Multi-parameter intelligent adjustment energy efficiency management method and system for biogas purification

Abstract

The invention discloses a multi-parameter intelligent adjustment energy efficiency management method and system for biogas purification, and belongs to the technical field of biogas purification. The method comprises the following steps: acquiring hydrogen sulfide concentration, carbon dioxide concentration and gas moisture content data, constructing an impurity parameter set based on historical sampling data, and calculating upper and lower envelope boundaries of each parameter to form an impurity parameter envelope boundary combination; calculating to obtain a comprehensive impurity fluctuation intensity index through the envelope width, and adding a fluctuation intensity label; and acquiring the accumulated regeneration times, adsorption efficiency and residual saturation of the adsorbent from a system background, calculating the health degree of the adsorbent, and distributing a health degree label. The intelligent regulation strategy table is constructed, and the corresponding regulation strategy is selected according to the real-time working condition, so that the dynamic energy efficiency management of the biogas purification process is realized, the pertinence and the stability of the regulation decision are improved, the system energy consumption is reduced while the purification effect is ensured, and the engineering application value is good.

Description

Technical Field

[0001] This invention relates to the field of biogas purification technology, specifically to a multi-parameter intelligent adjustment energy efficiency management method and system for biogas purification. Background Technology

[0002] With the continuous advancement of renewable energy and the circular economy concept, biogas, as a widely available biomass energy source with low carbon emission intensity, is widely used in agricultural waste treatment, urban organic waste resource utilization, and distributed energy systems. To meet the application requirements of gas grid connection, vehicle fuel, or high-efficiency power generation, raw biogas typically needs to undergo purification to reduce the content of impurities such as hydrogen sulfide, carbon dioxide, and moisture. Existing biogas purification technologies mainly include chemical absorption, physical adsorption, membrane separation, and their combinations. Among these, adsorption is widely used due to its relatively simple equipment structure and strong adaptability to operating pressure. In recent years, with the improvement of sensing technology, industrial automation, and data processing capabilities, some biogas purification systems have begun to introduce online monitoring methods to collect real-time data on changes in gas composition and, to some extent, assist in adjusting operating parameters, promoting the development of the biogas purification process from experience-driven to data-driven. However, research and engineering applications in areas such as multi-impurity coupling fluctuations, adsorbent performance degradation, and comprehensive system energy efficiency optimization are still in a stage of continuous evolution.

[0003] From the perspective of current technology, current biogas purification energy efficiency management mainly focuses on threshold control of single or a few key parameters, such as triggering operating condition switching based on hydrogen sulfide concentration or carbon dioxide content, lacking a systematic characterization of the overall fluctuation characteristics of multiple parameters. When the composition of impurities in the feed gas exhibits non-stationary fluctuations or simultaneous changes in multiple factors, existing methods struggle to reflect the true level of impurity fluctuation intensity in a timely manner, easily leading to lag or over-adjustment, which in turn causes increased energy consumption or decreased purification efficiency. Meanwhile, the adsorbent, as the core functional unit in the purification device, dynamically evolves its performance with changes in regeneration cycles, adsorption efficiency decay, and residual saturation. However, current technologies often manage it using only a single lifespan indicator or periodic manual assessments, failing to synergistically analyze the adsorbent's health status and the fluctuation characteristics of the feed gas, resulting in a mismatch between the adjustment strategy and the actual carrying capacity of the adsorbent. Furthermore, existing energy efficiency management strategies mostly employ fixed rules or static parameter configurations, lacking adaptability to changes in operating conditions, and struggling to balance purification stability and energy consumption control in multi-condition switching scenarios. Therefore, how to comprehensively reflect the intensity of impurity fluctuations and the health status of adsorbents under dynamic changes of multiple impurity parameters, and formulate targeted energy efficiency regulation strategies accordingly, has become a technical problem that urgently needs to be solved in the field of biogas purification. Summary of the Invention

[0004] The purpose of this invention is to provide a multi-parameter intelligent regulation energy efficiency management method and system for biogas purification, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A multi-parameter intelligent regulation energy efficiency management method for biogas purification includes the following steps: Step S1: Collect hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data entering the biogas purification device, and construct a set of biogas purification impurity parameters at historical data sampling time points; calculate the upper and lower envelope boundaries of the hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data respectively; Step S2: Construct a combination of biogas purification impurity parameter envelope boundaries, and calculate the envelope width and comprehensive impurity fluctuation intensity index of the hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data; Step S3: Preset the threshold of the comprehensive impurity fluctuation intensity index. The system sets a value range and assigns a fluctuation intensity label; it acquires hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data at the current data sampling time point in real time, and updates the envelope boundary combination of biogas purification impurity parameters, the comprehensive impurity fluctuation intensity index, and the fluctuation intensity label; Step S4: it acquires the adsorbent's operating condition events from the biogas purification device system backend and calculates the adsorbent's health; it presets a health threshold range and adds a health label to the adsorbent; Step S5: it constructs an intelligent adjustment strategy table based on the fluctuation intensity label and the health label; it acquires the corresponding adjustment strategy from the intelligent adjustment strategy table based on the fluctuation intensity label and the health label at the current data sampling time point.

