Fuel gas production energy efficiency optimization and carbon emission management method

Abstract

The invention relates to the technical field of energy management and environmental protection, and discloses a fuel gas production energy efficiency optimization and carbon emission management method, which comprises the following steps: collecting control variables in a fuel gas production process; simulating a fuel gas mixing diffusion process based on computational fluid mechanics; calculating fuel gas purification efficiency and unit gas purification energy consumption; dynamically adjusting a pressure variable and a temperature variable through a multi-objective optimization method; and feeding back, adjusting and optimizing parameters. Compared with the technical problems that in the prior art, a traditional gas production process depends on a single control parameter, balance optimization of energy efficiency and emission is difficult to achieve, and particularly under complex working conditions or variable loads, an existing method cannot achieve accurate dynamic adjustment, due to the fact that the dynamic adjustment strategy based on multi-target optimization is adopted, the energy efficiency and emission of the gas are greatly improved. Energy efficiency maximization and carbon emission minimization in the fuel gas production process are achieved, so that the problems that in a traditional method, energy efficiency and emission control is not accurate, pollution exceeds the standard and the like are solved, and intelligence and environment friendliness of fuel gas production are improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of energy management and environmental protection, and in particular relates to a method for optimizing gas production energy efficiency and managing carbon emissions. Background Art

[0002] At present, energy efficiency optimization and carbon emission management in the gas production process face many challenges. For example, most traditional gas production optimization methods rely on static control strategies, which are difficult to cope with complex changes in the production process, especially under load fluctuations and changing environmental conditions. These methods can usually provide certain energy efficiency optimization in the initial stage, but as production conditions change, they cannot adjust parameters in real time to adapt to new working conditions, resulting in unstable energy efficiency and ineffective control of carbon emissions. Specifically, most optimization methods in the existing technology lack the ability to respond to dynamic changes in real time. For complex variables in the gas production process (such as flow, temperature, pressure, etc.), the existing traditional control methods rely on empirical rules or simple PID controllers, which cannot monitor and adjust multiple variables in the production process in real time. Especially under high load or sudden working conditions, the control strategy cannot respond flexibly, which ultimately leads to unnecessary energy waste and excessive emissions. In addition, most existing technologies adopt a single optimization goal, usually focusing on energy efficiency optimization or carbon emission control, but lack a balanced treatment of the two. In actual production, how to reduce energy consumption and carbon emissions while ensuring gas production efficiency is a difficult problem that needs to be solved urgently. Traditional methods often face the problem of trade-offs between energy and environmental protection goals, and it is difficult to achieve the optimal balance between the two in a changing production environment. Therefore, there is an urgent need for an intelligent and dynamically adjusted gas production optimization method that can collect and analyze data in real time in a changing production environment, and achieve balanced optimization of energy efficiency and carbon emissions through a multi-objective optimization method, so as to improve the efficiency of gas production, reduce energy waste, and effectively control carbon emissions, thereby meeting environmental protection requirements and sustainable development needs. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a method for optimizing gas production energy efficiency and managing carbon emissions, aiming to solve the technical problem that the traditional gas production process in the prior art relies on a single control parameter, making it difficult to achieve balanced optimization of energy efficiency and emissions, especially under complex operating conditions or changing loads, and the existing methods are unable to achieve precise dynamic adjustment.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for optimizing gas production energy efficiency and managing carbon emissions.

[0005] The gas production energy efficiency optimization and carbon emission management method comprises:

[0006] Step S10: real-time collection of flow variables, pressure variables, temperature variables, concentration variables and energy consumption variables during the gas mixing and purification process;

[0007] Step S20: Simulate the gas mixing and diffusion process based on the computational fluid dynamics method, and calculate the mixing uniformity index U according to the concentration variable mix ;

[0008] Step S30: Establish an energy consumption model for the gas purification process based on the low temperature condensation method, and calculate the gas purification efficiency η according to the flow variable, concentration variable and energy consumption variable purify And unit gas purification energy consumption E purify ;

[0009] Step S40: Obtaining the carbon dioxide emission concentration C from the concentration variable CO2 , based on the carbon dioxide emission concentration C CO2 , Mixing uniformity index U mix , Gas purification efficiency η purify And unit gas purification energy consumption E purify , combined with multi-objective optimization methods to dynamically adjust pressure variables and temperature variables, and then adjust the carbon dioxide emission concentration;

[0010] Step S50: optimizing the weight coefficients in the multi-objective optimization method according to the adjusted carbon dioxide emission concentration feedback.

