REAL-TIME CONTROL SYSTEM FOR CARBON INTENSITY COMPLIANCE IN A HYDROGEN PRODUCTION FACILITY

Abstract

A method of operating a hydrogen production facility to meet carbon intensity (CI) requirements, the method comprising: 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, wherein 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.

Claims

1. A computer-implemented method of 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 being representative of measured and/or determined time-dependent values of one or more operational parameters of the hydrogen production facility; processing, using a computer system, the operational parameter data to define one or more linear terms, wherein the linear terms are linear with respect to one or more CI reference models; generating, from the one or more linear terms, control system CI values representative of the CI of hydrogen produced by the hydrogen production facility; generating, using a computer system and based on a function of the control system CI values, 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.

2. The computer-implemented method of claim 1, wherein the operational parameter data comprises measured and/or determined time-dependent values for one or more operational parameters relating to materials and/or energy input to the hydrogen production facility and materials and/or energy output from the hydrogen production facility.

3. The computer-implemented method of claim 2, wherein the one or more operational parameters are selected from the group of: a quantity of hydrogen produced; a quantity of electricity consumed; a quantity of steam produced; a quantity of syngas produced; a quantity of carbon monoxide produced; and a quantity of electricity produced.

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

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

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 one of: i) a total mass flow rate of hydrogen and coproducts produced at the hydrogen production facility; ii) a total molar flow rate of hydrogen and coproducts produced at the hydrogen production facility; iii) a total economic value of hydrogen and coproducts produced at the hydrogen production facility.

7. 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 coproducts produced at the hydrogen production facility.

8. The computer-implemented method of claim 7, wherein one or more coproducts comprises 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 a coproduct comprises electricity and the energy rate is the generated electric power.

10. The computer-implemented method of claim 1, wherein the step of generating control system CI values comprises determining a sum of the product of each of the one or more linear terms with 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 the step of updating the linear coefficients based on a perturbation of one or more of the CI reference models.

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

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 being representative of measured and/or determined time-dependent values of one or more operational parameters of the hydrogen production facility; a CI determination module configured: to process the operational parameter data to define one or more linear terms, wherein the linear terms are linear with respect to one or more carbon intensity reference models; and to generate, from the one or more linear terms, control system CI values representative of the CI of hydrogen produced by the hydrogen production facility; a production control module configured to generate, based on a function of the control system CI values, control variables for controlling one or more operational parameters of the hydrogen production facility; and a process controller configured to control the hydrogen production facility in accordance with the determined control variables.

14. The system of claim 13, wherein the operational parameter data comprises measured and/or determined time-dependent values for one or more operational parameters relating to materials and/or energy input to the hydrogen production facility and materials and/or energy output from the hydrogen production facility.

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

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

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 coproducts produced at the hydrogen production facility.

18. The system of claim 17, wherein one or more coproducts comprises a 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 wherein a coproduct comprises electricity and the energy rate is the generated electric power.

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

20. A non-transitory computer readable storage medium storing a program of instructions executable by a machine to perform a method of 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 being representative of measured and/or determined time-dependent values of one or more operational parameters of the hydrogen production facility; processing, using a computer system, the operational parameter data to define one or more linear terms, wherein the linear terms are linear with respect to one or more carbon intensity reference models; generating, from the one or more linear terms, control system CI values representative of the CI of hydrogen produced by the hydrogen production facility; generating, using a computer system and based on a function of the control system CI values, 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.

Description

BRIEF DESCRIPTION OF DRAWINGS

[0101] Embodiments of the present invention will now be described by example only and with reference to the figures in which:

[0102] FIG. 1 is a schematic diagram of a hydrogen production facility and associated control system;

[0103] FIG. 2 is a schematic diagram of a control system according to an embodiment;

[0104] FIG. 3 is a flow diagram of a method according to an embodiment; and

[0105] FIGS. 4 to 9 illustrate an operational example of an embodiment of the present invention.

[0106] Embodiments of the present disclosure and their advantages are best understood by referring to the detailed description that follows. It should be appreciated that like reference numbers are used to identify like elements illustrated in one or more of the figures, wherein showings therein are for purposes of illustrating embodiments of the present disclosure and not for purposes of limiting the same.

DETAILED DESCRIPTION OF THE INVENTION

[0107] Various examples and embodiments of the present disclosure will now be described. The following description provides specific details for a thorough understanding and enabling description of these examples. One of ordinary skill in the relevant art will understand, however, that one or more embodiments described herein may be practiced without many of these details. Likewise, one skilled in the relevant art will also understand that one or more embodiments of the present disclosure can 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, so as to avoid unnecessarily obscuring the relevant description.

[0108] The present invention is directed to the technical field of control systems for hydrogen production facilities to meet greenhouse gas intensity constraints.

[0109] The technology described herein provides technical improvements to the existing control of variables related to industrial hydrogen production in accordance with carbon intensity requirements and regulations. Technical improvements enable one or more production facilities to be controlled in order to manage technical considerations and constraints on hydrogen production.

Overview of Hydrogen Production Facility

[0110] FIG. 1 shows a general schematic diagram of a hydrogen production facility 10. In FIG. 1, the production facility 10 is operable to produce hydrogen from feedstocks. The production facility 10 is configured to produce hydrogen at a production rate which is selectable and can be varied as a function of time between maximum and minimum levels.

[0111] In other words, the production facility 10 can produce hydrogen at a selected variable rate. 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 and utilization or storage (CCUS) to reduce the GHG emissions from the production process or green hydrogen produced using renewable energy.

