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

Abstract

A method and system for selecting a design configuration of an industrial gas plant complex that includes one or more industrial gas plants and is 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 on 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 for an industrial gas production complex superstructure comprising one or more industrial gas plants and one or more renewable energy sources for powering the industrial gas plants. Background Art

[0004] An industrial gas plant complex includes one or more industrial process plants that produce gases or participate in the production of gases. In non-limiting examples, these gases may 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 for utilizing renewable energy for powering industrial gas plants and industrial gas plant complexes. However, a significant disadvantage of using renewable energy sources such as wind, solar, and tidal energy is the natural variability and instantaneous nature of such energy sources.

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

[0007] An exemplary industrial gas is hydrogen. Hydrogen is typically produced by the electrolysis of water. Another exemplary industrial gas is ammonia. Ammonia is produced using hydrogen from the electrolysis of water 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 pressure to produce ammonia.

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

[0009] Therefore, solutions to these technical problems are needed to enable efficient generation of industrial gases from renewable power sources. Summary of the invention

[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 a broad overview of the present disclosure and is not intended to identify the key or important elements of the present disclosure or to describe the scope of the present disclosure. The following only summarizes some concepts of the present disclosure as a preface to the more detailed description provided thereafter.

[0011] Disclosed herein are methods and systems (also referred to herein as "computer-implemented methods and systems") for selecting a design configuration for an industrial gas plant complex that includes one or more industrial gas plants and is powered by one or more renewable power sources.

[0012] Several preferred aspects of the method and system according to the invention are summarized below.

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

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

[0015] Aspect 3: A method according to Aspect 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 the same profile of the predicted time series power profile data but differing in the magnitude of the available maximum power.

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

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

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

[0019] Aspect 4: The method according to 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 according to any one of aspects 1, 2, 3 or 4, wherein the plurality of selectable modeled facility subsystems are arranged in groups of gas production facility subsystems and gas storage subsystems.

[0021] Aspect 6: A method according to Aspect 5, wherein the gas production equipment subsystem includes one or more of hydrogen production equipment, an air separation unit, and an ammonia production equipment; and wherein the gas storage subsystem includes one or more of a hydrogen storage, a hydrogen liquefier, a nitrogen storage, and an ammonia storage.

[0022] Aspect 7: A method according to Aspect 6, wherein at least one selected gas production equipment subsystem includes a hydrogen production equipment, and wherein the optional modeling components of the hydrogen production equipment can be selected from one or more of the following: electrolyzer type; electrolyzer capacity; compressor system; purifier system.

[0023] Aspect 8: The method according to any one of aspects 1 to 7, wherein the operation output parameter includes the amount of gas produced.

[0024] Aspect 8A: The method according to any one of aspects 1 to 8, wherein the predefined operating constraints include 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, the maximum value of the output parameter is achieved while minimizing the amount of time within the predetermined time period.

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

[0027] Aspect 9: The method according to any one of aspects 1 to 8, further comprising: constructing an industrial gas production complex superstructure according to the design.

[0028] Aspect 10: A system for configuring an industrial gas production complex superstructure, the industrial gas production complex superstructure including one or more equipment subsystems and being at least partially powered by one or more renewable power subsystems, the system comprising: at least one hardware processor; a subsystem module, the subsystem module being operable to: provide a model of the industrial gas production complex superstructure, the model having a plurality of selectable configurations representing 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 modeling components associated therewith; a simulation module, the simulation module being operable to: associate a plurality of operating parameters and a plurality of operating 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 modeling components; model components; selecting 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, determining predicted operation of the selected configuration of an industrial gas production complex superstructure over a predetermined time period to determine a maximum value of a predetermined operating 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 operating parameters and operating constraints associated with the selected configuration; and an optimization module operable to: utilize an agent model to identify one or more configurations of the industrial gas production complex superstructure based on the operating output parameter data and the selected configuration data for each configuration, the one or more configurations operable to maximize the value of the operating output parameter while satisfying predefined operating constraints; and generating one or more designs for the industrial gas production complex superstructure based on the identified one or more configurations.

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

[0030] Aspect 12: A system according to Aspect 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 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 according to 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 according to any one of aspects 10 to 13, wherein the plurality of selectable modeled facility subsystems are arranged in groups of gas production facility subsystems and gas storage subsystems.

[0033] Aspect 15: A system according to any one of Aspects 11 to 14, wherein the gas production equipment subsystem includes one or more of hydrogen production equipment, an air separation unit, and an ammonia production equipment, and wherein the gas storage subsystem includes one or more of a hydrogen storage, a hydrogen liquefier, a nitrogen storage, and an ammonia storage.

[0034] Aspect 16: A system according to Aspect 15, wherein at least one selected gas production equipment subsystem includes a hydrogen production equipment, and wherein the optional modeling components of the hydrogen production equipment can be selected from one or more of the following: 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 within 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 for 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 being performed by at least one hardware processor, the method comprising: providing a model of the industrial gas production complex superstructure, the model having a plurality of selectable configurations representing potential configurations of the industrial gas production complex 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 modeling components associated therewith; associating a plurality of operating parameters and a plurality of operating constraints with each of the plurality of modeled renewable power subsystems, each of the plurality of modeled equipment subsystems, and the plurality of selectable each selectable modeling component in the modeling component is associated; multiple configurations of the model are selected 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 the predicted operation of the selected configuration of the industrial gas production complex superstructure within a predetermined time period to determine the maximum value of a predetermined operating 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 operating parameters and operating constraints associated with the selected configuration; utilizing an agent model to identify one or more configurations of the industrial gas production complex superstructure based on the operating output parameter data and the selected configuration data for each configuration, the one or more configurations being operable to maximize the value of the operating output parameter while satisfying predefined operating constraints; and generating one or more designs for the industrial gas production complex 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 the group of a wind farm subsystem, a solar farm subsystem, a tidal power generation subsystem, and a hydroelectric power generation subsystem.

[0038] Aspect 20: A computer-readable storage medium according to Aspect 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 the same profile of the predicted time series power profile data but differing in the magnitude of available maximum power. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Embodiments of the present invention will now be described, by way of example only, with reference to the accompanying drawings, in which:

[0040] Figure 1 It is a schematic diagram of the industrial gas equipment complex and control system;

[0041] Figure 2 is a schematic diagram of a configuration system according to an implementation scheme;

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

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

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

[0045] Embodiments of the present disclosure and their advantages can be best understood by referring to the following detailed description. It should be understood that the same reference numerals are used to identify the same elements shown in one or more figures, wherein the illustrations herein are for the purpose of illustrating embodiments of the present disclosure, rather than for the purpose of limiting embodiments of 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 a thorough understanding and ability to describe these examples. However, it will be appreciated by those skilled in the relevant art that one or more embodiments described herein may be practiced without many of these details. Similarly, it will be appreciated by those skilled in the relevant art that one or more embodiments of the present disclosure may include other features and / or functions not described in detail herein. In addition, some well-known structures or functions may not be shown or described in detail below to avoid unnecessarily obscuring the relevant description.

