CN114674114B - Intelligent monitoring and operation optimizing method and system for LNG liquefaction process - Google Patents
Intelligent monitoring and operation optimizing method and system for LNG liquefaction process Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 79
- 230000008569 process Effects 0.000 title claims abstract description 45
- 238000012544 monitoring process Methods 0.000 title claims abstract description 29
- 238000005265 energy consumption Methods 0.000 claims abstract description 77
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims abstract description 74
- 239000003507 refrigerant Substances 0.000 claims abstract description 53
- 239000003345 natural gas Substances 0.000 claims abstract description 37
- 238000005457 optimization Methods 0.000 claims abstract description 29
- 238000010801 machine learning Methods 0.000 claims abstract description 19
- 238000004891 communication Methods 0.000 claims abstract description 10
- 239000003949 liquefied natural gas Substances 0.000 claims description 66
- 239000007788 liquid Substances 0.000 claims description 10
- 238000001816 cooling Methods 0.000 claims description 8
- 238000007405 data analysis Methods 0.000 claims description 7
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000013480 data collection Methods 0.000 claims description 6
- 230000007613 environmental effect Effects 0.000 claims description 6
- 239000007791 liquid phase Substances 0.000 claims description 5
- 230000005514 two-phase flow Effects 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 230000001105 regulatory effect Effects 0.000 claims description 3
- 125000004122 cyclic group Chemical group 0.000 claims description 2
- 238000010586 diagram Methods 0.000 description 4
- 238000010276 construction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 239000007789 gas Substances 0.000 description 2
- 239000002826 coolant Substances 0.000 description 1
- 239000000498 cooling water Substances 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 239000002360 explosive Substances 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000013486 operation strategy Methods 0.000 description 1
- 239000012071 phase Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000005057 refrigeration Methods 0.000 description 1
- 230000001932 seasonal effect Effects 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
Classifications
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25J—LIQUEFACTION, SOLIDIFICATION OR SEPARATION OF GASES OR GASEOUS OR LIQUEFIED GASEOUS MIXTURES BY PRESSURE AND COLD TREATMENT OR BY BRINGING THEM INTO THE SUPERCRITICAL STATE
- F25J1/00—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures
- F25J1/0002—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures characterised by the fluid to be liquefied
- F25J1/0022—Hydrocarbons, e.g. natural gas
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25J—LIQUEFACTION, SOLIDIFICATION OR SEPARATION OF GASES OR GASEOUS OR LIQUEFIED GASEOUS MIXTURES BY PRESSURE AND COLD TREATMENT OR BY BRINGING THEM INTO THE SUPERCRITICAL STATE
- F25J1/00—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures
- F25J1/02—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures requiring the use of refrigeration, e.g. of helium or hydrogen ; Details and kind of the refrigeration system used; Integration with other units or processes; Controlling aspects of the process
- F25J1/0243—Start-up or control of the process; Details of the apparatus used; Details of the refrigerant compression system used
- F25J1/0244—Operation; Control and regulation; Instrumentation
- F25J1/0252—Control strategy, e.g. advanced process control or dynamic modeling
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25J—LIQUEFACTION, SOLIDIFICATION OR SEPARATION OF GASES OR GASEOUS OR LIQUEFIED GASEOUS MIXTURES BY PRESSURE AND COLD TREATMENT OR BY BRINGING THEM INTO THE SUPERCRITICAL STATE
- F25J1/00—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures
- F25J1/003—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures characterised by the kind of cold generation within the liquefaction unit for compensating heat leaks and liquid production
- F25J1/0047—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures characterised by the kind of cold generation within the liquefaction unit for compensating heat leaks and liquid production using an "external" refrigerant stream in a closed vapor compression cycle
- F25J1/0052—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures characterised by the kind of cold generation within the liquefaction unit for compensating heat leaks and liquid production using an "external" refrigerant stream in a closed vapor compression cycle by vaporising a liquid refrigerant stream
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25J—LIQUEFACTION, SOLIDIFICATION OR SEPARATION OF GASES OR GASEOUS OR LIQUEFIED GASEOUS MIXTURES BY PRESSURE AND COLD TREATMENT OR BY BRINGING THEM INTO THE SUPERCRITICAL STATE
