hydrogen gas is compressed from a first feed pressure from the electrolyzer to a second intermediate pressure that is greater than the first feed pressure.

[0067] The second section comprises a medium pressure (MP) section 26 in which hydrogen gas is compressed from the second intermediate pressure to a third final pressure that is greater than the second pressure. The third pressure is selected as desired for any downstream processes.

[0068] In Figure 1 In the non-limiting embodiment shown in FIG. 1, the first (LP) section 24 has two compressor stages 24a, 24b. However, any suitable number can be used. For example, the LP section 24 can have a single compressor or can have multiple compressors.

[0069] As Figure 1 In the non-limiting embodiment shown in FIG. 1, the second (MP) section 26 is shown as a single compressor arrangement for simplicity. However, any suitable number of parallel compressor trains and / or compression stages can be provided as desired. For example, multiple compressor trains can be provided in parallel, with each compressor train including multiple compression stages.

[0070] The compressors forming part of the first (LP) compression section 24 and the second (MP) compression section 26 can take any suitable form. The form, number, and capacity of these compressors will be readily known to those skilled in the art. For example, for a total electrolyzer capacity of 1 GW, it can typically be necessary to have 2 to 4 compressors. For a total electrolyzer capacity of 2 GW, it can be necessary to have 5 or more.

[0071] The compressors used can also be selected as appropriate to the operating capacity and type of the gas production plant. For example, for a hydrogen application, the LP section 24 can include one or more centrifugal compressors, while the MR section 26 can include one or more reciprocating compressors. However, this is not intended to be limiting, and any suitable compression arrangement can be used as appropriate.

[0072] InFigure 1 In embodiments of the system 10, a purification section 28 is provided. The purification section 28 can be required in cases where, for example, any downstream processes require hydrogen of higher purity (i.e., with lower levels of water and / or oxygen that are inherently present in the compressed hydrogen gas produced by electrolysis). However, this need not necessarily be the case, and the section can be omitted if not required.

[0073] If provided, the purification section 28 includes a "DeOxo" unit operable to remove oxygen. The DeOxo unit operates by catalytic combustion of hydrogen to produce water compressed hydrogen gas from which oxygen has been removed.

[0074] The purification section 28 can further include a dryer. In this embodiment, the dryer includes a temperature swing adsorption (TSA) unit to produce dry compressed hydrogen gas for downstream processes. However, other suitable dryers and / or adsorption techniques can be used here. In embodiments, the dryer is located downstream of the DeOxo unit.

[0075] The downstream processing unit can be any unit that utilizes hydrogen gas as a feedstock or as a resource. In embodiments, the downstream processing unit is or includes an ammonia synthesis plant. An alternative or additional downstream processing unit can be a hydrogen liquefier as described below.

[0076] Hydrogen storage unit 30

[0077] Hydrogen can be stored in a hydrogen storage unit 30. The storage unit 30 can include multiple short and long term storage options with different sizes, fill / empty rates, and round trip efficiencies.

[0078] A typical storage system can include pressure vessels and / or pipe sections connected to a common inlet / outlet header. The pressure vessels can be, for example, spheres with a diameter of about 25 m, or "bullets" which are horizontal vessels with a large L / D ratio (typically up to about 12: 1) with a diameter up to about 12 m. In certain regions, underground caverns can be included as storage systems to eliminate seasonal variations associated with renewable power.

[0079] The hydrogen storage 30 is connected downstream of the hydrogen production plant 20 in a storage loop. An inlet supply line from the outlet header of the purification section 28 of the hydrogen production plant 20 to the hydrogen storage 30, and a return supply line from the hydrogen storage 30 to an output header downstream of the electrolyzer 22 and upstream of the compression sections 24, 26 extend from the hydrogen storage 30. Valves are located in the inlet and return supply lines to selectively control the flow of gas to / from the hydrogen storage 30.

[0080] In view of the variability of renewable power, it is often necessary to have a hydrogen reservoir 30 as a buffer. For example, if renewable power availability is low (e.g. during dark or low wind times), it can not be possible to run the electrolyser of the hydrogen production plant 20 at full capacity or potentially at all. To maintain hydrogen flow to the downstream processes, stored hydrogen can be mobilised.

[0081] The capacity of the hydrogen reservoir (or indeed any gas reservoir) needs to be configured and specified according to actual requirements. Gas reservoirs can take up considerable space in the industrial gas production complex 10 and require significant capital expenditure.

[0082] Thus, while in an ideal situation, sufficient gas reservoirs would be provided to ensure that all expected periods of low renewable power can be covered by the stored gas resources, physical, practical and capital expenditure constraints impose practical limits on the size and capacity of the available gas resources. This means that in a real-world context, the limited size of the gas resources must be taken into account when considering the methods and systems of the present invention.

[0083] In the context of embodiments of the present invention, the stored hydrogen can be used as a reservoir for the ammonia synthesis plant 50.

[0084] Hydrogen liquefier 32

[0085] In addition to or instead of using hydrogen for ammonia synthesis in the ammonia synthesis plant 50, the generated hydrogen can be liquefied for onward distribution into the supply network S1.

[0086] Typically, hydrogen liquefaction involves an initial compression to some extent using a compression system, followed by cryogenic cooling to about 30 K using one or more heat exchangers. An expansion step can then be performed in an expander. The gas then passes through a separator before being stored or transferred to the onward supply chain S1.

[0087] Air separation unit 40

[0088] In non-limiting embodiments, the nitrogen required for ammonia production is produced by cryogenic distillation of air in an air separation unit (ASU) 40. Typically, the ASU 40 has different stages operating at different pressures. For example, a high pressure (HP) column operates at about 10.5 bar gauge and a low pressure (LP) column operates at about 5 bar gauge. Gaseous nitrogen is produced by the ASU 40 at a pressure in excess of 25 bar gauge. The pressure is then reduced to provide a stream of nitrogen gas in one or more pipelines arranged to deliver the nitrogen to the ammonia synthesis plant 50. However, other sources of nitrogen can be used if desired, for example a liquid nitrogen reservoir 42.

[0089] The liquid nitrogen storage unit 42 can comprise any suitable liquid nitrogen storage, vaporization and distribution (LIN SVD) arrangement. The storage unit 42 can comprise a plurality of short-term and long-term storage options having different sizes, fill / drain rates and roundtrip efficiencies.

[0090] A typical storage system for liquid nitrogen can comprise a plurality of pressure vessels and / or pipe segments connected to a common inlet / outlet header. The pressure vessels can comprise low pressure flat bottom tanks (FBTs). Additionally or alternatively, the pressure vessels can be, for example, spheres of about 25 m in diameter, or "bullet bodies", which are horizontal vessels with a large L / D ratio, typically up to about 12:1, with diameters up to about 12 m.

[0091] As described above in relation to the hydrogen storage resource 30, the nitrogen storage 42 needs to be configured and specified according to actual requirements. The gas storage can occupy a considerable amount of space within the industrial gas production complex 10 and requires a significant capital expenditure.

[0092] Thus, while in an ideal situation, sufficient gas storage would be provided to ensure that all expected periods of low renewable power can be covered by the stored gas resource, physical, practical and capital expenditure constraints impose a practical limit on the size and capacity of the available gas resource. This means that, in a practical environment, the limited size of the gas resource must be taken into account when considering the method and system of the present invention.

[0093] Preferably, the nitrogen gas produced by the ASU 40 is compressed by a compressor and cooled for storage in liquid form in the nitrogen storage unit 42. However, gaseous nitrogen storage can also be provided. The storage unit 42 can serve as a reservoir for the ammonia synthesis plant 50, which can be fed through connecting pipes.

[0094] Ammonia synthesis plant 50

[0095] The ammonia synthesis plant 50 operates in a Haber-Bosch process and comprises an ammonia loop. The ammonia loop is a single unit balanced reaction system that processes a synthesis gas of nitrogen and hydrogen to produce ammonia.

[0096] Nitrogen is provided from one or more pipes from the ASU 40 (or storage 42), which can run continuously to provide nitrogen in embodiments. Hydrogen is provided directly from one or more pipes from the hydrogen production plant 20 (if operating based on availability of renewable power in a given situation) or from the hydrogen storage 30.

[0097] The stoichiometric composition of the synthesis gas is processed by a synthesis gas compressor system (not shown), and the resulting ammonia product is cooled by another set of compressors (not shown) and, if desired, sent to storage 60. The performance of the ammonia loop is controlled by the equilibrium conversion of the exothermic reaction. Parameters in this regard are discussed below.

[0098] Industrial gas production complex power supply

[0099] Electricity for powering the industrial gas plant complex superstructure 10 is provided by a main bus 70. The main bus 70 forms part of the industrial gas plant complex superstructure 10 and can be located on-site.

[0100] Renewable power sources 72, 74 supply electricity into the main bus 70 for onward distribution to the subsystems of the industrial gas plant complex superstructure 10. This is schematically shown in Figure 1 by dashed arrows.

[0101] The renewable energy sources include a wind energy source 72 (via a suitable wind farm comprising a plurality of wind turbines) and / or a solar energy source 74 (via a solar farm comprising a plurality of solar cells), although other forms of renewable energy sources (e.g. tidal or hydroelectric power sources) can also be utilized. The renewable energy sources 72, 74 form part of the industrial gas complex superstructure 10. While wind and solar energy are shown and described, other forms of renewable energy generation can be provided as part of the superstructure 10.

[0102] To address the intermittency of the power supply from the renewable energy sources 72, 74, the industrial gas production complex 10 includes an energy storage resource 76. In embodiments, the energy storage resource 76 is located on-site and forms part of the superstructure of the industrial gas production complex 10.

[0103] The energy storage resource 76 can include one or more energy storage devices. In embodiments, the energy storage resource 76 forms part of the industrial gas plant complex 10 and is thereby controlled and managed, as will be described below.

[0104] The energy storage resource 76 can take any suitable form. In embodiments, the energy storage devices can include one or more of a battery energy storage system (BESS) 76a or a compressed / liquid air energy system (CAES or LAES) 76b.

[0105] BESS 76a utilizes electrochemical technology and can include one or more of lithium-ion batteries, lead-acid batteries, zinc-bromine batteries, sodium-sulfur batteries, or redox flow batteries. Electrochemical devices such as batteries have an advantage in terms of fast charging rates and fast (nearly instantaneous) ramp rates to supply power to respond to sudden drops in energy supply. However, such devices tend to have more limited power capacity compared to other systems. Thus, they can be more suitable for use in situations where power shortfalls from renewable energy, for example, are expected to be temporary or short in duration.

[0106] CAES 76b compresses air and stores the air at high pressure of about 70 bar. It is typically stored in underground caverns. When power is needed, the compressed air is heated and expanded in an expansion turbine to drive an electrical generator.