[0112] The means for producing hydrogen may include any suitable technology or process that can produce low-carbon hydrogen from renewable or fossil sources. For example, the production facility 10 may comprise a hydrogen production plant comprising an electrolyser operable to produce hydrogen by electrolysis of feedstocks such as water, brine or steam. Alternatively, the production facility 10 may comprise a gasification plant or a reformer operable to produce liquid or gaseous hydrogen through steam reforming using methane as a feedstock.

[0113] Alternatively, hydrogen may be produced from a hydrogen production plant forming part of an ammonia production plant. An ammonia production plant may, in examples, comprise a hydrogen production plant, an air separation unit (ASU) and an ammonia synthesis plant. Storage for produced hydrogen may also be provided.

[0114] In order to reduce the carbon intensity of the production process, electricity for powering the production facility 10 may generated at least in part by renewable energy sources such as wind and/or the solar power sources although other sources may optionally be utilized. Green fuel produced using renewable power sources will have very low to zero CI at the point of production.

[0115] Different production facilities 10 may have different capacities, efficiencies, costs, and environmental impacts. In addition, whilst 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.

[0116] The production facility 10 is communicatively coupled to one or more sensor elements 10S. Sensor elements 10S are operable to measure the values of one or more operational parameters of the production facility 10. In embodiments, the sensor elements 10S are operable to measure the values of one or more operational parameters of the production facility 10 as a function of time (in other words, time-dependent variables are measured).

[0117] The measured operational parameters may be selected in dependence upon the type of production facility 10 that is in question. However, operational parameters may comprise one or more of the following: rate of production of hydrogen, power consumption (which may comprise electricity consumption), rate of consumption of feedstocks (e.g., natural gas, coal etc.), rate of capture of carbon dioxide, and rate of steam production (where appropriate).

[0118] The measured operational parameters may be measured directly (e.g., flow rate may be measured by a flow sensor) or indirectly through other parameters (e.g. calculated or otherwise determined from one or more other measurements).

[0119] In addition, the process plant 10 is communicatively coupled to one or more control elements 10C. Control elements 10C are operable to control, for example, the rate of the hydrogen production process in question, type of power supply, amount of power, ramp rates, throughput rate of materials (e.g., feedstocks, intermediates, and fuels to produce hydrogen).

[0120] Control systems typically maintain a control setpoint (e.g., a throughput or flow setpoint) and are associated with one or more sensors (such as, in non-limiting examples, flow rate meters, pressure sensors, temperature sensors etc.) and actuators (such as, in non-limiting examples, pumps, valves, compressors, or blowers) to regulate the throughput of materials through the process.

[0121] 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 function of these components will be described in more detail below with reference to FIG. 2.

Control System 100

[0122] FIG. 2 shows a schematic diagram of a control system 100 for controlling elements of the hydrogen production facility.

[0123] In embodiments, the present invention provides a method of, and system for, controlling processes within the hydrogen production facility to meet carbon intensity requirements. In embodiments, the control system 100 is operable to receive inputs from technical sources and determine optimum operating parameters for one or more production facilities 10 as described below in order to meet carbon intensity requirements.

[0124] In specific embodiments, the present invention is operable to enable interfacing between plant control systems and reference carbon intensity models to enable carbon intensity constraints to be met under multiple contexts.

[0125] The control system 100 comprises a controller 110. The controller 110 comprises at least one hardware processor 112 and at least one non-transitory memory 114. The controller 110 further comprises a data acquisition module 116, a carbon intensity determination module 118, and a control module 120.

[0126] The control system 100 further comprises a data block 130 for obtaining and transmitting data from the production facility 10 and a process controller 140 to enable control of the production facility 10. Finally, the control system 100 comprises a reference model module 150 and an update module 160.

[0127] The data block 130 is arranged to receive inputs from the sensors 10S or other components of the production facility 10. In embodiments, the data block 130 comprises one or more sensors 10-1S, 10-2S, 10-3S, 10-4S. In embodiments, the production facility 10 comprises one or more sensors 10-1S, 10-2S, 10-3S, 10-4S of the data block 130.

[0128] The sensors 10-1S, 10-2S, 10-3S enable reporting of operational process parameters. In embodiments, this may be in real-time and continuous. For example, in embodiments, data relating to operational process parameters may comprise the time-dependent values of one or more of the following parameters: flow rates, pressures, temperatures, rate of production of hydrogen, power consumption (which may comprise electricity consumption), rate of consumption of feedstocks (e.g., natural gas, coal etc.), rate of capture of carbon dioxide, and rate of steam production (where appropriate).

[0129] In addition, the data block 130 may comprise other data input elements 10-nS as required. These data input elements are not limited to sensors and sensor data and may include reporting or determined technical values on additional elements of the production facility 10. In embodiments, this may include technical information relating to production information, availability or CI values of fuels and processes, amongst others.

[0130] In summary, the data block 130 is operable to measure, derive and/or determine operational parameter data relating to time-dependent values of one or more operational parameters of the process plant 10. In embodiments, the data block 130 is operable to measure, derive and/or determine operational parameter data relating to time-dependent values of one or more operational parameters relating to materials and/or energy input to the process plant 10 and one or more operational parameters relating to materials and/or energy output from the process plant 10.

Data Acquisition Module 116

[0131] 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 the data input block 120. The data acquisition module 116 communicates with the carbon intensity determination module 118 and the control module 120 to provide them with the necessary data for their functions.

[0132] The data acquisition module 116 is operable to generate and/or collate input data for the carbon intensity determination module 118 as described below.

