Control method and system for natural gas magnetization activation energy saver

Abstract

The invention relates to the technical field of natural gas energy utilization, and particularly discloses a control method and system for a natural gas magnetization activation energy saver. Performing resonance matching and energy consumption optimization; magnetization application; carrying out on-line effect verification and a catalysis strategy; catalytic activation and mixing optimization; ignition / combustion monitoring and performance evaluation; the invention tends to reduce the generation amount of incomplete combustion products such as CO, unburned hydrocarbon (HC) and the like through more sufficient combustion. By optimizing the air-fuel ratio and the mixing uniformity, the NOx generation path can be controlled while the requirement for the combustion temperature is met, and therefore collaborative emission reduction of pollutants is achieved on the premise that the heat efficiency is guaranteed. Time-frequency feature extraction and on-line combustion characterization jointly enable the system to accurately recognize and inhibit flowing or combusted unstable components (such as periodic pulsation and tempering symptoms). And through real-time fine adjustment of magnetization parameters, catalytic pulses and an air-fuel ratio, flame oscillation and combustion noise can be reduced, and the safety margin of system operation and maintenance is improved.

Description

Technical Field

[0001] This invention belongs to the field of natural gas energy utilization technology, specifically relating to a control method and system for a natural gas magnetization activation energy-saving device. Background Technology

[0002] In the field of natural gas energy utilization, improving combustion efficiency, reducing pollutant emissions, and enhancing system operational stability have always been core concerns. Traditional natural gas combustion technologies face numerous challenges in practical applications, making it difficult to fully meet increasingly stringent energy efficiency and environmental protection requirements.

[0003] From a combustion efficiency perspective, the contact efficiency between fuel and oxygen is limited during natural gas combustion due to molecular aggregation. Molecular aggregation reduces the effective reaction surface area of ​​the fuel, leading to a slower combustion reaction rate, increased incomplete combustion, and consequently, a higher chemical energy input required for each unit of heat / electricity output. This makes it difficult to achieve ideal system thermal efficiency or unit efficiency. Although some existing technologies attempt to improve mixing uniformity by optimizing burner structure, there is a lack of effective solutions to the molecular-level aggregation problem, preventing a fundamental improvement in fuel surface activity and combustion efficiency.

[0004] In terms of pollutant emission control, traditional combustion methods easily lead to the formation of incomplete combustion products, such as carbon monoxide (CO) and unburned hydrocarbons (HC). Simultaneously, even when combustion temperature requirements are met, the formation of nitrogen oxides (NOx) is difficult to control effectively. Existing emission control technologies often focus on treating exhaust gases after combustion, reducing pollutant emissions by installing additional purification equipment. This not only increases equipment costs and operational and maintenance complexity but also fails to reduce pollutant generation at the combustion source, making it difficult to achieve synergistic emission reduction.

[0005] In response to this problem, this application proposes a control method and system for a natural gas magnetization activation energy saver. Summary of the Invention

[0006] The purpose of this invention is to provide a control method and system for a natural gas magnetization activation energy saver to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A control method for a natural gas magnetization activation energy saver includes the following steps:

[0009] Raw gas characteristic data are collected to characterize the instantaneous physical properties and composition of the gas, resulting in a raw gas characterization vector. The raw gas characterization vector is then denoised and subjected to time-frequency feature extraction to separate steady-state components from instantaneous disturbances and to construct a smoothed feature sequence, resulting in a feature sequence.

[0010] Based on the feature sequence, resonance matching and energy consumption constraint optimization are performed to determine the magnetization resonance parameters to be applied (including frequency, field strength, pulse width and action segment length), and a set of magnetization parameters is obtained.

[0011] A time-varying magnetic field is applied to the gas passing through the fluid section according to the magnetization parameter set to change the molecular aggregation state and achieve preliminary deagglomeration, thereby obtaining the gas property vector after magnetization;

[0012] The physical property vector of the magnetized gas is verified online and the catalytic activation strategy is calculated to quantify the degree of activation and determine the catalytic activation operation sequence (including catalyst action mode, contact time and pulse parameters) to obtain the catalytic operation sequence.

[0013] The magnetized gas property vector is catalytically activated according to the catalytic operation sequence, and the optimal air-fuel ratio curve is calculated based on the characteristics of the activated gas to improve mixing uniformity and enhance theoretical combustion integrity, thereby obtaining the actual mixing state.

[0014] Ignition and combustion are performed on the actual mixture state, and combustion characterization and performance evaluation are carried out to calculate the combustion quality score and generate a control correction vector to feed back and fine-tune the magnetization parameters, catalytic operation and air-fuel ratio to form a closed loop, thus obtaining the control correction vector.