- F25J1/00—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures
- F25J1/02—Processes or apparatus for liquefying or solidifying gases or gaseous mixtures requiring the use of refrigeration, e.g. of helium or hydrogen ; Details and kind of the refrigeration system used; Integration with other units or processes; Controlling aspects of the process
- F25J1/0243—Start-up or control of the process; Details of the apparatus used; Details of the refrigerant compression system used
- F25J1/0244—Operation; Control and regulation; Instrumentation
- F25J1/0254—Operation; Control and regulation; Instrumentation controlling particular process parameter, e.g. pressure, temperature
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25J—LIQUEFACTION, SOLIDIFICATION OR SEPARATION OF GASES OR GASEOUS OR LIQUEFIED GASEOUS MIXTURES BY PRESSURE AND COLD TREATMENT OR BY BRINGING THEM INTO THE SUPERCRITICAL STATE
- F25J2210/00—Processes characterised by the type or other details of the feed stream
- F25J2210/62—Liquefied natural gas [LNG]; Natural gas liquids [NGL]; Liquefied petroleum gas [LPG]
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25J—LIQUEFACTION, SOLIDIFICATION OR SEPARATION OF GASES OR GASEOUS OR LIQUEFIED GASEOUS MIXTURES BY PRESSURE AND COLD TREATMENT OR BY BRINGING THEM INTO THE SUPERCRITICAL STATE
- F25J2280/00—Control of the process or apparatus
- F25J2280/50—Advanced process control, e.g. adaptive or multivariable control
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F25—REFRIGERATION OR COOLING; COMBINED HEATING AND REFRIGERATION SYSTEMS; HEAT PUMP SYSTEMS; MANUFACTURE OR STORAGE OF ICE; LIQUEFACTION SOLIDIFICATION OF GASES
- F25J—LIQUEFACTION, SOLIDIFICATION OR SEPARATION OF GASES OR GASEOUS OR LIQUEFIED GASEOUS MIXTURES BY PRESSURE AND COLD TREATMENT OR BY BRINGING THEM INTO THE SUPERCRITICAL STATE
- F25J2290/00—Other details not covered by groups F25J2200/00 - F25J2280/00
- F25J2290/12—Particular process parameters like pressure, temperature, ratios
Abstract
The application relates to an intelligent monitoring and operation optimizing method and system for an LNG liquefaction process, wherein the method comprises the following steps: establishing network communication with an LNG factory DCS control system, collecting data and monitoring the running state of an LNG system and running data of each key device in real time; establishing a physical process prediction model for the compressor, the heat exchanger, the throttle valve and the separator by a machine learning prediction method; based on the prediction result of the physical process prediction model, utilizing the collected LNG system operation data to establish an operation optimization model based on system energy consumption, compressor frequency, refrigerant throttle opening, refrigerant flow, natural gas inlet and outlet parameters and refrigerant component proportion, and carrying out energy consumption optimization solution; and comparing the data corresponding to the overall energy consumption optimum obtained by solving with the historical similar working conditions close to the optimizing operation suggestion, outputting the operation parameters to the DCS control system according to the comparison result, and finally transmitting the operation parameters to the PLC of the liquefaction system equipment to enable the LNG system to reach the energy consumption optimum operation state.
Description
Technical Field
The application relates to the technical field of LNG liquefaction, in particular to an LNG liquefaction process intelligent monitoring and operation optimizing method and system based on machine learning.
Background
The natural gas is usually liquefied to solve the inconvenience in long-distance transportation, mass storage and use, which not only greatly reduces the transportation cost of the natural gas and promotes the utilization of clean energy, but also effectively accelerates the development of the natural gas industry technology. Natural gas liquefaction is a process of converting atmospheric natural gas, also known as LNG, into a liquid by lowering the temperature of the natural gas to-162 ℃ by an ultra-low temperature process. However, it should be noted that LNG process equipment is numerous, the flow path is complex, and a large amount of energy is consumed, and the running cost of the LNG process equipment is often more than 40% of the total cost; in addition, the natural gas and the refrigerant used in the cooling process belong to combustible gas, are inflammable and explosive, and need to know the running state in time. Thus, accurate and reliable monitoring of the operating conditions of LNG processes, as well as optimizing operations, improving liquefaction efficiency, and reducing operating costs are highly desirable and necessary.