[0011] Preferably, in step S10, the step of real-time acquisition of flow variables, pressure variables, temperature variables, concentration variables and energy consumption variables in the process of gas mixing and purification specifically includes:

[0012] Obtain the fuel gas raw material flow rate Q through the flow meter feed And the outlet gas flow rate Q after purification out ;

[0013] The gas mixing pressure P is monitored by a pressure sensor mix and separation tower pressure P sep ;

[0014] The mixed gas temperature T is measured by the temperature sensor mix and condensation temperature T cond ;

[0015] Obtain the gas mixture concentration C through the gas analyzer target and carbon dioxide emission concentration C CO2 ;

[0016] Monitor compressor power consumption E through power meter comp and cooling system energy consumption E cool ;

[0017] Among them, the flow variables include the fuel gas flow Q feed And the outlet gas flow rate Q after purification out ; Pressure variables include gas mixing pressure P mix and separation tower pressure P sep ; Temperature variables include mixed gas temperature mixed gas temperature and condensation temperature T cond ; Concentration variables include gas mixing concentration C target and CO 2 Emission concentration Energy consumption variables include compressor power consumption E comp and cooling system energy consumption E cool .

[0018] Preferably, in step S20, the mixing uniformity index U mix The calculation formula is:

[0019]

[0020] Among them, C actual,i is the actual gas mixture concentration of gas type i measured by the sensor, C target,i is the target gas mixing concentration of the preset gas type i.

[0021] Preferably, in step S30, the gas purification efficiency η purify The calculation formula is:

[0022]

[0023] Among them, C feed is the target gas concentration in the fuel gas raw material.

[0024] Preferably, in step S30, the unit gas purification energy consumption E purify The calculation formula is:

[0025]

[0026] Among them, the unit gas purification energy consumption E purify The lower it is, the more energy efficient the purification process is.

[0027] Preferably, in step S40, the carbon dioxide emission concentration is obtained from the concentration variable Based on CO2 emission concentration Mixing uniformity index U mix , Gas purification efficiency η purify And unit gas purification energy consumption E purify , the steps of dynamically adjusting the pressure variables and temperature variables by combining the multi-objective optimization method specifically include:

[0028] Step S401: Constructing a multi-objective optimization function

[0029]

[0030] Among them, w 1 ,w 2 ,w 3 ,w 4 is the dynamically adjusted weight coefficient;

[0031] Step S402: Using reinforcement learning to optimize the dynamically adjusted weight coefficient w 1 ,w 2 ,w 3 ,w 4 ;

[0032] Set the reinforcement learning state space S:

[0033]

[0034] Set the reinforcement learning action space A:

[0035] A={ΔP mix ,ΔP sep ,ΔT mix ,ΔT cond}

[0036] Where ΔP mix is the change in gas mixing pressure, ΔP sep is the pressure change of the separation tower, ΔT mix is the temperature change of the mixed gas, ΔT cond is the condensation temperature change;

[0037] Step S403: Combine the reinforcement learning state space S, the reinforcement learning action space A and the multi-objective optimization function The optimized pressure variables and optimized temperature variables are calculated, including the optimized gas mixing pressure P mix , optimize the separation tower pressure P sep , optimize the mixed gas temperature T mix and the optimal condensation temperature T cond ;

[0038] Step S404: After applying the optimized pressure variable and the optimized temperature variable, the adjusted carbon dioxide emission concentration is obtained through a gas analyzer.

