Intelligent matching method for energy efficiency of coal bed gas reciprocator

Abstract

The invention relates to the technical field of reciprocating compressors, and particularly discloses a coal bed gas reciprocating machine energy efficiency intelligent matching method which comprises the steps of S1, compressor energy-saving principle analysis, S2, compressor efficient area calculation research, S3, efficient area intelligent matching technology research, S4, intelligent cooling control technology and S5, intelligent optimization system design. S6, machine learning and artificial intelligence technology application; and S7, system integration. By optimizing the rotating speed and the pressure ratio of the compressor, the active power of the compressor is remarkably reduced, on the premise that the yield is not reduced, the operation power is reduced, and therefore the purposes of saving energy and reducing consumption are achieved, by establishing a mathematical model and a logic matching module, the compressor equipment always operates in a high-efficiency interval, and the working efficiency is improved. The compressor is always in an efficient operation state, the magnetic flux, the rotating speed, the torque, the current and the voltage of the motor are finely adjusted, high matching of wellhead pressure and working parameters of the compressor is ensured through fine adjustment, and the optimal pressure ratio is achieved.

Description

Technical Field

[0001] The invention belongs to the technical field of reciprocating compressors, and in particular relates to an intelligent energy efficiency matching method for a coalbed methane reciprocating machine. Background Art

[0002] In the field of coalbed methane mining and processing, reciprocating compressors, as key equipment, play a vital role in transporting coalbed methane from the wellhead to the gas gathering station and pressurizing it to the required pressure of the pipeline network. However, the traditional energy efficiency matching technology of coalbed methane reciprocating machines has several significant shortcomings, which limits its operating efficiency and energy consumption optimization potential.

[0003] First, manual adjustment and reliance on experience are major drawbacks of current technology. The adjustment of operating parameters of traditional compressors mainly relies on the operator's experience and judgment, which is not only inefficient, but also difficult to ensure that the compressor operates under optimal conditions. Due to the lack of accurate mathematical models and automated control methods, operators can often only adjust parameters based on limited operating data and intuitive feelings, resulting in frequent deviations from the high-efficiency range during compressor operation, and a significant increase in energy consumption.

[0004] Secondly, poor environmental adaptability is also a significant problem of traditional technology. The coalbed methane mining environment is complex and changeable, including factors such as ambient temperature, humidity, inlet gas pressure and cooling system efficiency, which will affect the performance of the compressor. Traditional systems often lack real-time monitoring and dynamic response mechanisms for these external conditions, resulting in large performance fluctuations of the compressor under different working conditions, making it difficult to achieve stable and efficient operation.

[0005] Furthermore, the lack of intelligence and automation has limited the further development of traditional technologies. In the current coalbed methane mining and processing process, the application of intelligent and automated technologies is becoming more and more widespread, but the traditional compressor energy efficiency matching technology is relatively lagging behind in this regard. The lack of an intelligent optimization system means that the compressor cannot automatically adjust operating parameters based on real-time data and historical trends, nor can it continuously optimize its performance through machine learning and artificial intelligence technologies. This not only increases labor costs, but also limits the further improvement of compressor energy efficiency.

[0006] In this regard, the inventors proposed a method for intelligent matching of energy efficiency of coalbed methane reciprocating machines to solve the above problems. Summary of the invention

[0007] The purpose of the present invention is to provide a method for intelligently matching the energy efficiency of a coalbed methane reciprocating machine to solve the problems raised in the above-mentioned background technology.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for intelligently matching energy efficiency of a coalbed methane reciprocating machine comprises the following steps:

[0010] S1. Analyze the energy-saving principle of compressors. Modify the compressor equipment to keep the cylinder size, cylinder compression degree, natural gas properties and cylinder sealing unchanged. The main factors that determine the working efficiency of the compressor are the compressor speed and pressure ratio. By adjusting the speed and pressure ratio, the active power of the compressor can be reduced;

