Real-time control system for carbon intensity compliance in hydrogen production facility

Abstract

To provide a real-time control system for carbon intensity compliance in a hydrogen production facility.SOLUTION: A method of operating a hydrogen production facility to meet carbon intensity (CI) requirements comprises: receiving operational parameter data from the hydrogen production facility, the operational parameter data being representative of measured and / or determined time-dependent values of operational parameters of the hydrogen production facility; processing the operational parameter data to define one or more linear terms, where the linear terms are linear with respect to one or more CI reference models; generating, from the linear terms, control system CI values representative of the CI of hydrogen produced by the hydrogen production facility; generating control variables for controlling one or more operational parameters of the hydrogen production facility; and controlling the hydrogen production facility in accordance with the determined control variables.SELECTED DRAWING: Figure 2

Description

[Background technology]

[0001] The present invention relates to a computer-implemented method and control system for operating a hydrogen production facility to meet carbon intensity (CI) requirements. In an embodiment, the present invention relates to a control system for low-carbon hydrogen supply with carbon intensity calculations and a carbon intensity reference model.

[0002] The control system may include a carbon intensity calculator, a carbon intensity reference model, and a controller. The carbon intensity calculator may determine the carbon intensity of the hydrogen produced by the hydrogen production unit based on input data from various sensors and sources. The carbon intensity reference model may provide a reference value for the carbon intensity of the hydrogen based on predefined criteria and parameters. The controller may calculate set points used to adjust operating parameters of the hydrogen production facility.

[0003] An industrial gas distribution network includes one or more processes that define the production, conversion, transportation, and distribution of gas for end-user applications.

[0004] Generally, the inputs to an industrial gas distribution network are raw materials (which may include raw materials and / or gas or liquid chemicals for the production of gases or gas precursors) and energy sources to power their production and purification processes. The final outputs of an industrial gas distribution network are gaseous and / or liquefied products that are delivered to end users. In certain applications, industrial gases can be used as fuel gases or liquefied fuel gases for end users.

[0005] Hydrogen distribution networks are critical because they provide a fuel essential for the functioning of economies around the world. However, hydrogen distribution networks are coming under increasing scrutiny because the production, processing, distribution, and end-use of the fuel are often associated with environmental pollutants.

[0006] The technological field of fuel supply and use has undergone significant changes in recent years. Many of these changes are driven by the urgent need to reduce greenhouse gas emissions and mitigate the effects of climate change. As a result, there has been increasing interest in developing low-carbon and renewable fuels that can help reduce the carbon intensity (CI) of transportation and other energy-intensive sectors.

[0007] Governments around the world have imposed strict limits on the carbon dioxide (CI) content of fuels used in a variety of applications. These limits have spurred innovation in the production, transportation, and processing of low-carbon fuels, as well as the development of new technologies and systems for managing those CIs across hydrogen supply networks.

[0008] Hydrogen is a versatile fuel and energy source that can be used for many applications, including power generation, transportation, industry, and heating. Hydrogen can also contribute to the decarbonization of the global energy supply chain due to the ability to produce hydrogen from renewable sources and to emit no greenhouse gases (GHGs) when used. However, not all hydrogen is produced in a low-carbon or carbon-neutral manner. Depending on the feedstock and production process, hydrogen can result in different levels of GHG emissions and environmental impacts.

[0009] Low-carbon hydrogen includes hydrogen produced in a manner such that the hydrogen has low greenhouse gas (GHG) emission intensity, which relates to the amount of GHG emissions per unit of energy output.

[0010] Low-carbon hydrogen can be produced from a variety of sources and methods, as long as they meet a predefined GHG emission reduction threshold compared to conventional hydrogen production from fossil fuels. GHG emission reduction thresholds may vary depending on the jurisdiction, regulations, or standards that apply to the production and use of low-carbon hydrogen. For example, the European Commission has set the threshold at 28.9 gCO2e / MJ.

[0011] There are two main categories of low-carbon hydrogen production methods: blue and green. Blue hydrogen is produced from fossil fuels such as natural gas or coal and involves carbon capture, utilization, and storage (CCUS) to reduce GHG emissions from the production process. Green hydrogen is produced from water electrolysis using electricity from renewable or nuclear sources, which does not generate direct GHG emissions.

[0012] Other categories of low-carbon hydrogen production methods can include turquoise hydrogen, produced from methane pyrolysis with solid carbon capture, yellow hydrogen, produced from solar-powered thermochemical water splitting, and pink hydrogen, produced from water electrolysis using electricity from nuclear fusion.

[0013] Low-carbon hydrogen has the potential to decarbonize the transportation and heavy industry sectors by replacing or blending with fossil fuels that have higher GHG emissions intensity. For example, low-carbon hydrogen could be used as a fuel for fuel cell vehicles such as cars, trucks, and trains, which emit only water as a by-product.

[0014] Low-carbon hydrogen can also be used as a feedstock for synthetic fuels such as ammonia or methanol, which can be used in internal combustion engines or turbines. Low-carbon hydrogen can also be used as a substitute for natural gas or coal in industrial processes that require high-temperature heat or chemical reactions, such as oil refining, petrochemicals, iron and steel, biofuels, and power generation. The use of low-carbon hydrogen in these sectors could significantly reduce their GHG emissions and contribute to achieving climate goals and objectives.

[0015] Recognizing the enormous potential of hydrogen for decarbonizing the transportation and industrial sectors, governments around the world have developed incentive programs for the production and use of low-carbon hydrogen. Examples of these programs include the California Low Carbon Fuel Standard, the Oregon Clean Fuel Standard, the Washington State Clean Fuel Standard, the US Inflation Control Act, the British Columbia Low Carbon Fuel Standard, and the UK Low Carbon Hydrogen Standard.

[0016] While these programs have some important differences, they all share some common core features: First, to be eligible for incentives, producers and users of low-carbon hydrogen must demonstrate that the carbon intensity of the hydrogen must be below a specified threshold.

[0017] Second, the demonstration must be conducted through specific requirements, including verification of carbon intensity, and third, the verification must be based on a specific Microsoft Excel-based life cycle model mandated by the incentive program.

[0018] Further details regarding the California Low Carbon Fuel Standard, Oregon Clean Fuel Standard, Washington Clean Fuel Standard, U.S. Inflation Control Act, British Columbia Low Carbon Fuel Standard, and UK Low Carbon Hydrogen Standard are provided below for reference.

[0019] California Low Carbon Fuel Standard (LCFS)

[0020] The California Low Carbon Fuel Standard (LCFS) is a regulation aimed at reducing GHG emissions from transportation fuels by requiring fuel providers to gradually reduce the CI of their output. The LCFS sets annual CI targets for different types of fuels, including gasoline, diesel, ethanol, biodiesel, natural gas, hydrogen, and electricity. Fuel providers must meet their CI targets by blending low-carbon fuels with conventional fuels or by purchasing credits from other providers that have excess low-carbon fuels.

[0021] The CA GREET model is a California-specific version of Argonne National Laboratory's GREET (Greenhouse Gas, Regulated Emissions, and Energy Use in Transportation) model, a well-to-wheel life cycle analysis tool implemented in Microsoft Excel that calculates GHG emissions for various transportation fuels. The CA GREET model is used to generate CI values ​​for all fuel pathways in the LCFS program, including lookup table pathways and applicant-specific Tier 1 and Tier 2 pathways.

[0022] The CA GREET model has been updated several times since its inception in 2009. The current version is CA-GREET3.0, which was based on GREET1_2016 and released in 2018. The CA-GREET3.0 model includes new feedstocks, processes, and pathways for biofuels, such as used cooking oil, corn oil, cellulosic ethanol from corn fiber, biomethane from anaerobic digestion of organic waste, dairy manure, wastewater sludge, and landfill gas. The CA-GREET3.0 model also allows for the selection of different regions for feedstock production, electricity generation, crude oil basket, and natural gas production parameters.

[0023] In December 2023, the California Air Resources Board published a proposed updated version of the model, known as CA-GREET4.0, based on US GREET1_2023. It also published a proposed Tier 1 calculator, also implemented in Microsoft Excel.

[0024] Oregon Clean Fuel Standards

[0025] The Oregon Clean Fuel Standard (OCFS) is a regulation aimed at reducing GHG emissions from transportation fuels by requiring fuel providers to gradually reduce the carbon intensity (CI) of their output. CI is a measure of GHG emissions per unit of energy in a fuel, expressed in grams of carbon dioxide equivalent per megajoule (gCO2e / MJ). OCFS sets annual CI targets for different types of fuels, including gasoline, diesel, ethanol, biodiesel, renewable diesel, natural gas, hydrogen, and electricity. Fuel providers must meet their CI targets by blending low-carbon fuels with conventional fuels or by purchasing credits from other providers with excess low-carbon fuels.

[0026] The Oregon GREET Excel model is a modified version of Argonne National Laboratory's GREET model, a well-to-wheel life cycle analysis tool that calculates GHG emissions for various transportation fuels. The Oregon GREET model is used to generate CI values ​​for all fuel pathways in the OCFS program, including lookup table pathways and applicant-specific Tier 1 and Tier 2 pathways.

[0027] The Oregon GREET model has been updated several times since its inception in 2016. The current version, OR-GREET3.0, is based on GREET1_2016 and was released in 2018. The OR-GREET3.0 model includes new feedstocks, processes, and pathways for biofuels, such as used cooking oil, corn oil, cellulosic ethanol from corn fiber, biomethane from anaerobic digestion of organic waste, dairy manure, wastewater sludge, and landfill gas. The OR-GREET3.0 model also allows for the selection of different regions for feedstock production, electricity generation, crude oil basket, and natural gas production parameters.

[0028] Washington Clean Fuel Standards

[0029] The Washington State Clean Fuel Program is a regulation aimed at reducing the carbon intensity of transportation fuels by 20% below 2017 levels by 2034. Carbon intensity is a measure of greenhouse gas emissions produced per unit of energy. The program requires fuel suppliers to produce or blend low-carbon fuels, such as low-carbon hydrogen, or purchase credits from low-carbon fuel providers.

