A denitrification temperature management system based on municipal solid waste incineration power generation

Abstract

This invention relates to the field of data analysis and prediction technology, and particularly to a denitrification temperature management system based on municipal solid waste incineration power generation. The system includes an intelligent monitoring module, a data analysis and prediction module, a dynamic control module, and a comprehensive feedback and response module. By planning the sensor layout of the incinerator, it collects and processes various parameter data to provide data support for subsequent data analysis and prediction. Machine learning algorithms are used to predict future temperature and NOx concentrations, providing decision-making basis for the dynamic control module. Based on the prediction results and current operating conditions, the system automatically adjusts the fuel parameters of the incinerator, thereby precisely controlling the furnace temperature. By comprehensively evaluating the dynamic control effect of the incinerator and providing timely feedback to the system, the system ensures the long-term stable operation and efficient emission management of the incinerator. This invention addresses the technical problems of incomplete monitoring and inaccurate temperature control in current incinerator operation and management.

Description

Technical Field

[0001] This invention relates to the field of data analysis and prediction technology, and in particular to a denitrification temperature management system based on municipal solid waste incineration power generation. Background Technology

[0002] Data analysis and prediction technology is a comprehensive technology that combines multiple fields such as data science, statistics, machine learning and artificial intelligence. It aims to extract valuable information from large and complex data and predict future trends or outcomes based on this information.

[0003] With continuous technological advancements and expanding application areas, the environmental protection and energy sector has also made new progress. However, the following problems still exist in the operation and management of incinerators: low data processing efficiency; traditional incinerator data collection and processing may rely on manual operation and traditional monitoring tools, a time-consuming and error-prone process that results in low data processing efficiency and difficulty in quickly reflecting the actual operating status of the incinerator; lack of real-time monitoring and prediction capabilities; traditional incinerator management may lack real-time monitoring and prediction capabilities, failing to promptly perceive changes in the incinerator environment and predict future trends, thus hindering rapid and accurate adjustments; lack of comprehensive evaluation and delayed response feedback; traditional response feedback mechanisms often rely on manual judgment and decision-making, leading to long response times and an inability to timely evaluate and adjust the control effects, thereby limiting the improvement of incinerator operating efficiency and environmental performance. Therefore, this invention proposes a denitrification temperature management system based on municipal solid waste incineration for power generation. Summary of the Invention

[0004] The purpose of this invention is to solve the problems in the background art by proposing a denitrification temperature management system based on municipal solid waste incineration power generation.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A denitrification temperature management system based on municipal solid waste incineration power generation includes: an intelligent monitoring module, a data analysis and prediction module, a dynamic control module, and a comprehensive feedback and response module;

[0007] The intelligent monitoring module is used to plan the sensor layout of the incinerator, realize comprehensive monitoring of the incinerator's internal environment, collect and process incinerator parameter data, obtain monitoring data packets, and upload the monitoring data packets to the data management center in real time through a wireless sensor network, reducing wiring complexity and improving system flexibility. The intelligent monitoring module includes a sensor layout planning unit and a data acquisition and processing unit.

[0008] The data management center is responsible for receiving monitoring data packets transmitted in real time from various sensors via low-power wireless sensor network technology, and for performing data parsing, processing, and storage.

[0009] The data analysis and prediction module is used to predict the temperature and NOx concentration in the future based on historical time series data of the incinerator using machine learning algorithms;

[0010] The dynamic control module is used to automatically adjust the fuel parameters of the incinerator based on the prediction results and the current operating conditions, precisely control the temperature inside the furnace to keep it in the optimal temperature range for the denitrification reaction, and obtain the optimal amount of reducing agent through quantitative analysis to achieve precise denitrification; among them, the combustion parameters include fuel supply rate, combustion air volume and secondary air configuration.

[0011] The integrated feedback and response module is used to comprehensively evaluate the dynamic control effect of the incinerator and provide timely feedback to the system to continuously optimize the control strategy.

[0012] It should be noted that the application of the denitrification temperature management system based on municipal solid waste incineration power generation in this embodiment of the invention can be used for the operation management or emission control of incinerators in the field of environmental energy. Specifically, it can comprehensively analyze and accurately evaluate the collected temperature data, NOx concentration data, pressure, etc. through advanced technologies such as the Internet of Things, big data analysis, and artificial intelligence, so as to realize real-time monitoring of the denitrification process of the incinerator and timely control of the denitrification temperature. Through multiple links such as data acquisition, transmission, processing, and feedback control, a closed-loop intelligent management system is formed, which enables the system to automatically optimize the denitrification reaction conditions, ensure that NOx emissions meet the standards and accurately control the temperature, while improving the operating efficiency and stability of the incinerator. In addition, the system also promotes the intelligent upgrading of environmental protection technologies and meets the urgent needs for energy conservation, emission reduction, and environmental protection.

[0013] Furthermore, the sensor layout planning unit's process for planning the sensor layout of the incinerator includes:

[0014] Determine the total number of sensors N, and then obtain the installation locations of each type of sensor in sequence, including the installation location A of the high-precision temperature sensor. i Location B of the flue gas composition analyzer installation j Pressure sensor installation location D b and the installation location of the flame detector E l Where i, j, b, and l represent the indices of the installation locations of the high-precision temperature sensor, the flue gas composition analyzer, the pressure sensor, and the flame detector, respectively.