[0039] Preferably, in step S50, the step of optimizing the weight coefficients in the multi-objective optimization method according to the adjusted carbon dioxide emission concentration feedback specifically includes:

[0040] Get the adjusted CO2 emission concentration Calculate the CO2 emission concentration relative to the preset target emission concentration CO2 concentration deviation

[0041] Preset carbon dioxide concentration deviation threshold, when carbon dioxide concentration deviation When the concentration is less than the CO2 concentration deviation threshold, the weight coefficient in the multi-objective optimization method is maintained;

[0042] When the carbon dioxide concentration deviates When the concentration is greater than or equal to the CO2 concentration deviation threshold, the weight coefficient in the multi-objective optimization method is adjusted, including:

[0043] like Increase w 1 The weight of the increase in carbon dioxide emissions constraints: in is the weight coefficient in the adjusted multi-objective optimization method, k 1 is the preset carbon dioxide emission constraint intensity adjustment coefficient;

[0044] like Reduce w 3 and w 4 To allow greater energy consumption for purification.

[0045] The present invention also provides a gas production energy efficiency optimization and carbon emission management system comprising:

[0046] Real-time data acquisition module, used to collect flow variables, pressure variables, temperature variables, concentration variables and energy consumption variables in the process of gas mixing and purification in real time;

[0047] The mixing and diffusion simulation module is used to simulate the gas mixing and diffusion process based on the computational fluid dynamics method and calculate the mixing uniformity index U according to the concentration variable. mix ;

[0048] The gas purification energy consumption model module is used to establish the energy consumption model of the gas purification process based on the low-temperature condensation method, and calculate the gas purification efficiency η based on the flow variable, concentration variable and energy consumption variable purify And unit gas purification energy consumption E purify ;

[0049] Multi-objective optimization module, used to obtain the carbon dioxide emission concentration C from the concentration variable CO2 , based on the carbon dioxide emission concentration C CO2 , Mixing uniformity index U mix , Gas purification efficiency η purify And unit gas purification energy consumption E purify, combined with multi-objective optimization methods to dynamically adjust pressure variables and temperature variables;

[0050] The feedback optimization module is used to obtain the adjusted carbon dioxide emission concentration and to optimize the weight coefficient in the multi-objective optimization method according to the adjusted carbon dioxide emission concentration.

[0051] The present invention also provides a computer program product, including a gas production energy efficiency optimization and carbon emission management program, which implements the gas production energy efficiency optimization and carbon emission management method when executed by a processor.

[0052] The beneficial effect of the present invention is that the present invention effectively solves the balance problem between energy efficiency optimization and carbon emission control in the gas production process by introducing a multi-objective optimization method, combining real-time data feedback and intelligent control. Specifically, after the implementation of the present invention, it can significantly save energy and reduce energy waste while ensuring gas production efficiency, and save carbon and reduce carbon dioxide emissions by accurately controlling emission sources.

[0053] The present invention uses an optimization model based on computational fluid dynamics (CFD) and intelligent control of reinforcement learning (RL), which can dynamically adapt to different production conditions and load changes, adjust optimization parameters in real time, ensure that energy efficiency is always maintained at the optimal state, and minimize carbon emissions. In this way, the present invention not only improves production efficiency in traditional gas production, but also makes significant contributions to green production. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0055] Figure 1 This is a flow chart of a first embodiment of a method for optimizing gas production energy efficiency and managing carbon emissions according to the present invention.

[0056] Figure 2 This is a schematic diagram of equipment for a method of optimizing gas production energy efficiency and managing carbon emissions according to the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Embodiment 1: Figure 1 As shown, it is a flow chart of the first embodiment of the method for optimizing gas production energy efficiency and managing carbon emissions of the present invention, and the first embodiment of the method for optimizing gas production energy efficiency and managing carbon emissions of the present invention is proposed.

[0059] In a first embodiment, the gas production energy efficiency optimization and carbon emission management method includes:

[0060] Step S10: real-time collection of flow variables, pressure variables, temperature variables, concentration variables and energy consumption variables during the gas mixing and purification process;

[0061] It should be noted that in step S10, the step of real-time collection of flow variables, pressure variables, temperature variables, concentration variables and energy consumption variables in the process of gas mixing and purification specifically includes:

[0062] Obtain the fuel gas raw material flow rate Q through the flow meter feed And the outlet gas flow rate Q after purification out ;

[0063] The gas mixing pressure P is monitored by a pressure sensor mix and separation tower pressure P sep ;

[0064] The mixed gas temperature T is measured by the temperature sensor mix and condensation temperature T cond ;

[0065] Obtain the gas mixture concentration C through the gas analyzer target and carbon dioxide emission concentration C CO2 ;

[0066] Monitor compressor power consumption E through power meter comp and cooling system energy consumption E cool ;

[0067] Among them, the flow variables include the fuel gas flow Q feed And the outlet gas flow rate Q after purification out , the pressure variables include the gas mixing pressure P mix and separation tower pressure P sep ; Temperature variables include mixed gas temperature mixed gas temperature and condensation temperature T cond; Concentration variables include gas mixing concentration C target and CO 2 Emission concentration C CO2 ; Energy consumption variables include compressor power consumption E comp and cooling system energy consumption E cool .

