A flue gas treatment analysis method and system based on multi-stage adsorption

Abstract

The application provides a flue gas treatment analysis method and system based on multistage adsorption, and belongs to the technical field of environmental protection. The flue gas treatment analysis method based on multistage adsorption comprises the following steps: obtaining flue gas inlet flow data, flue gas component concentration data, outlet pollutant concentration data of each stage of adsorption units, and bed layer temperature data of each stage of adsorption units; performing deviation calculation according to the flue gas component concentration data and a preset pollutant reference concentration to obtain a pollutant concentration deviation vector; combining a dynamic load distribution model, performing multistage load redistribution calculation according to the flue gas inlet flow data, the pollutant concentration deviation vector and the current adsorption capacity state of each stage of adsorption units to obtain target treatment loads of each stage of adsorption units.

Description

Technical Field

[0001] This invention belongs to the field of environmental protection technology and relates to a method and system for flue gas treatment and analysis based on multi-stage adsorption. Background Technology

[0002] Currently, flue gas treatment technology has become a crucial link in ensuring environmental quality in industrial waste gas treatment. Although traditional single-stage adsorption or single-purification processes are simple in structure and stable in operation, they have limitations in terms of pollutant removal efficiency, adsorbent utilization rate, and multi-component synergistic removal capability when dealing with flue gas with complex components. Therefore, multi-stage adsorption treatment technology has emerged. It uses multiple adsorption units connected in series or parallel to capture and transform pollutants in stages according to their different characteristics, thereby improving the overall purification effect and system adaptability.

[0003] In one existing technology, a multi-stage flue gas treatment system uses adsorption materials such as activated carbon, molecular sieves, or metal oxides to form a multi-stage bed, based on the SO2 and NO content in the flue gas. x VOCs s Based on the concentration gradient and reactivity of pollutants such as heavy metals, the adsorbent undergoes physical adsorption, chemical adsorption, or catalytic conversion sequentially. This method can improve the removal rate of pollutants to a certain extent and extend the service life of the adsorbent. However, in actual operation, the load distribution between each adsorption stage in this multi-stage treatment system relies on empirical settings and lacks a real-time response mechanism to dynamic changes in flue gas composition. Furthermore, when fluctuating flue gas flow rate or composition, the saturation of the preceding adsorption stage may affect the treatment efficiency of subsequent stages, leading to deviations in overall emission indicators. A fixed adsorbent regeneration cycle may also reduce system stability due to premature local deactivation.

[0004] In summary, existing technologies mostly employ preset operating parameters and static allocation strategies for regulation. However, this approach has limited dynamic matching and optimization capabilities for adsorption units at each stage under varying operating conditions and multi-component flue gas conditions, thus restricting further improvements in flue gas treatment accuracy and resource utilization efficiency. Summary of the Invention

[0005] This invention provides a method and system for flue gas treatment and analysis based on multi-stage adsorption, which solves the problems of pollutant removal efficiency fluctuation, low adsorbent utilization rate and system response lag caused by static distribution strategy in existing multi-stage flue gas adsorption treatment systems under varying operating conditions.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a flue gas treatment and analysis method based on multi-stage adsorption, comprising:

[0007] Acquire flue gas inlet flow rate data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit;

[0008] Based on the flue gas component concentration data and the preset pollutant baseline concentration, the deviation is calculated to obtain the pollutant concentration deviation vector;

[0009] By combining the dynamic load allocation model, multi-level load redistribution calculations are performed based on the flue gas inlet flow data, the pollutant concentration deviation vector, and the current adsorption capacity status of each adsorption unit, to obtain the target treatment load of each adsorption unit.

[0010] Based on the target treatment load, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data, the adsorption efficiency is evaluated, and an adsorption efficiency index is generated.

[0011] Based on the adsorption efficiency index and the preset regeneration trigger threshold, determine whether to initiate a local regeneration command;

[0012] Based on the local regeneration command or normal operation command, adjust the opening degree of the air intake valve and the bypass ratio of each adsorption unit, and update the weight parameters in the dynamic load distribution model simultaneously.

