System and method for optimizing a petroleum distribution system

Abstract

A method for operating a piping system includes obtaining sensor data of a gas in the piping system from a sensor of a sensing unit. The method also includes executing a real-time and closed-loop control scheme using the sensor data and a material model of the gas to determine one or more control decisions. The method also includes operating one or more controllable piping elements to adjust a temperature, a pressure, a flow rate, or a composition of the gas in accordance with the one or more control decisions.

Description

[0001] Cross-referencing of related patent applications

[0002] This application claims the benefit and priority of U.S. Patent Application No. 17 / 511,358, filed October 26, 2021, and U.S. Provisional Application No. 63 / 114,407, filed November 16, 2020, the entire disclosure of which is incorporated herein by reference. Background Technology

[0003] This disclosure relates to control systems or solutions for petroleum systems. More specifically, this disclosure relates to closed-loop control systems or solutions for oil and gas systems, including but not limited to natural gas systems, crude oil systems, gasoline systems, and other mixtures of oil and gas products. Summary of the Invention

[0004] According to some embodiments, one implementation of this disclosure is a method for operating a piping system. In some embodiments, the method includes obtaining sensor data of a gas in the piping system from a sensor of a sensing unit. In some embodiments, the method further includes using the sensor data and a material model of the gas to perform real-time and closed-loop control schemes to determine one or more control decisions. In some embodiments, the method further includes operating one or more controllable piping elements to adjust the temperature, pressure, flow rate, or composition of the gas based on one or more control decisions.

[0005] In some embodiments, the sensor data includes any one of gas temperature, gas pressure, gas flow rate, and gas composition. In some embodiments, the sensor of the sensing unit includes any one of the following: a temperature sensor configured to measure the temperature of a gas; a pressure sensor configured to measure the pressure of a gas; a flow meter configured to measure the flow rate of a gas; and any one of a gas chromatograph, laser interferometer, water sensor, density sensor, or hydrogen sulfide sensor configured to measure the composition of a gas.

[0006] In some embodiments, sensor data is obtained from multiple sensing units positioned around the piping system. In some embodiments, the material model is configured to estimate at least one of the following: the critical condensation temperature of the gas, the critical condensation pressure of the gas, the critical point of the gas, the viscosity, density, flow characteristics, or phase of the gas.

[0007] In some embodiments, one or more control decisions are determined to satisfy one or more control objectives. In some embodiments, one or more control objectives include at least one of the following: limiting the formation of hydrates in the gas; maintaining the gas in a desired phase; minimizing gas flow resistance; converting the gas to a desired phase; or reducing the likelihood of pipe breakage in the piping system.

[0008] In some embodiments, the method further includes generating display data for a user, the display data including any one of the following: graphs having hydrate curves, envelope curves, and processing paths; phase diagrams of gases; sensor data; or one or more thermodynamic properties estimated by one or more material models. In some embodiments, the method includes operating a display device to provide the display data to the user.

[0009] In some embodiments, one or more thermodynamic properties estimated by one or more material models include any one of the gas's critical condensation temperature, critical condensation pressure, or critical point. In some embodiments, one or more material models are selected, generated, or adjusted based on the gas composition.

[0010] According to some embodiments, another implementation of this disclosure is a controller for a piping system. In some embodiments, the controller includes a processing circuitry configured to acquire sensor data of gas in the piping system from sensors of a sensing unit. In some embodiments, the processing circuitry is configured to use the sensor data and a material model of the gas to perform real-time and closed-loop control schemes to determine one or more control decisions. In some embodiments, the processing circuitry is configured to operate one or more controllable piping elements based on one or more control decisions to adjust the temperature, pressure, flow rate, or composition of the gas.

[0011] In some embodiments, the sensor data includes any one of gas temperature, gas pressure, gas flow rate, or gas composition. In some embodiments, the sensor of the sensing unit includes any one of the following: a temperature sensor configured to measure the temperature of a gas; a pressure sensor configured to measure the pressure of a gas; a flow meter configured to measure the flow rate of a gas; and any one of a gas chromatograph, laser interferometer, water sensor, density sensor, or hydrogen sulfide sensor configured to measure the composition of a gas.

[0012] In some embodiments, sensor data is obtained from multiple sensing units positioned around the piping system. In some embodiments, the material model is configured to estimate at least one of the following: the critical condensation temperature of the gas, the critical condensation pressure of the gas, the critical point of the gas, the viscosity, density, flow characteristics, or phase of the gas.

[0013] In some embodiments, one or more control decisions are determined to satisfy one or more control objectives. In some embodiments, one or more control objectives include at least one of the following: limiting the formation of hydrates in the gas; maintaining the gas in a desired phase; minimizing gas flow resistance; converting the gas to a desired phase; or reducing the likelihood of pipe breakage in the piping system.

[0014] In some embodiments, the processing circuitry is also configured to generate display data for a user. In some embodiments, the display data includes any of the following: a graph with hydrate curves, envelope curves, and processing paths; a phase diagram of a gas; sensor data; or one or more thermodynamic properties estimated by one or more material models. In some embodiments, the processing circuitry is configured to operate a display device to provide display data to a user.

[0015] In some embodiments, one or more thermodynamic properties estimated by one or more material models include any one of the gas's critical condensation temperature, critical condensation pressure, or critical point. In some embodiments, one or more material models are selected, generated, or adjusted based on the gas composition.

[0016] According to some embodiments, another implementation of this disclosure is a pipeline system. In some embodiments, the pipeline system includes a pipeline, a station, pipeline equipment, and a controller. In some embodiments, the station includes a sensing unit configured to provide sensor data. In some embodiments, the pipeline equipment is configured to adjust the temperature, pressure, flow rate, or composition of a gas. In some embodiments, the controller is configured to obtain sensor data of the gas in the pipeline from sensors of the sensing unit. In some embodiments, the controller is configured to use the sensor data and a material model of the gas to perform real-time and closed-loop control schemes to determine one or more control decisions for the pipeline equipment. In some embodiments, the controller is configured to operate the pipeline equipment to adjust the temperature, pressure, flow rate, or composition of the gas based on one or more control decisions.

[0017] In some embodiments, the material model is configured to estimate at least one of the following: the critical condensation temperature of the gas, the critical condensation pressure of the gas, the critical point of the gas, the viscosity of the gas, the density of the gas, the flow characteristics of the gas, or the phase of the gas. In some embodiments, one or more control decisions are determined to satisfy one or more control objectives. In some embodiments, one or more control objectives include at least one of the following: limiting the formation of hydrates in the gas; maintaining the gas in a desired phase; minimizing gas flow resistance; converting the gas to a desired phase; or reducing the likelihood of pipe breakage in the piping system.

[0018] In some embodiments, the controller is also configured to generate display data for the user. In some embodiments, the display data includes any of the following: graphs with hydrate curves, envelope curves, and processing paths; phase diagrams of gases; sensor data; or one or more thermodynamic properties estimated by one or more material models. In some embodiments, the controller is configured to operate a display device to provide display data to the user.

[0019] According to some embodiments, another implementation of this disclosure is a method for optimizing a pipeline. In some embodiments, the method includes determining the pipeline's optimization and operating modes. In some embodiments, the method further includes obtaining an objective function that quantifies performance variables as a function of one or more control decisions for the pipeline over a future time horizon. In some embodiments, the method includes optimizing an objective function subject to one or more constraints to determine control decisions for the pipeline that result in optimal values ​​for the performance variables. In some embodiments, the method includes equipping the pipeline according to the control decisions.

[0020] In some implementations, the optimization mode includes at least one of an emissions or energy consumption mode, a monetary cost mode, or a throughput mode. In some implementations, in the emissions or energy consumption mode, the performance variable of the objective function is energy consumption, and optimizing the objective function includes minimizing the objective function to determine a control decision that results in a minimum value for the performance variable over a future time horizon. In some implementations, in the monetary cost mode, the performance variable of the objective function is monetary cost, and optimizing the objective function includes minimizing the objective function to determine a control decision that results in a minimum value for the monetary cost over a future time horizon. In some implementations, in the throughput mode, the performance variable of the objective function is the quantity of products delivered, and optimizing the objective function includes maximizing the objective function to determine a control decision that results in a maximum quantity of products delivered over a future time horizon.

[0021] In some implementations, the method is performed on the entire pipeline to determine control decisions for the entire pipeline. In some implementations, the method is performed on a station within the pipeline to determine control decisions for that station.

[0022] In some implementations, the objective function includes a model of the pipeline station. In some implementations, the pipeline station includes multiple pumps arranged in series or parallel, configured to pump products through the pipeline. In some implementations, the station model predicts one or more operating parameters of the station based on control decisions.

[0023] In some implementations, control decisions are made for each time step within a future timeframe. In some implementations, control decisions include at least one of the following: setpoints for one or more pumps in the pipeline; the amount of drag-reducing agent (DRA) to be added to the pipeline; or the amount of energy to be purchased from a utility provider to operate the pipeline.

[0024] In some embodiments, the pipeline is configured to transport any one of a liquid product, a gaseous product, or a mixture of liquids and gases. In some embodiments, control decisions include the amount and type of additives to be added to the product in the pipeline.

[0025] According to some embodiments, another implementation of this disclosure is a pipeline. In some embodiments, the pipeline includes multiple pipeline stations, each pipeline station including at least one pump or compressor, pipeline connecting the pipeline stations, and a controller. In some embodiments, the controller is configured to determine the optimization and operating modes of the pipeline. In some embodiments, the controller is also configured to obtain an objective function that quantifies performance variables as a function of one or more control decisions for the pipeline over a future time horizon. In some embodiments, the controller is also configured to optimize the objective function, which is subject to one or more constraints, to determine control decisions for the pipeline that result in optimal values ​​for the performance variables. In some embodiments, the controller is also configured to operate at least one pump or compressor in each of the pipeline stations of the pipeline according to the control decisions.

[0026] In some implementations, the optimization mode includes at least one of an emissions or energy consumption mode, a monetary cost mode, or a throughput mode. In some implementations, in the emissions or energy consumption mode, the performance variable of the objective function is energy consumption, and optimizing the objective function includes minimizing the objective function to determine a control decision that results in a minimum value for the performance variable over a future time horizon. In some implementations, in the monetary cost mode, the performance variable of the objective function is monetary cost, and optimizing the objective function includes minimizing the objective function to determine a control decision that results in a minimum value for the monetary cost over a future time horizon. In some implementations, in the throughput mode, the performance variable of the objective function is the quantity of products delivered, and optimizing the objective function includes maximizing the objective function to determine a control decision that results in a maximum quantity of products delivered over a future time horizon.

[0027] In some implementations, the controller is configured to determine control decisions for the pipeline as a whole based on an overall optimization objective function. In other implementations, the controller is configured to determine control decisions for a pipeline station based on an optimization objective function specific to the pipeline.

[0028] In some embodiments, the objective function includes a model of at least one of the pipeline stations. In some embodiments, at least one pipeline station includes a plurality of pumps configured to pump products through the pipeline in a series or parallel arrangement, wherein the model of the pipeline station predicts one or more operating parameters of the station based on control decisions.

[0029] In some implementations, control decisions are made for each time step within a future timeframe. In some implementations, control decisions include at least one of the following: setpoints for one or more pumps in the pipeline; the amount of drag-reducing agent (DRA) to be added to the pipeline; or the amount of energy to be purchased from a utility provider to operate the pipeline.

[0030] In some embodiments, the pipeline is configured to transport any one of a liquid product, a gaseous product, or a mixture of liquids and gases. In some embodiments, control decisions include the amount and type of additives to be added to the product in the pipeline.

[0031] According to some embodiments, another implementation of this disclosure is a controller for optimizing a pipeline. In some embodiments, the controller is configured to determine the optimization and operating mode of the pipeline. In some embodiments, the controller is configured to obtain an objective function that quantifies performance variables as a function of one or more control decisions for the pipeline over a future time horizon. In some embodiments, the controller is configured to optimize the objective function, which is subject to one or more constraints, to determine control decisions for the pipeline that result in optimal values ​​for the performance variables. In some embodiments, the controller is configured to operate the pipeline's equipment according to the control decisions.

[0032] In some implementations, the optimization mode includes at least one of an emissions or energy consumption mode, a monetary cost mode, or a throughput mode. In some implementations, in the emissions or energy consumption mode, the performance variable of the objective function is energy consumption, and optimizing the objective function includes minimizing the objective function to determine a control decision that results in a minimum value for the performance variable over a future time horizon. In some implementations, in the monetary cost mode, the performance variable of the objective function is monetary cost, and optimizing the objective function includes minimizing the objective function to determine a control decision that results in a minimum value for the monetary cost over a future time horizon. In some implementations, in the throughput mode, the performance variable of the objective function is the quantity of products delivered, and optimizing the objective function includes maximizing the objective function to determine a control decision that results in a maximum value for the quantity of products delivered over a future time horizon.

[0033] In some implementations, the objective function includes a model of the pipeline station. In some implementations, the pipeline station includes multiple pumps arranged in series or parallel, configured to pump products through the pipeline. In some implementations, the station model predicts one or more operating parameters of the station based on control decisions.