The prior LNG factory is usually subjected to professional process design or technical transformation after construction before construction, so that the system keeps certain operation energy consumption under the optimal working condition, and the later operation is usually based on the operation experience of operators, and corresponding operation is adopted aiming at different working conditions, thereby generating energy-saving effect. This has the following problems: in the actual operation process, the natural gas parameters and the external environment parameters are changed along with time and cannot always operate under the designed optimal working condition, so that the operation energy-saving effect is poor under the deviation from the designed working condition, the operation is also mostly based on the experience of workers, the measures aiming at the influence factors of seasons and surrounding environments are fewer, and the LNG system cannot achieve the optimal overall energy consumption.
Disclosure of Invention
Aiming at the problems, the application aims to provide an intelligent monitoring and operation optimizing method and system for an LNG liquefaction process based on machine learning, which can adapt to operation optimizing requirements under various loads and environmental conditions, and feed back operation suggestions to operators or control equipment in real time so as to enable an LNG system to achieve an energy consumption optimal operation state.
In order to achieve the above purpose, the present application adopts the following technical scheme: an intelligent monitoring and operation optimizing method for an LNG liquefaction process, which comprises the following steps: establishing network communication with an LNG factory DCS control system, collecting data of a liquefying system sensor and an environment sensor, and monitoring the running state of the LNG system and running data of each key device in real time; utilizing the collected LNG system operation data, and establishing a physical process prediction model for the compressor, the heat exchanger, the throttle valve and the separator by a machine learning prediction method; based on the prediction result of the physical process prediction model, utilizing the collected LNG system operation data to establish an operation optimization model based on system energy consumption, compressor frequency, refrigerant throttle valve opening, refrigerant flow, natural gas inlet and outlet parameters and refrigerant component proportion, and carrying out energy consumption optimization solution to solve the compressor operation frequency, the refrigerant flow, the refrigerant component proportion and the throttle valve opening with optimal overall energy consumption; and comparing the data corresponding to the overall energy consumption optimum obtained by solving with the historical similar working conditions close to the optimizing operation suggestion, outputting the operation parameters to the DCS control system according to the comparison result, and finally transmitting the operation parameters to the PLC of the liquefaction system equipment to enable the LNG system to reach the energy consumption optimum operation state.
Further, the establishing network communication with the LNG plant DCS control system includes: and establishing network communication with the LNG factory DCS control system through an OPC or MODBUS protocol to acquire operation data.
Further, the building a physical process prediction model for the compressor, the heat exchanger, the throttle valve and the separator through the machine learning prediction method comprises the following steps:
the machine learning prediction method adopts one or more of a deep neural network, a deep cyclic neural network and polynomial regression;
the physical process prediction model comprises a compressor energy consumption prediction module, a heat exchanger outlet temperature prediction module and a throttle valve outlet temperature prediction module, and a gas-liquid separator outlet two-phase flow prediction module; the method is used for predicting compressor energy consumption, heat exchanger refrigerant and natural gas outlet temperature, throttle valve outlet refrigerant temperature and separator outlet gas-liquid and liquid-phase refrigerant flow respectively.
Further, the energy consumption optimization solving includes:
an energy consumption optimal solving module is established by adopting an energy consumption optimal solving method to solve;
the input of the energy consumption optimal solving module is natural gas inlet flow, pressure and temperature and ambient temperature;
the output of the energy consumption optimal solving module is the frequency of the compressor, the opening of the throttle valve, the component proportion of the refrigerant and the flow rate of the refrigerant;
setting constraint conditions in the energy consumption optimal solving module: and taking the cooling load adjustable range of the water cooler, the compressor shaft power adjustable range, the compressor non-surge area, the inlet superheat allowable range and the liquefied natural gas outlet temperature allowable range as constraint conditions, wherein the solved optimal operation condition meets the constraint condition range.
Further, the comparing the data corresponding to the overall energy consumption optimal obtained by solving with the historical similar working conditions close to the optimizing operation suggestion includes:
the comparison data are the total energy consumption of the system, the temperature, the pressure and the flow parameters of the natural gas, the frequency modulation parameters of the compressor and the opening parameters of the throttle valve;
and if the comparison result is smaller than the set threshold value, the operation is considered to be safe.
An intelligent monitoring and operation optimizing system for an LNG liquefaction process is used for realizing the intelligent monitoring and operation optimizing method for the LNG liquefaction process, and comprises the following steps: the LNG system sensor is used for acquiring running state data and natural gas data of all main equipment of the liquefaction system and transmitting the running state data and the natural gas data to the DCS control system; the environment sensor is used for acquiring weather environment data and transmitting the weather environment data to the DCS control system; the data collection unit is connected with the DCS control system and is used for receiving data information transmitted by the DCS control system; the data analysis unit is connected with the data collection unit to receive data, and transmits an operation instruction obtained after analysis processing of the received data to the DCS control system; and the PLC controller unit of the liquefaction system is used for receiving the operation instruction transmitted by the DCS control system, so that the LNG system reaches the optimal running state of energy consumption.