[0015] Maximize the coverage of the monitoring area using the objective function:

[0016]

[0017] In the formula, C m This represents the m-th critical monitoring area; m represents the index of the critical monitoring area; M represents the number of critical monitoring areas; N A N B N D N E These represent the quantities of high-precision temperature sensors, flue gas composition analyzers, pressure sensors, and flame detectors, respectively; Cov(C m A i Cov(C) represents the coverage of the i-th high-precision temperature sensor to the m-th critical monitoring area; m B j ) represents the coverage of the j-th flue gas composition analyzer over the m-th key monitoring area; Cov(C m D b ) represents the coverage of the b-th pressure sensor to the m-th critical monitoring area; Cov(C m E l ) represents the coverage of the m-th critical monitoring area by the l-th flame detector;

[0018] Understandably, regarding the division of key monitoring areas, since the temperature distribution within an incinerator is typically uneven, and temperature is a critical factor affecting incineration efficiency and denitrification, several high-precision temperature sensors are usually required to monitor the temperature in different areas, thus dividing the furnace into multiple temperature monitoring zones. To avoid significant differences in NOx generation and distribution within the incinerator, more precise NOx concentration emission control is needed, necessitating the division into multiple NOx concentration monitoring zones. Because pressure is a relatively uniform physical quantity, and the spatial distribution of pressure within the incinerator varies little, only one or two pressure sensors located in suitable positions are required. Simultaneously, to ensure comprehensive monitoring of the flame state, this invention requires the installation of multiple flame detectors to cover different areas or angles.

[0019] Constraints are set for all sensors, including location constraints, coverage constraints, and performance constraints. Location constraints limit the installation location of the sensors, ensuring they are in feasible physical locations and do not interfere with each other. Coverage constraints require that each critical monitoring area be covered by at least a predetermined number of sensors to ensure comprehensive monitoring. Performance constraints specifically stipulate that the sensor's performance parameters must meet predetermined target requirements, namely the sensor's measurement range, accuracy, and response time.

[0020] Based on the structure and dimensions of the incinerator, high-precision temperature sensors are deployed to monitor the temperature at various points inside the furnace, ensuring comprehensive coverage of all key locations within the furnace (such as the combustion zone, high-temperature zone, and cooling zone). High-temperature resistant, high-precision, and fast-response temperature sensors are selected and installed in predetermined locations to ensure accurate measurement of the real-time temperature at various points inside the furnace. The predetermined locations include the combustion zone, high-temperature zone, and any areas with significant temperature changes within the furnace.

[0021] Sampling points representing the average composition of the flue gas within the entire flue are selected and equipped with flue gas composition analyzers to monitor NOx concentrations. These analyzers must be capable of measuring NOx concentrations and can be extended to measure other harmful gases (such as SO2 and CO) as needed. Understandably, the selection of sampling points should avoid areas with excessively high or low local concentrations to ensure that the flue gas composition analyzer's measurement results accurately reflect the flue gas conditions throughout the entire flue, mitigating the risk of measurement errors caused by excessively high or low local concentrations.

[0022] Pressure sensors are installed at key locations inside the furnace and in the flue to monitor pressure changes and ensure the safe and stable combustion process. Understandably, key locations inside the furnace and in the flue include the furnace inlet and outlet, the bends in the flue, or any area sensitive to pressure changes. At the same time, the pressure sensors must have high accuracy and fast response capabilities to detect and handle abnormal pressure situations in a timely manner.

[0023] Flame detectors are installed in the combustion zone of the incinerator to monitor the flame status inside the furnace in real time, including flame intensity, color, and stability. Understandably, the installation position of the flame detector should avoid obstructions to ensure that the flame detector can clearly capture the flame image. At the same time, it should be close enough to the flame to detect changes in the flame in a timely manner. The flame detector should have high sensitivity and anti-interference ability to ensure that it can accurately judge the flame status even in complex environments.

[0024] Furthermore, the data acquisition and processing unit is used to acquire and process incinerator parameter data, and the process of obtaining monitoring data packets includes:

[0025] Each sensor automatically collects various parameters of the furnace environment, namely temperature, NOx concentration, pressure, and flame status, according to the preset sampling frequency and accuracy.

[0026] The collected raw data undergoes preliminary processing to obtain real-time temperature data T(t) measured by a high-precision temperature sensor, NOx concentration change data G(t), real-time pressure data P(t) measured by a pressure sensor, and flame intensity index data F(t). Among them, G(t) is the NOx concentration measured by a flue gas composition analyzer, which is a function that reflects the change law of NOx concentration over time through calculation and analysis. The flame intensity index data F(t) is a quantitative evaluation value obtained by real-time monitoring of the flame state by a flame detector, extracting flame characteristics (such as brightness, area, etc.), and converting them.

[0027] The processed data will be packaged according to a predetermined format, which is a monitoring data packet containing a timestamp t, i.e., {T(t),G(t),P(t),F(t)}. Using wireless sensor network technology, the monitoring data packet will be transmitted to the data management center in real time, and the upload frequency will be set to once every Δt seconds, thereby forming continuous time series data.

[0028] Understandably, after receiving a data packet, the data management center performs an integrity check to ensure that the data packet has not been tampered with or damaged during transmission, and parses and stores the data packets that pass the check.

[0029] Furthermore, the data analysis and prediction module uses machine learning algorithms to predict future temperature and NOx concentrations based on historical time-series data from the incinerator. This process includes:

[0030] Historical time series data of the incinerator are extracted from the data management center, including incinerator temperature data {T(t-τ)}, NOx concentration change data {G(t-τ)}, pressure data {P(t-τ)}, and flame intensity index data {F(t-τ)} for past time periods; where τ represents the historical time offset.

[0031] Based on historical time-series data of the incinerator, the operating characteristics of the incinerator are calculated and obtained:

[0032]

[0033] In the formula, ψ(t) represents the characteristic value of the incinerator operation; T1 and T2 represent the minimum and maximum temperatures inside the incinerator, respectively, used to normalize the temperature data; G re P represents a reference value indicating the NOx concentration inside the incinerator. av P st F represents the average and standard deviation of the pressure inside the incinerator, used to standardize the pressure data; baThe baseline value represents the flame intensity index inside the incinerator; α, β, and γ represent the weighting coefficients of the temperature, pressure, and flame intensity index data inside the incinerator, respectively, and are used to adjust the degree of influence of different parameters on the incinerator's operating characteristic value ψ(t).

