Intelligent optimization control method and system for boosting, adsorbing and purifying marsh gas

Abstract

The invention discloses an intelligent optimization control method and system for boosting, adsorbing and purifying marsh gas, and belongs to the technical field of marsh gas treatment. According to the method, a standard process parameter range set is generated by overall planning of all process links and identification of process node parameters; dividing process stages according to purification tasks, and identifying and locking real-time processing flow paths meeting requirements; constructing an operation state matrix based on a path locking result, and evaluating the process stage stability by combining a process similarity mean value and a standard deviation; and determining an intelligent optimization control coupling gap when the running state fluctuation is minimum through clustering analysis. The system comprises a process path and standard parameter construction module and a real-time path identification and locking module, which cooperatively complete optimization control. According to the invention, precise and intelligent control of the whole process of biogas boosting, adsorption and purification is realized, the problems of inaccurate parameter matching, lack of stability evaluation and the like in the prior art are solved, the purification efficiency and the product purity are remarkably improved, the energy consumption is reduced, and the system is adaptive to complex working conditions.

Description

Technical Field

[0001] This invention relates to the field of biogas treatment technology, specifically to an intelligent optimized control method and system for biogas pressurization adsorption purification. Background Technology

[0002] Biogas, as a clean and renewable energy source, has its main component, methane, which, after purification, can be widely used in industrial production and residential gas supply, playing a significant role in alleviating the energy crisis and reducing environmental pollution. Biogas pressurized adsorption purification technology has become one of the mainstream purification technologies due to its simple operation and moderate cost. Its core lies in achieving efficient separation of methane from impurities such as carbon dioxide and hydrogen sulfide through precise control of key parameters in process stages such as pressurization and stabilization, pretreatment filtration, adsorption separation, and purification.

[0003] However, existing biogas pressurization adsorption purification control technologies still have many problems that urgently need to be solved: First, existing control methods mostly adopt fixed parameter control modes, setting parameter thresholds only for a single process step, without considering the overall correlation of the entire process steps, and ignoring the coupling relationship between process node parameters (pressure value, temperature value, adsorption time, biogas flow rate), resulting in a lack of systematic planning of the treatment process path and low parameter matching degree; Second, the accuracy of real-time treatment process path identification is insufficient. Existing technologies only judge the effectiveness of the path by whether a single parameter meets the standard, without forming a complete set of real-time process parameter ranges and... The comprehensive matching mechanism of the standard set is prone to misjudgment or omission of qualified paths, affecting the continuity of purification; third, the lack of quantitative assessment methods for the stability of process stages makes it impossible to accurately reflect the changes in the operating status of the same stage within different process cycles, resulting in poor adaptability to fluctuations in operating conditions; fourth, the optimization of control coupling gaps is mostly set based on experience and is not dynamically adjusted in combination with the characteristics of fluctuations in operating conditions, resulting in insufficient control precision, which in turn leads to problems such as low purification efficiency, large fluctuations in product purity (usually fluctuating by 3%-5%), and high energy consumption (energy consumption per unit of methane purification is 15%-20% higher than the ideal value).

[0004] Furthermore, the existing system structure is loose, with a lack of coordinated operation between functional modules, making it difficult to achieve closed-loop control throughout the entire process, from process parameter calibration to control gap optimization. Under complex operating conditions (such as fluctuations in biogas feedstock composition and changes in ambient temperature), existing technologies cannot quickly adjust control strategies, resulting in poor operational stability of the purification system and limiting the large-scale application of biogas purification technology. These problems make it difficult for existing biogas pressurization adsorption purification control technology to meet the demands of efficient, stable, and low-consumption industrial production. There is an urgent need for an intelligent control scheme that can coordinate the entire process, accurately identify pathways, quantitatively assess stability, and dynamically optimize control gaps. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent optimization control method and system for biogas pressurization adsorption purification, to solve the problems mentioned in the background art. This invention is based on the core logic of "standard calibration - real-time screening - state assessment - dynamic optimization" to construct a full-process intelligent control system. First, by coordinating all process links in biogas treatment, four key process node parameters—pressure, temperature, adsorption time, and biogas flow rate—are identified to form a standardized parameter range set, providing a benchmark for subsequent control (step S1). Second, the process stages are broken down according to the purification task, and through comprehensive matching of real-time parameter ranges with standard ranges, effective treatment paths are accurately locked, ensuring process compliance (step S2). Third, the path locking results are quantified using an operating state matrix. Process similarity is calculated through Boolean logic operations on adjacent period matrices, and stability is quantitatively assessed by combining the mean and standard deviation, accurately capturing changes in operating state (step S3). Finally, a two-dimensional coordinate system is constructed based on stability, and the clustering scale is dynamically adjusted through cluster analysis to find the clustering scale with the smallest operating state fluctuation as the control coupling gap, achieving precise adaptation of the control strategy (step S4).

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] An intelligent optimization control system for biogas pressurization adsorption purification is disclosed. The system includes: a process path and standard parameter construction module, a real-time path identification and locking module, an operating state matrix construction and stability assessment module, and an optimization control coupling gap determination module. Each module works in concert to sequentially complete the construction of the processing flow path and standard parameters, real-time path identification and locking, operating state stability assessment, and optimization control coupling gap determination, thereby realizing intelligent optimization control for biogas pressurization adsorption purification.

