SCR denitrification efficiency improvement method based on catalyst activity monitoring

Abstract

The present invention discloses a method for improving SCR denitration efficiency based on catalyst activity monitoring, which relates to the technical field of improving denitration efficiency, including: constructing a sensor data network; constructing a catalytic activity-efficiency relationship network; performing denitration, activating the sensor data network for collection, obtaining multiple reaction data, performing activity analysis on the catalyst, extracting catalyst activity dynamic monitoring data, and synchronizing to the activity-efficiency relationship network for retrospective update, obtaining the activity change trend of the catalyst, combining multiple reaction data for calculation, and obtaining the first denitration efficiency data; feedback adjustment of the operating parameter set of the SCR reactor according to the activity-efficiency relationship optimization network, generating a denitration operation optimization parameter set, dynamically analyzing the first denitration efficiency data, and generating the second denitration efficiency data. The present invention solves the technical problem of catalyst activity decay in the prior art, and achieves the technical effect of improving the denitration efficiency of the SCR reactor.

Description

Technical Field

[0001] The present invention relates to the technical field of improving denitration efficiency, and in particular to a method for improving SCR denitration efficiency based on catalyst activity monitoring. Background Art

[0002] In the selective catalytic reduction (SCR) denitrification technology, the activity decay of the catalyst and the fluctuation of the reaction efficiency are the key factors affecting the denitrification performance. As the reaction process proceeds, the catalyst may reduce its activity due to high temperature, chemical poisoning or mechanical wear, thereby affecting the NOx conversion efficiency. In the existing technology, fixed operating parameters and periodic catalyst replacement or cleaning are usually relied on to maintain the denitrification efficiency, but the actual state of the catalyst and the changes in reaction conditions are not monitored in real time, resulting in the inability to dynamically optimize the denitrification efficiency. Summary of the invention

[0003] The present application provides a method for improving SCR denitration efficiency based on catalyst activity monitoring, which is used to solve the technical problem of catalyst activity decay in the prior art.

[0004] In view of the above problems, the present application provides a method for improving SCR denitrification efficiency based on catalyst activity monitoring.

[0005] The present application provides a method for improving SCR denitration efficiency based on catalyst activity monitoring, the method comprising:

[0006] Traversing the SCR reactor, multiple sensors are deployed to collect data and construct a sensor data network; correlation analysis is performed based on the reaction kinetic data of the catalyst in combination with the historical nitrogen oxide conversion efficiency to construct a catalytic activity-efficiency relationship network; the catalyst is placed in the SCR reactor for denitration, the sensor data network is activated for collection, a plurality of reaction data are obtained, the activity of the catalyst is analyzed based on the plurality of reaction data, and dynamic monitoring data of the catalyst activity is extracted; the dynamic monitoring data of the catalyst activity is synchronized to the activity-efficiency relationship network for retrospective update to obtain the activity change trend of the catalyst, calculation is performed based on the activity change trend in combination with the plurality of reaction data to obtain first denitration efficiency data; feedback adjustment is performed on the operating parameter set of the SCR reactor according to the activity-efficiency relationship optimization network based on the first denitration efficiency data to generate a denitration operation optimization parameter set, the first denitration efficiency data is dynamically analyzed according to the denitration operation optimization parameter set to generate second denitration efficiency data.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] The present application traverses the SCR reactor and arranges multiple sensors for data collection, constructs a sensor data network; performs correlation analysis based on the reaction kinetic data of the catalyst in combination with the historical nitrogen oxide conversion efficiency, and constructs a catalytic activity-efficiency relationship network; places the catalyst in the SCR reactor for denitration, activates the sensor data network for collection, obtains multiple reaction data, performs activity analysis on the catalyst based on the multiple reaction data, and extracts catalyst activity dynamic monitoring data; synchronizes the catalyst activity dynamic monitoring data to the activity-efficiency relationship network for retrospective update, obtains the activity change trend of the catalyst, calculates based on the activity change trend combined with the multiple reaction data, and obtains the first denitration efficiency data; according to the first denitration efficiency data, the operating parameter set of the SCR reactor is feedback-adjusted according to the activity-efficiency relationship optimization network, generates a denitration operation optimization parameter set, and dynamically analyzes the first denitration efficiency data according to the denitration operation optimization parameter set to generate the second denitration efficiency data. The present invention solves the technical problem of catalyst activity decay in the prior art, and achieves the technical effect of improving the denitration efficiency of the SCR reactor by real-time monitoring of the catalyst activity and combining the reaction data for dynamic optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a method for improving SCR denitration efficiency based on catalyst activity monitoring provided in an embodiment of the present application;

[0011] Figure 2 A schematic diagram of a process for extracting dynamic monitoring data of catalyst activity in a method for improving SCR denitration efficiency based on catalyst activity monitoring provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The present application provides a method for improving the SCR denitrification efficiency based on catalyst activity monitoring, which is used to solve the technical problem of catalyst activity decay in the prior art. By real-time monitoring of catalyst activity and dynamic optimization combined with reaction data, the technical effect of improving the denitrification efficiency of the SCR reactor is achieved.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0015] like Figure 1 As shown, the present application provides a method for improving SCR denitration efficiency based on catalyst activity monitoring, the method comprising:

[0016] Step S100: traverse the SCR reactor to deploy multiple sensors for data collection and build a sensor data network.