[0030] Low-carbon hydrogen is hydrogen produced with minimal or no greenhouse gas emissions, such as through electrolysis using renewable electricity or steam methane reforming with carbon capture and storage. The program incentivizes the production and use of low-carbon hydrogen by assigning it a low or negative carbon intensity value depending on the production method and electricity source. This means that low-carbon hydrogen can generate credits that can be sold to fuel suppliers who are required to comply with the program.

[0031] Carbon intensity is calculated by using a life cycle analysis approach that considers the energy and emissions associated with fuel production, transportation, and use. The program uses the GREET model, developed by Argonne National Laboratory, as its primary tool for calculating carbon intensity values ​​for different fuels and pathways. The GREET model is a spreadsheet-based model that covers various stages of the fuel life cycle, including feedstock extraction, fuel conversion, distribution, and vehicle operation. The model also allows users to compare the energy and environmental impacts of different fuel and vehicle technologies.

[0032] The Washington State GREET model, or WA-GREET, is a modified version of the California GREET model, or CA-GREET, which is based on the US GREET model developed by Argonne National Laboratory. The WA-GREET model is used to calculate the carbon intensity of transportation fuels for the Washington State Clean Fuel Program, which aims to reduce greenhouse gas emissions from the transportation sector.

[0033] The main differences between the WA-GREET model and the US GREET model are as follows:

[0034] The WA-GREET model uses more recent and region-specific data on fuel production and consumption in Washington state, including electric grid mix, natural gas composition, ethanol and biodiesel feedstocks, and petroleum refining.

[0035] The WA-GREET model incorporates several updates and modifications from the CA-GREET model, including revised emission factors for fossil fuel extraction and processing, updated carbon capture and storage parameters, and improved allocation methods for by-products.

[0036] The WA-GREET model includes several new fuel pathways not available in the US GREET model, such as renewable diesel from tullow, renewable jet fuel from corn oil, and hydrogen from landfill gas.

[0037] The WA-GREET model reflects the specific conditions and characteristics of the Washington State transportation fuel market and is intended to assist in the implementation of clean fuel programs. The US GREET model is a more general and comprehensive tool covering a variety of fuel lifecycles and vehicle technologies across the United States.

[0038] United States Inflation Control Act

[0039] The American Inflation Control Act (IRA) was introduced into federal law in August 2022. It aims to reduce inflation by reducing the federal budget deficit, lowering prescription drug prices, and investing in domestic energy production while promoting clean energy.

[0040] One of the key provisions of the IRA is to support the development and deployment of low-carbon hydrogen as a clean fuel for various sectors, including transportation, industry, and power generation. Low-carbon hydrogen is hydrogen with a low lifecycle GHG emissions intensity, which measures the amount of GHG emissions per unit of energy output.

[0041] The IRA offers financial incentives for the production and use of low-carbon hydrogen, including tax credits, grants, and loans. The amount of the incentive depends on the carbon intensity of the hydrogen, calculated using a standardized methodology specified in the IRA. The lower the carbon intensity, the higher the incentive.

[0042] The IRA employs the GREET model as its official tool for calculating the carbon intensity of hydrogen from well to gate. The GREET model is a complete lifecycle model developed by Argonne National Laboratory that evaluates the energy and emissions impacts of various fuel and vehicle technologies. The GREET model considers all GHG emissions associated with hydrogen production, including feedstock extraction, processing, transportation, and conversion. The GREET model also considers the GHG emissions avoided by using low-carbon hydrogen instead of fossil fuels.

[0043] The GREET model is widely used by researchers, regulators, and industry to evaluate the environmental benefits of low-carbon hydrogen and other biofuels. To qualify for incentives, the IRA requires hydrogen producers to report their carbon intensity to the IRS and other relevant agencies using the GREET model.

[0044] The latest version of the GREET model is known as R&D GREET2023. R&D GREET2023 provides the background data required for use in establishing interim emissions rates, making it appropriate for assessing eligibility for 45V credits under the Inflation Control Act. Argonne National Laboratory also released a new Excel-based model, designated 45VH2-GREET, which is the Excel model that must be used to perform carbon intensity calculations to qualify hydrogen production for tax credits under Section 45V of the U.S. Tax Code.

[0045] British Columbia Low Carbon Fuel Standard

[0046] The British Columbia Low Carbon Fuel Standard (BC-LCFS) is a provincial regulation designed to reduce the CI of transportation fuels used in the province. CI is a measure of GHG emissions per unit of energy output over the fuel's entire life cycle. The BC-LCFS sets decreasing CI targets for gasoline and diesel fuel pools and requires fuel suppliers to either supply fuel with a lower CI than the targets or purchase credits from other suppliers who do so.

[0047] The BC-LCFS encourages the consumption of low-carbon hydrogen by rewarding fuel suppliers who blend it with gasoline or diesel or offer it as the sole fuel for vehicles. Hydrogen has a lower CI than fossil fuels, especially when produced from renewable sources or with carbon capture and storage. Fuel suppliers can generate credits by supplying low-carbon hydrogen and sell them to other suppliers who need to comply with the BC-LCFS. The price of the credits reflects the value of reducing GHG emissions from transportation fuels.

[0048] The GHGenius model is a life cycle analysis tool that calculates the energy and emissions impacts of various fuel and vehicle technologies. It was developed by (S&T) Squared Consultants for Natural Resources Canada and is available as a free download. The BC-LCFS uses the GHGenius model as the official method for determining the CI of hydrogen and other fuels. Fuel suppliers must report their CI to the provincial government using the GHGenius model and follow the guidelines and assumptions specified in the BC-LCFS.

[0049] The GHGenius model calculates the CI for hydrogen by considering all GHG emissions associated with hydrogen production, distribution, and use. It accounts for factors such as feedstock type, production technology, energy input, transportation mode, storage losses, and vehicle efficiency. It also considers the GHG emissions avoided by replacing fossil fuels with hydrogen. The GHGenius model uses data from a variety of sources, including government statistics, industry reports, and scientific literature, to estimate GHG emissions for each stage of the hydrogen lifecycle.

[0050] UK Low Carbon Hydrogen Standard

[0051] The UK Low Carbon Hydrogen Standard (LCHS) is a federal regulation that defines what constitutes "low carbon hydrogen" at the point of production. It sets the maximum threshold of GHG emissions allowed in the production process for hydrogen to be considered "low carbon hydrogen." The LCHS aims to ensure that new hydrogen production supported by the government contributes to the UK's carbon reduction and net zero targets.

[0052] The LCHS is closely linked to the UK Hydrogen Production Business Model (HPBM), a contractual support mechanism that provides revenue support to low-carbon hydrogen producers. The HPBM is designed to bridge the cost gap between low-carbon and high-carbon hydrogen fuels and encourage investment in the production and use of low-carbon hydrogen. The HPBM will be delivered through a private law contract (Low Carbon Hydrogen Contract) between a government-appointed counterparty and the hydrogen producer.

[0053] To qualify for HPBM, hydrogen producers must comply with the LCHS, which means they must calculate and report the CI for their hydrogen production pathway using standardized methodologies specified in the LCHS guidance. CI is a measure of GHG emissions per unit of energy output over the fuel's entire life cycle. The LCHS sets a CI threshold of 20 grams of CO2 equivalent per megajoule of hydrogen.

[0054] The UK Hydrogen Emissions Calculator (HEC) is an Excel-based tool that helps hydrogen producers calculate and report their CI under the LCHS. The HEC is based on a life cycle analysis model that considers all GHG emissions associated with the production, distribution, and use of hydrogen. It takes into account factors such as feedstock type, production technology, energy inputs, transport mode, storage losses, and vehicle efficiency. It also considers GHG emissions avoided by substituting fossil fuels with hydrogen.

[0055] The HEC is mandatory for hydrogen producers applying for HPBM or the Net Zero Hydrogen Fund, both government funding schemes that support low-carbon hydrogen production. The HEC is also recommended for other hydrogen producers who wish to demonstrate compliance with the LCHS or claim low-carbon status for their hydrogen.

[0056] overview

[0057] There is a need in the art to provide a control system operable to enable precise control of hydrogen production facilities to meet carbon intensity requirements and constraints.

[0058] However, there are many technical challenges to achieving this. First, any control system presents significant challenges because there are multiple calculation contexts for carbon intensity. For example, as discussed above, the United States, Canada, California, Oregon, Washington, British Columbia, and Alberta all specify different models for calculating carbon intensity.

[0059] In addition, with the exception of the Canadian model, all carbon intensity models are in the form of complex Microsoft Excel workbooks with dozens of sheets and tens of thousands of parameters. These models are practically and computationally inadequate to allow effective production facility control and to provide any kind of continuous (and possibly real-time) information.

[0060] Therefore, there is a need in the art to provide more effective methods and control systems for addressing these problems. Summary of the Invention

[0061] The following presents a selection of concepts in a simplified form in order to provide a basic understanding of some aspects of the disclosure. The following is not an extensive overview of the disclosure and is not intended to identify key or critical elements or to delineate the scope of the disclosure. The following merely summarizes some of the concepts of the disclosure as a prelude to the more detailed description provided later.

[0062] Generally, the present disclosure is directed to a process and low-carbon hydrogen control system for controlling the production of hydrogen to meet carbon intensity requirements specified by government incentive programs.

[0063] The plurality of hydrogen production facilities comprise means for producing hydrogen at a variable rate, which may include any suitable technology or process capable of producing low-carbon hydrogen from renewable or fossil resources, such as electrolysis, gasification, steam methane reforming with carbon capture, utilization, and storage (CCUS), or any combination thereof.

[0064] Some preferred aspects of the method and system according to the present invention are outlined below.