[0013] In one optional implementation, acquiring flue gas inlet flow rate data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit includes:

[0014] Flue gas inlet flow data are obtained using a thermal mass flow meter;

[0015] Flue gas component concentration data are obtained using an online gas chromatograph or a Fourier transform infrared spectrometer.

[0016] The pollutant concentration data at the outlet of each adsorption unit is obtained by a multi-component gas sensor array installed at the outlet of each adsorption unit.

[0017] Bed temperature data is acquired by a distributed temperature sensing network embedded within each level of the adsorption bed.

[0018] In one optional implementation, the step of calculating the deviation between the flue gas component concentration data and a preset pollutant baseline concentration to obtain a pollutant concentration deviation vector includes:

[0019] The measured concentrations of sulfur dioxide, nitrogen oxides, volatile organic compounds, and heavy metal vapors in the flue gas were compared with their respective emission limit benchmarks.

[0020] The results of each difference are normalized to form a pollutant concentration deviation vector with the same dimension as the number of pollutant types.

[0021] In one optional implementation, the step of combining a dynamic load allocation model to perform multi-level load redistribution calculations based on the flue gas inlet flow data, the pollutant concentration deviation vector, and the current adsorption capacity status of each adsorption unit, to obtain the target treatment load for each adsorption unit, includes:

[0022] A nonlinear mapping function is constructed with the adsorption unit sequence as the input variable and the pollutant removal amount per unit time as the output variable;

[0023] An adaptive adjustment factor is introduced, which is determined by the historical adsorption efficiency decay rate of each adsorption unit and the current bed temperature gradient.

[0024] The optimal load allocation scheme that satisfies the total processing capacity constraint is solved by weighted least squares method, and the target processing load of each adsorption unit is output.

[0025] In one optional implementation, the step of evaluating adsorption efficiency and generating an adsorption efficiency index based on the target treatment load, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data includes:

[0026] Calculate the ratio of the actual pollutant removal rate to the theoretical maximum removal rate for each adsorption unit;

[0027] The ratios above are adjusted for temperature based on the degree to which the bed temperature deviates from the optimal reaction temperature range.

[0028] The corrected ratio is weighted and fused with the completion rate of the target treatment load to generate an adsorption efficiency index between 0 and 1.

[0029] In one optional implementation, determining whether to initiate a local regeneration command based on the adsorption efficiency index and a preset regeneration trigger threshold includes:

[0030] When the adsorption efficiency index of any first-level adsorption unit is lower than the first preset regeneration trigger threshold for multiple consecutive sampling cycles, a local regeneration command is generated for that level.

[0031] When the adsorption efficiency index of two adjacent adsorption units decreases synchronously and the difference between them is less than the second preset synergistic deactivation criterion, a joint regeneration command is generated.

[0032] The regeneration command includes parameters such as regeneration start time, regeneration duration, and regeneration energy input intensity.

[0033] In one optional implementation, adjusting the inlet valve opening and bypass ratio of each adsorption unit according to the local regeneration command or normal operation command, and synchronously updating the weight parameters in the dynamic load allocation model, includes:

[0034] The electric regulating valves installed on the inlet pipes of each adsorption unit are controlled to adjust their opening degree according to the target treatment load ratio.

[0035] Open the switching valve in the bypass pipeline connected in parallel with the adsorption unit to be regenerated, so that the flue gas bypasses the unit.

[0036] The changing trend of the adsorption efficiency index during this operating cycle is used as a feedback signal, and the weights of the adaptive adjustment factors in the dynamic load allocation model are updated online using the recursive least squares algorithm.

[0037] Secondly, the present invention provides a flue gas treatment and analysis system based on multi-stage adsorption, comprising:

[0038] The data acquisition module is used to acquire flue gas inlet flow data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit.

[0039] The concentration deviation calculation module is used to calculate the deviation between the flue gas component concentration data and the preset pollutant benchmark concentration to obtain the pollutant concentration deviation vector.

