A method and system for optimizing parameters of plasma catalytic dry reforming of methane

Abstract

The application discloses a kind of plasma catalytic methane dry reforming parameter optimization method and system;The method comprises: one, working condition parameter screening;Two, prior constraint experimental design;Three, multiple output experimental collection;Four, parameter identification and calibration;Five, optimal working condition recommendation: the present application is obtained by constructing based on literature big data screening multiple working condition parameters The existing value is uniformly analyzed by normalization processing and outlier rejection, obtains each working condition parameter prior interval, realizes the scientific design and effective coverage of experimental condition, avoids the problem that parameter selection is blind in traditional trial-and-error experiment, experimental cost is high and data is large Discrete nature.Meanwhile, the present application constructs and introduces parameter coverage, experimental cost and discharge stability and other multiple constraints in prior interval Experimental condition design method, and experimental sequence construction is carried out by combining control variable method, realizes the clear identification of single parameter influence law under fewer experimental times.

Description

Technical Field

[0001] This invention belongs to the field of chemical process simulation and optimization technology; specifically, it relates to a method and system for optimizing parameters of plasma-catalyzed methane dry reforming. Background Technology

[0002] Dry methane reforming (DRM) technology, as one of the core pathways for achieving carbon resource recycling and low-carbon chemical development, can efficiently convert methane (CH4) and carbon dioxide (CO2), two major greenhouse gases, into high-value-added syngas such as carbon monoxide (CO) and hydrogen (H2). It holds irreplaceable strategic significance in mitigating the greenhouse effect and reducing dependence on fossil fuels, and its industrial application has always been a research hotspot in the energy and chemical industry. Although DRM technology possesses significant environmental and economic value, its large-scale implementation has long been limited by two key bottlenecks: first, catalyst deactivation is a prominent problem, with widespread carbon deposition and sintering of active components during the reaction process, leading to rapid decline in catalytic efficiency and high operating costs; second, the reaction system has high energy consumption, and the stringent reaction conditions further restrict the technology's economic viability and practicality.

[0003] In the field of catalyst and reaction system research and development, the traditional approach relies heavily on trial and error, requiring significant investment of manpower, resources, and time. Furthermore, the experimental process is often unpredictable, making it difficult to reveal the structure-activity relationship and reaction mechanism at the microscopic scale, severely hindering the development of high-performance catalytic systems. In recent years, with the development of computational simulation technology, a research paradigm combining numerical simulation and experimentation has gradually emerged. Theoretical calculation methods based on plasma fluid models and other methods have provided a new perspective for analyzing DRM reaction mechanisms and predicting reaction results, becoming important tools for optimizing catalyst design and reactor structure. However, existing theoretical calculation methods still have significant shortcomings: on the one hand, the calculation process relies on idealized reaction assumptions, which differ significantly from the complex actual reaction environment in industrial scenarios; on the other hand, the selection of core parameters such as discharge frequency, gas-to-liquid ratio, and reduced electric field lacks unified standards and practical references, relying heavily on researchers' experience. This leads to large deviations between model predictions and experimental data, severely affecting the accuracy and universality of methane dry reforming kinetic models and failing to provide reliable theoretical support for industrial applications.

[0004] Currently, there is no systematic model parameter optimization system, failing to effectively couple theoretical calculations with experimental data and making it difficult to continuously iterate and optimize model parameters through data feedback. Furthermore, the catalytic behavior of catalysts is not fully incorporated into the simulation scope of kinetic models, further exacerbating the disconnect between models and practical engineering applications. Therefore, developing a methane dry reforming model parameter optimization method that balances efficiency, accuracy, and practicality to address the inefficiencies of traditional R&D models and the adaptability deficiencies of existing models has become an urgent need to promote the industrialization of DRM technology. Summary of the Invention

[0005] The purpose of this invention is to provide a big data and experiment-driven method and system for optimizing parameters of plasma-catalyzed dry reforming of methane. By constructing a closed-loop optimization system that deeply couples theory and experiment, the core parameters of the model are accurately calibrated, improving the accuracy, universality, and engineering adaptability of the methane dry reforming kinetic model. This enables efficient screening and optimization of the catalytic system, providing reliable technical support and intelligent design path for the industrial application of DRM technology.

[0006] In a first aspect, the present invention provides a method for optimizing parameters of plasma-catalyzed dry reforming of methane, comprising the following steps:

[0007] Step 1: Screening of Operating Parameters: Multiple operating parameters related to performance indicators in plasma-catalyzed dry reforming of methane are screened. These parameters include discharge frequency, gas ratio, and reduced electric field E / N; the gas ratio is the molar ratio of methane, carbon dioxide, and argon in the reaction gas. Values ​​of each operating parameter and their corresponding output performance indicators are extracted from existing plasma-catalyzed dry reforming data (specifically, publicly available literature). The extracted results are then normalized, missing values ​​are processed, and outliers are removed to obtain the prior intervals for the operating parameters.