[0068] It is understandable that by collecting data such as gas flow, temperature, and pressure in real time, the present invention can dynamically adjust various parameters in the production process, especially the mixed gas flow and pressure, to ensure the uniformity of the mixing process, thereby improving the efficiency of gas purification and optimizing energy efficiency. Compared with traditional gas production control methods, the present invention can monitor and adjust the production process in real time to ensure the optimal production state under different working conditions. Accurate data collection enables rapid response to external environmental changes (such as changes in air pressure, temperature fluctuations, etc.) during the gas production process, thereby ensuring that the system can remain stable under extreme conditions and avoiding abnormal fluctuations in production, resulting in unnecessary energy consumption and carbon emissions.

[0069] Step S20: Simulate the gas mixing and diffusion process based on the computational fluid dynamics method, and calculate the mixing uniformity index U according to the concentration variable mix ;

[0070] It should be noted that in step S20, the mixing uniformity index U mix The calculation formula is:

[0071]

[0072] Among them, C actual,i is the actual gas mixture concentration of gas type i measured by the sensor, C target,i is the target gas mixing concentration of the preset gas type i.

[0073] It can be understood that this formula is used to quantify the uniformity of mixing, and the goal is to make the actual measured gas concentration consistent with the target concentration as much as possible, thereby improving the effect of gas mixing.

[0074] It should be understood that when the difference between the actual concentration and the target concentration is large, the mixing uniformity index will become smaller, which means that the mixing is uneven and further optimization and adjustment of control parameters such as flow, pressure or temperature are required. By calculating the mixing uniformity index, the uniformity of the gas mixing can be evaluated in real time. When the difference between the actual gas mixing concentration and the target concentration is large, the system can identify the uneven mixing and then adjust the optimization strategy (such as flow, temperature, pressure, etc.) to improve the mixing effect and ensure that the gas components are evenly distributed. Through this precise control, the present invention can improve the mixing efficiency in the gas production process, avoid the waste of resources caused by uneven gas composition, and thus improve the overall energy efficiency of the system.

[0075] For example, if the target gas concentration is set to 50% in a gas production process and the actual concentration measured by the sensor is 45%, the mixing uniformity index will be adjusted based on the difference. If there are concentration differences among multiple gas categories, the formula will take into account the concentration deviations of all categories and give an overall mixing uniformity assessment.

[0076] Step S30: Establish an energy consumption model for the gas purification process based on the low temperature condensation method, and calculate the gas purification efficiency η according to the flow variable, concentration variable and energy consumption variable purify And unit gas purification energy consumption E purify ;

[0077] It should be noted that in step S30, the gas purification efficiency η purify The calculation formula is:

[0078]

[0079] Among them, C feed is the target gas concentration in the fuel gas raw material.

[0080] This formula calculates the efficiency of gas purification, reflecting the ratio of the target gas concentration increase after purification to the raw gas concentration.

[0081] Unit gas purification energy consumption E purify The calculation formula is:

[0082]

[0083] Among them, the unit gas purification energy consumption E purify The lower it is, the more energy efficient the purification process is.

[0084] This formula calculates the energy consumed in the purification process of a unit of gas, which represents the ratio of the total energy consumption of the compressor and cooling system to the flow rate of the purified gas. purify The lower it is, the more energy efficient the purification process is.

[0085] It should be noted that the target gas concentration refers to the volume fraction or mass fraction of a specific gas that is expected to be achieved during the gas purification process, that is, the proportion of the target component (such as methane, hydrogen or carbon dioxide, etc.) in the gas mixture. The target gas concentration is usually used to measure the extraction efficiency of the target gas during the purification process and the purity of the final product.