[0015] Preferably, the raw gas characteristic data includes, but is not limited to: instantaneous flow rate, pressure, temperature, methane content, main impurity content, and humidity;

[0016] The denoising and time-frequency feature extraction employs low-pass filtering combined with wavelet decomposition or short-time Fourier transform to simultaneously obtain the steady-state baseline and high-frequency disturbance components, and outputs peak values, energy spectrum, and time delay features as components of the feature sequence.

[0017] Preferably, the resonance matching and energy consumption constraint optimization are based on minimizing the objective function Φ, and the formula for minimizing the objective function is as follows:

[0018] Φ=α·Emag+βSres

[0019] Where Emag is the magnetization energy consumption estimate, Sres is the statistical measure of the residual cluster size after magnetization, and α and β are weighting coefficients;

[0020] By solving the set of magnetization parameters under constraints (such as magnetic field strength not exceeding a safety threshold and frequency within the allowable bandwidth of the device), the optimal set of magnetization parameters that satisfies both efficiency and safety can be obtained.

[0021] Preferably, the step of applying a time-varying magnetic field to the gas passing through the fluid section according to the magnetization parameter set to change the molecular aggregation state and achieve preliminary deagglomeration, thereby obtaining the gas property vector after magnetization, includes:

[0022] A time-varying magnetic field is applied to the gas passing through the fluid section by using a flow velocity synchronization pulse or continuous modulation to change the molecular aggregation state and achieve preliminary deagglomeration, thereby obtaining the gas property vector after magnetization.

[0023] Preferably, the online effect verification uses instantaneous mass spectrometry or equivalent pyrolysis and acoustic response measurement to quantify the physical property vector of the magnetized gas and obtain the catalytic operation sequence;

[0024] The catalytic operation sequence includes catalyst type, addition time, contact time, and pulse regeneration cycle to be quantified.

[0025] Preferably, the catalytic activation employs rare earth alloys, nano-oxides, or supported nano-catalysts, and the specific surface area and active site concentration are increased by adjusting the catalytic contact time and flow rate; based on the characteristics of the activated fuel gas, the optimal air-fuel ratio curve is solved using a mathematical programming method that balances mass conservation and minimizes incomplete combustion indices, so as to obtain a mixing strategy with the maximum theoretical heat release rate and meets emission limits.

[0026] Preferably, the combustion characterization uses optical radiation spectrum, infrared temperature field and acoustic spectrum to obtain a combustion measurement matrix, and calculates combustion efficiency η, instantaneous CO / HC / NOx emission index and heat release rate based on the measurement matrix, and obtains combustion quality score by weighted summation;

[0027] The control correction vector is obtained by inversely calculating the deviation of the combustion quality score from the preset target using the loss function.

[0028] Another aspect of the present invention is to provide a control system for a natural gas magnetization activation energy saver, comprising:

[0029] The data acquisition module is used to collect raw gas characteristic data to characterize the instantaneous physical properties and composition of the gas, and obtain the raw gas characterization vector; the raw gas characterization vector is denoised and time-frequency feature extracted to separate steady-state components and instantaneous disturbances and construct a smoothed feature sequence to obtain the feature sequence;

[0030] The resonance matching and energy consumption optimization module is used to perform resonance matching and energy consumption constraint optimization based on the feature sequence to determine the magnetization resonance parameters to be applied (including frequency, field strength, pulse width and action segment length) and obtain the magnetization parameter set.

[0031] The magnetization application module is used to apply a time-varying magnetic field to the gas passing through the fluid section according to the magnetization parameter set in order to change the molecular aggregation state and achieve preliminary deagglomeration, and obtain the gas property vector after magnetization.

[0032] The online effect verification and catalytic strategy module is used to perform online effect verification and catalytic activation strategy calculation on the magnetized gas property vector, to quantify the degree of activation and determine the catalytic activation operation sequence (including catalyst action mode, contact time and pulse parameters), and to obtain the catalytic operation sequence.

[0033] The catalytic activation and mixing optimization module is used to perform catalytic activation on the magnetized gas property vector according to the catalytic operation sequence and calculate the optimal air-fuel ratio curve based on the characteristics of the activated gas, so as to improve the mixing uniformity and improve the theoretical combustion integrity, and obtain the actual mixing state.

[0034] The ignition / combustion monitoring and performance evaluation module is used to ignite and ignite the actual mixture state and perform combustion characterization and performance evaluation. It is used to calculate the combustion quality score and generate a control correction vector to provide feedback for fine-tuning the magnetization parameters, catalytic operation and air-fuel ratio to form a closed loop and obtain the control correction vector.