[0017] In the embodiment of the present application, the SCR reactor is first traversed, and sensors are arranged at preset positions of the SCR reactor, including the inlet, outlet, and middle of the catalyst bed, etc. The sensors include nitrogen oxide sensors, temperature sensors, flow sensors, etc. Data is collected through the arranged multiple sensors to obtain the operating status of the reactor.

[0018] After multiple sensors are deployed, the data collected by multiple sensors are managed in a networked manner, and the data are transmitted to a unified processing platform through wireless or wired communication technology. The platform integrates, stores and analyzes the data of sensors at different locations, thus forming a complete sensor data network.

[0019] Step S200: performing correlation analysis based on the reaction kinetics data of the catalyst in combination with the historical nitrogen oxide conversion efficiency to construct a catalytic activity-efficiency relationship network.

[0020] In the embodiment of the present application, the reaction kinetic data of the catalyst are first obtained from the historical database. These data are the reaction rate, activation energy, reaction temperature and other data recorded when the catalyst is tested under different operating conditions (such as different temperatures, gas flow rates, reactant concentrations, etc.). Specifically, these data reflect the performance of the catalyst under different reaction conditions, including reaction rate constants, reaction heat, and temperature and pressure coefficients. At the same time, the historical nitrogen oxide conversion efficiency is obtained from the historical database, reflecting the NOx conversion efficiency of the SCR reactor under different operating conditions.

[0021] Next, we conduct correlation analysis based on the catalyst reaction kinetics data combined with the historical nitrogen oxide conversion efficiency, and use regression analysis methods and regression models to establish the relationship between the catalyst reaction kinetics data and the NOx conversion efficiency data. Specifically, through regression analysis, we quantitatively describe the dependence between the catalyst reaction rate, activation energy and other reaction kinetic parameters and the NOx conversion efficiency, thereby constructing a relationship network between catalytic activity and denitrification efficiency, and obtaining a catalytic activity-efficiency relationship network.

[0022] Step S300: placing a catalyst in an SCR reactor for denitration, activating the sensor data network for collection, obtaining a plurality of reaction data, performing activity analysis on the catalyst according to the plurality of reaction data, and extracting catalyst activity dynamic monitoring data.

[0023] In the embodiment of the present application, firstly, the catalyst is selected and configured according to the requirements of the SCR reactor to obtain relevant configuration parameters. Then, the catalyst is placed in the reactor for denitration by using a radial bed loading method, and a sensor activation instruction is generated to activate the sensor data network. Through the activated sensor network, multiple data in the reaction process are collected in real time, including key parameters such as temperature, gas flow, and NOx concentration.

[0024] Based on the collected multiple reaction data and the reaction kinetic data of the catalyst, the real-time conversion efficiency of nitrogen oxides is calculated, and multiple weight coefficients are generated by the weighted average method. According to these weight coefficients, the reaction data is sorted to generate a reaction data sequence, and the activity of the catalyst is evaluated based on this to obtain an activity impact score. Further, the activity decay of the catalyst is predicted based on the activity impact score to generate an activity decay prediction rate. Finally, the activity decay prediction rate is used as the activity dynamic monitoring data of the catalyst.

[0025] Furthermore, in the method provided in the embodiment of the application, a catalyst is placed in an SCR reactor for denitration, the sensor data network is activated for collection, and a plurality of reaction data are obtained, and the method further includes:

[0026] A catalyst is selected and configured based on an SCR reactor to obtain configuration parameters, wherein the catalyst is a cylindrical particle structure; the catalyst is placed in an SCR reactor for denitration using radial bed loading according to the configuration parameters, and a sensor activation instruction is generated; three-dimensional structural information is obtained based on the cylindrical particle structure to calculate a reaction contact area; the sensor data network is activated through the sensor activation instruction, and the SCR reactor is comprehensively collected based on the sensor data network and the reaction contact area to obtain a plurality of reaction data.

[0027] In the embodiment of the present application, firstly, based on the working requirements of the SCR reactor, a technical expert selects and configures the catalyst according to the specific denitration reaction conditions of the reactor to obtain configuration parameters, wherein the catalyst is a cylindrical particle structure. The configuration parameters include the particle size and packing density of the catalyst.

[0028] Next, the catalyst is placed in the SCR reactor for denitration using radial bed loading according to the configuration parameters. Radial bed loading ensures that the catalyst is evenly distributed in the reactor and that the airflow has sufficient contact when passing through the catalyst bed, thereby improving the denitration efficiency. At the same time, a sensor activation command is generated during denitration.

[0029] The effective reaction contact area of ​​the catalyst is then calculated based on the cylindrical particle structure of the catalyst. Specifically, the outer surface area of ​​the catalyst particles and the effective area of ​​the internal pores are calculated using a cylindrical geometry model. Specifically, the height, diameter, and porosity of the cylinder are used to estimate its surface area, and then the reaction contact area is obtained.