[0008] Step 2, Experimental Design with Prior Constraints: Under the prior interval constraints, a candidate set of operating conditions covering all operating parameters is generated; each operating condition sample in the candidate set corresponds to a combination of operating parameters. An experimental operating condition sequence is then selected from the candidate set according to preset criteria; these criteria include at least parameter coverage, experimental cost constraints, and reaction safety / discharge stability constraints.

[0009] Step 3: Multi-output experimental data acquisition: Conduct plasma-catalyzed methane dry reforming experiments according to the experimental operating condition sequence, and collect output performance indicators to form an experimental dataset. The output performance indicators include reactant conversion rate and the yield and selectivity of the target oxygen-containing product.

[0010] Step 4, Parameter Identification and Calibration: Simulate each experimental condition using the methane dry reforming kinetic model, establish a weighted error function that includes each output performance index, and use an optimization algorithm to iteratively update the parameters of the methane dry reforming kinetic model until the convergence condition is met; the criteria for judging the convergence condition include at least the average error threshold or the error reduction threshold.

[0011] Step 5: Optimal operating condition recommendation: Calculate the predicted output performance values ​​corresponding to different combinations of operating parameters within the prior interval of each operating parameter using the methane dry reforming kinetic model; screen the operating parameter combinations that satisfy multi-objective constraints as the optimized plasma-catalyzed methane dry reforming operating parameter combinations.

[0012] Preferably, the outlier removal includes removal based on the absolute deviation of the median or the range of quantiles, and the unit of electric field-related parameters (including voltage, gap, and gas number density) in different data is converted to Td (Townsends) using a unified conversion rule.

[0013] Preferably, the prior range of the discharge frequency in the candidate operating condition set is 2kHz to 10kHz; the discharge frequency of the experimental operating condition is selected from 2kHz, 4kHz, 6kHz, 8kHz, and 10kHz.

[0014] Preferably, the a priori interval of the reduced electric field in the candidate operating condition set is in the range of 115Td to 180Td; the reduced electric field of the experimental operating condition is selected from 115Td, 125Td, 150Td, 160Td, and 180Td.

[0015] Preferably, the a priori interval of the gas ratio in the candidate operating condition set is (1~5):(0.2):(1~5), and the gas ratio of the experimental operating condition is selected from 1:2:7, 3:2:5, and 5:2:3.

[0016] As a preferred option, the experimental operating condition sequence for controlling variables in step two includes: multiple experimental operating conditions with varying discharge frequency when the reduced electric field is 150 Td and the gas ratio is 1:2:7; multiple experimental operating conditions with varying gas ratio when the reduced electric field is 150 Td and the discharge frequency is 8 kHz; and multiple experimental operating conditions with varying reduced electric field when the discharge frequency is 8 kHz and the gas ratio is 3:2:5.

[0017] As a preferred embodiment, the experimental discharge voltages corresponding to the reduced electric fields of 115Td, 125Td, 150Td, 160Td, and 180Td in step two are 90, 100, 110, 120, and 130 V, respectively.

[0018] Preferably, the convergence condition includes that the average relative error between the predicted output performance index of the methane dry reforming kinetic model and the experimentally measured value is less than or equal to an average error threshold. The error threshold is set at 4% to 6%, preferably 5%.

[0019] As a preferred option, after obtaining the combination of parameters for plasma-catalyzed dry reforming of methane, the dry reforming of methane was simulated using a methane dry reforming kinetic model to screen out the copper-based catalyst with the best catalytic performance.

[0020] Secondly, the present invention provides a plasma-catalyzed dry reforming parameter optimization system for performing the aforementioned plasma-catalyzed dry reforming parameter optimization method for methane; the plasma-catalyzed dry reforming parameter optimization system includes a parameter extraction module, an experimental condition generation module, an experimental module, a product data acquisition module, a simulation module, a parameter identification module, and an operating condition recommendation module.

[0021] The parameter extraction module is used to extract the values ​​of various operating parameters from publicly available data on plasma-catalyzed dry reforming of methane and generate prior intervals for each operating parameter.

[0022] The experimental condition generation module is used to generate multiple different experimental conditions within the prior interval of each condition parameter.

[0023] The experimental module is used to conduct experiments on plasma-catalyzed dry reforming of methane according to various experimental conditions.

[0024] The product data acquisition module is used to collect the output performance indicators of plasma-catalyzed methane dry reforming.

[0025] The simulation module is used to predict the output performance indicators corresponding to different combinations of operating parameters using a methane dry reforming kinetic model.