[0047] The present invention relates to a method and system for selecting a design configuration of an industrial gas plant complex superstructure, which industrial gas plant complex superstructure includes 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 a non-limiting embodiment, the industrial gas plant complex may include a hydrogen production plant and / or an ammonia production plant powered by renewable energy. However, the present invention has applicability to other types of industrial gas plant complexes. For example, the present invention has applicability to air separation plants for producing nitrogen from the atmosphere.

[0049] General configuration of the industrial gas equipment complex

[0050] Now refer to Figure 1 Components of an exemplary industrial gas plant complex superstructure are described. The methods and systems of the present invention are operable to design and / or configure respective elements of an industrial gas plant complex to achieve an optimized and / or maximized configuration or design to achieve a desired capacity given available power resources in a particular area and 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 may be designed and / or configured in accordance with embodiments of the present invention is shown.

[0052] In this embodiment, the industrial gas plant complex includes an ammonia plant complex 10. However, this should be viewed as exemplary and not limiting. Other types of industrial gas plant complex superstructures may be designed and / or configured with the disclosed embodiments of the present invention; for example, a hydrogen production plant, a nitrogen production plant, or other industrial gas production facilities.

[0053] The industrial gas plant complex 10 includes 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 S1 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 includes 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 equipment 20

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

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

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

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

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

[0061] The PEM electrolyzer utilizes a solid plastic material as an 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 an electrolyte that selectively conducts negatively charged oxygen ions (O 2- ). Water at the cathode combines with electrons from the external circuit to form hydrogen gas and negatively charged oxygen ions. The oxygen ions pass through the solid ceramic membrane and react at the anode to form oxygen gas and generate electrons for the external circuit.

[0063] The electrolytic cells may be arranged in any suitable grouping. For example, they may be arranged in parallel.

[0064] Hydrogen is produced by hydrogen production equipment 20 at approximately atmospheric pressure. The hydrogen stream so generated 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 downstream compressor systems.

[0065] In an embodiment, hydrogen production facility 20 further comprises a hydrogen compression stage and a purification stage.

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

[0067] The second section comprises a medium pressure (MP) section 26, wherein the hydrogen is compressed from a second intermediate pressure to a third final pressure greater than the second pressure. The third pressure is selected as required by any downstream processes.

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

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

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

[0071] The compressors used may also be appropriately selected depending on the operating capacity and type of the gas production facility. For example, for hydrogen applications, the LP section 24 may include one or more centrifugal compressors, while the MR section 26 may include one or more reciprocating compressors. However, this is not intended to be limiting, and any suitable compression arrangement may be appropriately used.

[0072] exist Figure 1 In an embodiment of the invention, a purification section 28 is provided. Purification section 28 may be required where, for example, any downstream process requires higher purity hydrogen (i.e., having lower levels of water and / or oxygen inherently present in the compressed hydrogen produced by electrolysis). However, this need not be the case, and this section may be omitted if it is not required.

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

[0074] Purification section 28 may further include a dryer. In this embodiment, the dryer includes a temperature swing adsorption (TSA) unit to produce dry compressed hydrogen for downstream processes. However, other suitable dryers and / or adsorption technologies may be used herein. In an embodiment, the dryer is located downstream of the deoxygenation unit.

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

[0076] Hydrogen storage unit 30

[0077] The hydrogen may be stored in a hydrogen storage unit 30. The storage unit 30 may include a number of short-term and long-term storage options having different sizes, fill / discharge rates, and round-trip efficiencies.

[0078] A typical storage system may include pressure vessels and / or pipe segments connected to a common inlet / outlet header. The pressure vessel may be, for example, a sphere of about 25 m in diameter, or a "bullet," which is a horizontal vessel with a large L / D ratio (typically up to about 12:1) with a diameter of up to about 12 m. In some regions, underground caverns may be included as a storage system to smooth out seasonal variations associated with renewable power.

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

[0080] Given the variability of renewable power, hydrogen storage 30 is often required as a buffer. For example, if renewable power availability is low (e.g., during times of darkness or low wind), then it may not be possible to operate the electrolyzer of the hydrogen production facility 20 at full capacity, or potentially at all. To maintain a flow of hydrogen to downstream processes, stored hydrogen may be mobilized.

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

[0082] Therefore, while in an ideal situation sufficient gas storage would be provided to ensure that all expected periods of low renewable power could be covered by stored gas resources, physical, practical and capital expenditure constraints impose practical limits on the size and capacity of available gas resources. 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.

[0083] In the context of an embodiment of the present invention, the stored hydrogen may 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 produced hydrogen can be liquefied for onward distribution into the supply network S1 .

[0086] Typically, hydrogen liquefaction involves a degree of initial compression 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 a non-limiting embodiment, the nitrogen required for ammonia production is produced by cryogenic distillation of air in an air separation unit (ASU) 40. Typically, ASU 40 has different stages operating at different pressures. For example, the high pressure (HP) tower operates at about 10.5 bar gauge, and the low pressure (LP) tower operates at about 5 bar gauge. Gaseous nitrogen is produced by ASU 40 at a pressure exceeding 25 bar gauge. The pressure is then reduced to provide a nitrogen gas stream in one or more pipelines arranged to deliver nitrogen to an ammonia synthesis device 50. However, if desired, other nitrogen sources, such as liquid nitrogen storage 42, may be used.

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

[0090] A typical storage system for liquid nitrogen may include multiple pressure vessels and / or pipe segments connected to a common inlet / outlet header. The pressure vessel may include a low pressure flat bottom tank (FBT). Additionally or alternatively, the pressure vessel may be a sphere, for example, having a diameter of about 25 m, or a "bullet", which is a horizontal container with a large L / D ratio (typically up to about 12:1), wherein the diameter is up to about 12 m.

[0091] As described above with respect to hydrogen storage resources 30, nitrogen storage 42 needs to be configured and specified according to actual requirements. Gas storage can take up considerable space within an industrial gas production complex 10 and require significant capital expenditures.

[0092] Therefore, while in an ideal situation sufficient gas storage would be provided to ensure that all expected periods of low renewable power could be covered by stored gas resources, physical, practical and capital expenditure constraints impose practical limits on the size and capacity of available gas resources. 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 produced by the ASU 40 is compressed by a compressor and cooled and stored in liquid form in a nitrogen storage unit 42. However, gaseous nitrogen storage may also be provided. The storage unit 42 may serve as a reservoir for an ammonia synthesis plant 50, which may be fed via a connecting pipeline.