[0030] Once the catalyst is loaded into the SCR reactor and starts working, the sensor network installed in the reactor is activated through the sensor activation command. Through this command, the sensor network monitors key data such as temperature, gas concentration, flow rate, etc. during the reaction process in real time. These data reflect the working status of the reactor. The final result is multiple reaction data collected by the activated sensor network, including gas concentration, temperature, flow rate, etc.

[0031] Further, such as Figure 2 As shown, in the method provided in the application embodiment, the activity of the catalyst is analyzed according to the multiple reaction data, and the dynamic monitoring data of the catalyst activity is extracted, which also includes:

[0032] Based on the multiple reaction data, the catalyst is calculated according to the reaction kinetic data of the catalyst to obtain a real-time conversion efficiency of nitrogen oxides; a weighted average of the catalyst is performed according to the real-time conversion efficiency in combination with the multiple reaction data to obtain a plurality of weight coefficients; the multiple reaction data are arranged in descending order according to the plurality of weight coefficients to generate a reaction data sequence; based on the reaction data sequence, the activity of the catalyst is evaluated to generate a plurality of activity influence scores, and the plurality of activity influence scores correspond to the multiple reaction data; the activity decay of the catalyst is predicted according to the plurality of activity influence scores to generate an activity decay prediction rate; and the activity decay prediction rate is added to the catalyst activity dynamic monitoring data.

[0033] In the embodiment of the present application, the real-time conversion efficiency of nitrogen oxides is first calculated using the reaction kinetics data of the catalyst and the reaction kinetics model. Specifically, the reaction kinetics data of the catalyst (such as reaction rate constant, activation energy, temperature, etc.) reflect the activity of the catalyst under different reaction conditions. By substituting these reaction kinetics data into a reaction model, such as the Arrhenius equation, and combining with real-time reaction data (such as gas concentration, temperature, flow rate, etc.), the real-time conversion efficiency of the catalyst under the current operating conditions is calculated.

[0034] Next, in order to evaluate the performance of the catalyst based on multiple reaction data, a weighted average method is used to calculate multiple weight coefficients of the catalyst. Specifically, first, a correlation analysis method such as the Pearson correlation coefficient is used to quantify the relationship between each reaction data and the real-time conversion efficiency. When weighting, the sum of the weight coefficients is required to be 1, which is achieved by normalization. Specifically, for each reaction data (such as gas flow, temperature, etc.), the Pearson correlation coefficient is used to calculate its correlation with the real-time conversion efficiency. These correlation values ​​are normalized so that their sum is 1. After normalization, the weight coefficient of each reaction data is obtained. Through this process, multiple weight coefficients are obtained.

[0035] Next, the reaction data are arranged in descending order using multiple weight coefficients, and a sorting algorithm is used to put the reaction data with larger weight coefficients in front and the reaction data with smaller weight coefficients in the back, thereby generating a reaction data sequence.

[0036] Based on the obtained reaction data sequence, the activity of the catalyst is evaluated. Here, the weighted scoring method is used to calculate the catalyst activity score under each reaction condition based on the data of each reaction condition in the reaction data sequence and its corresponding weight coefficient. Each reaction data (such as a specific temperature, gas flow rate, etc.) corresponds to an activity impact score. This activity impact score reflects the conversion ability or reaction efficiency of the catalyst under the reaction conditions. Through calculation, multiple activity impact scores are obtained, and each score directly corresponds to the corresponding reaction data.

[0037] After generating multiple activity impact scores, continue to predict the activity decay of the catalyst. The purpose of decay prediction is to deduce the activity decay rate of the catalyst by analyzing the performance of the catalyst under different conditions. Use a linear regression model to input data such as activity impact scores, reaction time, and operating conditions under various reaction conditions to train an activity decay prediction model. The model will deduce the future activity decay rate of the catalyst based on the current activity state, reaction conditions, and historical scores of the catalyst. This decay rate reflects the rate at which the activity of the catalyst decreases over a period of time in the future.

[0038] Finally, the generated activity decay prediction rate is used as the dynamic monitoring data of catalyst activity.

[0039] Step S400: synchronizing the catalyst activity dynamic monitoring data to the activity-efficiency relationship network for retrospective update to obtain the activity change trend of the catalyst, and performing calculations based on the activity change trend combined with the multiple reaction data to obtain the first denitration efficiency data.

[0040] In the embodiment of the present application, firstly, normalization processing is performed based on the activity decay prediction rate and the real-time conversion efficiency to obtain a normalized processing result. Then, this result is used as an index to traverse and trace back the activity-efficiency relationship network to determine the target traversal result. Then, according to the target traversal result, the normalized processing result is mapped to the activity-efficiency relationship network, and iterative updates are performed to generate an optimized activity-efficiency relationship network. Using this optimization network, the dynamic monitoring data of the catalyst activity is correlated and analyzed according to the time series to construct the activity change trend of the catalyst.

[0041] Finally, multiple reaction data were combined and calculated based on the activity change trend to obtain the first denitrification efficiency data.