[0133] For the purpose of illustration, in specific embodiments the input data obtained may include one or more of the following measurable and/or determinable parameters: a quantity of hydrogen produced; a quantity of electricity consumed; a quantity of steam produced; a quantity of syngas produced; a quantity of carbon monoxide produced; and a quantity of electricity produced.

Carbon Intensity Determination Module 118

[0134] The carbon intensity determination module 118 is operable to process the sensor and other operational characteristic data obtained from the data block 130 and received by the data acquisition module 116 and perform computational processing thereon to generate data corresponding to the carbon intensity of the hydrogen produced by the production facility 10 such that the data is consistent with carbon intensity reference models. This data so generated can then be utilized by the control module 120 to control the process controller 140 to adjust setpoints of the controls 10C of the production plant 10.

[0135] The carbon intensity determination module 118 may be configured to determine the carbon intensity of hydrogen produced at the production facility 10 and to enable control thereof. In embodiments, CI values are dependent on control variables such as production facility 10 rates because production facility 10 efficiencies have a dependence upon production facility rates.

[0136] In embodiments, the sensors 10-1S, 10-2S, 10-3S enable real-time and continuous reporting of production facility 10 operational process parameters. For example, in embodiments, operational process parameters may comprise one or more of the following: flow rates, pressures, temperatures, rate of production of hydrogen, power consumption (which may comprise electricity consumption), rate of consumption of feedstocks (e.g., natural gas, coal etc.), rate of capture of carbon dioxide, and rate of steam production (where appropriate).

[0137] The sensor data is received from the data acquisition module 116 by the carbon intensity determination module 118 and the received data is processed therein.

[0138] In embodiments, the processing comprises performing one or more mathematical transforms on the received operational parameter data (which may comprise measured sensor data). In embodiments, the mathematical transforms comprise nonlinear transformations which are operable to convert the received operational parameter data into one or more linear terms.

[0139] In embodiments, the transformation of the received operational parameter data to linear terms enable a direct correlation with one or more reference model carbon intensities. In other words, the linear terms have a linear effect on reference model carbon intensities. Once the linear terms have been derived, they may be stored in the memory 114.

[0140] In embodiments, the linear terms have a specific form. In embodiments, the linear terms may be representative of ratio of inputs to the process plant 10 to outputs from the process plant 10.

[0141] For example, in specific embodiments, the specific form of one or more of the linear terms may comprise expressions specifying a measured energy or material flow forming an input to the process plant 10, divided by expressions specifying a total mass flow rate of hydrogen and coproducts produced at the process plant 10.

[0142] In embodiments, the form of one or more of the linear terms may comprise expressions specifying a measured energy or material flow that is an input to the hydrogen production facility, divided by expressions specifying a total energy rate of hydrogen and coproducts produced at the facility.

[0143] In embodiments, if one or more coproducts 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 coproduct is electricity, the energy rate is the power of the electricity.

[0144] In alternative or additional specific embodiments, the specific form of one or more of the linear terms may comprise expressions specifying a measured energy or material flow forming an input to the process plant 10, divided by expressions specifying a total molar flow rate of hydrogen and coproducts produced at the process plant 10.

[0145] In alternative or additional specific embodiments, the specific form of one or more of the linear terms may comprise expressions specifying a measured energy or material flow forming an input to the process plant 10, divided by expressions specifying a total a total economic value of hydrogen and coproducts produced at the process plant 10.

[0146] Finally, for illustration purposes, consider the specific examples given above in relation to the data acquisition module 116 where the input data obtained includes one or more of the following measurable and/or determinable parameters: a quantity of hydrogen produced; a quantity of electricity consumed; a quantity of steam produced; a quantity of syngas produced; a quantity of carbon monoxide produced; and a quantity of electricity produced.

[0147] 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).

[0148] In the above examples, the non-linear transformation in each case is the determination of the ratio. Consequently, the non-linear transformation of the input parameters of natural gas consumed and hydrogen consumed is (natural gas consumed)/(hydrogen produced) to produce the relevant linear term, and so on.

[0149] Once the linear terms have been generated, the carbon intensity values for the control of the production facility 10 can be derived. The carbon intensity values are calculated from a linear combination of the linear terms. By this is meant that the carbon intensity values are obtained from the sum of the product of each linear term with a corresponding linear coefficient.

[0150] The linear coefficient corresponds to a scaling factor or weighting applied to each linear term. This may be selected as appropriate based on suitable physical, dynamic or mathematical considerations. In embodiments, as described below, the linear coefficients may be determined from one or more carbon intensity reference models.

[0151] Once the carbon intensity values have been derived, they can be sent to the production control module 120 to enable control of the production facility 10. In embodiments, the generated carbon intensity values correspond to those of one or more reference models. However, the approach of the present invention enables the generated carbon intensity values to be produced on timescales and frequencies that enable real-time (or substantially real-time) control of the production facility 10. This is in contrast to known arrangements and models which are computationally complex and incapable of being used to directly control production processes in real time.

Production Control Module 120

[0152] The production control module 120 is configured to generate a signal operable to enable control the production rates and other operational parameters of the production facility 10 based on the carbon intensity values derived by the carbon intensity determination module 118.

[0153] In embodiments, the processing performed by the carbon intensity determination module 118 generates a set of linear terms based on non-linear transformations of the relevant input parameters for the production facility 10. The carbon intensity values are then calculated as a linear combination of the generated linear terms.