[0040] The load allocation calculation module is used to combine the dynamic load allocation model and perform multi-level load redistribution calculations based on the flue gas inlet flow data, the pollutant concentration deviation vector and the current adsorption capacity status of each adsorption unit to obtain the target treatment load of each adsorption unit.

[0041] The performance evaluation module is used to evaluate the adsorption performance based on the target treatment load, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data, and generate an adsorption performance index.

[0042] The regeneration decision module is used to determine whether to initiate a local regeneration command based on the adsorption efficiency index and the preset regeneration trigger threshold.

[0043] The execution control module is used to adjust the opening degree and bypass ratio of the air intake valve of each adsorption unit according to the local regeneration command or normal operation command, and to update the weight parameters in the dynamic load distribution model simultaneously.

[0044] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the flue gas treatment and analysis method based on multi-stage adsorption as described in any one of the above.

[0045] Fourthly, the present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the flue gas treatment and analysis method based on multi-stage adsorption as described above.

[0046] Compared with existing technologies, the present invention has the following advantages: By constructing a dynamic load allocation model with real-time flue gas composition, flow rate, and adsorption unit status as input, the present invention abandons the static allocation strategy that relies on fixed empirical parameters, enabling the processing load of each adsorption unit to adaptively adjust with changes in flue gas conditions; by introducing the adsorption efficiency index as a regeneration trigger criterion, the present invention achieves accurate identification of the adsorbent deactivation state, avoiding energy waste or premature penetration risks caused by fixed-cycle regeneration; by linking the control of the inlet valve and bypass pipeline, and updating the model weight parameters online, a closed-loop optimization mechanism of "perception-analysis-decision-execution-learning" is formed, which significantly improves the synergistic removal accuracy of multi-component pollutants, the adsorbent resource utilization efficiency, and the system's operational stability under varying conditions. Attached Figure Description

[0047] Figure 1 This is a schematic flowchart of the flue gas treatment and analysis method based on multi-stage adsorption of the present invention;

[0048] Figure 2 This is a schematic diagram of the flue gas treatment and analysis system based on multi-stage adsorption of the present invention. Detailed Implementation

[0049] 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.

[0050] like Figure 1 As shown, the first embodiment of the present invention provides a flue gas treatment and analysis method based on multi-stage adsorption, including the following steps:

[0051] S101, acquire flue gas inlet flow data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit.

[0052] S102, Based on the flue gas component concentration data and the preset pollutant benchmark concentration, the deviation is calculated to obtain the pollutant concentration deviation vector;

[0053] S103, combined with the dynamic load allocation model, multi-level load redistribution calculation is performed based on the flue gas inlet flow data, the pollutant concentration deviation vector and the current adsorption capacity status of each adsorption unit to obtain the target treatment load of each adsorption unit.

[0054] S104. Based on the target treatment load, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data, the adsorption efficiency is evaluated, and an adsorption efficiency index is generated.

[0055] S105, Based on the adsorption efficiency index and the preset regeneration trigger threshold, determine whether to initiate a local regeneration command;

[0056] S106, according to the local regeneration command or normal operation command, adjust the opening degree of the air intake valve and the bypass ratio of each adsorption unit, and update the weight parameters in the dynamic load distribution model simultaneously.

[0057] In step S101, it is necessary to acquire flue gas inlet flow rate data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit, including:

[0058] Flue gas inlet flow rate data are obtained through a thermal mass flow meter; flue gas component concentration data are obtained through an online gas chromatograph or Fourier transform infrared spectrometer; pollutant concentration data at the outlet of each adsorption unit are obtained through a multi-component gas sensor array installed at the outlet of each adsorption unit; and bed temperature data are obtained through a distributed temperature sensing network embedded inside each adsorption bed.