[0026] The parameter identification module is used to iteratively optimize the parameters in the methane dry reforming kinetic model using a dataset obtained from experiments.

[0027] The operating condition recommendation module is used to output an optimized combination of plasma catalytic methane dry reforming operating condition parameters based on the output performance index prediction results of the simulation module for multiple different operating condition parameter combinations and in combination with multi-objective constraints.

[0028] Preferably, the experimental module includes a discharge driving unit, and a gas supply unit, a mixing and transport unit, and a reaction unit connected in sequence.

[0029] The gas supply unit includes cylinders for methane, carbon dioxide, and argon, as well as a flow controller for each cylinder, used to output mixed gas according to a set gas ratio.

[0030] The mixing and transport unit includes a mixing tank and a constant-temperature heated transport pipeline, used to homogenize the mixed gas and transport it to the reaction unit; the mixing tank is configured to receive and mix methane, carbon dioxide and argon gas output from the gas source supply unit.

[0031] The reaction unit includes a dielectric barrier discharge reactor for carrying out methane dry reforming under plasma conditions.

[0032] The discharge drive unit includes a pulse power supply, a high-voltage probe, and an oscilloscope. The pulse power supply applies a drive current with a preset discharge frequency and voltage to the dielectric barrier discharge reactor. The high-voltage probe and oscilloscope are used to acquire and display the voltage, current, and discharge frequency in real time.

[0033] Preferably, the product data acquisition module includes a gas chromatograph and a gas chromatography-mass spectrometry system, used to detect the content of methane, carbon dioxide, carbon monoxide, hydrogen, and target oxygen-containing products in the output system of the reaction unit.

[0034] Preferably, it also includes a data link synchronization processing unit that communicates with the discharge drive unit and the product data acquisition module respectively.

[0035] The data link synchronization processing unit performs the following tasks in the experiment:

[0036] a. Receive the discharge parameters from the discharge drive unit and the product content detection results from the product data acquisition module, and timestamp them.

[0037] b. Time alignment matching of discharge parameters and product content detection results.

[0038] c. Output a structured dataset for optimizing the parameters of the dynamic model.

[0039] Thirdly, the present invention provides a plasma-catalyzed dry methane reforming apparatus, comprising a mixed gas supply module and a plasma discharge reaction module connected in sequence. The mixed gas supply module is configured to supply a mixed gas of methane, carbon dioxide, and argon to the plasma discharge reaction module. The plasma discharge reaction module performs plasma-catalyzed dry methane reforming on the input mixed gas. The plasma-catalyzed dry methane reforming apparatus operates according to the combination of methane dry reforming operating parameters optimized by the plasma-catalyzed dry methane reforming parameter optimization method as described in claim 1.

[0040] Preferably, the catalyst is a Cu-based catalyst, and the support is one or more of CeO2, HZSM-5, γ-Al2O3 or TiO2.

[0041] The beneficial effects of this invention are:

[0042] 1. This invention constructs an existing set of operating parameters such as discharge frequency, gas ratio, and reduced electric field based on a large literature database. Through normalization and outlier removal, a unified analysis is performed to obtain prior intervals for each operating parameter. This achieves scientific design and effective coverage of experimental conditions, avoiding the problems of blind parameter selection, high experimental costs, and large data dispersion in traditional trial-and-error experiments. Furthermore, this invention constructs an experimental condition design method within the prior intervals, incorporating multiple constraints such as parameter coverage, experimental cost, and discharge stability. Combined with the controlled variable method for experimental sequence construction, this achieves clear identification of the influence of a single parameter with fewer experiments. While ensuring experimental safety and discharge stability, it significantly reduces experimental costs and improves data quality.

[0043] 2. This invention introduces an optimization mechanism that combines multi-output experimental data acquisition with kinetic model parameter identification, thereby controlling the average relative error between model prediction results and experimental results to within 5%. This significantly improves the prediction accuracy, stability, and applicability of the plasma-catalyzed methane dry reforming kinetic model under different operating conditions, providing reliable model support for process optimization.

[0044] 3. This invention, through a collaborative optimization process of "calculation screening - experimental verification - model calibration - multi-objective parameter recommendation", can quickly obtain the optimal values ​​of the combination of operating parameters, including discharge frequency, gas ratio, and reduced electric field. Applying this parameter combination to the operation of a specific device achieves efficient conversion of methane and carbon dioxide and simultaneous improvement of methanol yield, providing a replicable technical path for the engineering scale-up of methane dry reforming reaction and its low-carbon chemical application.

