CN114674114A - Intelligent monitoring and operation optimization method and system for LNG liquefaction process - Google Patents
Intelligent monitoring and operation optimization method and system for LNG liquefaction process Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 32
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- 239000003345 natural gas Substances 0.000 claims abstract description 35
- 238000010801 machine learning Methods 0.000 claims abstract description 19
- 238000009826 distribution Methods 0.000 claims abstract description 11
- 238000004891 communication Methods 0.000 claims abstract description 10
- 239000003949 liquefied natural gas Substances 0.000 claims description 67
- 238000001816 cooling Methods 0.000 claims description 10
- 239000007788 liquid Substances 0.000 claims description 10
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 8
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- 230000005514 two-phase flow Effects 0.000 claims description 4
- 238000012545 processing Methods 0.000 claims description 3
- 125000004122 cyclic group Chemical group 0.000 claims description 2
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- 238000013461 design Methods 0.000 description 1
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- 238000006467 substitution reaction Methods 0.000 description 1
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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
- F25J1/003—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
- F25J1/0252—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
- F25J1/02—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
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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
- 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
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Abstract
The invention relates to an intelligent monitoring and operation optimization method and system for an LNG liquefaction process, which comprises the following steps: establishing network communication with a DCS (distributed control system) of an LNG factory, collecting data and monitoring the operation state of the LNG system and the operation 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, establishing 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 distribution ratio by using the collected LNG system operation data, and performing energy consumption optimization solution; and comparing the data obtained by solving and corresponding to the optimal overall energy consumption with the historical similar working conditions close to the optimized operation suggestions, outputting operation parameters to the DCS 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 optimal energy consumption running state.
Description
Technical Field
The invention relates to the technical field of LNG liquefaction, in particular to a method and a system for intelligently monitoring and optimizing operation of an LNG liquefaction process based on machine learning.
Background
The natural gas is usually liquefied to solve a lot of inconveniences existing in the processes of long-distance transportation, mass storage and use, so that the transportation cost of the natural gas is greatly reduced, the utilization of clean energy is promoted, and the development of the natural gas industrial technology is effectively accelerated. Natural gas liquefaction is a process of converting atmospheric natural gas, also known as LNG, into liquid by lowering the temperature of the natural gas to-162 ℃ through an ultra-low temperature process. However, it should be noted that LNG process equipment is numerous, flow is complex, and consumes a large amount of energy, and its operation cost is often more than 40% of the total cost; in addition, natural gas and the refrigerant used in the cooling process are all combustible gases, are flammable and explosive, and need to know the operation state in time. Therefore, accurate and reliable monitoring of the operating conditions of an LNG process, as well as optimizing operations, increasing liquefaction efficiency, and reducing operating costs, is highly desirable and necessary.
The existing LNG factory is designed by a professional process before construction or is technically improved after construction, so that the system keeps certain operation energy consumption under the optimum design working condition, and corresponding operation is adopted aiming at different working conditions on the basis of the operation experience of operators in later operation, thereby generating an energy-saving effect. This has the following problems: in the actual operation process, the natural gas parameters and the parameters of the external environment are changed along with time and cannot be operated under the designed optimal working condition all the time, so that the operation energy-saving effect is poor under the condition deviating from the designed working condition, the operation is based on the experience of workers, the measures for influencing factors of seasons and the surrounding environment are fewer, and the LNG system cannot achieve the optimal overall energy consumption.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a method and a system for intelligently monitoring and optimizing the operation of an LNG liquefaction process based on machine learning, which can meet the operation optimization requirements under various loads and environmental conditions, and feed back operation advice to operators or control equipment in real time, so that the LNG system can achieve the optimal operation state of energy consumption.
In order to realize the purpose, the invention adopts the following technical scheme: an intelligent monitoring and operation optimization method for an LNG liquefaction process comprises the following steps: establishing network communication with a DCS (distributed control system) of an LNG factory, collecting data of a sensor and an environmental sensor of a liquefaction system, and monitoring the running state of the LNG system and the 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 using the collected LNG system operation data through a machine learning prediction method; based on the prediction result of the physical process prediction model, establishing 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 distribution ratio by using the collected LNG system operation data, performing energy consumption optimization solution, and solving the compressor operation frequency, the refrigerant flow, the refrigerant component distribution ratio and the throttle valve opening with optimal overall energy consumption; and comparing the data obtained by solving and corresponding to the optimal overall energy consumption with the historical similar working conditions close to the optimized operation suggestions, outputting operation parameters to the DCS 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 optimal energy consumption running state.