[0059] It should be noted that the flue gas inlet flow rate data is used to characterize the total flue gas flux entering the multi-stage adsorption system. The measurement point is located at the inlet of the main flue gas duct of the system. The thermal mass flow meter outputs a volumetric flow rate signal in standard cubic meters per hour (Nm³ / h) in real time through the principle of heat conduction. Flue gas component concentration data includes sulfur dioxide (SO2) and nitrogen oxides (NOx). x ), volatile organic compounds (VOCs) s ) and heavy metal vapors (such as Hg) o The concentration values ​​of pollutants were obtained through continuous sampling and analysis using an online gas chromatograph or FTIR spectrometer installed on the main flue, with a sampling frequency of no less than 1 Hz. Pollutant concentration data at the outlet of each adsorption unit were collected by a multi-component gas sensor array integrated on the corresponding outlet pipe. This array included electrochemical sensors, metal oxide semiconductor sensors, and a laser absorption spectroscopy module, which were cross-validated for different pollutant types. Bed temperature data was acquired through a distributed fiber optic temperature measurement system or an embedded thermocouple network. The temperature measurement points were uniformly distributed along the axial and radial directions of the adsorption bed, achieving a spatial resolution of 0.5 m and a temporal resolution better than 5 s.

[0060] In step S102, the deviation is calculated based on the flue gas component concentration data and the preset pollutant baseline concentration to obtain the pollutant concentration deviation vector.

[0061] The measured concentrations of sulfur dioxide, nitrogen oxides, volatile organic compounds, and heavy metal vapors in the flue gas were compared with their respective emission limit benchmarks.

[0062] The results of each difference are normalized to form a pollutant concentration deviation vector with the same dimension as the number of pollutant types.

[0063] In one implementation, the formula for calculating the pollutant concentration deviation vector D is as follows:

[0064]

[0065] in, These represent the measured concentrations of sulfur dioxide, nitrogen oxides, volatile organic compounds, and mercury vapor, respectively. This refers to the corresponding national or local emission limit benchmarks.

[0066] It should be noted that the preset pollutant baseline concentration is a legal emission standard value or an internal control target value, stored in the control system database. Deviation vector After normalization, the values ​​of each component range from [-1, +∞), with positive values ​​indicating exceeding the limit and negative values ​​indicating falling below the baseline. This vector, as one of the core inputs of the dynamic load allocation model, directly reflects the degree of deviation of the current flue gas pollution load from the allowable limit, providing a quantitative basis for subsequent load redistribution.

[0067] The degree of deviation of the pollution load from the allowable limit provides a quantitative basis for subsequent load redistribution.

[0068] In step S103, combined with the dynamic load allocation model, multi-level load redistribution calculation is performed based on the flue gas inlet flow data, the pollutant concentration deviation vector, and the current adsorption capacity status of each adsorption unit to obtain the target treatment load of each adsorption unit.

[0069] A nonlinear mapping function is constructed with the adsorption unit sequence as the input variable and the pollutant removal amount per unit time as the output variable;

[0070] An adaptive adjustment factor is introduced, which is determined by the historical adsorption efficiency decay rate of each adsorption unit and the current bed temperature gradient.

[0071] The optimal load allocation scheme that satisfies the total processing capacity constraint is solved by weighted least squares method, and the target processing load of each adsorption unit is output.

[0072] In one implementation, a nonlinear mapping function is constructed. , with adsorption unit sequence As input variables, the amount of pollutants removed per unit time For output variables; introduce adaptive adjustment factors. The formula for calculating the number is:

[0073]

[0074] in, For the first Historical average adsorption efficiency of the adsorption unit. For its initial efficiency, The current average temperature of the bed. For the optimal reaction temperature, This is the temperature tolerance threshold. Weighting coefficient (0.6 ≤ ≤ 0.8).

[0075] The following optimization problem is solved using the weighted least squares method:

[0076]

[0077] in, For the first The target treatment load of the level (expressed as a percentage of flue gas flow). The pollutant removal tasks required at this level, The weights are set based on the adsorbent type and remaining lifetime. Given the total inlet flow rate, and based on the aforementioned adjustment factors, the pollutant removal rate per unit time is... It can be represented as: .