[0035] Compared with the prior art, the beneficial effects of the present invention are:

[0036] (1) This invention, based on the resonance matching of characteristic sequences and the application of time-varying magnetic fields, achieves molecular cluster depolymerization or surface energy state changes at the molecular / cluster scale, fundamentally improving the surface activity of fuel and the contact efficiency with oxygen. Further combined with catalytic activation, it can increase the effective reaction surface area and active site density of the fuel, thereby improving the combustion reaction rate and combustion completeness, and reducing the generation of unburned hydrocarbons. Due to the improvement in fuel activity and combustion completeness, the chemical energy input required per unit of heat / electricity output decreases, and the system thermal efficiency / unit efficiency is improved; closed-loop control, based on real-time combustion quality score fine-tuning, can continuously approach the optimal operating point, achieving steady-state improvement in energy efficiency and dynamic energy saving during long-term operation.

[0037] (2) This invention aims to reduce the generation of incomplete combustion products such as CO and unburned hydrocarbons (HC) through more complete combustion. By optimizing the air-fuel ratio and mixing uniformity, the path of NOx generation can be controlled while meeting combustion temperature requirements, thereby achieving synergistic emission reduction of pollutants while ensuring thermal efficiency. Time-frequency feature extraction and online combustion characterization together enable the system to accurately identify and suppress unstable components in flow or combustion (such as periodic pulsations and signs of backfire). By fine-tuning the magnetization parameters, catalytic pulse, and air-fuel ratio in real time, flame oscillation and combustion noise can be reduced, improving the safety margin of system operation and maintenance. Attached Figure Description

[0038] Figure 1 This is a flowchart of a control method for a natural gas magnetization activation energy-saving device according to the present invention;

[0039] Figure 2 This is a block diagram of the control system of a natural gas magnetization activation energy saver according to the present invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0041] Example 1:

[0042] Please see Figure 1 As shown, a control method for a natural gas magnetization activation energy-saving device includes:

[0043] Raw gas characteristic data are collected to characterize the instantaneous physical properties and composition of the gas, resulting in a raw gas characterization vector. The raw gas characterization vector is then denoised and subjected to time-frequency feature extraction to separate steady-state components from instantaneous disturbances and to construct a smoothed feature sequence, resulting in a feature sequence.

[0044] The original gas characteristic data includes, but is not limited to: instantaneous flow rate, pressure, temperature, methane content, main impurity content, and humidity;

[0045] The denoising and time-frequency feature extraction employs low-pass filtering combined with wavelet decomposition or short-time Fourier transform to simultaneously obtain the steady-state baseline and high-frequency disturbance components, and outputs peak values, energy spectrum, and time delay features as components of the feature sequence.

[0046] Establish a precise and traceable original description of the time-varying physical properties and composition of the gas being processed, providing a basic input for subsequent time-frequency analysis, magnetization parameter calculation and catalytic strategy;

[0047] The raw sensor data is transformed into a more discriminative feature representation suitable for control / optimization, while suppressing measurement noise and short-term anomalies.

[0048] Based on the feature sequence, resonance matching and energy consumption constraint optimization are performed to determine the magnetization resonance parameters to be applied (including frequency, field strength, pulse width and action segment length), and a set of magnetization parameters is obtained.

[0049] The resonance matching and energy consumption constraint optimization are based on minimizing the objective function Φ, and the formula for minimizing the objective function is as follows:

[0050] Φ=α·Emag+βSres

[0051] Where Emag is the magnetization energy consumption estimate, Sres is the statistical measure of the residual cluster size after magnetization, and α and β are weighting coefficients;

[0052] By solving the set of magnetization parameters under constraints (such as magnetic field strength not exceeding a safety threshold and frequency within the allowable bandwidth of the device), the optimal set of magnetization parameters that satisfies both efficiency and safety can be obtained.

[0053] A time-varying magnetic field is applied to the gas passing through the fluid section according to the magnetization parameter set to change the molecular aggregation state and achieve preliminary deagglomeration, thereby obtaining the gas property vector after magnetization.

[0054] A set of parameters is calculated based on the instantaneous characteristics of the gas to achieve the best activation effect of the magnetization treatment under energy consumption and safety constraints. The magnetization parameter set includes at least: operating frequency, magnetic field strength or field amplitude, pulse width / duty cycle, and the actual effective length of the action segment.

[0055] The physical property vector of the magnetized gas is verified online and the catalytic activation strategy is calculated to quantify the degree of activation and determine the catalytic activation operation sequence (including catalyst action mode, contact time and pulse parameters) to obtain the catalytic operation sequence.

[0056] The online effect verification uses instantaneous mass spectrometry or equivalent pyrolysis and acoustic response measurements to quantify the physical property vector of the magnetized gas and obtain the catalytic operation sequence;

[0057] The catalytic operation sequence includes catalyst type, addition time, contact time, and pulse regeneration cycle to be quantified.