Further, the environmental sensor includes a temperature sensor and a humidity sensor.
Further, the liquefying system sensor comprises a temperature sensor, a pressure/pressure difference sensor, a component analyzer, a flow sensor, a valve opening sensor, a current sensor, a frequency sensor and a rotating speed sensor which are arranged at the inlet and the outlet of each main device on the system.
Further, the data analysis unit is connected with the DCS control system through an OPC or MODBUS protocol, and the DCS control system is arranged on an LNG industrial site.
Further, the liquefaction system PLC controller unit comprises a compressor PLC controller, a flow regulating valve PLC controller, a refrigerant filling valve PLC controller and a throttle valve PLC controller.
Due to the adoption of the technical scheme, the application has the following advantages:
1. the application can be optimized under various operating loads and natural gas parameter conditions, and has strong applicability.
2. The application gets rid of excessive dependence on process optimization software, does not need operators to accumulate experience, and can intelligently give running operation suggestions.
3. The application is not limited to a single liquefaction process, and has strong universality.
Drawings
FIG. 1 is a schematic diagram of an LNG liquefaction system according to an embodiment of the present application;
FIG. 2 is a schematic diagram of a modular system architecture based on machine learning in an embodiment of the application;
FIG. 3 is a schematic diagram of outlet temperature prediction in one embodiment of the application;
FIG. 4 is a schematic diagram of compressor power consumption prediction in one embodiment of the application.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present application. It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the application, fall within the scope of protection of the application.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application. As used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
Since the LNG process is mainly composed of a compressor, a heat exchanger, a throttle valve, and a water cooling system, as shown in fig. 1. The coolant is added by a compressor, cooled by a water cooling system, throttled and evaporated by a heat exchanger and exchanges heat with the raw material natural gas, liquefied after the natural gas is cooled, evaporated after absorbing heat, and then circulated by the compressor, so that liquefied natural gas is produced repeatedly and circularly. The application can monitor the running state of the liquefaction system in real time based on machine learning and provide the optimization operation suggestion, so that the liquefaction system can achieve the overall energy consumption optimization.
The application provides an intelligent monitoring and operation optimizing method and system for an LNG liquefaction process based on machine learning, wherein network connection is established with an LNG industrial site DCS control system to acquire liquefaction system sensor and environment sensor data, so that real-time operation state of a liquefaction factory is monitored on line; the main operation parameters of key equipment (a compressor, a heat exchanger, a throttle valve and the like) of the liquefaction system under different environment parameters, different operation loads and operation strategies under different natural gas parameters (temperature, pressure and flow), different seasonal environments (environment temperature) and different operation parameters (valve opening, refrigerant proportioning and refrigerant flow) are predicted by machine learning prediction on the data obtained through collection and monitoring; the operation energy consumption of the liquefaction system is analyzed through a machine learning optimization algorithm, the operation parameters with optimal energy consumption are solved, the operation guidance parameters such as the optimal refrigerant flow, the optimal refrigerant component ratio, the optimal cooling water temperature, the optimal compressor frequency and the optimal valve opening degree of the liquefaction system are obtained, the operation advice is fed back in real time or the operation parameters are directly output to the DCS control system after safety judgment, and the LNG system achieves the optimal operation state with optimal energy consumption.
In one embodiment of the application, an intelligent monitoring and operation optimization method for an LNG liquefaction process is provided. In this embodiment, as shown in fig. 2, the method includes the following steps:
1) Establishing network communication with an LNG factory DCS control system, collecting data of a liquefying system sensor and an environment sensor, and monitoring the running state of the LNG system and running data of each key device in real time;
2) Utilizing the collected LNG system operation data, and establishing a physical process prediction model for the compressor, the heat exchanger, the throttle valve and the separator by a machine learning prediction method;
3) Based on the prediction result of the physical process prediction model, using the collected LNG system operation data to establish an operation optimization model (i.e. an energy consumption optimization model) based on system energy consumption, compressor frequency, refrigerant throttle opening, refrigerant flow, natural gas inlet and outlet parameters and refrigerant component proportion, and carrying out energy consumption optimization solution to solve the compressor operation frequency, the refrigerant flow, the refrigerant component proportion and the throttle opening with optimal overall energy consumption;
4) And comparing the data corresponding to the overall energy consumption optimum obtained by solving with the historical similar working conditions close to the optimizing operation suggestion, outputting the operation parameters to the DCS control system according to the comparison result, and finally transmitting the operation parameters to the PLC of the liquefaction system equipment to enable the LNG system to reach the energy consumption optimum operation state.