[0107] LAES 76b includes an air liquefier to extract air from the environment and compress and cool the air to achieve liquefaction. The liquefied air is then stored in an insulated tank until power is needed. To convert the liquefied air into usable energy, the liquid air is pumped to high pressure and heated through a heat exchanger. The resulting high pressure gas is used to drive a turbine to generate electricity.

[0108] CAES and LAES are capable of storing significantly more energy compared to most BESS 76a systems. However, CAES and LAES have slower ramp rates than electrochemical storage devices and require more time to store larger amounts of energy. For example, it can take about 5-10 minutes for a compression stage to operate at full load, and 10-20 minutes to generate full power on demand. Thus, such storage devices are more suitable for long-term storage and more suitable for supplying power during long periods of renewable energy shortfalls.

[0109] While all of these elements are shown in Figure 1 , this is for illustrative purposes only. Energy storage resource 76 need not include each and every element described, and can include only one or more of the elements described. Moreover, energy resource 76 can include additional elements.

[0110] Elements 72, 74, 76 feed into main bus 70 as shown by the arrows in Figure 1 . Element 76 is operable to supply power to main bus 70 when demand requires it, and to store energy from main bus 70 when demand is low. In other words, given the variability of renewable energy such as wind energy 72 and solar energy 74, energy storage resource 76 is used to smooth power delivery to the network.

[0111] The selection of the type and capacity of energy storage resources 76 is another parameter that needs to be considered in the design and configuration of an industrial gas production complex. During the design process, the available space, capital expenditure, and specific ramp rate for each type of energy storage need to be taken into account.

[0112] While the examples of renewable power above have been given concerning wind and solar power, this is not intended to be limiting. For example, other renewable energy sources such as hydropower (not shown) and / or tidal power (not shown) can be used.

[0113] like Figure 1 As shown, the main bus 70 is connected to the local power grid infrastructure 80. The local power grid infrastructure 80 is outside the scope of the superstructure 10. The industrial gas equipment complex superstructure 10 is configured and / or designed to minimize or eliminate dependence on external power sources such as the local power grid 80.

[0114] However, in emergency situations or rare cases where sufficient power from components 72, 74, and 76 is temporarily unavailable, a power connection is required as an emergency backup, and power from an external source (such as the local power grid infrastructure 80) is needed to prevent the subsystems of the industrial gas equipment complex superstructure 10 from shutting down.

[0115] Superstructure design and configuration methods

[0116] In one embodiment, the present invention relates to methods and systems for designing and configuring superstructures such as industrial gas equipment complexes. In another embodiment, the industrial gas equipment complex includes an ammonia production facility.

[0117] The design of such superstructures is multifactorial and highly complex. In embodiments, the present invention seeks to provide methods and systems for designing such superstructures based on technical constraints such as site location, availability of renewable power, performance of superstructure components, utilization rate, safety requirements, efficiency and performance standards, capital expenditure, and expected productivity of industrial gases.

[0118] In the implementation, the method and system can utilize optimization methods to define a "configuration space" for the superstructure of an industrial gas equipment complex, and within the defined configuration space, seek configurations that achieve improved productivity within predetermined parameters. In the context of this invention, the optimization method aims to identify configurations or configuration ranges within the predetermined configuration space that meet specific criteria or parameters to achieve specific technical objectives.

[0119] For example, the optimization method can be used to identify an industrial gas plant superstructure that is operable to produce a desired amount (e.g., a maximum amount or an amount above a predefined threshold) of an industrial gas (e.g., ammonia and / or liquid hydrogen) based on a preferred input power profile given the actual or potential renewable energy available, with minimal or no dependence on external power sources such as a local grid.

[0120] For example, the method can produce one or more configurations that are capable of producing a maximum amount of one or more industrial gases, given economic, security, regulatory, and infrastructure constraints and requirements, while operating within h

[0121] In embodiments, the method utilizes a plurality of technical inputs that define constraints on the design to be produced. The subsystems defining the design and their technical parameters, components, and associated constraints define a configuration space within which one or more maximum or optimized configurations can be selected.

[0122] The configuration space can be defined by a combination of pre-specified elements and automatically defined elements. For example, a user can select a particular type of industrial gas plant superstructure (e.g., a hydrogen production plant or an ammonia production plant) which then requires particular subsystems (e.g., a hydrogen production plant subsystem and a hydrogen storage subsystem) to operate in an expected manner.

[0123] The user can also specify particular design parameters for the industrial gas plant superstructure that impose further requirements and constraints. For example, a maximum power demand from renewable energy, or a maximum or minimum desired production output of industrial gas.

[0124] Within the user-specified range, particular components can be selected in the model as needed. These can be user-defined or can be automatically defined based on the initial input requirements.

[0125] Thus, it can be seen that the configuration space is defined (or produced) by the combination of user requirements, component availability, and technical parameters and constraints.

[0126] The configuration of each subsystem of the industrial gas plant superstructure is selected from the defined configuration space and a simulation is run on the configuration to determine a maximum production for that configuration using an optimization strategy. This process is repeated for different configurations and the resulting data is used in a surrogate model to determine the optimal configuration.

[0127] A computer-implemented method and system will now be described. Figure 2 A schematic diagram of a configuration system 100 according to an embodiment is shown. The configuration system 100 comprises a plurality of modules.

[0128] Configuration system 100 includes subsystem module 102, simulation module 104, and optimization module 106. Configuration system 100 is operable to select one or more maximized or optimized configurations of the industrial gas equipment complex superstructure at a desired location.

[0129] Configuration system 10 runs on computer hardware. For example, configuration system 100 may use the central processing unit (CPU) and / or graphics processing unit (GPU) components of a computer system. Alternatively, other specialized hardware may be used, such as field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), or other stream processor technologies.

[0130] In addition, the model execution computer can optionally be connected to other computer database systems, which may store weather data services or other external data, for example.

[0131] Subsystem Module 102

[0132] Subsystem module 102 can define and specify the infrastructure subsystems that form the superstructure of the industrial equipment complex. Figure 2 The diagram shows a schematic of the components of subsystem module 102.

[0133] Subsystem module 102 enables the specification of an initial model of the industrial gas equipment complex superstructure 108 to be configured and designed, and can specify one or more power subsystems 110 and one or more equipment subsystems 112 therein. Components 114 within each subsystem 110, 112 can then be specified. Constraints 116 can then be applied to the power subsystems 110 and equipment subsystems 112 within the industrial equipment complex superstructure 108, as well as the components 114 of these subsystems 110, 112.

[0134] In other words, subsystem module 102 defines a configurable model with multiple selectable subsystems 110, 112. Subsystems 110, 112 can be selected from a group or pool of available subsystems. Some subsystems can be user-specified and are part of the basic design requirements of the industrial gas production complex superstructure (e.g., the type of industrial gas production complex superstructure required in terms of gas production or renewable power generation levels).

[0135] During the configuration process, other subsystems can be optional or selected from a set of available subsystems. "Available" means that a particular subsystem is compatible with the overall design requirements or can be used as part of the overall design requirements and is included in the model.

[0136] The subsystem module 102 receives data specifying the type and configuration of subsystems of the industrial gas plant complex superstructure 108 to be designed and configured. The data depends on the nature of the industrial gas plant complex superstructure, such as its intended use and configuration (e.g., an ammonia production plant or a hydrogen production plant).

[0137] For each subsystem 110, 112, parameters and components 114 can be selected within certain bounds and constraints 116. The specified parameters, components 114, and available ranges of constraints 116 define a configuration space within which one or more configurations of the industrial gas plant complex superstructure 108 can be selected, as will be described below.

[0138] The following disclosure illustrates how the specifications and constraints input into the subsystem module 102 enable the industrial gas plant complex superstructure 108 to be configured and designed, and to specify the power subsystems 110 and plant subsystems 112 within the configuration space thus defined.

[0139] The detailed disclosure is illustrated with reference to an industrial gas plant complex superstructure 108 in the form of an ammonia production plant powered by renewable power sources in the form of wind power and solar power generation resources.

[0140] Power subsystems 110

[0141] Each power subsystem 110 has certain design parameters. In embodiments, the power subsystems 110 can be grouped as power generation (e.g., renewable power subsystems 110R), and / or supporting power infrastructure (e.g., energy storage 76, main bus 70).

[0142] Renewable power subsystems 110R

[0143] The renewable power subsystems 110R can be selected automatically or manually during configuration of the model of the subsystem module 102. For the renewable power subsystems 110R, the specific components can not be important to the present invention, and in embodiments, the renewable power subsystems 110R can be defined only by parameters and any associated constraints.

[0144] This means that the detailed specifications of the renewable power subsystems 110R components of one or more renewable power subsystems 110R (e.g., the type, number, and configuration of wind turbines or solar panels) are not important to the present invention. However, in embodiments, the parameters of each power subsystem 110 can be specified based on certain design and / or configuration requirements.

[0145] In embodiments, one or more renewable power subsystems 110R can be selected by a user or automatically. Each renewable power subsystem 110R is grouped by type (e.g., wind, solar, tidal, etc.). Within each group, a range of renewable power subsystems 110R can be selected with different operating parameters.

[0146] The operating parameters can include maximum and minimum power profiles for a given renewable power subsystem 110R. How the power profiles are derived is explained in the section below regarding the power prediction module 110A. In embodiments, the predicted power profile includes an estimated power produced by a given configuration of renewable power subsystems 110R for a given number of intervals (e.g., 1 hour) over a predetermined time period (e.g., 1 year). This shows the predicted daily power availability of a given renewable power subsystem 110R.

[0147] As explained above, each available renewable power subsystem 110R can be selected as an entity with particular operating parameters. In embodiments, no internal components are selectable. However, parameters such as maximum power production of the renewable power subsystem 110R can be specified. This allows for the selection of renewable power subsystems 110R with available power production that is scaled to meet the needs of the industrial gas plant complex superstructure.

[0148] Consider an available renewable power subsystem 110R with maximum available physical dimensions that is capable of 2000 MW of maximum power production and has a given power profile. However, for a particular industrial gas production complex superstructure, such power output can not be needed, or other renewable energy sources can be used in combination with the given subsystem 110R, meaning that the full 2000 MW capacity is not needed.

[0149] In such scenarios, the set of selectable renewable power subsystems 110R can be scaled from the maximum, such that subsystems 110R with reduced maximum power production (such as 1500 MW, 1000 MW, or 500 MW) can be selectable. The selection can be continuous (where a renewable power subsystem 110R can be selected with any value below the maximum power production and above the minimum required power production) or discrete (e.g., multiple different selectable subsystems 110R have discrete maximum power production values).