[0008] The process path and standard parameter construction module is used to coordinate all process steps of biogas treatment and uniformly identify them, identify the process node parameters corresponding to each process step, form a biogas pressurization adsorption purification process path according to the process sequence reflected by the process node parameters, and generate a set of standard process parameter ranges for the corresponding process path based on the parameter combination of each process node.

[0009] The real-time path identification and locking module divides the biogas purification process into multiple process stages according to the purification task requirements within the process cycle. It identifies the real-time processing flow path within each process stage and generates a set of real-time process parameter ranges. By matching the real-time process parameter range set with the standard process parameter range set, it locks the real-time processing flow path that meets the requirements.

[0010] The operation status matrix construction and stability evaluation module constructs the operation status matrix of each process stage in different process cycles based on the locking results of the real-time processing flow path. By calculating the process similarity of the operation status matrix of the same process stage in adjacent process cycles, and combining the mean and standard deviation of the process similarity, the stability of the process stage is evaluated.

[0011] The optimized control coupling gap determination module constructs a two-dimensional scattered coordinate system based on the stability of each process stage. By initializing the clustering scale, statistically analyzing the fluctuation value of the operating state, and adjusting the clustering scale, it determines the clustering scale with the smallest fluctuation value of the operating state and uses this clustering scale as the intelligent optimized control coupling gap.

[0012] As a preferred embodiment of the present invention, the process path and standard parameter construction module includes a process link overall identification unit, a process node parameter identification unit, a processing flow path generation unit, and a standard parameter range set generation unit.

[0013] The process flow identification unit is used to uniformly organize and identify all process flows in biogas treatment;

[0014] The process node parameter identification unit is used to collect and identify the process node parameters corresponding to each process step. The process node parameters include pressure value, temperature value, adsorption time and biogas flow rate.

[0015] The process flow path generation unit is used to connect each process step in series according to the process sequence reflected by the process node parameters to form a complete biogas pressurization adsorption purification process flow path.

[0016] The standard parameter range set generation unit is used to determine the range of values ​​for pressure, temperature, adsorption time and biogas flow rate based on the parameter combination of each process node, and form a standard process parameter range set for the corresponding treatment process path.

[0017] As a preferred embodiment of the present invention, the real-time path identification and locking module includes a process stage division unit, a real-time path identification unit, a parameter range matching unit, and a path locking unit.

[0018] The process stage division unit is used to divide the process cycle into pressure boosting and stabilization stage, pretreatment and filtration stage, adsorption and separation stage and purification and refining stage according to the purification task requirements of a single biogas purification process cycle.

[0019] The real-time path identification unit is used to capture the actual processing flow path in each process stage and collect the real-time process parameters corresponding to each path to form a set of real-time process parameter ranges.

[0020] The parameter range matching unit is used to compare the real-time process parameter range set with the standard process parameter range set generated by the process path and standard parameter construction module to determine the overlap between the two in each parameter type.

[0021] The path locking unit is used to lock the real-time processing flow path when there is overlap between the range of parameters of the same type in the real-time process parameter range set and the standard process parameter range set.

[0022] As a preferred embodiment of the present invention, the operating state matrix construction and stability evaluation module includes an operating state matrix construction unit, a process similarity calculation unit, and a stability evaluation unit;

[0023] The operation status matrix construction unit, based on the path locking results of the real-time path identification and locking module, constructs the operation status matrix of each process stage within the corresponding process cycle with the process node number as the row index and the processing flow path number as the column index, and identifies whether the path is locked through the matrix elements.

[0024] The process similarity calculation unit is used to calculate the number of valid paths in the Boolean logic AND result of the running state matrix of the same process stage within two adjacent process cycles, and the number of valid paths in the Boolean logic OR result. The process similarity is obtained by the ratio of the two.

[0025] The stability assessment unit is used to statistically analyze the mean and standard deviation of process similarity over multiple process cycles. By combining the deviation of individual process similarity from the mean, the stability of the corresponding process stage is calculated.

[0026] As a preferred embodiment of the present invention, the optimized control coupling gap determination module includes a two-dimensional coordinate system construction unit, a cluster analysis unit, a fluctuation value statistics unit, and a coupling gap determination unit;

[0027] The two-dimensional coordinate system construction unit is used to construct a two-dimensional scattered coordinate system of biogas treatment operation status with the combination of process cycle and process stage as the horizontal independent variable and stability as the vertical dependent variable.

[0028] The clustering analysis unit is used to initialize the clustering scale, forming multiple clustering circles with each stability data point as the clustering point and the initialized clustering scale as the radius;

[0029] The fluctuation value statistics unit is used to calculate the variance of all stability data within each cluster circle and the stability of the cluster circle point, and to use this variance as the fluctuation value of the corresponding cluster circle's operating state.

[0030] The coupling gap determination unit is used to gradually adjust the clustering scale, repeat the clustering analysis and fluctuation value statistics process until the clustering scale that minimizes the fluctuation value of the running state of all clusters is found, and this clustering scale is determined as the intelligent optimization control coupling gap.

[0031] A smart optimization control method for biogas pressurization adsorption purification includes the following steps:

[0032] Step S1: Coordinate all process steps of biogas treatment and uniformly identify them. Identify the process node parameters corresponding to each process step. Form a biogas pressurization adsorption purification process path based on the process sequence reflected by the process node parameters. Generate a set of standard process parameter ranges for the corresponding process path based on the parameter combination of each process node.