[0065] Aspect 1: A computer-implemented method for operating a hydrogen production facility to meet carbon intensity (CI) requirements, the method being executed by at least one hardware processor, the computer-implemented method including: receiving, using a computer system, operational parameter data from the hydrogen production facility, the operational parameter data representing measured and / or determined time-dependent values ​​of one or more operating parameters of the hydrogen production facility; processing, using the computer system, the operational parameter data to define one or more linear terms, the linear terms being linear with respect to one or more CI reference models; generating, from the one or more linear terms, control system CI values ​​representing the CI of hydrogen produced by the hydrogen production facility; generating, using the computer system, control variables for controlling the one or more operating parameters of the hydrogen production facility based on a function of the control system CI values; and controlling the hydrogen production facility in accordance with the determined control variables.

[0066] Aspect 2: The computer-implemented method of aspect 1, wherein the operational parameter data includes measured and / or determined time-dependent values ​​of one or more operational parameters associated with material and / or energy inputs to and material and / or energy outputs from the hydrogen production facility.

[0067] Aspect 3: The computer-implemented method of aspect 2, wherein the one or more operating parameters are selected from the group of an amount of hydrogen produced, an amount of electricity consumed, an amount of steam produced, an amount of syngas produced, an amount of carbon monoxide produced, and an amount of electricity produced.

[0068] Aspect 4: The computer-implemented method of aspect 2 or 3, wherein the processing step includes applying one or more non-linear transformations to the operational parameter data of the one or more operational parameters to define one or more linear terms.

[0069] Aspect 5: The computer-implemented method of aspect 4, wherein one or more of the linear terms specify a ratio of material and / or energy inputs to the hydrogen production facility and material and / or energy outputs from the hydrogen production facility.

[0070] Aspect 6: The computer-implemented method of aspect 5, wherein one or more of the linear terms specify a measured energy or material flow input to the hydrogen production facility divided by one of a total mass flow rate of hydrogen and by-products produced at the hydrogen production facility, a total molar flow rate of hydrogen and by-products produced at the hydrogen production facility, or a total economic value of hydrogen and by-products produced at the hydrogen production facility.

[0071] Aspect 7: The computer-implemented method of aspect 5 or 6, wherein one or more of the linear terms specify a measured energy or material flow input to the hydrogen production facility divided by the total energy rate of hydrogen and by-products produced at the hydrogen production facility.

[0072] Embodiment 8: The computer-implemented method of embodiment 7, wherein the one or more byproducts include a gas, and the total energy rate is determined as the product of the flow rate of the gas and its lower heating value.

[0073] Aspect 9: The computer-implemented method of aspect 8, wherein the by-products include electricity and the energy rate is the generated power.

[0074] Aspect 10: A computer-implemented method described in any one of aspects 1 to 9, wherein the step of generating the control system CI value includes determining a sum of products of each of one or more linear terms and a corresponding linear coefficient, the linear coefficients being derived from one or more of the CI reference models.

[0075] Aspect 11: The computer-implemented method of aspect 10, further comprising updating the linear coefficients based on perturbations of one or more of the CI reference models.

[0076] Aspect 12: The computer-implemented method of any one of Aspects 1 to 11, wherein generating the control variables and controlling the hydrogen production facility utilizes model predictive control.

[0077] Aspect 13: A system for operating a hydrogen production facility to meet carbon intensity (CI) requirements, the system comprising: at least one hardware processor; a data acquisition module configured to receive operational parameter data from the hydrogen production facility, the operational parameter data representing measured and / or determined time-dependent values ​​of one or more operating parameters of the hydrogen production facility; a CI determination module configured to process the operational parameter data to define one or more linear terms, the linear terms being linear with respect to one or more carbon intensity criteria models, and generate from the one or more linear terms a control system CI value representing a CI of hydrogen produced by the hydrogen production facility; a production control module configured to generate control variables for controlling one or more operating parameters of the hydrogen production facility based on a function of the control system CI value; and a process controller configured to control the hydrogen production facility in accordance with the determined control variables.

[0078] Aspect 14: The system of aspect 13, wherein the operational parameter data includes measured and / or determined time-dependent values ​​of one or more operational parameters related to material and / or energy inputs to and material and / or energy outputs from the hydrogen production facility.

[0079] Aspect 15: The system described in aspect 14, wherein the CI determination module is configured to apply one or more nonlinear transformations to the operational parameter data of the one or more operational parameters to define one or more linear terms.

[0080] Aspect 16: The system of aspect 14 or 15, wherein one or more of the linear terms specify a ratio of material and / or energy input to the hydrogen production facility and material and / or energy output from the hydrogen production facility.

[0081] Aspect 17: The system of aspect 16, wherein one or more of the linear terms specify a measured energy or material flow input to the hydrogen production facility divided by the total energy rate of hydrogen and by-products produced at the hydrogen production facility.

[0082] Aspect 18: The system of aspect 17, wherein the one or more by-products include gas and the total energy rate is determined as the product of the flow rate of the gas and its lower heating value, and / or the by-products include electricity and the energy rate is the generated power.

[0083] Aspect 19: The system of any one of Aspects 13-18, wherein the production control module and the process controller comprise a model predictive controller operable to generate the control variables and control the hydrogen production facility.

[0084] Aspect 13A: A system for operating a hydrogen production facility to meet carbon intensity (CI) requirements, the system comprising at least one hardware processor, the system configured to implement a method using a computer system including: receiving operational parameter data from the hydrogen production facility, the operational parameter data representing measured and / or determined time-dependent values ​​of one or more operating parameters of the hydrogen production facility; processing the operational parameter data to define one or more linear terms, the linear terms being linear with respect to one or more carbon intensity criteria models; generating from the one or more linear terms a control system CI value representing a CI of hydrogen produced by the hydrogen production facility; generating control variables for controlling the one or more operating parameters of the hydrogen production facility based on a function of the control system CI value; and controlling the hydrogen production facility in accordance with the determined control variables.

[0085] Aspect 13B: The system of aspect 13A, wherein the processing step includes applying one or more nonlinear transformations to the operational parameter data of the one or more operational parameters to define one or more linear terms.

[0086] Embodiment 13C: The system of embodiment 13A or 13B, further comprising a model predictive controller operable to generate the control variables and control the hydrogen production facility.

[0087] Aspect 20: A non-transitory computer-readable storage medium storing a program of instructions executable by a machine to implement a method for operating a hydrogen production facility to meet carbon intensity (CI) requirements, the method being executed by at least one hardware processor and including: receiving, using a computer system, operational parameter data from the hydrogen production facility, the operational parameter data representing measured and / or determined time-dependent values ​​of one or more operational parameters of the hydrogen production facility; processing, using the computer system, the operational parameter data to define one or more linear terms, the linear terms being linear with respect to one or more carbon intensity criteria models; generating, from the one or more linear terms, control system CI values ​​that represent the CI of hydrogen produced by the hydrogen production facility; generating, using the computer system, control variables for controlling the one or more operational parameters of the hydrogen production facility based on a function of the control system CI values; and controlling the hydrogen production facility in accordance with the determined control variables.

[0088] Aspect A1: A computer-implemented method for updating a control system for a hydrogen production facility using one or more carbon intensity (CI) reference models, the control system being operable to control one or more operating parameters of the hydrogen production facility based on a generated control system CI value representing a CI of hydrogen produced by the hydrogen production facility, the method being executed by at least one hardware processor and comprising: a) receiving, using the computer system, operating parameter data from the hydrogen production facility, the operating parameter data representing measured and / or determined time-dependent values ​​of the one or more operating parameters of the hydrogen production facility; and b) using the computer system to update a control system for the hydrogen production facility using one or more carbon intensity (CI) reference models. a) processing the operational parameter data to define one or more linear terms, the linear terms being linear with respect to one or more CI reference models; b) generating CI values ​​for the control system as a function of the one or more linear terms and the one or more linear coefficients; c) extracting one or more reference model inputs from the one or more linear terms; e) inputting the reference model inputs into the one or more CI reference models to generate updated reference CI values ​​for the hydrogen production facility; f) generating updated linear coefficients using the one or more CI reference models using the updated CI reference values; and g) updating the control system with the updated linear coefficients.

[0089] Aspect A2: The method of aspect A1, wherein steps a) to c) are repeated more frequently than steps d) and e).

[0090] Aspect A3: The method of aspect A1 or A2, wherein steps d) and e) are repeated more frequently than steps f) and g).

[0091] Aspect A4: The method of aspect A1, A2, or A3, wherein step d) includes applying one or more nonlinear transformations to the linear terms to generate one or more reference model inputs.

[0092] Aspect A5: The method of any one of aspects A1-A4, wherein the operating parameter data includes measured and / or determined time-dependent values ​​of one or more operating parameters related to material and / or energy inputs to and material and / or energy outputs from the hydrogen production facility.

[0093] Aspect A6: The method of aspect A5, wherein the one or more operating parameters are selected from the group of an amount of hydrogen produced, an amount of electricity consumed, an amount of steam produced, an amount of syngas produced, an amount of carbon monoxide produced, and an amount of electricity produced.

[0094] Aspect A7: The method of any one of aspects A1-A6, wherein step b) includes applying one or more non-linear transformations to the operational parameter data of the one or more operational parameters to define one or more linear terms.

[0095] Aspect A8: The method of aspect A5 or A6, wherein one or more of the linear terms specify a ratio of material and / or energy inputs to the hydrogen production facility and material and / or energy outputs from the hydrogen production facility.

[0096] Aspect A9: The method of aspect A5 or A6, wherein one or more of the linear terms specify a measured energy or material flow input to the hydrogen production facility divided by one of a total mass flow rate of hydrogen and by-products produced at the hydrogen production facility, a total molar flow rate of hydrogen and by-products produced at the hydrogen production facility, or a total economic value of hydrogen and by-products produced at the hydrogen production facility.

[0097] Aspect A10: A control system for a hydrogen production facility using one or more carbon intensity (CI) reference models, the control system configured to control one or more operating parameters of the hydrogen production facility based on a generated control system CI value representing a CI of hydrogen produced by the hydrogen production facility, the control system including at least one hardware processor and a data acquisition module configured to receive operating parameter data from the hydrogen production facility, the operating parameter data representing measured and / or determined time-dependent values ​​of the one or more operating parameters of the hydrogen production facility, and a CI determination module configured to process the operating parameter data to define one or more linear terms. and generating a CI value for the control system as a function of the one or more linear terms and the one or more linear coefficients; a reference model module configured to extract one or more reference model inputs from the one or more linear terms and input the reference model inputs to the one or more CI reference models to generate updated reference CI values ​​for the hydrogen production facility; and an update module configured to utilize the updated CI reference values ​​to generate updated linear coefficients using the one or more CI reference models and update the control system with the updated linear coefficients.