[0150] In either scenario, in embodiments, the power profiles of each subsystem 110R of the same type (e.g., wind / solar) have the same profile and form, but with different magnitudes. In other words, if each power profile of each subsystem 110R within a given group is normalized relative to the maximum available power of each subsystem 110R, the profiles would in fact be identical and overlap.

[0151] This selectivity can be derived from real-world design decisions. For example, there can be a particular land area available to provide renewable resources (wind and / or solar). If the entire land area is used for wind power, then that energy resource can produce a particular power profile (maximum or expected power delivered over a predefined time period, such as a year). This defines an upper bound or constraint on the maximum wind power that can be produced with the available resource. The same is true if the entire resource is used for solar power.

[0152] However, if only a portion of the available resource area is used for wind, for example the minimum commercially or technically viable wind farm resource scale, then this defines a lower bound on the power profile for the wind power resource. However, for each scale, the power profile will be essentially the same, albeit scaled in magnitude to the chosen scale of the wind farm.

[0153] The same applies to solar power. Thus, in this example, it can be seen that a range of parameters for each renewable power subsystem 110R can be defined and used as part of a global superstructure optimization problem to select a suitable power profile for a desired superstructure configuration.

[0154] The defined range (discrete or continuous) of configurations of available renewable power subsystems 110R that can provide a particular maximum power generation amount for a given power profile enables the mix of renewable energy to be investigated and an optimal configuration to be selected.

[0155] For example, the optimization process can utilize data related to the customized selection of wind and solar resources. Solar power can provide more consistent power during the day, but wind power can provide greater flexibility and power generation during the night. Thus, a particular mix of these power profiles can be used as part of the configuration selection to identify a maximum power profile for a particular configuration of device subsystems 112.

[0156] The maximum and minimum power generation can be constraints and parameters that can be selected. However, other constraints can be appropriately allocated.

[0157] For example, constraints 116 can apply safety considerations in terms of maximum capacity and limits on power generation or rate of change of power generation to maintain component integrity and safety.

[0158] Further, the constraints 116 can also be applied to the renewable power subsystem 110R over a longer time frame; for example, to take into account degradation of performance and efficiency over time, or to specify time intervals for repair and replacement of the renewable power subsystem.

[0159] The constraints 116 can also be applied to the interdependence of parameters between the renewable power subsystem 110R and the plant subsystems 112 of the industrial gas plant complex superstructure 108 to be configured and designed. For example, additional constraints can be applied to ensure that the ramp rate of the renewable power subsystem 110R does not exceed the technical limit of the ramp rate of the plant subsystems 112 powered.

[0160] Power profile module 110A

[0161] The acquisition of power profile information for one or more renewable power subsystems 110R will now be described.

[0162] The configuration system 100 further comprises a power profile module 110A operable to obtain predicted time-dependent operation and meteorological data for one or more renewable power subsystems 110R over a predetermined time period. In embodiments, the time period is at least one year, which in embodiments can be multiple years. The time series data represents the available power from the renewable energy as a function of time.

[0163] For the purposes of the present embodiments, only wind power and solar power are considered. However, as mentioned above, this is non-limiting and other renewable sources of power can be used in embodiments.

[0164] In embodiments, if the renewable power subsystem 110 under consideration is comparable to an existing renewable power source such as a wind farm or a solar farm (or selected portions thereof), then the variables for wind power Wpi and solar power SPi in the time series data for the predetermined time period can be available to be used as prediction data for future evaluation and design. In embodiments, the index i represents time from period n to n+k, and the data can be available in fixed duration intervals, where the power generated is represented as a function of time in MW.

[0165] More commonly, if the renewable power site has not yet been designed or constructed, then the time series power data can be estimated using suitable metrics and / or models. In embodiments, the time series power data can be estimated from weather sources and technical information.

[0166] For example, historical and current wind energy data can be obtained from publicly available sources such as https: / / globalwindatlas.info or NREL. Historical and current solar energy data can be obtained from publicly available sources such as https: / / globalsolaratlas.info or NREL, and detailed irradiance and albedo data can be obtained from https: / / solargis.com.

[0167] Additionally or alternatively, given the challenge of predicting local wind speeds and variations, local measurements can be obtained by, for example, installing measurement poles at identified sites with one or more anemometers at different height levels. Field data can be collected over a period of time (e.g., a minimum of one year). Additionally or alternatively, modeling simulations can be used to determine the wind energy profile for an entire wind farm by creating a wind prediction model for a specified geographic area using historical data.

[0168] Technical data can also be used. For a wind farm, this can include known wind farm layout and design, selection and number of turbines. For a solar farm, technical details such as type, area, efficiency, and number of panels, and their location and orientation can be modeled with suitable software.

[0169] This data can then be used to generate a predicted power profile for a predetermined time period. For example, this time period can be based on historical data (e.g., past wind energy data over a period of one or more years) or can be based on predicted future data derived from a machine learning process.

[0170] The predicted average power data can be used to generate a P50 and P90 power profile for the predetermined time period. P50 represents the median of the annual estimate of power production from the renewable resource, such that over the life of the project, there is a 50% probability that power production at any given time will be below the P50 value, and a 50% probability that it will exceed the P50 value.

[0171] The P90 value is more conservative and represents the average power value that will be reached or exceeded 90% of the time.

[0172] However, while P50 and P90 are widely used in the respective industries, any suitable metric can be used. For example, P25, P75, or any other suitable metric.

[0173] It is further noted that the data utilized by the power subsystem 110 can be obtained by any suitable means, and the above discussion does not limit the power subsystem 110 to any data generation requirements. In fact, the data can be provided by an external source.

[0174] Average wind power generation WPi and average solar power generation SPi can be provided or generated over a period of time, in embodiments, the period of time is a year or more. The data can include a time series, where index i represents time from period n to n+k in fixed duration intervals. In non-limiting embodiments, the intervals can include 15 minutes, 30 minutes, or 1 hour.

[0175] In embodiments, additional environmental and meteorological signals can be used to refine the determination of average power profiles. These can include, but are not limited to, time dependent environmental data, which includes: air temperature Ti; atmospheric pressure Pi; wind speed WSi; cloud cover CCi; precipitation Pi; humidity Hi; where index i represents time from period n-m to n+k.

[0176] The above data can be used to define constraints 116 on the renewable power subsystem 110R, such as site size, scaling, power profiles, and capacity. These constraints define a configuration space for the renewable power subsystem 110R, from which appropriate configurations can be selected and executed during the simulation phase.

[0177] Supporting power subsystem 110S

[0178] The supporting power subsystem 110S includes power infrastructure elements, such as the main bus 70 and energy storage 76. In embodiments, certain supporting power subsystem 110S can be automatically designated in response to selections made with respect to the renewable power subsystem 110R described above; for example, the main bus 70 selected to handle the selected maximum power value resulting from the selection of one or more renewable power subsystems 110R. However, in embodiments, certain elements, such as the energy storage 76, can be designated.

[0179] In some cases, one or more supporting power subsystems 110S can include one or more components 114. The components 114 correspond to functional elements of the subsystem, and are each selectable from a library of components. The components 114 can be modular, and a portion of the design and configuration process can involve determining the number and size of any type of component 114.

[0180] The subsystem module 102 is further operable to define constraints 116 on the construction and operation of the components 114 within each supporting power subsystem 110S and between each component 114.

[0181] The constraints can include technical constraints in normal operation, such as power consumption, maximum and minimum capacity, efficiency, and efficiency variation with load.

[0182] Constraints 116 can also take into account dynamic processes - e.g., ramp rates for start-up and shut-down of energy storage resources. These constraints 116 can also be associated with more general constraints and problems - e.g., safety considerations in terms of maximum capacity and limits on ramp rates to maintain component integrity and safety.

[0183] Furthermore, constraints can also be applied over longer time frames; e.g., to take into account degradation of performance and efficiency over time, or to specify time intervals for repair and replacement of battery modules.

[0184] Constraints 116 can also be applied to interdependencies of parameters between power subsystems 110 (both renewable power subsystems 110R and support power subsystems 110S) and plant subsystems 112 of the industrial gas plant complex superstructure 108 to be configured and designed. For example, additional constraints can be applied to ensure that ramp rates of power subsystems 110 do not exceed the technical limit ramp rates of powered plant subsystems 112.

[0185] Support Power Subsystems 110S - Energy Storage Resources

[0186] Support power subsystems 110S are energy storage resource selection and configuration. While this feature can be optional, in most renewable systems some form of smoothing or backup power source is required. Selection and configuration parameters are as follows:

[0187] Energy storage type: battery energy storage system (BESS) or compressed / liquid air energy system (CAES or LAES).

[0188] Energy storage technology parameters: capacity, configuration, construction (e.g., lithium ion, lead acid, zinc bromine, sodium sulfur, or redox flow batteries, physical size).

[0189] Energy storage operability constraints: (ramp rates, charge rates, rates of change of performance and storage efficiency over time (i.e., aging and degradation), degradation of charge storage materials, average or averaged time intervals between replacement or repair, average or averaged time to repair or replace components).

[0190] Energy storage safety and regulatory constraints: maximum capacity, maximum power draw, limits on ramp rates, regulations to prevent certain components (e.g., electrolytic tanks) from being powered solely or exclusively by energy storage resources.

[0191] Energy storage interdependency considerations (power supplied to only certain subsystems, power management on bus 70).

[0192] Support Power Subsystems 110S - Main Bus 70

[0193] The support power subsystem 110S that can be selected and configured is for the main power bus (e.g., main bus 70). The main power bus must be operable to monitor and control the input power and output power draw of the subsystems of the device complex superstructure. The main power bus can be configured as needed, taking into account the other selected subsystems 110, 112.

[0194] Constraints applicable to the main power bus include the total available power (which can depend on the selection of components 114 of the main power bus) and the maximum power draw, which sets an upper limit on the power that the particular configuration or element of the main power bus can draw at any time.

[0195] In embodiments, the selection of the main bus 70 can be done automatically based on the selection of the renewable power subsystem 110R. However, in some embodiments, manual selection is available.

[0196] Device subsystems 112

[0197] Each device subsystem 112 includes one or more components 114. The components 114 correspond to functional elements of the subsystem and are each selectable from a library of components 114. The components 114 can be modular, and part of the design and configuration process can involve determining the number and size of any type of component 114.

[0198] For example, consider an ammonia production plant with a hydrogen production device subsystem 112. In embodiments, this subsystem 112 includes one or more electrolyzers. The electrolyzers can be available from different manufacturers, can have different configurations and capacities, and can be different types. For example, the electrolyzers can be selected from one or more of: alkaline electrolyzers; PEM electrolyzers; and solid oxide electrolyzers.

[0199] The electrolyzers can be modular, and multiple electrolyzer modules can be used together. For example, a single module can contain many cells and have a total capacity of 20 MW, and the subsystem module 102 can enable any number of 20 MW electrolyzers to be selected as part of the hydrogen production device subsystem 112.