[0094] Ammonia synthesis equipment 50

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

[0096] Nitrogen is provided from one or more pipelines from ASU 40 (or storage 42), which in embodiments may be operated continuously to provide nitrogen. Hydrogen is provided directly from one or more pipelines from hydrogen production facility 20 (if operated based on the availability of renewable power in a given situation) or from 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 sent to storage 60 when required. The performance of the ammonia loop is controlled by the equilibrium conversion of the exothermic reaction. Parameters in this regard will be discussed below.

[0098] Power supply for industrial gas production complexes

[0099] The electrical power used to power the industrial gas complex superstructure 10 is provided by a main bus 70. The main bus 70 forms part of the industrial gas complex superstructure 10 and may be located on-site.

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

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

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

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

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

[0105] The BESS 76a utilizes electrochemical technology and may 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 advantages in fast charging rates and fast (almost instantaneous) ramp rates to supply power to cope with sudden drops in energy supply. However, such devices tend to have more limited power capacity than other systems. Therefore, they may be more suitable for use in situations where, for example, power shortages from renewable energy sources are expected to be temporary or short-lived.

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

[0107] LAES 76b includes an air liquefier to draw 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. In order 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 than most BESS 76a systems. However, CAES and LAES have slower ramp rates than electrochemical storage devices and require longer times to store larger amounts of energy. For example, it may take about 5-10 minutes for the compression stage to operate at full load, and 10-20 minutes to generate full power on demand. Therefore, such storage devices are more suitable for long-term storage and are more suitable for supplying power during long periods of time when renewable energy is in short supply.

[0109] Although in Figure 1 All of these elements are shown in FIG. 7 , but this is for illustration purposes only. The energy storage resource 76 need not include each and every described element, and may include only one or more of the described elements. Furthermore, the energy resource 76 may include additional elements.

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

[0111] The selection of the type and capacity of the energy storage resource 76 is an additional parameter to be considered in the design and configuration of the industrial gas production complex. In the design process, the available space, capital expenditure and specific ramp rate of each type of energy storage need to be considered.

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

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

[0114] However, in an emergency or in the rare event that sufficient power from elements 72, 74, 76 is temporarily unavailable, a power connection is required as an emergency backup and power is required from an external source (such as a local grid infrastructure 80) to prevent shutdown of the subsystems of the industrial gas plant complex superstructure 10.

[0115] Superstructure design and configuration methods

[0116] In an embodiment, the present invention is directed to methods and systems for designing and configuring a superstructure such as an industrial gas plant complex. In an embodiment, the industrial gas plant complex comprises an ammonia production plant.

[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, renewable power availability, performance of the superstructure's components, utilization, safety requirements, efficiency and performance standards, capital expenditures, and desired production rates of industrial gases.

[0118] In an embodiment, the method and system may utilize an optimization method to define a "configuration space" for an industrial gas plant complex superstructure, and to seek within the defined configuration space an improved configuration that achieves a desired productivity within predetermined parameters. In the context of the present invention, the optimization method is intended to identify within a predetermined configuration space a configuration or range of configurations that meet specific criteria or parameters to achieve a specific technical goal.

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

[0120] For example, the method can generate one or more configurations that, for given economic, safety, regulatory, and infrastructure constraints and requirements, can produce the maximum amount of one or more industrial gases while operating within h

[0121] In an embodiment, the method utilizes multiple 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 maximized 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 specific type of industrial gas plant complex superstructure (e.g., a hydrogen production plant or an ammonia production plant), which then requires specific subsystems (e.g., a hydrogen production plant subsystem and a hydrogen storage subsystem) to operate in the expected manner.

[0123] Users can also specify specific design parameters for the Industrial Gas Complex superstructure that impose further requirements and constraints, such as maximum power demand from renewable energy sources, or maximum or minimum desired production output of industrial gases.

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

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

[0126] A configuration for each subsystem of the industrial gas plant complex superstructure is selected from the defined configuration space and a simulation is run for that configuration to determine the maximum output 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] Computer implemented methods and systems will now be described. Figure 2 A schematic diagram of a configuration system 100 according to an embodiment is shown. The configuration system 100 includes a plurality of modules.

[0128] The configuration system 100 includes a subsystem module 102, a simulation module 104, and an optimization module 106. The configuration system 100 is operable to select one or more maximized or optimized configurations of an industrial gas plant complex superstructure in a desired location.

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

[0130] Furthermore, the model execution computer may optionally be connected to other computer database systems where, for example, weather data services or other external data may be stored.

[0131] Subsystem module 102

[0132] The subsystem module 102 enables the definition and specification of the infrastructure subsystems that form the industrial plant complex superstructure. Figure 2 A schematic diagram of the elements of the subsystem module 102 is shown in FIG.

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

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

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

[0136] Subsystem module 102 receives data specifying the type and configuration of subsystems to be designed and configured for industrial gas complex superstructure 108. This data depends on the nature of the industrial gas 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 may be selected within certain bounds and constraints 116. The available ranges of specified parameters, components 114, and constraints 116 define a configuration space within which one or more configurations of the industrial gas plant complex superstructure 108 may be selected, as will be described below.

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

[0139] The detailed disclosure is described 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 and solar power resources.

[0140] Power Subsystem 110

[0141] Each power subsystem 110 has specific design parameters. In an embodiment, the power subsystems 110 can be grouped into power generation (eg, renewable power subsystems 110R), and / or supporting power infrastructure (eg, energy storage 76, main bus 70).

[0142] Renewable Power Subsystem 110R

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

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

[0145] In an embodiment, one or more renewable power subsystems 110R may 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 series of renewable power subsystems 110R with different operating parameters may be selected.

[0146] The operating parameters may include a maximum and minimum power profile for a given renewable power subsystem 110R. How to derive a power profile is explained in the section below about the power prediction module 110A. In an embodiment, the predicted power profile includes an estimated power generated by a given configuration of the renewable power subsystem 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 for a given renewable power subsystem 110R.

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

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

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

[0150] In either scenario, in an embodiment, the power profiles for 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 for each subsystem 110R within a given group is normalized relative to the maximum available power of each subsystem 110R, then the profiles will be virtually identical and overlapping.

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

[0152] However, if only a portion of the available resource area is used for wind energy, such as the smallest commercially or technically viable wind farm resource size, this will define a lower limit to the power profile of the wind power resource. However, for each size, the power profile will be essentially the same, albeit scaled so that the magnitude is proportional to the selected size of the wind farm.