[0034] In some implementations, control decisions are made for each time step within a future timeframe. In some implementations, control decisions include at least one of the following: setpoints for one or more pumps in the pipeline; the amount of drag-reducing agent (DRA) to be added to the pipeline; or the amount of energy to be purchased from a utility provider to operate the pipeline. Attached Figure Description

[0035] The present disclosure will be more fully understood from the following detailed description taken in conjunction with the accompanying drawings, in which like reference numerals indicate like elements:

[0036] Figure 1 It is a schematic diagram of a piping system and a control system including instruments, according to some implementation methods.

[0037] Figure 2It is based on some implementation methods Figure 1 A diagram of a discrete portion of gas within a piping system, the length of which increases as the discrete portion of gas flows along the piping system.

[0038] Figure 3 It is based on some implementation methods Figure 1 The diagram shows the portion of the piping system that combines the main line and transverse piping lines at the mixing point.

[0039] Figure 4 It is based on some implementation methods Figure 3 A diagram of a portion of the piping system.

[0040] Figure 5 It is a diagram showing the mixing of discrete portions of the main gas and discrete portions of the transverse gas according to some implementation methods to form new discrete portions of the gas.

[0041] Figure 6 This is another diagram showing the mixing of discrete portions of the main gas and discrete portions of the transverse gas according to some implementation methods to form new discrete portions of the gas.

[0042] Figure 7 It is based on some implementation methods Figures 5 to 6 A diagram of the new discrete portion of the gas.

[0043] Figure 8 It is based on some implementation methods Figure 1 Phase diagram of fluids in a piping system.

[0044] Figure 9 This illustrates, according to some implementations, the situation before one or more control decisions have been made. Figure 1 The phase envelope curve, hydrate curve, and processing path curve of the fluid.

[0045] Figure 10 This illustrates, according to some implementations, after one or more control decisions have been made. Figure 1 The phase envelope curve, hydrate curve, and processing path curve of the fluid.

[0046] Figure 11 It is based on some implementation methods Figure 1 A block diagram of the control system.

[0047] Figure 12 It can be implemented on some implementation methods. Figure 1 and Figure 11 The system infrastructure of the control system.

[0048] Figure 13It is a flowchart of a process for operating a pipeline system based on a material model of a gas used in a pipeline system according to some implementation methods.

[0049] Figure 14 It is a block diagram of a pipeline system including different stations according to some implementation methods.

[0050] Figure 15 It is configured to be based on some implementation methods. Figure 14 piping systems or Figure 14 A block diagram of a controller for optimizing the station operation of a pipeline system.

[0051] Figure 16 This is a block diagram of a cloud computing system that communicates with controllers at different stations in a pipeline system, according to some implementation methods, with each controller configured to perform independent optimizations.

[0052] Figure 17 It is a flowchart of a process for selecting between different optimization schemes for a piping system, according to some implementation methods.

[0053] Figure 18 It is a flowchart of a process for optimizing a piping system to minimize operating costs, according to some implementation methods.

[0054] Figure 19 It is a flowchart of a process for optimizing a piping system to minimize emissions or energy use, according to some implementation methods.

[0055] Figure 20 This is a flowchart of a process for optimizing a piping system to maximize the throughput of the piping system, according to some implementation methods. Detailed Implementation

[0056] Before turning to the accompanying drawings, which provide a detailed description of exemplary embodiments, it should be understood that this application is not limited to the details or methods set forth in the specification or shown in the drawings. It should also be understood that the terminology is for descriptive purposes only and should not be considered limiting.

[0057] Overview

[0058] Referring generally to the accompanying drawings, systems and methods for optimizing pipelines according to some embodiments are illustrated. According to some embodiments, the pipeline includes various stations, each including pumps or compressors (e.g., in series or parallel) for pumping products from storage tanks (e.g., tank farms at hydrocarbon sites) to customers. In some embodiments, the station may include generators (e.g., combined heat and power, wind turbines, solar panels, etc.) configured to generate energy for the operation of the station. In some embodiments, the station also includes various energy storage devices (e.g., battery farms) for storing energy for later use by the station or its equipment (e.g., pumps). In some embodiments, a controller is configured to define an objective function using system information of the pipeline and station equipment, utility costs, mathematical models of the various equipment of the pipeline and / or station, sensor data from the pipeline, optimization patterns, etc. The objective function can be defined as energy consumption, emissions, throughput, or monetary costs based on one or more decision variables over a future time horizon. Optimization is performed to minimize or maximize the objective function to determine the optimal control decision as minimizing energy consumption, minimizing emissions, minimizing monetary costs, or maximizing throughput. In some implementations, control decisions are output to the pipeline for pipeline operation. Optimizations can be performed locally at each station to optimize the operation of each station, or globally to optimize the entire pipeline.

[0059] According to some embodiments, a control system for a pipeline system (e.g., a transportation system, a refinery, a distribution center, a processing system, etc.) includes a sensing unit configured to measure the temperature, pressure, flow rate, and composition of a gas flowing through the pipeline as sensor data. According to some embodiments, the control system also includes a controller configured to acquire the sensor data from the sensing unit and use a material model of the gas in a closed-loop control scheme to determine one or more control decisions. According to some embodiments, the sensor data can be acquired and used by the controller, in conjunction with a material model, to determine control decisions in real time. According to some embodiments, control decisions (e.g., increasing or decreasing pressure, increasing or decreasing temperature by heating or cooling, changing composition by injecting additives, etc.) are implemented to achieve one or more control objectives. According to some embodiments, control objectives may include reducing the likelihood of hydrate formation, maintaining the gas in a desired phase, converting the gas to a desired phase, operating with a gas above its critical condensation temperature, etc.

[0060] Low-level optimization

[0061] Gas pipeline

[0062] Reference Figure 1A system 10 is shown, according to some embodiments, for monitoring a conduit 12 (e.g., a conduit for a fluid such as a gas or liquid or a mixture of both, a conduit for a gas such as a compressible gas, natural gas including methane and contaminants, an acidic gas such as carbon dioxide and hydrogen sulfide, or a conduit for a liquid such as natural gas, gasoline, aviation fuel, crude oil, distillate, diesel, butane, propane, ethane, etc.). The system 10 may be configured to monitor one or more conditions of a fluid 16 (e.g., hydrocarbons, natural gas, gas, liquid / gas mixture, etc.) flowing or traveling within the conduit 12. The system 10 may include a control system 100 configured to receive and use sensor inputs from one or more sensing units 30 that measure one or more conditions or characteristics of the fluid (e.g., temperature, pressure, dynamic pressure, static pressure, flow rate, etc.) to adjust the operation of one or more devices of the system 10 (e.g., to influence the fluid 16 within the conduit 12). In some embodiments, pipeline 12 is used for crude oil, natural gas, gasoline, acid gases (e.g., mixtures including carbon dioxide and hydrogen sulfide) or other petroleum products, including but not limited to mixtures of oil and gas products.

[0063] Pipeline 12 may be part of piping system 20. Piping system 20 may be a distribution, production, or consumption system for distributing fluid 16, producing fluid 16, or consuming fluid 16. In some embodiments, piping system 20 is configured to collect fluid 16 (e.g., receive gas and / or oil from a well), transport fluid 16 (e.g., transport gas and / or oil nationwide), and / or distribute fluid 16 (e.g., distribute gas and / or oil to end customers). It should be understood that, although Figure 1 Only a portion of the piping system 20 is shown, but the piping system 20 can be much more extensive and can include any number of pipes, conduits, tubular components, etc. In some embodiments, such as Figure 1 The control system 100 shown repeats at various intervals along the piping system 20.

[0064] The fluid 16 (or other petroleum product or mixture) flowing through pipe 12 can be modeled as one or more fluid slugs 18. For example, a fluid slug 18 can represent a quantity, volume, portion, or number of fluid 16 flowing through pipe 12. Control system 100 also includes a sensing unit 30 comprising one or more sensors 104. A first sensor 104a can be configured to measure the temperature of the fluid 16 (or fluid slug 18) flowing through pipe 12. A second sensor 104b can be configured to measure the pressure (e.g., dynamic, static, etc.) of the fluid 16 (or fluid slug 18) flowing through pipe 12. A third sensor 104c can be configured to measure the velocity or flow rate (e.g., volumetric flow rate, mass flow rate, etc., or any combination thereof) of the fluid 16 (or fluid slug 18) flowing through pipe 12. A fourth sensor 104d can be configured to measure the composition of the fluid 16 (or fluid slug 18) flowing through pipe 12. For example, the fourth sensor 104d may be a collection of one or more sensors configured to measure or detect the presence or concentration of any of the following: methane, nitrogen, carbon dioxide, ethane, propane, water, hydrogen sulfide, hydrogen, carbon monoxide, oxygen, isobutane, n-butane, isopentane, n-pentane, hexane, heptane, octane, nonane, decane, helium, argon, benzene, ethylbenzene, toluene, methanol, ethylene glycol, etc. It should be understood that the sensing unit 30 may include any number of sensors configured to measure other conditions or characteristics of the fluid 16 (or fluid slug 18), or to measure / obtain values ​​of the characteristics or conditions of the fluid 16, which may be used (e.g., by a controller) to estimate or calculate other characteristics of the fluid 16 (e.g., using a model of the composition of the fluid 16).

[0065] In some embodiments, the fourth sensor 104d is or includes a gas chromatograph configured to obtain a sample of fluid 16, separate the chemical components of fluid 16, and detect or sense the presence and / or concentration of each of the different chemical components of fluid 16. According to some embodiments, the fourth sensor 104d may be configured to provide the detected presence and / or concentration of each of the different chemical components of fluid 16 to the controller 102 of the control system 100 for closed-loop or feedback control. In some embodiments, the fourth sensor 104d is or includes a laser interferometer configured to monitor certain chemical components of fluid 16. In some embodiments, the fourth sensor 104d is or includes a water sensor and / or a hydrogen sulfide (H2S) sensor configured to detect the presence and / or concentration of water / moisture and / or H2S.

[0066] It should be understood that the pipe 12 described herein can transport gases, liquids, fluids, etc. In some embodiments, the fluid 16 is diesel fuel, gasoline, propane, etc. In some embodiments, the fluid 16 is configured to transport different types of gases or substances. For example, according to some embodiments, the pipe 12 can be configured to transport both diesel fuel and gasoline. When different gases or liquids or substances are transported through the pipe 12, the different gases, liquids, or substances can mix at the interface between the different substances (thus producing a turbid liquid or a transformed mixture).

[0067] The control system 100 includes a controller 102 (e.g., a programmable logic controller (PLC), feedback controller, processing unit, processing circuitry system, etc.) configured to acquire sensor data from sensing unit 30 or from various sensors 104 of sensing unit 30. The controller 102 can use the sensor data acquired from sensing unit 30 to determine one or more characteristics (e.g., phase) of the fluid 16 or fluid slug 18 flowing within pipe 12, and can generate control decisions for one or more controllable pipe elements 106. Controllable pipe elements 106 can be configured to adjust the operation of pipe system 20 (e.g., a shut-off valve or pressure control valve), or to adjust / control one or more characteristics of the fluid 16 (or fluid slug 18) flowing through pipe 12 (e.g., adjust the operation of a pump or compressor). In this way, the controller 102 can execute a closed-loop feedback control scheme to adjust the operation of controllable pipe elements 106 based on real-time or current sensor data acquired from one or more sensing units 30. In some embodiments, temperature, pressure, flow rate, and composition can be controlled by various devices (e.g., heating coils, cooling coils, boilers, heat exchangers, ports for inserting or removing material, compressors or pumps for controlling pressure, mixers for altering material homogeneity). Controller 102 can also estimate the phase of fluid 16 (or fluid slug 18) using a model of the composition of the fluid 16 (or fluid slug 18) flowing through conduit 12. Controller 102 can generate control signals for the controllable conduit element 106 to maintain fluid 16 (or fluid slug 18) at a desired phase or desired temperature and pressure. Controller 102 can operate the controllable conduit element 106 to maintain fluid 16 (or fluid slug 18) at a desired phase to reduce the likelihood of conduit 12 rupture, or to reduce the amount of hydrate in fluid 16 (or fluid slug 18), or to maintain the fluid at a particular phase or a particular temperature or pressure.

[0068] Gas phase diagram

[0069] Now refer to Figure 8This diagram illustrates a phase diagram 800 of fluid 16 according to an exemplary embodiment. Phase diagram 800 includes an X-axis showing the temperature of fluid 16 in degrees Celsius and a Y-axis showing the pressure of fluid 16 in MPa or psia. Phase diagram 800 includes a liquid and gas phase 818, a liquid phase 802, a dense phase 804, and a gas phase 806. When fluid 16 is located in the liquid and gas phase 818, fluid 16 (or any fluid slug described herein) comprises both liquid and gaseous portions. When fluid 16 is located in the liquid phase 802, fluid 16 (or any fluid slug described herein) comprises only liquid. When fluid 16 is located in the dense phase 804, fluid 16 may exhibit both liquid and gaseous properties (e.g., dense fluid, supercritical fluid, etc.). When fluid 16 is located in the gas phase 806, fluid 16 may be completely gaseous and may exhibit gaseous properties.