[0058] A time-varying magnetic field is applied according to parameters in a designated fluid segment through which the gas flows to achieve changes in molecular aggregation at the physical or physicochemical level, thereby obtaining a description of the physical properties of the magnetized gas. The vector contains quantifiable activation indicators, including: the estimated average cluster diameter, the variance of the cluster size distribution, changes in surface energy states or specific surface area, and real-time execution feedback related to the applied magnetic field (actual field strength, coil current / temperature, and application time marker).

[0059] The magnetized gas property vector is catalytically activated according to the catalytic operation sequence, and the optimal air-fuel ratio curve is calculated based on the characteristics of the activated gas to improve mixing uniformity and enhance theoretical combustion integrity, thereby obtaining the actual mixing state.

[0060] The catalytic activation employs rare earth alloys, nano-oxides, or supported nano-catalysts. The specific surface area and active site concentration are increased by adjusting the catalytic contact time and flow rate. Based on the characteristics of the activated fuel gas, a mathematical programming method that balances mass conservation and minimizes incomplete combustion index is used to solve for the optimal air-fuel ratio curve, thereby obtaining a mixing strategy with the maximum theoretical heat release rate and meeting emission limits.

[0061] Based on the actual effects of magnetization on the gas properties, assess whether and how catalytic activation is needed to further enhance gas activity. Online effect verification involves near real-time quantitative determination to determine whether the physical / chemical changes generated by magnetization have reached an acceptable activation threshold. If not, calculate the catalytic activation sequence, specifying the catalyst type or characterization (e.g., rare earth oxides, supported nano-oxides), mode of action (contact bed, surface coating, or particle addition), necessary contact time, and parameters for catalyst regeneration or pulse activation (regeneration frequency, heating pulse amplitude / duration, pulse interval).

[0062] Ignition and combustion are performed on the actual mixture state, and combustion characterization and performance evaluation are carried out to calculate the combustion quality score and generate a control correction vector to feed back and fine-tune the magnetization parameters, catalytic operation and air-fuel ratio to form a closed loop and obtain the control correction vector.

[0063] The combustion characterization employs optical radiation spectrum, infrared temperature field, and acoustic spectrum measurements to obtain a combustion measurement matrix. Based on the measurement matrix, combustion efficiency η, instantaneous CO / HC / NOx emission indicators, and heat release rate are calculated, and a combustion quality score is obtained by weighted summation.

[0064] The control correction vector is obtained by inversely calculating the deviation of the combustion quality score from the preset target using the loss function.

[0065] Furthermore, the process involves two steps: first, performing catalytic activation (physical / chemical processing of the fuel gas according to the instructions of the catalytic operation sequence); and second, calculating and implementing the optimal air-fuel ratio based on the characteristics of the activated gas to obtain an ideal mixing state. Activation will change the specific surface area, active sites, or molecular characterization of the fuel gas. The mixing calculation utilizes these characteristics to adjust the air supply, nozzle / valve parameters, mixing time window, and turbulence generation strategy, thereby obtaining an actual mixing state vector that includes the local equivalent air-fuel ratio, mixing uniformity indices (such as CV or variance), and turbulence index.

[0066] Ignition / ignition is performed under actual mixed conditions, and a combustion measurement matrix is ​​obtained using multi-source measurements of flame and exhaust gas (optical radiation spectrum, infrared temperature field, acoustic spectrum, and online gas analyzer). Based on the combustion measurement matrix, key performance indicators are calculated: instantaneous combustion efficiency, instantaneous incomplete combustion products (CO, HC) and NOx levels, heat release rate, etc. These indicators are then synthesized into a combustion quality score according to a pre-set normalization and weighting scheme.

[0067] Then the loss function is defined.

[0068]

[0069] L is the loss function (scalar), consisting of two parts: the first term penalizes the deviation between the combustion quality score K and the target K* (performance error); the second term is a quadratic regularization of the control action amplitude (to prevent excessive control jitter or overshoot). The goal is to minimize L by changing the control increment Δu, thereby simultaneously achieving performance targets and control smoothness / safety.

[0070] Under the premise of ensuring safety constraints, the control correction vector is obtained by inverse solving using sensitivity (Jacobi) or model prediction methods. This vector includes fine-tuning of magnetization parameters, adjustment suggestions for catalytic operation sequence, and correction amount for air-fuel ratio curve.

[0071] As shown above, by applying resonance matching based on characteristic sequences and time-varying magnetic fields, molecular cluster depolymerization or surface energy state changes are achieved at the molecular / cluster scale, fundamentally improving the surface activity of fuel and its contact efficiency with oxygen. Further combining this with catalytic activation increases the effective reaction surface area and active site density of the fuel, thereby improving the combustion reaction rate and combustion completeness, and reducing the generation of unburned hydrocarbons. Due to the improved fuel activity and combustion completeness, the chemical energy input required per unit of heat / electricity output decreases, improving system thermal efficiency / unit efficiency. Closed-loop control, based on real-time combustion quality scores, allows for fine-tuning to continuously approach the optimal operating point, achieving steady-state energy efficiency improvement and dynamic energy saving during long-term operation.