[0200] In addition, the hydrogen production device subsystem 112 can include one or more purification and compression stages. The purification stages can be selected (or deselected) from a library of available components. Likewise, the compression stages can be selected from a library of possible compressor configurations and components based on type, compression ratio, downstream pressure, etc.

[0201] Furthermore, the subsystem module 102 can be able to implement the selection of bespoke components. For example, an electrolyser module with specific required properties can be specified as the optimal solution, which can then be manufactured to order.

[0202] The subsystem module 102 is further operable to define constraints on the construction and operation of components within each equipment subsystem 112 and between each component 114. This will be described in detail below in relation to an exemplary ammonia production plant.

[0203] However, again taking the equipment subsystems 112 of a hydrogen production plant as an example, the constraints can include technical constraints in normal operation, such as power consumption, maximum and minimum capacity, efficiency (e.g. how much input energy is required to produce NM 3 of hydrogen) and variation of efficiency with load.

[0204] The constraints can also take into account dynamic processes - for example, ramp rates for start-up and shut-down of electrolyser modules. These constraints can also be associated with more general constraints and problems - for example, safety considerations in terms of maximum capacity and limits on ramp rates to maintain component integrity and safety.

[0205] Furthermore, the constraints can also apply over longer time frames; for example, to take into account degradation of performance and efficiency over time, or to specify time intervals for repair and replacement of electrolyser modules and tanks.

[0206] Constraints can also be applied in relation to interdependencies of parameters between equipment subsystems 112 of the industrial gas plant superstructure 108 to be configured and designed. For example, additional constraints can be applied to the ramp rate of an upstream process that exceeds the technical limit of the ramp rate of that process to ensure that a downstream process is not affected by a change in gas flow that exceeds the design rate of change of the downstream process.

[0207] Subsystem module - example of an ammonia production plant

[0208] The following non-limiting example relates to the design and configuration of an ammonia production plant superstructure.

[0209] The subsystems required for an ammonia production plant include, as set out in relation to Figure 1 the hydrogen production plant 20, hydrogen storage unit 30, hydrogen liquefier 32, air separation unit (ASU) 40, ASU storage unit 42, ammonia synthesis plant 50, ammonia storage unit 60 and energy storage resource 76.

[0210] The subsystems and their interconnections (e.g. power supply connections, upstream / downstream process connections, etc.) are specified in the subsystem module 102.

[0211] Device subsystem 112 - hydrogen production device subsystem instance

[0212] For the hydrogen production device subsystem 112, the non-limiting categories of components to be specified are from the group of electrolyzer, purification stage, and compression stage.

[0213] For the electrolyzer, the subsystem module 102 can enable selection of:

[0214] Electrolyzer type (e.g., alkaline electrolyzer; PEM electrolyzer; solid oxide electrolyzer);

[0215] Electrolyzer technical performance (capacity per module (MW), number of cells per module, number of modules, manufacturer or module design).

[0216] Electrolyzer design constraints and parameters include:

[0217] Electrolyzer operating characteristics (power consumption, maximum and minimum capacity, efficiency, variation of efficiency as a function of load).

[0218] Electrolyzer specific parameters (softened water flow, average cell temperature, average cell pressure, cell voltage, cell current)

[0219] Electrolyzer operational constraints (rate of change of performance and efficiency over time (i.e., aging and degradation), average or averaged time interval between cell replacement or repair, average or averaged time for repair or replacement of electrolyzer modules and cells).

[0220] Electrolyzer safety constraints (maximum voltage, current, maximum capacity, maximum load, limits on ramp rates)

[0221] Electrolyzer interdependence considerations (proportion of power draw to available power, ramp rates to ensure proper flow rates to downstream processes).

[0222] Other components that can be selected include purification systems, which can be selected from:

[0223] Temperature swing adsorption (TSA) components; deoxygenation systems (operational capacity, power draw, flow pressure)

[0224] Compressors (type, number of compressor banks, number of stages, compression ratio, efficiency, power consumption, capacity, ramp rates for partial / total shutdown or startup of compressors).

[0225] Compressor operating variables and constraints (compressor pressure and flow).

[0226] Device subsystem 112 - hydrogen storage subsystem instance

[0227] Additional subsystems can include hydrogen storage resources 30. Components and constraints can include:

[0228] Storage type (spheres, bullets, caverns, size of each, number of each).

[0229] Storage infrastructure (physical size and space availability, capital expenditure, piping).

[0230] Storage parameters and constraints (maximum and minimum storage pressure, maximum and minimum storage capacity, constraints on desired fill level).

[0231] Storage operational data (storage pressure, temperature, volume, leak management, time interval between repair or replacement, flow rate to / from gas storage).

[0232] Plant subsystem 112 - hydrogen liquefier subsystem instance

[0233] In addition to or instead of using hydrogen for ammonia synthesis in ammonia synthesis plant 50, the generated hydrogen can be liquefied for onward distribution into supply network S1.

[0234] Components of a hydrogen liquefier can include compression components, cooling components, expansion components, and storage components. Technical constraints and operational parameters of these components can include ramp rates and turndown rates, as well as storage volume.

[0235] In view of the fact that a hydrogen liquefier can be designed to supply liquid hydrogen to forward supply chain S1, additional constraints can apply in terms of market demand and carbon intensity of the production and forward transportation process.

[0236] For example, a hydrogen liquefier, if used as part of an ammonia plant, requires production of sufficient liquid hydrogen to meet the demand of supply network S1, while maintaining sufficient hydrogen for ammonia production. These aspects impose constraints on the operational rate and ramp rate of the liquefaction system.

[0237] Plant subsystem 112 - air separation unit subsystem instance

[0238] Additional subsystems can include ASU 40. Components and constraints can include:

[0239] Air separation unit type (process, manufacturer, capacity).

[0240] Air separation unit technical performance (efficiency, efficiency vs. load, specific power, nitrogen recovery).

[0241] Air separation unit operational constraints and parameters (maximum and minimum capacity, efficiency, temperature difference in heat exchangers).

[0242] Air separation unit operational constraints (rate of change of performance and efficiency over time (i.e. aging and degradation), average or mean time between replacement or repair, average or mean time to repair or replace ASU components).

[0243] Air separation unit safety constraints (maximum capacity, maximum load, limits on ramp rates).

[0244] Air separation unit interdependencies considerations (proportion of power draw to available power, ramp rates to ensure proper flow rates to downstream processes).

[0245] Plant subsystem 112 - air separation unit storage subsystem instance

[0246] Additional subsystems can include nitrogen storage resources 42. Components and constraints can include:

[0247] Storage type (spheres, bullets, caverns, size of each, number of each).

[0248] Storage infrastructure (physical size and space availability, capital expenditures, piping).

[0249] Storage parameters and constraints (maximum and minimum storage pressure, maximum and minimum storage capacity, constraints on desired fill level).

[0250] Storage operational data (storage pressure, temperature, volume, leak management, time interval between repair or replacement, flow rate to / from gas storage).

[0251] Plant subsystem 112 - ammonia production plant subsystem instance

[0252] Ammonia production plant type (process, manufacturer, capacity).

[0253] Ammonia production plant type technical performance (efficiency, efficiency vs. load, capacity).

[0254] Ammonia production plant operational constraints and parameters (maximum and minimum capacity, ramp rate and drop out limits, response to varying input gas streams (hydrogen and nitrogen)).

[0255] Ammonia production plant operational parameters (e.g. power consumed by ammonia loop, ammonia loop pressure and temperature, feed flow rates of nitrogen and hydrogen streams, ammonia plant synthesis gas compressor pressure).

[0256] Ammonia production plant operational constraints (rate of change of performance and conversion loop efficiency over time (i.e. aging and degradation), degradation of catalyst beds, average or mean time between replacement or repair, average or mean time to repair or replace ammonia production plant components).

[0257] Ammonia production plant safety constraints (maximum capacity, maximum load, limits on ramp rates).

[0258] Ammonia production plant interdependence considerations (proportion of power draw to available power, ramp rates to ensure proper flow from upstream processes).

[0259] Plant subsystem 112 - ammonia production plant storage subsystem instance

[0260] Additional subsystems can include ammonia storage resources 60. Components and constraints can include:

[0261] Storage types (spheres, bullets, caverns, size of each, number of each).

[0262] Storage infrastructure (physical size and space availability, capital expenditures, piping).

[0263] Storage parameters and constraints (maximum and minimum storage pressure, maximum and minimum storage capacity, constraints on desired fill level).

[0264] Storage operational data (storage pressure, temperature, volume, leak management, time interval between repair or replacement, flow rate to / from gas storage).

[0265] Further, in view of the stored ammonia then being supplied to the forward supply chain S2, additional constraints can be applied in terms of market demand and carbon intensity of the production and forward transportation processes of the ammonia.

[0266] For example, ammonia plant 50 needs to produce enough ammonia to meet the demand of the supply network S2 without needing to store beyond practical design considerations. Further, in certain embodiments, an additional constraint can be to produce enough ammonia to meet the supply network S2, which also enables production of enough liquid hydrogen for the supply chain S1. These aspects impose constraints on the operational rates and ramp rates of the ammonia production plant 50 and the storage 60.

[0267] Simulation module 104

[0268] Simulation module 104 is operable to receive data from power subsystem 110 and subsystem module 102, and to simulate operation of the industrial gas plant complex superstructure 108 for a plurality of different configurations selected from the configuration space, for a predetermined period of time at the configuration. The predetermined period of time can include a year or more of operation.

[0269] The simulation module 104 is operable to build a model of the industrial gas plant complex superstructure 108 with the data received, determined and / or generated by the subsystem module 106 in the selected configuration. The model includes the plant subsystems 112 as defined in the subsystem module 102 and the components 114 of those subsystems in addition to all the relevant constraints 116 defined with respect to the components 114 in the subsystem module 102.

[0270] The power prediction data from the renewable power subsystem 110R is then utilized and the simulation of the selected configuration is allowed to proceed in accordance with the particular configuration in operation as the power data is predicted.

[0271] Simulation model 104 - subsystem models

[0272] The simulation model 104 utilizes physics-based models of the various subsystems to simulate the behavior of the predefined configurations of the plant. The physics-based models are primarily concerned with capturing the energy consumption of the subsystems at different rates of operation. The following example of a physics-based model is now given.

[0273] For the simulation of the hydrogen production plant 20, in embodiments, the relevant physics-based model is based on the polarization curve of the electrolyzer and can represent the power consumption at different hydrogen production rates. The polarization curve varies over time and the resulting power consumption response varies in response thereto. In embodiments, such time-based degradation can be incorporated in the physics-based model of the hydrogen production plant 20.