[0070] Phase diagram 800 also includes a bubble point curve 808 separating liquid phase 802 from liquid and gas phases 818 and a dew point curve 810 separating gas phase 806 from liquid and gas phases 818. Phase diagram 800 also includes a critical condensation pressure point 812, a critical point 814, and a critical condensation temperature point 816. The critical condensation temperature point 816 indicates the temperature above which neither of the two states (e.g., both liquid and gas) can exist, regardless of pressure. The critical condensation pressure point 812 indicates the pressure above which neither of the two states (e.g., both liquid and gas) can exist, regardless of temperature. In some embodiments, control system 100 is intended to operate controllable piping element 106 to maintain fluid 16 in dense phase 804 (e.g., above critical condensation pressure point 812) or in gas phase 806 (e.g., above critical condensation temperature point 816). Phase diagram 800 as shown can be a model generated by controller 102 (e.g., as shown in...). Figure 11 The material model 120 shown is used to determine what phase the fluid 16 is currently in and what steps should be taken to adjust or maintain the fluid 16 in the desired phase. Phase diagrams 800 can be generated differently based on the composition of the fluid 16. For example, bubble point curves 808, critical points 814, critical condensation pressure points 812, dew point curves 810 and critical condensation temperature points 816, and the different phase regions they define, can be based on the composition of the fluid 16 and can differ for different compositions of the fluid 16.

[0071] Based on sensor data obtained from sensing unit 30, phase diagram 800 can be adjusted, generated, selected, etc. For example, phase diagram 800 or its various parameters (e.g., bubble point curve 808, critical point 814, critical condensation pressure point 812, dew point curve 810, and critical condensation temperature point 816) can be generated based on the composition of fluid 16 obtained by the fourth sensor 104d of sensing unit 30. In some embodiments, a model (e.g., material model 120) provides values ​​such as bubble point curve 808, critical point 814, critical condensation pressure point 812, dew point curve 810, and critical condensation temperature point 816 for use in rule-based control schemes for controlling piping element 106. Controller 102 can use a tuned PID loop. In one example, the mixture can be cooled to maximize water extraction (dehydration). In some embodiments, controller 102 injects methanol or ethylene glycol based on parameters provided from the model to prevent hydrate formation. In some implementations, the model receives a composition including up to 250 compounds and provides appropriate parameters based on pressure composition and temperature. Parameters can be physical constants such as specific gravity, hydrate temperature, bubble point curve 808, critical point 814, critical condensation pressure point 812, dew point curve 810, critical condensation temperature point 816, etc.

[0072] Controller diagram

[0073] Now refer to Figure 11 The control system 100 is shown in more detail according to some embodiments. The control system 100 includes a controller 102, a plurality of sensing units 30, a controllable piping element 106, and a user interface 126 (e.g., a device including a display screen, user input devices, etc.). The controller 102 is configured to receive sensor input from each of the sensing units 30, including the temperature of the fluid 16, the pressure of the fluid 16, the flow rate of the fluid 16 (e.g., velocity, volumetric flow rate, etc.), and the composition of the fluid 16. The controller 102 can use the sensor input in the model to determine control operations or control signals for the controllable piping element 106 to maintain the fluid 16 within a desired phase or desired state to reduce the likelihood of pipe 12 breakage, etc. The controller 102 can also generate and output display information (e.g., XY graph, tables, etc.) for the display device 126, enabling the display device 126 to be operated to display the current status of the fluid 16 to an operator or technician.

[0074] Controller 102 includes a processing circuitry system 108, which includes a processor 110 and a memory 112. Processor 110 may be a general-purpose or special-purpose processor, an application-specific integrated circuit (ASIC), one or more field-programmable gate arrays (FPGAs), a set of processing units, or other suitable processing units. Processor 110 may be configured to execute computer code and / or instructions stored in memory 112 or received from other computer-readable media (e.g., CD-ROM, network storage device, remote server, etc.).

[0075] Memory 112 may include one or more means (e.g., memory cells, memory devices, storage devices, etc.) for storing data and / or computer code for performing and / or facilitating the various processes described herein. Memory 112 may include random access memory (RAM), read-only memory (ROM), hard disk drive storage devices, temporary storage devices, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and / or computer instructions. Memory 112 may include database components, object code components, scripting components, or any other type of information structure for supporting the various activities and information structures described herein. Memory 112 may be communicatively connected to processor 110 via processing circuitry 108 and may include computer code for performing (e.g., executed by processor 110) one or more processes described herein.

[0076] The memory 112 is shown to include a fracture limit database 114, an operating phase manager 116, a fluid slug manager 118, a material model 120, a pipeline control manager 122, and a display data manager 124. According to some embodiments, the material model 120 may be stored locally in the memory 112 of the controller 102 or may be stored remotely (e.g., in a server), and the material model 120 may be accessed by the controller 102 (e.g., by the operating phase manager 116). The fracture limit database 114 may store various fracture parameters that can be used by the pipeline control manager 122 to determine whether adjustments should be made (e.g., adjusting the operating pressure of the pipeline 12) to reduce the likelihood of fracture. The operating phase manager 116 may use sensor data (e.g., temperature, pressure, flow rate, and composition) obtained from any of the sensing units 30 to determine the current phase of the fluid 16 within the pipeline 12 (e.g., determining which phase the fluid 16 is in) using the material model 120 of the fluid 16. In some embodiments, the operating phase manager 116 uses different material models 120 based on different detected compositions of fluid 16. According to some embodiments, the fluid slug manager 118 is configured to use sensor data obtained from sensing unit 30 to identify one or more fluid slugs (e.g., fluid slug 18) and track fluid slug 18 through piping system 20. According to some embodiments, the fluid slug manager 118 is configured to use sensor data from sensing unit 30 to predict one or more conditions of fluid 16 at a location further downstream than where the sensor data was obtained. Pipeline control manager 122 is configured to determine control operations to maintain fluid 16 in a desired phase, maintain fluid 16 in a desired composition, etc., by providing control signals to controllable piping element 106, using any one of the following: fracture parameters provided by fracture limit database 114; the current phase of fluid 16 or fluid slugs determined by operating phase manager 116 using material model 120; or the predicted condition of fluid 16 (e.g., as predicted by fluid slug manager 118).

[0077] According to some embodiments, memory 112 is shown as including a fracture limit database 114 for pipe 12. According to some embodiments, if the operating pressure of pipe 12 is below 1900 psi, fracture of pipe 12 is negligible or unlikely. However, if the operating pressure of pipe 12 is above 1900 psi, fracture of pipe 12 may be more likely, in conjunction with the current conditions of the fluid 16 (or any fluid slug) within pipe 12. For example, different compositions, temperatures, pressures, or flow rates may result in a higher probability of pipe 12 fracture. Fracture limit database 114 may include a series of lookup tables that can be used to estimate the percentage of stagnant pipe (%Arrest Pipe) within pipe 12, based on conditions (e.g., temperature, pressure, flow rate, composition, etc.) of the fluid 16 within pipe 12. For example, %Arrest Pipe can be determined or selected from fracture limit database 114 based on the temperature, pressure, flow rate, composition, etc. of the fluid 16 within pipe 12. In some embodiments, controller 102 seeks to maintain %Arrest Pipe below 80% or any other acceptable predetermined threshold. According to some implementations, if %Arrest Pipe exceeds a predetermined threshold, the pipeline control manager 122 may determine one or more responsive control actions (e.g., adding additional materials or mixtures to change composition, reducing or increasing pressure, heating or cooling fluid 16, etc.) to maintain %Arrest Pipe below the predetermined threshold. In some implementations, the %Arrest Pipe limit, or any other limit described herein, is established by regulatory and / or engineering studies.

[0078] In some embodiments, the fracture limit database 114 is optional. In some embodiments, the fracture limit database 114 is an operating characteristic database that includes values ​​for various parameters, such as maximum operating pressure, maximum chemical injection concentration, etc., or any other limitations or parameters in which the control logic of the pipeline control manager 122 operates.

[0079] According to some embodiments, memory 112 is shown to include an operational phase manager 116. The operational phase manager 116 is configured to determine, using a material model 120 (e.g., a model of the composition of fluid 16), temperature, pressure, and flow rate, which phase fluid 16 (or one of the fluid slugs) is currently in. For example, given the composition of fluid 16 and based on the fluid's current temperature and pressure, the operational phase manager 116 can determine that fluid 16 is currently in the dense phase 804. Similarly, given the composition of fluid 16 and based on the fluid's current temperature and pressure, the operational phase manager 116 can determine that fluid 16 is currently in both the liquid and gas phases 818. In some embodiments, the operational phase manager 116 is configured to use multiple different material models 120 for different compositions of fluid 16. For example, material model 120 can use different values ​​or equations of parameters (e.g., bubble point curve 808, critical point 814, critical condensation pressure point 812, dew point curve 810, and critical condensation temperature point 816) with respect to phase diagram 800 based on the detected composition of fluid 16 (e.g., as detected by a fourth sensor 104d in one of the sensing units 30). In some embodiments, material model 120 uses any one of bubble point curve 808, critical point 814, critical condensation pressure point 812, dew point curve 810, or critical condensation temperature point 816 as parameters. According to some embodiments, these parameters can be adjusted based on the detected composition of fluid 16. For example, operating phase manager 116 may include one or more predetermined relationships or equations for adjusting the parameters of material model 120 based on different concentrations of the composition of fluid 16.

[0080] In some implementations, the operation phase manager 116 selects different material models 120 for the fluid 16 based on the detected presence of various elements, molecules, compounds, etc. in the fluid 16. For example, the operation phase manager 116 may select a specific model from a set of predetermined material models 120, each material model having different parameters.

[0081] According to some embodiments, the operating phase manager 116 is configured to determine the current phase of the fluid 16 and can provide that phase to any one of the fluid slug manager 118, display data manager 124, pipeline control manager 122, or fluid slug manager 118. In some embodiments, the operating phase manager 116 is also configured to provide any parameters of the material model 120 (e.g., bubble point curve 808, critical point 814, critical condensation pressure point 812, dew point curve 810, and critical condensation temperature point 816) to any one of the fluid slug manager 118, pipeline control manager 122, or display data manager 124. For example, the display data manager 124 can use the parameters of the material model 120 to present them to an operator or user via a display device 126.

[0082] According to some embodiments, the operating phase manager 116 may also provide one or more phase envelopes of model 120 to the display data manager 124. In some embodiments, the operating phase manager 116 is also configured to use sensor data obtained from the sensing unit 30 to generate mass curves and hydrate curves (e.g., hydrate formation curves). According to some embodiments, the operating phase manager 116 may provide mass curves and / or hydrate curves to the display data manager 124 (and / or to the fluid slug manager 118, pipeline control manager 122, fluid slug manager 118, etc.). According to some embodiments, the hydrate curve may be a pressure-temperature diagram (e.g., as shown in the image). Figures 9 to 10 (As shown). In some embodiments, the operation phase manager 116 also generates an envelope curve based on sensor data obtained from the sensing unit 30. The operation phase manager 116 may also use sensor data obtained from different sensing units 30 at different locations along the piping system 20 to generate processing paths (e.g., temperature and pressure points at different locations along the pipe 12).

[0083] Specific reference Figures 9 to 11 According to some embodiments, the operating phase manager 116 can be configured to generate graphs 900 and 1000. Graphs 900 and 1000 show dew point or phase envelope curves 904 and 1004 for fluid 16, and hydrate curves 906 and 1006. As fluid 16 flows through piping system 20, fluid 16 may experience pressure and temperature changes, as shown by processing paths 902 and 1002. According to some embodiments, processing paths 902 and 1002 may be defined by points 903 and 1003, respectively. Points 903 and 1003 may show different temperature and pressure points of fluid 16 recorded or measured by sensing unit 30 at different locations along piping system 20. Envelope curves 904 and 1004 and hydrate curves 906 and 1006 can be generated by the operating phase manager 116 using material model 120, the detected composition of fluid 16, and / or the expected composition of fluid 16. Hydrate curves 906 and 1006 can be generated by the operating phase manager 116 based on water or moisture detected by one or more fourth sensors 104d of the sensing units 30 and the composition of the detected fluid 16 and / or the expected composition of the fluid 16.

[0084] Specific reference Figure 9 Graph 900 shows the processing path 902, hydrate curve 906, and envelope curve 904 before controller 102 initiates one or more control decisions to adjust the operation of one or more controllable piping elements 106. (As shown in the graph...) Figure 9As shown, processing path 902 crosses envelope curve 904 at path portion 908 and hydrate curve 906 at path portion 910. When processing path 902 crosses hydrate curve 906, hydrates may form in fluid 16, which is undesirable. To limit hydrate formation in fluid 16, controller 102 may determine control decisions to adjust the composition of fluid 16 (e.g., inject ethanol) to adjust hydrate curve 906 and / or adjust envelope curve 904. Similarly, controller 102 may determine control decisions to adjust processing path 902 (e.g., by applying heating or cooling to pipe 12 to increase or decrease the temperature of fluid 16 at different locations along pipe system 20, increase or decrease the pressure of fluid 16 at different locations along pipe system 20, etc.). In some embodiments, controller 102 operates controllable piping element 106 to influence at least one of envelope curve 904, hydrate curve 906 and / or processing path 902 at different locations along piping system 20, such that processing path 902 does not cross envelope curve 904 and / or hydrate curve 906.