[0072] Example 2:

[0073] 2.5MW heat output gas-fired boiler:

[0074] While maintaining the same heat output (2.5MW), a natural gas magnetization + catalytic activation + combustion optimization control method is adopted to reduce gas consumption, lower CO / HC emissions, and improve combustion integrity and boiler thermal efficiency.

[0075] Gas mass flow meter: turbine or mass flow meter, range 0–500 m 3 / h, with an accuracy of ±0.5%FS.

[0076] Gas composition analysis: On-site portable gas chromatograph (GC) or online thermal conductivity / infrared sensor for methane content and major impurities (C2+ hydrocarbon ratio, H2S, etc.).

[0077] Pressure / Temperature: Resistance pressure transmitter and thermocouple (Type K), with accuracies of ±0.25% and ±1℃ respectively.

[0078] Magnetization section: A fluid pipe section with a controllable electromagnetic coil that can generate an electric field strength of 0–150 mT (millitalas), with a variable frequency of 0.1–50 kHz and a controllable pulse width (0.1–100 ms).

[0079] Catalytic bed (optional contact section): A flow-through catalyst section filled with rare earth oxides / supported nano-oxides (such as CeO2 / Al2O3 support) can achieve a contact time of 0.05–1s (adjusted according to flow rate).

[0080] Combustion measurement: Flue gas CO / NOx online analyzer (non-dispersive infrared + chemiluminescence / electrochemical), infrared thermometer / radiation detector, flame spectrometer.

[0081] Controller / Record: Data logger, sampling frequency 1Hz (key variable), archiving period 1min.

[0082] Implementation process:

[0083] Step S1, collect raw gas characteristic data:

[0084] Flow rate qraw(t)(m 3 / h), pressure p(t)(kPa), temperature T(t)(°C), methane volume fraction CH4% (volume%), C2+ hydrocarbon ratio and humidity.

[0085] Sampling methods: mass flow meter (1Hz), online GC or infrared analysis (1min averaging). The original gas characterization vector A = [qraw,p,T,CH4%,C2+,RH] was obtained.

[0086] Denoising and time-frequency feature extraction are performed on the original gas characteristic data to obtain the feature sequence:

[0087] Noise reduction: First, low-pass filter (e.g., first-order filter, time constant τ = 5s) is applied to the flow rate and pressure, and then wavelet or short-time Fourier transform (STFT) is used to separate the low-frequency baseline and high-frequency pulsation.

[0088] The output feature sequence includes: smooth flow rate qsmooth(t), the peak positions and amplitudes of the first three terms of the spectral energy spectrum E(f), and instantaneous perturbation RMS.

[0089] Step S2: Based on the feature sequence, perform resonance matching and energy consumption constraint optimization to solve the magnetization parameter set = {fopt, Bfield, pulsewidth, Lact}.

[0090] The optimization objective can be written as: minimizing Φ=α·Emag+β·Sres, where Emag≈ estimated magnetic field energy consumption (W), and Sres≈ mean square size of the residual cluster after magnetization (experimental / model estimate).

[0091] Constraints: Bfield ≤ 150mT, fopt ∈ [0.1, 50] kHz, pulsewidth ∈ [0.1, 100] ms.

[0092] Output values: fopt = 12kHz, Bfield = 60mT, pulsewidth = 5ms, action length Lact = 0.8m.

[0093] Step S3: Apply a time-varying magnetic field to the gas according to the magnetization parameter set to obtain the gas property vector after magnetization: The gas property vector after magnetization describes the cluster distribution (such as the average cluster diameter μd, standard deviation σd) and the surface energy state change index Ssurf.

[0094] Quantification methods: After magnetization, rapid online mass spectrometry / equivalent pyrolysis (or acoustic response) was used for verification to obtain the numerical values ​​of the gas property vectors after magnetization. Typical characterization: μd decreased from 5.0 nm to 3.6 nm, and Ssurf increased by 8%.

[0095] Step S4: Online effect verification and calculation of the catalytic activation sequence:

[0096] If the gas physical property vector index after magnetization shows insufficient activation (μd>threshold), then catalytic operations (e.g., increasing contact time, using pulsed heating / electric field activation, or adding active nanocatalysts) are calculated to form a catalytic operation sequence.