[0033] Step S2: Based on the purification task requirements within the biogas purification process cycle, the process cycle is divided into multiple process stages. The real-time processing flow path within each process stage is identified, and a set of real-time process parameter ranges is generated. By matching the real-time process parameter range set with the standard process parameter range set, the real-time processing flow path that meets the requirements is locked.

[0034] Step S3: Based on the locking results of the real-time processing flow path, construct the operating status matrix of each process stage in different process cycles. By calculating the process similarity of the operating status matrix of the same process stage in adjacent process cycles, and combining the mean and standard deviation of the process similarity, evaluate the stability of the process stage.

[0035] Step S4: Construct a two-dimensional scatter coordinate system based on the stability of each process stage. Determine the clustering scale with the smallest operating state fluctuation value by initializing the clustering scale, statistically analyzing the operating state fluctuation value, and adjusting the clustering scale. Use this clustering scale as the coupling gap for intelligent optimization control.

[0036] As a preferred embodiment of the present invention, the specific implementation process of step S1 includes:

[0037] All process steps in biogas treatment are coordinated and uniformly identified, and additional process node parameters in the process steps are identified. Each process node corresponds to one process step, and the process flow path of biogas pressurization adsorption purification is formed according to the process sequence reflected in the process step flow of the process node parameters.

[0038] The process node parameters include pressure, temperature, adsorption time, and biogas flow rate. Any process node is labeled m, and all processing flow paths formed by process node m are captured. The e-th processing flow path formed by process node m is labeled as... Where r is the process node number and r≠m, and process node r is the process node traversed in the e-th processing flow path formed by process node m. This refers to the combination of process node parameter labels corresponding to the r-th process node, including the pressure value. Temperature value Adsorption time and biogas flow R represents the total number of process nodes;

[0039] Process flow paths are generated based on combinations of process node parameter labels. Standard process parameter range set .

[0040] As a preferred embodiment of the present invention, the specific implementation process of step S2 includes:

[0041] Based on the purification task requirements in the fth biogas purification process cycle, the fth biogas purification process cycle is divided into G process stages. The process stages are the execution stages of each purification sub-task, including the pressure boosting and stabilizing stage, the pretreatment and filtration stage, the adsorption and separation stage, and the purification and refining stage.

[0042] Identify all processing paths generated within the g-th process stage, and obtain the e-th real-time processing path formed by process node m within the g-th process stage, denoted as . And generate a real-time processing flow path. The set of real-time process parameter ranges will be used to define the real-time processing flow path. Real-time process parameter range set and processing flow path The standard process parameter range set is matched. If the process parameter ranges generated by the same process node parameter type in the real-time process parameter range set overlap with those in the standard process parameter range set, then the real-time processing flow path is locked. If the real-time process parameter range set overlaps with the process parameter ranges generated by the same process node parameter type in the standard process parameter range set, the real-time processing flow path will not be locked. .

[0043] As a preferred embodiment of the present invention, the specific implementation process of step S3 includes:

[0044] Based on real-time processing workflow path The locked results are used to construct the operating state matrix of the g-th process stage within the f-th biogas purification process cycle. The row index of the running status matrix corresponds to the process node number, and the column index corresponds to the processing flow path number. If the real-time processing flow path... If locked, set the position of the matrix in the m-th row and e-th column of the running status matrix to 1. If the real-time processing flow path... If not locked, set the position of the matrix in the m-th row and e-th column of the running state matrix to 0;

[0045] Based on the operating state matrix, the stability of the g-th process stage under the cycle flow of the biogas purification process is evaluated. In the formula, Let g be the process similarity of the g-th process stage when the process transitions from the f-th biogas purification process cycle to the (f+1)-th biogas purification process cycle. This represents the stability of the g-th process stage when the f-th biogas purification process cycle transitions to the (f+1)-th biogas purification process cycle. This represents the operating state matrix of the g-th process stage within the f-th biogas purification process cycle. The operating state matrix of the g-th process stage within the (f+1)-th biogas purification process cycle The number of 1s contained in the result of an inter-Boolean logical AND operation. This represents the operating state matrix of the g-th process stage within the f-th biogas purification process cycle. The operating state matrix of the g-th process stage within the (f+1)-th biogas purification process cycle The number of 1s contained in the result of an inter-Boolean OR operation. The average of process similarity and , For the standard deviation of process similarity and .

[0046] As a preferred embodiment of the present invention, the specific implementation process of step S4 includes:

[0047] Based on stability Construct a two-dimensional scattered coordinate system to represent the operating status of biogas treatment, with the horizontal independent variable being... The longitudinal dependent variable is ;

[0048] The initial clustering scale for stability is x, and the stability is expressed in a two-dimensional scatter coordinate system. Using the clustering point x as the clustering radius, we obtain the clustering circle. Statistical clustering circle Stability and stability The variance of stability is used as a measure of stability. The fluctuation value of the operating state of the clustering points;

[0049] Adjust the size of the clustering scale x to determine the clustering scale x until each clustering circle in the two-dimensional scatter coordinate system satisfies the minimum value of the running state fluctuation, and use the clustering scale x as the coupling gap of intelligent optimization control.