[0085] Specific reference Figure 10 Graph 1000 shows the processing path 1002, hydrate curve 1006, and envelope curve 1004 after controller 102 initiates one or more control decisions to adjust the operation of one or more controllable piping elements 106. (As shown in the graph...) Figure 9 and Figure 10 The comparison shows that processing path 1002 does not cross the hydrate curve 1006 at path portion 1010 (corresponding to path portion 910 of graph 900) and does not cross the envelope curve 1004 at path portion 1008 (corresponding to path portion 908 of graph 900). Advantageously, controller 102 uses sensor data obtained from sensing unit 30 (e.g., at different locations along piping system 20) to construct or generate graph 900 (e.g., using material model 120), identify locations where hydrates may form or where fluid 16 may transition to an undesirable phase, and implement adjustments (e.g., adjustments to the composition of fluid 16 at different locations along piping system 20, adjustments to the temperature and / or pressure of fluid 16 at different locations along piping system 20, etc.) to prevent / limit hydrate formation or to prevent / limit fluid 16 from transitioning to an undesirable phase (e.g., liquid and gas phase, liquid phase, etc.). The controller 102 can also check the sensor data obtained from the sensing unit 30 after the adjustment is implemented to ensure that the adjustment successfully limits the formation of hydrates and / or prevents the fluid 16 from transitioning to an undesirable phase. In this way, according to some embodiments, the controller 102 can operate in a closed-loop manner using sensor data in real time.

[0086] Specific reference Figure 11 as well as Figures 2 to 7 According to some embodiments, the controller 102 can acquire sensor data from one or more sensing units 30 at time intervals (e.g., every five minutes, every minute, every second, etc.). For example, according to some embodiments, the controller 102 can acquire sensor data from sensing unit 30a every five minutes. During this time interval, a certain amount of fluid 16 or a certain volume of fluid 16 can flow through pipe 12 (e.g., depending on the flow rate of fluid 16 through pipe 12 at sensing unit 30a). This amount of fluid 16 in Figures 2 to 3 This is shown as fluid slug 18. As fluid slug 18 travels through pipe 12, the length 202 of fluid slug 18 can increase due to the decrease in pressure along pipe 12. For example, as... Figure 2 As shown, according to some embodiments, the length 202 of the fluid slug 18 is increased from length 202a to length 202b, to length 202c, to length 202d, to length 202e, etc. According to some embodiments, both the volume and length 202 of the fluid slug 18 can increase as it flows along the conduit 12. In some embodiments, the standard volume of the fluid slug 18 remains the same as that of the fluid slug 18 traveling along the conduit 12.

[0087] According to some implementations, the fluid slug manager 118 can be configured to monitor and track different slugs throughout the piping system 20. For example, the fluid slug manager 118 can track the leading edge of the fluid slug 18 as it travels through the piping system 20 or as it mixes with other fluid slugs (e.g., lateral slugs, as described in more detail below) to form a new fluid slug. The fluid slug manager 118 can calculate the dimensions of the fluid slug 18 as follows:

[0088] V 段塞 =Q 主线 ×t 样本

[0089] Where V 段塞 Q is the standard volume of fluid slug 18. 主线 It is the volumetric flow rate of fluid 16 through pipe 12 (e.g., a predetermined value or a measurement at a corresponding point in sensing unit 30), and t 样本 It is a time interval (e.g., five minutes).

[0090] The fluid slug manager 118 can also be configured to store the standard volume V of the fluid slug 18. 段塞 Converted to the actual volume V occupied by fluid slug 18 in pipe 12 实际 This is to calculate the length of the pipe 12 along which the fluid slug 18 extends. For example, the fluid slug manager 118 can estimate the actual volume V using the following equation.实际 :

[0091]

[0092] Where P 标准 This is standard or atmospheric pressure (e.g., 14.696 psia), and T 标准 It is a standard temperature (e.g., 59 degrees Fahrenheit), T 实际 It is the actual temperature of the fluid 16 as measured by the sensing unit 30, and P 实际 This is the actual pressure of the fluid 16 as measured by the sensing unit 30. The fluid slug manager 118 can then use the following equation to estimate or calculate the length L of the fluid slug 18. 段塞 :

[0093]

[0094] Where A c,主线 It is the cross-sectional area of ​​pipe 12.

[0095] The fluid slug manager 118 can use the average velocity of the fluid slug 18 (or any other fluid slug described herein) as it moves downward along the conduit 12 to determine the distance of the fluid slug 18 from the sensing unit 30 to the mixing point (e.g., as described herein). Figures 3 to 4 The average velocity of the mixing point 22 shown. The velocity of the fluid slug 18 can be measured by the sensing unit 30, or it can be calculated based on a measurement of flow velocity (e.g., volumetric flow velocity) or the expected flow rate of the fluid 16 through the pipe 12.

[0096] According to some embodiments, the leading edge of the fluid slug 18 can be defined by the location and time at which the sensing unit 30 acquires a gas sample for analysis. For example... Figures 3 to 4 As shown, conduit 12 may intersect with transverse conduit 13 (e.g., for the injection of one or more chemicals as determined by controller 102). Figure 3 As shown, according to some embodiments, pipe 12 is the mainline, and fluid slug 18 will mix with two lateral slugs 24 and 26 at mixing point 22. In some embodiments, a new fluid slug is defined as the leading edge of the mainline, or when the lateral slugs reach the mainline at the mixing point. This can be targeted at new slugs (e.g., as...). Figures 4 to 7 The composition of the mixed gas is calculated using the new slug 35 shown.

[0097] Specific reference Figure 5According to some embodiments, the new slug 35 may include a first sub-slug 34, a second sub-slug 36, and a third sub-slug 38. In some embodiments, the leading edge of the third sub-slug 38 is created when the leading edge of the fluid slug 18 reaches the mixing point 22. In some embodiments, the leading edge of the second sub-slug 36 is created when the leading edge of the fluid slug 19 (e.g., a downstream slug relative to the fluid slug 18 along the conduit 12) reaches the mixing point 22.

[0098] Specific reference Figure 6 According to some embodiments, the third sub-slug 38 may be a mixture of the fluid slug 19 and the first transverse slug 24. In some embodiments, the second sub-slug 36 is a mixture of the fluid slug 19 and the transverse slug 32 (e.g., a transverse slug located downstream of the first transverse slug 24). In some embodiments, the first sub-slug 34 is a mixture of another fluid slug and the transverse slug 32 located downstream of the fluid slug 19 along the conduit 12.

[0099] Specific reference Figure 11 and Figure 7 According to some embodiments, controller 102 (or more specifically, fluid slug manager 118) can track fluid slug 18 and new fluid slug 35 throughout the piping system 20. In some embodiments, fluid slug manager 118 is configured to compare the composition of fluid 16 (or gas supplied laterally, or chemicals such as methanol injected through transverse conduit 13) using sensor data about the composition of fluid slug 18 and transverse slugs 24, 26, and 32 obtained from sensing units 30 upstream and downstream of mixing point 22. In some embodiments, fluid slug manager 118 is configured to identify new composition at a point located downstream of mixing point 22 (e.g., based on sensor data from sensing unit 30). According to some embodiments, fluid slug manager 118 can provide the new composition to piping control manager 122 for determining whether additional control decisions should be implemented to further adjust the new composition.

[0100] Specific reference Figure 11According to some embodiments, the fluid slug manager 118 can be configured to predict the expected downstream composition or characteristics of the fluid 16 (e.g., downstream of mixing point 22) using upstream sensor data obtained from one or more of the sensing units 30. In some embodiments, the fluid slug manager 118 is configured to predict the composition of downstream, new, or mixed fluid slugs. According to some embodiments, the predicted composition can be verified based on sensor data obtained from the sensing units 30 located downstream of mixing point 22. In some embodiments, the fluid slug manager 118 calculates the carbon molar concentration in any of the sub-slugs 34, 36, or 38, or the new slug 35. For example, the fluid slug manager 118 can use the following equation to calculate the carbon molar concentration (e.g., mole %) of the third sub-slug 38. Cx,D3 ):

[0101]

[0102] Where V D3 It is the volume of the third sub-segment plug 38, V ML(A-1) It is the volume of fluid slug 19 (e.g., mainline fluid slug A-1), mole% Cx,ML(A-1) It is the carbon molar concentration (e.g., in percentage) of fluid slug 19, V LS1 It is the volume of the transverse septum 24, and mole%. Cx,LS1 This is the carbon molar concentration (e.g., in percentage) of the transverse slug 24. According to some embodiments, the fluid slug manager 118 can similarly calculate or estimate the predicted composition of the second sub-slug 36 and the first sub-slug 34.

[0103] In some implementations, the fluid slug manager 118 is configured to measure the predicted molar concentration (e.g., mole %) of the slug located downstream of the mixing point 22. Cx,D3 ) and the actual molar concentration (e.g., mole% as measured using sensing unit 30). Cx,D3 The upstream calculated composition (e.g., predicted composition) and downstream measured composition of different gas slugs can be compared. This comparison can be used to calibrate or verify the predictions performed by the fluid slug manager 118 and / or calibrate or verify downstream time calculations. According to some embodiments, the predicted composition of fluid 16 downstream of mixing point 22, the measured composition of fluid 16 downstream of mixing point 22, and / or the measured composition of fluid 16 upstream of mixing point 22 can be provided to the pipeline control manager 122 and / or the display data manager 124.

[0104] Specific reference Figure 11The pipeline control manager 122 is configured to use the output of the operating phase manager 116, fracture parameters (e.g., %Arrest Pipe) from the fracture limit database 114, the output of the fluid slug manager 118, or any of the outputs of the fluid slug manager 118, in combination with any sensor data obtained from the sensing unit 30, to determine one or more control decisions for the controllable pipeline element 106. According to some embodiments, the control decisions may include: injecting or adding one or more chemicals or additives (e.g., the amount of propane-rich gas injected, the rate of propane-rich gas injection, ethanol, etc.); removing one or more substances from the fluid 16 (e.g., removing moisture or water); applying heating or cooling to adjust the temperature or pressure of the fluid 16; adjusting the operation of one or more compressors driving the fluid 16 through the pipeline system 20, etc. In some embodiments, the control decisions result in changes in one or more properties of the fluid 16 at different locations within the pipeline system 20. For example, control decisions can change the composition of fluid 16, the temperature and / or pressure of fluid 16, the flow rate of fluid 16, etc., so that fluid 16 avoids the critical condensation temperature point 816 (e.g., so that fluid 16 located downstream of the injection site is in the desired phase, such as dense phase 804), thereby reducing the possibility of pipe 12 rupture, reducing the possibility of hydrate formation, and preventing fluid 16 from crossing the hydrate curve, thereby reducing the possibility of forming liquids or gases, etc.

[0105] According to some embodiments, the pipeline control manager 122 uses the output of the operating phase manager 116 and sensor data obtained in real time from the sensing unit 30 to perform closed-loop control in order to maintain desired objectives (e.g., keeping fluid 16 in a desired phase, limiting hydrate formation in fluid 16, keeping fluid 16 sufficiently away from the critical condensation temperature 816, reducing the likelihood of pipeline 12 breakage, etc.) by operating the controllable pipeline element 106. In some embodiments, the controllable pipeline element 106 includes heating devices (e.g., heating coils, boilers, heat exchangers, etc.), cooling devices (e.g., cooling coils, heat exchangers, Joule-Thompson pressure cooling devices, etc.), and injection devices (e.g., injection systems configured to inject propane-rich gases, methanol, or ethanol, etc., into fluid 16, for example, through transverse pipes). According to some implementations, after the controllable piping element 106 operates according to control decisions as determined by the piping control manager 122, the piping control manager 122 can acquire new sensor data, and the operating phase manager 116, fluid slug manager 118, material model 120, and display data manager 124 can be refunctionalized to provide the piping control manager 122 with new, updated, or recalculated inputs (e.g., new phases of fluid 16 at different locations along the piping system 20, new model parameters such as the critical condensation temperature points of fluid 16 at different locations along the piping system 20, etc.). The piping control manager 122 can then determine whether new control decisions should be implemented by the controllable piping element 106. For example, the piping control manager 122 can determine whether one or more objectives are met (e.g., maintaining fluid 16 in the desired phase, sufficient limitation of hydrate formation, etc.), and if these objectives are not met, new control decisions can be generated for the controllable piping element 106.

[0106] It should be understood that, according to some embodiments, the pipeline control manager 122 can operate to achieve the different purposes described herein for discrete portions of the pipeline system 20. For example, according to some embodiments, the sensing unit 30 is shown as including a first sensing unit 30a, a second unit 30b, a third sensing unit 30c, ..., and an nth sensing unit 30n, each of which is located at a different location around the pipeline system 20. In some embodiments, controllable pipeline elements 106 (e.g., heating elements, cooling elements, injection systems, compressors, etc.) are positioned around the pipeline system 20. In this way, according to some embodiments, the pipeline control manager 122 can execute multiple control schemes to ensure that objectives are met at all different locations or portions of the pipeline system 20. For example, if the pipeline control manager 122 identifies that the fluid 16 is not in the desired phase at a specific location in the pipeline system 20 (e.g., based on sensor data obtained from the sensing unit 30 and / or the output of the operating phase manager 116), the pipeline control manager 122 may determine one or more control decisions (or operations to influence the fluid 16 at the specific location) of the controllable pipeline element 106 near the specific location to satisfy the target at the specific location.