[0007] As a preferred embodiment of the multi-parameter intelligent regulation energy efficiency management method for biogas purification described in this invention, during the operation of the biogas purification device, gas concentration sensors and gas humidity sensors installed in the gas processing channel of the biogas purification device are used to collect multi-parameter data of the raw gas entering the biogas purification device. The multi-parameter data of the raw gas includes hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data.

[0008] Construct the historical data sampling time period, denoted as ,in, Let A represent the a-th historical data sampling time point, and A represent the total number of historical data sampling time points; the historical data sampling time points are respectively... The hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data collected below are recorded as follows: , and And construct historical data sampling time points The following set of impurity parameters for biogas purification is denoted as: .

[0009] As a preferred embodiment of the multi-parameter intelligent regulation energy efficiency management method for biogas purification described in this invention, the biogas purification impurity parameter set is obtained at all A historical data sampling time points, and the mean and average absolute offset of hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data at all A historical data sampling time points are calculated respectively.

[0010] Based on the mean and average absolute offset of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data, the upper and lower envelope boundaries of the hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data are calculated respectively, using the following formulas:

[0011] ;

[0012] Where x represents the x-th parameter in the set of impurity parameters for biogas purification. Let x represent the upper envelope boundary of the x-th parameter in the set of impurity parameters for biogas purification. Let x represent the lower envelope boundary of the x-th parameter in the set of impurity parameters for biogas purification. Let x represent the mean of the x-th parameter in the set of impurity parameters for biogas purification. This represents the average absolute offset of the x-th parameter in the set of impurity parameters for biogas purification. This represents the envelope coefficient of the x-th parameter in the preset set of impurity parameters for biogas purification.

[0013] As a preferred embodiment of the multi-parameter intelligent regulation and energy efficiency management method for biogas purification described in this invention, a combination of biogas purification impurity parameter envelope boundaries is constructed based on the upper and lower envelope boundaries of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data, denoted as... ;

[0014] Based on the envelope boundary combination of impurity parameters in biogas purification, the envelope widths of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data are calculated and denoted as follows: , and ;

[0015] Data Envelopment Width Based on Hydrogen Sulfide Concentration Carbon dioxide concentration data envelopment width and gas moisture content data envelopment width The comprehensive impurity fluctuation intensity index is calculated using the following formula:

[0016] ;

[0017] in, This represents the overall impurity fluctuation intensity index. , and These represent the influencing factors of the preset data envelopment widths for hydrogen sulfide concentration, carbon dioxide concentration, and gas moisture content, respectively.

[0018] As a preferred embodiment of the multi-parameter intelligent adjustment and energy efficiency management method for biogas purification described in this invention, a preset threshold range for the comprehensive impurity fluctuation intensity index is defined. If the comprehensive impurity fluctuation intensity index... If the value is less than the minimum value within the threshold range of the comprehensive impurity fluctuation intensity index, the comprehensive impurity fluctuation intensity is determined to be low, and a low fluctuation intensity label is added.

[0019] If the comprehensive impurity fluctuation intensity index If the comprehensive impurity fluctuation intensity index is within the threshold range, then the comprehensive impurity fluctuation intensity is determined to be subject to an additional fluctuation intensity label.

[0020] If the comprehensive impurity fluctuation intensity index If the value is greater than the maximum value within the threshold range of the comprehensive impurity fluctuation intensity index, the comprehensive impurity fluctuation intensity is determined to be high, and a high fluctuation intensity label is added.

[0021] The system acquires hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data at the (A+1)th data sampling time point in real time, and updates the envelope boundary combination of biogas purification impurity parameters, comprehensive impurity fluctuation intensity index, and fluctuation intensity label at the (A+1)th data sampling time point in real time.

[0022] As a preferred embodiment of the multi-parameter intelligent adjustment and energy efficiency management method for biogas purification described in this invention, the operating condition events of the adsorbent are obtained from the background of the biogas purification device system. These operating condition events include cumulative regeneration count data, adsorption efficiency data, and residual saturation data. The cumulative regeneration count data, adsorption efficiency data, and residual saturation data at the (A+1)th data sampling time point are normalized and denoted as follows: , and ;

[0023] Based on the cumulative regeneration count, adsorption efficiency, and residual saturation data at the (A+1)th data sampling time point after normalization, the health of the adsorbent is calculated using the following formula:

[0024] ;

[0025] in, Indicates the health status of the adsorbent. , and These represent the weighting factors for the preset cumulative regeneration times data, adsorption efficiency data, and residual saturation data, respectively.

[0026] Preset health threshold range, if the health of the adsorbent If the value is less than the minimum value within the health threshold range, the adsorbent is judged to have low health and a low health label is attached.

[0027] If the health of the adsorbent Within the stated health threshold range, a medium health label is added to the determination of the adsorbent's health.

[0028] If the health of the adsorbent If the value is greater than the maximum value within the health threshold range, the adsorbent is determined to have a high health status and a high health status label is added.

[0029] As a preferred embodiment of the multi-parameter intelligent regulation energy efficiency management method for biogas purification described in this invention, an intelligent regulation strategy table is constructed based on the fluctuation intensity label and the health label.

[0030] Based on the fluctuation intensity label and health label at the A+1th data sampling time point, the corresponding adjustment strategy is obtained from the intelligent adjustment strategy table to perform intelligent energy efficiency management.

[0031] The system acquires the fluctuation intensity label and health label at each data sampling time point in real time and dynamically adjusts energy efficiency.