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

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

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

[0156] Maximum and minimum power production may be constraints and parameters that may be selected. However, other constraints may be assigned as appropriate.

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

[0158] Additionally, the constraints 116 may also be applied to the renewable power subsystem 110R over longer time frames; for example, to account for degradation of performance and efficiency over time, or to specify time intervals for repairing and replacing the renewable power subsystem.

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

[0160] Power Profile Module 110A

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

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

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

[0164] In an embodiment, 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 a selected portion thereof), the variables of wind power generation WPi and solar power generation SPi in time series data for a predetermined time period may be available to be used as predictive data for future assessment and design. In an embodiment, the index i represents the time from period n to n+k, and the data may be available in intervals of fixed duration, where the generated power is expressed in MW as a function of time.

[0165] More generally, if the renewable power site has not yet been designed or built, the time series power data may be estimated using appropriate metrics and / or models. In an embodiment, the time series power data may be estimated from weather sources and technical information.

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

[0167] Additionally or alternatively, given the challenges of predicting local wind speeds and variations, local measurements can be obtained by, for example, installing measuring poles at different height levels in identified locations with one or more anemometers. 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 the entire wind farm by creating a wind prediction model for a specified geographic area using historical data.

[0168] Technical data may also be used. For a wind farm, this may include the known wind farm layout and design, the choice and number of turbines. For a solar farm, technical details such as the type, area, efficiency and number of panels as well as 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, the time period can be based on historical data (e.g., past wind energy data over a one or more year period) or can be based on predicted future data derived from a machine learning process.

[0170] The forecasted average power data can be used to generate P50 and P90 power profiles for a predetermined time period. P50 represents the median annual estimate of power generation from renewable resources, such that over the life of the project, power generation at any given time has a 50% probability of being below the P50 value and a 50% probability of exceeding the P50 value.

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

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

[0173] It should be further noted that the data utilized by the power subsystem 110 may 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 may be provided by an external source.

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

[0175] In an embodiment, additional environmental and meteorological signals may be used to refine the determination of the average power profile. These may include, but are not limited to, time-dependent environmental data including: air temperature Ti; atmospheric pressure Pi; wind speed WSi; cloud cover CCi; precipitation Pi; humidity Hi; where index i represents the time from period nm 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 profile and capacity. These constraints define a configuration space for the renewable power subsystem 110R, from which an appropriate configuration can be selected and executed during the simulation phase.

[0177] Support power subsystem 110S

[0178] The supporting power subsystems 110S include power infrastructure elements, such as the main bus 70 and energy storage 76. In an embodiment, certain supporting power subsystems 110S may be automatically designated in response to selections made regarding the above-described renewable power subsystems 110R; 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 an embodiment, certain elements, such as the energy storage 76, may be designated.

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

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

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

[0182] The constraints 116 may also take into account dynamic processes - for example, ramp rates for startup and shutdown of energy storage resources. These constraints 116 may also be associated with broader constraints and issues - for example, 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 to longer time frames; for example, to take into account degradation of performance and efficiency over time, or to specify time intervals for repairing and replacing battery modules.

[0184] Constraints 116 may also be applied to the interdependencies of parameters between the power subsystems 110 (both the renewable power subsystem 110R and the supporting power subsystem 110S) and the plant subsystems 112 of the industrial gas plant complex superstructure 108 to be configured and designed. For example, additional constraints may be applied to ensure that the ramp rate of the power subsystem 110 does not exceed the ramp rate of the technical limitations of the plant subsystem 112 being powered.

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

[0186] Supporting the power subsystem 110S is the energy storage resource selection and configuration. While this feature may be optional, in most renewable systems some form of smoothing or backup power source is required. The 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 battery, physical dimensions).

[0189] Energy storage operability constraints: (ramp rates, charge rates, rate 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 average time to repair or replace components).

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

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

[0192] Supports power subsystem 110S – main bus 70

[0193] The supporting power subsystem 110S that may be selected and configured is 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 equipment complex superstructure. The main power bus may be configured as desired in view of the other selected subsystems 110, 112.

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

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

[0196] Equipment Subsystem 112

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

[0198] For example, consider an ammonia production facility having a hydrogen production facility subsystem 112. In an embodiment, the subsystem 112 includes one or more electrolyzers. The electrolyzers may be available from different manufacturers, may have different configurations and capacities, and may be of different types. For example, the electrolyzers may be selected from one or more of the following: alkaline electrolyzers; PEM electrolyzers; and solid oxide electrolyzers.

[0199] The electrolyzer 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 20MW, and the subsystem module 102 can enable any number of 20MW electrolyzers to be selected as part of the hydrogen production equipment subsystem 112.

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

[0201] In addition, the subsystem module 102 may enable the selection of bespoke components. For example, an electrolyser module with certain desired properties may be specified as the best solution, which may then be manufactured to order.

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

[0203] However, considering again the plant subsystem 112 of the hydrogen production plant as an example, the constraints may include technical constraints in normal operation, such as power consumption, maximum and minimum capacities, efficiency (e.g., production of NM 3 how much input energy is required to produce hydrogen) and how the efficiency varies with load.

[0204] Constraints can also take into account dynamic processes – for example, ramp rates for start-up and shutdown of electrolyser modules. These constraints can also be linked to wider constraints and issues – for example, safety considerations in terms of maximum capacity and limits on ramp rates to maintain component integrity and safety.

[0205] Furthermore, constraints may also be applied to 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 cells.

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

[0207] Subsystem Module - Example of Ammonia Production Plant

[0208] The following non-limiting examples relate to the design and configuration of an ammonia production facility complex superstructure.

[0209] The subsystems required for the ammonia production plant complex include, for example, Figure 1 As set forth in , the hydrogen production facility 20, the hydrogen storage unit 30, the hydrogen liquefier 32, the air separation unit (ASU) 40, the ASU storage unit 42, the ammonia synthesis facility 50, the ammonia storage unit 60 and the energy storage resource 76.

[0210] Subsystems and their interconnections (eg, power connections, upstream / downstream process connections, etc.) are specified in subsystem modules 102 .

[0211] Equipment Subsystem 112-Hydrogen Production Equipment Subsystem Example

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

[0213] For the electrolyzer, the subsystem module 102 enables the selection of the following items:

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

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

[0216] The design constraints and parameters of the electrolyser include:

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

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

[0219] Electrolyser operability constraints (rate of change of performance and efficiency over time (i.e., aging and degradation), mean or averaged time interval between cell replacement or repair, mean or averaged time for repair or replacement of electrolyser modules and cells).

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

[0221] Electrolyser interdependency considerations (power draw as a proportion of available power, ramp rates to ensure appropriate flow rates to downstream processes).