[0042] Furthermore, in the method provided in the embodiment of the application, the catalyst activity dynamic monitoring data is synchronized to the activity-efficiency relationship network for retrospective update to obtain the activity change trend of the catalyst, and further includes:

[0043] Based on the activity decay prediction rate and the real-time conversion efficiency, normalization processing is performed to generate a normalized processing result; the normalized processing result is used as an index to traverse and trace back the activity-efficiency relationship network to determine a target traversal result; according to the target traversal result, the normalized processing result is mapped to the activity-efficiency relationship network for iterative update to generate an activity-efficiency relationship optimization network; according to the activity-efficiency relationship optimization network, correlation analysis is performed on the catalyst activity dynamic monitoring data according to a time series, and the activity change trend of the catalyst is constructed according to multiple correlation coefficients.

[0044] In the present embodiment, the activity decay prediction rate and the real-time conversion efficiency are first normalized. To ensure that the two data are in the same scale range, a linear normalization method is used to convert them into values ​​in the interval [0, 1] to generate a normalized result.

[0045] Next, the normalized result is used as an index to traverse and backtrack the activity-efficiency relationship network to determine the target traversal result. In this process, the normalized result is used as a query condition to enter the activity-efficiency relationship network for backtracking. This step uses a binary search algorithm or an interpolation method. Through this method, the node closest to the current catalyst activity state is quickly found between known nodes. If the exact node corresponding to the normalized result does not have a direct match, an interpolation method, such as linear interpolation or polynomial interpolation, is used to infer the closest target node value. Through this process, the target traversal result is determined.

[0046] Afterwards, the normalized processing results are mapped to the activity-efficiency relationship network according to the target traversal results for iterative update to generate an activity-efficiency relationship optimization network. Specifically, after the target traversal results are found, the weighted average method is used for data mapping and network iterative update. The operation of the weighted average method is to apply the normalized results to the target node, and combine the historical data of the node (such as the historical efficiency value) to obtain the new efficiency value of the node through weighted calculation. This new value is used to update the weights of the nodes in the network to form a more accurate relationship network. This process is repeated through iterations, so that the efficiency value of each node is closer to the actual catalyst performance after each update, and finally an optimized activity-efficiency relationship network is generated.

[0047] Next, the dynamic monitoring data of catalyst activity is analyzed according to the time series according to the activity-efficiency relationship optimization network, and the activity change trend of the catalyst is constructed according to multiple correlation coefficients. After completing the iterative update of the relationship network, the sliding window method is used to perform the correlation analysis of the dynamic monitoring data of catalyst activity. The specific operation is to set a fixed time window, calculate the activity change of the catalyst within the time range, and perform correlation analysis with other reaction data (such as temperature, gas flow rate, NOx concentration, etc.). By continuously sliding the time window, the change trend of catalyst activity is captured, and it is compared with the reaction data to find out the potential relationship between them. In this process, the calculation of the correlation coefficient is crucial. When calculating the correlation coefficient, first, the correlation between the catalyst activity change and the reaction data is calculated, and the Pearson correlation coefficient is used to measure the linear relationship between them. The correlation coefficient ranges from -1 to 1, indicating the strength and direction of the correlation between the two variables. In this way, the correlation coefficient between the catalyst activity and the reaction conditions is obtained, and these coefficients are used to construct the activity change trend of the catalyst.

[0048] The activity change trend of the catalyst is constructed based on multiple correlation coefficients. Finally, the weighted average method is used to integrate multiple correlation coefficients to calculate the overall trend of catalyst activity over time. Specifically, the correlation coefficient at each time point reflects the strength of the correlation between the catalyst activity and the reaction conditions, and a comprehensive activity change trend is obtained by weighted average.

[0049] Furthermore, in the method provided in the embodiment of the application, the first denitrification efficiency data is obtained by calculating according to the activity change trend in combination with the plurality of reaction data, and further includes:

[0050] Retrieve historical denitration record logs, and extract the denitration reaction cycle based on the historical denitration record logs; perform effectiveness analysis on the catalyst according to the denitration reaction cycle in combination with the multiple reaction data to generate a reaction analysis result, wherein the reaction analysis result includes a catalytically effective result and a catalytically ineffective result; perform determination and calculation based on the reaction analysis result to obtain the first denitration efficiency.

[0051] In the embodiment of the present application, the historical denitration log is first retrieved, which contains the denitration reaction data of the catalyst at different time points, recording the use of the catalyst, reaction conditions and denitration efficiency. By retrieving these historical logs, detailed data of each reaction cycle can be obtained, which generally include variables such as reaction time, temperature, gas flow rate, and NOx concentration.

[0052] Next, based on the retrieved historical denitrification record log, the denitrification reaction cycle is extracted. This process is implemented using the threshold method. Specifically, technical experts first set certain reaction condition thresholds (such as upper and lower limits of temperature, NOx concentration, etc.). When the fluctuations of these conditions exceed the preset range, it marks the beginning of a new reaction cycle. By analyzing the range of change of the reaction data, it can be determined which data belongs to the same cycle. By comparing the thresholds, the denitrification reaction cycle is obtained.