[0034] A prediction model is constructed using machine learning algorithms; among them, the temporal prediction model LSTM is selected, and the number of layers and the number of hidden units in each layer of the temporal prediction model LSTM network are labeled as L and H, respectively. L is a structural parameter used to define the depth of the model.

[0035] All calculated incinerator operating characteristic values ​​are collected and divided into training and test sets. The model is trained on the training set and then evaluated on the test set.

[0036] The training process and optimization steps of the LSTM time-series prediction model are as follows:

[0037] S1. Assume there exists a time series dataset I(x) r ,y r )={(x1,y1),(x2,y2),...,(x n ,y n )};where, x r =ψ(t) r ) represents the eigenvector, y r This represents the corresponding target value, namely the temperature and NOx concentration at future times, and r represents the index of the feature vector;

[0038] S2. Initialize random values ​​for network weights W and biases q;

[0039] S3. For each sample r, calculate the network output. Where f is the forward propagation function of the network;

[0040] S4. Calculate the predicted value using mean squared error. With the target value y r The difference between them, i.e., the loss value Lo:

[0041]

[0042] S5. Update the parameters using gradient descent, namely the network weights W and biases q:

[0043] In the formula, ε represents the learning rate, which controls the step size of parameter updates;

[0044] S6. Repeat steps S3 to S5 until a preset stopping condition is reached; the preset stopping condition can be that the loss value is less than a loss threshold, which is used to evaluate whether the model's performance on the training set has reached a sufficient level.

[0045] The optimized time-series forecasting model LSTM is used to predict temperature and NOx concentration: outputting the predicted temperature value at the next time step h. and predicted NOx concentration

[0046] Prediction results and Transmitted to the dynamic control module.

[0047] Furthermore, the dynamic control module automatically adjusts the fuel parameters of the incinerator based on the prediction results and current operating conditions, precisely controls the furnace temperature, and obtains the optimal amount of reducing agent through quantitative analysis.

[0048] Based on temperature prediction results and the current temperature inside the incinerator Calculate temperature deviation

[0049] A PID controller is used to adjust fuel parameters, and a control law is established:

[0050]

[0051] In the formula, u(t) represents the control output of the PID controller, which is used to adjust the controlled object (such as the adjustment amount of fuel supply rate, combustion air volume, and secondary air configuration) to eliminate deviation, and the value of u(t) changes with time t; K1, K2, and K3 represent proportional, integral, and derivative gains, respectively. K1 determines the proportional relationship between the control input and the current deviation. When the deviation is large, the proportional term will generate a large control input to quickly respond to the deviation. K2 is used to handle the cumulative effect of the deviation, that is, to integrate the historical value of the deviation to consider the influence of past deviations on the system. K3 adjusts the control input according to the rate of change of the deviation and predicts the future deviation trend by differentiating the deviation.

[0052] Based on the PID controller output u(t), the fuel supply rate FR(t), combustion air volume RA(t), and secondary air configuration SA(t) (including wind speed and direction) are adjusted to make the temperature deviation ΔT approach 0, thereby precisely controlling the furnace temperature and maintaining it within the optimal temperature range for the denitrification reaction.

[0053] FR(t)←FR(t)+φ FR u(t)

[0054] RA(t)←RA(t)+φ RA u(t)

[0055] SA(t)←SA(t+φ SA u(t)

[0056] In the formula, φ FR φ RA φ SA These represent adjustment factors for fuel supply rate, combustion air volume, and secondary air configuration, respectively, used to convert the controller output into actual operating parameter adjustments.

[0057] Combined with NOx concentration prediction results By using real-time monitoring of NOx concentration, the optimal reducing agent dosage is quantitatively analyzed to determine the optimal injection position, thereby employing precision injection technology to achieve accurate denitrification. This involves estimating the flue gas flow rate (GC) and reducing agent denitrification efficiency (DE) within the furnace under current operating conditions to obtain the optimal reducing agent dosage. In the formula, ts is the time step, which represents the time interval from the current time to the predicted time point t+h, λ represents the safety factor, and σ represents the reaction rate;

[0058] Understandably, determining the optimal injection position requires using computational fluid dynamics simulations or empirical data to understand the flow characteristics of the flue gas in the furnace, including velocity distribution and temperature distribution. It also requires considering the reaction kinetics between the reducing agent and NOx, and combining the results of flow simulations and reaction kinetics to make a comprehensive judgment, selecting the position that allows the reducing agent and NOx to mix fully and react rapidly as the optimal injection position.

[0059] Furthermore, the process of comprehensively evaluating the dynamic control effect of the incinerator through multiple integrated feedback and response modules, and providing timely feedback to the system to continuously optimize the control strategy includes:

[0060] The actual monitoring parameters of the incinerator are compared and evaluated with the control target values. If the deviation between the actual monitoring parameters and the control target values ​​is greater than the preset deviation threshold, a detection signal is generated, and the performance of the PID controller is optimized in a timely manner or the fuel supply rate, combustion air volume, and secondary air configuration are adjusted, while the injection volume and position of the reducing agent are adjusted. If the deviation between the actual monitoring parameters and the control target values ​​is not greater than the preset deviation threshold, a normal operation signal is generated, and the operating status of the incinerator is continuously monitored. The deviation is the difference between the actual monitoring parameters and the control target values. The actual monitoring parameters include temperature and NOx concentration, and the control target values ​​refer to the optimal denitrification reaction temperature range and the NOx concentration emission limit.