[0050] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0051] First, existing technologies lack unified identification and parameter coordination for all process stages. This invention, by constructing a standard set of process parameter ranges including four key parameters, achieves coordinated linkage among various process stages, solves the problem of parameter fragmentation, and significantly improves the standardization of the processing flow path. Second, existing technologies lack quantitative evaluation methods for the stability of process stages. This invention innovatively adopts an evaluation method combining an operating state matrix with the mean and standard deviation of process similarity, achieving accurate quantification of stability for the first time, and enabling early prediction of operating condition fluctuation risks. Third, existing technologies mostly use fixed values ​​for control coupling gaps. This invention dynamically determines the clustering scale with the smallest operating state fluctuation as the coupling gap through cluster analysis, achieving adaptive optimization of the control strategy to adapt to the operating characteristics of different process cycles and stages.

[0052] Compared to existing technologies, this invention overcomes the limitations of fixed parameter control, single-dimensional path judgment, and experience-based coupling gap setting. In application scenarios, whether it is a large-scale biogas project (processing capacity of 500-1000 m³ / h) or a small-to-medium-sized purification system, parameter synergy optimization can be achieved through the overall planning of the entire process, avoiding the decrease in purification efficiency caused by parameter imbalance in a single link. The real-time path comprehensive matching mechanism improves the accuracy of qualified path identification, solving the problem of misjudgment and omission in existing technologies. The quantitative assessment of stability makes operating condition fluctuations perceptible and knowable, providing data support for control adjustments. The dynamic coupling gap setting enables the system to adapt to complex changes in raw material composition, ambient temperature, etc. This invention not only improves the purity of methane purification (up to 97.5% or more), but also reduces energy consumption and operational difficulty, and can adapt to complex operating condition changes, solving the technical bottleneck of existing technologies that struggle to balance stability and efficiency. Attached Figure Description

[0053] 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.

[0054] Figure 1 This is a schematic diagram of the steps of an intelligent optimization control method for biogas pressurization adsorption purification according to the present invention. Detailed Implementation

[0055] 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.

[0056] In this first embodiment, an intelligent optimization control system for biogas pressurization adsorption purification is provided. The system includes: a process path and standard parameter construction module, a real-time path identification and locking module, an operating state matrix construction and stability evaluation module, and an optimization control coupling gap determination module. Each module works in concert to sequentially complete the construction of the processing flow path and standard parameters, real-time path identification and locking, operating state stability evaluation, and optimization control coupling gap determination, thereby realizing intelligent optimization control for biogas pressurization adsorption purification.

[0057] The process path and standard parameter construction module is used to coordinate all process steps of biogas treatment and uniformly identify them. It identifies the process node parameters corresponding to each process step, forms a biogas pressurization adsorption purification process path based on the process sequence reflected by the process node parameters, and generates a set of standard process parameter ranges for the corresponding process path based on the parameter combination of each process node.

[0058] Specifically, the process path and standard parameter construction module includes a process link overall identification unit, a process node parameter identification unit, a processing flow path generation unit, and a standard parameter range set generation unit;

[0059] The process flow identification unit is used to uniformly organize and identify all process flows in biogas treatment;

[0060] The process node parameter identification unit is used to collect and identify the process node parameters corresponding to each process step. The process node parameters include pressure value, temperature value, adsorption time and biogas flow rate.

[0061] The process flow path generation unit is used to connect each process step in series according to the process sequence reflected by the process node parameters to form a complete biogas pressurization adsorption purification process flow path.

[0062] The standard parameter range set generation unit is used to determine the range of values ​​for pressure, temperature, adsorption time and biogas flow rate based on the parameter combination of each process node, and form a standard process parameter range set for the corresponding treatment process path.

[0063] The real-time path identification and locking module divides the biogas purification process into multiple process stages according to the purification task requirements within the process cycle. It identifies the real-time processing flow path within each process stage and generates a set of real-time process parameter ranges. By matching the real-time process parameter range set with the standard process parameter range set, it locks the real-time processing flow path that meets the requirements.

[0064] Specifically, the real-time path identification and locking module includes a process stage division unit, a real-time path identification unit, a parameter range matching unit, and a path locking unit.

[0065] The process stage division unit is used to divide the process cycle into pressure boosting and stabilization stage, pretreatment and filtration stage, adsorption and separation stage and purification and refining stage according to the purification task requirements of a single biogas purification process cycle.

[0066] The real-time path identification unit is used to capture the actual processing flow path in each process stage and collect the real-time process parameters corresponding to each path to form a set of real-time process parameter ranges.

[0067] The parameter range matching unit is used to compare the real-time process parameter range set with the standard process parameter range set generated by the process path and standard parameter construction module to determine the overlap between the two in each parameter type.

[0068] The path locking unit is used to lock the real-time processing flow path when there is overlap between the range of parameters of the same type in the real-time process parameter range set and the standard process parameter range set.

[0069] The module for constructing the operational status matrix and evaluating stability constructs the operational status matrix of each process stage in different process cycles based on the locking results of the real-time processing flow path. By calculating the process similarity of the operational status matrix of the same process stage in adjacent process cycles, and combining the mean and standard deviation of the process similarity, the stability of the process stage is evaluated.

[0070] Specifically, the operating state matrix construction and stability assessment module includes an operating state matrix construction unit, a process similarity calculation unit, and a stability assessment unit;

[0071] The operation status matrix construction unit, based on the path locking results of the real-time path identification and locking module, constructs the operation status matrix of each process stage within the corresponding process cycle with the process node number as the row index and the processing flow path number as the column index, and identifies whether the path is locked through the matrix elements.

[0072] The process similarity calculation unit is used to calculate the number of valid paths in the Boolean logic AND result of the running state matrix of the same process stage within two adjacent process cycles, and the number of valid paths in the Boolean logic OR result. The process similarity is obtained by the ratio of the two.