[0154] In embodiments, the linear terms may be represented as rational functions, i.e. functions where the numerator and denominator comprise polynomials. Any of the linear terms described above in relation to the carbon intensity determination module 118 may be described in this manner.

[0155] Consequently, in embodiments, the rational functions so generated may be utilized by the production control module 120 to enable control of the processes of the production facility 10. In embodiments, the process control module 120 may comprise a digital control system (DCS) using a model predictive controller which supports a family of variable transformations such as rational functions.

[0156] As a result, a model predictive controller so configured may be operable to utilize the rational functions generated from the linear terms and/or carbon intensity values and generate control signals therefrom. This process is described in detail below.

[0157] The production control module 120 may use any suitable algorithm or technique to generate control setpoint values to enable adjustment of the production rates of the production facility 10 in real time according to the changes in the network conditions and demands. The production control module 120 may also communicate with the production facility 10.

[0158] In embodiments, the production control module 120 may further comprise control elements operable to provide control signals (e.g., control setpoints) to the process controller 140 to control one or more control elements 10-1C, 10-2C, 10-3C, 10-4C of the process controller 140. In embodiments, the production control module 120 is operable to generate production facility 10-1, 10-2, 10-3, 10-4 rates which are communicated to the process controller 140.

[0159] The production control module 120 is operable to communicate with process controller 140 as described below.

[0160] The control system 100 comprises the process controller 140 which comprises a plurality of control elements 10-1C, 10-2C, 10-3C, 10-4C . . . 10-nC associated with at least some of the production facility 10 . . . 10-n.

[0161] In embodiments, the parameters controlled by the process controller 140 may include operation rate of the hydrogen production process in question, type of power supply, amount of power, ramp rates, flow rates, pressures, temperatures, throughput rate of materials (e.g., feedstocks, intermediates, and fuels to produce hydrogen). These parameters are regulated by the control elements/control systems 10-1C, 10-20, 10-3C, 10-4C . . . 10-nC associated with the production facility 10.

[0162] Control systems typically maintain a control setpoint (e.g., a throughput or flow setpoint) and are associated with one or more sensors (such as, in non-limiting examples, flow rate meters, pressure sensors, temperature sensors etc.) and actuators (such as, in non-limiting examples, pumps, valves, compressors, or blowers) to regulate the throughput of materials through the process. These systems may comprise any suitable controller, for example, proportional-integral-derivative (PID) controllers.

[0163] Each production process of the production facility 10 will, in practice, have a maximum and minimum operational capacity. In general, in a dynamic operation a maximum rate of change will apply (this corresponds to the ramp rate). These constraints are typically set by safety, mechanical, electronic, material or other physical constraints within the equipment.

[0164] The difference between the maximum and minimum operating points defines the range of operation. Process constraints place constraints on the maximum and minimum capacity for each process, together with constraints on the rate of change of production capacity (i.e., ramp rates) in response to controller set point changes. Physical equipment limitations, quality and/or safety parameters may also apply.

[0165] The above limitations may be determined by the process controller 140 and the process(es) controlled by a throughput setpoint value either determined locally or provided by the control module 120 of the controller 110.

[0166] In embodiments, the production facility 10-1, 10-2, 10-3, 10-4 rate values determined by the process control module 120 are provided to the process controller 140 which is operable to control the production facility 10 dynamically (through control elements 10-1C, 10-20, 10-3C, 10-4C . . . 10-nC) in response to this data.

[0167] In specific embodiments, the process controller 140 comprises a model predictive control (MPC) system. In embodiments, the MPC system comprises a multivariable control algorithm that utilizes an internal dynamic model 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 a plurality of control inputs to control elements 10-1C, 10-2C, 10-3C, 10-4C . . . 10-nC.

[0168] However, in embodiments, alternative functions may be used. These may involve, for example, similarity functions which are maximized.

[0169] The process controller 140 is arranged to receive production facility 10-1, 10-2, 10-3, 10-4 rate values determined by the process control module 118 and derive a production facility 10-1, 10-2, 10-3, 10-4 operation policy including set point operation parameters over a predetermined future time horizon. These are then fed to the control elements 10-1C, 10-20, 10-30, 10-4C . . . 10-nC to control the relevant processes controlled thereby. In embodiments, it may utilize linear empirical models obtained by system identification of the various processes.

[0170] Alternatively, or additionally, in embodiments, it may utilize non-linear high-fidelity models or non-linear models created from a machine learning algorithm. The process controller 140, knowing the desired production facility 10-1, 10-2, 10-3, 10-4 rates, may utilize the MPC system to optimize the control set points and processes for a present time period, whilst also being able to adapt for future time periods. This is achieved, in embodiments, by optimizing a finite time horizon for the processes whilst implementing the current time period. The optimization is then performed again at the next time period for another finite time horizon.

[0171] In embodiments, the present invention provides a control system 100 operable to enable selective control of processes within the hydrogen supply network N to achieve predetermined outcomes when operating the hydrogen supply network N.

[0172] It will be understood that the above terms module, block and element are non-limiting terms and do not necessarily imply any interconnection or grouping between the component parts of the systems 100, 110, 130, 140 which may be illustrated in a common grouping for clarity purposes only.

[0173] Variations are possible. For example, the carbon intensity determination module 118 may not be a separate module and may be directly integrated with the production control module 120. This may, in embodiments, be the case if the production control module 120 comprises a model predictive controller such as ASPEN DMC which supports a family of variable transformations such as rational functions.

[0174] Alternatively, in embodiments, the production control module 120 may operate under open loop conditions. For example, data generated by the carbon intensity determination module 118 may be manually entered or programmed into the production control module 120 without automatic feedback.