[0061] Compared with existing technologies, the advantages of the denitrification temperature management system and method based on municipal solid waste incineration power generation provided by this invention are as follows:

[0062] 1. This invention ensures comprehensive coverage of operating parameters within the incinerator by planning the sensor layout. By collecting and processing incinerator parameter data, monitoring data packets are obtained and uploaded in real time via wireless network, making system deployment more flexible and easier to adjust and optimize according to the actual situation of the incinerator. Based on the historical time series data of the incinerator, machine learning algorithms are used to predict the temperature and NOx concentration in the future, providing a scientific basis for dynamic regulation.

[0063] 2. Based on the prediction results and current operating conditions, this invention automatically adjusts the fuel parameters of the incinerator, precisely controls the furnace temperature, and keeps it within the optimal temperature range for the denitrification reaction. It can respond to changes in the operating status of the incinerator in real time. Through quantitative analysis, the optimal amount of reducing agent is obtained, achieving precise denitrification and reducing resource waste.

[0064] 3. This invention comprehensively evaluates the dynamic control effect of the incinerator, feeds the evaluation results back to the system, provides data support for the continuous optimization of the control strategy, and improves the operating efficiency and stability of the incinerator.

[0065] In summary, this invention achieves precise control of furnace temperature and optimizes the combustion process by leveraging advantages such as planned sensor layout, prediction of future temperature changes, and automatic adjustment of fuel parameters. The various modules in the invention cooperate and work together to form a closed-loop control process of monitoring, analysis, prediction, regulation, and feedback, improving the continuity and stability of incinerator temperature management and the overall performance of the system. This ensures the efficient and stable operation of a subsequent denitrification temperature management system based on municipal solid waste incineration for power generation. Attached Figure Description

[0066] Figure 1 This is a block diagram of a denitrification temperature management system based on municipal solid waste incineration power generation proposed in this invention. Detailed Implementation

[0067] 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.

[0068] Reference Figure 1 A denitrification temperature management system based on municipal solid waste incineration power generation, the system includes an intelligent monitoring module, a data analysis and prediction module, a dynamic control module and a comprehensive feedback and response module;

[0069] The intelligent monitoring module is used to plan the sensor layout of the incinerator, realize comprehensive monitoring of the incinerator's internal environment, collect and process incinerator parameter data, obtain monitoring data packets, and upload the monitoring data packets to the data management center in real time through a wireless sensor network, reducing wiring complexity and improving system flexibility. The intelligent monitoring module includes a sensor layout planning unit and a data acquisition and processing unit.

[0070] The data management center is responsible for receiving monitoring data packets transmitted in real time from various sensors via low-power wireless sensor network technology, and for performing data parsing, processing, and storage.

[0071] The data analysis and prediction module is used to predict the temperature and NOx concentration in the future based on historical time series data of the incinerator using machine learning algorithms;

[0072] The dynamic control module is used to automatically adjust the fuel parameters of the incinerator based on the prediction results and the current operating conditions, precisely control the temperature inside the furnace to keep it in the optimal temperature range for the denitrification reaction, and obtain the optimal amount of reducing agent through quantitative analysis to achieve precise denitrification; among them, the combustion parameters include fuel supply rate, combustion air volume and secondary air configuration.

[0073] The integrated feedback and response module is used to comprehensively evaluate the dynamic control effect of the incinerator and provide timely feedback to the system to continuously optimize the control strategy.

[0074] It should be noted that the application of the denitrification temperature management system based on municipal solid waste incineration power generation in this embodiment of the invention can be used for the operation management or emission control of incinerators in the field of environmental energy. Specifically, it can comprehensively analyze and accurately evaluate the collected temperature data, NOx concentration data, pressure, etc. through advanced technologies such as the Internet of Things, big data analysis, and artificial intelligence, so as to realize real-time monitoring of the denitrification process of the incinerator and timely control of the denitrification temperature. Through multiple links such as data acquisition, transmission, processing, and feedback control, a closed-loop intelligent management system is formed, which enables the system to automatically optimize the denitrification reaction conditions, ensure that NOx emissions meet the standards and accurately control the temperature, while improving the operating efficiency and stability of the incinerator. In addition, the system also promotes the intelligent upgrading of environmental protection technologies and meets the urgent needs for energy conservation, emission reduction, and environmental protection.

[0075] The intelligent monitoring module performs sensor layout planning for the incinerator, collects and processes incinerator parameter data, acquires monitoring data packets, and uploads these monitoring data packets to the data management center in real time via a wireless sensor network. The steps include:

[0076] Step 101: Determine the total number of sensors N, and obtain the installation locations of each type of sensor in sequence, including the installation location A of the high-precision temperature sensor. iLocation B of the flue gas composition analyzer installation j Pressure sensor installation location D b and the installation location of the flame detector E l Where i, j, b, and l represent the indices of the installation locations of the high-precision temperature sensor, the flue gas composition analyzer, the pressure sensor, and the flame detector, respectively.

[0077] Step 102: Maximize the coverage of the monitoring area using the objective function:

[0078]

[0079] In the formula, C m This represents the m-th critical monitoring area; m represents the index of the critical monitoring area; M represents the number of critical monitoring areas; N A N B N D N E These represent the quantities of high-precision temperature sensors, flue gas composition analyzers, pressure sensors, and flame detectors, respectively; Cov(C m A i Cov(C) represents the coverage of the i-th high-precision temperature sensor to the m-th critical monitoring area; m B j ) represents the coverage of the j-th flue gas composition analyzer over the m-th key monitoring area; Cov(C m D b ) represents the coverage of the b-th pressure sensor to the m-th critical monitoring area; Cov(C m E l ) represents the coverage of the m-th critical monitoring area by the l-th flame detector;

[0080] In step 102, regarding the division of key monitoring areas, since the temperature distribution inside the incinerator is usually uneven, and temperature is a key factor affecting combustion efficiency and denitrification effect, several high-precision temperature sensors are usually needed to monitor the temperature in different areas, thus dividing the furnace into multiple temperature monitoring zones. To avoid significant differences in NOx generation and distribution in the incinerator, more precise NOx concentration emission control is required, which necessitates the division into multiple NOx concentration monitoring zones. Because pressure is a relatively uniform physical quantity, and the spatial distribution of pressure inside the incinerator has small differences, only one or two pressure sensors located in suitable positions are needed. At the same time, to ensure comprehensive monitoring of the flame state, this invention requires the installation of multiple flame detectors to cover different areas or angles.