[0078] It should be noted that the dynamic load allocation model is deployed in the central controller, which receives multi-source signals from the data acquisition module in real time and outputs load allocation instructions for each adsorption unit online according to the algorithm described above. This model abandons the traditional fixed ratio allocation method and instead dynamically adjusts the weights according to the actual state of the adsorbent (efficiency decay rate) and thermodynamic conditions (temperature deviation), ensuring that highly active units bear a higher load and preventing inefficient units from penetrating prematurely.

[0079] In step S104, the adsorption efficiency is evaluated based on the target treatment load, the pollutant concentration data at the outlet of each adsorption unit, and the bed temperature data, and an adsorption efficiency index is generated.

[0080] Calculate the ratio of the actual pollutant removal rate to the theoretical maximum removal rate for each adsorption unit;

[0081] The ratios above are adjusted for temperature based on the degree to which the bed temperature deviates from the optimal reaction temperature range.

[0082] The corrected ratio is weighted and fused with the completion rate of the target treatment load to generate an adsorption efficiency index between 0 and 1.

[0083] In one implementation, the adsorption efficiency index Ei is calculated using the following formula:

[0084]

[0085] in, This represents the actual removal rate. This represents the theoretical maximum removal rate (determined by the adsorption isotherm). This represents the actual flue gas flow rate passing through this stage. To handle the target load, For performance weighting coefficients (0.7≤ ≤ 0.9), This is the temperature sensitivity coefficient.

[0086] It should be noted that, The value ranges from [0,1], with values ​​closer to 1 indicating better operating conditions for that adsorption unit. The temperature correction term uses an exponential decay form to reflect the nonlinear effect of temperature deviation on the rate of chemisorption or catalytic reaction. This index comprehensively reflects the unit's "capacity-task" matching degree and is a key criterion for regeneration decisions.

[0087] In step S105, based on the adsorption efficiency index and the preset regeneration trigger threshold, it is determined whether to initiate a local regeneration command.

[0088] When the adsorption efficiency index of any first-level adsorption unit is lower than the first preset regeneration trigger threshold for multiple consecutive sampling cycles, a local regeneration command is generated for that level.

[0089] When the adsorption efficiency index of two adjacent adsorption units decreases synchronously and the difference between them is less than the second preset synergistic deactivation criterion, a joint regeneration command is generated.

[0090] The regeneration command includes parameters such as regeneration start time, regeneration duration, and regeneration energy input intensity.

[0091] In one implementation, a first preset regeneration trigger threshold is set. = 0.45, Second preset co-activation criterion = 0.15. When any level satisfy < And when the values ​​are below this threshold for 5 consecutive sampling periods (10 s per period), a local regeneration command is generated for that level; when the values ​​of two adjacent levels are below this threshold, a local regeneration command is generated for that level. and +1 simultaneously satisfies < When both parameters show a downward trend, a joint regeneration command is generated. The regeneration command includes the regeneration start time, duration (usually 30-120 min), and energy input intensity (such as steam flow rate and electric heating power).

[0092] It should be noted that the regeneration triggering logic is executed by the regeneration decision module, whose input is the time-series data of each Ei. This mechanism avoids energy waste (such as regeneration before the adsorbent is saturated) or safety risks (such as regeneration only after penetration) caused by fixed-cycle regeneration, and achieves precise regeneration on demand.

[0093] In step S106, the opening degree of the air intake valve and the bypass ratio of each adsorption unit are adjusted according to the local regeneration command or normal operation command, and the weight parameters in the dynamic load distribution model are updated synchronously.

[0094] The electric regulating valves installed on the inlet pipes of each adsorption unit are controlled to adjust their opening degree according to the target treatment load ratio.

[0095] Open the switching valve in the bypass pipeline connected in parallel with the adsorption unit to be regenerated, so that the flue gas bypasses the unit.

[0096] The changing trend of the adsorption efficiency index during this operating cycle is used as a feedback signal, and the weights of the adaptive adjustment factors in the dynamic load allocation model are updated online using the recursive least squares algorithm.