[0053] Next, the catalyst effectiveness analysis is performed based on the extracted denitrification reaction cycle data. Specifically, the NOx conversion rate or NOx removal efficiency is used as a standard to evaluate whether the catalyst is effective. In order to perform effectiveness analysis, a conversion rate threshold is set. For example, when the NOx conversion rate is higher than 90% during the reaction cycle, the catalyst is considered to be in an effective state; when the conversion rate is lower than this standard, the catalyst is considered to be ineffective or has poor performance. By comparing the NOx conversion rate of each cycle with this standard, the performance of the catalyst is divided into "catalytically effective" or "catalytically ineffective", and the corresponding reaction analysis results are generated. Among them, the NOx conversion rate is calculated by subtracting the difference between the intake NOx concentration and the outlet NOx concentration from the intake NOx concentration.

[0054] Finally, the first denitration efficiency is obtained by making a judgment and calculation based on the reaction analysis results. This step is based on the catalyst effectiveness analysis results in the previous step. By calculating the NOx conversion rate of the catalyst in multiple reaction cycles, the overall denitration efficiency is obtained. Based on the analysis results of these reaction cycles, the first denitration efficiency data is obtained.

[0055] Furthermore, in the method provided in the embodiment of the application, the first denitration efficiency is obtained by performing a determination calculation based on the reaction analysis result, and further comprising:

[0056] When the reaction analysis result is the catalytically effective result, a reaction start-up module is triggered; the reaction start-up module is used to calculate according to the denitration reaction cycle to obtain first denitration efficiency data; when the reaction analysis result is the catalytically ineffective result, an abnormal reaction module is triggered; the abnormal reaction module is used to perform abnormal reaction activity analysis on the catalyst in combination with the multiple reaction data to generate an abnormal reaction activity label; the multiple reaction data are abnormally marked with the abnormal reaction activity label to generate an abnormal feedback prompt; the catalyst is adjusted according to the abnormal feedback prompt, and calculation is performed according to the adjustment result to obtain the first denitration efficiency data.

[0057] In an embodiment of the present application, when the reaction analysis result is a catalytically effective result, the reaction start-up module is triggered, and the reaction start-up module is used to perform calculations according to the denitration reaction cycle. Specifically, first, the NOx removal efficiency in each cycle is calculated based on the change in NOx concentration during the denitration reaction cycle. The denitration efficiency is calculated by subtracting the difference between the input NOx concentration and the output NOx concentration from the input NOx concentration. Among them, the input NOx concentration and the output NOx concentration represent the NOx concentrations entering the reactor and discharged from the reactor, respectively. Through this calculation, the first denitration efficiency data is obtained.

[0058] When the reaction analysis result is "catalytic ineffective", the abnormal reaction module is triggered. The task of the abnormal reaction module is to analyze whether the catalyst has activity decline or failure, further identify the problem and make adjustments. Specifically, by combining the reaction data of the catalyst (such as temperature, flow rate, NOx concentration, etc.), it is determined whether the reaction is within the normal range. Usually, this analysis is based on a set threshold. For example, if the NOx removal efficiency in a certain cycle is lower than the set threshold, or the temperature fluctuates too much, the reaction conditions are unstable, etc., it will be regarded as a reaction abnormality. Once an abnormality is detected, the reaction data is marked and a reaction activity abnormality label is generated, indicating that the catalyst performance of the cycle does not meet expectations. For example, in the case of low NOx removal efficiency and abnormal temperature, an abnormal label will be generated to remind the operator to intervene. According to the feedback prompt, the operator can choose whether to adjust the reaction conditions, such as adjusting the gas flow rate, temperature or replacing the catalyst. If the operator adjusts the reaction conditions according to the abnormal feedback prompt, the new denitrification efficiency is recalculated. The new calculation process is similar to the above-mentioned effective calculation steps, and the NOx removal efficiency of the adjusted reaction is still calculated based on the change in NOx concentration. The new denitration efficiency is calculated by subtracting the difference between the new input NOx concentration and the new output NOx concentration, and the new denitration efficiency is taken as the first denitration efficiency data.

[0059] Step S500: According to the first denitration efficiency data, feedback adjustment is performed on the operating parameter set of the SCR reactor according to the activity-efficiency relationship optimization network to generate a denitration operation optimization parameter set, and the first denitration efficiency data is dynamically analyzed according to the denitration operation optimization parameter set to generate second denitration efficiency data.

[0060] In the embodiment of the present application, based on the first denitration efficiency data, growth analysis is first performed to generate efficiency growth space, and feasibility determination is performed in combination with the activity-efficiency relationship optimization network to generate a determination result. If the determination result shows that growth is feasible, the operating parameter set is obtained through the sensor data network. Next, the first denitration efficiency data is mapped to the operating parameter set, and the parameters to be adjusted are determined through retrieval and matching, and the denitration operation optimization parameter set is updated and generated.

[0061] On this basis, a reaction test is conducted based on the denitration operation optimization parameter set to obtain reaction test data. Then, these test data are used to perform dynamic regression analysis on the first denitration efficiency data to calculate multiple regression coefficients. The regression coefficients are screened according to the efficiency growth space to obtain the target regression coefficients, and the reactor is calculated based on these coefficients to finally generate the second denitration efficiency data.