[0045] 4. By comparing and analyzing the catalytic performance of copper-based catalysts with different supports under optimal operating parameters, this invention clarifies the advantages of Cu / HZSM-5 in the plasma-catalyzed dry reforming of methane to methanol, providing a clear direction for subsequent catalyst structure design and performance regulation, and enhancing the scalability of this invention in the screening and application of catalytic materials. Attached Figure Description

[0046] Figure 1 This is a schematic flowchart of the plasma-catalyzed methane dry reforming parameter optimization method provided in Embodiment 1 of the present invention;

[0047] Figure 2 This is a schematic diagram of the methane dry reforming experimental platform used in Example 1 of the present invention.

[0048] Figure descriptions: 1. Gas cylinder; 2. Gas flow controller (MFC); 3. Pulse power supply; 4. Oscilloscope; 5. Mixing vessel; 6. Dielectric barrier discharge reactor; 7. Cold trap; 8. Gas chromatography-mass spectrometry system; 9. Gas chromatograph; 10. High-pressure probe. Detailed Implementation

[0049] The present invention will be further described below.

[0050] Example 1

[0051] like Figure 1 As shown, a method for optimizing parameters of plasma-catalyzed dry reforming of methane is presented. Taking the dielectric barrier discharge (DBD) plasma-catalyzed dry reforming system of methane as the object, the method systematically optimizes the reaction performance under multiple operating conditions. The overall process includes steps such as screening operating conditions, experimental design, experimental data acquisition, model parameter calibration, and optimization recommendations, as detailed below:

[0052] (I) Screening of operating parameters and construction of prior intervals

[0053] In this embodiment, the operating parameters that significantly affect the performance of plasma-catalyzed dry reforming of methane were first screened. Using AI big data technology, experimental data related to plasma dry reforming of methane were collected and integrated from the BMP and WOS databases by keyword search. A systematic review of publicly available literature in this field was conducted, focusing on three core parameters: discharge frequency, gas ratio, and reduced electric field. The gas ratio refers to the volume ratio of methane, carbon dioxide, and the inert gas argon in the reaction gases.

[0054] During the data processing, the operating parameters used in different documents were standardized:

[0055] On the one hand, the original parameters are normalized to eliminate dimensional differences between different data sources; on the other hand, data with missing or obvious anomalies are screened and corrected. In this embodiment, outlier identification preferably uses the median absolute deviation (MAD) or quantile interval method to remove data that deviates significantly from the main distribution interval, so as to avoid extreme values ​​interfering with subsequent analysis.

[0056] For the reduced electric field parameters, since different literature may describe them using different physical quantities such as discharge voltage, electrode gap, or gas number density, this embodiment adopts a unified conversion rule to convert the relevant parameters into Townsends (Td) as the standard characterization method for the reduced electric field. Through the above processing, the a priori intervals of each operating condition parameter are finally obtained, providing constraint boundaries for subsequent experimental design.

[0057] (II) Experimental Condition Design Introducing Prior Constraints

[0058] After obtaining the prior intervals for each operating condition parameter, this embodiment constructs a candidate operating condition set within these prior intervals. In the candidate operating condition set, each operating condition sample corresponds to a combination of discharge frequency, gas ratio, and reduced electric field.

[0059] To avoid a significant increase in experimental costs due to an excessive number of experiments, while ensuring effective coverage of the parameter space, this embodiment introduces multiple constraint criteria for screening the candidate operating condition set. These criteria include at least:

[0060] (1) Parameter coverage constraint, that is, to ensure that each parameter has a representative value in its prior interval.

[0061] (2) Experimental cost constraints, that is, reducing unnecessary number of experiments while ensuring the accuracy of model identification.

[0062] (3) Reaction safety and discharge stability constraints, i.e., excluding parameter combinations that may cause discharge instability or abnormal reactor operation.

[0063] In this embodiment, the experimental condition sequence was constructed using a controlled variable approach. For example, the discharge frequency was changed while maintaining a fixed reduced electric field and gas ratio; the gas ratio was changed sequentially while maintaining a fixed discharge frequency; and the reduced electric field was changed while maintaining a fixed discharge frequency and gas ratio. This method clearly identifies the impact of single parameter changes on reaction performance, providing high-quality experimental data for subsequent model calibration.

[0064] (III) Multi-output experimental data collection and dataset construction

[0065] Following the experimental sequence described above, experiments were conducted sequentially on the plasma-catalyzed methane dry reforming experimental platform. During the experiments, the reactor operated stably under preset discharge frequency, voltage, and gas ratio conditions. Reaction product data were collected after the system reached steady state.

[0066] In this embodiment, the output performance indicators include at least the conversion rates of methane and carbon dioxide, as well as the yields and selectivity of the target oxygen-containing products such as carbon monoxide and hydrogen. Each experimental condition corresponds to a complete set of input parameters and output performance indicators, ultimately forming a structured experimental dataset for subsequent model parameter identification and optimization.