[0032] A multi-parameter intelligent regulation and energy efficiency management system for biogas purification, the system includes: a data acquisition and envelope boundary calculation module, an envelope width and index calculation module, an intensity label allocation module, a health calculation and label allocation module, and a strategy table construction and regulation management module;

[0033] The data acquisition and envelope boundary calculation module acquires hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data entering the biogas purification device, and constructs a set of biogas purification impurity parameters at historical data sampling time points; and calculates the upper and lower envelope boundaries of the hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data respectively.

[0034] The envelope width and index calculation module: constructs the envelope boundary combination of biogas purification impurity parameters, and calculates the envelope width and comprehensive impurity fluctuation intensity index of hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data.

[0035] The intensity label allocation module: presets the threshold range of the comprehensive impurity fluctuation intensity index and allocates fluctuation intensity labels; acquires hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data at the current data sampling time point in real time, and updates the biogas purification impurity parameter envelope boundary combination, comprehensive impurity fluctuation intensity index and fluctuation intensity label;

[0036] The health calculation and tag assignment module: obtains the operating conditions of the adsorbent from the background of the biogas purification device system and calculates the health of the adsorbent; presets a health threshold range and adds health tags to the adsorbent;

[0037] The strategy table construction and adjustment management module: constructs an intelligent adjustment strategy table based on fluctuation intensity labels and health labels; and obtains the corresponding adjustment strategy from the intelligent adjustment strategy table based on the fluctuation intensity labels and health labels at the current data sampling time point.

[0038] Furthermore, the envelope width and index calculation module includes an envelope width calculation unit and an index calculation unit;

[0039] The envelope width calculation unit: constructs a combination of envelope boundaries for biogas purification impurity parameters based on the upper and lower envelope boundaries of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data; and calculates the envelope width of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data based on the combination of envelope boundaries of biogas purification impurity parameters.

[0040] The index calculation unit calculates the comprehensive impurity fluctuation intensity index based on the data envelopment width of hydrogen sulfide concentration, carbon dioxide concentration, and gas moisture content.

[0041] Furthermore, the health score calculation and tag allocation module includes a health score calculation unit and a tag allocation unit;

[0042] The health calculation unit: acquires the adsorbent's operating condition events from the biogas purification device system backend. The operating condition events include cumulative regeneration count data, adsorption efficiency data, and residual saturation data. It normalizes the cumulative regeneration count data, adsorption efficiency data, and residual saturation data at the (A+1)th data sampling time point. Based on the normalized cumulative regeneration count data, adsorption efficiency data, and residual saturation data at the (A+1)th data sampling time point, it calculates the health of the adsorbent.

[0043] The label allocation unit has a preset health threshold range. If the health of the adsorbent is less than the minimum value within the health threshold range, the adsorbent is determined to have low health and a low health label is attached. If the health of the adsorbent is within the health threshold range, the adsorbent is determined to have medium health and a medium health label is attached. If the health of the adsorbent is greater than the maximum value within the health threshold range, the adsorbent is determined to have high health and a high health label is attached.

[0044] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides a multi-parameter intelligent regulation energy efficiency management method and system for biogas purification. By simultaneously collecting multiple parameters such as hydrogen sulfide concentration, carbon dioxide concentration, and gas moisture content during the biogas purification process, a set of impurity parameters based on historical sampling data is constructed. The upper and lower envelope boundaries of each impurity parameter are formed using the mean and average absolute offset, thereby characterizing the normal fluctuation range of impurities in the raw gas. Based on this, a comprehensive impurity fluctuation intensity index is obtained through envelope width calculation and influence factor weighting, quantifying the overall instability of impurities in the raw gas. Furthermore, a clear fluctuation intensity label is formed through threshold division, enabling graded identification of the fluctuation state on the gas source side. Simultaneously, by acquiring operating events such as the cumulative regeneration times, adsorption efficiency, and residual saturation of the adsorbent, an adsorbent health index is constructed and a health label is attached, providing a direct reflection of the adsorbent's usage status and performance degradation. Finally, the impurity fluctuation intensity label and the adsorbent health label are combined to construct an intelligent regulation strategy table, enabling differentiated energy efficiency regulation strategies corresponding to different gas source fluctuation states and adsorbent health states. Through the synergistic effect of the above steps, this invention achieves simultaneous identification of gas source instability and adsorbent carrying capacity during biogas purification, transforming energy efficiency management from experience-based judgment to intelligent decision-making based on multi-parameter status. While ensuring purification effect, it effectively reduces energy consumption and extends adsorbent lifespan, thereby improving the overall operational stability and energy efficiency of the biogas purification system. Attached Figure Description

[0045] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0046] Figure 1 This is a schematic diagram illustrating the steps of a multi-parameter intelligent regulation and energy efficiency management method for biogas purification according to the present invention.

[0047] Figure 2 This is a schematic diagram of the structure of a multi-parameter intelligent regulation energy efficiency management system for biogas purification according to the present invention. Detailed Implementation

[0048] 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 skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] Please see Figure 1 In this first embodiment: a multi-parameter intelligent adjustment energy efficiency management method for biogas purification is provided, the method including the following steps:

[0050] Step S1: Collect hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data entering the biogas purification device, and construct a set of biogas purification impurity parameters at historical data sampling time points; calculate the upper and lower envelope boundaries of the hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data respectively.