[0062] Furthermore, in the method provided in the embodiment of the application, feedback adjustment is performed on the operating parameter set of the SCR reactor according to the first denitration efficiency data according to the activity-efficiency relationship optimization network to generate a denitration operation optimization parameter set, and the method also includes:

[0063] A growth analysis is performed based on the first denitrification efficiency data to generate an efficiency growth space; a feasibility determination is performed according to the efficiency growth space in combination with an activity-efficiency relationship optimization network to generate a determination result; operation sensing of the SCR reactor is performed through the sensor data network to generate the operation parameter set; when the determination result is that growth is feasible, the operation parameter set is mapped to the activity-efficiency relationship optimization network according to the first denitrification efficiency for retrieval and matching to generate a parameter matching result; a comparative analysis is performed with the operation parameter set according to the parameter matching result to determine a plurality of parameters to be adjusted; the plurality of parameters to be adjusted are fed back to the operation parameter set for optimization and replacement to generate the denitrification operation optimization parameter set.

[0064] In the embodiment of the present application, firstly, by performing a time series analysis on the first denitration efficiency data, the historical data is reviewed, and the potential for changes in the denitration efficiency is calculated using a trend analysis method. Specifically, by performing a linear regression analysis on the historical denitration efficiency data, the change trend of the current efficiency is calculated, and then the efficiency increase that this trend may bring in a certain period of time in the future is predicted, generating an efficiency growth space.

[0065] After the efficiency growth space is generated, the feasibility of the growth space is determined in combination with the activity-efficiency relationship optimization network. This step uses the threshold determination method to compare the generated efficiency growth space with the pre-set efficiency improvement threshold. If the calculated growth space exceeds the preset acceptable range, the growth is considered feasible; if the growth space is small, it is determined to be infeasible. Through this process, a determination result is generated.

[0066] Next, through the real-time data acquisition method, the sensor data network is used to sense the operation of the SCR reactor, obtain multiple real-time operation data (such as temperature, gas flow rate, NOx concentration, etc.), and generate an operation parameter set.

[0067] When the result is that growth is feasible, the nearest neighbor algorithm (KNN) is used to map the current operating parameter set to the activity-efficiency relationship optimization network and perform search matching. By using similarity calculation methods such as Euclidean distance to calculate the similarity between the current operating parameters and historical data, the most suitable parameter matching result is found.

[0068] Then, the generated parameter matching results are compared and analyzed with the current operating parameter set to determine multiple parameters to be adjusted. This step compares the difference between the current operating parameters and the best matching historical parameters to find out the key parameters that affect the denitrification efficiency and determine which parameters need to be adjusted. For example, if a parameter (such as temperature or flow rate) is more than the preset range away from the ideal value in the matching result, it means that the parameter may be one of the reasons for the low denitrification efficiency and needs to be adjusted.

[0069] To ensure the rationality of the optimization and replacement process, thresholds are set to screen the parameters to be adjusted. By analyzing the impact of each parameter to be adjusted on the denitrification efficiency, technical experts set a suitable threshold and only adjust those parameters that have a significant impact on the efficiency change. If a parameter to be adjusted contributes little to the efficiency improvement, or the effect after adjustment is not obvious, the parameter will not be replaced, but the current value will be retained. Through this screening mechanism, it is ensured that the optimized replacement parameters can indeed improve the denitrification efficiency of the system, rather than blindly replacing all parameters to be adjusted.

[0070] Finally, based on the selected parameters to be adjusted, these parameters are fed back to the current operating parameter set for optimization replacement. The optimization replacement process is to adjust the current operating parameter set based on the determined best matching parameters. The adjusted operating parameter set is called the denitrification operation optimization parameter set.

[0071] Furthermore, in the method provided in the embodiment of the application, the first denitration efficiency data is dynamically analyzed according to the denitration operation optimization parameter set to generate second denitration efficiency data, and further includes:

[0072] Based on the denitration operation optimization parameter set and in combination with a catalyst, a reaction test is performed on the SCR reactor to obtain reaction test data; based on the reaction test data, a dynamic regression analysis is performed on the first denitration efficiency data to generate a plurality of regression coefficients; the efficiency growth space is used as a constraint condition to screen the plurality of regression coefficients to determine a target regression coefficient; based on the target regression coefficient, the SCR reactor is calculated in combination with the catalyst to generate updated denitration efficiency data; the updated denitration efficiency data is compared with the first denitration efficiency data, and when the updated denitration efficiency data is greater than the first denitration efficiency data, the target regression coefficient is marked as feasible.

[0073] In the embodiment of the present application, the reaction test of the SCR reactor is first performed based on the denitration operation optimization parameter set combined with the catalyst, and the key data in the reaction process are collected through the sensor network deployed in the reactor (such as temperature, flow rate, NOx concentration sensor, etc.) to obtain the reaction test data. These data include the NOx concentration in the reactor, the catalyst temperature, the gas flow rate, etc., and the NOx concentration emitted from the reactor.