[0011] S2. Research on the calculation of the high-efficiency zone of the compressor. Through fine adjustment, the deviation of the compressor's operating condition is corrected, so that the current process is always in the high-efficiency zone, and restored to the design value when it was just put into operation, matching with the gas gathering station. Through mathematical modeling, the current production capacity condition is divided into several small intervals, and the calculation is performed item by item. Finally, the device performance curve of the gas gathering station and the equipment performance curve of the compressor are obtained. Through the logic matching module, the two curves are always dynamically crossed. The intersection is the high-efficiency point of the current production capacity interval. Repeat the calculation several times to match the high-efficiency zone;

[0012] S3. Research on high-efficiency zone intelligent matching technology. Through the vector control module, combined with the ambient temperature, inlet pressure, and cooling oil temperature parameters, the motor's magnetic flux, speed, torque, current, and voltage are finely adjusted for the first time to match the wellhead pressure into the high-efficiency zone. At the same time, the wellhead pressure is tracked and the motor is fine-tuned again to make the current working condition highly matched with the high-efficiency zone, so that the compressor can achieve the best pressure ratio;

[0013] S4, intelligent cooling control technology, intelligently adjusts the fan speed according to the first and second exhaust temperatures and oil temperature of the compressor, tracks the temperature in real time, operates on demand, and reduces fan energy consumption;

[0014] S5. Intelligent optimization system design integrates real-time monitoring and control modules, which can collect the operating data and environmental parameters of the compressor in real time. The data analysis and processing module analyzes the collected data and generates corresponding adjustment instructions. The feedback control module adjusts the operating parameters of the compressor in real time according to the adjustment instructions to ensure that it operates within the high-efficiency range.

[0015] S6. Application of machine learning and artificial intelligence technology: Through machine learning and artificial intelligence technology, the operating parameters of the compressor are continuously optimized to improve its ability to adapt to different working conditions. The model is trained using historical data and real-time data to predict the optimal operating parameters of the compressor under different working conditions, thus achieving more accurate optimization control.

[0016] S7, system integration, integrate the intelligent matching system, test the system to ensure its stable operation under different working conditions, and continuously optimize the system according to the test results to improve its adaptability and stability.

[0017] Preferably, the calculation expression for the indicated power W of the natural gas compressor in step S1 is:

[0018]

[0019] Where:

[0020] W is the work consumed by the compressor per unit time, i.e. indicated power, kW;

[0021] n is the speed of the compressor, revolutions per minute;

[0022] P 吸 is the cylinder suction pressure, kgf / cm 2 ;

[0023] P 排 Cylinder exhaust pressure, kgf / cm 2 ;

[0024] Vh is the volume of the cylinder, m 3 ;

[0025] λv is the volume coefficient of the cylinder;

[0026] K is the natural gas adiabatic index;

[0027] α is the percentage of pressure loss in the cylinder, %.

[0028] Preferably, the mathematical modeling expression in step S2 is:

[0029] Assume that the capacity condition is divided into m small intervals, and the performance curve corresponding to each interval is C 装置 (i) and C 设备 (i), where i=1,2,…,m;

[0030] The calculation formula for the intersection of the performance curve is:

[0031] C 装置 (i) = C 设备 (i)

[0032] Let the efficient interval be E 高效 , the calculated efficient point is P 高效 :

[0033]

[0034] Preferably, in step S3, the expression for initially finely adjusting the magnetic flux Φ, speed n, torque T, current I and voltage V of the motor by the vector control module is:

[0035] T=k1·Φ·I

[0036] V=k2·n·Φ

[0037] Among them, k1 and k2 are constants.

[0038] Preferably, the pressure ratio optimization expression in step S3 is:

[0039]

[0040] Among them, P 井口 : Wellhead pressure;

[0041] Pressure ratio.