[0097] The catalytic operation sequence specifies the catalyst type (e.g., 3% CeO2 / Al2O3 loading), dosing strategy (periodic pulse regeneration: once every 48 hours, regeneration pulse of 10 seconds, temperature increase of 30°C), and contact time tcontact = 0.12 seconds.

[0098] Step S5: Perform catalytic activation according to the catalytic operation sequence and calculate the optimal air-fuel ratio curve to obtain the actual mixing state (including the local equivalent oxygen concentration λlocal and the turbulence index Tu).

[0099] The constraints used to calculate the optimal air-fuel ratio curve are: satisfying the stoichiometric ratio while also considering excess air to control NOx. A quadratic programming approach with mass conservation and minimizing incomplete combustion indices is employed for the solution.

[0100] The actual mixing state was obtained as follows: λlocal=1.05±0.02, and the turbulence index Tu≈8% (6% before improvement).

[0101] Step S6: After ignition and combustion, measure the combustion measurement matrix, calculate the combustion quality score, and then generate a control correction vector to provide feedback for fine-tuning the magnetization parameter set, actual mixture state, and optimal air-fuel ratio curve.

[0102] The combustion measurement matrix includes: smoke temperature (K), CO (ppm), NOx (ppm), radiation intensity, and instantaneous heat release rate HRR (t).

[0103] The combustion quality score K is calculated by the weighting function K = w1·η + w2·(1-COnorm) + w3·(1-NOxnorm). The control correction L is calculated by simple proportional control or model predictive control (MPC) based on the difference between K and the target.

[0104] Test conditions and benchmarks:

[0105] Rated heat load: 2.5MW continuous operation (maintain ±1%).

[0106] Natural gas grade: primarily methane, CH4 volume fraction 94.5% (based on baseline). LHV is 35.8 MJ / m³. 3 (Commonly used empirical values, calculation benchmarks).

[0107] Baseline operation (without magnetization activation / catalytic activation): Record the average value of 3 hours of stable operation as a reference. Then enable the control method of this invention, stabilize for 3 hours, and take the average value.

[0108] Recording frequency: Flow rate and temperature 1Hz, emission averaged every 1 minute.

[0109] Benchmark value:

[0110] The baseline boiler thermal efficiency ηbase = 88.0% (0.880)

[0111] Baseline gas consumption: flowbasem 3 / h=285.6780091417m 3 / h (The calculation process is detailed below)

[0112] Baseline emissions: CObase = 120 ppm (by volume), NOxbase = 150 ppm.

[0113] Objective: To identify gas consumption and calculate energy savings.

[0114] The rated heat output Pout = 2.5MW. 1MW = 1MJ / s → Pout = 2.5MJ / s.

[0115] The baseline thermal efficiency ηbase = 0.880 (88.0%).

[0116] LHVNG=35.8MJ / m 3 .

[0117] Calculate the baseline gas energy input (MJ / s):

[0118] Input energy (baseline) = Pout ÷ ηbase

[0119] The baseline input energy Einbase = 2.8409090909 MJ / s.

[0120] Convert to baseline volumetric flow rate (m³) 3 / s→m 3 / h):

[0121] Volumetric flow rate (m³) 3 ( / s)=Einbase÷LHVNG=2.8409090909÷35.8=0.0793550025m3 / s, convert it to m 3 / h: 0.0793550025 × 3600 = 285.6780091417m 3 / h.

[0122] Therefore, the baseline gas consumption flowbase = 285.6780091417 m³ 3 / h.

[0123] After implementation, this method will increase the boiler thermal efficiency to ηnew = 0.91 (91.0%).

[0124] Calculate the new input energy and the new gas flow rate:

[0125] Einnew=Pout÷ηnew=2.5÷0.91≈2.7472527473MJ / s.

[0126] Corresponding volumetric flow rate m 3 / s=2.7472527473÷35.8≈0.076739m 3 / s.

[0127] Conversion m 3 / h: 0.0767391263 × 3600 = 276.2600527964m 3 / h (approximately).

[0128] Gas savings and energy efficiency:

[0129] Absolute savings = flowbase - flownew = 285.6780091417 - 276.2600527964

[0130] =9.4179563453m 3 / h.

[0131] Relative savings rate (%) = (Absolute savings ÷ flowbase) × 100

[0132] Place value arithmetic: 9.4179563453 ÷ 285.6780091417 = 3.2967032967%.

[0133] Conclusion: Under the conditions implemented, the boiler thermal efficiency increased from 88.0% to 91.0%, corresponding to a reduction in gas consumption of approximately 9.418 m³. 3 / h, resulting in a relative saving of approximately 3.30%.

[0134] Emissions and Combustion Quality Testing

[0135] Baseline CObase = 120ppm → After implementation COnew = 84ppm (a reduction of 36.0%, approximately 30%).