[0074] Next, a dynamic regression analysis is performed on the first denitration efficiency data based on the reaction test data to generate multiple regression coefficients. In the regression analysis, the relationship between multiple independent variables in the reaction test data (such as NOx concentration, temperature, gas flow rate, etc.) and the first denitration efficiency is modeled. A multivariate linear regression or nonlinear regression model is used to calculate the contribution of each parameter to the denitration efficiency. For example, through calculation, a linear relationship between factors such as temperature, flow rate, NOx concentration and denitration efficiency is obtained. Specifically, the regression coefficient of temperature on denitration efficiency may be 0.3, which means that for every 1°C increase in temperature, the denitration efficiency will increase by 0.3%. Similarly, the regression coefficient of gas flow rate may be -0.2, indicating that for every 1 unit increase in flow rate, the denitration efficiency will decrease by 0.2%. These regression coefficients reflect the actual impact of each parameter on the denitration efficiency.

[0075] After obtaining these regression coefficients, the next step is to screen them in combination with the efficiency growth space. The efficiency growth space refers to the gap between the current denitrification efficiency and the theoretical maximum efficiency, which represents the potential for optimization. If the growth space is large, it means that there is a possibility of further optimization, otherwise it means that it is close to its theoretical optimal value. According to this growth space, the regression coefficients are screened. By setting a threshold (such as the growth space is greater than 10%), the regression coefficients are selected to ensure that the selected coefficients can maximize the improvement of denitrification efficiency. The screened coefficients are determined as the target regression coefficients.

[0076] Next, based on these target regression coefficients, the catalyst and SCR reactor are calculated to generate updated denitration efficiency data. During the calculation process, the target regression coefficient is combined with the input parameters of the reactor (such as temperature, flow rate, etc.) to calculate the new denitration efficiency value. Specifically, the regression coefficient is combined with the various input parameters of the reactor to obtain new denitration efficiency data. This process ensures that the denitration efficiency is improved under the optimized parameter settings.

[0077] Finally, the updated denitration efficiency data is compared with the first denitration efficiency data. If the updated denitration efficiency is greater than the first denitration efficiency, it means that the adopted target regression coefficient is effective and can further improve the denitration efficiency. Therefore, the target regression coefficient is marked as feasible. And the updated denitration efficiency data is used as the second denitration efficiency data.

[0078] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0079] The present application traverses the SCR reactor and arranges multiple sensors for data collection, constructs a sensor data network; performs correlation analysis based on the reaction kinetic data of the catalyst in combination with the historical nitrogen oxide conversion efficiency, and constructs a catalytic activity-efficiency relationship network; places the catalyst in the SCR reactor for denitration, activates the sensor data network for collection, obtains multiple reaction data, performs activity analysis on the catalyst based on the multiple reaction data, and extracts catalyst activity dynamic monitoring data; synchronizes the catalyst activity dynamic monitoring data to the activity-efficiency relationship network for retrospective update, obtains the activity change trend of the catalyst, calculates based on the activity change trend combined with the multiple reaction data, and obtains the first denitration efficiency data; according to the first denitration efficiency data, the operating parameter set of the SCR reactor is feedback-adjusted according to the activity-efficiency relationship optimization network, generates a denitration operation optimization parameter set, and dynamically analyzes the first denitration efficiency data according to the denitration operation optimization parameter set to generate the second denitration efficiency data. The present invention solves the technical problem of catalyst activity decay in the prior art, and achieves the technical effect of improving the denitration efficiency of the SCR reactor by real-time monitoring of the catalyst activity and combining the reaction data for dynamic optimization.

[0080] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0082] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A method for improving SCR denitration efficiency based on catalyst activity monitoring, characterized in that: The method comprises: Multiple sensors are deployed throughout the SCR reactor to collect data and build a sensor data network; Based on the reaction kinetic data of the catalyst and the historical nitrogen oxide conversion efficiency, a correlation analysis was conducted to construct a catalytic activity-efficiency relationship network; Placing a catalyst in an SCR reactor for denitration, activating the sensor data network for collection, and obtaining a plurality of reaction data; performing activity analysis on the catalyst according to the plurality of reaction data, and extracting dynamic monitoring data of the catalyst activity, the method comprising: Calculating the catalyst according to the reaction kinetic data of the catalyst based on the plurality of reaction data to obtain a real-time conversion efficiency of nitrogen oxides; Performing weighted averaging on the catalyst according to the real-time conversion efficiency combined with the plurality of reaction data to obtain a plurality of weight coefficients; Arrange the plurality of reaction data in descending order according to the plurality of weight coefficients to generate a reaction data sequence; Evaluate the activity of the catalyst based on the reaction data sequence to generate a plurality of activity impact scores, wherein the plurality of activity impact scores correspond to the plurality of reaction data; Predicting the activity decay of the catalyst according to the plurality of activity impact scores to generate an activity decay prediction rate; adding the activity decay prediction rate to the catalyst activity dynamic monitoring data; The catalyst activity dynamic monitoring data is combined with the real-time conversion efficiency to synchronize to the activity-efficiency relationship network for retrospective update, to generate an activity-efficiency relationship optimization network, and to obtain the activity change trend of the catalyst, and to calculate according to the activity change trend in combination with the plurality of reaction data to obtain the first denitration efficiency data; According to the first denitration efficiency data, the operating parameter set of the SCR reactor is feedback-adjusted according to the activity-efficiency relationship optimization network to generate a denitration operation optimization parameter set, and the first denitration efficiency data is dynamically analyzed according to the denitration operation optimization parameter set to generate second denitration efficiency data.