Reference Model Module 150

[0175] 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 to apply one or more non-linear transformations to the linear terms to generate reference model inputs.

[0176] The reference model inputs are inputted into a reference model. In embodiments, the reference model may comprise a Microsoft Excel workbook. The reference model is then operable to determine reference carbon intensities directly using an applicable carbon intensity reference model. The resulting carbon intensity data may be stored in memory 114.

[0177] The reference model module 150 may process linear term data at a lower frequency than the processing of measurement sensor data by the carbon intensity determination module 118.

Update Module 160

[0178] The update module 160 is operable to use the determined reference carbon intensities produced by the reference model in the reference model module 150 to update the linear coefficients used to generate the control carbon intensity values by the carbon intensity determination module 118.

[0179] The update module 160 utilizes the determined reference carbon intensities produced by the reference model in the reference model module 150 and the linear terms generated by the carbon intensity determination module 118 to generate updated linear coefficients. The updated linear coefficients are sent to the carbon intensity determination module 118 periodically.

[0180] In embodiments, the update module 160 may process data and generate updated data at a lower frequency than the processing of measurement sensor data by the carbon intensity determination module 118. In embodiments, the update module 160 may process data and generate updated data at a lower frequency than the reference model module 150 processes data to generate an updated reference model.

Method

[0181] FIG. 3 shows a method 200 according to an embodiment. In embodiments, there is provided a computer-implemented method of operating a hydrogen production facility to meet carbon intensity (CI) requirements. The method is implemented on a computer system utilizing at least one hardware processor 112 and having memory 114 and operable to receive inputs from the data block 130 amongst others.

[0182] The method comprises three control groups. Steps 210-250 define a first control loop for generation of carbon intensity control data for control of the process facility 10. In embodiments, this may be a closed loop system. Steps 220 and 260 to 270 comprise a second control process for generating data from a reference model. Finally, steps 280 and 230 define a final process for updating linear coefficients.

Step 210: Obtain Operational Parameter Data

[0183] At step 210, operational parameter data (which may comprise measured sensor data) is received from the hydrogen production facility. The operational parameter data is representative of measured and/or determined time-dependent values of one or more operational parameters of the hydrogen production facility.

[0184] In embodiments, the sensors 10-1S, 10-2S, 10-3S may be used to measure and report time-dependent values of one or more operational process parameters of the hydrogen process facility 10. In embodiments, this may be in real-time and continuous.

[0185] In embodiments, operational process parameters may comprise one or more of the following: flow rates, pressures, temperatures, rate of production of hydrogen, power consumption (which may comprise electricity consumption), rate of consumption of feedstocks (e.g., natural gas, coal etc.), rate of capture of carbon dioxide, and rate of steam production (where appropriate).

[0186] In addition, the data block 130 may comprise other data input elements 10-nS as required. These data input elements are not limited to sensors and sensor data and may include reporting or determined technical values on additional elements of the production facility 10. In embodiments, this may include technical information relating to production information, availability or CI values of fuels and processes, amongst others.

[0187] For the purpose of illustration, in specific embodiments the operational parameter data obtained may include one or more of the following measurable and/or determinable parameters: a quantity of hydrogen produced; a quantity of electricity consumed; a quantity of steam produced; a quantity of syngas produced; a quantity of carbon monoxide produced; and a quantity of electricity produced.

[0188] In summary, step 210 is operable to measure, derive and/or determine operational parameter data relating to time-dependent values of one or more operational parameters of the process plant 10. In embodiments, step 210 is operable to measure, derive and/or determine operational parameter data relating to time-dependent values of one or more operational parameters relating to materials and/or energy input to the process plant 10 and one or more operational parameters relating to materials and/or energy output from the process plant 10.

[0189] Once the operational parameter data (which may comprise measured sensor data) is obtained, the method proceeds to step 220.

Step 220; Determine Linear Terms

[0190] At step 220, the operational parameter data is processed to define one or more linear terms. The linear terms are linear with respect to one or more carbon intensity reference models.

[0191] In embodiments, the processing comprises performing one or more mathematical transforms on the received measured sensor data to generate the linear terms. In embodiments, the mathematical transforms comprise nonlinear transformations which are operable to convert the received operational parameter data into one or more linear terms.

[0192] In embodiments, the transformation of the received operational parameter data to linear terms enable a direct correlation with one or more reference model carbon intensities. In other words, the linear terms have a linear effect on reference model carbon intensities. Once the linear terms have been derived, they may be stored in the memory 114.

[0193] In embodiments, the linear terms have a specific form. In embodiments, the linear terms may be representative of ratio of inputs to the process plant 10 to outputs from the process plant 10.

[0194] For example, in specific embodiments, the specific form of one or more of the linear terms may comprise expressions specifying a measured energy or material flow forming an input to the process plant 10, divided by expressions specifying a total mass flow rate of hydrogen and coproducts produced at the process plant 10.

[0195] In embodiments, the form of one or more of the linear terms may comprise expressions specifying a measured energy or material flow that is an input to the hydrogen production facility, divided by expressions specifying a total energy rate of hydrogen and coproducts produced at the facility.

[0196] In embodiments, if one or more coproducts 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 coproduct is electricity, the energy rate is the power of the electricity.

[0197] In alternative or additional specific embodiments, the specific form of one or more of the linear terms may comprise expressions specifying a measured energy or material flow forming an input to the process plant 10, divided by expressions specifying a total molar flow rate of hydrogen and coproducts produced at the process plant 10.