[0097] In one implementation, the control system sends an opening control signal to the electrically adjustable valves on the inlet pipes of each adsorption unit, thereby adjusting their opening degree. satisfy Simultaneously, the pneumatic switching valve on the parallel bypass pipeline of the unit to be regenerated is opened, allowing the flue gas to completely bypass the unit. Furthermore, during this operating cycle... slope of change As a feedback signal, the adaptive adjustment factor is updated online using a recursive least squares algorithm. Weights in With coefficient .

[0098] It should be noted that both the electric regulating valve and the bypass switching valve are driven by a PLC controller, with a response delay of less than 2 seconds. Online updates of model parameters ensure that the dynamic load distribution model can continuously adapt to long-term changes such as adsorbent aging and operating condition drift, forming a closed-loop learning mechanism.

[0099] In summary, this invention discloses a flue gas treatment analysis method based on multi-stage adsorption, including acquiring flue gas inlet flow rate data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit; calculating the deviation between the flue gas component concentration data and a preset pollutant baseline concentration to obtain a pollutant concentration deviation vector; combining a dynamic load allocation model, performing multi-stage load redistribution calculations based on the flue gas inlet flow rate data, the pollutant concentration deviation vector, and the current adsorption capacity status of each adsorption unit to obtain the target treatment load of each adsorption unit; evaluating adsorption efficiency based on the target treatment load, the pollutant concentration data at the outlet of each adsorption unit, and the bed temperature data to generate an adsorption efficiency index; determining whether to initiate a local regeneration command based on the adsorption efficiency index and a preset regeneration trigger threshold; adjusting the inlet valve opening and bypass ratio of each adsorption unit according to the local regeneration command or normal operation command, and synchronously updating the weight parameters in the dynamic load allocation model.

[0100] This method constructs a dynamic load allocation model with real-time multi-source sensor data as input, enabling adaptive reconfiguration of the processing load of each adsorption unit and solving the efficiency fluctuation problem caused by traditional static allocation strategies under varying operating conditions. By introducing the adsorption efficiency index as a regeneration criterion, it achieves accurate identification of adsorbent deactivation and on-demand regeneration, significantly improving adsorbent utilization. Through coordinated control of the inlet valve and bypass pipeline, and online updating of model parameters, a closed-loop optimization mechanism of perception, analysis, decision-making, execution, and learning is formed, effectively ensuring the stability of the synergistic removal of multi-component pollutants and the economic efficiency of system operation.

[0101] like Figure 2 As shown, the second embodiment of the present invention provides a flue gas treatment and analysis system based on multi-stage adsorption, comprising:

[0102] Data acquisition module 10 is used to acquire flue gas inlet flow data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit.

[0103] The concentration deviation calculation module 20 is used to calculate the deviation between the flue gas component concentration data and the preset pollutant benchmark concentration to obtain the pollutant concentration deviation vector.

[0104] The load allocation calculation module 30 is used to combine the dynamic load allocation model and perform multi-level load redistribution calculation based on the flue gas inlet flow data, the pollutant concentration deviation vector and the current adsorption capacity status of each adsorption unit to obtain the target treatment load of each adsorption unit.

[0105] The performance evaluation module 40 is used to evaluate the adsorption performance based on the target treatment load, the pollutant concentration data at the outlet of each adsorption unit, and the bed temperature data, and generate an adsorption performance index.

[0106] Regeneration decision module 50 is used to determine whether to initiate a local regeneration command based on the adsorption efficiency index and the preset regeneration trigger threshold.

[0107] The execution control module 60 is used to adjust the opening degree and bypass ratio of the air intake valve of each adsorption unit according to the local regeneration command or normal operation command, and to update the weight parameters in the dynamic load distribution model simultaneously.

[0108] It should be noted that the flue gas treatment and analysis system based on multi-stage adsorption provided in this embodiment of the invention is used to execute all the process steps of the flue gas treatment and analysis method based on multi-stage adsorption in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0109] Thirdly, the present invention also provides an electronic device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor executes the computer program to implement the flue gas treatment and analysis method based on multi-stage adsorption as described in any one of the above.