[0042] Preferably, the expression for generating the corresponding adjustment instruction in step S5 is:

[0043] C(t)=f(D(t))

[0044] Where D(t) is the data collected in real time;

[0045] C(t) is the adjustment instruction;

[0046] f is the data processing function.

[0047] Preferably, the operating parameter expression of the compressor adjusted in step S5 is:

[0048] P 反馈 (t) = g(C(t))

[0049] Where g is the feedback control function.

[0050] Preferably, the machine learning and artificial intelligence technology in step S6 includes training model and prediction optimization, wherein the training model expression is:

[0051] M=T(D 历史 , D 实时 )

[0052] D 历史 is historical data, D 实时 is real-time data, M is the training model, and T is model training;

[0053] Predict the optimal operating parameters P under different working conditions by training model M 预测 , the expression is:

[0054] P 预测 =M(D 实时 ).

[0055] Preferably, the system integration expression in step S7 is:

[0056] S 集成 =Integrate(C 控制 , P 反馈 , M).

[0057] Preferably, in step S7, the expression for optimizing the system according to the test results of the system integration is:

[0058] S 优化 =Optimize(S 集成 , Test Results).

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] (1) The present invention significantly reduces the active power of the compressor by optimizing the rotation speed and pressure ratio of the compressor, thereby reducing the operating power while maintaining the output, thereby achieving the purpose of energy saving and consumption reduction. By establishing a mathematical model and a logic matching module, the compressor equipment is always operated in the high-efficiency range, and the performance of the compressor is perfectly matched with the needs of the gas gathering station, ensuring that the compressor is always in an efficient operating state.

[0061] (2) The vector control module in the present invention combines factors such as ambient temperature, inlet pressure, cooling oil temperature, etc. to fine-tune the magnetic flux, speed, torque, current and voltage of the motor. The fine adjustment ensures a high degree of matching between the wellhead pressure and the compressor operating parameters to achieve the optimal pressure ratio.

[0062] (3) The present invention realizes automatic identification and intelligent operation of the compressor throughout the entire process through an intelligent optimization system, without the need for manual operation. It integrates a real-time monitoring and control module, can collect and analyze the operating data of the compressor in real time, generate and execute adjustment instructions, and uses machine learning and artificial intelligence technology to train models through historical data and real-time data to continuously optimize the operating parameters of the compressor. It uses AI technology to predict the optimal operating parameters under different working conditions to achieve more accurate optimization control. The intelligent matching method for the energy efficiency of coalbed methane reciprocating machines can systematically optimize the operating efficiency of the compressor and achieve the goal of stabilizing production and reducing consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 A flow chart of a method for intelligently matching energy efficiency of a coalbed methane reciprocating machine according to the present invention;

[0064] Figure 2 The block diagram of the factors affecting the working efficiency of the compressor of the present invention;

[0065] Figure 3 Design a block diagram of the intelligent optimization system of the present invention;

[0066] Figure 4 It is a block diagram of the mathematical modeling composition of the present invention;

[0067] Figure 5 It is a block diagram of the vector control module of the present invention;

[0068] Figure 6It is a block diagram of the machine learning and artificial intelligence technology composition of the present invention. DETAILED DESCRIPTION

[0069] 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.

[0070] Embodiment 1:

[0071] See also Figures 1 to 6 As shown, a method for intelligently matching energy efficiency of a coalbed methane reciprocating machine comprises the following steps:

[0072] S1. Analyze the energy-saving principle of compressors. Modify the compressor equipment to keep the cylinder size, cylinder compression degree, natural gas properties and cylinder sealing unchanged. The main factors that determine the working efficiency of the compressor are the compressor speed and pressure ratio. By adjusting the speed and pressure ratio, the active power of the compressor can be reduced;