[0073] The stability assessment unit is used to statistically analyze the mean and standard deviation of process similarity over multiple process cycles. By combining the deviation of individual process similarity from the mean, the stability of the corresponding process stage is calculated.

[0074] The optimization control coupling gap determination module constructs a two-dimensional scattered coordinate system based on the stability of each process stage. By initializing the clustering scale, statistically analyzing the fluctuation value of the operating state, and adjusting the clustering scale, it determines the clustering scale with the smallest fluctuation value of the operating state and uses this clustering scale as the intelligent optimization control coupling gap.

[0075] Specifically, the optimization control coupling gap determination module includes a two-dimensional coordinate system construction unit, a cluster analysis unit, a fluctuation value statistics unit, and a coupling gap determination unit.

[0076] The two-dimensional coordinate system construction unit is used to construct a two-dimensional scattered coordinate system of biogas treatment operation status with the combination of process cycle and process stage as the horizontal independent variable and stability as the vertical dependent variable.

[0077] The clustering analysis unit is used to initialize the clustering scale, forming multiple clustering circles with each stability data point as the clustering point and the initialized clustering scale as the radius;

[0078] The fluctuation value statistics unit is used to calculate the variance of all stability data within each cluster circle and the stability of the cluster circle point, and to use this variance as the fluctuation value of the corresponding cluster circle's operating state.

[0079] The coupling gap determination unit is used to gradually adjust the clustering scale, repeat the clustering analysis and fluctuation value statistics process until the clustering scale that minimizes the fluctuation value of the running state of all clusters is found, and this clustering scale is determined as the intelligent optimization control coupling gap.

[0080] Please see Figure 1 In this second embodiment, an intelligent optimization control method for biogas pressurization adsorption purification is provided, applicable to the above embodiment one. This embodiment is applied to a large-scale livestock and poultry breeding waste biogas treatment project. The biogas treatment capacity of the project is 500 m³ / h. The volume fraction of methane in the raw biogas is 60%-65%, carbon dioxide is 30%-35%, and there are small amounts of hydrogen sulfide and impurities. The target is that the methane purity after purification is ≥97%. The continuous operation cycle of the system is 24 hours / process cycle, and a total of 5 process cycles are set (F=5).

[0081] The method includes the following steps:

[0082] Step S1: Coordinate all process steps of biogas treatment and uniformly identify them. Identify the process node parameters corresponding to each process step. Form a biogas pressurization adsorption purification process path based on the process sequence reflected by the process node parameters. Generate a set of standard process parameter ranges for the corresponding process path based on the parameter combination of each process node.

[0083] For example, all process steps in biogas treatment are coordinated and uniformly identified, and additional process node parameters in the process steps are identified. Each process node corresponds to one process step, and a biogas pressurization adsorption purification process path is formed according to the process sequence reflected in the process step flow of the process node parameters.

[0084] Process node parameters include pressure, temperature, adsorption time, and biogas flow rate. Any process node is labeled m. All processing flow paths formed by process node m are captured, and the e-th processing flow path formed by process node m is labeled as... Where r is the process node number and r≠m, and process node r is the process node traversed in the e-th processing flow path formed by process node m. This refers to the combination of process node parameter labels corresponding to the r-th process node, including the pressure value. Temperature value Adsorption time and biogas flow R represents the total number of process nodes;

[0085] Process flow paths are generated based on combinations of process node parameter labels. Standard process parameter range set ;

[0086] For example, by comprehensively considering all aspects of the biogas treatment process, a total of 8 process nodes (R=8) were identified, uniformly labeled as 1-8. The process node parameters include pressure (P), temperature (W), adsorption time (T), and biogas flow rate (Q). Three treatment flow paths (e=1, 2, 3) formed by process node 1 (m=1) were captured. Taking path L11 as an example, it passes through process nodes r=2-8, and the parameter label combination of each node is (Pᵣ, Wᵣ, Tᵣ, Qᵣ). Based on multiple sets of experimental data, a set of standard process parameter ranges was generated: pressure [0.6MPa, 1.2MPa], temperature [25℃, 40℃], adsorption time [30min, 60min], and biogas flow rate [400m³ / h, 600m³ / h].

[0087] Step S2: Based on the purification task requirements within the biogas purification process cycle, the process cycle is divided into multiple process stages. The real-time processing flow path within each process stage is identified, and a set of real-time process parameter ranges is generated. By matching the real-time process parameter range set with the standard process parameter range set, the real-time processing flow path that meets the requirements is locked.

[0088] For example, based on the purification task requirements in the fth biogas purification process cycle, the fth biogas purification process cycle is divided into G process stages. Each process stage is the execution stage of each purification sub-task, including the pressure boosting and stabilizing stage, the pretreatment filtration stage, the adsorption separation stage, and the purification and refining stage.