[0081] Step 103: Set constraints for all sensors, including location constraints, coverage constraints, and performance constraints. Location constraints limit the installation location of the sensors, ensuring they are in feasible physical locations and do not interfere with each other. Coverage constraints require that each critical monitoring area be covered by at least a predetermined number of sensors to ensure comprehensive monitoring. Performance constraints specifically stipulate that the sensor's performance parameters must meet predetermined target requirements, namely, the sensor's measurement range, accuracy, and response time.

[0082] For example, taking a high-precision temperature sensor as an example, the position constraint is specifically expressed as follows:

[0083]

[0084]

[0085] In the formula, FL A A represents the set of feasible installation locations for a high-precision temperature sensor. e J represents any high-precision temperature sensor other than the i-th high-precision temperature sensor, where e represents the index of that high-precision temperature sensor; min MinD(A) represents the minimum permissible distance between two sensors. i A e ) indicates a high-precision temperature sensor A i and high-precision temperature sensor A e The shortest distance between them;

[0086] The coverage constraint is specifically expressed as:

[0087]

[0088] In the formula, Thre m This represents the minimum number of sensors required for each critical monitoring area, ensuring that each critical monitoring area C... m At least Thre m Covered by one sensor;

[0089] Performance constraints are expressed as follows:

[0090]

[0091] In the formula, Per(A) i R represents the performance parameters of a high-precision temperature sensor. A This indicates the target requirements for high-precision temperature sensors.

[0092] Step 104: Based on the structure and dimensions of the incinerator, deploy high-precision temperature sensors to monitor the temperature at various points inside the furnace, ensuring comprehensive coverage of all key locations within the furnace (such as the combustion zone, high-temperature zone, and cooling zone); select high-temperature resistant, high-precision, and fast-response temperature sensors and install them in predetermined locations to ensure accurate measurement of the real-time temperature at various points inside the furnace; the predetermined locations are the combustion zone, high-temperature zone, and any area with significant temperature changes within the furnace.

[0093] Sampling points representing the average composition of the flue gas within the entire flue are selected and equipped with flue gas composition analyzers to monitor NOx concentrations. These analyzers must be capable of measuring NOx concentrations and can be extended to measure other harmful gases (such as SO2 and CO) as needed. The selection of sampling points should avoid areas with excessively high or low local concentrations to ensure that the flue gas composition analyzer's measurement results accurately reflect the flue gas conditions throughout the entire flue, mitigating the risk of measurement errors caused by excessively high or low local concentrations.

[0094] Pressure sensors are installed at key locations inside the furnace and in the flue to monitor pressure changes and ensure the safe and stable combustion process. These key locations include the furnace inlet and outlet, flue bends, or any area sensitive to pressure changes. The pressure sensors must also have high accuracy and fast response capabilities to detect and handle abnormal pressure situations in a timely manner.

[0095] Flame detectors are installed in the combustion zone of the incinerator to monitor the flame status inside the furnace in real time, including flame intensity, color, and stability. The installation position of the flame detector should avoid obstructions to ensure that the flame detector can clearly capture the flame image. At the same time, it should be close enough to the flame to detect changes in the flame in a timely manner. The flame detector should have high sensitivity and anti-interference ability to ensure accurate judgment of the flame status even in complex environments.

[0096] Step 105: Each sensor automatically collects various parameters of the furnace environment, namely temperature, NOx concentration, pressure, and flame status, according to the preset sampling frequency and accuracy.

[0097] Step 106: Perform preliminary processing on the collected raw data to obtain real-time temperature data T(t) measured by a high-precision temperature sensor, NOx concentration change data G(t), real-time pressure data P(t) measured by a pressure sensor, and flame intensity index data F(t).

[0098] In step 106, G(t) is the NOx concentration measured by the flue gas composition analyzer, and is a function that reflects the change of NOx concentration over time through calculation and analysis. The flame intensity index data F(t) is a quantitative evaluation value obtained by real-time monitoring of the flame state by a flame detector, extracting flame characteristics (such as brightness, area, etc.), and converting them.

[0099] Step 107: The processed data will be packaged according to a predetermined format, wherein the predetermined format is a monitoring data packet containing a timestamp t, i.e., {T(t),G(t),P(t),F(t)}; using wireless sensor network technology, the monitoring data packet will be transmitted to the data management center in real time, and the upload frequency will be set to once every Δt seconds, thereby forming continuous time series data.

[0100] In step 107, after receiving the data packet, the data management center performs an integrity check to ensure that the data packet has not been tampered with or damaged during transmission. The data packets that pass the check are then parsed and stored.

[0101] The data analysis and prediction module uses machine learning algorithms to predict future temperature and NOx concentrations based on historical time-series data from incinerators. The steps include:

[0102] Step 201: Extract historical time series data of the incinerator from the data management center, including incinerator temperature data {T(t-τ)}, NOx concentration change data {G(t-τ)}, pressure data {P(t-τ)}, and flame intensity index data {F(t-τ)} within the past time period; where τ represents the historical time offset.