[0073] S2. Research on the calculation of the high-efficiency zone of the compressor. Through fine adjustment, the deviation of the compressor's operating condition is corrected, so that the current process is always in the high-efficiency zone, and restored to the design value when it was just put into operation, matching with the gas gathering station. Through mathematical modeling, the current production capacity condition is divided into several small intervals, and the calculation is performed item by item. Finally, the device performance curve of the gas gathering station and the equipment performance curve of the compressor are obtained. Through the logic matching module, the two curves are always dynamically crossed. The intersection is the high-efficiency point of the current production capacity interval. Repeat the calculation several times to match the high-efficiency zone;

[0074] S3. Research on high-efficiency zone intelligent matching technology. Through the vector control module, combined with the ambient temperature, inlet pressure, and cooling oil temperature parameters, the motor's magnetic flux, speed, torque, current, and voltage are finely adjusted for the first time to match the wellhead pressure into the high-efficiency zone. At the same time, the wellhead pressure is tracked and the motor is fine-tuned again to make the current working condition highly matched with the high-efficiency zone, so that the compressor can achieve the best pressure ratio;

[0075] S4, intelligent cooling control technology, intelligently adjusts the fan speed according to the first and second exhaust temperatures and oil temperature of the compressor, tracks the temperature in real time, operates on demand, and reduces fan energy consumption;

[0076] S5. Intelligent optimization system design integrates real-time monitoring and control modules, which can collect the operating data and environmental parameters of the compressor in real time. The data analysis and processing module analyzes the collected data and generates corresponding adjustment instructions. The feedback control module adjusts the operating parameters of the compressor in real time according to the adjustment instructions to ensure that it operates within the high-efficiency range.

[0077] S6. Application of machine learning and artificial intelligence technology: Through machine learning and artificial intelligence technology, the operating parameters of the compressor are continuously optimized to improve its ability to adapt to different working conditions. The model is trained using historical data and real-time data to predict the optimal operating parameters of the compressor under different working conditions, thus achieving more accurate optimization control.

[0078] S7, system integration, integrate the intelligent matching system, test the system to ensure its stable operation under different working conditions, and continuously optimize the system according to the test results to improve its adaptability and stability.

[0079] From the above, it can be seen that by optimizing the speed and pressure ratio of the compressor, the active power of the compressor can be significantly reduced, and the operating power can be reduced while maintaining the output, thereby achieving the purpose of energy saving and consumption reduction. By establishing a mathematical model and a logical matching module, the compressor equipment can always operate in the high-efficiency range, and the performance of the compressor can be perfectly matched with the needs of the gas gathering station, ensuring that the compressor is always in an efficient operating state.

[0080] Embodiment 2:

[0081] refer to Figures 1 to 6 As shown, the calculation expression for the indicated power W of the natural gas compressor is:

[0082]

[0083] Where:

[0084] W is the work consumed by the compressor per unit time, i.e. indicated power, kW;

[0085] n is the speed of the compressor, revolutions per minute;

[0086] P 吸 is the cylinder suction pressure, kgf / cm 2 ;

[0087] P 排 Cylinder exhaust pressure, kgf / cm 2 ;

[0088] Vh is the volume of the cylinder, m 3 ;

[0089] λv is the volume coefficient of the cylinder;

[0090] K is the natural gas adiabatic index;

[0091] α is the percentage of pressure loss in the cylinder, %.

[0092] Specifically, the mathematical modeling expression is:

[0093] Assume that the capacity condition is divided into m small intervals, and the performance curve corresponding to each interval is C 装置 (i) and C 设备 (i), where i=1,2,…,m;

[0094] The calculation formula for the intersection of the performance curve is:

[0095] C 装置 (i) = C 设备 (i)

[0096] Let the efficient interval be E 高效 , the calculated efficient point is P 高效 :

[0097]

[0098] By matching the intersection of the device performance curve and the equipment performance curve, the high efficiency interval E is determined. 高效 , so that the compressor can operate under the best working conditions.