[0067] In this embodiment, the methane dry reforming experimental platform is a detection and analysis system for methanol production via CH4 / CO2 reforming. The experimental gases CH4, CO2, and Ar are output from high-pressure gas cylinders and their flow rates are precisely controlled by a mass flow meter (MFC). Each component gas undergoes dynamic homogenization in a mixer. The thoroughly mixed reaction gases are then transported through a constant-temperature heated pipeline to the plasma reaction chamber of a dielectric barrier discharge structure. The input voltage and frequency are set via a high-voltage power supply, and the voltage, current, and frequency information are displayed in real time on an oscilloscope. The experiment uses a GC-7900 gas chromatography system (equipped with an FID / TCD detector) for online detection of greenhouse gases such as CH4 and CO2, CO / H2 syngas components, and CH3OH oxygen-containing compounds.

[0068] like Figure 2 As shown, the methane dry reforming experimental platform includes a gas supply unit, a mixing and transport unit 5, a reaction unit 6, and a discharge drive unit. The gas supply unit, mixing and transport unit 5, and reaction unit 6 are connected in sequence; the gas supply unit includes gas cylinders 1 for methane, carbon dioxide, and argon, and a flow controller 2 for each gas cylinder; the mixing and transport unit 5 includes a mixing tank and a constant-temperature heated transport pipeline; the reaction unit 6 includes a dielectric barrier discharge reactor for carrying out the methane dry reforming reaction under plasma conditions.

[0069] The gases from the three gas cylinders 1 are mixed in a mixing tank after passing through their respective flow controllers 2. The mixed gas from the mixing tank is then delivered to the dielectric barrier discharge reactor via a constant-temperature heated transport pipeline.

[0070] The discharge drive unit includes a pulse power supply 3, a high-voltage probe 10, and an oscilloscope 4. The pulse power supply applies a drive current with a preset discharge frequency and voltage to the dielectric barrier discharge reactor. The high-voltage probe 10 and the oscilloscope 4 are used to acquire and display the voltage, current, and discharge frequency in real time.

[0071] The methane dry reforming experimental platform is equipped with a corresponding product data acquisition module. This module includes a gas acquisition unit and a data analysis unit. The gas acquisition unit uses a cold trap 7 to preprocess the product system output from the reaction unit, achieving enrichment and purification of the gaseous target components. The data analysis unit includes a gas chromatography-mass spectrometry (GC-MS) instrument 8 and a gas chromatograph 9, used to detect the content of methane, carbon dioxide, carbon monoxide, hydrogen, and the target oxygen-containing product in the reaction unit output system.

[0072] In this embodiment, the experiment is divided into the following three stages:

[0073] Phase 1: Discharge Frequency Control Experiment

[0074] In this embodiment, a mass flow meter was used to precisely control the gas ratio CH4:CO2:Ar = 1:2:7. A fixed voltage of 110V was set through a high-voltage power supply, and frequencies of 2, 4, 6, 8, and 10 kHz were input as five control experiments. The experimental results detected by the GC-7900 gas chromatography system are shown in Table 1 below. Comparing the yields of H2, CO, and CH3OH, it can be concluded that the methanol yield is the highest at 8 kHz, and all three products show good performance. The optimal discharge frequency obtained from the experiment is 8 kHz.

[0075] Table 1. Yield at different discharge frequencies

[0076]

[0077] Phase Two: Gas Proportioning Control Experiment

[0078] In this embodiment, a fixed input voltage of 110 V and a fixed frequency of 8 kHz were set using a high-voltage power supply. The CH4:CO2:Ar ratio was precisely adjusted to 1:2:7, 3:2:5, and 5:2:3 using a mass flow meter as three control experiments. The experimental results detected by the GC-7900 gas chromatography system are shown in Table 2 below. Comparing the yields of H2, CO, and CH3OH, in experimental group 1:2:7, the reaction gas ratio was biased towards Ar. Although this could improve the methanol yield to some extent, the syngas yield was low, and the raw material utilization rate was insufficient, resulting in a low overall efficiency. In experimental group 5:2:3, the reaction gas ratio was biased towards CH4, and the syngas yield was improved, but the methanol yield decreased significantly, and the overall process efficiency was not optimal. When the reaction gas ratio was 3:2:5, both the methanol and syngas yields remained at a high level, which could take into account the yield requirements of both methanol and syngas and maximize the overall process efficiency. This ratio is the optimal reaction gas ratio suitable for the process of this invention.