[0098] Aspect A11: The control system of aspect A10, further configured to generate control system CI values ​​more frequently than updated reference CI values.

[0099] Aspect A12: The control system of aspect A10 or A11, further configured to generate updated reference CI values ​​at a higher frequency than the control system is updated with updated linear coefficients.

[0100] Aspect A13: A control system described in any one of aspects A10, 11, or 12, wherein the reference model module is further configured to apply one or more nonlinear transformations to the linear terms to generate one or more reference model inputs.

[0101] Aspect A14: The control system of any one of aspects A10, 11, 12, or 13, wherein the operational parameter data includes measured and / or determined time-dependent values ​​of one or more operational parameters related to material and / or energy inputs to and material and / or energy outputs from the hydrogen production facility.

[0102] Aspect A15: The control system of any one of aspects A10-A14, wherein the one or more operating parameters are selected from the group of an amount of hydrogen produced, an amount of electricity consumed, an amount of steam produced, an amount of syngas produced, an amount of carbon monoxide produced, and an amount of electricity produced.

[0103] Aspect A16: A control system described in any one of aspects A10 to A15, wherein the CI determination module is configured to apply one or more non-linear transformations to the operational parameter data of the one or more operational parameters to define one or more linear terms.

[0104] Aspect A17: The control system of any one of aspects A10-A16, wherein one or more of the linear terms specify a ratio of material and / or energy input to the hydrogen production facility and material and / or energy output from the hydrogen production facility.

[0105] Aspect A18: The control system of any one of aspects A10-A17, wherein one or more of the linear terms specify a measured energy or material flow input to the hydrogen production facility divided by one of a total mass flow rate of hydrogen and by-products produced at the hydrogen production facility, a total molar flow rate of hydrogen and by-products produced at the hydrogen production facility, or a total economic value of hydrogen and by-products produced at the hydrogen production facility.

[0106] 1. A non-transitory computer-readable storage medium storing a program of instructions executable by a machine to implement a method for updating a control system for a hydrogen production facility using one or more carbon intensity (CI) reference models, the control system being operable to control one or more operating parameters of the hydrogen production facility based on generated control system CI values ​​representative of CI of hydrogen produced by the hydrogen production facility, the method being executed by at least one hardware processor and comprising: a) receiving, using the computer system, operating parameter data from the hydrogen production facility, the operating parameter data representing measured and / or determined time-dependent values ​​of the one or more operating parameters of the hydrogen production facility; a) generating a CI value for a control system as a function of the one or more linear terms and the one or more linear coefficients; b) processing the operational parameter data using a computer system to define one or more linear terms, the linear terms being linear with respect to one or more CI reference models; c) generating a CI value for a control system as a function of the one or more linear terms and the one or more linear coefficients; d) extracting one or more reference model inputs from the one or more linear terms; e) inputting the reference model inputs into the one or more CI reference models to generate updated reference CI values ​​for the hydrogen production facility; f) generating updated linear coefficients using the one or more CI reference models using the updated CI reference values; and g) updating the control system with the updated linear coefficients. [Brief explanation of the drawings]

[0107] Embodiments of the present invention will now be described, by way of example only, with reference to the following drawings in which:

[0108] [Figure 1] 1 is a schematic diagram of a hydrogen production facility and associated control system. [Figure 2] FIG. 1 is a schematic diagram of a control system according to an embodiment. [Figure 3] FIG. 1 is a flow diagram of a method according to an embodiment. [Figure 4] An example of the operation of an embodiment of the present invention will now be illustrated. [Figure 5] An example of the operation of an embodiment of the present invention will now be illustrated. [Figure 6] An example of the operation of an embodiment of the present invention will now be illustrated. [Figure 7] An example of the operation of an embodiment of the present invention will now be illustrated. [Figure 8] An example of the operation of an embodiment of the present invention will now be illustrated. [Figure 9] An example of the operation of an embodiment of the present invention will now be illustrated.

[0109] Embodiments of the present disclosure and their advantages are best understood by reference to the following detailed description, wherein like reference numerals are used to identify like elements illustrated in one or more of the figures, it being understood that the illustrations are for purposes of illustrating embodiments of the present disclosure and not for purposes of limiting them. DETAILED DESCRIPTION OF THE INVENTION

[0110] Various examples and embodiments of the present disclosure are described below. The following description provides specific details to enable a thorough understanding and description of these examples. However, those skilled in the relevant art will understand that one or more embodiments described herein may be practiced without many of these details. Likewise, those skilled in the relevant art will also understand that one or more embodiments of the present disclosure may include other features and / or functions not described in detail herein. Additionally, some well-known structures or functions may not be shown or described in detail below to avoid unnecessarily obscuring the relevant description.

[0111] The present invention is directed to the technical field of control systems for hydrogen production facilities to meet greenhouse gas intensity constraints.

[0112] The technology described herein provides a technological improvement to the existing control of variables related to industrial hydrogen production, in accordance with carbon intensity requirements and regulations, that allows for control of one or more production facilities to manage the technical considerations and constraints related to the production of hydrogen.

[0113] Overview of the hydrogen production facility

[0114] Figure 1 shows a general schematic diagram of a hydrogen production facility 10. In Figure 1, the production facility 10 is operable to produce hydrogen from a feedstock. The production facility 10 is configured to produce hydrogen at a production rate that is selectable and that can vary as a function of time between a maximum level and a minimum level.

[0115] In other words, production facility 10 may produce hydrogen at a selected variable rate. The hydrogen produced in the production step may be, but is not necessarily limited to, "blue" hydrogen produced from fossil fuels (such as natural gas or coal) with carbon capture, utilization, and storage (CCUS) to reduce GHG emissions from the production process, or "green" hydrogen produced using renewable energy.

[0116] The means for producing hydrogen may include any suitable technology or process capable of producing low-carbon hydrogen from renewable or fossil resources. For example, production facility 10 may comprise a hydrogen production plant comprising an electrolyzer operable to produce hydrogen by electrolysis of a feedstock such as water, brine, or steam. Alternatively, production facility 10 may comprise a gasification plant or reformer operable to produce liquid or gaseous hydrogen through steam reforming using methane as a feedstock.

[0117] Alternatively, hydrogen may be produced from a hydrogen production plant forming part of an ammonia production plant, which may, for example, comprise a hydrogen production plant, an air separation unit (ASU), and an ammonia synthesis plant. Storage for the produced hydrogen may also be provided.

[0118] To reduce the carbon intensity of the production process, the electricity to power production facility 10 may be generated, at least in part, by renewable energy sources such as wind and / or solar power sources, although other sources may optionally be utilized. Green fuels produced using renewable power sources have a very low to zero CI at the point of production.

[0119] Different production facilities 10 may have different capacities, efficiencies, costs, and environmental impacts. Additionally, while only a single hydrogen production facility 10 is shown for clarity, there is no practical limit to the number of hydrogen production facilities that may be included.

[0120] The production facility 10 is communicatively coupled to one or more sensor elements 10S. The sensor elements 10S are operable to measure values ​​of one or more operating parameters of the production facility 10. In an embodiment, the sensor elements 10S are operable to measure values ​​of the one or more operating parameters of the production facility 10 as a function of time (in other words, time-dependent variables are measured).

[0121] The measured operating parameters may be selected depending on the type of production facility 10 in question. However, the operating parameters may include one or more of the following: hydrogen production rate, power consumption (which may include electricity consumption), feedstock (e.g., natural gas, coal, etc.) consumption rate, carbon dioxide capture rate, and steam production rate (if appropriate).

[0122] The measured operating parameter may be measured directly (e.g., flow rate may be measured by a flow sensor) or may be measured indirectly through other parameters (e.g., calculated or otherwise determined from one or more other measurements).

[0123] Additionally, the process plant 10 is communicatively coupled to one or more control elements 10C operable to control, for example, the rate of the relevant hydrogen production process, the type of power source, the amount of power, the ramp rate, and the throughput rate of materials (e.g., feedstocks, intermediates, and fuels for producing hydrogen).

[0124] A control system is typically associated with one or more sensors (e.g., in non-limiting examples, flow meters, pressure sensors, temperature sensors, etc.) and actuators (e.g., in non-limiting examples, pumps, valves, compressors, or blowers) to maintain a control setpoint (e.g., a throughput or flow setpoint) and regulate the throughput of material through a process.

[0125] The hydrogen production facility 10 is communicatively coupled to a control system 100. The control system 100 is operable to receive data from the sensor elements 10S and to control the control elements 10C. The functionality of these components is described in more detail below with reference to FIG. 2.

[0126] Control System 100

[0127] FIG. 2 shows a schematic diagram of a control system 100 for controlling elements of a hydrogen production facility.

[0128] In an embodiment, the present invention provides a method and system for controlling processes within a hydrogen production facility to meet carbon intensity requirements. In an embodiment, a control system 100 is operable to receive input from technical sources and determine optimal operating parameters for one or more production facilities 10, as described below, to meet carbon intensity requirements.

[0129] In particular embodiments, the present invention is operable to enable an interface between a plant control system and a baseline carbon intensity model to enable carbon intensity constraints to be met under multiple circumstances.

[0130] The control system 100 includes a controller 110. The controller 110 includes at least one hardware processor 112 and at least one non-transitory memory 114. The controller 110 further includes a data acquisition module 116, a carbon intensity determination module 118, and a control module 120.

[0131] The control system 100 further comprises a data block 130 for acquiring and transmitting data from the production facility 10, and a process controller 140 for enabling control of the production facility 10. Finally, the control system 100 comprises a reference model module 150 and an update module 160.