[0051] Specifically, during the operation of the biogas purification device, gas concentration sensors and gas humidity sensors installed in the gas processing channel of the biogas purification device collect multi-parameter data of the raw gas entering the biogas purification device. The multi-parameter data of the raw gas includes hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data.

[0052] Construct the historical data sampling time period, denoted as ,in, Let A represent the a-th historical data sampling time point, and A represent the total number of historical data sampling time points; the historical data sampling time points are respectively... The hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data collected below are recorded as follows: , and And construct historical data sampling time points The following set of impurity parameters for biogas purification is denoted as: .

[0053] Furthermore, obtain the set of biogas purification impurity parameters for all A historical data sampling time points, and calculate the mean and average absolute offset of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data for all A historical data sampling time points respectively.

[0054] Based on the mean and average absolute offset of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data, the upper and lower envelope boundaries of the hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data are calculated respectively, using the following formulas:

[0055] ;

[0056] Where x represents the x-th parameter in the set of impurity parameters for biogas purification. Let x represent the upper envelope boundary of the x-th parameter in the set of impurity parameters for biogas purification. Let x represent the lower envelope boundary of the x-th parameter in the set of impurity parameters for biogas purification. Let x represent the mean of the x-th parameter in the set of impurity parameters for biogas purification. This represents the average absolute offset of the x-th parameter in the set of impurity parameters for biogas purification. This represents the envelope coefficient of the x-th parameter in the preset set of impurity parameters for biogas purification.

[0057] It should be noted that 'x' corresponds to three core impurity parameters (hydrogen sulfide, carbon dioxide, and gas moisture content), which are key indicators that need to be controlled during biogas purification. The historical average value of a certain impurity parameter reflects the baseline concentration level of that impurity under normal operating conditions (e.g., the average concentration of hydrogen sulfide in long-term monitoring is 80 ppm). The average absolute offset of a certain impurity parameter reflects the average fluctuation range of the historical data of that impurity from the mean (for example, the average fluctuation of hydrogen sulfide concentration around the mean is 15 ppm). The preset envelope coefficient (set according to purification process requirements, such as the strong corrosiveness of hydrogen sulfide), It can be set to 1.8; since moisture has a relatively small impact, it can be set to 1.2), which is used to amplify or reduce the normal fluctuation range.

[0058] This formula, based on the statistical characteristics of historical sampling data, uses a weighted average of the mean and fluctuation amplitude to define a reasonable fluctuation range for impurity parameters. Essentially, it establishes a baseline for impurity fluctuations through training with historical data, preventing unintended adjustments due to short-term random fluctuations.

[0059] Existing technologies mostly use fixed thresholds (such as preset hydrogen sulfide concentration ≥100ppm to trigger adjustment), but the sources of biogas (straw fermentation, landfill, livestock manure) are different, and the fluctuation patterns of impurities vary greatly (for example, the fluctuation of hydrogen sulfide in landfill gas is more drastic). This formula dynamically calculates the boundary based on the mean and average absolute offset of historical data, and can automatically adapt to the fluctuation characteristics of different gas sources. It eliminates the need for manual adjustment of thresholds according to changes in gas sources, reducing operation and maintenance costs.

[0060] Compared to a single fixed threshold, the envelope boundary can accommodate reasonable random fluctuations in impurities (such as slight concentration fluctuations caused by short-term changes in fermentation raw materials), avoiding over-adjustment (frequent start-ups and shutdowns of the regeneration system); at the same time, it can accurately capture abnormal fluctuations (such as sudden concentration changes caused by equipment leaks), avoiding adjustment lag and balancing system stability and purification effect.

[0061] Step S2: Construct the envelope boundary combination of impurity parameters for biogas purification, and calculate the envelope width and comprehensive impurity fluctuation intensity index of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data.

[0062] Specifically, based on the upper and lower envelope boundaries of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data, a combination of envelope boundaries for biogas purification impurity parameters is constructed, denoted as... ;

[0063] Based on the envelope boundary combination of impurity parameters in biogas purification, the envelope widths of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data are calculated and denoted as follows: , and ;

[0064] Data Envelopment Width Based on Hydrogen Sulfide Concentration Carbon dioxide concentration data envelopment width and gas moisture content data envelopment width The comprehensive impurity fluctuation intensity index is calculated using the following formula:

[0065] ;

[0066] in, This represents the overall impurity fluctuation intensity index. , and These represent the influencing factors of the preset data envelopment widths for hydrogen sulfide concentration, carbon dioxide concentration, and gas moisture content, respectively.

[0067] It should be noted that, considering the different weights of the impact of various impurities on biogas purification, a simple summation is not used. Instead, priority is assigned through influencing factors, ultimately transforming multi-dimensional fluctuations into single-dimensional quantitative indicators. This solves the problem of how to judge the overall risk when multiple parameters fluctuate synchronously. Furthermore, this formula can quantify the overall instability of the raw gas: for example, in a certain monitoring, hydrogen sulfide fluctuates widely but has a high influencing factor. Even if carbon dioxide fluctuates little, the comprehensive index will still increase, avoiding the one-sidedness of adjustment caused by judging a single parameter.