[0110] The present invention also provides a computer-readable storage medium comprising a stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the flue gas treatment and analysis method based on multi-stage adsorption as described above.

[0111] This invention also provides an electronic device. The electronic device includes a processor, a memory, and a computer program stored in the memory and executable on the processor, such as a flue gas treatment and analysis program. When the processor executes the computer program, it implements the steps described in the various embodiments of the multi-stage adsorption-based flue gas treatment and analysis method, for example... Figure 1 The step S101 shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit in the above-described device embodiments, such as the performance evaluation module.

[0112] For example, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the electronic device.

[0113] The electronic device may be an industrial control computer, a programmable logic controller (PLC), or an edge computing gateway. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that the above components are merely examples of electronic devices and do not constitute a limitation on the electronic device. It may include more or fewer components than described above, or combine certain components, or different components. For example, the electronic device may also include analog input modules, digital output modules, industrial Ethernet interfaces, etc.

[0114] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the electronic device, connecting all parts of the electronic device via various interfaces and lines.

[0115] The memory can be used to store the computer programs and / or modules. The processor implements various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and by calling data stored in the memory. The memory may mainly include a program storage area and a data storage area. The program storage area may store the operating system, application programs required for at least one function (such as data acquisition services, model inference engines, etc.), etc.; the data storage area may store data created based on system operation (such as historical performance indices, model parameters, etc.). In addition, the memory may include high-speed random access memory and may also include non-volatile memory, such as solid-state drives, SD cards, flash memory chips, or other volatile solid-state storage devices.

[0116] Wherein, if the modules / units integrated in the electronic device are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content included in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electrical carrier signals and telecommunication signals.

[0117] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0118] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

[0119] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principles of this invention are further supplemented below with a specific application scenario.

Claims

1. A method for flue gas treatment and analysis based on multi-stage adsorption, characterized in that, Includes the following steps: Acquire flue gas inlet flow rate data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit; Based on the flue gas component concentration data and the preset pollutant baseline concentration, the deviation is calculated to obtain the pollutant concentration deviation vector; By combining the dynamic load allocation model, multi-level load redistribution calculations are performed based on the flue gas inlet flow data, the pollutant concentration deviation vector, and the current adsorption capacity status of each adsorption unit, to obtain the target treatment load of each adsorption unit. Based on the target treatment load, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data, the adsorption efficiency is evaluated, and an adsorption efficiency index is generated. Based on the adsorption efficiency index and the preset regeneration trigger threshold, determine whether to initiate a local regeneration command; Based on the local regeneration command or normal operation command, adjust the opening degree of the air intake valve and the bypass ratio of each adsorption unit, and update the weight parameters in the dynamic load distribution model simultaneously.

2. The flue gas treatment and analysis method based on multi-stage adsorption according to claim 1, characterized in that, Acquire flue gas inlet flow rate data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit, including: Flue gas inlet flow data are obtained using a thermal mass flow meter; Flue gas component concentration data are obtained using an online gas chromatograph or a Fourier transform infrared spectrometer. The pollutant concentration data at the outlet of each adsorption unit is obtained by a multi-component gas sensor array installed at the outlet of each adsorption unit. Bed temperature data is acquired by a distributed temperature sensing network embedded within each level of the adsorption bed.

3. The flue gas treatment and analysis method based on multi-stage adsorption according to claim 1, characterized in that, Based on the flue gas component concentration data and the preset pollutant baseline concentration, a deviation calculation is performed to obtain the pollutant concentration deviation vector, including: The measured concentrations of sulfur dioxide, nitrogen oxides, volatile organic compounds, and heavy metal vapors in the flue gas were compared with their respective emission limit benchmarks. The results of each difference are normalized to form a pollutant concentration deviation vector with the same dimension as the number of pollutant types.