[0132] Data block 130 is arranged to receive input from sensors 10S or other components of production facility 10. In an embodiment, data block 130 comprises one or more sensors 10-1S, 10-2S, 10-3S, 10-4S. In an embodiment, production facility 10 comprises one or more sensors 10-1S, 10-2S, 10-3S, 10-4S of data block 130.

[0133] Sensors 10-1S, 10-2S, 10-3S enable reporting of operational process parameters, which in embodiments may be real-time and continuous. For example, in embodiments, data related to operational process parameters may include time-dependent values ​​of one or more of the following parameters: flow rate, pressure, temperature, hydrogen production rate, power consumption (which may include electricity consumption), feedstock (e.g., natural gas, coal, etc.) consumption rate, carbon dioxide capture rate, and steam production rate (if appropriate).

[0134] Additionally, data block 130 may include other data input elements 10-nS as needed. These data input elements are not limited to sensors and sensor data, but may include reported or determined technical values ​​related to additional elements of production facility 10. In an embodiment, this may include, among other things, technical information related to production information, fuel and process availability, or CI values.

[0135] In summary, the data block 130 is operable to measure, derive, and / or determine operational parameter data related to time-dependent values ​​of one or more operational parameters of the process plant 10. In an embodiment, the data block 130 is operable to measure, derive, and / or determine operational parameter data related to time-dependent values ​​of one or more operational parameters related to material and / or energy inputs to the process plant 10 and one or more operational parameters related to material and / or energy outputs from the process plant 10.

[0136] Data Acquisition Module 116

[0137] The data acquisition module 116 is configured to collect data on the production facility 10 and is operable to receive and process sensor data and other data inputs from a data input block 120. The data acquisition module 116 may also communicate with a carbon intensity determination module 118 and a control module 120 to provide the data necessary for their functions.

[0138] The data acquisition module 116 is operable to generate and / or collate input data for the carbon intensity determination module 118, as described below.

[0139] For illustrative purposes, in a specific embodiment, the obtained input data may include one or more of the following measurable and / or determinable parameters: amount of hydrogen produced, amount of electricity consumed, amount of steam produced, amount of syngas produced, amount of carbon monoxide produced, and amount of electricity produced.

[0140] Carbon Intensity Determination Module 118

[0141] The carbon intensity determination module 118 is operable to process the sensor and other operating characteristic data obtained from the data block 130 and received by the data acquisition module 116 and perform computations on the data to generate data corresponding to the carbon intensity of the hydrogen produced by the production facility 10 such that the data is consistent with the carbon intensity reference model. This data so generated may then be utilized by the control module 120 to control the process controller 140 to adjust the setpoints of the control 10C of the production plant 10.

[0142] The carbon intensity determination module 118 may be configured to determine and enable control of the carbon intensity of the hydrogen produced at the production facility 10. In an embodiment, the CI value depends on a control variable, such as the rate of the production facility 10, since the efficiency of the production facility 10 depends on the rate of the production facility.

[0143] In embodiments, sensors 10-1S, 10-2S, 10-3S enable real-time and continuous reporting of operating process parameters of production facility 10. For example, in embodiments, the operating process parameters may include one or more of the following: flow rate, pressure, temperature, hydrogen production rate, power consumption (which may include electricity consumption), feedstock (e.g., natural gas, coal, etc.) consumption rate, carbon dioxide capture rate, and steam production rate (if appropriate).

[0144] The sensor data is received from the data acquisition module 116 by the carbon intensity determination module 118, where the received data is processed.

[0145] In an embodiment, the processing includes performing one or more mathematical transforms on the received operational parameter data (which may include measured sensor data). In an embodiment, the mathematical transforms include a non-linear transform operable to convert the received operational parameter data into one or more linear terms.

[0146] In an embodiment, converting the received operational parameter data into linear terms allows for direct correlation with one or more reference model carbon intensities. In other words, the linear terms have a linear effect on the reference model carbon intensities. Once the linear terms are derived, they may be stored in memory 114.

[0147] In an embodiment, the linear term has a particular form: In an embodiment, the linear term may represent the ratio of input to the process plant 10 to output from the process plant 10.

[0148] For example, in particular embodiments, one or more particular forms of the linear terms may include an equation specifying the measured energy or material flows that form the inputs to the process plant 10 divided by an equation specifying the total mass flow rate of hydrogen and by-products produced in the process plant 10.

[0149] In an embodiment, one or more forms of linear terms may include an equation specifying a measured energy or material flow that is an input to a hydrogen production facility divided by an equation specifying the total energy rate of hydrogen and by-products produced at the facility.

[0150] In an embodiment, if one or more by-products is a gas, the total energy rate is determined as the product of the flow rate of the gas and its lower heating value. Alternatively or additionally, if the by-product is electricity, the energy rate is the power of the electricity.

[0151] In alternative or additional specific embodiments, one or more particular forms of the linear terms may include an equation specifying the measured energy or material flow forming the input to the process plant 10 divided by an equation specifying the total molar flow rate of hydrogen and by-products produced in the process plant 10.

[0152] In alternative or additional specific embodiments, one or more particular forms of the linear terms may include an equation specifying the measured energy or material flows that form the inputs to process plant 10 divided by an equation specifying the total aggregate economic value of the hydrogen and by-products produced in process plant 10.

[0153] Finally, for purposes of illustration, consider the specific example described above in connection with data acquisition module 116, where the obtained input data includes one or more of the following measurable and / or determinable parameters: amount of hydrogen produced, amount of electricity consumed, amount of steam produced, amount of syngas produced, amount of carbon monoxide produced, and amount of electricity produced.

[0154] In this context, examples of linear terms may include the ratio (natural gas consumed) / (hydrogen produced), the ratio (electricity consumed) / (hydrogen produced), the ratio (steam produced) / (hydrogen produced), and the ratio (electricity produced) / (hydrogen produced).

[0155] In the above example, the nonlinear transformation in each case is a determination of the ratio. Thus, the nonlinear transformation of the input parameters of consumed natural gas and consumed hydrogen is (consumed natural gas) / (hydrogen produced) to yield the associated linear terms, etc.

[0156] Once the linear terms are generated, a carbon intensity value can be derived for control of the production facility 10. The carbon intensity value is calculated from a linear combination of the linear terms, which means that the carbon intensity value is obtained from the sum of the products of each linear term and the corresponding linear coefficient.

[0157] The linear coefficients correspond to scaling factors or weightings applied to each linear term, which may be appropriately selected based on suitable physical, dynamic, or mathematical considerations. In an embodiment, the linear coefficients may be determined from one or more carbon intensity criteria models, as described below.

[0158] Once the carbon intensity values ​​are derived, they may be sent to the production control module 120 to enable control of the production facility 10. In an embodiment, the generated carbon intensity values ​​correspond to values ​​of one or more reference models. However, the inventive approach enables the generated carbon intensity values ​​to be produced on a time scale and frequency that enables real-time (or substantially real-time) control of the production facility 10. This is in contrast to known arrangements and models that are computationally complex and cannot be used to directly control the production process in real time.

[0159] Production Control Module 120

[0160] The production control module 120 is configured to generate signals operable to enable control of production rates and other operating parameters of the production facility 10 based on the carbon intensity values ​​derived by the carbon intensity determination module 118.

[0161] In an embodiment, the processing performed by the carbon intensity determination module 118 generates a set of linear terms based on non-linear transformations of relevant input parameters of the production facility 10. A carbon intensity value is then calculated as a linear combination of the generated linear terms.

[0162] In an embodiment, the linear terms may be expressed as rational functions, i.e., functions whose numerators and denominators include polynomials. Any of the linear terms described above in connection with the carbon intensity determination module 118 may be described in this manner.

[0163] As a result, in an embodiment, the rational functions so generated may be utilized by the production control module 120 to enable control of processes in the production facility 10. In an embodiment, the process control module 120 may comprise a digital control system (DCS) that uses a model predictive controller that supports a family of variable transformations, such as rational functions.

[0164] As a result, a model predictive controller so configured may be operable to utilize the linear terms and / or rational functions generated from the carbon intensity values ​​and generate control signals therefrom, a process which is described in more detail below.

[0165] The production control module 120 may use any suitable algorithms or techniques to generate control set points to allow for real-time adjustments to the production rate of the production facility 10 in response to changing network conditions and demand. The production control module 120 may also be in communication with the production facility 10.

[0166] In an embodiment, the production control module 120 may further comprise a control element operable to provide a control signal (e.g., a control set point) to the process controller 140 for controlling one or more control elements 10-1C, 10-2C, 10-3C, 10-4C of the process controller 140. In an embodiment, the production control module 120 is operable to generate ratios for the production facilities 10-1, 10-2, 10-3, 10-4 that are communicated to the process controller 140.

[0167] The production control module 120 is operable to communicate with a process controller 140, as described below.

[0168] The control system 100 comprises a process controller 140 that comprises a plurality of control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC associated with at least some of the production facilities 10...10-n.

[0169] In an embodiment, parameters controlled by the process controller 140 may include the operating rate of the hydrogen production process in question, the type of power source, the amount of power, the ramp rate, the flow rate, the pressure, the temperature, the throughput rate of materials (e.g., feedstocks, intermediates, and fuels for producing hydrogen), etc. These parameters are regulated by control elements / control systems 10-1C, 10-2C, 10-3C, 10-4C...10-nC associated with the production facility 10.

[0170] Control systems are typically associated with one or more sensors (e.g., flow meters, pressure sensors, temperature sensors, etc., as non-limiting examples) and actuators (e.g., pumps, valves, compressors, or blowers, as non-limiting examples) to maintain a control setpoint (e.g., a throughput or flow setpoint) and regulate the throughput of material through a process. These systems may comprise any suitable controller, for example, a proportional-integral-derivative (PID) controller.

[0171] Each production process in the production facility 10 actually has a maximum and minimum operating capacity. Generally, in dynamic operation, a maximum rate of change applies (which corresponds to the ramp rate). These constraints are typically set by safety, mechanical, electronic, material, or other physical constraints within the equipment.