[0068] Some technologies focus on a single parameter (such as monitoring only hydrogen sulfide concentration), but impurities in biogas purification fluctuate in a coupled manner (e.g., an increase in carbon dioxide concentration may be accompanied by an increase in moisture content, both affecting adsorbent performance). The formula weighted and integrated the fluctuation widths of the three core impurities, which can comprehensively reflect the overall instability of the feed gas and avoid substandard purification results caused by a single parameter being normal but other parameters being abnormal (e.g., ignoring moisture fluctuations leading to adsorbent deactivation due to moisture).

[0069] The impact factor can be weighted according to the priority of the harm of impurities to the purification system (e.g., hydrogen sulfide corrodes equipment, poisons catalysts, etc.). Set at 0.4; carbon dioxide only affects the calorific value of biogas. Set the value to 0.3; moisture accelerates adsorbent degradation. (Setting the value to 0.3), this design makes the overall volatility intensity more in line with the actual risk (e.g., hydrogen sulfide volatility has a higher weight, so even if other impurities have small volatility, the overall index will reflect high risk in a timely manner), making subsequent adjustment strategies more targeted.

[0070] Furthermore, the formula transforms multi-dimensional fluctuation data into a single-dimensional quantitative indicator, that is... Without the need for manual comparison of fluctuations in the three types of impurities, low / medium / high labels can be directly defined by thresholds, enabling rapid integration with adjustment strategies and achieving an automated closed loop of data collection → indicator calculation → label allocation → strategy execution, thereby improving adjustment efficiency.

[0071] Step S3: Preset the threshold range of the comprehensive impurity fluctuation intensity index and assign fluctuation intensity labels; acquire hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data at the current data sampling time point in real time, and update the envelope boundary combination of biogas purification impurity parameters, comprehensive impurity fluctuation intensity index and fluctuation intensity labels.

[0072] Specifically, a preset threshold range for the comprehensive impurity fluctuation intensity index is defined. If the value is less than the minimum value within the threshold range of the comprehensive impurity fluctuation intensity index, the comprehensive impurity fluctuation intensity is determined to be low, and a low fluctuation intensity label is added.

[0073] If the comprehensive impurity fluctuation intensity index If the comprehensive impurity fluctuation intensity index is within the threshold range, then the comprehensive impurity fluctuation intensity is determined to be subject to an additional fluctuation intensity label.

[0074] If the comprehensive impurity fluctuation intensity index If the value is greater than the maximum value within the threshold range of the comprehensive impurity fluctuation intensity index, the comprehensive impurity fluctuation intensity is determined to be high, and a high fluctuation intensity label is added.

[0075] The system acquires hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data at the (A+1)th data sampling time point in real time, and updates the envelope boundary combination of biogas purification impurity parameters, comprehensive impurity fluctuation intensity index, and fluctuation intensity label at the (A+1)th data sampling time point in real time.

[0076] Step S4: Obtain the operating conditions of the adsorbent from the background of the biogas purification device system and calculate the health of the adsorbent; preset the health threshold range and attach a health label to the adsorbent.

[0077] Specifically, the operating conditions of the adsorbent are obtained from the background of the biogas purification device system. These operating conditions include cumulative regeneration count data, adsorption efficiency data, and residual saturation data. The cumulative regeneration count data, adsorption efficiency data, and residual saturation data at the (A+1)th data sampling time point are normalized and denoted as follows: , and ;

[0078] It should be noted that the adsorbent, as a physical adsorption unit located within the gas processing channel of the biogas purification device, generates regeneration records, adsorption performance changes, and post-regeneration adsorption capacity decay during operation. These records correspond to the cumulative regeneration count, current cycle adsorption efficiency, and residual saturation parameters, respectively. These parameters collectively characterize the health status of the adsorbent. In the regeneration control process corresponding to the adsorbent, the event of completing one regeneration operation is recorded, and the number of records is accumulated to form the cumulative regeneration count of the adsorbent. After the adsorbent completes the regeneration process, its impurity reduction capacity at the beginning of the next adsorption cycle is compared with the initial calibrated adsorption capacity to determine the residual saturation parameter of the adsorbent.

[0079] Based on the cumulative regeneration count, adsorption efficiency, and residual saturation data at the (A+1)th data sampling time point after normalization, the health of the adsorbent is calculated using the following formula:

[0080] ;

[0081] in, Indicates the health status of the adsorbent. , and These represent the weighting factors for the preset cumulative regeneration times data, adsorption efficiency data, and residual saturation data, respectively.

[0082] It should be noted that this formula comprehensively evaluates the actual working capacity of the adsorbent from three dimensions: degree of loss (the more times the regeneration is performed, the smaller the value of this term), current performance (the higher the efficiency, the larger the value of this term), and decay trend (the lower the residual saturation, the larger the value of this term), thus avoiding misjudgment caused by a single indicator (such as only looking at the number of regenerations).

[0083] This formula can accurately capture the true health status of the adsorbent. For example, if the adsorption efficiency is high despite multiple regeneration cycles and the residual saturation is low, the health status is still relatively high, and the adsorbent can continue to be used, thus extending its service life and reducing consumable costs.

[0084] Existing technologies often use the cumulative number of regenerations as a single indicator to judge the lifespan of adsorbents (e.g., replace after 10 regenerations). However, in reality, the state of adsorbents is affected by a combination of factors, including loss (number of regenerations), current performance (adsorption efficiency), and decay trend (residual saturation). For example, some adsorbents may have many regenerations but still meet the adsorption efficiency standards, and premature replacement would increase costs.