In the step 1), establishing network communication with the LNG plant DCS control system, including: and establishing network communication with the LNG factory DCS control system through an OPC or MODBUS protocol to acquire operation data.
In this embodiment, the data obtained by the environmental sensor includes the ambient temperature and humidity surrounding the liquefaction system; key equipment of the liquefaction system includes, but is not limited to, compressors, heat exchangers, water cooling systems, gas-liquid separators, and throttles; the operating state data content includes, but is not limited to, temperature, pressure/drop, flow, refrigerant composition ratio, rotational speed, frequency, valve opening, current, voltage, and power.
In the step 2), a physical process prediction model is built for the compressor, the heat exchanger, the throttle valve and the separator by a machine learning prediction method, wherein the machine learning prediction method adopts one or more of a deep neural network, a deep circulating neural network and polynomial regression;
the physical process prediction model comprises a compressor energy consumption prediction module, a heat exchanger outlet temperature prediction module and a throttle valve outlet temperature prediction module, and a gas-liquid separator outlet two-phase flow prediction module; the method is used for predicting compressor energy consumption, heat exchanger refrigerant and natural gas outlet temperature, throttle valve outlet refrigerant temperature and separator outlet gas-liquid and liquid-phase refrigerant flow respectively.
In this embodiment, inputs to the energy consumption prediction module include, but are not limited to: the energy consumption of the compressor is predicted according to the input.
Inputs to the heat exchanger outlet temperature prediction module include, but are not limited to: the flow, temperature and pressure of the gas-phase refrigerant, the flow, temperature and pressure of the liquid-phase refrigerant, the throttled temperature, the heat exchange area, the flow, temperature and pressure of the natural gas inlet, and the temperature of the heat exchanger refrigerant and the natural gas outlet are predicted according to the input.
Inputs to the throttle outlet temperature prediction module include, but are not limited to: the pressure of the refrigerant before the throttle valve, the temperature, the component proportion and the pressure of the refrigerant after the throttle valve are predicted according to the input.
Inputs to the gas-liquid separator outlet two-phase flow prediction module include, but are not limited to: inlet refrigerant pressure, temperature, flow and component proportion, and the gas-liquid and liquid-phase refrigerant flow of the separator outlet is predicted according to the input.
In the step 3), the system energy consumption is the sum of the compressor energy consumption, the water cooling system energy consumption and the refrigerant pump energy consumption. The energy consumption optimization solution adopts an energy consumption optimal solution method to establish an energy consumption optimal solution module for solving; wherein:
the input of the energy consumption optimal solving module is natural gas inlet flow, pressure, temperature and environmental temperature;
the output of the energy consumption optimal solving module is the compressor frequency, the throttle opening, the refrigerant component proportion and the refrigerant flow;
constraint conditions in an energy consumption optimal solving module are set: and taking the cooling load adjustable range of the water cooler, the compressor shaft power adjustable range, the compressor non-surge area, the inlet superheat allowable range and the liquefied natural gas outlet temperature allowable range as constraint conditions, namely the solved optimal operation condition meets the constraint condition range.
In this embodiment, the energy consumption optimal solution method includes, but is not limited to: and one or more of a simulated annealing algorithm, a genetic algorithm and an ant colony algorithm.
In the step 4), the data corresponding to the overall energy consumption optimal obtained by solving is compared with the similar historical working conditions close to the optimizing operation suggestion, and the method specifically comprises the following steps:
the comparison data are the total energy consumption of the system, the temperature, the pressure and the flow parameters of the natural gas, the frequency modulation parameters of the compressor and the opening parameters of the throttle valve;
and if the comparison result is smaller than the set threshold value, the operation is considered to be safe.