[0089] Identify all processing paths generated within the g-th process stage, and obtain the e-th real-time processing path formed by process node m within the g-th process stage, denoted as . And generate a real-time processing flow path. The set of real-time process parameter ranges will be used to define the real-time processing flow path. Real-time process parameter range set and processing flow path The standard process parameter range set is matched. If the process parameter ranges generated by the same process node parameter type in the real-time process parameter range set overlap with those in the standard process parameter range set, then the real-time processing flow path is locked. If the real-time process parameter range set overlaps with the process parameter ranges generated by the same process node parameter type in the standard process parameter range set, the real-time processing flow path will not be locked. ;

[0090] For example, each process cycle (f=1-5) is divided into four process stages (G=4): pressure boosting and stabilization stage (g=1), pretreatment and filtration stage (g=2), adsorption and separation stage (g=3), and purification and refining stage (g=4). Taking the adsorption and separation stage (g=3) of the third process cycle (f=3) as an example, five real-time processing paths were identified, and the real-time parameter ranges for each path were collected and matched with standard ranges. The results show that only path g... 13 (corresponding to L) 11 The real-time parameter range (pressure 0.7-1.1MPa, temperature 28-38℃, adsorption time 35-55min, flow rate 420-580m³ / h) completely overlaps with the standard range and is therefore locked; the other 4 paths are not locked because some parameters exceed the standard range.

[0091] Step S3: Based on the locking results of the real-time processing flow path, construct the operating status matrix of each process stage in different process cycles. By calculating the process similarity of the operating status matrix of the same process stage in adjacent process cycles, and combining the mean and standard deviation of the process similarity, evaluate the stability of the process stage.

[0092] For example, based on the real-time processing flow path The locked results are used to construct the operating state matrix of the g-th process stage within the f-th biogas purification process cycle. The row index of the running status matrix corresponds to the process node number, and the column index corresponds to the processing flow path number. If the real-time processing flow path... If locked, set the position of the matrix in the m-th row and e-th column of the running status matrix to 1. If the real-time processing flow path... If not locked, set the position of the matrix in the m-th row and e-th column of the running state matrix to 0;

[0093] Based on the operating state matrix, the stability of the g-th process stage under the cycle flow of the biogas purification process is evaluated. In the formula, Let g be the process similarity of the g-th process stage when the process transitions from the f-th biogas purification process cycle to the (f+1)-th biogas purification process cycle. This represents the stability of the g-th process stage when the f-th biogas purification process cycle transitions to the (f+1)-th biogas purification process cycle. This represents the operating state matrix of the g-th process stage within the f-th biogas purification process cycle. The operating state matrix of the g-th process stage within the (f+1)-th biogas purification process cycle The number of 1s contained in the result of an inter-Boolean logical AND operation. This represents the operating state matrix of the g-th process stage within the f-th biogas purification process cycle. The operating state matrix of the g-th process stage within the (f+1)-th biogas purification process cycle The number of 1s contained in the result of an inter-Boolean OR operation. The average of process similarity and , For the standard deviation of process similarity and ;

[0094] For example, based on the path locking results, a running status matrix U_fg for each process stage is constructed. The row index of the matrix is ​​the process node number (1-8), and the column index is the processing flow path number (1-5). The matrix element corresponding to the locked path is set to 1, and the element that is not locked is set to 0. Taking g=3 (adsorption separation stage) as an example, the process similarity Δf of adjacent process cycles (f=1-2, f=2-3, f=3-4, f=4-5) is calculated to be 0.85, 0.88, 0.92, and 0.89, respectively. The mean process similarity Δ̄=0.885 and the standard deviation Δ̄̄=0.023 are also calculated. The stability H_fg of each cycle is calculated to be 0.217, 0.130, 0.152, and 0.022, respectively.

[0095] Step S4: Construct a two-dimensional scatter coordinate system based on the stability of each process stage. Determine the clustering scale with the smallest operating state fluctuation value by initializing the clustering scale, statistically analyzing the operating state fluctuation value, and adjusting the clustering scale. Use this clustering scale as the coupling gap for intelligent optimization control.

[0096] For example, based on stability Construct a two-dimensional scattered coordinate system to represent the operating status of biogas treatment, with the horizontal independent variable being... The longitudinal dependent variable is ;

[0097] The initial clustering scale for stability is x, and the stability is expressed in a two-dimensional scatter coordinate system. Using the clustering point x as the clustering radius, we obtain the clustering circle. Statistical clustering circle Stability and stability The variance of stability is used as a measure of stability. The fluctuation value of the operating state of the clustering points;

[0098] Adjust the size of the clustering scale x to determine the clustering scale x until each clustering circle in the two-dimensional scatter coordinate system satisfies the minimum value of the running state fluctuation, and use the clustering scale x as the coupling gap of intelligent optimization control;

[0099] A two-dimensional scatter coordinate system was constructed with (f, g) as the horizontal independent variable and H_fg as the vertical dependent variable. The initial clustering scale x=0.05 was used to count the fluctuation values ​​of the operating state of each cluster circle. x was gradually adjusted to 0.06, 0.07, 0.08, and 0.09. When x=0.08, the fluctuation value of each cluster circle reached the minimum value (average fluctuation value 0.0015). This clustering scale was determined to be the intelligent optimization control coupling gap. After applying this coupling gap to the system control, the stability H53 of the adsorption separation stage in the 5th process cycle was 0.018, the methane purification purity reached 97.8%, and the unit energy consumption was reduced to 2.8 kWh / m³. Compared with the initial control (coupling gap 0.1, purity 95.2%, energy consumption 3.2 kWh / m³), the purity increased by 2.6% and the energy consumption decreased by 12.5%.

[0100] 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.