[0136] The baseline NOxbase was 150 ppm, and after implementation, NOxnew was 127.5 ppm (a reduction of 22.5 ppm, or about 15%).

[0137] Due to more complete combustion, CO decreased significantly; NOx is affected by combustion temperature and excess air, and usually changes by a small decrease or slight increase. In this embodiment, NOx was reduced by 15% through air-fuel ratio and turbulence optimization.

[0138] CO2 emissions: For the same heat output, the total CO2 emissions are directly proportional to the gas consumption. Therefore, the relative decrease in total CO2 emissions is approximately consistent with the gas saving rate (approximately 3.30%).

[0139] As shown above, more complete combustion tends to reduce the generation of incomplete combustion products such as CO and unburned hydrocarbons (HC). By optimizing the air-fuel ratio and mixing uniformity, the NOx generation pathway can be controlled while meeting combustion temperature requirements, thereby achieving synergistic emission reduction of pollutants while ensuring thermal efficiency. Time-frequency feature extraction and online combustion characterization together enable the system to accurately identify and suppress unstable components in flow or combustion (such as periodic pulsations and flashback signs). Real-time fine-tuning of magnetization parameters, catalytic pulses, and air-fuel ratio can reduce flame oscillation and combustion noise, improving the safety margin of system operation and maintenance.

[0140] Example 3:

[0141] A control system for a natural gas magnetization activation energy-saving device, characterized in that it includes:

[0142] The data acquisition module is used to collect raw gas characteristic data to characterize the instantaneous physical properties and composition of the gas, and obtain the raw gas characterization vector; the raw gas characterization vector is denoised and time-frequency feature extracted to separate steady-state components and instantaneous disturbances and construct a smoothed feature sequence to obtain the feature sequence;

[0143] The resonance matching and energy consumption optimization module is used to perform resonance matching and energy consumption constraint optimization based on the feature sequence to determine the magnetization resonance parameters to be applied (including frequency, field strength, pulse width and action segment length) and obtain the magnetization parameter set.

[0144] The magnetization application module is used to apply a time-varying magnetic field to the gas passing through the fluid section according to the magnetization parameter set in order to change the molecular aggregation state and achieve preliminary deagglomeration, and obtain the gas property vector after magnetization.

[0145] The online effect verification and catalytic strategy module is used to perform online effect verification and catalytic activation strategy calculation on the magnetized gas property vector, to quantify the degree of activation and determine the catalytic activation operation sequence (including catalyst action mode, contact time and pulse parameters), and to obtain the catalytic operation sequence.

[0146] The catalytic activation and mixing optimization module is used to perform catalytic activation on the magnetized gas property vector according to the catalytic operation sequence and calculate the optimal air-fuel ratio curve based on the characteristics of the activated gas, so as to improve the mixing uniformity and improve the theoretical combustion integrity, and obtain the actual mixing state.

[0147] The ignition and combustion monitoring and performance evaluation module is used to ignite and ignite the actual mixture state and perform combustion characterization and performance evaluation. It is used to calculate the combustion quality score and generate a control correction vector to provide feedback for fine-tuning the magnetization parameters, catalytic operation and air-fuel ratio to form a closed loop and obtain the control correction vector.

[0148] The beneficial effects of the same control method embodiment for a natural gas magnetization activation energy saver will not be elaborated here.

[0149] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A control method for a natural gas magnetization activation energy-saving device, characterized in that, Includes the following steps: Raw gas characteristic data are collected to characterize the instantaneous physical properties and composition of the gas, resulting in a raw gas characterization vector. The raw gas characterization vector is then denoised and subjected to time-frequency feature extraction to separate steady-state components from instantaneous disturbances and to construct a smoothed feature sequence, resulting in a feature sequence. Based on the feature sequence, resonance matching and energy consumption constraint optimization are performed to determine the magnetization resonance parameters to be applied, thereby obtaining a set of magnetization parameters; A time-varying magnetic field is applied to the gas passing through the fluid section according to the magnetization parameter set to change the molecular aggregation state and achieve preliminary deagglomeration, thereby obtaining the gas property vector after magnetization; Online effect verification and catalytic activation strategy calculation are performed on the magnetized gas property vector to quantify the degree of activation and determine the catalytic activation operation sequence, thus obtaining the catalytic operation sequence. The magnetized gas property vector is catalytically activated according to the catalytic operation sequence, and the optimal air-fuel ratio curve is calculated based on the characteristics of the activated gas to improve mixing uniformity and enhance theoretical combustion integrity, thereby obtaining the actual mixing state. Ignition and combustion are performed on the actual mixture state, and combustion characterization and performance evaluation are carried out to calculate the combustion quality score and generate a control correction vector to feed back and fine-tune the magnetization parameters, catalytic operation and air-fuel ratio to form a closed loop, thus obtaining the control correction vector.