[0107] Still refer to Figure 11 According to some embodiments, the display data manager 124 is configured to generate display data and provide the display data to the display device 126. According to some embodiments, the display device 126 may be a remote device, a user device, a display screen, etc. In some embodiments, the display data generated by the display data manager 124 includes the current phase of the fluid 16, phase diagram 800 (or any other similar phase diagram), and / or graphs 900 and 1000 (e.g., hydrate curves, envelope curves, processing paths, etc.). According to some embodiments, the display data may also include any sensor data obtained from the sensing unit 30, and / or any outputs, inputs, or determined values ​​from the fracture limit database 114, the operating phase manager 116, the fluid slug manager 118, or the pipeline control manager 122. In some embodiments, the display data also includes control decisions made by the pipeline control manager 122.

[0108] Potential implementation infrastructure

[0109] Specific reference Figure 12According to some embodiments, the control system 100 may be implemented on system infrastructure 1200. According to some embodiments, system infrastructure 1200 may include a control logic processor 1202 and a computer module 1204. According to some embodiments, computer module 1204 includes an input / output (I / O) module 1206, a computing module 1208, a user experience (UX) engine 1210, and a display server 1212. In some embodiments, the control logic processor 1202 is configured to implement any functions of the pipeline control manager 122. In some embodiments, the control logic processor 1202 is configured to acquire sensor data (e.g., temperature, pressure, composition, and flow rate) from sensing unit 30 and provide the sensor data to the I / O module 1206 of computer module 1204. According to some embodiments, computer module 1204 may be located remotely from the control logic processor 1202, and communication between computer module 1204 and control logic processor 1202 may be wireless.

[0110] In some embodiments, I / O module 1206 is configured to provide sensor data (e.g., composition, pressure, temperature, etc.) to computing module 1208. In some embodiments, computing module 1208 is configured to implement any functions of operating phase manager 116 and / or material model 120. For example, according to some embodiments, computing module 1208 may store and use material model 120. According to some embodiments, computing module 1208 is configured to use sensor data and may output values ​​of critical condensation temperature, critical condensation pressure, and critical point to I / O module 1206. According to some embodiments, I / O module 1206 is configured to provide values ​​of critical condensation temperature, critical condensation pressure, and critical point to control logic processor 1202 for generating control decisions in a closed-loop control scheme. In some embodiments, computing module 1208 is also configured to provide real-time and historical data of pressure, temperature, composition, phase envelope, and hydrate curve points to UX engine 1210. In some embodiments, the UX engine 1210 is configured to perform the functions of the display data manager 124. In some embodiments, the UX engine 1210 is configured to generate display data and provide the display data as an HTML or HTML5 file to the server 1212. According to some embodiments, the display data can be accessed and viewed by a remote device via the display server 1212. In some embodiments, in response to receiving a historical data request from the UX engine 1210, the calculation module 1208 provides real-time and historical pressure, temperature, composition, phase, envelope, and hydrate curve points to the UX engine 1210.

[0111] deal with

[0112] Now refer to Figure 13 A flowchart of a process 1300 for operating a piping system using real-time sensor data and a material model, according to some embodiments, is shown. According to some embodiments, process 1300 includes steps 1302 to 1314 and can be performed by system 10 and / or control system 100. In some embodiments, process 1300 is performed by system infrastructure 1200.

[0113] According to some embodiments, processing 1300 includes obtaining sensor data (step 1302) from one or more sensing units located at different locations around the piping system, including the temperature, pressure, flow rate, and composition of the gas. In some embodiments, step 1302 is performed by sensing unit 30. For example, according to some embodiments, temperature may be obtained by a first sensor 104a, pressure may be obtained by a second sensor 104b, flow rate may be obtained by a third sensor 104c, and fluid composition may be obtained by a fourth sensor 104d. According to some embodiments, the sensor data may be provided from sensing unit 30 to controller 102 and / or control logic processor 1202.

[0114] According to some embodiments, processing 1300 includes obtaining one or more material models of the fluid (e.g., material model 120) based on the composition of the fluid at different locations around the piping system (step 1304). According to some embodiments, one or more material models can predict various thermodynamic properties of the fluid (e.g., phase, critical condensation temperature, critical condensation pressure, critical point, bubble point curve, hydrate curve, envelope curve, dew point curve, etc.) based on the fluid's temperature and pressure. In some embodiments, one or more material models are determined, selected, or generated based on the fluid composition detected by sensing unit 30 (or more specifically, fourth sensor 104d). In some embodiments, various parameters of one or more models are adjusted based on the detected or sensed fluid composition. In some embodiments, step 1304 is performed by operating phase manager 116 or computer module 1208 (e.g., using the techniques described in more detail above with reference to operating phase manager 116).

[0115] According to some embodiments, process 1300 includes using one or more material models of the fluid to determine one or more thermodynamic properties of the fluid at different locations around the piping system (step 1306). In some embodiments, step 1306 includes using one or more material models and the temperature and pressure of the fluid at different locations around the piping system 20, as detected by sensing unit 30. In some embodiments, step 1306 includes using one or more material models to determine critical points, critical condensation temperature points, critical condensation pressure points, etc., of the fluid at one or more locations around the piping system 20. According to some embodiments, step 1306 is performed by operating phase manager 116 and / or computer module 1208.

[0116] According to some embodiments, processing 1300 includes using sensor data and one or more thermodynamic properties to perform closed-loop control to determine one or more control decisions (step 1308) to achieve one or more control objectives. In some embodiments, the sensor data used includes temperature, pressure, and flow rate. In some embodiments, one or more thermodynamic properties include a critical condensation temperature, a critical condensation pressure, a critical point, and the fluid's position on a phase diagram (e.g., output from a material model based on temperature and pressure). In some embodiments, step 1308 is performed by a pipeline control manager 122 or a control logic processor 1202. In some embodiments, one or more control objectives include: limiting or preventing hydrate formation in the fluid; maintaining the fluid in a desired phase (e.g., a dense phase); maintaining the fluid above the critical condensation temperature, etc. In some embodiments, one or more control decisions include: injecting additives into the fluid (e.g., adding propane to adjust the fluid composition); applying heating or cooling (to adjust temperature); adjusting compressor operation (to adjust pressure). According to some embodiments, the pipeline control manager 122 may implement a PID control scheme to determine control decisions that satisfy the control objectives.

[0117] According to some embodiments, process 1300 includes operating one or more controllable piping elements to achieve one or more control objectives (step 1310). In some embodiments, step 1310 includes providing one or more control decisions from controller 102 (e.g., piping control manager 122) to controllable piping element 106. In some embodiments, the controllable piping element includes an injection system for pumping or injecting additives into fluids, a heating element (e.g., a heating coil), a cooling element, a compressor, a separator, etc. According to some embodiments, the controllable piping element is controllable piping element 106.

[0118] According to some embodiments, processing 1300 includes generating display data for one or more thermodynamic properties, sensor data, and one or more phase diagrams (step 1312). According to some embodiments, one or more thermodynamic properties may include critical condensation temperature points, critical condensation pressure points, critical points, operating positions of the fluid on the phase diagram, etc. In some embodiments, sensor data includes the temperature of the fluid, the pressure of the fluid, the flow rate of the fluid, and / or the composition of the fluid. In some embodiments, the phase diagram includes hydrate curves, envelope curves, processing paths, etc. In some embodiments, one or more phase diagrams include graphs or charts similar to graph 900, graph 1000, or phase diagram 800. In some embodiments, step 1312 is performed by the display data manager 124 and / or the UX engine 1210.

[0119] According to some embodiments, process 1300 includes operating a display device to provide display data to a user (step 1314). In some embodiments, the display device is display device 126. In some embodiments, the display device is configured to access the display data via a server or web page (e.g., server 1212).

[0120] The appendices describe various exemplary implementations of the systems and methods described herein, as well as exemplary system architectures, frameworks, operating environments, etc., in which the systems and methods described herein may be implemented. Systems of this disclosure may include any of the hardware, software, or other components described in the appendices and may be configured to perform any of the functions described in the appendices. Similarly, methods or processes of this disclosure may include any processing steps described in the appendices. In some implementations, the systems and methods described herein may be implemented using any of the systems, methods, or other features described in the appendices, or in any combination thereof. It should be understood that the disclosures provided in the appendices are provided by way of example only and should not be considered as limitations.

[0121] Advanced optimization

[0122] Reference Figures 14 to 20This document illustrates various systems and methods for performing advanced optimization of pipelines according to some embodiments. In some embodiments, advanced optimization can be performed to determine how to operate various pumps in the pipeline, and / or to determine the amount of drag-reducing agent (DRA), diluent, or other additives to be injected into the pipeline, the location where DRA should be injected, the time when DRA should be injected, when to use electrical energy from a utility provider or energy storage device, etc., or otherwise operate the pipeline to minimize energy consumption, emissions generated by the pipeline system, costs associated with operating the pipeline, or product throughput through the pipeline. In some embodiments, advanced optimization can be performed on the entire pipeline to determine the optimal control decisions for the pipeline over a future time period (e.g., a future time frame). In some embodiments, advanced optimization is performed independently at each of multiple stations in the pipeline, such that each station operates to optimize its throughput, emissions, energy consumption, operating costs, etc.

[0123] Piping system

[0124] Reference Figure 14 The diagram illustrates a pipeline 1400 according to some embodiments. According to some embodiments, pipeline 1400 includes a tank 1402 configured to store a product for delivery to a customer. In some embodiments, the product includes any one of diesel fuel, gasoline fuel, fuel oil, jet fuel, or any other hydrocarbon fluid. In some embodiments, tank 1402 is or includes multiple tanks (e.g., a tank farm) configured for storing and emptying the product. In some embodiments, the product is stored as a liquid in tank 1402.

[0125] According to some embodiments, pipeline 1400 also includes a first station 1404, a second station 1406, and a third station 1408. In some embodiments, each of stations 1404 to 1408 includes one or more pumps configured to drive product flow through pipeline 1400. For example, according to some embodiments, the first station 1404 includes pumps 1406 to 1410, the second station 1406 includes pumps 1412 to 1414, and the third station 1408 includes stations 1416 to 1418. According to some embodiments, pumps 1406 to 1418 of stations 1404 to 1408 are configured to operate to pump product from tank 1402 to a delivery point (e.g., a customer). In some embodiments, pumps 1406 to 1414 are arranged in parallel (e.g., at stations 1404 and 1406), and pumps 1416 to 1418 are arranged in series (e.g., at station 1408). It should be understood that although only three stations are shown—stations 1404 to 1408—pipeline 1400 may include any number of stations, which may include any number of pumps connected in series, in parallel, or in any combination thereof.

[0126] In some embodiments, pumps 1406 to 1410 are arranged in parallel at station 1404 to facilitate the use of smaller pumps capable of consistent operation to pump large quantities of product. In some embodiments, pumps 1416 and 1418 at station 1408 are arranged in series to increase the pressure of the product before delivery to the customer. For example, if there is a change in altitude from station 1408 to the customer, the series arrangement of pumps 1416 and 1418 can facilitate increasing the pressure of the product to drive it to the higher altitude where the customer is located.

[0127] like Figure 14 As shown, according to some embodiments, each of stations 1404 to 1408 includes equipment 1432 to 1434 (e.g., transformer, controller, electric motor, internal combustion engine, etc.) configured to operate pumps of the station. In some embodiments, station 1404 includes equipment 1430 configured to operate pumps 1406 to 1410. In some embodiments, station 1406 includes equipment 1432 configured to operate pumps 1412 to 1414. In some embodiments, station 1408 includes equipment 1434 configured to operate pumps 1416 to 1418.

[0128] According to some embodiments, each of stations 1404 to 1408 further includes an energy storage device, shown as energy storage device 1436, energy storage device 1438, and energy storage device 1440. In some embodiments, energy storage devices 1436 to 1440 are configured to store electrical energy supplied by a utility provider (e.g., an energy provider) or generated locally at stations 1404 to 1408. Energy storage devices 1436 to 1442 may include capacitors, batteries, battery fields, etc., and are configured to be charged with electrical energy and discharged to equipment 1430 to 1434 for operation of stations 1404 to 1408 or their pumps.

[0129] In some embodiments, each of stations 1404 to 1408 further includes power generation equipment 1442 to 1446. For example, according to some embodiments, station 1404 may include power generation equipment 1442, station 1406 may include power generation equipment 1444, and station 1408 may include power generation equipment 1446. In some embodiments, power generation equipment 1442 to 1446 is or includes wind turbines, solar panels, water turbines, hydroelectric generators, diesel generators, etc., configured to generate electrical energy for use by equipment 1430 to 1434 and / or stored in any of power storage devices 1436 to 1440 for later use by equipment 1430 to 1434 to operate pumps 1406 to 1418.