[0274] To simulate the gas storage elements (e.g., for hydrogen, nitrogen and / or ammonia) in the industrial gas production complex superstructure, the storage can be represented by the minimum and maximum allowable storage mass and flow rate that the gas can be stored or extracted.

[0275] One or more components of the hydrogen production plant 20 include compressors. In addition, hydrogen liquefaction requires compression. To simulate the one or more compressors, the power curve of the compressor and the operating principle that causes the compressor to enter different modes at different flow rates are utilized. Overall, the compressor model represents the power consumption at different rates with the same pressure rise.

[0276] For the models of the ammonia production plant 50, these models represent the power consumption of the ammonia synthesis gas compressor and the refrigeration compressor at different ammonia production rates. Separate models can also be used to represent the power generated by the steam turbine that operates using steam from the ammonia. Overall, these models can represent the net power consumed by the ammonia production system at different ammonia production rates.

[0277] The air separation unit 40 can be modeled by a simulation model that represents the power consumption of the ASU 40 compressors at different rates. This can also be based on compressor curves.

[0278] Other components can be modeled as elements that consume a constant power draw per unit of time.

[0279] With the above, the simulation module 104 can capture power consumption realistically by using non-linear equations, modeling, and empirical analysis.

[0280] Simulation module output parameters

[0281] The output parameters can then be generated to act as an index metric. For example, in the case of an ammonia production plant being designed and configured, the output parameter can be the amount of ammonia produced over a predetermined time period. This time period can be, for example, one year.

[0282] In embodiments, the simulation can determine an optimized value or maximized value of the output parameter over a predetermined time period. This can be done by varying the process variables of the simulated plant within the bounds of defined constraints and in response to predicted available power data to achieve a maximum or optimized value of the output parameter.

[0283] In embodiments, the output parameter can be the amount of ammonia produced over a predetermined time period (e.g., 1 year). Thus, the output parameter from this model is an estimate of the maximum amount of ammonia that can be generated for any one particular configuration based on the most suitable selection of available ranges from renewable resources.

[0284] This optimization can utilize the set points of the simulation of the control processes in the plant complex 110 at a particular time period to balance predicted available power with consumed power so that the right amount of hydrogen is produced and the ammonia plant is running at the right rate to maximize ammonia production.

[0285] In other words, the simulation module 104 solves an optimization algorithm applied to a dynamic mathematical model of the configuration of the industrial gas plant complex superstructure 108 under consideration. The predicted available renewable power WPi and SPi and the constraints of the various components and subsystems of the simulated configuration of the industrial gas plant complex superstructure 108 are taken as inputs and applied to the optimization algorithm to come up with the optimal rate at which to run the ammonia plant over a particular predetermined time period.

[0286] Alternatively, the output parameter can be a plant complex superstructure configuration in which the reliance on external power sources such as the local grid 80 is minimized given a range of available renewable resource power profiles.

[0287] The simulation module 104 can also utilize data related to the energy storage devices 76, if implemented in a particular configuration. The state, operational characteristics, availability, resource storage level, and ease of power availability of each of the units of the storage resource 76 can be factored into the optimization problem.

[0288] In embodiments, the simulation of the plant complex 110, including the selected plant subsystems 112 and all of their components 114, along with the applicable constraints 116, can be defined as a mixed integer linear programming (MILP) problem. However, other optimization solution techniques are available.

[0289] The predicted power data can be on a hourly time scale, and the model can simulate the full operation of the plant 110 for the specified configuration, including plant failures and repairs over a time scale of at least one year, preferably over multiple years.

[0290] Simulation model - selection of configuration

[0291] In the example of an ammonia production plant, the configuration space of plant subsystems 112 selections and components 114 selections is very large and multi-dimensional. Thus, while a particular configuration can be selected manually, this can represent a difficult problem in certain configuration spaces. Thus, it is necessary to automatically select different configurations to be able to explore the configuration space.

[0292] In embodiments, a selection method is used to select a plurality of configurations for simulation. In embodiments, a selection protocol is implemented to automatically select a particular configuration for simulation from the available configuration space.

[0293] In non-limiting embodiments, a sampling method can be used. In non-limiting embodiments, a Latin hypercube sampling technique can be used. Latin hypercube sampling is a statistical method operable to generate an approximately random sample of values from a multi-dimensional distribution space. However, other methods can be used; for example, random sampling or orthogonal sampling.

[0294] In the present invention, the distribution space represents the possible configurations of the plant complex superstructure 108 from which to select a near-random sample. Once a plurality of configurations is selected, each configuration can be run in a simulation to determine the maximum or optimized value of the output parameter for that particular configuration. When these values are obtained, a variable in the configuration space can be obtained, where the value of the output parameter as a function of the configuration can be obtained.

[0295] Optimization module 106

[0296] Once the configurations have been generated and simulated by the simulation module 104, the optimization module 106 can access the configuration data space in which various configurations have been simulated, which defines a plurality of configuration data points within the configuration space. This will give the simulated values of the output parameters for that configuration.

[0297] In embodiments, the output parameter can be the maximum ammonia production over a predetermined time frame (e.g. 1 year) determined for each configuration. Alternatively, the output parameter can be the proportional or absolute usage of external power resources (such as the local grid 80) over a predetermined time frame (e.g. 1 year).

[0298] However, given the large number of possible configurations, as described above, a plurality of configurations are selected according to a random or pseudo-random technique in the configuration space. Therefore, further optimization is required to select the optimal configuration for production.

[0299] In embodiments, this is handled by the optimization module 106. The optimization model 108 seeks to identify one or more maximized or optimized configurations within the configuration space that satisfy the technical, safety, efficiency and business requirements, while optimizing, maximizing or minimizing the desired output parameter. In embodiments, the output parameter can be the maximum amount of ammonia from the selected power profile of the available power profiles.

[0300] The optimization module 106 is able to identify relationships between the configuration options and identify dependencies of the ammonia production values on the selection or deselection of particular components or subsystems.

[0301] In embodiments, the optimization module 106 utilizes a proxy optimization model in the configuration space to identify a configuration that produces the maximum ammonia production while satisfying the technical, safety, efficiency and business requirements (e.g. to identify the most efficient, most reliable and safest system with the lowest capital expenditure, resulting in the lowest LCOA (Levelized Cost of Ammonia)).

[0302] In embodiments, the proxy model uses any suitable model or statistical process operable to estimate the relationship between the dependent variable of the maximized ammonia production and a plurality of independent variables of the subsystem and component selection for each configuration.

[0303] In embodiments, a regression model is used as the proxy model. Alternatively or additionally, the proxy model can be based on a machine learning framework. Any suitable machine learning algorithm can be used.

[0304] For example, the model can utilize techniques such as gradient boosting (utilizing e.g. XGboost), long short-term memory (LSTM), support vector machines (SVM), or a random decision forest can be used in such models.

[0305] Gradient boosting is a machine learning technique for regression and classification problems. A strong predictive model is formed that includes a collection of weak predictive models such as decision trees. A staged process can be used to generate the model by steepest descent minimization (among other methods).

[0306] An LSTM is an artificial recurrent neural network architecture with feedback connections as well as feedforward connections. A common LSTM unit consists of a cell, an input gate, an output gate, and a forget gate. The cell is operable to remember values over arbitrary time intervals given the flow of information into and out of the cell is regulated by the gates.

[0307] A support vector machine utilizes a set of training instances, each instance being contained in one of two classes, and generates a model that assigns new instances to a particular class. Thus, an SVM includes a non-probabilistic binary linear classifier.

[0308] A random decision forest includes an ensemble machine learning method that operates by constructing a large number of decision trees during a training process and outputting the class (classification) or a pattern of median / average predictions (regression) as the individual trees.

[0309] In embodiments, the output of the optimization module 106 can be a configuration of the industrial gas production plant superstructure 108 that satisfies all necessary efficiency, safety, regulatory, space, engineering, and business constraints while producing an optimal or maximum amount of ammonia at a minimum cost based on the available renewable power resources.

[0310] Such optimization is not possible using conventional methods. For example, the inventors have found that a plant superstructure can be designed using the methods of the present invention that has a much higher utilization of available power from renewable resources compared to plants designed using other methods.

[0311] Method of operation

[0312] In embodiments, a method and system for selecting a design configuration of an industrial gas plant comprising one or more industrial gas plants and powered by one or more renewable power sources is provided. The method is performed by at least one hardware processor.

[0313] Step 200 - defining a model of the superstructure subsystem

[0314] In step 200, a computational model defining a model of the industrial gas being modeled is provided. The computational model of the industrial gas production superstructure includes selectable elements such that a plurality of selectable configurations representing potential configurations of the industrial gas production superstructure can be defined therein.

[0315] The computational model includes a plurality of selectable modeled renewable power subsystems 110R and selectable modeled equipment subsystems 112. Optionally or additionally, supporting power subsystems 110S can also be defined in the model.

[0316] The subsystems 110R, 110S, 112 can be selected from a group or library of available subsystems. Some subsystems can be user specified and part of the basic design requirements for the industrial gas production complex superstructure (e.g., the type of industrial gas production complex superstructure required in terms of gas production or renewable power generation levels).

[0317] Other subsystems can be optional or selectable from a group of available subsystems during the configuration process. By “available” it is meant that the particular subsystem is compatible with the overall design requirements or can be used as part of the overall design requirements and is included in the model.

[0318] By “selectable” it is meant that the model can be provided with a plurality of different subsystems that can be selected to define a particular configuration of the modeled industrial gas plant complex superstructure 108 within the model. These selections can be made available or provided manually or can be system defined based on available data or predictive dates.

[0319] In step 200, the subsystem module 102 receives data specifying the type and configuration of the subsystems of the industrial gas plant complex superstructure 108 to be designed and configured. This data depends on the nature of the industrial gas plant complex superstructure, such as its intended use and configuration (e.g., an ammonia production plant or a hydrogen production plant).

[0320] In embodiments, the subsystem module 102 can be used to specify or determine the subsystems that form part of the industrial gas plant complex superstructure 108 to be analyzed and optimized. The subsystem module 102 can be used in this step to specify an initial model of the industrial gas plant complex superstructure 108 to be configured and designed, and is able to specify one or more renewable power subsystems 110R and one or more equipment subsystems 112 in accordance with subsequent steps.

[0321] In embodiments, the industrial gas plant complex superstructure 108 comprises an ammonia production plant. The subsystems required for an ammonia production plant complex can include a hydrogen production plant 20, a hydrogen storage unit 30, an air separation unit (ASU) 40, an ASU storage unit 42, an ammonia synthesis plant 50, an ammonia storage unit 60, a main bus 70, wind and solar renewable power sources 72, 74, and an energy storage resource 76. Optionally, a hydrogen liquefier 32 can also be provided.