In one embodiment of the present application, an intelligent monitoring and operation optimizing system for LNG liquefaction process is provided, which is used to implement the intelligent monitoring and operation optimizing method for LNG liquefaction process in each of the above embodiments. The system comprises:
the LNG system sensor is used for acquiring running state data and natural gas data of all main equipment of the liquefaction system and transmitting the running state data and the natural gas data to the DCS control system;
the environment sensor is used for acquiring weather environment data and transmitting the weather environment data to the DCS control system;
the data collection unit is connected with the DCS control system and is used for receiving data information transmitted by the DCS control system;
the data analysis unit is connected with the data collection unit to receive data, and transmits an operation instruction obtained after analysis processing of the received data to the DCS control system; the operation instruction is an energy consumption optimization parameter;
and the PLC controller unit of the liquefaction system is used for receiving the operation instruction transmitted by the DCS control system, so that the LNG system reaches the optimal running state of energy consumption.
In the above embodiment, the environmental sensor includes a temperature sensor and a humidity sensor.
In the above embodiment, the liquefaction system sensor includes a temperature sensor, a pressure/pressure difference sensor, a component analyzer, a flow sensor, a valve opening sensor, a current sensor, a frequency sensor and a rotation speed sensor, which are provided at the inlet and outlet of each main device on the system.
In the above embodiment, the data analysis unit is connected to the DCS control system through OPC or MODBUS protocol, and the DCS control system is disposed in the LNG industrial site. The data analysis unit comprises state monitoring, machine learning prediction, energy consumption optimal solution and safety judgment functions. The state monitoring is mainly used for displaying the running parameters of the heat exchanger system and the inlet and outlet parameters of each device in real time; machine learning is used for learning historical data and predicting; the energy consumption optimal solution is used for calculating left and right operation parameters of the current system; the safety judgment is used for judging whether the optimization operation suggestion is safe or not.
Wherein, the safety judgment is: comparing historical similar conditions proximate to the optimization operation recommendation, the comparison data includes, but is not limited to: total energy consumption of the system, natural gas temperature, natural gas pressure and natural gas flow parameters, compressor frequency modulation parameters and throttle opening parameters. And if the comparison result is smaller than the set threshold value, the operation is considered to be safe, wherein the set threshold value is defined by an operation user.
In the above-described embodiments, the liquefaction system PLC controller unit includes, but is not limited to, a compressor PLC controller, a flow regulating valve PLC controller, a refrigerant charge valve PLC controller, and a throttle valve PLC controller.
In summary, when the system is used, the operation state of the liquefaction system can be monitored in real time by optimizing the monitoring interface of the system, wherein the operation state comprises the energy consumption of the system, the LNG yield and the operation parameters of each device of the system, the energy consumption data comprises the total energy consumption of the system and the unit liquefaction energy consumption, and the operation parameters of each device of the system comprise: refrigerant circulation flow, natural gas inlet and outlet parameters, throttle opening and the like. And the optimization interface of the optimization system can give out optimization operation suggestions in real time, the optimization operation suggestions given on the interface comprise refrigerant circulation flow, compressor frequency and valve opening, and the given operation suggestions can reduce the compressor energy consumption by 267kW.
As shown in fig. 3 and 4, respectively, the heat exchangers in some embodimentsThe temperature of the outlet of the throttle valve of the liquefaction section and the time curve of the predicted value and the measured value of the energy consumption of the refrigerant compressor. In the figure, the LNG factory adopts a three-stage mixed refrigeration cycle process, the design capacity is 30 square/day, a spiral wound tube type heat exchanger is adopted, the temperature of raw gas is 31-34 ℃, the pressure is 4.2-4.3bar, and the flow is 5800-5900m 3 And/h. In an embodiment, the steady state prediction average error is less than 5%.