[0172] The difference between the maximum and minimum operating points defines the operating range. Process constraints impose constraints on the maximum and minimum capacity of each process, along with constraints on the rate of change of production capacity in response to changes in controller setpoints (i.e., ramp rate). Physical equipment limits, quality, and / or safety parameters may also apply.

[0173] The above limits may be determined by the process controller 140 and the process may be controlled by a locally determined throughput setpoint or may be provided by the control module 120 of the controller 110 .

[0174] In an embodiment, the ratio values ​​for the production facilities 10-1, 10-2, 10-3, 10-4 determined by the process control module 120 are provided to the process controller 140, which is operable to dynamically control the production facilities 10 (via control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC) in response to this data.

[0175] In particular embodiments, the process controller 140 comprises a model predictive control (MPC) system. In embodiments, the MPC system includes a multivariable control algorithm that utilizes internal dynamic models of the production facility 10-1, 10-2, 10-3, 10-4 components, an appropriate cost function, and an optimization algorithm. In embodiments, the optimization algorithm is operable to minimize the cost function using multiple control inputs to the control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC.

[0176] However, in embodiments, alternative functions may be used, which may involve, for example, a similarity function that is maximized.

[0177] The process controller 140 is configured to receive the ratio values ​​of the production facilities 10-1, 10-2, 10-3, 10-4 determined by the process control module 118 and derive operating policies for the production facilities 10-1, 10-2, 10-3, 10-4, including setpoint operating parameters over a predetermined future time horizon. These are then provided to the control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC to control the associated processes controlled thereby. In an embodiment, it may utilize linear empirical models obtained by system identification of the various processes.

[0178] Alternatively or additionally, embodiments may utilize nonlinear high-fidelity models or nonlinear models created from machine learning algorithms. Because the process controller 140 knows the desired production facility 10-1, 10-2, 10-3, 10-4 ratios, it may be possible to utilize an MPC system to optimize the control set points and process for the current time period while also adapting to future time periods. In embodiments, this is accomplished by optimizing a finite time horizon of the process while implementing the current time period. Then, in the next time period, the optimization is performed again for another finite time horizon.

[0179] In an embodiment, the present invention provides a control system 100 operable to enable selective control of processes within a hydrogen supply network N to achieve a predetermined result when operating the hydrogen supply network N.

[0180] It should be understood that the above terms "module," "block," and "element" are non-limiting terms and do not necessarily imply any interconnection or grouping between components of systems 100, 110, 130, 140, which may be illustrated with common groupings for purposes of clarity only.

[0181] Variations are possible. For example, the carbon intensity determination module 118 may not be a separate module, but may be directly integrated with the production control module 120. This may be the case in an embodiment where the production control module 120 comprises a model predictive controller, such as the ASPEN DMC, that supports a family of variable transformations, such as rational functions.

[0182] Alternatively, in embodiments, production control module 120 may operate under open-loop conditions, for example, data generated by carbon intensity determination module 118 may be manually entered or programmed into production control module 120 without automatic feedback.

[0183] Reference Model Module 150

[0184] The control system 100 further comprises a reference model module 150. The reference model module 150 is operable to receive the linear terms determined by the carbon intensity determination module 118 and apply one or more non-linear transformations to the linear terms to generate reference model inputs.

[0185] The reference model inputs are entered into the reference model. In an embodiment, the reference model may include a Microsoft Excel workbook. The reference model is then operable to directly determine the reference carbon intensity using the applicable carbon intensity reference model. The resulting carbon intensity data may be stored in memory 114.

[0186] The reference model module 150 may process the linear term data less frequently than the carbon intensity determination module 118 processes the measured sensor data.

[0187] Update module 160

[0188] The update module 160 is operable to use the determined reference carbon intensity generated by the reference model in the reference model module 150 to update the linear coefficients used to generate the control carbon intensity value by the carbon intensity determination module 118.

[0189] The update module 160 generates updated linear coefficients using the determined baseline carbon intensity generated by the baseline model in the baseline model module 150 and the linear terms generated by the carbon intensity determination module 118. The updated linear coefficients are sent to the carbon intensity determination module 118 periodically.

[0190] In embodiments, update module 160 may process data to generate updated data less frequently than measured sensor data is processed by carbon intensity determination module 118. In embodiments, update module 160 may process data to generate updated data less frequently than reference model module 150 processes data to generate updated reference models.

[0191] method

[0192] 3 illustrates a method 200 according to an embodiment. In an embodiment, a computer-implemented method for operating a hydrogen production facility to meet carbon intensity (CI) requirements is provided. The method is implemented on a computer system utilizing at least one hardware processor 112, having memory 114, and operable to receive input from, among other things, a data block 130.

[0193] The method includes three control groups. Steps 210-250 define a first control loop for generating carbon intensity control data for control of the process facility 10. In an embodiment, this may be a closed-loop system. Steps 220 and 260-270 include a second control process for generating data from a reference model. Finally, steps 280 and 230 define a final process for updating the linear coefficients.

[0194] Step 210: Obtaining Operating Parameter Data

[0195] In step 210, operating parameter data (which may include measured sensor data) is received from the hydrogen production facility. The operating parameter data represents measured and / or determined time-dependent values ​​of one or more operating parameters of the hydrogen production facility.

[0196] In an embodiment, sensors 10-1S, 10-2S, 10-3S may be used to measure and report time-dependent values ​​of one or more operating process parameters of hydrogen processing facility 10. In an embodiment, this may be real-time and continuous.

[0197] In an embodiment, the operating process parameters may include one or more of the following: flow rate, pressure, temperature, hydrogen production rate, power consumption (which may include electricity consumption), feedstock (e.g., natural gas, coal, etc.) consumption rate, carbon dioxide capture rate, and steam production rate (if appropriate).

[0198] Additionally, data block 130 may include other data input elements 10-nS as needed. These data input elements are not limited to sensors and sensor data, but may include reported or determined technical values ​​related to additional elements of production facility 10. In an embodiment, this may include, among other things, technical information related to production information, fuel and process availability, or CI values.

[0199] For illustrative purposes, in specific embodiments, the obtained operating parameter data may include one or more of the following measurable and / or determinable parameters: amount of hydrogen produced, amount of electricity consumed, amount of steam produced, amount of syngas produced, amount of carbon monoxide produced, and amount of electricity produced.

[0200] In summary, step 210 is operable to measure, derive, and / or determine operational parameter data related to time-dependent values ​​of one or more operational parameters of the process plant 10. In an embodiment, step 210 is operable to measure, derive, and / or determine operational parameter data related to time-dependent values ​​of one or more operational parameters related to material and / or energy inputs to the process plant 10 and one or more operational parameters related to material and / or energy outputs from the process plant 10.

[0201] Once the operating parameter data (which may include measured sensor data) is obtained, the method proceeds to step 220 .

[0202] Step 220: Determine the linear term

[0203] In step 220, the operating parameter data is processed to define one or more linear terms, which are linear with respect to one or more carbon intensity criteria models.

[0204] In an embodiment, the processing includes performing one or more mathematical transforms on the received measured sensor data to generate linear terms. In an embodiment, the mathematical transforms include a non-linear transform operable to convert the received operational parameter data into one or more linear terms.

[0205] In an embodiment, converting the received operational parameter data into linear terms allows for direct correlation with one or more reference model carbon intensities. In other words, the linear terms have a linear effect on the reference model carbon intensities. Once the linear terms are derived, they may be stored in memory 114.

[0206] In an embodiment, the linear term has a particular form: In an embodiment, the linear term may represent the ratio of input to the process plant 10 to output from the process plant 10.

[0207] For example, in particular embodiments, one or more particular forms of the linear terms may include an equation specifying the measured energy or material flows that form the inputs to the process plant 10 divided by an equation specifying the total mass flow rate of hydrogen and by-products produced in the process plant 10.

[0208] In an embodiment, one or more forms of linear terms may include an equation specifying a measured energy or material flow that is an input to a hydrogen production facility divided by an equation specifying the total energy rate of hydrogen and by-products produced at the facility.

[0209] In an embodiment, if one or more by-products is a gas, the total energy rate is determined as the product of the flow rate of the gas and its lower heating value. Alternatively or additionally, if the by-product is electricity, the energy rate is the power of the electricity.

[0210] In alternative or additional specific embodiments, one or more particular forms of the linear terms may include an equation specifying the measured energy or material flow forming the input to the process plant 10 divided by an equation specifying the total molar flow rate of hydrogen and by-products produced in the process plant 10.

[0211] In alternative or additional specific embodiments, one or more particular forms of the linear terms may include an equation specifying the measured energy or material flows that form the inputs to process plant 10 divided by an equation specifying the total aggregate economic value of the hydrogen and by-products produced in process plant 10.

[0212] Finally, for purposes of illustration, consider the specific example described above in connection with data acquisition module 116, where the obtained input data includes one or more of the following measurable and / or determinable parameters: amount of hydrogen produced, amount of electricity consumed, amount of steam produced, amount of syngas produced, amount of carbon monoxide produced, and amount of electricity produced.

[0213] In this context, examples of linear terms may include the ratio (natural gas consumed) / (hydrogen produced), the ratio (electricity consumed) / (hydrogen produced), the ratio (steam produced) / (hydrogen produced), and the ratio (electricity produced) / (hydrogen produced).

[0214] In the above example, the nonlinear transformation in each case is a determination of the ratio. Thus, the nonlinear transformation of the input parameters of consumed natural gas and consumed hydrogen is (consumed natural gas) / (hydrogen produced) to yield the associated linear terms, etc.

[0215] The method proceeds to step 230 .

[0216] Step 230: Generate a carbon intensity value for the control system

[0217] In step 230, the linear terms derived in step 220 may be utilized to generate a control system carbon intensity value that represents the carbon intensity of the hydrogen produced by the hydrogen production facility.

[0218] The carbon intensity value of the control system is calculated from a linear combination of linear terms, which means that the carbon intensity value is obtained from the sum of the products of each linear term and the corresponding linear coefficient.

[0219] The linear coefficients in each case correspond to scaling factors or weightings applied to each linear term, which may be appropriately selected based on suitable physical, dynamic, or mathematical considerations. In embodiments, the linear coefficients may be determined from one or more carbon intensity criteria models, as described below.