[0085] Preset health threshold range, if the health of the adsorbent If the value is less than the minimum value within the health threshold range, the adsorbent is judged to have low health and a low health label is attached.

[0086] If the health of the adsorbent Within the stated health threshold range, a medium health label is added to the determination of the adsorbent's health.

[0087] If the health of the adsorbent If the value is greater than the maximum value within the health threshold range, the adsorbent is determined to have a high health status and a high health status label is added.

[0088] Step S5: Construct an intelligent adjustment strategy table based on the fluctuation intensity label and health label; obtain the corresponding adjustment strategy from the intelligent adjustment strategy table based on the fluctuation intensity label and health label at the current data sampling time point.

[0089] Specifically, an intelligent adjustment strategy table is constructed based on the fluctuation intensity label and the health label;

[0090] Based on the fluctuation intensity label and health label at the A+1th data sampling time point, the corresponding adjustment strategy is obtained from the intelligent adjustment strategy table to perform intelligent energy efficiency management.

[0091] The system acquires the fluctuation intensity label and health label at each data sampling time point in real time and dynamically adjusts energy efficiency.

[0092] The specific intelligent adjustment strategy table is shown below:

[0093] Health status label\fluctuation intensity label Low fluctuation intensity In terms of fluctuation intensity High fluctuation intensity High health Adjustment objectives: Energy saving optimization: Reduce regeneration frequency, optimize adsorption tower switching sequence, reduce system operating pressure, and record as a high-efficiency and stable mode. Adjustment Target: Stable Operation: Maintain the current regeneration strategy, slightly increase the pretreatment intensity, monitor fluctuation trends, and record as a stable operation mode. Adjustment objectives: Suppress fluctuations: Appropriately increase the regeneration frequency, enhance the processing capacity of the preprocessing unit, increase the system buffer capacity, and record as an anti-fluctuation mode. Health status is medium Adjustment Objectives: Maintenance and Prevention: Maintain regeneration frequency, monitor adsorbent performance degradation, optimize energy recovery, and record data for preventative maintenance. Adjustment Objective: Balanced Adjustment: Slightly increase regeneration time, balance adsorption tower load, maintain stable system pressure, and record as balanced operation mode. Adjustment objectives: Equipment protection: Increase regeneration time and frequency, activate backup adsorption towers, increase system pressure to cope with shocks, and record operation as a protective mode. Low health Adjustment objectives: Extend lifespan: Reduce adsorption load, extend regeneration time, reduce system operating intensity, and indicate upcoming maintenance. Adjustment targets: Emergency maintenance: Significantly reduce adsorption load, increase regeneration frequency and duration, enhance pretreatment intensity, and indicate the need for prompt maintenance. Adjustment Target: Emergency Protection: Significantly reduce throughput, activate backup adsorption system, enter high-energy-consumption maintenance mode, trigger alarm and prompt immediate maintenance or replacement.

[0094] Please see Figure 2 In this second embodiment: a multi-parameter intelligent regulation energy efficiency management system for biogas purification is provided. The system includes: a data acquisition and envelope boundary calculation module, an envelope width and index calculation module, an intensity label allocation module, a health calculation and label allocation module, and a strategy table construction and regulation management module.

[0095] The data acquisition and envelope boundary calculation module acquires hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data entering the biogas purification device, and constructs a set of biogas purification impurity parameters at historical data sampling time points; and calculates the upper and lower envelope boundaries of the hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data respectively.

[0096] The envelope width and index calculation module: constructs the envelope boundary combination of biogas purification impurity parameters, and calculates the envelope width and comprehensive impurity fluctuation intensity index of hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data.

[0097] The intensity label allocation module: presets the threshold range of the comprehensive impurity fluctuation intensity index and allocates fluctuation intensity labels; acquires hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data at the current data sampling time point in real time, and updates the biogas purification impurity parameter envelope boundary combination, comprehensive impurity fluctuation intensity index and fluctuation intensity label;

[0098] The health calculation and tag assignment module: obtains the operating conditions of the adsorbent from the background of the biogas purification device system and calculates the health of the adsorbent; presets a health threshold range and adds health tags to the adsorbent;

[0099] The strategy table construction and adjustment management module: constructs an intelligent adjustment strategy table based on fluctuation intensity labels and health labels; and obtains the corresponding adjustment strategy from the intelligent adjustment strategy table based on the fluctuation intensity labels and health labels at the current data sampling time point.

[0100] Furthermore, the envelope width and index calculation module includes an envelope width calculation unit and an index calculation unit;

[0101] The envelope width calculation unit: constructs a combination of envelope boundaries for biogas purification impurity parameters based on the upper and lower envelope boundaries of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data; and calculates the envelope width of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data based on the combination of envelope boundaries of biogas purification impurity parameters.

[0102] The index calculation unit calculates the comprehensive impurity fluctuation intensity index based on the data envelopment width of hydrogen sulfide concentration, carbon dioxide concentration, and gas moisture content.