[0099] Specifically, in step S3, the expression for initially finely adjusting the magnetic flux Φ, speed n, torque T, current I and voltage V of the motor by the vector control module is:

[0100] T=k1·Φ·I

[0101] V=k2·n·Φ

[0102] Among them, k1 and k2 are constants, which are adjusted through the formula of the vector control module to accurately match the motor's magnetic flux, speed, torque, current and voltage so that the compressor operates within the high-efficiency range.

[0103] The pressure ratio optimization expression in step S3 is:

[0104]

[0105] Among them, P 井口 : Wellhead pressure;

[0106] Pressure ratio.

[0107] Specifically, the expression for generating the corresponding adjustment instruction in step S5 is:

[0108] C(t)=f(D(t))

[0109] Where D(t) is the data collected in real time;

[0110] C(t) is the adjustment instruction;

[0111] f is the data processing function.

[0112] Specifically, the operating parameter expression of the compressor adjusted in step S5 is:

[0113] P 反馈 (t) = g(C(t))

[0114] Where g is the feedback control function;

[0115] Through real-time data analysis and processing, and feedback control, we ensure that the compressor always operates in the high-efficiency range, thereby optimizing energy efficiency.

[0116] Specifically, the machine learning and artificial intelligence technology in step S6 includes training model and prediction optimization, wherein the training model expression is:

[0117] M=T(D 历史 , D 实时 )

[0118] D 历史 is historical data, D 实时 is real-time data, M is the training model, and T is model training;

[0119] Predict the optimal operating parameters P under different working conditions by training model M 预测 , the expression is:

[0120] P 预测 =M(D 实时 )

[0121] Utilize machine learning and artificial intelligence technologies to continuously optimize the operating parameters of the compressor to achieve precise control and energy saving and consumption reduction.

[0122] Specifically, the system integration expression in step S7 is:

[0123] S 集成 =Integrate(C 控制 , P 反馈 , M).

[0124] Specifically, in step S7, the expression for optimizing the system according to the test results of the system integration is:

[0125] S 优化 =Optimize(S 集成 , Test Results)

[0126] System integration and test optimization ensure the stability and efficiency of the intelligent matching system in actual operation.

[0127] From the above, it can be seen that the vector control module combines factors such as ambient temperature, inlet pressure, cooling oil temperature, etc. to fine-tune the motor's magnetic flux, speed, torque, current and voltage. The fine adjustment ensures a high match between the wellhead pressure and the compressor operating parameters to achieve the optimal pressure ratio.

[0128] Embodiment three:

[0129] refer to Figures 1 to 6 As shown, the energy-saving principle of compressor is analyzed:

[0130] Fixed parameters:

[0131] Cylinder size (Vh): 2.5m 3 ;

[0132] Cylinder compression degree (λv): 0.85;

[0133] Natural gas properties (K): 1.2;

[0134] Cylinder sealing (α): 0.95;

[0135] Key influencing factors:

[0136] Compressor speed (n): 900RPM;

[0137] Pressure ratio (P discharge / P suction): 4;

[0138] Adjustment strategy:

[0139] Fuzzy matching intelligent optimization technology is used to adjust the speed and pressure ratio to reduce active power.

[0140] 2. Research on efficient area calculation:

[0141] Mathematical modeling:

[0142] Divide the current capacity condition into 10 small intervals and calculate the device performance curve C of each interval item by item 装置( i) and equipment performance curve C 设备 (i), as shown in Table 1 below:

[0143]

[0144]

[0145] Table 1

[0146] pass Determine that the efficient point is in the 4th interval.

[0147] 3. Vector control module:

[0148] The motor's magnetic flux, speed, torque, current and voltage are fine-tuned for the first time. The motor parameters after the initial adjustment are as follows:

[0149] Magnetic flux Φ: 0.8Wb;

[0150] Speed: 850RPM;

[0151] Torque T: 150Nm;

[0152] Current I: 100A;

[0153] Voltage V: 400V;

[0154] The high efficiency zone is determined by the formula T = k1·Φ·I and V = k2·n·Φ;

[0155] Second fine tuning:

[0156] Wellhead pressure Pwellhead: 3.8MPa;

[0157]

[0158] The motor is fine-tuned again to adjust its speed n to 820RPM, and finally it is determined that the compressor is operating at the optimal pressure ratio.