[0086] It should be understood that by calculating the gas purification efficiency, the effect of the purification process can be quantified, and the operating parameters in the gas purification process can be dynamically optimized according to the real-time target concentration and actual concentration difference. By calculating the unit gas purification energy consumption, the energy consumption in the purification process can be effectively monitored and controlled to ensure that energy waste is reduced while optimizing the purification efficiency.

[0087] For example, in a gas production process, the purification efficiency is lower than expected, but the unit gas purification energy consumption is high, which may indicate that excessive energy is consumed in the purification process, resulting in a suboptimal purification effect. At this time, by adjusting control variables such as temperature and pressure, the gas purification process can be optimized, efficiency can be improved, and energy consumption can be reduced, so that the purification effect is optimized and carbon dioxide emissions can be reduced.

[0088] Step S40: Obtaining the carbon dioxide emission concentration from the concentration variable Based on CO2 emission concentration Mixing uniformity index U mix , Gas purification efficiency η purify And unit gas purification energy consumption E purify , combined with multi-objective optimization methods to dynamically adjust pressure variables and temperature variables;

[0089] It should be noted that the carbon dioxide emission concentration is obtained from the concentration variable Based on CO2 emission concentration Mixing uniformity index U mix , Gas purification efficiency η purify And unit gas purification energy consumption E purify , combining the multi-objective optimization method to dynamically adjust the pressure variables and temperature variables, and then adjust the carbon dioxide emission concentration, the specific steps include:

[0090] Step S401: Constructing a multi-objective optimization function

[0091]

[0092] Among them, w 1 ,w 2 ,w 3 ,w 4 is the dynamically adjusted weight coefficient;

[0093] Step S402: Using reinforcement learning to optimize the dynamically adjusted weight coefficient w 1 ,w 2 ,w 3 ,w 4 ;

[0094] Set the reinforcement learning state space S:

[0095]

[0096] Set the reinforcement learning action space A:

[0097] A={ΔP mix ,ΔP sep ,ΔT mix ,ΔT cond}

[0098] Where ΔP mix is the change in gas mixing pressure, ΔP sep is the pressure change of the separation tower, ΔT mix is the temperature change of the mixed gas, ΔT cond is the condensation temperature change;

[0099] Step S403: Combine the reinforcement learning state space S, the reinforcement learning action space A and the multi-objective optimization function The optimized pressure variables and optimized temperature variables are calculated, including the optimized gas mixing pressure P mix , optimize the separation tower pressure P sep , optimize the mixed gas temperature T mix and the optimal condensation temperature T cond ;

[0100] Step S404: After applying the optimized pressure variable and the optimized temperature variable, the adjusted carbon dioxide emission concentration is obtained through a gas analyzer.

[0101] It can be understood that the optimization method uses the reinforcement learning algorithm to automatically adjust the weight coefficient of the optimization target, so that the system can dynamically adjust the control parameters under different operating conditions to ensure that the energy efficiency and carbon emissions of gas production are optimized without affecting the gas quality.

[0102] It should be understood that through reinforcement learning and multi-objective optimization methods, the gas production process no longer relies on static control strategies, but can be dynamically adjusted according to real-time data to ensure that the production process always runs in the optimal state and reduce energy consumption and emissions.

[0103] For example, suppose that in a production process, the gas mixing is uneven, resulting in U mix Decreases, while the purification efficiency η purify Below the standard value, carbon dioxide emission concentration Exceeded. At this time:

[0104] Traditional method: It is necessary to manually adjust the mixing pressure, separation tower pressure and temperature, which has a long adjustment cycle and is difficult to respond in real time.

[0105] The method of the present invention: the reinforcement learning agent detects Too high, U mix When it is too low, the weight of the optimization objective function is automatically adjusted to increase w 1 Reduce w 4 , making the system more inclined to reduce carbon emissions; the multi-objective optimization module calculates new optimal pressure and temperature parameters, increasing the gas mixing pressure P mix To enhance gas diffusion, improve mixing uniformity, and adjust the condensation temperature T cond To optimize the purification process and improve the gas purification efficiency; appropriately reduce the separation tower pressure P sep In order to reduce carbon dioxide emissions, after the above-mentioned optimization and adjustment, carbon dioxide emissions can be automatically reduced, mixing uniformity can be improved, energy consumption can be optimized, and intelligent control and dynamic optimization of the gas production process can be achieved.