[0103] Furthermore, the health score calculation and tag allocation module includes a health score calculation unit and a tag allocation unit;

[0104] The health calculation unit: acquires the adsorbent's operating condition events from the biogas purification device system backend. The operating condition events include cumulative regeneration count data, adsorption efficiency data, and residual saturation data. It normalizes the cumulative regeneration count data, adsorption efficiency data, and residual saturation data at the (A+1)th data sampling time point. Based on the normalized cumulative regeneration count data, adsorption efficiency data, and residual saturation data at the (A+1)th data sampling time point, it calculates the health of the adsorbent.

[0105] The label allocation unit has a preset health threshold range. If the health of the adsorbent is less than the minimum value within the health threshold range, the adsorbent is determined to have low health and a low health label is attached. If the health of the adsorbent is within the health threshold range, the adsorbent is determined to have medium health and a medium health label is attached. If the health of the adsorbent is greater than the maximum value within the health threshold range, the adsorbent is determined to have high health and a high health label is attached.

[0106] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0107] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-parameter intelligent adjustment energy efficiency management method for biogas purification, characterized in that, The method includes the following steps: Step S1: Collect hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data entering the biogas purification device, and construct a set of biogas purification impurity parameters at historical data sampling time points; calculate the upper and lower envelope boundaries of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data respectively. Step S2: Construct the envelope boundary combination of impurity parameters for biogas purification, and calculate the envelope width and comprehensive impurity fluctuation intensity index of hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data. Step S3: Preset the threshold range of the comprehensive impurity fluctuation intensity index and assign fluctuation intensity labels; acquire hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data at the current data sampling time point in real time, and update the envelope boundary combination of biogas purification impurity parameters, comprehensive impurity fluctuation intensity index and fluctuation intensity labels. Step S4: Obtain the operating conditions of the adsorbent from the background of the biogas purification device system and calculate the health of the adsorbent; preset the health threshold range and attach a health label to the adsorbent; Step S5: Construct an intelligent adjustment strategy table based on the fluctuation intensity label and health label; obtain the corresponding adjustment strategy from the intelligent adjustment strategy table based on the fluctuation intensity label and health label at the current data sampling time point.

2. The multi-parameter intelligent adjustment energy efficiency management method for biogas purification according to claim 1, characterized in that, The specific implementation process of step S1 includes: During the operation of the biogas purification device, gas concentration sensors and gas humidity sensors installed in the gas processing channel of the biogas purification device collect multi-parameter data of the raw gas entering the biogas purification device. The multi-parameter data of the raw gas includes hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data. Construct the historical data sampling time period, denoted as ,in, Let A represent the a-th historical data sampling time point, and A represent the total number of historical data sampling time points; the historical data sampling time points are respectively... The hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data collected below are recorded as follows: , and And construct historical data sampling time points The following set of impurity parameters for biogas purification is denoted as: .

3. The multi-parameter intelligent adjustment energy efficiency management method for biogas purification according to claim 2, characterized in that, The specific implementation process of step S1 also includes: Obtain the set of biogas purification impurity parameters for all A historical data sampling time points, and calculate the mean and average absolute offset of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data for all A historical data sampling time points respectively. Based on the mean and average absolute offset of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data, the upper and lower envelope boundaries of the hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data are calculated respectively, using the following formulas: ； Where x represents the x-th parameter in the set of impurity parameters for biogas purification. Let x represent the upper envelope boundary of the x-th parameter in the set of impurity parameters for biogas purification. Let x represent the lower envelope boundary of the x-th parameter in the set of impurity parameters for biogas purification. Let x represent the mean of the x-th parameter in the set of impurity parameters for biogas purification. This represents the average absolute offset of the x-th parameter in the set of impurity parameters for biogas purification. This represents the envelope coefficient of the x-th parameter in the preset set of impurity parameters for biogas purification.

4. The multi-parameter intelligent adjustment energy efficiency management method for biogas purification according to claim 3, characterized in that, The specific implementation process of step S2 includes: Based on the upper and lower envelope boundaries of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data, a combination of envelope boundaries for biogas purification impurity parameters is constructed, denoted as […]. ; Based on the envelope boundary combination of impurity parameters in biogas purification, the envelope widths of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data are calculated and denoted as follows: , and ; Data Envelopment Width Based on Hydrogen Sulfide Concentration Carbon dioxide concentration data envelopment width and gas moisture content data envelopment width The comprehensive impurity fluctuation intensity index is calculated using the following formula: ； in, This represents the overall impurity fluctuation intensity index. , and These represent the influencing factors of the preset data envelopment widths for hydrogen sulfide concentration, carbon dioxide concentration, and gas moisture content, respectively.

5. The multi-parameter intelligent adjustment energy efficiency management method for biogas purification according to claim 4, characterized in that, The specific implementation process of step S3 includes: The preset threshold range for the comprehensive impurity fluctuation intensity index is as follows: If the value is less than the minimum value within the threshold range of the comprehensive impurity fluctuation intensity index, the comprehensive impurity fluctuation intensity is determined to be low, and a low fluctuation intensity label is added. If the comprehensive impurity fluctuation intensity index If the comprehensive impurity fluctuation intensity index is within the threshold range, then the comprehensive impurity fluctuation intensity is determined to be subject to an additional fluctuation intensity label. If the comprehensive impurity fluctuation intensity index If the value is greater than the maximum value within the threshold range of the comprehensive impurity fluctuation intensity index, the comprehensive impurity fluctuation intensity is determined to be high, and a high fluctuation intensity label is added. The system acquires hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data at the (A+1)th data sampling time point in real time, and updates the envelope boundary combination of biogas purification impurity parameters, comprehensive impurity fluctuation intensity index, and fluctuation intensity label at the (A+1)th data sampling time point in real time.