Further, the establishing network communication with the DCS control system of the LNG factory comprises: and establishing network communication with the LNG factory DCS control system through an OPC or MODBUS protocol to acquire operation data.
Further, the establishing of the physical process prediction model for the compressor, the heat exchanger, the throttle valve and the separator by 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 a polynomial regression;
the established physical process prediction model comprises a compressor energy consumption prediction module, a heat exchanger outlet temperature prediction module, a throttle valve outlet temperature prediction module and a gas-liquid separator outlet two-phase flow prediction module; the method is used for predicting the energy consumption of the compressor, the outlet temperatures of the heat exchanger refrigerant and the natural gas, the outlet refrigerant temperature of the throttling valve and the gas-liquid and liquid-phase refrigerant flow of the outlet of the separator respectively.
Further, the energy consumption optimization solving comprises:
establishing an energy consumption optimal solving module by adopting an energy consumption optimal solving method for solving;
the input of the energy consumption optimal solving module is natural gas inlet flow, pressure, temperature and ambient temperature;
the output of the energy consumption optimal solving module is compressor frequency, throttle opening, refrigerant component proportion and refrigerant flow;
and setting constraint conditions in the energy consumption optimal solution module: the adjustable range of the cooling load of the water cooler, the adjustable range of the shaft power of the compressor, the non-surge area of the compressor, the allowable range of the superheat degree of the inlet and the allowable range of the outlet temperature of the liquefied natural gas are used as constraint conditions, and the solved optimal operation working condition meets the constraint condition range.
Further, the comparing the data obtained by solving the optimal corresponding overall energy consumption with the historical similar working conditions close to the optimized operation suggestions includes:
the comparison data comprises total energy consumption of the system, natural gas temperature, pressure and flow parameters, compressor frequency modulation parameters and throttle valve opening parameters;
and if the comparison result is smaller than the set threshold value, the operation is safe.
An intelligent monitoring and operation optimizing system for an LNG liquefaction process, which is used for realizing the intelligent monitoring and operation optimizing method for the LNG liquefaction process, comprises the following steps: the LNG system sensor is used for acquiring running state data and natural gas data of each main device of the liquefaction system and transmitting the running state data and the natural gas data to the DCS; the environment sensor is used for acquiring weather environment data and transmitting the weather environment data to the DCS; the data collection unit is connected with the DCS control system and 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 by analyzing and processing the received data to the DCS control system; and the liquefaction system PLC unit is used for receiving the operation instruction transmitted by the DCS control system and enabling the LNG system to reach the optimal energy consumption running state.
Further, the environmental sensor includes a temperature sensor and a humidity sensor.
Further, the liquefaction system sensor comprises a temperature sensor, a pressure/differential pressure 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 field.
Further, the liquefaction system PLC controller unit comprises a compressor PLC controller, a flow regulating valve PLC controller, a refrigerant charging valve PLC controller and a throttle valve PLC controller.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. the invention can be optimized under various operating load and natural gas parameter conditions, and has strong applicability.
2. The invention gets rid of the excessive dependence on process optimization software and does not need the accumulated experience of operators, and can intelligently give operation suggestions.
3. The invention is not limited to a single liquefaction process and has strong universality.
Drawings
FIG. 1 is a schematic illustration of a liquefied natural gas liquefaction system configuration in an embodiment of the present invention;
FIG. 2 is a block diagram of a modular system architecture based on machine learning in an embodiment of the present invention;
FIG. 3 is a schematic illustration of outlet temperature prediction in one embodiment of the present invention;
fig. 4 is a schematic diagram of compressor power consumption prediction in one embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention.
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 example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
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 method comprises the steps of firstly increasing a coolant through a compressor, then cooling through a water cooling system, then entering a heat exchanger for throttling evaporation and exchanging heat with raw natural gas, liquefying the natural gas after cooling, evaporating the coolant after absorbing heat, then entering the compressor for circulation, and repeatedly producing the liquefied natural gas in such a way. The invention can monitor the running state of the liquefaction system in real time based on machine learning, and provides an optimized operation suggestion, so that the liquefaction system can achieve the optimal overall energy consumption.