[0220] Step 240: Generate control settings

[0221] In step 240, the control system carbon intensity value generated in step 230 may be used to generate control variables for controlling one or more operating parameters of the hydrogen production facility.

[0222] Once the carbon intensity values ​​are derived, they may be sent to production control module 120 to enable control of production facility 10. In an embodiment, the generated carbon intensity values ​​correspond to values ​​of one or more reference models, which may be used by a suitable control system to generate control settings that enable control of production facility 10 according to desired carbon intensity requirements.

[0223] The control system 110 is operable to adjust one or more operating parameters of the production facilities 10 by generating production rate setpoints for each production facility 10 and communicating the setpoints to each respective production facility 10.

[0224] In an embodiment, the processing performed in step 220 generates a set of linear terms based on a non-linear transformation of relevant input parameters of the production facility 10. A carbon intensity value is then calculated in step 230 as a linear combination of the generated linear terms.

[0225] In an embodiment, the linear terms may be expressed as rational functions, i.e., functions whose numerators and denominators contain polynomials. Any of the linear terms generated in step 220 may be described in this manner.

[0226] As a result, in an embodiment, the rational functions so generated may be utilized in step 240 by the control system 110 to generate control set points for controlling the processes of the production facility 10 .

[0227] In an embodiment, the process control module 120 of the control system 110 may comprise a digital control system (DCS) using a model predictive controller that supports a family of variable transformations, such as rational functions.

[0228] As a result, step 240 may include implementing a model predictive controller operable to utilize the linear terms and / or rational functions generated from the carbon intensity values ​​and generate control signals therefrom.

[0229] Step 250: Manage your production facilities

[0230] In step 250, the hydrogen production facility 10 may be controlled according to the determined control variable set points.

[0231] Operating parameter setpoints may be determined by the process control module 120 and provided to the process controller 140, which is operable to dynamically control the production facility 10 (through control elements 10-1C, 10-2C, 10-3C, 10-4C, ... 10-nC) in response to this data.

[0232] In particular embodiments, the generated control variables for production rate control may be utilized in a model predictive control (MPC) system. In embodiments, the MPC system includes a multivariable control algorithm that utilizes internal dynamic models of the production facility 10-1, 10-2, 10-3, 10-4 components, an appropriate cost function, and an optimization algorithm. In embodiments, the optimization algorithm is operable to minimize the cost function using multiple control inputs to the control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC.

[0233] However, in embodiments, alternative functions may be used, which may involve, for example, a similarity function that is maximized.

[0234] In an embodiment, controlling the hydrogen production facility 10 according to the values ​​of the control variables may include receiving, by the process controller 140, ratio values ​​for the production facilities 10-1, 10-2, 10-3, 10-4 determined by the process control module 120, and deriving operating policies for the production facilities 10-1, 10-2, 10-3, 10-4 including setpoint operating parameters over a predetermined future time range.

[0235] Control is then exercised via control elements 10-1C, 10-2C, 10-3C, 10-4C...10-nC, which control the associated processes controlled thereby. In an embodiment, this process may utilize linear empirical models obtained by system identification of the various processes.

[0236] Alternatively or additionally, in embodiments, the process may utilize a nonlinear high-fidelity model or a nonlinear model created from a machine learning algorithm. Because the process controller 140 knows the desired production facility 10-1, 10-2, 10-3, 10-4 ratios, it may be possible to utilize an MPC system to optimize the control set points and process for the current time period while also adapting to future time periods. In embodiments, this is accomplished by optimizing a finite time horizon of the process while implementing the current time period. Then, in the next time period, the optimization is performed again for another finite time horizon.

[0237] In an embodiment, steps 210-250 may be repeated in a closed control loop cycle with a frequency of minutes. In a specific embodiment, the control loop cycle frequency may be every 5 minutes. In a specific embodiment, the control loop cycle frequency may be every 15 minutes.

[0238] Steps 260-270 define additional processes that are described below.

[0239] Step 260: Process linear terms

[0240] In step 260, the linear terms determined by the carbon intensity determination module 118 in step 220 are taken and one or more non-linear transformations are applied to the linear terms to generate reference model inputs.

[0241] The reference model inputs may, in embodiments, include inputs to one or more cells in an Excel reference model. The nonlinear transformation allows for determining the correct reference model inputs based on linear terms. The nonlinear transformation may depend on the reference model used.

[0242] For example, in the US GREET model, one of the baseline model inputs is the efficiency ratio η = (hydrogen produced) / (natural gas consumed + electricity consumed). If the linear terms include A = (natural gas consumed) / (hydrogen produced) and B = (electricity consumed) / (hydrogen produced), then the nonlinear transformation T that converts A and B to η is η = T(A, B) = 1 / (A + B). The accuracy of the transformation T can be verified by algebraic manipulation.

[0243] The method proceeds to step 270.

[0244] Step 270: Generate a reference model

[0245] The reference model inputs are entered into the reference model. In an embodiment, the reference model may include a Microsoft Excel workbook. The reference model inputs thus generate an updated reference model.

[0246] Step 280: Determine the Reference Carbon Intensity

[0247] The updated reference model is then operable to directly determine the reference carbon intensity using the applicable carbon intensity reference model, step 280. The resulting carbon intensity data may be stored in memory 114.

[0248] The reference model module 150 may process the linear term data less frequently than the carbon intensity determination module 118 processes the measured sensor data.

[0249] Step 290: Update the linear coefficients

[0250] In step 290, the update module 160 is operable to update the linear coefficients used to generate the control carbon intensity value by the carbon intensity determination module 118 using the determined reference carbon intensity generated by the reference model in the reference model module 150 in step 280.

[0251] The update module 160 generates updated linear coefficients using the determined reference carbon intensity produced by the reference model in the reference model module 150 and the linear terms generated by the carbon intensity determination module 118. The updated linear coefficients are periodically sent to the carbon intensity determination module 118. They are then used in step 230 to generate updated carbon intensity control data.

[0252] In embodiments, update module 160 may process data to generate updated data less frequently than measured sensor data is processed by carbon intensity determination module 118. In embodiments, update module 160 may process data to generate updated data less frequently than reference model module 150 processes data to generate updated reference models.

[0253] Example of how it works

[0254] 4-8 show implementations for the control of hydrogen production from natural gas with carbon intensities calculated using the US GREET reference model.

[0255] In an embodiment, a hydrogen production facility is used to produce hydrogen from natural gas. Electricity is used to drive fans, pumps, and controllers within the facility. The plant produces a steam by-product. Carbon dioxide produced in the process of converting hydrogen to natural gas is captured and sequestered.

[0256] The U.S. Inflation Control Act requires hydrogen producers to quantify the carbon intensity of their hydrogen production using the US GREET model. The US GREET model is a large Excel workbook with over 60 worksheets and tens of thousands of parameters. A screenshot of a portion of one worksheet from the US GREET model is shown in Figure 4.

[0257] The US GREET Excel model typically takes 60 seconds to calculate carbon intensity, and furthermore, it is not possible to directly integrate the Excel model into real-time digital control systems (DCS) such as those typically used to control large industrial facilities.

[0258] Therefore, it is desirable to utilize the method and system of the present invention to calculate carbon intensity for purposes of controlling a facility, as typically done with a digital control system (DCS), and modeling predictive control, as typically done using ASPEN DMC Plus.

[0259] FIG. 5 is a screenshot illustrating the configuration of measured quantities used in an embodiment of the present invention, where the measured quantities used to calculate carbon intensity include a measured hydrogen production rate, a measured electricity consumption rate, a measured natural gas consumption rate, a measured carbon dioxide capture rate, and a measured steam production rate.

[0260] 6 is a screenshot illustrating the configuration of the nonlinear transformation and linear terms used in an exemplary embodiment of the present invention. As shown in the Python code associated with the object property "linear_input_formulas", there are four linear terms, including:

[0261] Quantity specified as ELEC_IN / H2_OUT (electricity consumption rate) / (hydrogen production rate).

[0262] The quantity specified as NG_IN / H2_OUT is (natural gas consumption rate) / (hydrogen production rate).

[0263] The quantity specified as CO2_OUT / H2_OUT (carbon dioxide capture rate) / (hydrogen production rate).

[0264] Quantity specified as STEAM_OUT / H2_OUT (steam production rate) / (hydrogen production rate).

[0265] Figure 7 shows the configuration of a calculation model for the carbon intensity of hydrogen produced from natural gas. Specifically, it shows the linear coefficients used to form a linear combination of linear terms. For example, GHG emissions for combustion in a steam methane reformer (SMR) are calculated using the following linear equation:

[0266] [GHG from SMR combustion emissions]=-254.6+18311*[NG_IN / H2_OUT]-2800*[CO2_CAPTURED / H2_OUT].

[0267] 8 shows the configuration of baseline model inputs 70 used in the baseline model to calculate GHG emissions associated with producing hydrogen from natural gas. For this model, the baseline model inputs include:

[0268] Cell E9 in the "Input" sheet of the GREET2022 Excel model

[0269] Cell F721 in the "Input" sheet of the GREET2022 Excel model

[0270] Cell K1078 in the "Input" sheet of the GREET2022 Excel model

[0271] Cell K1079 in the "Input" sheet of the GREET2022 Excel model

[0272] Cell K1081 in the "Input" sheet of the GREET2022 Excel model

[0273] Cell K1082 in the "Input" sheet of the GREET2022 Excel model

[0274] Cell K1083 in the "Input" sheet of the GREET2022 Excel model

[0275] Cell B11 of the "Hydrogen" sheet in the GREET2022 Excel model

[0276] Cell F251 in the "Input" sheet of the GREET2022 Excel model

[0277] A nonlinear transformation 60 is used to calculate the values ​​of the reference model inputs 70. An example of a nonlinear transformation 60 is provided in Figure 9. In this example, the nonlinear transformation 60 used to calculate the reference model input cell K1078 on the input model sheet of the reference model 80GREET2022 as a function of the linear terms 30 is as follows:

[0278] [Cell K1078]=1 / (NG_IN / H2_OUT+ELEC_IN / H2_OUT)

[0279] In a real-time control system, measured quantities are read from sensors and nonlinear transformations are used to produce linear terms which are then multiplied with linear coefficients to produce linear combinations which are summed to produce control system carbon intensities which are then used by the plant control system to adjust plant rates. The linear terms and control system carbon intensities are stored. This mechanism for calculating carbon intensities is very accurate and very computationally efficient.