[0130] In some embodiments, pipe 1400 includes DRA addition points 1420 to 1426, at which DRA can be introduced into pipe 1400 to reduce friction and improve the efficiency of pipe 1400 (e.g., reducing power consumption, operating costs, etc. at stations 1404 to 1408). In some embodiments, when DRA is injected or introduced into the product in pipe 1400, the DRA reduces the energy required to propel the product through pipe 1400. In some embodiments, the type of product in pipe 1400 determines how much DRA can be introduced. For example, if the product transported through pipe 1400 is jet fuel, DRA cannot be provided to the product. Similarly, diesel, gasoline, fuel oil, etc., may have different DRA requirements. In this way, the amount of DRA supplied to the product in pipe 1400 can be determined based on the type of product currently transported through pipe 1400. In some embodiments, DRA is a chemical additive that reduces friction between the product and the interior of pipe 1400. In some embodiments, DRA may have a molecular structure with a length of 10 to 12 inches. In some embodiments, as DRA passes through pumps 1406 to 1418, the DRA molecules are cut or severed by the blades or turbine of pumps 1406 to 1418. Additional DRA can then be introduced downstream of the pumps. For example, as... Figure 14As shown, DRA addition point 1422 is downstream of pumps 1406 to 1410 at station 1404, DRA addition point 1424 is downstream of pumps 1412 to 1414 at station 1406, and DRA addition point 1426 is downstream of pumps 1416 to 1418 at station 1408.

[0131] In some embodiments, stations 1404 to 1408 are positioned 40 to 100 miles apart. In this way, when a product leaves station 1404, it can travel until it reaches station 1406, where it is repressurized so that it can reach the third station 1408. In some embodiments, multiple different products are simultaneously supplied through pipeline 1400. For example, jet fuel, diesel fuel, gasoline, different grades of gasoline, etc., can all be transported through pipeline 1400 to a delivery location (e.g., a customer). Different products can be transported through pipeline 1400 as different product slugs. For example, a first product can be introduced into pipeline 1400 first and travel through pipeline 1400 as a first slug, while a second product is introduced into pipeline 1400 after the first product and travels through pipeline 1400 as a second slug. In some embodiments, the first and second slugs mix with each other at their boundary to form a transition mixture. In some embodiments, the conversion mixture is removed from pipeline 1400 when the product arrives at its destination and is sent to the refinery. In some embodiments, for similar types of products, the conversion mixture is acceptable and is not removed before being sent to the refinery. For example, if different grades of gasoline are blended, such a conversion mixture may be acceptable and does not require removal and refining. The above references can be used. Figures 1 to 7 Any techniques for tracking acceptable transformation mixtures through piping system 20, described in more detail.

[0132] Optimize controller

[0133] Specific reference Figure 15This illustration shows a system 1500 for optimizing the operation of a pipeline 1400 according to some embodiments. In some embodiments, the system 1500 is configured to generate control decisions for the pipeline 1400 to optimally operate the pipeline 1400, equipment, pumps, DRA introductions, etc. In some embodiments, the system 1500 includes a controller 1502, a database 1528, a power meter 1514, a product instrument 1512, a user interface 1532, and the pipeline 1400. In some embodiments, the database 1528 is stored locally on the controller 1502 or on a cloud computing system. In some embodiments, the power meter 1514 and the product instrument 1512 are components or sensors of the pipeline 1400. In some embodiments, controller 1502 is configured to: receive system information about pipeline 1400 from database 1528; receive power consumption data for various components of pipeline 1400 from power meter 1514; receive delivery rate or quantity data from product meter 1512 of pipeline 1400; receive electricity cost, forecast, or scheduling data from utility provider 1510; and receive sensor and / or operational data from pipeline 1400 or its sensors (e.g., pressure sensors, flow meters, temperature sensors, etc.). In some embodiments, controller 1502 is also configured to receive one or more user inputs from user interface 1532 indicating a desired optimization mode for controller 1502. In some embodiments, controller 1502 is configured to operate user interface 1532 to display optimization results. In some embodiments, controller 1502 is configured to provide control decisions to pipeline 1400 to operate pipeline 1400 based on control decisions generated as optimization results. In some embodiments, if the power meter 1514 is not provided or used, the power usage can be estimated based on processing conditions and pump curves (e.g., integrating the amount of energy consumed over time). In some embodiments, the power usage is calculated by the controller 1502.

[0134] Controller 1502 includes processing circuitry 1504, which includes processor 1506 and memory 1508. Processor 1506 may be a general-purpose or special-purpose processor, application-specific integrated circuit (ASIC), one or more field-programmable gate arrays (FPGAs), a set of processing units, or other suitable processing units. Processor 1506 may be configured to execute computer code and / or instructions stored in memory 1508 or received from other computer-readable media (e.g., CD-ROM, network storage device, remote server, etc.).

[0135] Memory 1508 may include one or more means (e.g., memory cells, memory devices, storage devices, etc.) for storing data and / or computer code for performing and / or facilitating the various processes described herein. Memory 1508 may include random access memory (RAM), read-only memory (ROM), hard disk drive storage device, temporary storage device, non-volatile memory, flash memory, optical memory, or any other suitable memory for storing software objects and / or computer instructions. Memory 1508 may include database components, object code components, scripting components, or any other type of information structure for supporting the various activities and information structures described herein. Memory 1508 may be communicatively connected to processor 1506 via processing circuitry 1504 and may include computer code for performing (e.g., by processor 1506) one or more processes described herein.

[0136] Still refer to Figure 15According to some embodiments, memory 1508 includes an objective function generator 1518, an optimizer 1526, and a user input manager 1530. In some embodiments, the objective function generator 1518 is configured to receive optimization modes, such as those provided by user input or selected by the user input manager 1530. In some embodiments, the objective function generator 1518 includes a cost objective function generator 1520, an energy objective function generator 1522, and a delivery objective function generator 1524. According to some embodiments, the objective function generator 1518 is configured to receive system information and / or real-time sensor data to generate an objective function based on the optimization mode. In some embodiments, the system information is system information provided by database 1528. The system information may include information about the equipment of pipeline 1400, equipment models, interrelationships between different equipment, stations, pumps, etc., layout, etc. In some embodiments, the system information includes models of different stations (e.g., stations 1404 to 1408), or mathematical models of various components of the equipment at stations 1404 to 1408. For example, the models for different stations can be equipment performance curves, mathematical equations, multidimensional graphs, etc., illustrating the modeled or predicted operation of stations 1404 to 1408 relative to different control decisions (e.g., how much energy is consumed when operating station 1404 to drive the product through pipe 1400 at a specific flow rate, assuming all pumps 1406 to 1410 are operational; how much energy is consumed when operating station 1404 to drive the product through pipe 1400 at a specific flow rate, assuming only one or two of pumps 1406 to 1410 are operational). In some embodiments, the system information also includes information about the distance between stations 1404 to 1408, the height variation between stations 1404 to 1408, etc. In some implementations, the model of station 1404 predicts or estimates one or more output variables (e.g., energy consumption, output flow rate, product delivery rate, output emissions, operating costs, etc.) based on one or more input variables (e.g., operating parameters of the station's pumps, the amount of energy discharged or charged from the power storage device 1436, power generation, weather data, type of product transported through the station, etc.).

[0137] In some implementations, real-time sensor data includes power consumption, delivery rate, power cost, and / or sensor and operational data. In some implementations, the objective function generator 1518 uses real-time sensor data to generate the objective function.

[0138] When an optimization mode is selected to optimize energy consumption, objective function generator 1518 implements energy objective function generator 1522 and generates an objective function that quantitatively predicts or estimates the energy consumption of pipeline 1400 or individual stations of pipeline 1400 based on one or more control decisions subject to one or more constraints (e.g., operation of pumps 1406 to 1418). In some embodiments, the objective function includes models (e.g., pump models, pump curves, etc.) of one or more stations or components of stations in pipeline 1400 to predict or output performance variables or the energy consumption of each station or component of a station.

[0139] For example, the objective function can represent the energy consumption of pipe 1400 over a future time range subject to one or more constraints. The energy consumption objective function can have the following form:

[0140]

[0141] Where k is the time step within the optimization period or time range, m is the total number of time steps within the optimization period or time range, and x is a set of decision or controllable variables used for optimization.

[0142] In some implementations, the optimization takes the following form:

[0143] Minimize E(x)

[0144] This allows optimizer 1526 to be configured to determine the value of the decision or control variable x that minimizes the total energy consumption E over the optimization period or time range.

[0145] In some implementations, the decision variable x is or includes the flow rate or pressure passing through different pumps 1406 to 1418 or through stations 1404 to 1408. For example, the nth pump may have the following function or model:

[0146] E 泵，n =f 泵，n (x)

[0147] Where E 泵，n It is an estimate of the energy used by the nth pump to achieve the control decision x (e.g., flow rate Q, pressure difference Δp, etc.) within a time step or instantaneously, and f 泵，n It is a function of the nth pump, which predicts energy usage based on control or decision variables.

[0148] In some implementations, the inputs to the model for the nth pump include the amount or quantity of DRA introduced into pipe 1400, the location of the amount or quantity of DRA introduced into pipe 1400, the type of product currently being transported through pipe 1400, etc. For example, the nth pump may have the following functions or models:

[0149] E 泵，n =f 泵，n (x Δp x DRA x DRA，位置 x 产品 )

[0150] Where x Δp It is the pressure difference passing through the nth pump, x DRA Including one or more DRAs introduced into the pipeline, x DRA，位置 This involves introducing one or more DRA units into the pipe at position 1400, and x 产品 It is a product type pumped through pipe 1400 or a slug product type pumped through pipe 1400.

[0151] In some implementations, the energy objective function is minimized while subject to one or more constraints. In some implementations, constraints include: limits on pressurization of each pump in the pipeline, pump operating time, limits on the amount of DRA that can be introduced into the product based on the type of product currently flowing through the pipeline, tracking of product slugs as they pass through pipeline 1400, interrelationships between pumps in pipeline 1400 or between different stations in pipeline 1400 (e.g., increasing the pressurization of the first pump can affect the pressurization capacity of different pumps, etc.), and so on. In some implementations, constraints are any limitations (e.g., upper and lower limits) in the decision or control variables x. In some implementations, constraints are interrelationships between different control or decision variables x. For example, a constraint that adjusting one control decision can affect another in the control decision. In some implementations, constraints include a minimum amount of product delivered to the customer (e.g., finding the optimal control decision that minimizes energy consumption but still delivers a specific amount of product to the customer).

[0152] In some embodiments, the energy objective function also includes one or more models for tracking slugs of different types of products passing through pipe 1400. For example, since different products may lead to different constraints on pump pressurization or different constraints on the amount of DRA that can be provided to the product slugs, the objective function generated by objective function generator 1518 can take into account and selectively adjust or control constraints based on the location of different slugs in pipe 1400. Slugs passing through pipe 1400 can be tracked based on a mathematical model that predicts the transport of various products' slugs through pipe 1400 based on different pump control decisions (e.g., increasing pressure can increase the rate at which slugs travel through pipe 1400). In some embodiments, the slug position is obtained in real time based on sensor data obtained from pipe 1400 (e.g., chemical sensors detecting different types of products transported through pipe 1400). In some embodiments, processing circuitry system 1504 or system 1500 is configured to use as referenced above. Figures 1 to 11 The controller 102 may be described in more detail using any of the sensors, piping structures, or technologies used to identify and track product segments passing through the pipe 1400.

[0153] In some embodiments, the energy objective function also considers the electricity generated by any of the power generation devices 1442 to 1446. For example, the objective function generator 1518 may include terms or models that predict the electricity generation of the power generation devices 1442 to 1446 based on predicted weather conditions. For example, if the power generation devices 1442 to 1446 include solar panels, the weather conditions may indicate sunshine, cloud cover, etc., or the expected amount of solar radiation that the solar panels will experience and can be used to predict the energy generation of the solar panels. Similarly, if the power generation devices 1442 to 1446 include wind turbines, the weather conditions may indicate the average wind speed that can be used to predict the energy generation of the wind turbines over a future time period. In some embodiments, the energy objective function also considers losses associated with charging or discharging the power storage device 1436.

[0154] In some embodiments, optimization of the energy objective function generated or defined by the energy objective function generator 1522 leads to the determination of an optimal or minimum emission solution. For example, minimizing the energy consumption of the energy objective function generator 1522 can lead to minimal equipment usage and thereby minimal emissions (e.g., carbon dioxide, greenhouse gases, etc.) released into the environment or atmosphere. In some embodiments, the energy optimization mode is also referred to as an emission optimization mode that minimizes emissions.

[0155] In some implementations, the objective function generator 1518 is configured to construct the objective function using system information and to populate one or more terms or variables of the objective function using real-time sensor data. For example, real-time sensor data can be used to inform the objective function generator 1518 about the current condition of the pipeline 1400, the current operating status of the equipment on the pipeline 1400, weather conditions at different locations along the pipeline 1400, etc.

[0156] Still refer to Figure 15 According to some embodiments, the objective function generator 1518 includes a cost objective function generator 1520. In some embodiments, the cost objective function generator 1520 is configured to generate a cost objective function that predicts the monetary cost of pipeline 1400 or a single station of pipeline 1400 based on one or more control decisions (e.g., operation of pumps 1406 to 1418) subject to one or more control decisions. In some embodiments, the cost objective function is similar to the energy objective function, but also takes into account the costs associated with purchasing energy at different times of the day (e.g., costs associated with purchasing electricity, natural gas, etc.), the storage of electricity for later use (e.g., charging energy storage devices 1436 to 1440 with electricity and using the stored electricity at different times of the day when energy costs are higher), power generation, carbon tax costs, etc.