The system provided in this embodiment is used to execute the above method embodiments, and specific flow and details refer to the above embodiments, which are not described herein.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present application, and are not limiting; although the application has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (7)
1. An intelligent monitoring and operation optimizing method for an LNG liquefaction process is characterized by comprising the following steps:
establishing network communication with an LNG factory DCS control system, collecting data of a liquefying system sensor and an environment sensor, and monitoring the running state of the LNG system and running data of each key device in real time;
utilizing the collected LNG system operation data, and establishing a physical process prediction model for the compressor, the heat exchanger, the throttle valve and the separator by a machine learning prediction method;
based on the prediction result of the physical process prediction model, utilizing the collected LNG system operation data to establish an operation optimization model based on system energy consumption, compressor frequency, refrigerant throttle valve opening, refrigerant flow, natural gas inlet and outlet parameters and refrigerant component proportion, and carrying out energy consumption optimization solution to solve the compressor operation frequency, the refrigerant flow, the refrigerant component proportion and the throttle valve opening with optimal overall energy consumption;
comparing the data corresponding to the overall energy consumption optimum obtained by solving with the historical similar working conditions close to the optimizing operation advice, outputting the operation parameters to the DCS control system according to the comparison result, and finally transmitting the operation parameters to the PLC of the liquefaction system equipment to enable the LNG system to reach the energy consumption optimum operation state;
the method for establishing a physical process prediction model for the compressor, the heat exchanger, the throttle valve and the separator by a machine learning prediction method comprises the following steps:
the machine learning prediction method adopts one or more of a deep neural network, a deep cyclic neural network and polynomial regression;
the physical process prediction model comprises a compressor energy consumption prediction module, a heat exchanger outlet temperature prediction module and a throttle valve outlet temperature prediction module, and a gas-liquid separator outlet two-phase flow prediction module; the device is used for predicting compressor energy consumption, heat exchanger refrigerant and natural gas outlet temperature, throttle valve outlet refrigerant temperature and separator outlet gas-liquid and liquid-phase refrigerant flow respectively;
the energy consumption optimization solving comprises the following steps:
an energy consumption optimal solving module is established by adopting an energy consumption optimal solving method to solve;
the input of the energy consumption optimal solving module is natural gas inlet flow, pressure and temperature and ambient temperature;
the output of the energy consumption optimal solving module is the frequency of the compressor, the opening of the throttle valve, the component proportion of the refrigerant and the flow rate of the refrigerant;
setting constraint conditions in the energy consumption optimal solving module: taking the cooling load adjustable range of the water cooler, the compressor shaft power adjustable range, the compressor non-surge area, the inlet superheat allowable range and the liquefied natural gas outlet temperature allowable range as constraint conditions, wherein the solved optimal operation condition meets the constraint condition range;
the comparing the data corresponding to the overall energy consumption optimal obtained by solving with the historical similar working conditions close to the optimizing operation suggestion comprises the following steps:
the comparison data are the total energy consumption of the system, the temperature, the pressure and the flow parameters of the natural gas, the frequency modulation parameters of the compressor and the opening parameters of the throttle valve;
and if the comparison result is smaller than the set threshold value, the operation is considered to be safe.
2. The intelligent monitoring and operational optimization method for LNG liquefaction process of claim 1, wherein establishing network communication with an LNG plant DCS control system comprises: and establishing network communication with the LNG factory DCS control system through an OPC or MODBUS protocol to acquire operation data.
3. An intelligent monitoring and operation optimization system for an LNG liquefaction process, for implementing the intelligent monitoring and operation optimization method for an LNG liquefaction process according to any one of claims 1 to 2, characterized in that the system comprises:
the LNG system sensor is used for acquiring running state data and natural gas data of all main equipment of the liquefaction system and transmitting the running state data and the natural gas data to the DCS control system;
the environment sensor is used for acquiring weather environment data and transmitting the weather environment data to the DCS control system;
the data collection unit is connected with the DCS control system and is used for receiving data information transmitted by the DCS control system;
the data analysis unit is connected with the data collection unit to receive data, and transmits an operation instruction obtained after analysis processing of the received data to the DCS control system;
and the PLC controller unit of the liquefaction system is used for receiving the operation instruction transmitted by the DCS control system, so that the LNG system reaches the optimal running state of energy consumption.
4. The LNG liquefaction process intelligent monitoring and operation optimization system of claim 3, wherein the environmental sensors comprise temperature sensors and humidity sensors.
5. The intelligent monitoring and operation optimizing system for LNG liquefaction process according to claim 3, wherein the liquefaction system sensor comprises a temperature sensor, a pressure/pressure difference sensor, a component analyzer, a flow sensor, a valve opening sensor, a current sensor, a frequency sensor and a rotation speed sensor which are arranged at the inlet and outlet of each main equipment on the system.
6. The LNG liquefaction process intelligent monitoring and operation optimization system according to claim 3, wherein the data analysis unit is connected with the DCS control system through OPC or MODBUS protocol, and the DCS control system is installed in LNG industrial site.
7. The LNG liquefaction process intelligent monitoring and operation optimization system of claim 3, wherein the liquefaction system PLC controller unit comprises a compressor PLC controller, a flow regulating valve PLC controller, a refrigerant charge valve PLC controller and a throttle valve PLC controller.
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