6. The multi-parameter intelligent adjustment energy efficiency management method for biogas purification according to claim 5, characterized in that, The specific implementation process of step S4 includes: The operating conditions of the adsorbent are obtained from the background of the biogas purification device system. These operating conditions include cumulative regeneration count data, adsorption efficiency data, and residual saturation data. The cumulative regeneration count data, adsorption efficiency data, and residual saturation data at the (A+1)th data sampling time point are normalized and denoted as follows: , and ; Based on the cumulative regeneration count, adsorption efficiency, and residual saturation data at the (A+1)th data sampling time point after normalization, the health of the adsorbent is calculated using the following formula: ； in, Indicates the health status of the adsorbent. , and These represent the weighting factors for the preset cumulative regeneration times data, adsorption efficiency data, and residual saturation data, respectively. Preset health threshold range, if the health of the adsorbent If the value is less than the minimum value within the health threshold range, the adsorbent is judged to have low health and a low health label is attached. If the health of the adsorbent Within the stated health threshold range, a medium health label is added to the determination of the adsorbent's health. If the health of the adsorbent If the value is greater than the maximum value within the health threshold range, the adsorbent is determined to have a high health status and a high health status label is added.

7. A multi-parameter intelligent adjustment energy efficiency management method for biogas purification according to claim 6, characterized in that, The specific implementation process of step S5 includes: Based on the fluctuation intensity label and the health label, an intelligent adjustment strategy table is constructed; Based on the fluctuation intensity label and health label at the A+1th data sampling time point, the corresponding adjustment strategy is obtained from the intelligent adjustment strategy table to perform intelligent energy efficiency management. The system acquires the fluctuation intensity label and health label at each data sampling time point in real time and dynamically adjusts energy efficiency.

8. A multi-parameter intelligent regulation energy efficiency management system for biogas purification, executing the multi-parameter intelligent regulation energy efficiency management method for biogas purification as described in any one of claims 1-7, characterized in that, The system includes: a data acquisition and envelope boundary calculation module, an envelope width and index calculation module, an intensity label allocation module, a health calculation and label allocation module, and a strategy table construction and adjustment management module; The data acquisition and envelope boundary calculation module acquires hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data entering the biogas purification device, and constructs a set of biogas purification impurity parameters at historical data sampling time points; and calculates the upper and lower envelope boundaries of the hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data respectively. The envelope width and index calculation module: constructs the envelope boundary combination of biogas purification impurity parameters, and calculates the envelope width and comprehensive impurity fluctuation intensity index of hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data. The intensity label allocation module: presets the threshold range of the comprehensive impurity fluctuation intensity index and allocates fluctuation intensity labels; acquires hydrogen sulfide concentration data, carbon dioxide concentration data and gas moisture content data at the current data sampling time point in real time, and updates the biogas purification impurity parameter envelope boundary combination, comprehensive impurity fluctuation intensity index and fluctuation intensity label; The health calculation and tag assignment module: obtains the operating conditions of the adsorbent from the background of the biogas purification device system and calculates the health of the adsorbent; presets a health threshold range and adds health tags to the adsorbent; The strategy table construction and adjustment management module: constructs an intelligent adjustment strategy table based on fluctuation intensity labels and health labels; and obtains the corresponding adjustment strategy from the intelligent adjustment strategy table based on the fluctuation intensity labels and health labels at the current data sampling time point.

9. A multi-parameter intelligent regulation and energy efficiency management system for biogas purification according to claim 8, characterized in that: The envelope width and index calculation module includes an envelope width calculation unit and an index calculation unit; The envelope width calculation unit: constructs a combination of envelope boundaries for biogas purification impurity parameters based on the upper and lower envelope boundaries of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data; and calculates the envelope width of hydrogen sulfide concentration data, carbon dioxide concentration data, and gas moisture content data based on the combination of envelope boundaries of biogas purification impurity parameters. The index calculation unit calculates the comprehensive impurity fluctuation intensity index based on the data envelopment width of hydrogen sulfide concentration, carbon dioxide concentration, and gas moisture content.

10. A multi-parameter intelligent energy efficiency management system for biogas purification according to claim 9, characterized in that: The health score calculation and label allocation module includes a health score calculation unit and a label allocation unit; The health calculation unit obtains the adsorbent's operating condition events from the biogas purification device system backend. The operating condition events include cumulative regeneration times data, adsorption efficiency data, and residual saturation data. The cumulative regeneration times data, adsorption efficiency data, and residual saturation data at the A+1th data sampling time point are normalized respectively. Based on the cumulative regeneration times, adsorption efficiency, and residual saturation data at the A+1th data sampling time point after normalization, the health of the adsorbent is calculated. The label allocation unit has a preset health threshold range. If the health of the adsorbent is less than the minimum value within the health threshold range, the health of the adsorbent is determined to be low, and a low health label is attached. If the health status of the adsorbent is within the health status threshold range, then a medium health status label is added to the health status of the adsorbent. If the health of the adsorbent is greater than the maximum value within the health threshold range, the adsorbent is determined to have a high health status and a high health status label is attached.

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