[0079] Table 2 Yields of different gas ratios

[0080]

[0081] Phase 3: Reduced Electric Field Control Experiment

[0082] In a reaction system with a standard atmospheric pressure, an initial temperature of 400 K, a total reaction time of 0.15 s, and an electrode gap of d = 0.001 cm, input voltages of 90 V, 100 V, 110 V, 120 V, and 130 V were applied respectively. The current variation range under each voltage was recorded, and the maximum value was substituted into the calculation. The corresponding reduced electric field values ​​were obtained as 115 Td, 125 Td, 150 Td, 160 Td, and 180 Td respectively.

[0083] The calculation conditions are set as follows: pressure 1 atm, initial temperature 400 K, frequency 8 kHz, and the composition of the discharge gas mixture is 0.3 CH4 / 0.2 CO2 / 0.5 Ar. The electric field strength is calculated based on Equation 3-1 using the current supplied by the system.

[0084]

[0085] in: For current, For electron charge, For electron density, For electron mobility, For electric field strength, Let be the cross-sectional area of ​​the conductive channel. After obtaining the electric field strength, the reduced electric field is obtained using Equation 3-2.

[0086]

[0087] in: To reduce the electric field, For electric field strength, This represents the density of neutral particles.

[0088] In this embodiment, the CH4:CO2:Ar ratio was precisely controlled to 3:2:5 using a mass flow meter. A fixed frequency of 8 kHz was set using a high-voltage power supply, with voltages of 90, 100, 110, 120, and 130 V inputs, serving as five control experiments. The experimental results detected by the GC-7900 gas chromatography system are shown in Table 3 below. Comparing the yields of H2, CO, and CH3OH, it can be concluded that the methanol yield is highest at 130 V, while the syngas yield also remains at a high level. Therefore, the optimal discharge voltage (reduced electric field) determined by the experiment is 130 V (180 Td).

[0089] Table 3 Yields at different discharge voltages

[0090]

[0091] (iv) Identification and calibration of dynamic model parameters

[0092] After obtaining the experimental dataset, this embodiment uses a methane dry reforming kinetic model to perform numerical simulations of various experimental conditions. By comparing the differences between the model predictions and experimental results, a weighted error function containing multiple output performance indicators is constructed. The operating parameters of the methane dry reforming kinetic model are set to use a built-in square wave nanosecond pulse discharge mode.

[0093] After each iteration of the parameter identification process, the convergence condition is determined based on the change in the error function. In this embodiment, the preferred convergence condition is that the average relative error between the model prediction and the experimental measured values ​​is less than a preset threshold (0.5 in this embodiment), or the error decrease is less than a set threshold. When the convergence condition is met, the dynamic model parameters are considered to have been calibrated.

[0094] For the discharge frequency, the initial mole fractions of the components were set to 0.1, 0.2, and 0.7 (CH4, CO2, Ar), the reduced electric field was set to 150 Td, and the pulse discharge frequencies were set to 2, 4, 6, 8, and 10 kHz to simulate the reaction process. The validity of the model is verified as shown in Table 4 below. The calculated average error of the model is < ±5%, indicating that the model is reliable. Analysis of the simulation results shows that the optimal discharge frequency for the model operation is consistent with the experimental results, which is 8 kHz.

[0095] Table 4. Simulated CH4 and CO2 conversion rates at different discharge frequencies

[0096]

[0097] For the gas mixture ratio, the reduced electric field was set to 150 Td, the pulse discharge frequency was set to 8 kHz, and the initial mole fractions of components (CH4, CO2, Ar) were set to 0.1, 0.2, 0.7; 0.3, 0.2, 0.5; 0.5, 0.2, 0.3, respectively. The simulation reaction process was performed, and the validity of the model was verified as shown in Table 5 below. The calculated average error of the model was < ±5%, indicating that the model is reliable. Analysis of the simulation results shows that the optimal gas mixture ratio of the model is consistent with the experiment at 3:2:5 (CH4:CO2:Ar).

[0098] Table 5. Simulated CH4 and CO2 conversion rates in experiments with different gas ratios.

[0099]

[0100] For the reduced electric field, the initial mole fractions of the components were set to 0.3, 0.2, and 0.5 (CH4, CO2, Ar), the pulse discharge frequency was set to 8 kHz, and the reduced electric fields were set to 115, 125, 150, 160, and 180 Td, respectively. The reaction process was simulated, and the validity of the model is verified as shown in Table 6 below. The calculated average error of the model is < ±5%, indicating that the model is reliable. Analysis of the simulation results shows that the optimal reduced electric field for model operation is consistent with the experimental results, which is 180 Td (130 V).