[0322] The subsystems and their interconnections (e.g., power connections, upstream / downstream process connections, etc.) are specified in the subsystem module 102, as discussed below.

[0323] The model provides a configuration space in which different configurations of the modeled industrial gas plant superstructure 108 can be defined. The selectable components are derived from the definitions in steps 210 and 220.

[0324] Step 210 - Specifying Superstructure Renewable Power Subsystems

[0325] In this step, a plurality of selectable modeled renewable power subsystems are specified in the model. Each modeled renewable power subsystem has predicted time series power profile data for a predetermined time period associated therewith.

[0326] In this step, one or more renewable power subsystems 110R can be selected by the user or automatically. Renewable power subsystems 110R are available to be automatically or manually selected during configuration of the model of the subsystem module 102. For renewable power subsystems 110R, the specific components can not be important to the present invention, and in embodiments, the renewable power subsystems 110R can be defined only by parameters and any associated constraints.

[0327] This means that the detailed specifications of the renewable power subsystem 110R components of the one or more renewable power subsystems 110R (e.g., the type, number, and configuration of wind turbines or solar panels) are not important to the present invention. However, in embodiments, the parameters of each power subsystem 110 can be specified based on specific design and / or configuration requirements.

[0328] Each renewable power subsystem 110R is grouped by type (e.g., wind, solar, tidal, etc.). As described in step 220, within each group, a range of renewable power subsystems 110R can be selected having different operating parameters associated therewith.

[0329] Each modeled renewable power subsystem has predicted time series power profile data for a predetermined time period associated therewith. In embodiments, the predicted power profile includes an estimated power produced by a given configuration of renewable power subsystems 110R for a given number of intervals (e.g., 1 hour) over a predetermined time period (e.g., 1 year). This shows the predicted daily power availability of a given renewable power subsystem 110R.

[0330] The power profile module 110A is operable to receive time dependent power profile data for one or more renewable power sources. In non-limiting embodiments, time dependent operational and meteorological data for a location or site of one or more renewable power sources is received for a predetermined time period.

[0331] In embodiments, the time period is at least one year. In embodiments, this can be multiple years. The time series data represents the available power from the renewable energy source as a function of time.

[0332] In embodiments, if the renewable power subsystem 110 under consideration is comparable to an existing renewable power source such as a wind farm or a solar farm (or selected portions thereof), then the variables for wind power Wpi and solar power Spt in the time series data for the predetermined time period can be available to be used as predictive data for future assessment and design. In embodiments, the index i represents time from period n to n+k, and the data can be available for intervals of fixed duration, where the power generated is represented as a function of time in MW.

[0333] However, if no existing time series power data is available (for example, if the renewable power site has not yet been built), then the time series power data can be estimated. In embodiments, the time series power data can be estimated from weather sources and technical information.

[0334] Technical data can also be used in this step. For a wind farm, this can include known wind farm layouts and designs, selection and number of turbines. For a solar farm, technical details such as type, area, efficiency and number of panels and their location and orientation can be modeled with suitable software. This data can then be used to generate predicted power profiles for a predetermined time period. For example, this time period can be based on historical data (for example, past wind energy data over a period of one or more years) or can be based on predicted future data derived from a machine learning process.

[0335] The predicted average power data can be used to generate P50 and P90 power profiles for the predetermined time period. P50 represents the median of the annual estimates of power production from the renewable resource, such that over the lifetime of the project, there is a 50% probability that the power production at any given time is below the P50 value, and a 50% probability that it exceeds the P50 value.

[0336] The P90 value is more conservative and represents the average power value that will be reached or exceeded 90% of the time.

[0337] However, while P50 and P90 are widely used in the respective industries, any suitable metric can be used. For example, P25, P75 or any other suitable metric.

[0338] It is further noted that the data utilized by the power subsystem 110 can be obtained by any suitable means, and the above discussion does not limit the power subsystem 110 to any requirement of data generation. In fact, the data can be provided by an external source.

[0339] The average wind power generation WPi and average solar power generation SPi can be provided or generated over a period of time, in embodiments, the period of time is a year or more. The data can include a time series, where the index i represents time from period n to n+k in fixed duration intervals. In non-limiting embodiments, the intervals can include 15 minutes, 30 minutes, or 1 hour.

[0340] The predicted time series data (which can form part of step 210, in embodiments) can be modified for each selectable modeled renewable power subsystem according to the operational parameters and constraints described in step 220. In embodiments, the magnitude of the predicted time series data can be scaled according to the operational parameters and constraints, such as the size of the wind / solar power farm, as described below.

[0341] Step 220 - associating operational parameters and constraints with renewable power subsystems

[0342] Step 220 can occur simultaneously with and / or be integrated into step 210, or can occur as a separate stage. The configuration space of the renewable power subsystems is defined in step 210. Then, in step 220, a plurality of operational parameters and a plurality of operational constraints can be associated with each of the plurality of modeled renewable power subsystems.

[0343] The operational parameters can include maximum and minimum power profiles for a given renewable power subsystem 110R. How the power profiles are derived is explained in the section below regarding the power prediction module 110A.

[0344] Each available renewable power subsystem 110R is arranged to be selectable in subsequent steps as an entity with particular operational parameters. In embodiments, no internal components are selectable. However, parameters such as maximum power production of the renewable power subsystem 110R can be specified. This allows the selection of renewable power subsystems 110R with available power production that is scaled to meet the needs of the industrial gas plant complex superstructure.

[0345] In other words, a predicted time series power profile data for a subsystem 110R-1 having operational parameters and constraints defining a usable farm area that is half of another subsystem 110R-2 will have an equivalent predicted time series power profile data having half the magnitude of the data for the subsystem 110R-2.

[0346] This selectivity can be derived from real-world design decisions. For example, there can be a particular land area available to provide a renewable resource (wind power and / or solar power). If the entire land area is used for wind power, then that energy resource can produce a particular power profile (maximum or expected power delivered over a predefined time period, such as a year). This defines an upper bound or constraint on the maximum wind power that can be produced with the available resource. The same is true if the entire resource is used for solar power.

[0347] However, if only a portion of the available resource area is used for wind power, for example the minimum commercially or technically viable wind farm resource scale, then this will define a lower bound on the power profile for the wind power resource. However, for each scale, the power profile will be essentially the same, albeit scaled in magnitude so that the magnitude is proportional to the selected scale of the wind farm.

[0348] The same applies to solar power. Thus, in this example, it can be seen that a range of parameters for each renewable power subsystem 110R can be defined and used as part of a global superstructure optimization problem to select a suitable power profile for a desired superstructure configuration.

[0349] The defined range (discrete or continuous) of configurations of available renewable power subsystems 110R that can provide a particular maximum power production for a given power profile enables a mix of renewable energy to be investigated and an optimal configuration to be selected.

[0350] For example, the optimization process can utilize data related to the customized selection of wind and solar resources. Solar power can provide more consistent power during the day, but wind power can provide greater flexibility and power production during the night. Thus, a particular mix of these power profiles can be used as part of the configuration selection to identify a maximum power profile for a particular configuration of device subsystems 112.

[0351] Maximum and minimum power production can be constraints and parameters that can be selected. However, other constraints can be appropriately allocated.

[0352] For example, constraints 116 can apply safety considerations in terms of maximum capacity and limits on power production or rate of change of power generation to maintain component integrity and safety.

[0353] In addition, constraints 116 can also be applied to the renewable power subsystem 110R over a longer time frame; for example, to account for degradation of performance and efficiency over time, or to specify time intervals for repair and replacement of the renewable power subsystem.

[0354] Constraints 116 can also be applied to interdependencies of parameters between the renewable power subsystem 110R and the equipment subsystems 112 of the industrial gas plant complex superstructure 108 to be configured and designed. For example, additional constraints can be applied to ensure that ramp rates of the renewable power subsystem 110R do not exceed the ramp rates of the technically limited powered equipment subsystems 112.

[0355] Step 230 - Specify superstructure support power subsystem, associate operational parameters and constraints

[0356] This step is optional, and can specify a support power subsystem 110S if needed.

[0357] In some cases, one or more components 114 of the support power subsystem 110S can be specified. The components 114 correspond to functional elements of the subsystem, and are each selectable from a library of components. The components 114 can be modular, and part of the design and configuration process can involve determining the number and size of any type of component 114.

[0358] The subsystem module 102 is further operable to define constraints 116 on the construction and operation of the components 114 within each power subsystem 110 and between each component 114.

[0359] The constraints can include technical constraints in normal operation, such as power consumption, maximum and minimum capacity, efficiency, and variation of efficiency with load.

[0360] The constraints 116 can also account for dynamic processes - for example, ramp rates of start-up and shut-down of energy storage resources. These constraints 116 can also be associated with broader constraints and problems - for example, safety considerations in maximum capacity and limits on ramp rates to maintain component integrity and safety.

[0361] In addition, constraints can also be applied to longer time frames; for example, to account for degradation of performance and efficiency over time, or to specify time intervals for repair and replacement of the battery modules.

[0362] Constraints 116 can also be applied with respect to the interdependencies of parameters between power subsystems 110 and plant subsystems 112 of the industrial gas plant complex superstructure 108 to be configured and designed. For example, additional constraints can be applied to ensure that the ramp rate of a power subsystem 110 does not exceed the technical limit ramp rate of a powered plant subsystem 112.

[0363] However, for other power subsystems 110 such as renewable power subsystems 110R, the components can not be important to the present invention and these power subsystems 110R can be defined only by parameters and any associated constraints.

[0364] This means that the detailed selection of renewable power subsystem 110R components of one or more renewable power subsystems 110R (e.g., the type, number, and configuration of wind turbines or solar panels) is not important to the present invention. However, in this case, the parameters of each renewable power subsystem 110R can be specified based on specific configuration requirements. Thus, the configuration space of a renewable power subsystem 110R is related to the scaling of the subsystem from a maximum to a minimum value.

[0365] Step 240 - Specifying Superstructure Plant Subsystems

[0366] In this step, a plurality of selectable modeled plant subsystems 112 are specified in the model. Each modeled selectable modeled plant subsystem 112 has a plurality of selectable modeled components associated therewith.

[0367] In this step, one or more modeled plant subsystems 112 can be selected by a user or automatically. During configuration of the model of the subsystem module 102, modeled plant subsystems 112 are available to be selected automatically or manually.

[0368] Each modeled plant subsystem 112 is grouped by type, such as a gas production plant subsystem or a gas storage subsystem. Within each group, the subsystems can include one or more of a hydrogen production plant, an air separation unit, and an ammonia production plant, and wherein the gas storage subsystem includes one or more of a hydrogen gas storage, a hydrogen liquefier, a nitrogen storage, and an ammonia storage.