[0101] 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 smart optimization control method for biogas pressurization adsorption purification, characterized in that, The method includes the following steps: Step S1: Coordinate all process steps of biogas treatment and uniformly identify them. Identify the process node parameters corresponding to each process step. Form a biogas pressurization adsorption purification process path based on the process sequence reflected by the process node parameters. Generate a set of standard process parameter ranges for the corresponding process path based on the parameter combination of each process node. Step S2: Based on the purification task requirements within the biogas purification process cycle, the process cycle is divided into multiple process stages. The real-time processing flow path within each process stage is identified, and a set of real-time process parameter ranges is generated. By matching the real-time process parameter range set with the standard process parameter range set, the real-time processing flow path that meets the requirements is locked. Step S3: Based on the locking results of the real-time processing flow path, construct the operating status matrix of each process stage in different process cycles. By calculating the process similarity of the operating status matrix of the same process stage in adjacent process cycles, and combining the mean and standard deviation of the process similarity, evaluate the stability of the process stage. Step S4: Construct a two-dimensional scatter coordinate system based on the stability of each process stage. Determine the clustering scale with the smallest operating state fluctuation value by initializing the clustering scale, statistically analyzing the operating state fluctuation value, and adjusting the clustering scale. Use this clustering scale as the coupling gap for intelligent optimization control.

2. The intelligent optimization control method for biogas pressurization adsorption purification according to claim 1, characterized in that, The specific implementation process of step S1 includes: All process steps in biogas treatment are coordinated and uniformly identified, and additional process node parameters in the process steps are identified. Each process node corresponds to one process step, and the process sequence reflected in the process step flow of the process node parameters is used to form a biogas pressurization adsorption purification process path. The process node parameters include pressure, temperature, adsorption time, and biogas flow rate. Any process node is labeled m, and all processing flow paths formed by process node m are captured. The e-th processing flow path formed by process node m is labeled as... Where r is the process node number and r≠m, and process node r is the process node traversed in the e-th processing flow path formed by process node m. This refers to the combination of process node parameter labels corresponding to the r-th process node, including the pressure value. Temperature value Adsorption time and biogas flow R represents the total number of process nodes; Process flow paths are generated based on combinations of process node parameter labels. Standard process parameter range set .

3. The intelligent optimization control method for biogas pressurization adsorption purification according to claim 2, characterized in that, The specific implementation process of step S2 includes: Based on the purification task requirements in the fth biogas purification process cycle, the fth biogas purification process cycle is divided into G process stages. The process stages are the execution stages of each purification sub-task, including the pressure boosting and stabilizing stage, the pretreatment and filtration stage, the adsorption and separation stage, and the purification and refining stage. Identify all processing paths generated within the g-th process stage, and obtain the e-th real-time processing path formed by process node m within the g-th process stage, denoted as . And generate a real-time processing flow path. The set of real-time process parameter ranges will be used to define the real-time processing flow path. Real-time process parameter range set and processing flow path The standard process parameter range set is matched. If the process parameter ranges generated by the same process node parameter type in the real-time process parameter range set overlap with those in the standard process parameter range set, then the real-time processing flow path is locked. If the real-time process parameter range set overlaps with the process parameter ranges generated by the same process node parameter type in the standard process parameter range set, the real-time processing flow path will not be locked. .

4. The intelligent optimization control method for biogas pressurization adsorption purification according to claim 3, characterized in that, The specific implementation process of step S3 includes: Based on real-time processing workflow path The locked results are used to construct the operating state matrix of the g-th process stage within the f-th biogas purification process cycle. The row index of the running status matrix corresponds to the process node number, and the column index corresponds to the processing flow path number. If the real-time processing flow path... If locked, set the position of the matrix in the m-th row and e-th column of the running status matrix to 1. If the real-time processing flow path... If not locked, set the position of the matrix in the m-th row and e-th column of the running state matrix to 0; Based on the operating state matrix, the stability of the g-th process stage under the cycle flow of the biogas purification process is evaluated. In the formula, Let g be the process similarity of the g-th process stage when the process transitions from the f-th biogas purification process cycle to the (f+1)-th biogas purification process cycle. This represents the stability of the g-th process stage when the f-th biogas purification process cycle transitions to the (f+1)-th biogas purification process cycle. This represents the operating state matrix of the g-th process stage within the f-th biogas purification process cycle. The operating state matrix of the g-th process stage within the (f+1)-th biogas purification process cycle The number of 1s contained in the result of an inter-Boolean logical AND operation. This represents the operating state matrix of the g-th process stage within the f-th biogas purification process cycle. The operating state matrix of the g-th process stage within the (f+1)-th biogas purification process cycle The number of 1s contained in the result of an inter-Boolean OR operation. The average of process similarity and , For the standard deviation of process similarity and .

5. The intelligent optimization control method for biogas pressurization adsorption purification according to claim 4, characterized in that, The specific implementation process of step S4 includes: Based on stability Construct a two-dimensional scattered coordinate system to represent the operating status of biogas treatment, with the horizontal independent variable being... The longitudinal dependent variable is ; The initial clustering scale for stability is x, and the stability is expressed in a two-dimensional scatter coordinate system. Using the clustering point x as the clustering radius, we obtain the clustering circle. Statistical clustering circle Stability and stability The variance of stability is used as a measure of stability. The fluctuation value of the operating state of the clustering points; Adjust the size of the clustering scale x to determine the clustering scale x until each clustering circle in the two-dimensional scatter coordinate system satisfies the minimum value of the running state fluctuation, and use the clustering scale x as the intelligent optimization control coupling gap.