[0198] In alternative or additional specific embodiments, the specific form of one or more of the linear terms may comprise expressions specifying a measured energy or material flow forming an input to the process plant 10, divided by expressions specifying a total a total economic value of hydrogen and coproducts produced at the process plant 10.

[0199] Finally, for illustration purposes, consider the specific examples given above in relation to the data acquisition module 116 where the input data obtained includes one or more of the following measurable and/or determinable parameters: a quantity of hydrogen produced; a quantity of electricity consumed; a quantity of steam produced; a quantity of syngas produced; a quantity of carbon monoxide produced; and a quantity of electricity produced.

[0200] 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).

[0201] In the above examples, the non-linear transformation in each case is the determination of the ratio. Consequently, the non-linear transformation of the input parameters of natural gas consumed and hydrogen consumed is (natural gas consumed)/(hydrogen produced) to produce the relevant linear term, and so on.

[0202] The method proceeds to step 230.

Step 230: Generate Control System Carbon Intensity Values

[0203] At step 230, the linear terms derived in step 220 may be utilized to generate control system carbon intensity values representative of the carbon intensity of hydrogen produced by the hydrogen production facility.

[0204] The control system carbon intensity values are calculated from a linear combination of the linear terms. By this is meant that the carbon intensity values are obtained from the sum of the product of each linear term with a corresponding linear coefficient.

[0205] The linear coefficient in each case corresponds to a scaling factor or weighting applied to each linear term. This may be selected as appropriate based on suitable physical, dynamic or mathematical considerations. In embodiments, as described below, the linear coefficients may be determined from one or more carbon intensity reference models.

Step 240: Generate Control Setpoints

[0206] At step 240, using the control system carbon intensity values produced in step 230, control variables for controlling one or more operational parameters of the hydrogen production facility can be generated.

[0207] Once the carbon intensity values have been derived, they can be sent to the production control module 120 to enable control of the production facility 10. In embodiments, the generated carbon intensity values correspond to those of one or more reference models. These can be used by a suitable control system to generate control setpoints to enable control of the production facility 10 in accordance with desired carbon intensity requirements.

[0208] The control system 110 is operable to adjust one or more operational parameters of the production facility 10 by generating a setpoint value for the production rate of each production facility 10 and communicating the setpoint value to each respective production facility 10.

[0209] In embodiments, the processing performed in step 220 generates a set of linear terms based on non-linear transformations of the relevant input parameters for the production facility 10. The carbon intensity values are then calculated in step 230 as a linear combination of the generated linear terms.

[0210] In embodiments, the linear terms may be represented as rational functions, i.e, functions where the numerator and denominator comprise polynomials. Any of the linear terms generated in step 220 may be described in this manner.

[0211] Consequently, in embodiments, the rational functions so generated may be utilized in step 240 by the control system 110 to generate control setpoints for control of the processes of the production facility 10.

[0212] In embodiments, the process control module 120 of the control system 110 may comprise a digital control system (DCS) using a model predictive controller which supports a family of variable transformations such as rational functions.

[0213] As a result, step 240 may comprise implementing a model predictive controller operable to utilize the rational functions generated from the linear terms and/or carbon intensity values and generate control signals therefrom.

Step 250: Control Production Facility

[0214] At step 250 the hydrogen production facility 10 can be controlled in accordance with the determined control variable setpoints.

[0215] The operational parameter setpoint values may be determined by the process control module 120 and may be provided to the process controller 140 which is operable to control the production facility 10 dynamically (through control elements 10-1C, 10-20, 10-3C, 10-4C . . . 10-nC) in response to this data.

[0216] In specific embodiments, the generated control variables for control of production rates may be utilized in a model predictive control (MPC) system. In embodiments, the MPC system comprises a multivariable control algorithm that utilizes an internal dynamic model 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 a plurality of control inputs to control elements 10-1C, 10-20, 10-3C, 10-4C . . . 10-nC.

[0217] However, in embodiments, alternative functions may be used. These may involve, for example, similarity functions which are maximized.

[0218] In embodiments, controlling the hydrogen production facility 10 in accordance with the values of the control variables may comprise receiving, by the process controller 140, production facility 10-1, 10-2, 10-3, 10-4 rate values determined by the process control module 120 and deriving a production facility 10-1, 10-2, 10-3, 10-4 operation policy including set point operation parameters over a predetermined future time horizon.

[0219] Control is then effected through the control elements 10-1C, 10-20, 10-3C, 10-4C . . . 10-nC to control the relevant processes controlled thereby. In embodiments, this process may utilize linear empirical models obtained by system identification of the various processes.

[0220] Alternatively or additionally, in embodiments, the process may utilize non-linear high-fidelity models or non-linear models created from a machine learning algorithm. The process controller 140, knowing the desired production facility 10-1, 10-2, 10-3, 10-4 rates, may utilize the MPC system to optimize the control set points and processes for a present time period, whilst also being able to adapt for future time periods. This is achieved, in embodiments, by optimizing a finite time horizon for the processes whilst implementing the current time period. The optimization is then performed again at the next time period for another finite time horizon.

[0221] In embodiments, steps 210 to 250 may be repeated in a closed control loop cycle on a frequency of the order of minutes. In specific embodiments, the control loop cycle frequency may be every 5 minutes. In specific embodiments, the control loop cycle frequency may be every 15 minutes.

[0222] Steps 260 to 270 define an additional process, described below.