[0369] As described in step 220, within each group, a range of modeled plant subsystems 112 can be selected having different operating parameters associated therewith.

[0370] Step 250 - Associating Plant Operating Parameters, Components, and Constraints

[0371] Step 250 can occur simultaneously with and / or be integrated into step 240, or can be performed as a separate stage. In step 240, a configuration space for the device subsystems is defined. Then, in step 250, a plurality of operating parameters and a plurality of operating constraints can be associated with each of the plurality of modeled device subsystems.

[0372] In this step, the parameters and / or components 114 of each device subsystem 112 are specified. Then, constraints 116 are applied to the device subsystems 112 and components 114 within the industrial gas plant superstructure 108.

[0373] In more detail, the subsystem module 102 receives data specifying the type of industrial gas plant superstructure to be designed and configured, and the subsystems. This data depends on the nature of the industrial gas plant superstructure 108.

[0374] Each device subsystem 112 comprises one or more components 114. The components 114 correspond to functional elements of the device subsystem 112. The components 114 can be selected from a library of defined components. The components can be modular, and part of the design and configuration process can involve determining the number and size of any type of component.

[0375] In addition, the subsystem module 102 can be able to implement the selection of custom components. For example, an electrolyzer module with specific required properties can be specified as the optimal solution, which can then be manufactured to order.

[0376] The subsystem module 102 can specify and define constraints on the construction and operation of the components within each device subsystem 112 and between each component 114. The constraints can include technical constraints in normal operation, such as power consumption, maximum and minimum capacity, efficiency, and variation of efficiency with load.

[0377] The constraints can be related to dynamic processes - for example, ramp rates for start-up and shut-down of components. These constraints can also be associated with more general constraints and problems - for example, safety considerations in terms of maximum capacity and limits on ramp rates to maintain component integrity and safety.

[0378] In addition, the constraints can also be applied over a longer time frame; for example, to take into account degradation of performance and efficiency over time, or to specify time intervals for repair and replacement of electrolyzer modules and cells.

[0379] Constraints can also be applied to the interdependencies of parameters between the plant subsystems 112 of the industrial gas plant complex superstructure 108 to be configured and designed. For example, additional constraints can be applied to the ramp rate of an upstream process that exceeds the ramp rate of the technical limit of that process to ensure that a downstream process is not affected by a gas flow change that exceeds the design rate of change of the downstream process.

[0380] The subsystems defined in step 200, and possibly the library of components and associated technical parameters and constraints, define a configuration space from which a configuration can be selected in subsequent steps.

[0381] Step 260 - Selecting a configuration

[0382] In step 260, a plurality of configurations is selected from the configuration space defined in steps 200 to 250. This can be done by any suitable method. In step 260, the plurality of configurations is selected from the model by selecting for each configuration: one or more modelled renewable power subsystems, one or more modelled plant subsystems; and one or more components associated with the selected one or more modelled plant subsystems as defined in steps 200 to 250.

[0383] In embodiments, the selecting step selects a plurality of configurations for simulation. In embodiments, the selecting step comprises a sampling method. In non-limiting embodiments, a Latin hypercube sampling technique can be used. However, other methods can be used; for example, random sampling or orthogonal sampling.

[0384] In the present invention, the distribution space represents the possible configurations of the plant complex superstructure 108 from which a near-random sample is selected. Once a plurality of configurations is selected, each configuration can be run in a simulation in step 270 to determine the maximum or optimised value of the output parameter for that particular configuration. When these values are obtained, a variable in the configuration space can be obtained in which the value of the output parameter as a function of the configuration can be obtained.

[0385] In embodiments, a plurality of configurations is selected in step 260. In examples, the number of configurations selected can be greater than 1000.

[0386] Step 270 - Simulating the configurations

[0387] In step 270, once a plurality of configurations is selected, these configurations can be run in a simulation. In this step, the simulation module 104 is operable to utilise the data received, determined and / or generated by the subsystem module 106 in steps 200 to 250 and the configurations selected in step 260 to build a model of the industrial gas plant complex superstructure 108 in the selected configurations.

[0388] The model includes, in addition to all the relevant constraints 116 defined with respect to these subsystems 110R, 112 and components 114 in the subsystem modules 102, the renewable power subsystems 110R, the equipment subsystems 112 and components 114 of these subsystems as defined in the subsystem modules 102. If included, the above can also include the support power subsystems 110S.

[0389] For each simulated configuration, one of the configurations of the renewable power subsystems 110R is selected. A power profile is associated with this configuration of the renewable power subsystems 110R and this can then be used in the simulation of the selected configuration to simulate the particular equipment configuration in operation according to the power data as predicted from the selected renewable power subsystems 110R configuration. The simulation is then run to obtain the predetermined metric.

[0390] Step 280 - Generating an output parameter

[0391] In embodiments, steps 270 and 280 can be integrated. In step 280, the simulation in step 270 can be operable to determine a maximum value or an optimized value of the output parameter for each configuration. When these values are obtained, a variable in the configuration space can be obtained in which the values of the output parameter as a function of the configuration can be obtained.

[0392] In other words, steps 270 and 280 are capable of determining, for each selected configuration, a predicted operation of the selected configuration in step 260 of the industrial gas production superstructure over a predetermined time period to determine a maximum value of a predetermined operational output parameter for the selected configuration and for the predetermined time period. The predicted operation utilizes the power profile data associated with the one or more selected renewable power subsystems and the operational parameters and operational constraints associated with the selected configuration.

[0393] In other words, the output parameter is generated to act as an index metric. For example, in the context of an ammonia production plant being designed and configured, the output parameter can be the amount of ammonia produced over a predetermined time period. The time period can be, for example, one year.

[0394] In this step, the simulation can determine an optimized value or a maximized value of the output parameter over the predetermined time period. This can be done by varying the process variables of the simulated equipment within the bounds of the defined constraints and in response to the predicted available power data to achieve a maximum value or an optimized value of the output parameter.

[0395] In embodiments, the output parameter can be the amount of ammonia produced over a predetermined time period (e.g., 1 year). Thus, the output parameter from this model is an estimate of the maximum amount of ammonia that can be produced for any one particular configuration based on the available renewable power resources.

[0396] The optimization can utilize the set points of the simulation of the control processes in the plant complex 110 at a certain time period to balance the predicted available power with the consumed power so that the right amount of hydrogen is produced and the ammonia plant is running at the right rate to maximize the ammonia production.

[0397] In other words, the simulation module 104 solves an optimization algorithm applied to the dynamic mathematical model of the configuration of the industrial gas plant complex superstructure 108 under consideration. The predicted available renewable power WPi and SPi and the constraints of the simulated configuration of the various components and subsystems of the industrial gas plant complex superstructure 108 are considered as inputs and applied to the optimization algorithm to come up with the optimal rate at which to run the ammonia plant for a certain predetermined time period.

[0398] Step 290 - building a proxy model

[0399] Once a plurality of configurations have been selected in step 260 and simulated by the simulation module 104 in steps 270 and 280, the optimization module 106 accesses a configuration data space in which a large number of configurations have been simulated and for each simulated configuration an output parameter (e.g. maximum ammonia production over a predetermined time frame (e.g. 1 year)) has been determined.

[0400] In embodiments, the proxy model can comprise any suitable model or statistical process operable to estimate the relationship between the dependent variable of the maximum ammonia production and a plurality of independent variables selected for each configuration of subsystems and components.

[0401] In embodiments, a regression model is used as the proxy model. Alternatively or additionally, the proxy model can be based on a machine learning framework. Any suitable machine learning algorithm can be used.

[0402] For example, the model can utilize techniques such as gradient boosting (utilizing e.g. XGboost), long short-term memory (LSTM), support vector machines (SVM), or a random decision forest can be used in such models.

[0403] Step 300 - optimization

[0404] In step 300, the proxy model formed part of the optimization model 108 and built in step 290 is used to identify the optimal configuration within the configuration space that meets one or more predetermined parameters. In embodiments, the predetermined parameters include the optimized amount of ammonia for a given available power profile while meeting technical, safety, efficiency and business requirements.

[0405] The optimization module 106 is able to identify the relationship between the configuration options and identify the dependency of the ammonia production value on the selection or deselection of a particular component or subsystem.

[0406] In embodiments, the optimization module 106 utilizes a proxy model in the configuration space to identify a configuration that produces the maximum ammonia production and satisfies the technical, safety, efficiency, and business requirements (e.g., to identify the most efficient, most reliable, and safest system with the lowest capital expenditure, resulting in the lowest LCOA (levelized cost of ammonia).

[0407] In embodiments, the output of the optimization module 106 can be a configuration of the industrial gas production plant complex superstructure 108 that satisfies all necessary efficiency, safety, regulatory, space, engineering, and business constraints while producing the best or maximum amount of ammonia at the lowest cost based on the available renewable power resources.

[0408] In other words, the step 300 outputs one or more optimized designs of the industrial gas production plant complex superstructure 108 for specific implementation and construction. Each design can include one or more selected renewable power subsystems 110R with specific parameters, as well as one or more selected plant subsystems 112 and components 114 thereof.

[0409] The design can then be used to inform the design of a real-world plant that has improved efficiency and is well-matched to one or more renewable power sources. The present invention is the first to be able to configure and optimize both the renewable power source and the industrial gas plant system, resulting in significant technical benefits.

[0410] Step 310 - Construction of Plant

[0411] At step 310, the generated design in step 300 can be constructed as needed.

[0412] While the present invention has been described with reference to the preferred embodiments as depicted in the drawings, it is to be understood that various modifications are possible within the spirit or scope of the present invention as defined in the appended claims.

[0413] In the specification and claims, the term “industrial gas plant” is intended to mean a processing plant that produces or is involved in the production of industrial gases, commercial gases, medical gases, inorganic gases, organic gases, fuel gases, and green fuel gases in gaseous form, liquefied form, or compressed form.

[0414] For example, the term "industrial gas equipment" can include process equipment for the manufacture of gases such as those described in NACE 20.11 category and including but not limited to: elemental gases; liquid or compressed air; refrigeration gases; mixed industrial gases; inert gases such as carbon dioxide; and barrier gases. In addition, the term "industrial gas equipment" can also include process equipment for the manufacture of industrial gases in NACE 20.15 category such as ammonia, for the extraction and / or manufacture of methane, ethane, butane or propane (NACE 06.20 and 19.20 categories) and for the manufacture of gaseous fuels as defined by NACE 35.21 category. The above has been described in relation to the European NACE system but is intended to cover equivalent categories under the North American classification SIC and NAICS. In addition, the above list is non-limiting and non-exhaustive.

[0415] In some examples, a hydrogen storage system is shown and in some cases a purification unit. However, it will be appreciated that the present application can be implemented without the use of a hydrogen storage system or purification unit, which are shown herein for completeness only.