The invention provides a method and a system for intelligently monitoring and optimizing the operation of an LNG liquefaction process based on machine learning, wherein the method and the system are used for acquiring the data of a liquefaction system sensor and an environmental sensor by establishing network connection with an LNG industrial field DCS control system and realizing the online monitoring of the real-time operation state of a liquefaction factory; by carrying out machine learning prediction on data obtained by collection and monitoring, main operation parameters of key equipment (a compressor, a heat exchanger, a throttle valve and the like) of a liquefaction system under the conditions of different environment parameters, different operation loads and operation strategies under the conditions of different natural gas parameters (temperature, pressure and flow), different seasonal environments (environment temperature) and different operation parameters (valve opening, refrigerant ratio and refrigerant flow) are predicted; the running energy consumption of the liquefaction system is analyzed through a machine learning optimization algorithm, the operation parameters with the optimal energy consumption are solved, the operation guide parameters such as the refrigerant flow, the refrigerant component distribution ratio, the cooling water temperature, the compressor frequency and the valve opening degree with the optimal energy consumption of the liquefaction system are obtained, the operation suggestions are fed back in real time or the operation parameters are directly output to a DCS (distributed control system) after safety judgment, so that the LNG system reaches the optimal running state of the energy consumption.
In one embodiment of the invention, 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 a DCS (distributed control system) of an LNG factory, collecting data of a sensor and an environmental sensor of a liquefaction system, and monitoring the running state of the LNG system and the running data of each key device in real time;
2) establishing a physical process prediction model for the compressor, the heat exchanger, the throttle valve and the separator by using the collected LNG system operation data through a machine learning prediction method;
3) based on the prediction result of the physical process prediction model, establishing an operation optimization model (namely 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 distribution ratio by using the collected LNG system operation data, performing energy consumption optimization solution, and solving the compressor operation frequency, the refrigerant flow, the refrigerant component distribution ratio and the throttle opening with optimal overall energy consumption;
4) and comparing the data obtained by solving and corresponding to the optimal overall energy consumption with the historical similar working conditions close to the optimized operation suggestions, outputting operation parameters to the DCS 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 optimal energy consumption running state.
In the step 1), establishing network communication with the DCS control system of the LNG plant includes: 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 sensors includes the ambient temperature and humidity surrounding the liquefaction system; key equipment of the liquefaction system comprises but is not limited to a compressor, a heat exchanger, a water cooling system, a gas-liquid separator and a throttle valve; operating condition data content includes, but is not limited to, temperature, pressure/pressure drop, flow rate, refrigerant component distribution ratio, rotational speed, frequency, valve opening, current, voltage, and power.
In the step 2), a physical process prediction model is established 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 circulation neural network and a polynomial regression;
the established physical process prediction model comprises a compressor energy consumption prediction module, a heat exchanger outlet temperature prediction module, a throttle valve outlet temperature prediction module and a gas-liquid separator outlet two-phase flow prediction module; the method is used for predicting the energy consumption of the compressor, the outlet temperatures of the refrigerant and the natural gas of the heat exchanger, the outlet temperature of the refrigerant of the throttle valve and the gas-liquid and liquid-phase refrigerant flow at the outlet of the separator respectively.
In this embodiment, the input of the compressor energy consumption prediction module includes, but is not limited to: the energy consumption of the compressor is predicted according to the input of the refrigerant flow, the refrigerant component distribution ratio, the refrigerant inlet and outlet pressure and the cooler outlet water temperature.
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 and the temperature after throttling, the heat exchange area, the flow, temperature and pressure of the natural gas inlet, and the temperature of the refrigerant and the natural gas outlet of the heat exchanger are predicted according to the input.
Inputs to the throttle outlet temperature prediction module include, but are not limited to: the refrigerant pressure, temperature, group ratio and refrigerant pressure after the throttle valve before the throttle valve, and the temperature of the refrigerant at the outlet of the throttle valve is predicted based on the above inputs.
The inputs to the gas-liquid separator outlet two-phase flow prediction module include, but are not limited to: and the pressure, the temperature, the flow and the component distribution ratio of the inlet refrigerant are used for predicting the flow of the gas-liquid and liquid-phase refrigerants at the outlet of the separator according to the input.