[0103] Step 202: Based on the historical time series data of the incinerator, calculate and obtain the operating characteristics of the incinerator:

[0104]

[0105] In the formula, ψ(t) represents the characteristic value of the incinerator operation; T1 and T2 represent the minimum and maximum temperatures inside the incinerator, respectively, used to normalize the temperature data; G re P represents a reference value indicating the NOx concentration inside the incinerator. av P st F represents the average and standard deviation of the pressure inside the incinerator, used to standardize the pressure data; ba The baseline value represents the flame intensity index inside the incinerator; α, β, and γ represent the weighting coefficients of the temperature, pressure, and flame intensity index data inside the incinerator, respectively, and are used to adjust the degree of influence of different parameters on the incinerator's operating characteristic value ψ(t).

[0106] Step 203: Construct a prediction model using machine learning algorithms; wherein, select the temporal prediction model LSTM, and label the number of layers and the number of hidden units in each layer of the temporal prediction model LSTM network as L and H respectively, where L is a structural parameter used to define the depth of the model;

[0107] Step 204: Collect all the calculated incinerator operating characteristic values ​​and divide them into training set and test set. After training the model on the training set, use the test set to evaluate the model performance.

[0108] In step 204, the training process and optimization steps of the time series prediction model LSTM are as follows:

[0109] S1. Assume there exists a time series dataset I(x) r ,y r )={(x1,y1),(x2,y2),...,(x n ,y n )};where, x r =ψ(t) r ) represents the eigenvector, y r This represents the corresponding target value, namely the temperature and NOx concentration at future times, and r represents the index of the feature vector;

[0110] S2. Initialize random values ​​for network weights W and biases q;

[0111] S3. For each sample r, calculate the network output. Where f is the forward propagation function of the network;

[0112] S4. Calculate the predicted value using mean squared error. With the target value y r The difference between them, i.e., the loss value Lo:

[0113]

[0114] S5. Update the parameters using gradient descent, namely the network weights W and biases q:

[0115] In the formula, ε represents the learning rate, which controls the step size of parameter updates;

[0116] S6. Repeat steps S3 to S5 until a preset stopping condition is reached; the preset stopping condition can be that the loss value is less than a loss threshold, which is used to evaluate whether the model's performance on the training set has reached a sufficient level.

[0117] Step 205: Use the optimized time-series prediction model LSTM to predict temperature and NOx concentration: Output the predicted temperature value for the next time step h. and predicted NOx concentration

[0118] Step 206: Obtain the prediction results and Transmitted to the dynamic control module.

[0119] The dynamic control module automatically adjusts the fuel parameters of the incinerator based on prediction results and current operating conditions, precisely controls the furnace temperature, and obtains the optimal amount of reducing agent through quantitative analysis. The steps include:

[0120] Step 301: Based on the temperature prediction results and the current temperature inside the incinerator Calculate temperature deviation

[0121] Step 302: Use a PID controller to adjust fuel parameters and formulate a control law:

[0122]

[0123] In the formula, u(t) represents the control output of the PID controller, which is used to adjust the controlled object (such as the adjustment amount of fuel supply rate, combustion air volume, and secondary air configuration) to eliminate deviation, and the value of u(t) changes with time t; K1, K2, and K3 represent proportional, integral, and derivative gains, respectively. K1 determines the proportional relationship between the control input and the current deviation. When the deviation is large, the proportional term will generate a large control input to quickly respond to the deviation. K2 is used to handle the cumulative effect of the deviation, that is, to integrate the historical value of the deviation to consider the influence of past deviations on the system. K3 adjusts the control input according to the rate of change of the deviation and predicts the future deviation trend by differentiating the deviation.

[0124] Step 303: Based on the PID controller output u(t), adjust the fuel supply rate FR(t), combustion air volume RA(t), and secondary air configuration SA(t) (including wind speed and direction) to make the temperature deviation ΔT approach 0, thereby precisely controlling the furnace temperature and maintaining it within the optimal temperature range for the denitrification reaction.

[0125] FR(t)←FR(t)+φ FR u(t)

[0126] RA(t)←RA(t)+φ RA u(t)

[0127] SA(t)←SA(t+φ SA u(t)

[0128] In the formula, φ FR φ RA φ SAThese represent adjustment factors for fuel supply rate, combustion air volume, and secondary air configuration, respectively, used to convert the controller output into actual operating parameter adjustments.

[0129] Step 304: Combine NOx concentration prediction results By using real-time monitoring of NOx concentration, the optimal reducing agent dosage is quantitatively analyzed to determine the optimal injection position, thereby employing precision injection technology to achieve accurate denitrification. This involves estimating the flue gas flow rate (GC) and reducing agent denitrification efficiency (DE) within the furnace under current operating conditions to obtain the optimal reducing agent dosage. In the formula, ts is the time step, which represents the time interval from the current time to the predicted time point t+h, λ represents the safety factor, and σ represents the reaction rate;

[0130] In step 304, determining the optimal injection position requires using computational fluid dynamics simulations or empirical data to understand the flow characteristics of the flue gas in the furnace, including velocity distribution and temperature distribution. The reaction kinetics between the reducing agent and NOx are considered, and a comprehensive judgment is made by combining the results of flow simulation and reaction kinetics to select the position that allows the reducing agent and NOx to mix fully and react rapidly as the optimal injection position.

[0131] The steps of the integrated feedback and response module to comprehensively evaluate the dynamic control effect of the incinerator and provide timely feedback to the system to continuously optimize the control strategy include:

[0132] Step 401: Compare and evaluate the actual monitoring parameters of the incinerator with the control target values. If the deviation between the actual monitoring parameters and the control target values ​​is greater than the preset deviation threshold, a detection signal is generated, and the performance of the PID controller is optimized or the fuel supply rate, combustion air volume, and secondary air configuration are adjusted in a timely manner, while the injection volume and position of the reducing agent are adjusted. If the deviation between the actual monitoring parameters and the control target values ​​is not greater than the preset deviation threshold, a normal operation signal is generated, and the operating status of the incinerator is continuously monitored. Here, the deviation is the difference between the actual monitoring parameters and the control target values. The actual monitoring parameters include temperature and NOx concentration, and the control target values ​​refer to the optimal denitrification reaction temperature range and the NOx concentration emission limit.