6. An intelligent optimization control system for biogas pressurization adsorption purification, executing the intelligent optimization control method for biogas pressurization adsorption purification as described in any one of claims 1-5, characterized in that, The system includes: a process path and standard parameter construction module, a real-time path identification and locking module, an operating status matrix construction and stability assessment module, and an optimization control coupling gap determination module. Each module works in concert to sequentially complete the construction of the processing flow path and standard parameters, real-time path identification and locking, operating status stability assessment, and optimization control coupling gap determination, thereby realizing intelligent optimization control for biogas pressurization adsorption purification. The process path and standard parameter construction module is used to coordinate all process steps of biogas treatment and uniformly identify them, identify the process node parameters corresponding to each process step, form a biogas pressurization adsorption purification process path according to the process sequence reflected by the process node parameters, and generate a set of standard process parameter ranges for the corresponding process path based on the parameter combination of each process node. The real-time path identification and locking module divides the biogas purification process into multiple process stages according to the purification task requirements within the process cycle. It identifies the real-time processing flow path within each process stage and generates a set of real-time process parameter ranges. By matching the real-time process parameter range set with the standard process parameter range set, it locks the real-time processing flow path that meets the requirements. The operation status matrix construction and stability evaluation module constructs the operation status matrix of each process stage in different process cycles based on the locking results of the real-time processing flow path. By calculating the process similarity of the operation status matrix of the same process stage in adjacent process cycles, and combining the mean and standard deviation of the process similarity, the stability of the process stage is evaluated. The optimized control coupling gap determination module constructs a two-dimensional scattered coordinate system based on the stability of each process stage. By initializing the clustering scale, statistically analyzing the fluctuation value of the operating state, and adjusting the clustering scale, it determines the clustering scale with the smallest fluctuation value of the operating state and uses this clustering scale as the intelligent optimized control coupling gap.

7. The intelligent optimized control system for biogas pressurization adsorption purification according to claim 6, characterized in that, The process path and standard parameter construction module includes a process link overall identification unit, a process node parameter identification unit, a processing flow path generation unit, and a standard parameter range set generation unit. The process flow identification unit is used to uniformly organize and identify all process flows in biogas treatment; The process node parameter identification unit is used to collect and identify the process node parameters corresponding to each process step. The process node parameters include pressure value, temperature value, adsorption time and biogas flow rate. The process flow path generation unit is used to connect each process step in series according to the process sequence reflected by the process node parameters to form a complete biogas pressurization adsorption purification process flow path. The standard parameter range set generation unit is used to determine the range of values ​​for pressure, temperature, adsorption time and biogas flow rate based on the parameter combination of each process node, and form a standard process parameter range set for the corresponding treatment process path.

8. The intelligent optimized control system for biogas pressurization adsorption purification according to claim 6, characterized in that, The real-time path identification and locking module includes a process stage division unit, a real-time path identification unit, a parameter range matching unit, and a path locking unit. The process stage division unit is used to divide the process cycle into pressure boosting and stabilization stage, pretreatment and filtration stage, adsorption and separation stage and purification and refining stage according to the purification task requirements of a single biogas purification process cycle. The real-time path identification unit is used to capture the actual processing flow path in each process stage and collect the real-time process parameters corresponding to each path to form a set of real-time process parameter ranges. The parameter range matching unit is used to compare the real-time process parameter range set with the standard process parameter range set generated by the process path and standard parameter construction module to determine the overlap between the two in each parameter type. The path locking unit is used to lock the real-time processing flow path when there is overlap between the range of parameters of the same type in the real-time process parameter range set and the standard process parameter range set.

9. The intelligent optimized control system for biogas pressurization adsorption purification according to claim 6, characterized in that, The operation state matrix construction and stability evaluation module includes an operation state matrix construction unit, a process similarity calculation unit, and a stability evaluation unit; The operation status matrix construction unit, based on the path locking results of the real-time path identification and locking module, constructs the operation status matrix of each process stage within the corresponding process cycle with the process node number as the row index and the processing flow path number as the column index, and identifies whether the path is locked through the matrix elements. The process similarity calculation unit is used to calculate the number of valid paths in the Boolean logic AND result of the running state matrix of the same process stage within two adjacent process cycles, and the number of valid paths in the Boolean logic OR result. The process similarity is obtained by the ratio of the two. The stability assessment unit is used to statistically analyze the mean and standard deviation of process similarity over multiple process cycles. By combining the deviation of individual process similarity from the mean, the stability of the corresponding process stage is calculated.

10. The intelligent optimized control system for biogas pressurization adsorption purification according to claim 6, characterized in that, The optimized control coupling gap determination module includes a two-dimensional coordinate system construction unit, a cluster analysis unit, a fluctuation value statistics unit, and a coupling gap determination unit. The two-dimensional coordinate system construction unit is used to construct a two-dimensional scattered coordinate system of biogas treatment operation status with the combination of process cycle and process stage as the horizontal independent variable and stability as the vertical dependent variable. The clustering analysis unit is used to initialize the clustering scale, forming multiple clustering circles with each stability data point as the clustering point and the initialized clustering scale as the radius; The fluctuation value statistics unit is used to calculate the variance of all stability data within each cluster circle and the stability of the cluster circle point, and to use this variance as the fluctuation value of the corresponding cluster circle's operating state. The coupling gap determination unit is used to gradually adjust the clustering scale, repeat the clustering analysis and fluctuation value statistics process until the clustering scale that minimizes the fluctuation value of the running state of all clusters is found, and this clustering scale is determined as the intelligent optimization control coupling gap.

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