[0222] Other components that may be selected include a purification system, which may be selected from:

[0223] Thermal Swing Adsorption (TSA) components; deoxygenation systems (operating capacity, power draw, flow pressure)

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

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

[0226] Equipment Subsystem 112 - Hydrogen Storage Subsystem Example

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

[0228] Stores the type (sphere, bullet, cave, size of each, quantity of each).

[0229] Storage infrastructure (physical size and space availability, capital expenditures, pipelines).

[0230] Storage parameters and constraints (maximum and minimum storage pressures, maximum and minimum storage capacities, constraints on desired fill levels).

[0231] Storage of operational data (storage pressure, temperature, volume, leak management, time intervals between repairs or replacements, flow rates to / from gas storage).

[0232] Equipment Subsystem 112-Hydrogen Liquefaction Subsystem Example

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

[0234] The components of the hydrogen liquefier may include compression components, cooling components, expansion components, and storage components. The technical constraints and operating parameters of these components may include ramp and turndown rates and storage volumes.

[0235] Given that the hydrogen liquefier may be designed to supply liquid hydrogen to the onward supply chain S1 , additional constraints may apply in terms of market demand and carbon intensity of the production and onward transportation processes.

[0236] For example, a hydrogen liquefier, if used as part of an ammonia plant, is required to produce enough liquid hydrogen to meet the needs of the supply network S1 while maintaining enough hydrogen for ammonia production. These aspects impose constraints on the operating rate and ramp rate of the liquefaction system.

[0237] Equipment Subsystem 112 - Air Separation Unit Subsystem Example

[0238] Additional subsystems may include the ASU 40. Components and constraints may 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 capacities, efficiencies, temperature differences in heat exchangers).

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

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

[0244] Air separation unit interdependency considerations (power draw as a proportion of available power, ramp rates to ensure appropriate flow rates to downstream processes).

[0245] Equipment Subsystem 112 - Air Separation Unit Storage Subsystem Example

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

[0247] Stores the type (sphere, bullet, cave, size of each, quantity of each).

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

[0249] Storage parameters and constraints (maximum and minimum storage pressures, maximum and minimum storage capacities, constraints on desired fill levels).

[0250] Storage of operational data (storage pressure, temperature, volume, leak management, time intervals between repairs or replacements, flow rates to / from gas storage).

[0251] Equipment Subsystem 112-Ammonia Production Equipment Subsystem Example

[0252] Type of ammonia production equipment (process, manufacturer, capacity).

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

[0254] Ammonia production plant operational constraints and parameters (maximum and minimum capacities, ramp rates and ramp-down limits, response to varying input gas streams (hydrogen and nitrogen)).

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

[0256] Ammonia production equipment operational constraints (rate of change of performance and conversion loop efficiency over time (i.e., aging and degradation), degradation of catalyst beds, average or averaged time intervals between replacement or repairs, average or averaged time for repair or replacement of ammonia production equipment components).

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

[0258] Ammonia production plant interdependency considerations (power draw as a proportion of available power, ramp rates to ensure appropriate flows from upstream processes).

[0259] Equipment Subsystem 112-Ammonia Production Equipment Storage Subsystem Example

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

[0261] Stores the type (sphere, bullet, cave, size of each, quantity of each).

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

[0263] Storage parameters and constraints (maximum and minimum storage pressures, maximum and minimum storage capacities, constraints on desired fill levels).

[0264] Storage of operational data (storage pressure, temperature, volume, leak management, time intervals between repairs or replacements, flow rates to / from gas storage).

[0265] Furthermore, given that the stored ammonia is then supplied to the onward supply chain S2, further constraints may apply in terms of market demand and carbon intensity of the ammonia production and onward transportation process.

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

[0267] Simulation Module 104

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

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

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

[0271] Simulation Model 104 - Subsystem Model

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

[0273] For simulation of hydrogen production equipment 20, in an embodiment, 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 changes over time, and the resulting power consumption responds to its changes. In an embodiment, such time-based degradation can be included in the physics-based model of hydrogen production equipment 20.

[0274] To simulate gas storage elements (e.g., for hydrogen, nitrogen and / or ammonia) in an industrial gas production complex superstructure, storage may be represented by minimum and maximum allowable storage masses and flow rates at which gas can be stored or withdrawn.

[0275] One or more components of the hydrogen production equipment 20 include compressors. In addition, hydrogen liquefaction requires compression. In order to simulate one or more compressors, the power curve of the compressor and the operating principle of the compressor entering different modes at different flow rates are used. In general, the compressor model represents the power consumption at different rates under the same pressure rise.

[0276] For the models of the ammonia production facility 50, these models represent the power consumption of the ammonia synthesis gas compressor and the refrigeration compressor at different ammonia production rates. A separate model can also be used to represent the power generated by the steam turbine running on 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 may be modeled by a simulation model that represents the power consumption of the ASU 40 compressor at different speeds. This may also be based on a compressor curve.

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

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

[0280] Analog module output parameters

[0281] An output parameter may then be generated to act as an indicator metric. For example, where an ammonia production facility is designed and configured, the output parameter may be the amount of ammonia produced over a predetermined time period. The time period may be, for example, one year.

[0282] In an embodiment, the simulation can determine an optimized or maximized value of an output parameter within a predetermined time period. This can be accomplished by varying the process variables of the simulated device within the limits of the defined constraints and responding to the predicted available power data to achieve the maximum or optimized value of the output parameter.

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

[0284] The optimization may utilize simulated set points for control processes in the plant complex 110 over specific time periods to balance predicted available power with consumed power so that the proper amount of hydrogen is produced and the ammonia plant operates at the correct 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 powers 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 input and applied to the optimization algorithm to propose the best rate for operating the ammonia plant within a specific predetermined period of time.

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

[0287] Simulation module 104 may also utilize data associated with energy storage device 76 if implemented in a particular configuration. The status, operating characteristics, availability, resource storage level, and ease of power availability of each of the units of storage resource 76 may be factored into the optimization problem.

[0288] In an embodiment, simulation of the plant complex 110 including the selected plant subsystem 112 and all of its components 114, along with applicable constraints 116, may be defined as a mixed integer linear programming (MILP) problem. However, other optimization solving techniques are available.

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

[0290] Simulation Model - Configuration Selection

[0291] In the example of an ammonia production plant, the configuration space for plant subsystem 112 selection and component 114 selection is very large and multi-dimensional. Therefore, while it is possible to manually select a specific configuration, in some configuration spaces this may represent an intractable problem. Therefore, it is necessary to automatically select different configurations to be able to explore the configuration space.