In the step 3), the energy consumption of the system is the sum of the energy consumption of the compressor, the energy consumption of the water cooling system and the energy consumption of the refrigerant pump. Energy consumption optimal solution an energy consumption optimal solution module is established by adopting an energy consumption optimal solution method for solution; wherein:
the input of the energy consumption optimal solving module is natural gas inlet flow, pressure, temperature and ambient temperature;
the output of the energy consumption optimal solving module is compressor frequency, throttle opening, refrigerant component proportion and refrigerant flow;
setting constraint conditions in the energy consumption optimal solution module: the adjustable range of the cooling load of the water cooler, the adjustable range of the shaft power of the compressor, the non-surge area of the compressor, the allowable range of the superheat degree of the inlet and the allowable range of the outlet temperature of the liquefied natural gas are used 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: simulating one or more of an annealing algorithm, a genetic algorithm, and an ant colony algorithm.
In the step 4), the data corresponding to the optimal overall energy consumption obtained by the solution is compared with the historical similar working conditions close to the optimized operation suggestions, and the method specifically comprises the following steps:
the comparison data comprises total energy consumption of the system, natural gas temperature, pressure and flow parameters, compressor frequency modulation parameters and throttle valve opening parameters;
and if the comparison result is smaller than the set threshold value, the operation is safe.
In an embodiment of the present invention, an intelligent monitoring and operation optimizing system for an LNG liquefaction process is provided, where the system is used to implement the intelligent monitoring and operation optimizing method for an 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 each main device of the liquefaction system and transmitting the running state data and the natural gas data to the DCS;
the environment sensor is used for acquiring weather environment data and transmitting the weather environment data to the DCS;
the data collection unit is connected with the DCS control system and 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 by analyzing and processing the received data to the DCS control system; wherein the operation instruction is an energy consumption optimization parameter;
and the PLC unit of the liquefaction system is used for receiving the operation instruction transmitted by the DCS control system so as to enable the LNG system to reach the optimal energy consumption running state.
In the above embodiments, 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 disposed at an inlet and an outlet of each main device in the system.
In the above embodiment, the data analysis unit is connected to the DCS control system via an OPC or MODBUS protocol, and the DCS control system is installed in the LNG industrial site. The data analysis unit comprises the functions of state monitoring, machine learning prediction, energy consumption optimal solution and safety judgment. The state monitoring is mainly used for displaying the operation parameters of the heat exchanger system and the parameters of the inlet and the outlet 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 the 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 as follows: comparing historical similar operating conditions that are close to the optimized operating recommendations, the comparison data including, but not limited to: the system comprises total energy consumption, natural gas temperature, pressure and flow parameters, compressor frequency modulation parameters and throttle valve opening parameters. And if the comparison result is smaller than a set threshold value, the operation is considered to be safe, wherein the set threshold value is defined by an operation user.
In the above 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 charging valve PLC controller, and a throttle valve PLC controller.
In summary, when the system is used, the monitoring interface of the system is optimized, so that the operation state of the liquefaction system can be monitored in real time, the operation state comprises system energy consumption, LNG yield and operation parameters of each device of the system, the energy consumption data comprises total system energy consumption and 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 valve opening degree and the like. And an optimization interface of the optimization system can give an optimization operation suggestion in real time, the optimization operation suggestion given on the interface comprises the refrigerant circulation flow, the compressor frequency and the valve opening degree, and the given operation suggestion can reduce the energy consumption of the compressor by 267 kW.
As shown in fig. 3 and fig. 4, the predicted value and the measured value of the energy consumption of the heat exchanger liquefaction segment throttle valve and the refrigerant compressor are respectively shown as time curves. In the figure, an LNG factory adopts a three-stage mixed refrigeration cycle process, the designed capacity is 30 ten thousand square/day, a spiral wound tube type heat exchanger is adopted, the temperature of raw material gas is 31-34 ℃, the pressure is 4.2-4.3bar, and the flow rate is 5800-3H is the ratio of the total weight of the catalyst to the total weight of the catalyst. In an embodiment, the steady state prediction average error is less than 5%.