[0106] Step S50: optimizing the weight coefficients in the multi-objective optimization method according to the adjusted carbon dioxide emission concentration feedback.

[0107] It should be noted that in step S50, the step of optimizing the weight coefficient in the multi-objective optimization method according to the adjusted carbon dioxide emission concentration feedback specifically includes:

[0108] Get the adjusted CO2 emission concentration Calculate the CO2 emission concentration relative to the preset target emission concentration CO2 concentration deviation

[0109] Preset carbon dioxide concentration deviation threshold, when carbon dioxide concentration deviation When the concentration is less than the CO2 concentration deviation threshold, the weight coefficient in the multi-objective optimization method is maintained;

[0110] When the carbon dioxide concentration deviates When the concentration is greater than or equal to the CO2 concentration deviation threshold, the weight coefficient in the multi-objective optimization method is adjusted, including:

[0111] like Increase w 1 The weight of the increase in carbon dioxide emissions constraints: in is the weight coefficient in the adjusted multi-objective optimization method, k 1 is the preset carbon dioxide emission constraint intensity adjustment coefficient;

[0112] like Reduce w 3 and w 4 To allow greater energy consumption for purification.

[0113] It can be understood that this step can dynamically adjust the weight coefficient of the optimization target to ensure that the gas production process can both guarantee efficient purification and minimize carbon dioxide emissions, thereby achieving the optimal balance between energy efficiency and environmental protection.

[0114] It should be understood that traditional methods usually adopt fixed-weight optimization strategies, which are difficult to cope with real-time operating condition changes, resulting in a difficult balance between energy efficiency and carbon emission control. However, the present invention can automatically optimize the control strategy according to the carbon dioxide emission situation to ensure that the gas production process is always in the optimal state.

[0115] For example, scenario 1: CO2 emissions exceed the limit

[0116] Phenomenon: The target emission concentration is set to However, the actual emission concentration measured is Threshold exceeded.

[0117] Optimization strategy: Increase w 1 weight, further reduce carbon dioxide emissions; appropriately reduce w 3 ,w 4 Make the system more inclined to control emissions rather than improve purification rate or energy saving.

[0118] Optimization effect: Readjust the optimization variable P mix ,P sep ,T mix ,T cond , reduce the carbon content in the gas flow, so that the final emission concentration drops to within the target value.

[0119] Scenario 2: CO2 emissions below target

[0120] Phenomenon: The target emission concentration is However, the actual emission concentration measured is Well below target.

[0121] Optimization strategy: Appropriately reduce w 1 The constraint strength allows the emission concentration to rise slightly; increasing w 3 ,w 4 Weight makes the system more inclined to optimize the purification rate and improve production efficiency.

[0122] Effect after optimization: Recalculating the optimization variables improves the gas purification efficiency, slightly increases the unit energy consumption, but improves the overall production efficiency.

[0123] Embodiment 2: In addition, the gas production energy efficiency optimization and carbon emission management system provided by the present invention adopts a gas production energy efficiency optimization and carbon emission management method in the above embodiment, which can solve the technical problem of gas production energy efficiency optimization and carbon emission management. Compared with the prior art, the beneficial effects of the gas production energy efficiency optimization and carbon emission management system provided by the present invention are the same as the beneficial effects of the gas production energy efficiency optimization and carbon emission management method provided by the above embodiment, and the other technical features of the gas production energy efficiency optimization and carbon emission management system are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0124] Embodiment 3: The present invention provides a gas production energy efficiency optimization and carbon emission management device, please refer to Figure 2, a gas production energy efficiency optimization and carbon emission management device includes: at least one processor; and a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the gas production energy efficiency optimization and carbon emission management method in the above-mentioned embodiment 1. A gas production energy efficiency optimization and carbon emission management device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A gas production energy efficiency optimization and carbon emission management device is only an example and should not bring any limitations to the functions and scope of use of the embodiments of the present invention. A gas production energy efficiency optimization and carbon emission management device may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In RAM 1004, various programs and data required for the operation of a gas production energy efficiency optimization and carbon emission management device are also stored. The processing device 1001, ROM 1002, and RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow a gas production energy efficiency optimization and carbon emission management device to communicate wirelessly or wired with other devices to exchange data. Although a gas production energy efficiency optimization and carbon emission management device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0125] Embodiment 4: The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of a method for optimizing gas production energy efficiency and managing carbon emissions as described above. The computer program product provided by the present invention can solve the technical problem of optimizing gas production energy efficiency and managing carbon emissions. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the method for optimizing gas production energy efficiency and managing carbon emissions provided in the above embodiment, which will not be described in detail here.