Step 260: Process Linear Terms

[0223] At step 260, the linear terms determined by the carbon intensity determination module 118 in step 220 are obtained and one or more non-linear transformations are applied to the linear terms to generate reference model inputs.

[0224] The reference model inputs may, in embodiments, comprise inputs to one or more cells in the Excel reference model. The non-linear transforms enable the correct reference model inputs to be determined based on the linear terms. The non-linear transforms may be dependent upon the reference model used.

[0225] For example, in the US GREET model one of the reference model inputs is the efficiency ratio n=(hydrogen produced)/(natural gas consumed+electricity consumed). If the linear terms comprise A=(natural gas consumed)/(hydrogen produced) and B=(electricity consumed)/(hydrogen produced), the nonlinear transformation T which converts A and B to is =T(A,B)=1/(A+B). It can be verified through algebraic manipulation that the transformation T is exactly correct.

[0226] The method proceeds to step 270.

Step 270: Generate Reference Model

[0227] The reference model inputs are inputted into a reference model. In embodiments, the reference model may comprise a Microsoft Excel workbook. The reference model inputs thus generate an updated reference model.

Step 280: Determine Reference Carbon Intensities

[0228] At step 280, the updated reference model is then operable to determine reference carbon intensities directly using an applicable carbon intensity reference model. The resulting carbon intensity data may be stored in memory 114.

[0229] The reference model module 150 may process linear term data at a lower frequency than the processing of measurement sensor data by the carbon intensity determination module 118.

Step 290: Update Linear Coefficients

[0230] At step 290, the update module 160 is operable to use the determined reference carbon intensities produced by the reference model in the reference model module 150 in step 280 to update the linear coefficients used to generate the control carbon intensity values by the carbon intensity determination module 118.

[0231] The update module 160 utilizes the determined reference carbon intensities produced by the reference model in the reference model module 150 and the linear terms generated by the carbon intensity determination module 118 to generate updated linear coefficients. The updated linear coefficients are sent to the carbon intensity determination module 118 periodically. These are then used in step 230 to generate updated carbon intensity control data.

[0232] In embodiments, the update module 160 may process data and generate updated data at a lower frequency than the processing of measurement sensor data by the carbon intensity determination module 118. In embodiments, the update module 160 may process data and generate updated data at a lower frequency than the reference model module 150 processes data to generate an updated reference model.

Operational Example

[0233] FIGS. 4 to 8 show aspects of implementation for control of hydrogen production from natural gas with carbon intensities calculated using the US GREET reference model.

[0234] In embodiments, a hydrogen production facility is used to produce hydrogen from natural gas. Electricity is used to drive fans, pumps, and controllers in the facility. The plant produces a steam coproduct. Carbon dioxide generated in the process of converting hydrogen to natural gas is captured and sequestered.

[0235] The United States Inflation Reduction Act requires that hydrogen producers quantify the carbon intensity of 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 in the US GREET model is show in FIG. 4.

[0236] The US GREET excel model typically takes 60 seconds to perform a carbon intensity calculation. Furthermore, it is not feasible to directly integrate an Excel model in a real-time digital control system (DCS) such as those which are typically used to control large industrial facilities.

[0237] Thus, it is desirable to utilize the method and system of the present invention for calculating carbon intensities for the purpose of controlling the facility such as typically done with a digital control system (DCS) and model predictive control such as is typically done using ASPEN DMC Plus.

[0238] FIG. 5 is a screenshot showing the configuration of measured quantities used in an embodiment of the invention, the measured quantities used to calculate carbon intensity comprise a measurement of the rate of production of hydrogen, a measurement of the rate of consumption of electricity, a measurement of the rate of consumption of natural gas, a measurement of the rate of capture of carbon dioxide, and a measurement of the rate of steam production.

[0239] FIG. 6 is a screenshot showing the configuration of the nonlinear transformations and the linear terms used in an exemplary embodiment of the invention. As shown in the Python code which is associated with the object property linear_input_formulas, there are four linear terms comprising:

[0240] The quantity (rate of consumption of electricity)/(rate of production of hydrogen), designated as ELEC_IN/H2_OUT.

[0241] The quantity (rate of consumption of natural gas)/(rate of production of hydrogen), designated as NG_IN/H2_OUT.

[0242] The quantity (rate of capture of carbon dioxide)/(rate of production of hydrogen), designated as CO2_OUT/H2_OUT.

[0243] The quantity (rate of steam production)/(rate of production of hydrogen), designated as STEAM_OUT/H2_OUT.

[0244] FIG. 7 shows the configuration of a model for the calculation of the carbon intensity of hydrogen produced from natural gas. Specifically, it shows the linear coefficients used to form linear combinations of the linear terms. For example, GHG emissions for combustion in a steam methane reformer (SMR) is calculated using the following linear formula:

[00001]$\left[\mathrm{GHGs}\mathrm{for}\mathrm{SMR}\mathrm{combustion}\mathrm{emmissions}\right]=-254.6+18311*\left[\mathrm{NG\_IN}/\mathrm{H2\_OUT}\right]-2800*\left[\mathrm{CO2\_CAPTURED}/\mathrm{H2\_OUT}\right].$