The system provided in this embodiment is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An intelligent monitoring and operation optimization method for an LNG liquefaction process is characterized by comprising the following steps:
establishing network communication with a DCS (distributed control system) of an LNG factory, collecting data of a sensor and an environmental sensor of a liquefaction system, and monitoring the running state of the LNG system and the 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 using the collected LNG system operation data through a machine learning prediction method;
based on the prediction result of the physical process prediction model, establishing 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 distribution ratio by using the collected LNG system operation data, performing energy consumption optimization solution, and solving the compressor operation frequency, the refrigerant flow, the refrigerant component distribution ratio and the throttle valve opening with optimal overall energy consumption;
and comparing the data obtained by solving and corresponding to the optimal overall energy consumption with the historical similar working conditions close to the optimized operation suggestions, outputting operation parameters to the DCS 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 optimal energy consumption running state.
2. The method of claim 1, wherein said establishing network communication with a DCS control system of an LNG plant comprises: and establishing network communication with the LNG factory DCS control system through an OPC or MODBUS protocol to acquire operation data.
3. The intelligent monitoring and operation optimization method for the LNG liquefaction process of claim 1, wherein the establishing of the physical process prediction model for the compressor, the heat exchanger, the throttling valve and the separator by the machine learning prediction method comprises:
the machine learning prediction method adopts one or more of a deep neural network, a deep cyclic neural network and a polynomial regression;
the established physical process prediction model comprises a compressor energy consumption prediction module, a heat exchanger outlet temperature prediction module, a throttle valve outlet temperature prediction module and a gas-liquid separator outlet two-phase flow prediction module; the method is used for predicting the energy consumption of the compressor, the outlet temperatures of the refrigerant and the natural gas of the heat exchanger, the outlet temperature of the refrigerant of the throttle valve and the gas-liquid and liquid-phase refrigerant flow at the outlet of the separator respectively.
4. The intelligent monitoring and operation optimization method for the LNG liquefaction process of claim 1, wherein said energy consumption optimization solution comprises:
establishing an energy consumption optimal solving module by adopting an energy consumption optimal solving method for solving;
the input of the energy consumption optimal solving module is natural gas inlet flow, pressure, temperature and ambient temperature;
the output of the energy consumption optimal solving module is compressor frequency, throttle opening, refrigerant component proportion and refrigerant flow;
and setting constraint conditions in the energy consumption optimal solution module: the adjustable range of the cooling load of the water cooler, the adjustable range of the shaft power of the compressor, the non-surge area of the compressor, the allowable range of the superheat degree of the inlet and the allowable range of the outlet temperature of the liquefied natural gas are used as constraint conditions, and the solved optimal operation working condition meets the constraint condition range.
5. The method for intelligently monitoring and optimizing the operation of the LNG liquefaction process according to claim 1, wherein the step of comparing the data corresponding to the optimal overall energy consumption obtained by the solution with historical similar operating conditions close to the optimal operation recommendation comprises the steps of:
the comparison data comprises total energy consumption of the system, natural gas temperature, pressure and flow parameters, compressor frequency modulation parameters and throttle valve opening parameters;
and if the comparison result is smaller than the set threshold value, the operation is safe.
6. An intelligent monitoring and operation optimizing system for LNG liquefaction process, which is used for realizing the intelligent monitoring and operation optimizing method for LNG liquefaction process as claimed in any one of claims 1 to 5, and is characterized in that the system comprises:
the LNG system sensor is used for acquiring running state data and natural gas data of each main device of the liquefaction system and transmitting the running state data and the natural gas data to the DCS;
the environment sensor is used for acquiring weather environment data and transmitting the weather environment data to the DCS;
the data collection unit is connected with the DCS control system and 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 by analyzing and processing the received data to the DCS control system;
and the liquefaction system PLC unit is used for receiving the operation instruction transmitted by the DCS control system and enabling the LNG system to reach the optimal energy consumption running state.
7. The intelligent LNG liquefaction process monitoring and operational optimization system of claim 6, wherein said environmental sensors include temperature and humidity sensors.
8. The intelligent monitoring and operation optimizing system for the LNG liquefaction process of claim 6, wherein the liquefaction system sensors include temperature sensors, pressure/differential pressure sensors, composition analyzers, flow sensors, valve opening sensors, current sensors, frequency sensors and rotational speed sensors disposed at the inlets and outlets of the main equipment on the system.
9. The intelligent monitoring and operation optimization system for the LNG liquefaction process of claim 6, wherein 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.
10. The intelligent monitoring and operation optimization system for the LNG liquefaction process of claim 6, wherein the liquefaction system PLC controller unit comprises a compressor PLC controller, a flow control valve PLC controller, a refrigerant charge valve PLC controller, and a throttle valve PLC controller.
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