[0157] In some implementations, the cost objective function has the following form:

[0158] J(x) = E(x)(cost(k))

[0159] or:

[0160]

[0161] Here, cost(k) is the cost per unit of energy at time step k. In some implementations, cost(k) includes the cost of energy units purchased based on the time of day. For example, utility provider 1510 may provide a schedule based on energy costs (e.g., electricity costs) that vary throughout the day based on demand. In some implementations, energy may be cheaper at night, and therefore the optimization or minimization of the cost objective function can be determined: from a cost perspective, it is optimal to operate pipeline 1400 at a higher rate at night to take advantage of cheaper energy prices at different times of the day. In some implementations, cost(k) includes the cost of energy that can be generated (e.g., free), stored, and used later. In some implementations, cost(k) includes the cost of energy that can be purchased when energy is cheaper (e.g., during the night) and the cost of energy used at times when the cost of purchasing energy is higher later. In this way, optimization of the cost objective function generated by cost objective function generator 1520 can determine the optimal control decisions for the purchase, use, storage, generation, and release of energy over future timeframes. In some implementations, the processing circuitry 1504 is configured to use machine learning to adjust or tune when to charge, discharge, or store energy in the power storage devices 1436 to 1440 of stations 1404 to 1408.

[0162] In some implementations, the cost objective function is also subject to one or more constraints minimized by optimizer 1526. The constraints used to optimize or minimize the cost objective function may be the same as or similar to the constraints used to optimize or minimize the energy objective function as described in more detail above. In this way, controller 1502 can determine the optimal control decision for pipeline 1400 or a single station of pipeline 1400 based on the monetary costs over a future time horizon (e.g., one day, one week, several days, etc.).

[0163] Still refer to Figure 15 According to some embodiments, the objective function generator 1518 includes a delivery objective function generator 1524. In some embodiments, the delivery objective function generator 1524 is configured to generate an objective function that defines or predicts the quantity of product delivered over a future time span. For example, the quantity of product delivered may be defined as the flow rate of pipe 1400 at a customer's location, or the quantity of product transported to a customer over a period of time (e.g., in gallons, liters, weight, etc.). In some embodiments, the delivery objective function defines the quantity of product delivered based on a control or decision variable x. In some embodiments, the delivery objective function is provided to optimizer 1526 and maximized to determine the control or decision variable that results in maximum product delivery. In this way, pumps 1406 to 1418 can be operated to deliver as much product as possible to the customer, regardless of the energy consumption, emissions, or costs associated with doing so.

[0164] Optimizer 1526 is configured to obtain any of the objective functions described herein from objective function generator 1518 and optimize (e.g., maximize or minimize) the objective functions to determine control or decision variables that result in the desired behavior of pipeline 1400. In some embodiments, control or decision variables include determinations of: how much DRA, diluent, or other additive is added; where and when DRA is added; which of pumps 1406 to 1418 is operated; how pumps 1406 to 1418 are operated, etc., to achieve minimum emissions and / or minimum energy use, minimum monetary cost, or maximize product delivery of pipeline 1400 over a period of time.

[0165] Optimizer 1526 is configured to provide detailed optimization results to user input manager 1530 for display on user interface 1532 (e.g., allowing the user to see how to operate pipeline 1400 to achieve a desired goal), and is also configured to provide control decisions to pipeline 1400 or its equipment to operate pipeline 1400 according to control or decision variables. In some embodiments, pipeline 1400 operates within that time period using control decisions determined by performing optimization.

[0166] In some implementations, the optimizations described herein are performed on different batches of product. For example, any of the optimizations described herein can be performed on the first batch of diesel product, subsequent batches of gasoline product, subsequent batches of jet fuel product, etc. Optimization patterns can be provided and used as determined by the user (e.g., the pipeline operator). In some implementations, the operator can use customer requirements (e.g., requirements for different optimization patterns) for different sections of pipeline 1400 (e.g., from one pumping station to the next) or across the entire pipeline 1400. For example, if a customer requires product delivery as quickly as possible, an optimization pattern that maximizes product delivery can be implemented. If another customer requires product delivery in a cost-effective manner, an optimization can be implemented to deliver the desired amount of product to the customer based on a cost-efficient solution (e.g., using a cost objective function generated by cost objective function generator 1520).

[0167] In some implementations, optimization is performed individually for each of stations 1404 to 1408. For example, optimization can be performed on a station-by-station basis to adequately pump the product to the next station in the most cost-effective, energy-efficient, or maximum delivery rate manner (e.g., taking into account altitude variations). Advantageously, implementing the functionality of controller 1502 at each of stations 1404 to 1408 facilitates, at least in part, sensor-based autonomous optimization, allowing pipeline 1400 to continue operating optimally even in the event of a communication interruption. For example, if the third station 1408 loses power or experiences a power outage, the optimization decision at the second station 1406 can be altered to address the inoperability of the third station 1408 without rendering the entire pipeline 1400 inoperable. Furthermore, unlike subject matter expert (SME) methods, the optimization techniques described herein are mathematically based.

[0168] Discrete optimization

[0169] Specific reference Figure 16 A block diagram 1600 of a discrete optimization system for each of stations 1404 to 1408 according to some embodiments is shown. In some embodiments, each of stations 1404 to 1408 includes a corresponding controller 1502a to 1502c. In some embodiments, the first station 1404 is configured to use controller 1502a to perform optimization on itself using real-time sensor data and / or user input indicating a desired optimization pattern. Similarly, the second station 1406 and the third station 1408 can use corresponding controllers 1502b and 1502c to perform optimization on themselves based on real-time sensor data and user input indicating a desired optimization pattern. In this way, optimization can be performed locally at each of stations 1404 to 1408 to determine the optimization results and / or control decisions of the equipment at stations 1404 to 1408. In some embodiments, optimization is performed autonomously at each of stations 1404 to 1408 to facilitate autonomous optimal operation of each of stations 1404 to 1408.

[0170] In some implementations, the cloud computing system 1602 is configured to obtain data from any of stations 1404 to 1408 or from pipeline 1400 in order to perform overall optimization of pipeline 1400 in a coordinated manner. In some implementations, the cloud computing system 1602 is configured to use any function of controller 1502 to perform overall optimization of pipeline 1400. In this way, reference above... Figure 15The optimization techniques described in more detail can be implemented locally at each station 1404 to 1408 to optimize the operation of each station 1404 to 1408, or they can be implemented globally on the cloud computing system 1602 for the entire pipeline 1400 to determine the optimal operation of the pipeline 1400. In some embodiments, the overall optimization of the pipeline 1400 is performed in a distributed manner among the controllers 1502a to 1502c at stations 1404 to 1408, wherein controllers 1502a to 1502c communicate with each other. In some embodiments, the overall optimization is performed (at the cloud computing system 1602 or distributed among controllers 1502a to 1502c), and in the event of a communication interruption, controllers 1502a to 1502c default to performing individual optimizations for each station 1404 to 1408.

[0171] deal with

[0172] Specific reference Figure 17 The diagram illustrates a process 1700 for optimizing pipeline operation according to some embodiments. Process 1700 includes steps 1702 to 1710, and can be performed by referring to the above. Figures 14 to 16 The controller 1502, controllers 1502a to 1502c, or cloud computing system 1602 are described in more detail to perform this action.

[0173] According to some embodiments, process 1700 includes receiving user input (step 1702) indicating a desired pattern of optimization and operation of the pipeline system or station. In some embodiments, step 1702 includes receiving input indicating whether process 1700 should be performed to optimize and operate the pipeline system or station based on its cost, its energy consumption, or its delivery rate or quantity. In some embodiments, the desired pattern of optimization and operation of the pipeline system or station is automatically determined based on the user input.

[0174] According to some implementations, process 1700 includes obtaining an objective function (step 1704) that quantifies performance variables as a function of control decisions for the pipeline system or station. In some implementations, the objective function predicts performance variables over future time periods based on control decisions for the pipeline system or station. In some implementations, the performance variables are any one of the pipeline system's or station's delivery rate or throughput, energy consumption or emissions, or operating costs. In some implementations, step 1704 is performed by an objective function generator 1518 of controller 1502, or more specifically, by various modules of the objective function generator 1518.

[0175] According to some implementations, process 1700 includes minimizing or maximizing performance variables that conform to an objective function subject to one or more constraints to determine a control decision for a piping system or station (step 1706). In some implementations, step 1706 includes minimizing or maximizing performance variables by changing or adjusting the values ​​of control decisions over a future time period. In some implementations, step 1706 is performed by optimizer 1526. In some implementations, constraints include limitations on different control decisions, internal parameters, pump parameters (e.g., maximum operating flow rate, etc.). In some implementations, step 1706 is performed to determine a control decision that results in the minimum or maximum performance variables over a future time period.

[0176] According to some embodiments, process 1700 includes operating the piping system or station according to control decisions (step 1708). In some embodiments, step 1708 includes adjusting various control parameters of different equipment in the piping system or station according to control decisions. In some embodiments, step 1708 includes providing control decisions to different equipment in the piping system or station. In some embodiments, step 1708 includes operating the equipment in the piping system or station for a future time period according to control decisions. Control decisions may be schedules for different pump setpoints, operating parameters, how much DRA to inject, when and where to inject DRA, etc. According to some embodiments, step 1708 may be performed by pipeline 1400.

[0177] According to some implementations, process 1700 includes displaying optimization results (step 1710). In some implementations, step 1710 includes operating user interface 1532 to provide display data of the optimization results. In some implementations, the optimization results include the display of different parameters predicted over a future time range, control decisions, etc. In some implementations, step 1710 is performed by user interface 1532.

[0178] Specific reference Figure 18 The diagram illustrates a process 1800 for optimizing the operating costs of a pipeline, according to some embodiments. Process 1800 includes steps 1802 to 1806, and can be performed by referring to the above. Figure 15 The objective function generator 1518 and optimizer 1526 are described in more detail. In some embodiments, process 1800 is performed to determine how to operate the piping system or booster station in the most cost-effective manner. In some embodiments, process 1800 is performed as steps 1704 to 1706 of process 1700.

[0179] According to some embodiments, process 1800 includes obtaining an objective function that defines the operating costs of the pipeline as a function of one or more control decisions over a future time span (step 1802). In some embodiments, step 1802 includes defining an objective function representing the summation of the operating costs of the pipeline system over a future time span. Operating costs may include: energy costs associated with purchasing energy (e.g., purchasing electricity from a utility provider); cost savings that may be realized based on available energy generation and / or weather conditions; costs associated with purchasing, storing, and using energy at different times of the day; adjustments to operating parameters throughout the day (e.g., more difficult operation at night when electricity is cheaper), etc. In some embodiments, operating costs are monetary costs determined based on the amount of energy purchased over a future time span and the energy price over the future time span. In some embodiments, step 1802 is performed by a cost objective function generator 1520.

[0180] According to some embodiments, process 1800 includes obtaining one or more constraints on the objective function (step 1804). In some embodiments, step 1804 includes defining, generating, or otherwise obtaining one or more constraints on the objective function. In some embodiments, constraints include limitations on the operability of various equipment of the pipeline, limitations on how much DRA can be provided to different types of products transported via the pipeline, etc. In some embodiments, constraints are additional equations or conditions that must be satisfied to make the solution feasible or practically achievable. In some embodiments, step 1804 is performed by an objective function generator 1518 or an optimizer 1526.

[0181] According to some implementations, process 1800 includes minimizing an objective function subject to one or more constraints to determine a control decision over a future time span that results in the lowest operating cost (step 1806). In some implementations, step 1806 includes performing multivariate optimization to determine a control decision that satisfies one or more constraints and results in the lowest or optimal operating cost. In some implementations, step 1806 is performed by optimizer 1526 based on the objective function obtained in step 1804.

[0182] Specific reference Figure 19 The diagram illustrates a process 1900 for optimizing energy consumption or emissions in a pipeline, according to some embodiments. Process 1900 includes steps 1902 to 1906 and can be performed by methods as described above. Figure 15The objective function generator 1518 and optimizer 1526 are described in more detail. In some embodiments, process 1900 is performed to determine how to operate the pipeline system 1400 or booster station in the most energy-efficient or most emission-efficient manner. In some embodiments, process 1900 is performed as steps 1704 to 1706 of process 1700.

[0183] According to some embodiments, process 1900 includes obtaining an objective function that defines the energy consumption of the pipeline as a function of one or more control decisions over a future time span (step 1902). In some embodiments, step 1902 includes defining an objective function that represents the summation of energy consumption or emissions of the pipeline system over a future time span. Energy consumption may include energy consumption from various pumps, equipment, DRA injectors, energy losses associated with charging energy storage devices, etc., as a function of one or more control decisions. In some embodiments, control decisions are adjustments to various controllable equipment of the pipeline that affect the energy consumption or emissions generated by the pipeline.