[0159] 4. Intelligent optimization system design:

[0160] Real-time monitoring and control:

[0161] Real-time collection of compressor operating data and environmental parameters, as follows:

[0162] Ambient temperature: 25℃;

[0163] Inlet pressure: 3.5MPa;

[0164] Cooling oil temperature: 50℃;

[0165] Data analysis and processing:

[0166] Generate adjustment instructions: C(t)=f(D(t)).

[0167] 5. Application of machine learning and artificial intelligence technology:

[0168] Train the model:

[0169] The model is trained using historical data and real-time data. The data is as follows:

[0170] Historical data: DHistory;

[0171] Running time: 10,000 hours;

[0172] Energy efficiency data: hourly energy consumption data;

[0173] Real-time data: D real-time;

[0174] Current time: 1200 hours;

[0175] Current energy efficiency data:

[0176] Training model M:

[0177] M=TrainMode 1(Dhistory, Drealtime);

[0178] Prediction Optimization:

[0179] Predict optimal operating parameters:

[0180] Pprediction=M(Drealtime).

[0181] From the above, it can be seen that through the intelligent optimization system, the compressor can be automatically identified and intelligently operated throughout the process without manual operation. The integrated real-time monitoring and control module can collect and analyze the operating data of the compressor in real time, generate and execute adjustment instructions, and use machine learning and artificial intelligence technology to train models through historical data and real-time data to continuously optimize the operating parameters of the compressor. AI technology is used to predict the optimal operating parameters under different operating conditions to achieve more accurate optimization control. The intelligent matching method of energy efficiency of coalbed methane reciprocating machine can systematically optimize the operating efficiency of the compressor and achieve the goal of stable production and reduced consumption.

[0182] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0183] In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other.

[0184] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent energy efficiency matching of a coalbed methane reciprocating machine, characterized in that: The following steps are involved: S1. Analyze the energy-saving principle of compressors. Modify the compressor equipment to keep the cylinder size, cylinder compression degree, natural gas properties and cylinder sealing unchanged. The factors that determine the working efficiency of the compressor are the compressor speed and pressure ratio. By adjusting the speed and pressure ratio, the active power of the compressor can be reduced; S2. Research on the calculation of the high-efficiency zone of the compressor. Through fine adjustment, the deviation of the compressor's operating condition is corrected, so that the current process is always in the high-efficiency zone, and restored to the design value when it was just put into operation, matching with the gas gathering station. Through mathematical modeling, the current production capacity condition is divided into several small intervals, and the calculation is performed item by item. Finally, the device performance curve of the gas gathering station and the equipment performance curve of the compressor are obtained. Through the logic matching module, the two curves are always dynamically crossed. The intersection is the high-efficiency point of the current production capacity interval. Repeat the calculation several times to match the high-efficiency zone; S3. Research on high-efficiency zone intelligent matching technology. Through the vector control module, combined with the ambient temperature, inlet pressure, and cooling oil temperature parameters, the motor's magnetic flux, speed, torque, current, and voltage are finely adjusted for the first time to match the wellhead pressure into the high-efficiency zone. At the same time, the wellhead pressure is tracked and the motor is fine-tuned again to make the current working condition highly matched with the high-efficiency zone, so that the compressor can achieve the best pressure ratio; S4, intelligent cooling control technology, intelligently adjusts the fan speed according to the first and second exhaust temperatures and oil temperature of the compressor, tracks the temperature in real time, operates on demand, and reduces fan energy consumption; S5. Intelligent optimization system design integrates real-time monitoring and control modules, which can collect the operating data and environmental parameters of the compressor in real time. The data analysis and processing module analyzes the collected data and generates corresponding adjustment instructions. The feedback control module adjusts the operating parameters of the compressor in real time according to the adjustment instructions to ensure that it operates within the high-efficiency range. S6. Application of machine learning and artificial intelligence technology: Through machine learning and artificial intelligence technology, the operating parameters of the compressor are continuously optimized to improve its ability to adapt to different working conditions. The model is trained using historical data and real-time data to predict the optimal operating parameters of the compressor under different working conditions, thus achieving more accurate optimization control. S7, system integration, integrate the intelligent matching system, test the system to ensure its stable operation under different working conditions, and continuously optimize the system according to the test results to improve its adaptability and stability.