[0126] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0127] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0128] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for optimizing gas production energy efficiency and managing carbon emissions, characterized in that: Methods include: Step S10: real-time collection of flow variables, pressure variables, temperature variables, concentration variables and energy consumption variables during the gas mixing and purification process; Step S20: Simulate the gas mixing and diffusion process based on the computational fluid dynamics method, and calculate the mixing uniformity index U according to the concentration variable mix ; Step S30: Establish an energy consumption model for the gas purification process based on the low temperature condensation method, and calculate the gas purification efficiency η according to the flow variable, concentration variable and energy consumption variable purify And unit gas purification energy consumption E purify ; Step S40: Obtaining the carbon dioxide emission concentration C from the concentration variable CO2 , based on the carbon dioxide emission concentration C CO2 , Mixing uniformity index U mix , Gas purification efficiency η purify And unit gas purification energy consumption E purify , combined with multi-objective optimization methods to dynamically adjust pressure variables and temperature variables, and then adjust the carbon dioxide emission concentration; Step S50: optimizing the weight coefficients in the multi-objective optimization method according to the adjusted carbon dioxide emission concentration feedback.

2. A method for optimizing gas production energy efficiency and managing carbon emissions as claimed in claim 1, characterized in that: In step S10, the step of real-time acquisition of flow variables, pressure variables, temperature variables, concentration variables and energy consumption variables in the process of gas mixing and purification specifically includes: Obtain the fuel gas flow rate Q through the flow meter feed And the outlet gas flow rate Q after purification out ; The gas mixing pressure P is monitored by a pressure sensor mix and separation tower pressure P sep ; The mixed gas temperature T is measured by the temperature sensor mix and condensation temperature T cond ; Obtain the gas mixture concentration C through the gas analyzer target and carbon dioxide emission concentration C CO2 ; Monitor the compressor power consumption E through a power meter comp and cooling system energy consumption E cool ; Among them, the flow variables include the fuel gas flow Q feed And the outlet gas flow rate Q after purification out ; Pressure variables include gas mixing pressure P mix and separation tower pressure P sep ; Temperature variables include mixed gas temperature mixed gas temperature and condensation temperature T cond ; Concentration variables include gas mixing concentration C target and CO2 emission concentration C CO2 ; Energy consumption variables include compressor power consumption E comp and cooling system energy consumption E cool .

3. A method for optimizing gas production energy efficiency and managing carbon emissions as claimed in claim 2, characterized in that: In step S20, the mixing uniformity index U mix The calculation formula is: Among them, C actual,i is the actual gas mixture concentration of gas type i measured by the sensor, C target,i is the target gas mixing concentration of the preset gas type i.

4. A method for optimizing gas production energy efficiency and managing carbon emissions as claimed in claim 2, characterized in that: In step S30, the gas purification efficiency η purify The calculation formula is: Among them, C feed is the target gas concentration in the fuel gas raw material.

5. A method for optimizing gas production energy efficiency and managing carbon emissions as claimed in claim 2, characterized in that: In step S30, the unit gas purification energy consumption E purify The calculation formula is: Among them, the unit gas purification energy consumption E purify The lower it is, the more energy efficient the purification process is.