2. The method for improving SCR denitration efficiency based on catalyst activity monitoring according to claim 1, characterized in that: Placing a catalyst in an SCR reactor for denitration, activating the sensor data network for collection, and obtaining a plurality of reaction data, the method comprising: Selecting and configuring a catalyst based on an SCR reactor to obtain configuration parameters, wherein the catalyst is a cylindrical particle structure; Placing the catalyst in the SCR reactor for denitration using radial bed loading according to the configuration parameters, and generating a sensor activation instruction; Calculate the reaction contact area based on the three-dimensional structural information obtained from the cylindrical particle structure; The sensor data network is activated by the sensor activation instruction, and the SCR reactor is fully collected based on the sensor data network combined with the reaction contact area to obtain a plurality of reaction data.

3. The method for improving SCR denitration efficiency based on catalyst activity monitoring according to claim 1, characterized in that: The catalyst activity dynamic monitoring data is combined with the real-time conversion efficiency and synchronized to the activity-efficiency relationship network for retrospective update, an activity-efficiency relationship optimization network is generated, and the activity change trend of the catalyst is obtained, the method comprising: Performing normalization processing based on the activity decay prediction rate combined with the real-time conversion efficiency to generate a normalized processing result; Using the normalized processing result as an index, traversing and backtracking the activity-efficiency relationship network to determine a target traversal result; According to the target traversal result, the normalized processing result is mapped to the activity-efficiency relationship network for iterative update to generate an activity-efficiency relationship optimization network; According to the activity-efficiency relationship optimization network, correlation analysis is performed on the catalyst activity dynamic monitoring data according to time series, and the activity change trend of the catalyst is constructed according to multiple correlation coefficients.

4. The method for improving SCR denitration efficiency based on catalyst activity monitoring according to claim 1, characterized in that: Calculating according to the activity change trend in combination with the plurality of reaction data to obtain first denitration efficiency data, the method comprising: Retrieving a historical denitration record log, and extracting a denitration reaction cycle based on the historical denitration record log; Performing effectiveness analysis on the catalyst according to the denitration reaction cycle and the plurality of reaction data to generate a reaction analysis result, wherein the reaction analysis result includes a catalytically effective result and a catalytically ineffective result; A determination calculation is performed based on the reaction analysis result to obtain the first denitration efficiency.

5. The method for improving SCR denitration efficiency based on catalyst activity monitoring according to claim 4, characterized in that: The first denitration efficiency is obtained by performing a determination calculation according to the reaction analysis result, and the method includes: When the reaction analysis result is the catalytically effective result, a reaction start module is triggered; The reaction start-up module calculates according to the denitration reaction cycle to obtain first denitration efficiency data; When the reaction analysis result is the catalytic invalid result, the abnormal reaction module is triggered; Performing reaction activity anomaly analysis on the catalyst in combination with the plurality of reaction data through the abnormal reaction module to generate a reaction activity anomaly label; Using the reaction activity abnormality label to mark the plurality of reaction data as abnormal, and generating an abnormal feedback prompt; The catalyst is adjusted according to the abnormal feedback prompt, and calculation is performed according to the adjustment result to obtain the first denitration efficiency data.

6. The method for improving SCR denitration efficiency based on catalyst activity monitoring according to claim 1, characterized in that: Feedback adjustment is performed on the operating parameter set of the SCR reactor according to the first denitration efficiency data according to the activity-efficiency relationship optimization network to generate a denitration operation optimization parameter set, the method comprising: Perform growth analysis based on the first denitration efficiency data to generate efficiency growth space; Perform feasibility determination according to the efficiency growth space combined with the activity-efficiency relationship optimization network to generate a determination result; Performing operation sensing on the SCR reactor through the sensor data network to generate the operation parameter set; When the determination result is that the increase is feasible, the operating parameter set is mapped to the activity-efficiency relationship optimization network according to the first denitration efficiency for retrieval and matching to generate a parameter matching result; Comparing and analyzing the parameter matching result with the operating parameter set, determining a plurality of parameters to be adjusted; The multiple parameters to be adjusted are fed back to the operating parameter set for optimization and replacement to generate the denitration operation optimization parameter set.

7. The method for improving SCR denitration efficiency based on catalyst activity monitoring according to claim 6, characterized in that: Dynamically analyzing the first denitration efficiency data according to the denitration operation optimization parameter set to generate second denitration efficiency data, the method further includes: Performing a reaction test on the SCR reactor based on the denitration operation optimization parameter set in combination with a catalyst to obtain reaction test data; Performing a dynamic regression analysis on the first denitration efficiency data based on the reaction test data to generate a plurality of regression coefficients; Using the efficiency growth space as a constraint condition to screen the multiple regression coefficients and determine a target regression coefficient; Calculating the SCR reactor in combination with the catalyst based on the target regression coefficient to generate updated denitration efficiency data; The updated denitration efficiency data is compared with the first denitration efficiency data, and when the updated denitration efficiency data is greater than the first denitration efficiency data, the target regression coefficient is marked as feasible.

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