[0101] Table 6. Simulated CH4 and CO2 conversion rates under different reduced electric fields

[0102]

[0103] (v) Recommendation of optimal operating parameters

[0104] After calibrating the kinetic model parameters, the calibrated model is used to predict the performance of different parameter combinations within the prior interval of each operating condition. The prediction results are then filtered by introducing multi-objective constraints, which include at least high reactant conversion rate, high selectivity of target product, and energy efficiency or discharge stability requirements.

[0105] Finally, the combination of operating parameters that satisfies the multi-objective constraints is output as the optimized combination of operating parameters for plasma-catalyzed methane dry reforming, thus achieving closed-loop update of parameter optimization.

[0106] (vi) Optimal catalyst selection

[0107] Under optimized plasma-catalyzed dry reforming parameters, kinetic models were used to simulate plasma-catalyzed dry reforming of methane with various copper-based catalysts on different supports. The CH4 and CO2 conversions and CH3OH yields of different copper-based catalysts were obtained, and the optimal copper-based catalyst was selected. In this embodiment, among the screened catalysts Cu / CeO2, Cu / HZSM-5, Cu / γ-Al2O3, and Cu / TiO2, the optimal copper-based catalyst was Cu / HZSM-5.

[0108] Example 2

[0109] A plasma-catalyzed dry reforming apparatus for methane includes a mixed gas supply module and a plasma discharge reaction module connected in sequence. The mixed gas supply module supplies a mixture of methane, carbon dioxide, and argon to the reaction module according to a set gas ratio; the plasma discharge reaction module performs a plasma-catalyzed dry reforming reaction of the mixed gas under an external discharge driving condition.

[0110] When the device is in operation, the discharge frequency, reduced electric field and gas ratio parameters are all set according to the optimal combination of operating parameters obtained by the parameter optimization method provided in Example 1, so as to achieve better reaction performance while ensuring discharge stability.

[0111] A catalyst was placed in the discharge region of the plasma discharge reaction module. The catalyst was prepared using four substances—CeO2, HZSM-5 molecular sieve, γ-Al2O3, and TiO2—as supports via a wet impregnation method, resulting in Cu-based catalysts with loadings of 5 wt%. Under reaction conditions of a discharge voltage of 130 V, a discharge frequency of 8 kHz, and a gas ratio of CH4:CO2:Ar = 3:2:5, the catalytic performance of these four copper-based catalysts in plasma-catalyzed CH4 / CO2 reforming to CH3OH was compared. The yields are shown in Table 7. The optimal catalyst for methanol production was Cu / HZSM-5. Further optimization and control of this catalyst can be directly implemented to improve both the conversion rates of CH4 and CO2 and the selectivity of CH3OH.

[0112] Table 7 Yields of different catalysts

[0113]

[0114] The above embodiments are merely preferred embodiments of the present invention and are not intended to limit it. It should be noted that those skilled in the art can still modify and improve the solutions proposed in the foregoing embodiments, and these modifications and improvements should also be considered within the scope of protection of the present invention, without affecting the effectiveness of the implementation of the present invention or the practicality of the patent.

Claims

1. A method for optimizing parameters of plasma-catalyzed dry reforming of methane, characterized in that: Includes the following steps: Step 1: Screen multiple operating parameters related to performance indicators in plasma-catalyzed dry reforming of methane; the operating parameters include discharge frequency, gas ratio and reduced electric field; the gas ratio is the molar ratio of methane, carbon dioxide and argon in the reaction gas; extract the values ​​of each operating parameter from the existing plasma-catalyzed dry reforming data and establish the prior interval of the operating parameters. Step 2: Under the prior interval constraints, generate a candidate working condition set and select an experimental working condition sequence from the candidate working condition set; Step 3: Conduct plasma-catalyzed dry reforming experiments of methane according to the experimental operating condition sequence, and collect output performance indicators to form an experimental dataset; the output performance indicators include reactant conversion rate and the yield and selectivity of the target oxygen-containing product; Step 4: Simulate each experimental condition using the methane dry reforming kinetic model, establish a weighted error function that includes each output performance index, and iteratively update the parameters of the methane dry reforming kinetic model until the convergence condition is met; the criteria for judging the convergence condition include at least the average error threshold or the error reduction threshold. Step 5: Calculate the predicted output performance values ​​corresponding to different combinations of operating parameters within the prior interval of each operating parameter using the methane dry reforming kinetic model; select the operating parameter combinations that satisfy the multi-objective constraints as the optimized plasma-catalyzed methane dry reforming operating parameter combinations.

2. The method for optimizing parameters of plasma-catalyzed dry reforming of methane according to claim 1, characterized in that: Outlier removal was performed on parameters extracted from existing plasma-catalyzed methane dry reforming data. Outlier removal included removal based on median absolute deviation or quantile range. The prior interval for discharge frequency in the candidate set of operating conditions was 2kHz to 10kHz. The prior interval for reduced electric field in the candidate set of operating conditions was 115Td to 180Td. The prior interval for gas ratio in the candidate set of operating conditions was (1 to 5):(0.2):(1 to 5). The criteria for selecting experimental operating condition sequences from the candidate set of operating conditions included at least parameter coverage, experimental cost constraints, and reaction safety / discharge stability constraints.