[0133] In this embodiment of the invention, comprehensive monitoring of multiple parameters such as temperature, pressure, and gas concentration within the incinerator is achieved through sensor layout planning, ensuring the comprehensiveness and accuracy of the data. Sensor data is collected and preliminarily processed to form monitoring data packets, providing a high-quality data source for subsequent analysis. Wireless network technology is used to upload the monitoring data, significantly reducing wiring requirements and system complexity. By utilizing machine learning algorithms to analyze historical time-series data of the incinerator, the trends in temperature and NOx concentration changes over a future period can be accurately predicted, providing a scientific basis for dynamic control. Based on the prediction results and current operating conditions, fuel parameters (such as fuel supply rate, combustion air volume, and secondary air configuration) are automatically adjusted to achieve precise temperature control within the furnace, ensuring that the denitrification reaction occurs within the optimal temperature range. Quantitative analysis yields the optimal amount of reducing agent, reducing unnecessary waste, lowering operating costs, and reducing pollutant emissions. Dynamic control can respond in real-time to changes in the incinerator's operating status, maintaining the system in optimal operating condition and improving the incinerator's processing efficiency and stability. A comprehensive evaluation of the incinerator's dynamic control effect is fed back to the system, providing data support for continuous optimization of the control strategy. In summary, this invention addresses the problems of incomplete monitoring and inaccurate temperature control in current incinerator operation and management. In practice, more data and contextual information may be needed to make specific decisions and optimization plans.

[0134] Furthermore, the formulas mentioned above are all dimensionless calculations, derived from software simulation using a large amount of collected data to approximate the real situation. The proportionality coefficients in the formulas and the preset thresholds in the analysis process are set by those skilled in the art based on the actual situation or obtained through large-scale data simulation. The magnitude of the proportionality coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The magnitude of the proportionality coefficient depends on the amount of sample data and the processing coefficients initially set by those skilled in the art for each set of sample data. As long as it does not affect the proportional relationship between the parameter and the quantified value, it is acceptable.

[0135] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. The focus of each embodiment is on its differences from other embodiments. In particular, the apparatus embodiments are described simply because they are fundamentally based on the method embodiments; relevant details can be found in the descriptions of the method embodiments.

[0136] For ease of description, the above devices are described separately by function as various units. Of course, in implementing this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0137] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other.

[0142] In conclusion, the above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A denitrification temperature management system based on municipal solid waste incineration power generation, characterized in that: It includes an intelligent monitoring module, a data analysis and prediction module, a dynamic control module, and a comprehensive feedback and response module; The intelligent monitoring module is used for sensor layout planning in the incinerator, collecting and processing incinerator parameter data, acquiring monitoring data packets, and uploading the monitoring data packets to the data management center in real time via a wireless sensor network. The intelligent monitoring module includes a sensor layout planning unit and a data acquisition and processing unit. The sensor layout planning unit's process for planning the sensor layout of the incinerator includes: Determine the total number of sensors N, and then obtain the installation locations of each type of sensor in sequence, including the installation location A of the high-precision temperature sensor. i Location B of the flue gas composition analyzer installation j Pressure sensor installation location D b and the flame detector installation location E l Where i, j, b, and l represent the indices of the installation locations of the high-precision temperature sensor, the flue gas composition analyzer, the pressure sensor, and the flame detector, respectively. Maximize the coverage of the monitoring area using the objective function: In the formula, C m This represents the m-th critical monitoring area; m represents the index of the critical monitoring area; M represents the number of critical monitoring areas; N A N B N D N E These represent the quantities of high-precision temperature sensors, flue gas composition analyzers, pressure sensors, and flame detectors, respectively; Cov(C m A i Cov(C) represents the coverage of the i-th high-precision temperature sensor to the m-th critical monitoring area; m B j ) represents the coverage of the j-th flue gas composition analyzer over the m-th key monitoring area; Cov(C m D b ) represents the coverage of the b-th pressure sensor to the m-th critical monitoring area; Cov(C m E l ) represents the coverage of the m-th critical monitoring area by the l-th flame detector; Constraints are set for all sensors, including location constraints, coverage constraints, and performance constraints. Location constraints limit the installation location of the sensors, ensuring they are located in feasible physical locations and do not interfere with each other. Coverage constraints require that each critical monitoring area be covered by at least a predetermined number of sensors. Performance constraints specifically stipulate that the sensor's performance parameters must meet predetermined target requirements, namely the sensor's measurement range, accuracy, and response time. The data analysis and prediction module is used to predict future temperature and NOx concentrations based on historical time-series data from the incinerator using machine learning algorithms. The process by which this module predicts future temperature and NOx concentrations based on historical time-series data includes: Historical time series data of the incinerator are extracted from the data management center, including incinerator temperature data {T(t-τ)}, NOx concentration change data {G(t-τ)}, pressure data {P(t-τ)}, and flame intensity index data {F(t-τ)} for past time periods; where τ represents the historical time offset. Based on historical time series data of the incinerator, the operating characteristics of the incinerator are calculated and obtained; A prediction model is constructed using machine learning algorithms; among them, the temporal prediction model LSTM is selected, and the number of layers and the number of hidden units in each layer of the temporal prediction model LSTM network are labeled as L and H, respectively. L is a structural parameter used to define the depth of the model. All calculated incinerator operating characteristic values ​​are collected and divided into training and test sets. The model is trained on the training set and then evaluated on the test set. The optimized time-series forecasting model LSTM is used to predict temperature and NOx concentration: outputting the predicted temperature value at the next time step h. and predicted NOx concentration Prediction results and Transmitted to the dynamic control module; The dynamic control module is used to automatically adjust the fuel parameters of the incinerator based on the prediction results and the current operating conditions, accurately control the temperature inside the furnace, and obtain the optimal amount of reducing agent through quantitative analysis. The integrated feedback and response module is used to comprehensively evaluate the dynamic control effect of the incinerator and provide timely feedback to the system to continuously optimize the control strategy.