[0416] In this specification, unless expressly stated otherwise, the word "or" is used in the sense of the operator that returns true if one or both of the stated conditions are met, as opposed to the operator "exclusive or" which requires only one of the conditions to be met. The word "comprise" is used in the sense of "include" rather than "consist of".

[0417] In the discussion of embodiments of the present application, pressures given are absolute pressures, unless otherwise stated.

[0418] All the foregoing prior teachings are hereby incorporated by reference. Acknowledgement in this text of any prior publication is not to be taken as an admission that the teaching of that publication was common general knowledge at the priority date in Australia or elsewhere.

[0419] Where applicable, the various embodiments provided by the present disclosure can be implemented using hardware, software, or a combination of hardware and software. Moreover, where applicable, the various hardware components and / or software components set forth herein can be combined into a composite component comprising software, hardware, and / or both, without departing from the spirit of the present disclosure. Where applicable, the various hardware components and / or software components set forth herein can be separated into sub-components, without departing from the scope of the present disclosure. In addition, where applicable, the various hardware components and / or software components set forth herein can be implemented as a "virtual component" or as a "cloud component", which is implemented using a combination of hardware and / or software components running on one or more computer servers, without departing from the scope of the present disclosure. Further, where applicable, it is contemplated that software components can be implemented as hardware components, and vice-versa.

[0420] According to the present disclosure, software, such as program code and / or data, can be stored on one or more computer-readable media. It is also contemplated that software identified herein can be implemented using one or more general purpose or special purpose computers and / or computer systems, networked and / or otherwise. Where applicable, the order of the blocks of the various steps described herein can be changed, combined into composite steps, and / or separated into sub-steps, to provide features described herein.

[0421] While various operations have been described herein according to "modules," "units," or "components," it should be noted that these terms are not limited to a single unit or function. Moreover, the functions attributed to some modules or components described herein can be combined and attributed to fewer modules or components. Furthermore, while the present application has been described with reference to specific examples, the description is illustrative and not restrictive. It will be apparent to those of ordinary skill in the art that changes, additions, deletions, etc. can be made to the disclosed embodiments without departing from the spirit and scope of the application. For example, one or more portions of the methods described above can be performed in a different order (or simultaneously), and still achieve desirable results.

Claims

1. A method of configuring an industrial gas production superstructure, the industrial gas production superstructure comprising one or more equipment subsystems and being powered at least in part by one or more renewable power subsystems, the method being performed by at least one hardware processor and comprising: providing a model of the industrial gas production superstructure, the model having a plurality of selectable configurations representing potential configurations of the industrial gas production superstructure; specifying a plurality of selectable modeled renewable power subsystems in the model, each modeled renewable power subsystem having predicted time series power profile data for a predetermined time period associated therewith; specifying a plurality of selectable modeled equipment subsystems in the model, each selectable modeled equipment subsystem having a plurality of selectable modeled components associated therewith; associating a plurality of operational parameters and a plurality of operational constraints with each of the plurality of modeled renewable power subsystems, each of the plurality of modeled equipment subsystems, and each of the plurality of selectable modeled components; selecting a plurality of configurations of the model by selecting, for each configuration: one or more modeled renewable power subsystems, one or more modeled equipment subsystems, and one or more components associated with the selected one or more modeled equipment subsystems; for each selected configuration, determining a predicted operation of the selected configuration of the industrial gas production superstructure over a predetermined time period to determine a maximum value of a predetermined operational output parameter for the selected configuration and for the predetermined time period, the predicted operation utilizing the power profile data associated with one or more selected renewable power subsystems and the operational parameters and the operational constraints associated with the selected configuration; utilizing a proxy model to identify one or more configurations of the industrial gas production superstructure operable to maximize a value of the operational output parameter while satisfying predefined operational constraints based on the operational output parameter data and the selected configuration data for each configuration; and generating one or more designs for the industrial gas production superstructure based on the identified one or more configurations.

2. The method of claim 1, wherein the plurality of selectable modeled renewable power subsystems are arranged in groups of wind farm subsystems, solar farm subsystems, tidal energy subsystems, and hydroelectric energy subsystems.

3. The method of claim 2, wherein within each of the groups, a plurality of selectable modeled renewable power subsystems are available for selection, each selectable modeled renewable power subsystem sharing a same profile of the predicted time series power profile data but differing in magnitude of available maximum power.

4. The method of claim 2, wherein a plurality of selectable modeled renewable power subsystems can be selected from at least two different groups. ​ 5. The method of claim 1, wherein the plurality of selectable modeled equipment subsystems are arranged in groups of a gas production equipment subsystem and a gas storage subsystem.

6. The method of claim 5, wherein the gas production equipment subsystems comprise one or more of a hydrogen production equipment, an air separation unit, and an ammonia production equipment; and wherein the gas storage subsystems comprise one or more of a hydrogen gas storage, a hydrogen liquefier, a nitrogen storage, and an ammonia storage.

7. The method of claim 6, wherein at least one selected gas production equipment subsystem comprises a hydrogen production equipment, and wherein the selectable modeled components of the hydrogen production equipment are selectable from one or more of: an electrolyzer type; an electrolyzer capacity; a compressor system; a purifier system.

8. The method of claim 1, wherein the predetermined operational output parameter comprises an amount of gas produced by the industrial gas production complex superstructure.

9. The method of claim 1, further comprising: designing an industrial gas production complex superstructure.

10. A system for configuring an industrial gas production complex superstructure, the industrial gas production complex superstructure comprising one or more equipment subsystems and being powered at least in part by one or more renewable power subsystems, the system comprising: at least one hardware processor; a subsystem module, the subsystem module operable to: provide a model of the industrial gas production complex superstructure, the model having a plurality of selectable configurations representative of potential configurations of the industrial gas production complex superstructure; specify a plurality of selectable modeled renewable power subsystems, each modeled renewable power subsystem having predicted time series power profile data for a predetermined time period associated therewith; and specify a plurality of selectable modeled equipment subsystems, each selectable modeled equipment subsystem having a plurality of selectable modeled components associated therewith; a simulation module, the simulation module operable to: associate a plurality of operational parameters and a plurality of operational constraints with each of the plurality of modeled renewable power subsystems, each of the plurality of modeled equipment subsystems, and each of the plurality of selectable modeled components; select a plurality of configurations by selecting, for each configuration: one or more modeled renewable power subsystems, one or more modeled equipment subsystems, and one or more components associated with the selected one or more modeled equipment subsystems; and for each selected configuration, determine a predicted operation of the selected configuration of the industrial gas production complex superstructure over a predetermined time period to determine a maximum value of a predetermined operational output parameter for the selected configuration and for the predetermined time period, the predicted operation utilizing the power profile data associated with one or more selected renewable power subsystems and the operational parameters and the operational constraints associated with the selected configuration; and an optimization module, the optimization module operable to: determine a selected configuration of the industrial gas production complex superstructure that maximizes the predetermined operational output parameter. identifying, using a proxy model, one or more configurations of the industrial gas production superstructure that are operable to maximize a value of the operational output parameter while satisfying predefined operational constraints based on the operational output parameter data and the selected configuration data for each configuration; and generating one or more designs for the industrial gas production superstructure based on the identified one or more configurations.

11. The system of claim 10, wherein the plurality of selectable modeled renewable power subsystems are arranged in a group of wind farm subsystems, solar farm subsystems, tidal energy subsystems, and hydroelectric subsystems.

12. The system of claim 11, wherein within each of the groups, a plurality of selectable modeled renewable power subsystems are available for selection, each selectable modeled renewable power subsystem sharing a same profile of the predicted time series power profile data, but differing in magnitude of available maximum power.

13. The system of claim 11, wherein a plurality of selectable modeled renewable power subsystems can be selected from at least two different groups.

14. The system of claim 10, wherein the plurality of selectable modeled equipment subsystems are arranged in a group of gas production equipment subsystems and gas storage subsystems.

15. The system of claim 11, wherein the gas production equipment subsystems include one or more of a hydrogen production equipment, an air separation unit, and an ammonia production equipment; and wherein the gas storage subsystems include one or more of a hydrogen storage, a hydrogen liquefier, a nitrogen storage, and an ammonia storage.

16. The system of claim 15, wherein at least one selected gas production equipment subsystem includes a hydrogen production equipment, and wherein the selectable modeled components of the hydrogen production equipment can be selected from one or more of: an electrolyzer type; an electrolyzer capacity; a compressor system; a purifier system.

17. The system of claim 10, wherein the predetermined operational output parameter includes an amount of gas produced by the industrial gas production superstructure.

18. A computer-readable storage medium storing a program of instructions executable by a machine to perform a method of controlling an industrial gas production facility, the industrial gas production facility including one or more industrial gas equipment powered by a power network including one or more renewable power sources, the method performed by at least one hardware processor, the method comprising: providing a model of the industrial gas production superstructure, the model having a plurality of selectable configurations representing potential configurations of the industrial gas production superstructure; specifying, in the model, a plurality of selectable modeled renewable power subsystems, each modeled renewable power subsystem having predicted time series power profile data for a predetermined time period associated therewith; a plurality of selectable modeled device subsystems are specified in the model, each selectable modeled device subsystem having associated therewith a plurality of selectable modeled components; a plurality of operational parameters and a plurality of operational constraints are associated with each of the plurality of modeled renewable power subsystems, each of the plurality of modeled device subsystems, and each of the plurality of selectable modeled components; a plurality of configurations of the model are selected by selecting for each configuration: one or more modeled renewable power subsystems, one or more modeled device subsystems, and one or more components associated with the selected one or more modeled device subsystems; for each selected configuration, a predicted operation of the selected configuration of the industrial gas production superstructure over a predetermined time period is determined to determine a maximum value of a predetermined operational output parameter for the selected configuration and for the predetermined time period, the predicted operation utilizing the power profile data associated with the one or more selected renewable power subsystems and the operational parameters and the operational constraints associated with the selected configuration; the proxy model is utilized to identify one or more configurations of the industrial gas production superstructure operable to maximize the value of the operational output parameter while satisfying the predefined operational constraints based on the operational output parameter data and the selected configuration data for each configuration; and one or more designs are generated for the industrial gas production superstructure based on the identified one or more configurations.

19. The computer readable storage medium of claim 18, wherein the plurality of selectable modeled renewable power subsystems are arranged in groups of wind farm subsystems, solar farm subsystems, tidal energy subsystems, and hydroelectric subsystems.

20. The computer readable storage medium of claim 19, wherein within each of the groups, a plurality of selectable modeled renewable power subsystems are available for selection, each selectable modeled renewable power subsystem sharing a same profile of the predicted time series power profile data but differing in magnitude of maximum power available.

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