[0292] In an embodiment, a selection method is used to select multiple configurations for simulation. In an embodiment, a selection protocol is implemented to automatically select a specific configuration for simulation from an available configuration space.

[0293] In a non-limiting embodiment, a sampling method may be used. In a non-limiting embodiment, a Latin Hypercube sampling technique may be used. Latin Hypercube sampling is a statistical method that is operable to generate an approximately random sample of values ​​from a multidimensional distribution space. However, other methods may be used; for example, random sampling or orthogonal sampling.

[0294] In the present invention, the distribution space represents the possible configurations of the equipment complex superstructure 108 from which a near-random sample is selected. Once a plurality of configurations are selected, each configuration can be run in a simulation to determine the maximum or optimized value of an output parameter for that particular configuration. When these values ​​are obtained, variables 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 a configuration has 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 various configuration data points within the configuration space. This will give simulated values ​​for the output parameters of that configuration.

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

[0298] However, given the large number of possible configurations, as described above, multiple configurations are selected according to random or pseudo-random techniques in the configuration space. Therefore, additional optimization is required to select the best configuration for production.

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

[0300] The optimization module 106 is capable of identifying relationships between configuration options and identifying the dependency of ammonia production value on the selection or deselection of specific components or subsystems.

[0301] In an embodiment, optimization module 106 utilizes a proxy optimization model in a configuration space to identify configurations that produce maximum ammonia production while meeting technical, safety, efficiency, and commercial requirements (e.g., to identify the most efficient, reliable, and safest system with the lowest capital expenditure, resulting in the lowest LCOA (levelized cost of ammonia)).

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

[0303] In an embodiment, a regression model is used as a 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 may utilize techniques such as gradient boosting (using, for example, XGboost), long short-term memory (LSTM), support vector machine (SVM), or random decision forests may 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 an ensemble of weak predictive models such as decision trees. A staged process can be used to generate the model by steepest descent minimization (and other methods).

[0306] 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 any time interval where 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 examples, each of which falls into one of two categories, and generates a model that assigns new examples to a particular category. Thus, an SVM comprises a non-probabilistic binary linear classifier.

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

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

[0310] Such optimizations are not possible using conventional methods. For example, the inventors have discovered that the methods of the present invention can be used to design plant complex superstructures that make much higher utilization of available power from renewable resources than plant complexes designed using other methods.

[0311] Method of operation

[0312] In an embodiment, a method and system for selecting a design configuration for an industrial gas plant complex including 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 - Define the model of the superstructure subsystem

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

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

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

[0317] Other subsystems may be optional or may be selected from a set of available subsystems during the configuration process. "Available" means that the particular subsystem is compatible with 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 may be provided with a number of different subsystems that may be selected to define a particular configuration of the modeled industrial gas plant complex superstructure 108 within the model. These selections may be made available or provided manually, or may be system defined based on available data or forecast dates.

[0319] In step 200, the subsystem module 102 receives data specifying the type and configuration of subsystems to be designed and configured for the industrial gas plant complex superstructure 108. 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 an embodiment, 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 can specify one or more renewable power subsystems 110R and one or more plant subsystems 112 therein according to subsequent steps.

[0321] In an embodiment, the industrial gas plant complex superstructure 108 includes an ammonia production plant. The subsystems required for the ammonia production plant complex may 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 energy storage resources 76. Optionally, a hydrogen liquefier 32 may also be provided.

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

[0323] The model provides a configuration space in which different configurations of the modeled industrial gas plant complex 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 may be selected by a user or automatically. The renewable power subsystems 110R are available to be automatically or manually selected during configuration of the model of the subsystem module 102. For the renewable power subsystems 110R, the specific components may not be important to the present invention, and in an embodiment, the renewable power subsystems 110R may be defined only by parameters and any associated constraints.

[0327] This means that the detailed specifications of the renewable power subsystem 110R components (e.g., the type, quantity, and configuration of wind turbines or solar panels) of one or more renewable power subsystems 110R are not important to the present invention. However, in an embodiment, 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 (eg, wind, solar, tidal, etc.) As described in step 220, within each group, a range of renewable power subsystems 110R having different operating parameters associated therewith may be selected.

[0329] Each modeled renewable power subsystem has a predicted time series power profile data for a predetermined time period associated therewith. In an embodiment, the predicted power profile includes an estimated power generated by a given configuration of the renewable power subsystem 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 for a given renewable power subsystem 110R.

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

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

[0332] In an embodiment, 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 a selected portion thereof), the variables of wind power generation WPi and solar power generation SPi in time series data for a predetermined time period may be available to be used as predictive data for future assessment and design. In an embodiment, the index i represents the time from period n to n+k, and the data may be available in intervals of fixed duration, where the generated power is expressed in MW as a function of time.

[0333] However, if no existing time series power data is available (eg, if the renewable power plant has not yet been built), the time series power data may be estimated. In an embodiment, the time series power data may be estimated from weather sources and technical information.

[0334] In this step, technical data may also be used. For a wind farm, this may include known wind farm layout and design, the choice and number of turbines. For a solar farm, technical details such as the type, area, efficiency and number of panels and their location and orientation may be modeled with suitable software. This data may then be used to generate a predicted power profile for a predetermined time period. For example, the time period may be based on historical data (e.g., past wind energy data over a one-year or multi-year time period) or may be based on predicted future data derived from a machine learning process.

[0335] The forecasted average power data can be used to generate P50 and P90 power profiles for a predetermined time period. P50 represents the median annual estimate of power generation from renewable resources, such that over the life of the project, power generation at any given time has a 50% probability of being below the P50 value and a 50% probability of exceeding the P50 value.

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

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

[0338] It should be further noted that the data utilized by the power subsystem 110 may 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 may be provided by an external source.

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

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

[0341] Step 220 - Associating operating parameters and constraints with the renewable power subsystem

[0342] Step 220 may occur simultaneously with and / or be integrated into step 210, or may be performed as a separate phase. A configuration space of renewable power subsystems is defined in step 210. Then, in step 220, a plurality of operating parameters and a plurality of operating constraints may be associated with each of a plurality of modeled renewable power subsystems.

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

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

[0345] In other words, the predicted time series power profile data for subsystem 110R-1 having operating parameters and constraints for an available farm area (farmarea) defined as half that of another subsystem 110R-2 will have equivalent predicted time series power profile data having an amplitude that is half that of the data for subsystem 110R-2.

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

[0347] However, if only a portion of the available resource area is used for wind energy, such as the smallest commercially or technically viable wind farm resource size, this will define a lower limit to the power profile of the wind power resource. However, for each size, the power profile will be essentially the same, albeit scaled so that the magnitude is proportional to the selected size of the wind farm.