[0245] FIG. 8 shows the configuration of reference model inputs 70 that are used in the reference model to calculate GHG emissions associated with producing hydrogen from natural gas. For this model, the reference model inputs comprise: [0246] Cell E9 on the Inputs sheet in the GREET2022 Excel model [0247] Cell F721 on the Inputs sheet in the GREET2022 Excel model [0248] Cell K1078 on the Inputs sheet in the GREET2022 Excel model [0249] Cell K1079 on the Inputs sheet in the GREET2022 Excel model [0250] Cell K1081 on the Inputs sheet in the GREET2022 Excel model [0251] Cell K1082 on the Inputs sheet in the GREET2022 Excel model [0252] Cell K1083 on the Inputs sheet in the GREET2022 Excel model [0253] Cell B11 on the Hydrogen sheet in the GREET2022 Excel model [0254] Cell F251 on the Inputs sheet in the GREET2022 Excel model

[0255] Nonlinear transformations 60 are used to compute the values of reference model inputs 70. An example of a nonlinear transformation 60 is provided in FIG. 9. In this example the nonlinear transformation 60 used to calculate the reference model input cell K1078 on the Inputs model sheet of the reference model 80 GREET2022, as a function of the linear terms 30 is

[00002]$\left[\mathrm{Cell}K1078\right]=1/\left(\mathrm{NG\_IN}/\mathrm{H2\_OUT}+\mathrm{ELEC\_IN}/\mathrm{H2\_OUT}\right)$

[0256] In the real-time control system, measured quantities are read from sensors and nonlinear transformations are used to produce linear terms, which are then multiplied by linear coefficients to produce linear combinations which are summed to produce control system carbon intensities which are then used by a plant control system to adjust plant rates. Linear terms and control system carbon intensities are stored. This mechanism for calculating carbon intensities is highly accurate and extremely computationally efficient.

[0257] At a lower frequency, and outside the real-time control system, nonlinear transformations are used to calculate reference model inputs which are provided to a reference model, typically a large Excel model, which is used to calculate reference carbon intensities which are also stored. The reference model may not be computationally efficient and may not be suitable for integration with a real-time control system. The reference carbon intensities are typically exactly equal to the control system carbon intensities and computing the reference carbon intensities with the reference model and comparing them to the control system carbon intensities can be used to validate the control system carbon intensities.

[0258] From time to time, the reference model may be updated by regulatory or scientific bodies. For example, the US GREET model is typically updated yearly by Argonne National Labs in the month of October. When these updates occur, the linear terms and the corresponding reference carbon intensities can be used to in a process of updating the linear coefficients. This ensures that the calculations for the control system for low-carbon hydrogen supply stay accurate when reference models are updated.

[0259] It will be appreciated by the person of skill in the art that various modifications may be made to the above-described examples without departing from the scope of the invention as defined by the appended claims.

[0260] While the invention has been described with reference to the preferred embodiments depicted in the figures, it will be appreciated that various modifications are possible within the spirit or scope of the invention as defined in the following claims.

[0261] For example, whilst some of the above exemplary embodiments have been described in the context of a hydrogen supply network for supplying hydrogen fuel, the invention is not so limited. The invention is equally applicable to the process of providing hydrogen having a defined carbon intensity value to an end user location for purposes other than as a fuel. In other words, in embodiments, the hydrogen supply network may be considered to be a hydrogen supply network for the supply of hydrogen for any suitable purpose.

[0262] It will be understood that the term control as used herein may, in embodiments, refer to a systematic plan or set of actions designed to manage and optimize the operation of one or more hydrogen production facilities, in order to produce fuel with a defined carbon intensity value, while considering factors such as feedstock carbon intensity, demand data, and process constraints.

[0263] It will be understood that the term control as used herein may refer to the management and regulation of the operations of the industrial plants of the industrial processing facility, ensuring that the production of hydrogen and/or hydrogen fuel adheres to the defined carbon intensity value and other constraints set by the optimization model.

[0264] It will be understood that the term fuel as used herein may refer to any type of fuel used to power processes (including industrial processes) for turning stored fuel energy into useful work. The term fuel 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.

[0265] It will be understood that the term Defined carbon intensity (CI) value as used herein may refer to a predetermined or specified value for the greenhouse gas emissions associated with the production, processing and distribution of a product such as hydrogen or hydrogen fuel, expressed in terms of mass of carbon dioxide equivalent per unit of energy, and used as a target or constraint in the optimization process for producing the fuel in an environmentally sustainable manner.

[0266] In this specification, unless expressly otherwise indicated, the word or is used in the sense of an operator that returns a true value when either or both of the stated conditions are met, as opposed to the operator exclusive or which requires only that one of the conditions is met. The word comprising is used in the sense of including rather than to mean consisting of.

[0267] Where applicable, various embodiments provided by the present disclosure may be implemented using hardware, software, or combinations of hardware and software. Also, where applicable, the various hardware components and/or software components set forth herein may be combined into composite components comprising software, hardware, and/or both without departing from the spirit of the present disclosure. Where applicable, the various hardware components and/or software components set forth herein may be separated into sub-components comprising software, hardware, or both without departing from the scope of the present disclosure. In addition, where applicable, it is contemplated that software components may be implemented as hardware components and vice-versa.

[0268] Software, in accordance with the present disclosure, such as program code and/or data, may be stored on one or more computer readable mediums. It is also contemplated that software identified herein may be implemented using one or more general purpose or specific purpose computers and/or computer systems, networked and/or otherwise. Where applicable, the ordering of various steps described herein may be changed, combined into composite steps, and/or separated into sub-steps to provide features described herein.

[0269] While various operations have been described herein in terms of modules, units or components, these terms should not limited to single units or functions. In addition, functionality attributed to some of the modules or components described herein may be combined and attributed to fewer modules or components.

[0270] It will be apparent to those of ordinary skill in the art that changes, 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 portions of methods described above may be performed in a different order (or concurrently) and still achieve desirable results.