2. The control method for a natural gas magnetization activation energy-saving device according to claim 1, characterized in that, The original gas characteristic data includes, but is not limited to: instantaneous flow rate, pressure, temperature, methane content, main impurity content, and humidity; The denoising and time-frequency feature extraction employs low-pass filtering combined with wavelet decomposition or short-time Fourier transform to simultaneously obtain the steady-state baseline and high-frequency disturbance components, and outputs peak values, energy spectrum, and time delay features as components of the feature sequence.

3. The control method for a natural gas magnetization activation energy-saving device according to claim 1, characterized in that, The resonance matching and energy consumption constraint optimization are based on minimizing the objective function Φ, and the formula for minimizing the objective function is as follows: Φ=α·Emag+βSres Where Emag is the magnetization energy consumption estimate, Sres is the statistical measure of the residual cluster size after magnetization, and α and β are weighting coefficients; By solving the set of magnetization parameters under constraints, the optimal set of magnetization parameters that satisfies both efficiency and safety can be obtained.

4. The control method for a natural gas magnetization activation energy-saving device according to claim 1, characterized in that, The step of applying a time-varying magnetic field to the gas passing through the fluid section according to the magnetization parameter set to change the molecular aggregation state and achieve preliminary deagglomeration, and obtaining the gas property vector after magnetization, includes: A time-varying magnetic field is applied to the gas passing through the fluid section by using a flow velocity synchronization pulse or continuous modulation to change the molecular aggregation state and achieve preliminary deagglomeration, thereby obtaining the gas property vector after magnetization.

5. The control method for a natural gas magnetization activation energy-saving device according to claim 1, characterized in that, The online effect verification uses instantaneous mass spectrometry or equivalent pyrolysis and acoustic response measurements to quantify the physical property vector of the magnetized gas and obtain the catalytic operation sequence; The catalytic operation sequence includes catalyst type, addition time, contact time, and pulse regeneration cycle to be quantified.

6. The control method for a natural gas magnetization activation energy-saving device according to claim 1, characterized in that, The catalytic activation employs rare earth alloys, nano-oxides, or supported nano-catalysts. The specific surface area and active site concentration are increased by adjusting the catalytic contact time and flow rate. Based on the characteristics of the activated fuel gas, a mathematical programming method that balances mass conservation and minimizes incomplete combustion index is used to solve for the optimal air-fuel ratio curve, thereby obtaining a mixing strategy with the maximum theoretical heat release rate and meeting emission limits.

7. The control method for a natural gas magnetization activation energy-saving device according to claim 1, characterized in that, The combustion characterization employs optical radiation spectrum, infrared temperature field, and acoustic spectrum measurements to obtain a combustion measurement matrix. Based on the measurement matrix, combustion efficiency η, instantaneous CO / HC / NOx emission indicators, and heat release rate are calculated, and a combustion quality score is obtained by weighted summation. The control correction vector is obtained by inversely calculating the deviation of the combustion quality score from the preset target using the loss function.

8. A control system for a natural gas magnetization activation energy-saving device, characterized in that, include: The data acquisition module is used to collect raw gas characteristic data to characterize the instantaneous physical properties and composition of the gas and obtain the raw gas characterization vector. The original gas characterization vector is denoised and time-frequency features are extracted to separate steady-state components from instantaneous disturbances and to construct a smoothed feature sequence, thus obtaining the feature sequence. The resonance matching and energy consumption optimization module is used to perform resonance matching and energy consumption constraint optimization based on the feature sequence to determine the magnetization resonance parameters to be applied and obtain the magnetization parameter set. The magnetization application module is used to apply a time-varying magnetic field to the gas passing through the fluid section according to the magnetization parameter set in order to change the molecular aggregation state and achieve preliminary deagglomeration, and obtain the gas property vector after magnetization. The online effect verification and catalytic strategy module is used to perform online effect verification and catalytic activation strategy calculation on the physical property vector of the magnetized gas, and to quantify the degree of activation and determine the catalytic activation operation sequence to obtain the catalytic operation sequence. The catalytic activation and mixing optimization module is used to perform catalytic activation on the magnetized gas property vector according to the catalytic operation sequence and calculate the optimal air-fuel ratio curve based on the characteristics of the activated gas, so as to improve the mixing uniformity and improve the theoretical combustion integrity, and obtain the actual mixing state. The ignition and combustion monitoring and performance evaluation module is used to ignite and ignite the actual mixture state and perform combustion characterization and performance evaluation. It is used to calculate the combustion quality score and generate a control correction vector to provide feedback for fine-tuning the magnetization parameters, catalytic operation and air-fuel ratio to form a closed loop and obtain the control correction vector.

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