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

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

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

[0351] Maximum and minimum power production may be constraints and parameters that may be selected. However, other constraints may be assigned as appropriate.

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

[0353] Additionally, the constraints 116 may also be applied to the renewable power subsystem 110R over longer time frames; for example, to account for degradation of performance and efficiency over time, or to specify time intervals for repairing and replacing the renewable power subsystem.

[0354] Constraints 116 may also be applied to the interdependencies of parameters between the renewable power subsystem 110R and the plant subsystem 112 of the industrial gas plant complex superstructure 108 to be configured and designed. For example, additional constraints may be applied to ensure that the ramp rate of the renewable power subsystem 110R does not exceed the ramp rate of the technical limitations of the plant subsystem 112 being powered.

[0355] Step 230 - Specify the superstructure to support the power subsystem, associating operating parameters and constraints

[0356] This step is optional and the supporting power subsystem 110S can be specified when needed.

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

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

[0359] Constraints may 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 may also take into account dynamic processes - for example, ramp rates for startup and shutdown of energy storage resources. These constraints 116 may also be associated with broader constraints and issues - for example, safety considerations in terms of maximum capacity and limits on ramp rates to maintain component integrity and safety.

[0361] Furthermore, constraints can also be applied to longer time frames; for example, to take into account degradation of performance and efficiency over time, or to specify time intervals for repairing and replacing battery modules.

[0362] Constraints 116 may also be applied to the interdependencies of parameters between the power subsystem 110 and the equipment subsystem 112 of the industrial gas equipment complex superstructure 108 to be configured and designed. For example, additional constraints may be applied to ensure that the ramp rate of the power subsystem 110 does not exceed the ramp rate of the technical limitations of the equipment subsystem 112 being powered.

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

[0364] This means that the detailed selection of the renewable power subsystem 110R components (e.g., the type, number, and configuration of wind turbines or solar panels) of the one or more renewable power subsystems 110R 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. Therefore, the configuration space of the renewable power subsystem 110R is related to the scaling of the subsystem from maximum to minimum values.

[0365] Step 240 - Specifying the Superstructure Equipment Subsystem

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

[0367] In this step, one or more modeled equipment subsystems 112 may be selected by a user or automatically.During configuration of the model of the subsystem module 102, the modeled equipment 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 subsystem may 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 storage, a hydrogen liquefier, a nitrogen storage, and an ammonia storage.

[0369] As depicted in step 220 , within each group, a series of modeled equipment subsystems 112 having different operating parameters associated therewith may be selected.

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

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

[0372] In this step, parameters of each equipment subsystem 112 and / or component 114 are specified. Constraints 116 are then applied to the equipment subsystems 112 and components 114 within the industrial equipment complex superstructure 108.

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

[0374] Each device subsystem 112 includes one or more components 114. Components 114 correspond to functional elements of device subsystem 112. Components 114 may be libraries of custom components. Components may be modular, and part of the design and configuration process may involve determining the number and size of any one type of component.

[0375] In addition, the subsystem module 102 may enable the selection of custom components. For example, an electrolyser module with certain desired properties may be specified as the best solution, which may then be manufactured to order.

[0376] Subsystem module 102 may specify and define constraints on the configuration and operation of components within each equipment subsystem 112 and between each component 114. Constraints may include technical constraints in normal operation such as power consumption, maximum and minimum capacities, efficiency, and variation of efficiency with load.

[0377] Constraints can be related to dynamic processes – for example, ramp rates for startup and shutdown of components. These constraints can also be linked to broader constraints and issues – for example, safety considerations in terms of maximum capacity and limits on ramp rates to maintain component integrity and safety.

[0378] Furthermore, constraints may also be applied to 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 cells.

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

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

[0381] Step 260 - Select Configuration

[0382] At step 260, a plurality of configurations are selected from the configuration space defined in steps 200 to 250. This may be accomplished by any suitable method. At step 260, a plurality of configurations are selected from 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 as defined in steps 200 to 250.

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

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

[0385] In an embodiment, a plurality of configurations are selected in step 260. In an example, the number of configurations selected may be greater than 1,000.

[0386] Step 270 - Simulate Configuration

[0387] Once the multiple configurations are selected, the configurations may be run in a simulation in step 270. In this step, the simulation module 104 may be operable to utilize the data received, determined, and / or generated by the subsystem module 106 in steps 200-250 and the configuration selected in step 260 to model the industrial gas plant complex superstructure 108 in the selected configuration.

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

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

[0390] Step 280 - Generate output parameters

[0391] In an embodiment, steps 270 and 280 may be integrated together. In step 280, the operation of the simulation in step 270 may be operated to determine the maximum or optimized value of the output parameter for each configuration. When these values ​​are obtained, variables in the configuration space may be obtained, wherein the value of the output parameter as a function of the configuration may be obtained.

[0392] In other words, steps 270 and 280 can determine, for each selected configuration, a predicted operation of the configuration selected in step 260 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 utilizes power profile data associated with one or more selected renewable power subsystems and operating parameters and operating constraints associated with the selected configuration.

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

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

[0395] In an embodiment, the output parameter may be the amount of ammonia produced over a predetermined time period (eg, 1 year). Thus, the output parameter from the 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 may utilize simulated set points for control processes in the plant complex 110 over specific time periods to balance predicted available power with consumed power so that the proper amount of hydrogen is produced and the ammonia plant operates at the correct rate to maximize ammonia production.

[0397] 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 powers 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 input and applied to the optimization algorithm to propose the best rate for operating the ammonia plant within a specific predetermined period of time.

[0398] Step 290 - Build the Proxy Model

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

[0400] In embodiments, the proxy model may include any suitable model or statistical process operable to estimate the relationship between the dependent variable of maximized ammonia production and a plurality of independent variables selected for each configured subsystem and component.

[0401] In an embodiment, a regression model is used as a 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 may utilize techniques such as gradient boosting (using, for example, XGboost), long short-term memory (LSTM), support vector machine (SVM), or random decision forests may be used in such models.

[0403] Step 300 - Optimization

[0404] In step 300, the proxy model forming part of the optimization model 108 and constructed in step 290 is used to identify an optimal configuration within the configuration space that satisfies one or more predetermined parameters. In an embodiment, the predetermined parameters include the amount of ammonia that is optimized for a given available power profile while satisfying technical, safety, efficiency, and commercial requirements.

[0405] The optimization module 106 is capable of identifying relationships between configuration options and identifying the dependency of ammonia production value on the selection or deselection of specific components or subsystems.

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

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

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

[0409] This design can then be used to inform the design of real world equipment that has improved efficiency and is well matched to one or more renewable power sources.The present invention is the first to enable configuration and optimization of both renewable power sources and industrial gas plant systems, resulting in significant technical benefits.