2. The method for intelligent energy efficiency matching of a coalbed methane reciprocating machine according to claim 1 is characterized in that: In step S1, the calculation expression for the indicated power W of the natural gas compressor is: Where: W is the work consumed by the compressor per unit time, i.e. indicated power, kW; n is the speed of the compressor, revolutions per minute; P 吸 is the cylinder suction pressure, kgf / cm 2 ; P 排 Cylinder exhaust pressure, kgf / cm 2 ; Vh is the volume of the cylinder, m 3 ; λv is the volume coefficient of the cylinder; K is the natural gas adiabatic index; α is the percentage of pressure loss in the cylinder, %.

3. The method for intelligent energy efficiency matching of a coalbed methane reciprocating machine according to claim 1, characterized in that: The mathematical modeling expression in step S2 is: Assume that the capacity condition is divided into m small intervals, and the performance curve corresponding to each interval is C 装置 (i) and C 设备 (i), where i=1,2,…,m; The calculation formula for the intersection of the performance curve is: C 装置 (i)=C 设备 (i) Let the efficient interval be E 高效 , the calculated efficient point is P 高效 :

4. The method for intelligent energy efficiency matching of a coalbed methane reciprocating machine according to claim 1, characterized in that: In step S3, the expression for the initial fine adjustment of the magnetic flux Φ, speed n, torque T, current I and voltage V of the motor by the vector control module is: T=k1·Φ·I V=k2·n·Φ Among them, k1 and k2 are constants.

5. The method for intelligent energy efficiency matching of a coalbed methane reciprocating machine according to claim 1 is characterized in that: The pressure ratio optimization expression in step S3 is: Among them, P 井口 : Wellhead pressure; Pressure ratio.

6. The method for intelligent energy efficiency matching of a coalbed methane reciprocating machine according to claim 1, characterized in that: The expression for generating the corresponding adjustment instruction in step S5 is: C(t)=f(D(t)) Where D(t) is the data collected in real time; C(t) is the adjustment instruction; f is the data processing function.

7. The method for intelligent energy efficiency matching of a coalbed methane reciprocating machine according to claim 1, characterized in that: The operating parameter expression of the compressor adjusted in step S5 is: P 反馈 (t)=g(C(t)) Where g is the feedback control function.

8. The method for intelligent energy efficiency matching of a coalbed methane reciprocating machine according to claim 1, characterized in that: In step S6, the machine learning and artificial intelligence technology includes training model and prediction optimization, wherein the training model expression is: M=T(D 历史 ,D 实时 ) D 历史 is historical data, D 实时 is real-time data, M is the training model, and T is model training; Predict the optimal operating parameters P under different working conditions by training model M 预测 , the expression is: P 预测 =M(D 实时 )。 9. The method for intelligent energy efficiency matching of a coalbed methane reciprocating machine according to claim 1, characterized in that: The system integration expression in step S7 is: S 集成 =Integrate(C 控制 ,P 反馈 ,M)。 10. A method for intelligent energy efficiency matching of a coalbed methane reciprocating machine according to claim 9, characterized in that: In step S7, the expression for optimizing the system according to the test results is: S 优化 =Optimize(S 集成 ,Test Results)。

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