2. The denitrification temperature management system based on municipal solid waste incineration power generation according to claim 1, characterized in that: The sensor layout planning unit also includes the following steps in the process of planning the sensor layout for the incinerator: Based on the structure and dimensions of the incinerator, high-precision temperature sensors are deployed to monitor the temperature at various points inside the furnace; sampling points representing the average composition of the flue gas throughout the flue are selected and flue gas composition analyzers are installed to monitor the NOx concentration in the flue gas; pressure sensors are installed at key locations inside the furnace and in the flue to monitor pressure changes inside the furnace, including the furnace inlet and outlet, flue bends, or any areas sensitive to pressure changes; flame detectors are installed in the combustion zone of the incinerator to monitor the flame status inside the furnace in real time.

3. The denitrification temperature management system based on municipal solid waste incineration power generation according to claim 1, characterized in that: The data acquisition and processing unit is used to acquire and process incinerator parameter data. The process of obtaining monitoring data packets includes: Each sensor automatically collects various parameters of the furnace environment, namely temperature, NOx concentration, pressure, and flame status, according to the preset sampling frequency and accuracy. The collected raw data undergoes preliminary processing to obtain real-time temperature data T(t) measured by a high-precision temperature sensor, NOx concentration change data G(t), real-time pressure data P(t) measured by a pressure sensor, and flame intensity index data F(t). Among them, G(t) is the NOx concentration measured by a flue gas composition analyzer, which is a function that reflects the change law of NOx concentration over time through calculation and analysis. The flame intensity index data F(t) is a quantitative evaluation value obtained by real-time monitoring of the flame state by a flame detector, extracting flame characteristics, and converting them. The processed data will be packaged according to a predetermined format, which is a monitoring data packet containing a timestamp t, i.e., {T(t),G(t),P(t),F(t)}. Using wireless sensor network technology, the monitoring data packet will be transmitted to the data management center in real time, and the upload frequency will be set to once every Δt seconds, thereby forming continuous time series data.

4. The denitrification temperature management system based on municipal solid waste incineration power generation according to claim 1, characterized in that: Based on historical time-series data of the incinerator, the formula for calculating and obtaining the operating characteristics of the incinerator is as follows: In the formula, ψ(t) represents the characteristic value of the incinerator operation; T1 and T2 represent the minimum and maximum temperatures inside the incinerator, respectively, used to normalize the temperature data; G re This indicates a reference value for NOx concentration inside the incinerator; P av 、P st F represents the average and standard deviation of the pressure inside the incinerator, used to standardize the pressure data; ba The baseline value represents the flame intensity index inside the incinerator; α, β, and γ represent the weighting coefficients of the temperature, pressure, and flame intensity index data inside the incinerator, respectively, and are used to adjust the degree of influence of different parameters on the incinerator's operating characteristic value ψ(t).

5. The denitrification temperature management system based on municipal solid waste incineration power generation according to claim 1, characterized in that: The dynamic control module automatically adjusts the fuel parameters of the incinerator based on prediction results and current operating conditions, precisely controls the furnace temperature, and obtains the optimal amount of reducing agent through quantitative analysis. This process includes: Based on temperature prediction results and the current temperature inside the incinerator Calculate temperature deviation A PID controller is used to adjust fuel parameters, and a control law is established: In the formula, u(t) represents the control output of the PID controller, which is used to adjust the controlled object to eliminate the deviation, and the value of u(t) changes with time t; K1, K2, and K3 represent the proportional, integral, and derivative gains, respectively. Based on the PID controller output u(t), the fuel supply rate FR(t), combustion air quantity RA(t), and secondary air configuration SA(t) are adjusted to make the temperature deviation ΔT approach 0. FR(t)←FR(t)+φ FR the(t) RA(t)←RA(t)+φ RA you SA(t)←SA(t)+φ SA you In the formula, φ FR φ RA φ SA These represent adjustment factors for fuel supply rate, combustion air volume, and secondary air configuration, respectively, used to convert the controller output into actual operating parameter adjustments. Combined with NOx concentration prediction results By using real-time monitoring of NOx concentration, the optimal reducing agent dosage is quantitatively analyzed, and the optimal injection position is determined, thereby employing precision injection technology for injection: estimating the flue gas flow rate (GC) and reducing agent denitrification efficiency (DE) in the furnace under current operating conditions to obtain the optimal reducing agent dosage. In the formula, ts is the time step, representing the time interval from the current time to the predicted time point t+h, λ represents the safety factor, and σ represents the reaction rate.

6. The denitrification temperature management system based on municipal solid waste incineration power generation according to claim 1, characterized in that: The integrated feedback and response module comprehensively evaluates the dynamic control effect of the incinerator and provides timely feedback to the system, continuously optimizing the control strategy. This process includes: The actual monitoring parameters of the incinerator are compared and evaluated with the control target values. If the deviation between the actual monitoring parameters and the control target values ​​is greater than the preset deviation threshold, a detection signal is generated, and the performance of the PID controller is optimized in a timely manner or the fuel supply rate, combustion air volume, and secondary air configuration are adjusted, while the injection volume and position of the reducing agent are adjusted. If the deviation between the actual monitoring parameters and the control target values ​​is not greater than the preset deviation threshold, a normal operation signal is generated, and the operating status of the incinerator is continuously monitored. The deviation is the difference between the actual monitoring parameters and the control target values. The actual monitoring parameters include temperature and NOx concentration, and the control target values ​​refer to the optimal denitrification reaction temperature range and the NOx concentration emission limit.

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