[0410] Step 310 - Build the Equipment

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

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

[0413] In the specification and claims, the term "industrial gas plant" is intended to mean a process 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, liquefied or compressed form.

[0414] For example, the term "industrial gas equipment" may include process equipment for manufacturing gases such as those described in NACE 20.11 class and including, but not limited to: elemental gases; liquid or compressed air; refrigeration gases; mixed industrial gases; inert gases, such as carbon dioxide; and isolation gases. In addition, the term "industrial gas equipment" may also include process equipment for manufacturing industrial gases (such as ammonia) in NACE 20.15 class, process equipment for extracting and / or manufacturing methane, ethane, butane or propane (NACE 06.20 class and 19.20 class) and manufacturing gaseous fuels as defined by NACE 35.21 class. The above has been described for the European NACE system, but is intended to cover equivalent categories under the North American classifications 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 is shown. However, it should be understood that the present invention can be implemented without the use of a hydrogen storage system or purification unit, which are shown here only for the sake of completeness.

[0416] In this specification, unless expressly stated otherwise, the word "or" is used in the sense of an operator that returns a true value when 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 "comprising" is used in the sense of "including", rather than meaning "consisting of".

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

[0418] All previous teachings above are hereby incorporated herein by reference. Acknowledgement of any previously published document herein is not to be taken as an acknowledgment or representation that the teachings of the document were common general knowledge in Australia or elsewhere at its date.

[0419] Where applicable, the various embodiments provided by the disclosure can be implemented using a combination of hardware, software or hardware and software. In addition, where applicable, the various hardware components and / or software components set forth herein can be combined into composite components comprising software, hardware and / or both, without departing from the spirit of the disclosure. Where applicable, the various hardware components and / or software components set forth herein can be divided into subcomponents comprising software, hardware or both, without departing from the scope of the disclosure. In addition, where applicable, it is expected 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 may be stored on one or more computer-readable media. It is also contemplated that the software identified herein may be implemented using one or more general or special-purpose computers and / or computer systems, networked and / or otherwise. Where applicable, the order of the various steps described herein may be varied, combined into composite steps, and / or divided into sub-steps to provide the features described herein.

[0421] Although various operations have been described herein in terms of "modules," "units," or "components," it should be noted that these terms are not limited to single units or functions. In addition, the functions attributed to some modules or components described herein may be combined and attributed to fewer modules or components. In addition, although the present invention has been described with reference to specific examples, these examples are merely illustrative and are not intended to limit the present invention. It will be apparent to those of ordinary skill in the art that the disclosed embodiments may be changed, added, or deleted without departing from the spirit and scope of the present invention. For example, one or more parts of the above method may be performed in a different order (or simultaneously) and still obtain the desired results.

Claims

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

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

3. A method according to 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 the same profile of the predicted time series power profile data but differing in the magnitude of available maximum power.

4. The method of claim 2, wherein the 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 facility subsystems are arranged in groups of gas production facility subsystems and gas storage subsystems.

6. A method according to claim 5, wherein the gas production equipment subsystem includes one or more of a hydrogen production equipment, an air separation unit and an ammonia production equipment; and wherein the gas storage subsystem includes one or more of a hydrogen storage, a hydrogen liquefier, a nitrogen storage and an ammonia storage.

7. A method according to claim 6, wherein at least one selected gas production equipment subsystem includes a hydrogen production equipment, and wherein the selectable modeling components of the hydrogen production equipment can be selected from one or more of the following: electrolyzer type; electrolyzer capacity; compressor system; 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 according to claim 1, further comprising: The superstructure of the industrial gas production complex is constructed according to the design.

10. A system for configuring an industrial gas production complex superstructure, the industrial gas production complex superstructure including one or more equipment subsystems and being at least partially powered by one or more renewable power subsystems, the system comprising: at least one hardware processor; A subsystem module, the subsystem module being operable to: providing a model of the industrial gas production complex superstructure, the model having a plurality of selectable configurations representing potential configurations of the industrial gas production complex superstructure; specifying 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; as well as specifying a plurality of selectable modeled equipment subsystems, each selectable modeled equipment subsystem having associated therewith a plurality of selectable modeled components; A simulation module, the simulation module being operable to: associating a plurality of operating parameters and a plurality of operating 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 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; as well as determining, for each selected configuration, 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 said selected configuration and for said predetermined time period, said predicted operation utilizing said power profile data associated with one or more selected renewable power subsystems and said operating parameters and said operating constraints associated with said selected configuration; as well as An optimization module, the optimization module being operable to: utilizing a proxy model to identify one or more configurations of the industrial gas production complex superstructure based on the operational output parameter data and selected configuration data for each configuration, the one or more configurations being operable to maximize a value of the operational output parameter while satisfying predefined operational constraints; as well as Based on the identified one or more configurations, one or more designs are generated for the industrial gas production complex superstructure.

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

12. A system according to 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 the same profile of the predicted time series power profile data but differing in the magnitude of available maximum power.

13. The system of claim 11, wherein the 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 facility subsystems are arranged in groups of gas production facility subsystems and gas storage subsystems.

15. A system according to claim 11, wherein the gas production equipment subsystem includes one or more of a hydrogen production equipment, an air separation unit, and an ammonia production equipment; and wherein the gas storage subsystem includes one or more of a hydrogen storage, a hydrogen liquefier, a nitrogen storage, and an ammonia storage.

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

17. The system of claim 10, wherein the predetermined operational output parameter comprises an amount of gas produced by an industrial gas production complex 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 comprising one or more industrial gas devices powered by a power grid comprising one or more renewable power sources, the method being performed by at least one hardware processor, the method comprising: providing a model of the industrial gas production complex superstructure, the model having a plurality of selectable configurations representing potential configurations of the industrial gas production complex 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; specifying in the model a plurality of selectable modeled equipment subsystems, each selectable modeled equipment subsystem having associated therewith a plurality of selectable modeled components; associating a plurality of operating parameters and a plurality of operating 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 equipment subsystems, and one or more components associated with the selected one or more modeled equipment subsystems; determining, for each selected configuration, 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 said selected configuration and for said predetermined time period, said predicted operation utilizing said power profile data associated with one or more selected renewable power subsystems and said operating parameters and said operating constraints associated with said selected configuration; utilizing a proxy model to identify one or more configurations of the industrial gas production complex superstructure based on the operational output parameter data and selected configuration data for each configuration, the one or more configurations being operable to maximize a value of the operational output parameter while satisfying predefined operational constraints; as well as Based on the identified one or more configurations, one or more designs are generated for the industrial gas production complex superstructure.

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

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

Images