3. The method for optimizing parameters of plasma-catalyzed dry reforming of methane according to claim 1, characterized in that: The convergence condition includes that the average relative error between the predicted output performance index of the methane dry reforming kinetic model and the experimentally measured value is less than or equal to the average error threshold; the error threshold is 4% to 6%.

4. The method for optimizing parameters of plasma-catalyzed dry reforming of methane according to claim 1, characterized in that: After obtaining the combination of parameters for plasma-catalyzed dry reforming of methane, the dry reforming of methane was simulated using a methane dry reforming kinetic model with different copper-based catalysts, and the copper-based catalyst with the best catalytic performance was selected.

5. A plasma-catalyzed methane dry reforming parameter optimization system, characterized in that: This system is used to execute the plasma-catalyzed dry reforming parameter optimization method as described in claim 1; the plasma-catalyzed dry reforming parameter optimization system includes a parameter extraction module, an experimental condition generation module, an experimental module, a product data acquisition module, a simulation module, a parameter identification module, and an operating condition recommendation module. The parameter extraction module is used to extract the values ​​of various operating parameters from publicly available data on plasma-catalyzed dry reforming of methane and generate the prior intervals of each operating parameter. The experimental condition generation module is used to generate multiple different experimental conditions within the prior interval of each condition parameter. The experimental module is used to conduct experiments on plasma-catalyzed dry reforming of methane according to various experimental conditions; The product data acquisition module is used to collect the output performance indicators of plasma-catalyzed methane dry reforming. The simulation module is used to predict the output performance indicators corresponding to different combinations of operating parameters using a methane dry reforming kinetic model. The parameter identification module is used to iteratively optimize the parameters in the methane dry reforming kinetic model using a dataset obtained from experiments. The operating condition recommendation module is used to output an optimized combination of plasma catalytic methane dry reforming operating condition parameters based on the output performance index prediction results of the simulation module for multiple different operating condition parameter combinations and in combination with multi-objective constraints.

6. The plasma-catalyzed methane dry reforming parameter optimization system according to claim 5, characterized in that: The experimental module includes a discharge driving unit, and a gas supply unit, a mixing and transport unit, and a reaction unit connected in sequence. The gas supply unit includes cylinders for methane, carbon dioxide, and argon, as well as a flow controller for each cylinder. The mixing and transport unit includes a mixing tank and a constant-temperature heat-traced transport pipeline; The reaction unit includes a dielectric barrier discharge reactor for carrying out methane dry reforming under plasma conditions. The discharge drive unit includes a pulse power supply, a high-voltage probe, and an oscilloscope. The pulse power supply applies a drive current with a preset discharge frequency and voltage to the dielectric barrier discharge reactor. The high-voltage probe and oscilloscope are used to acquire and display the voltage, current, and discharge frequency in real time.

7. The plasma-catalyzed methane dry reforming parameter optimization system according to claim 6, characterized in that: The product data acquisition module includes a gas chromatograph and a gas chromatography-mass spectrometry system, used to detect the content of methane, carbon dioxide, carbon monoxide, hydrogen, and target oxygen-containing products in the output system of the reaction unit.

8. The plasma-catalyzed methane dry reforming parameter optimization system according to claim 7, characterized in that: It also includes a data link synchronization processing unit that communicates with the discharge drive unit and the product data acquisition module, respectively; The data link synchronization processing unit performs the following tasks in the experiment: a. Receive the discharge parameters from the discharge drive unit and the product content detection results from the product data acquisition module, and timestamp them; b. Time-aligned matching of discharge parameters and product content detection results; c. Output a structured dataset for optimizing the parameters of the dynamic model.

9. A plasma-catalyzed dry reforming apparatus for methane, comprising a mixed gas supply module and a plasma discharge reaction module connected in sequence; the mixed gas supply module is configured to supply a mixed gas of methane, carbon dioxide, and argon to the plasma discharge reaction module; the plasma discharge reaction module performs plasma-catalyzed dry reforming of the input mixed gas for methane; characterized in that: The plasma-catalyzed methane dry reforming unit operates according to the combination of methane dry reforming operating parameters optimized by the plasma-catalyzed methane dry reforming parameter optimization method as described in claim 1.

10. The plasma-catalyzed methane dry reforming apparatus according to claim 9, characterized in that: The catalyst is a Cu-based catalyst, and the support is HZSM-5 molecular sieve.

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