[0184] According to some implementations, process 1900 includes obtaining one or more constraints on the objective function (step 1904). In some implementations, step 1904 is the same as or similar to step 1804 of process 1800. In some implementations, step 1904 includes defining one or more constraints that limit various parameters (e.g., control decisions, performance variables, pipeline operating parameters, etc.). In some implementations, step 1904 is performed by objective function generator 1518 or optimizer 1526.

[0185] According to some implementations, process 1900 includes minimizing an objective function subject to one or more constraints to determine a control decision (step 1906) that results in the lowest energy consumption or lowest emissions over a future timeframe. In some implementations, step 1906 is the same as or similar to step 1806 of process 1800.

[0186] Specific reference Figure 20 The diagram illustrates a process 2000 for performing optimizations in terms of throughput or product delivery, according to some embodiments. Process 2000 includes steps 2002 to 2006, and can be performed by referring to the above. Figure 15 The objective function generator 1518 and optimizer 1526 are described in more detail. In some embodiments, the execution process 2000 determines how to operate the pipeline (e.g., pipeline system 1400) to provide as much product as possible through the pipeline, regardless of the energy consumption or cost associated with such operation. In some embodiments, throughput is defined based on the volume, rate, etc., of the product passing through the pipeline.

[0187] According to some embodiments, process 2000 includes obtaining an objective function that defines the pipeline throughput as a function of one or more control decisions over a future time horizon (step 2002). In some embodiments, step 2002 includes defining an objective function that quantitatively predicts the quantity or flow rate of delivered products based on one or more control decisions. In some embodiments, step 2002 is the same as or similar to step 1902 or step 1802 of processes 1900 or 1800, respectively.

[0188] According to some embodiments, process 2000 includes obtaining one or more constraints on the objective function (step 2004). In some embodiments, step 2004 is the same as or similar to step 1904 of process 1900, or the same as or similar to step 1804 of process 1800. According to some embodiments, process 2000 also includes a control decision (step 2006) that maximizes the objective function subject to one or more constraints to determine a future time range leading to the highest throughput of the pipeline. In some embodiments, step 2006 is performed to maximize the throughput or product delivery of the pipeline, regardless of the energy consumption or cost associated with such operation. Process 2000 may be performed as steps 1704 to 1706 of process 1700.

[0189] Crude oil pipeline optimization

[0190] Refer again Figures 14 to 15 According to some embodiments, pipeline 1400 may be an oil pipeline, and controller 1502 may be configured to perform its functions for the oil pipeline. In some embodiments, the oil pipeline operates similarly to pipeline 1400, but instead of a DRA (Diluent Regulator), it provides a diluent (e.g., to dilute or adjust the viscosity of the oil). Controller 1502 may perform its functions to determine how much diluent to add, when to add the diluent, and where to add the diluent. In some embodiments, controller 1502 is also configured to obtain a temperature value of pipeline 1400 and use the temperature value to determine the viscosity of the oil and how much diluent to add to the pipeline to achieve a desired viscosity. In some embodiments, the objective function or constraint includes the relationship between the viscosity of the oil in the pipeline and the energy required to pump the oil at the current viscosity. In this way, the viscosity of the oil can be adjusted to improve the operating efficiency of the oil pipeline used for transporting or delivering the oil.

[0191] Gas pipeline optimization

[0192] Refer again Figures 14 to 15According to some embodiments, pipeline 1400 may be a gas pipeline (e.g., a pipeline for natural gas or acid gases), and controller 1502 may be configured to perform its functions for the gas pipeline. In some embodiments, the gas pipeline operates similarly to pipeline 1400, but typically no additives are supplied to pipeline 1400. Controller 1502 may perform its functions to determine how to operate the pumps at station 1404 to optimize power, emissions, cost, throughput, etc., according to an optimization mode. In some embodiments, controller 1502 is configured to optimize variables such as which compressors or pumps of pipeline 1400 are operated, the speed of the pumps or compressors of pipeline 1400, whether a cooler downstream of the compressor or pump is operated, and what level of cooling is provided.

[0193] Configuration of exemplary implementation

[0194] As used herein, the terms “about,” “approximately,” “substantially,” and similar terms are intended to have a broad meaning consistent with the common and accepted usage of those skilled in the art to which the subject matter of this disclosure pertains. Those skilled in the art who consult this disclosure will understand that these terms are intended to allow for the description of certain features described and claimed, without limiting the scope of those features to the precise numerical ranges provided. Therefore, these terms should be interpreted as indicating that non-substantial or irrelevant modifications or alterations to the described and claimed subject matter are considered to be within the scope of the invention as set forth in the appended claims.

[0195] It should be noted that the term “exemplary” used in this document to describe various embodiments is intended to indicate that such embodiments are possible examples, representations and / or illustrations of possible embodiments (and such term is not intended to imply that such embodiments are necessarily extraordinary or super-illustrative examples).

[0196] As used herein, the terms “coupled,” “connected,” etc., refer to two components joining each other directly or indirectly. Such a joining can be fixed (e.g., permanent) or movable (e.g., removable, releasable, etc.). Such a joining can be achieved by integrally forming two components or two components and any additional intermediate components into a single unit, or by attaching two components or two components and any additional intermediate components to each other.

[0197] The positions of the elements mentioned herein (e.g., "top," "bottom," "above," "below," etc.) are used only to describe the orientation of the various elements in the accompanying drawings. It should be noted that the orientation of the various elements may differ according to other exemplary embodiments, and such variations are intended to be covered by this disclosure.

[0198] Furthermore, the term "or" is used in its inclusive sense (rather than in its exclusive sense), such that, for example, when used to connect lists of elements, the term "or" means one, some, or all of the elements in the list. Unless otherwise explicitly stated, union language such as the phrase "at least one of X, Y, and Z" is otherwise understood in the context to generally mean that the terms, etc., can be X, Y, Z, X and Y, X and Z, Y and Z, or X, Y, and Z (i.e., any combination of X, Y, and Z). Therefore, unless otherwise specified, such union language is generally not intended to imply that certain implementations require at least one of X, at least one of Y, and at least one of Z to each exist.

[0199] It is important to note that the construction and arrangement of the elements of the systems and methods illustrated in the exemplary embodiments are merely illustrative. Although only a few embodiments of this disclosure have been described in detail, those skilled in the art upon reviewing this disclosure will readily understand that numerous modifications are possible (e.g., variations in the use of the size, dimensions, structure, shape and proportions, parameter values, mounting arrangements, materials, colors, orientations, etc., of various elements) without substantially departing from the novel teachings and advantages of the subject matter. For example, an element shown as integrally formed may be composed of multiple parts or elements. It should be noted that the components and / or parts described herein may be constructed from any of a variety of materials providing sufficient strength or durability, in any of a variety of colors, textures, and combinations. Therefore, all such modifications are intended to be included within the scope of the invention. Other substitutions, modifications, alterations, and omissions may be made in the design, operating conditions, and arrangement of the preferred and other exemplary embodiments without departing from the scope of this disclosure or the spirit of the appended claims.

Claims

1. A method for operating a piping system, the method comprising: Sensor data of the gas in the pipeline system is obtained from the sensor of the sensing unit; The sensor data and the material model of the gas are used to execute real-time and closed-loop control schemes to determine one or more control decisions. The material model of the gas is configured to receive the sensor data of the gas and predict the thermodynamic properties of the gas based on the sensor data. The thermodynamic properties of the gas predicted by the material model are different from those of the sensor data. as well as Operate one or more controllable piping elements according to one or more control decisions to adjust the phase of the gas to limit hydrate formation.

2. The method according to claim 1, wherein, The sensor data includes any one of the following: The temperature of the gas; The pressure of the gas; The flow rate of the gas; and The composition of the gas; The sensors of the sensing unit include any one of the following: a temperature sensor configured to measure the temperature of the gas; a pressure sensor configured to measure the pressure of the gas; a flow meter configured to measure the flow rate of the gas; and any one of a gas chromatograph, laser interferometer, water sensor, density sensor, or hydrogen sulfide sensor configured to measure the composition of the gas.

3. The method according to claim 1, wherein, The sensor data is obtained from multiple sensing units located around the piping system.

4. The method according to claim 1, wherein, The thermodynamic property predicted by the material model is at least one of the following: the critical condensation temperature of the gas, the critical condensation pressure of the gas, the critical point of the gas, the viscosity, density, or flow characteristics of the gas.

5. The method according to claim 1, wherein, The one or more control decisions are determined to satisfy one or more control objectives, wherein the one or more control objectives include limiting the formation of hydrates in the gas and at least one of the following: Maintain the gas in the desired phase; Minimize airflow resistance; To convert the gas into the desired phase; or Reduce the likelihood of pipe breakage in the pipeline system.

6. The method according to claim 1, further comprising: Generate display data for the user, the display data including at least one of the following: a graph containing hydrate curves, envelope curves and processing paths, a phase diagram of the gas, the sensor data, or one or more thermodynamic properties estimated by one or more of the material models; as well as Operate the display device to provide the display data to the user.

7. The method according to claim 6, wherein, The one or more thermodynamic properties estimated by the one or more material models include any one of the critical condensation temperature point, the critical condensation pressure point, or the critical point of the gas.

8. The method according to claim 1, wherein, The one or more material models are selected, generated, or adjusted based on the composition of the gas.

9. A controller for a piping system, the controller comprising a processing circuit system configured to: Sensor data of the gas in the pipeline system is obtained from the sensor of the sensing unit; The sensor data and a material model of the gas are used to execute real-time and closed-loop control schemes to determine one or more control decisions. The material model of the gas is configured to receive the sensor data of the gas and predict the thermodynamic properties of the gas based on the sensor data. The thermodynamic properties of the gas predicted by the material model differ from those predicted by the sensor data. Operate one or more controllable piping elements according to one or more control decisions to adjust the phase of the gas to limit hydrate formation.

10. The controller according to claim 9, wherein, The sensor data includes any one of the following: The temperature of the gas; The pressure of the gas; The flow rate of the gas; and The composition of the gas; The sensors of the sensing unit include any one of the following: a temperature sensor configured to measure the temperature of the gas; a pressure sensor configured to measure the pressure of the gas; a flow meter configured to measure the flow rate of the gas; and any one of a gas chromatograph, laser interferometer, water sensor, density sensor, or hydrogen sulfide sensor configured to measure the composition of the gas.

11. The controller according to claim 9, wherein, The sensor data is obtained from multiple sensing units located around the piping system.

12. The controller according to claim 9, wherein, The thermodynamic property predicted by the material model is at least one of the following: the critical condensation temperature of the gas, the critical condensation pressure of the gas, the critical point of the gas, the viscosity, density, or flow characteristics of the gas.

13. The controller according to claim 9, wherein, The one or more control decisions are determined to satisfy one or more control objectives, wherein the one or more control objectives include limiting the formation of hydrates in the gas and at least one of the following: Maintain the gas in the desired phase; Minimize airflow resistance; To convert the gas into the desired phase; or Reduce the likelihood of pipe breakage in the pipeline system.

14. The controller according to claim 9, wherein, The processing circuit system is also configured to: Generate display data for the user, the display data including any one of the following: a graph containing hydrate curves, envelope curves and processing paths, a phase diagram of the gas, the sensor data, or one or more thermodynamic properties estimated by the one or more material models; as well as Operate the display device to provide the display data to the user.

15. The controller according to claim 14, wherein, The one or more thermodynamic properties estimated by the one or more material models include any one of the critical condensation temperature point, the critical condensation pressure point, or the critical point of the gas.

16. The controller according to claim 9, wherein, The one or more material models are selected, generated, or adjusted based on the composition of the gas.

17. A piping system, comprising: pipeline; The station includes a sensing unit configured to provide sensor data; Piping equipment, the piping equipment being configured to adjust the temperature, pressure, flow rate, or composition of a gas; and The controller is configured to: Sensor data of the gas in the pipeline is obtained from the sensor of the sensing unit; The sensor data and the gas material model are used to execute real-time and closed-loop control schemes to determine one or more control decisions for the pipeline equipment. The gas material model is configured to receive the gas sensor data and predict the thermodynamic properties of the gas based on the gas sensor data. The thermodynamic properties of the gas predicted by the material model are different from those of the sensor data. as well as The piping equipment is operated according to one or more control decisions to adjust the phase of the gas to limit hydrate formation.

18. The piping system according to claim 17, wherein, The thermodynamic property predicted by the material model is at least one of the following: the critical condensation temperature of the gas, the critical condensation pressure of the gas, the critical point of the gas, the viscosity, density, or flow characteristics of the gas.

19. The piping system according to claim 17, wherein, The one or more control decisions are determined to satisfy one or more control objectives, wherein the one or more control objectives include limiting the formation of hydrates in the gas and at least one of the following: Maintain the gas in the desired phase; Minimize airflow resistance; To convert the gas into the desired phase; or Reduce the likelihood of pipe breakage in the pipeline system.

20. The piping system according to claim 17, wherein, The controller is also configured to: Generate display data for the user, the display data including any one of the following: a graph containing hydrate curves, envelope curves and processing paths, a phase diagram of the gas, the sensor data, or one or more thermodynamic properties estimated by the one or more material models; as well as Operate the display device to provide the display data to the user.

Images