6. A method for optimizing gas production energy efficiency and managing carbon emissions as claimed in claim 2, characterized in that: In step S40, the carbon dioxide emission concentration is obtained from the concentration variable Based on CO2 emission concentration Mixing uniformity index U mix , Gas purification efficiency η purify And unit gas purification energy consumption E purify , combining the multi-objective optimization method to dynamically adjust the pressure variables and temperature variables, and then adjust the carbon dioxide emission concentration, the specific steps include: Step S401: Constructing a multi-objective optimization function Among them, w1, w2, w3, w4 are dynamically adjusted weight coefficients; Step S402: using reinforcement learning to optimize dynamically adjusted weight coefficients w1, w2, w3, w4; Set the reinforcement learning state space S: Set the reinforcement learning action space A: A={ΔP mix ,ΔP sep ,ΔT mix ,ΔT cond } Among them, ΔP mix is the change in gas mixing pressure, ΔP sep is the pressure change of the separation tower, ΔT mix is the temperature change of the mixed gas, ΔT cond is the condensation temperature change; Step S403: Combine the reinforcement learning state space S, the reinforcement learning action space A and the multi-objective optimization function The optimized pressure variables and optimized temperature variables are calculated, including the optimized gas mixing pressure P mix , optimize the separation tower pressure P sep , optimize the mixed gas temperature T mix and the optimal condensation temperature T cond ; Step S404: After applying the optimized pressure variable and the optimized temperature variable, the adjusted carbon dioxide emission concentration is obtained through a gas analyzer.

7. A method for optimizing gas production energy efficiency and managing carbon emissions as claimed in claim 6, characterized in that: In step S50, the step of optimizing the weight coefficients in the multi-objective optimization method according to the adjusted carbon dioxide emission concentration feedback specifically includes: Get the adjusted CO2 emission concentration Calculate the CO2 emission concentration relative to the preset target emission concentration CO2 concentration deviation Preset carbon dioxide concentration deviation threshold, when carbon dioxide concentration deviation When the concentration is less than the CO2 concentration deviation threshold, the weight coefficient in the multi-objective optimization method is maintained; When the carbon dioxide concentration deviates When the concentration is greater than or equal to the CO2 concentration deviation threshold, the weight coefficient in the multi-objective optimization method is adjusted, including: like Increasing the weight of w1 will increase the constraints on carbon dioxide emissions: in is the weight coefficient in the adjusted multi-objective optimization method, k1 is the preset carbon dioxide emission constraint intensity adjustment coefficient; like Reduce w3 and w4 to allow more energy consumption for purification.

8. A gas production energy efficiency optimization and carbon emission management system, applied to a gas production energy efficiency optimization and carbon emission management method as claimed in any one of claims 1 to 7, characterized in that: The gas production energy efficiency optimization and carbon emission management system includes: Real-time data acquisition module, used to collect flow variables, pressure variables, temperature variables, concentration variables and energy consumption variables in the process of gas mixing and purification in real time; The mixing and diffusion simulation module is used to simulate the gas mixing and diffusion process based on the computational fluid dynamics method and calculate the mixing uniformity index U according to the concentration variable. mix ; The gas purification energy consumption model module is used to establish the energy consumption model of the gas purification process based on the low-temperature condensation method, and calculate the gas purification efficiency η based on the flow variable, concentration variable and energy consumption variable purify And unit gas purification energy consumption E purify ; Multi-objective optimization module, used to obtain the carbon dioxide emission concentration C from the concentration variable CO2 , based on the carbon dioxide emission concentration C CO2 , Mixing uniformity index U mix , Gas purification efficiency η purify And unit gas purification energy consumption E purify , combined with multi-objective optimization methods to dynamically adjust pressure variables and temperature variables, and then adjust the carbon dioxide emission concentration; The feedback optimization module is used to optimize the weight coefficients in the multi-objective optimization method according to the adjusted carbon dioxide emission concentration feedback.

9. A gas production energy efficiency optimization and carbon emission management device, characterized in that: The gas production energy efficiency optimization and carbon emission management device includes: a memory, a processor, and a gas production energy efficiency optimization and carbon emission management program stored on the memory and executable on the processor. When the gas production energy efficiency optimization and carbon emission management program is executed by the processor, a gas production energy efficiency optimization and carbon emission management method described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product includes a gas production energy efficiency optimization and carbon emission management program, and when the gas production energy efficiency optimization and carbon emission management program is executed by a processor, a gas production energy efficiency optimization and carbon emission management method as described in any one of claims 1 to 7 is implemented.

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