[0280] Less frequently, and outside of the real-time control system, nonlinear transformations are used to calculate baseline model inputs, which are provided to the baseline model, typically a large Excel model, and used to calculate a baseline carbon intensity, which is also stored. The baseline model may not be computationally efficient and may not be suitable for integration with a real-time control system. The baseline carbon intensity is typically exactly equal to the carbon intensity of the control system, and calculating the baseline carbon intensities using the baseline model and comparing them to the carbon intensity of the control system can be used to verify the carbon intensity of the control system.

[0281] From time to time, the reference model may be updated by regulatory or scientific organizations. For example, the US GREET model is typically updated annually by Argonne National Laboratory in October. When these updates occur, the linear terms and corresponding reference carbon intensities may be used in the process of updating the linear coefficients. This ensures that the calculations of the control system for low-carbon hydrogen supply are accurate when the reference model is updated.

[0282] Those skilled in the art will appreciate that various modifications can be made to the examples described above without departing from the scope of the present invention, which is defined by the appended claims.

[0283] Although the invention has been described with reference to the preferred embodiments illustrated in the drawings, it will be understood that various modifications can be made within the spirit and scope of the invention as defined in the following claims.

[0284] For example, although some of the above exemplary embodiments are described in the context of a hydrogen distribution network for supplying hydrogen fuel, the invention is not so limited. The invention is equally applicable to processes for providing hydrogen having a defined carbon intensity value to end-user locations for purposes other than as a fuel. In other words, in embodiments, a hydrogen distribution network may be considered to be a hydrogen distribution network for the supply of hydrogen for any suitable purpose.

[0285] It will be understood that the term "control," as used herein, may refer, in embodiments, to a systematic plan or series of actions designed to manage and optimize the operation of one or more hydrogen production facilities to produce fuels having defined carbon intensity values, taking into account factors such as feedstock carbon intensity, demand data, and process constraints.

[0286] It will be understood that the term "control" as used herein may refer to the management and regulation of the operation of an industrial plant at an industrial processing facility to ensure that the production of hydrogen and / or hydrogen fuel complies with the defined carbon intensity values ​​and other constraints set by the optimization model.

[0287] It will be understood that the term "fuel" as used herein may refer to any type of fuel used to power a process (including industrial processes) for converting stored fuel energy into useful work. The term "fuel" as used herein may include, but is not limited to, "transportation fuels" used to power vehicles for the purpose of facilitating the movement of people or goods.

[0288] It will be understood that the term "defined carbon intensity (CI) value" as used herein refers to a predetermined or specified value, expressed in mass of carbon dioxide equivalent per unit of energy, for greenhouse gas emissions associated with the production, processing, and distribution of a product such as hydrogen or hydrogen fuel, and may be used as a target or constraint in an optimization process for producing fuel in an environmentally sustainable manner.

[0289] In this specification, unless expressly indicated otherwise, the word "or" is used in the sense of an operator that returns a value of true when either 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 "consisting of."

[0290] Where applicable, various embodiments provided by the present disclosure may be implemented using hardware, software, or a combination of hardware and software. Also, where applicable, various hardware and / or software components described herein may be combined into composite components including software, hardware, and / or both without departing from the spirit of the present disclosure. Where applicable, various hardware and / or software components described herein may be separated into subcomponents including software, hardware, or both without departing from the scope of the present disclosure. Additionally, where applicable, it is contemplated that software components may be implemented as hardware components, and vice versa.

[0291] Software according to the present disclosure, such as program code and / or data, may be stored on one or more computer-readable media. It is also contemplated that the software specified herein may be networked and / or otherwise implemented using one or more general-purpose or special-purpose computers and / or computer systems. Where applicable, the order of various steps described herein may be changed, combined into composite steps, and / or separated into substeps to provide the features described herein.

[0292] Although various operations are described herein in terms of "modules," "units," or "components," these terms should not be limited to a single unit or function. In addition, functionality attributed to some of the modules or components described herein may be combined and attributed to fewer modules or components.

[0293] It will be apparent to those skilled in the art that modifications, additions, or deletions may be made to the disclosed embodiments without departing from the spirit and scope of the invention. For example, one or more parts of the methods described above may be performed in a different order (or simultaneously) and still achieve desirable results.

Claims

1. 1. A computer-implemented method for operating a hydrogen production facility to meet carbon intensity (CI) requirements, the method being executed by at least one hardware processor and comprising: receiving, using a computer system, operational parameter data from the hydrogen production facility, the operational parameter data representing measured and / or determined time-dependent values ​​of one or more operational parameters of the hydrogen production facility; processing the operational parameter data using a computer system to define one or more linear terms, the linear terms being linear with respect to one or more CI reference models; generating a control system CI value from the one or more linear terms that represents the CI of hydrogen produced by the hydrogen production facility; generating, using a computer system and based on the control system CI value, control variables for controlling one or more operating parameters of the hydrogen production facility; and controlling the hydrogen production facility according to the determined control variables.

2. 2. The computer-implemented method of claim 1, wherein the operational parameter data comprises measured and / or determined time-dependent values ​​of one or more operational parameters related to material and / or energy inputs to and material and / or energy outputs from the hydrogen production facility.

3. 3. The computer-implemented method of claim 2, wherein the one or more operating parameters are selected from the group of an amount of hydrogen produced, an amount of electricity consumed, an amount of steam produced, an amount of syngas produced, an amount of carbon monoxide produced, and an amount of electricity produced.

4. The computer-implemented method of claim 2 , wherein the processing step includes applying one or more non-linear transformations to the operational parameter data of one or more operational parameters to define the one or more linear terms.

5. 5. The computer-implemented method of claim 4, wherein one or more of the linear terms specify a ratio of material and / or energy inputs to the hydrogen production facility and material and / or energy outputs from the hydrogen production facility.

6. One or more of the linear terms may represent measured energy or material flow inputs to the hydrogen production facility as: i) the total mass flow rate of hydrogen and by-products produced at the hydrogen production facility; ii) the total molar flow rate of hydrogen and by-products produced at the hydrogen production facility; iii) the total economic value of hydrogen and by-products produced at the hydrogen production facility.

7. 6. The computer-implemented method of claim 5, wherein one or more of the linear terms specify a measured energy or material flow input to the hydrogen production facility divided by a total energy rate of hydrogen and by-products produced at the hydrogen production facility.

8. The computer-implemented method of claim 7 , wherein one or more by-products include a gas, and the total energy rate is determined as the product of the flow rate of the gas and its lower heating value.

9. The computer-implemented method of claim 8 , wherein by-products include electricity and the energy rate is power produced.

10. 2. The computer-implemented method of claim 1, wherein generating the control system CI value includes determining a sum of products of each of one or more linear terms and a corresponding linear coefficient, the linear coefficients being derived from one or more of the CI reference models.

11. The computer-implemented method of claim 10 , further comprising updating the linear coefficients based on one or more perturbations of the CI reference model.

12. The computer-implemented method of claim 1 , wherein generating control variables and controlling the hydrogen production facility utilizes model predictive control.

13. 1. A system for operating a hydrogen production facility to meet carbon intensity (CI) requirements, the system comprising: at least one hardware processor; a data acquisition module configured to receive operational parameter data from the hydrogen production facility, the operational parameter data representing measured and / or determined time-dependent values ​​of one or more operational parameters of the hydrogen production facility; and a CI determination module, processing the operational parameter data to define one or more linear terms, the linear terms being linear with respect to one or more carbon intensity reference models; and generating a control system CI value from the one or more linear terms that represents the CI of hydrogen produced by the hydrogen production facility. a production control module configured to generate control variables for controlling one or more operating parameters of the hydrogen production facility based on a function of the control system CI value; a process controller configured to control the hydrogen production facility according to the determined control variable.

14. 14. The system of claim 13, wherein the operational parameter data includes measured and / or determined time-dependent values ​​of one or more operational parameters related to material and / or energy inputs to and material and / or energy outputs from the hydrogen production facility.

15. 15. The system of claim 14, wherein the CI determination module is configured to apply one or more non-linear transformations to the operational parameter data of one or more operational parameters to define the one or more linear terms.

16. 15. The system of claim 14, wherein one or more of the linear terms specify a ratio of material and / or energy inputs to the hydrogen production facility and material and / or energy outputs from the hydrogen production facility.

17. 17. The system of claim 16, wherein one or more of the linear terms specify a measured energy or material flow input to the hydrogen production facility divided by a total energy rate of hydrogen and by-products produced at the hydrogen production facility.

18. 20. The system of claim 17, wherein one or more by-products include gas and the total energy rate is determined as the product of the flow rate of the gas and its lower heating value, and / or a by-product includes electricity and the energy rate is the generated power.

19. The system of claim 13 , wherein the production control module and process controller comprises a model predictive controller operable to generate the control variables and control the hydrogen production facility.

20. 1. A non-transitory computer-readable storage medium storing a program of machine-executable instructions for implementing a method for operating a hydrogen production facility to meet carbon intensity (CI) requirements, the method being executed by at least one hardware processor; receiving, using a computer system, operational parameter data from the hydrogen production facility, the operational parameter data representing measured and / or determined time-dependent values ​​of one or more operational parameters of the hydrogen production facility; processing the operational parameter data using a computer system to define one or more linear terms, the linear terms being linear with respect to one or more carbon intensity criteria models; generating a control system CI value from the one or more linear terms that represents the CI of hydrogen produced by the hydrogen production facility; generating, using a computer system and based on the control system CI value, control variables for controlling one or more operating parameters of the hydrogen production facility; and controlling the hydrogen production facility according to the determined control variable.

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