4. The flue gas treatment and analysis method based on multi-stage adsorption according to claim 1, characterized in that, The combined dynamic load allocation model, based on the flue gas inlet flow data, the pollutant concentration deviation vector, and the current adsorption capacity status of each adsorption unit, performs multi-level load redistribution calculations to obtain the target treatment load for each adsorption unit, including: A nonlinear mapping function is constructed with the adsorption unit sequence as the input variable and the pollutant removal amount per unit time as the output variable; An adaptive adjustment factor is introduced, which is determined by the historical adsorption efficiency decay rate of each adsorption unit and the current bed temperature gradient. The optimal load allocation scheme that satisfies the total processing capacity constraint is solved by weighted least squares method, and the target processing load of each adsorption unit is output.

5. The flue gas treatment and analysis method based on multi-stage adsorption according to claim 1, characterized in that, Based on the target treatment load, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data, adsorption efficiency is evaluated to generate an adsorption efficiency index, including: Calculate the ratio of the actual pollutant removal rate to the theoretical maximum removal rate for each adsorption unit; The ratios above are adjusted for temperature based on the degree to which the bed temperature deviates from the optimal reaction temperature range. The corrected ratio is weighted and fused with the completion rate of the target treatment load to generate an adsorption efficiency index between 0 and 1.

6. The flue gas treatment and analysis method based on multi-stage adsorption according to claim 1, characterized in that, Based on the adsorption efficiency index and the preset regeneration trigger threshold, determine whether to initiate a local regeneration command, including: When the adsorption efficiency index of any first-level adsorption unit is lower than the first preset regeneration trigger threshold for multiple consecutive sampling cycles, a local regeneration command is generated for that level. When the adsorption efficiency index of two adjacent adsorption units decreases synchronously and the difference between them is less than the second preset synergistic deactivation criterion, a joint regeneration command is generated. The regeneration command includes parameters such as regeneration start time, regeneration duration, and regeneration energy input intensity.

7. The flue gas treatment and analysis method based on multi-stage adsorption according to claim 1, characterized in that, Based on the local regeneration command or normal operation command, adjust the opening degree and bypass ratio of the inlet valves of each adsorption unit, and synchronously update the weight parameters in the dynamic load distribution model, including: The electric regulating valves installed on the inlet pipes of each adsorption unit are controlled to adjust their opening degree according to the target treatment load ratio. Open the switching valve in the bypass pipeline connected in parallel with the adsorption unit to be regenerated, so that the flue gas bypasses the unit. The changing trend of the adsorption efficiency index during this operating cycle is used as a feedback signal, and the weights of the adaptive adjustment factors in the dynamic load allocation model are updated online using the recursive least squares algorithm.

8. A flue gas treatment and analysis system based on multi-stage adsorption, comprising: The data acquisition module is used to acquire flue gas inlet flow data, flue gas component concentration data, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data of each adsorption unit. The concentration deviation calculation module is used to calculate the deviation between the flue gas component concentration data and the preset pollutant benchmark concentration to obtain the pollutant concentration deviation vector. The load allocation calculation module is used to combine the dynamic load allocation model and perform multi-level load redistribution calculations based on the flue gas inlet flow data, the pollutant concentration deviation vector and the current adsorption capacity status of each adsorption unit to obtain the target treatment load of each adsorption unit. The performance evaluation module is used to evaluate the adsorption performance based on the target treatment load, pollutant concentration data at the outlet of each adsorption unit, and bed temperature data, and generate an adsorption performance index. The regeneration decision module is used to determine whether to initiate a local regeneration command based on the adsorption efficiency index and the preset regeneration trigger threshold. The execution control module is used to adjust the opening degree and bypass ratio of the air intake valve of each adsorption unit according to the local regeneration command or normal operation command, and to update the weight parameters in the dynamic load distribution model simultaneously.

9. An electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein the processor, when executing the computer program, implements the flue gas treatment and analysis method based on multi-stage adsorption as described in any one of claims 1-8.

10. A computer-readable storage medium comprising a stored computer program, wherein, When the computer program is running, it controls the device containing the computer-readable storage medium to perform the flue gas treatment and analysis method based on multi-stage adsorption as described in any one of claims 1-8.

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