Denitration temperature management system based on household garbage incineration power generation

Abstract

The invention relates to the technical field of data analysis and prediction, in particular to a denitration temperature management system based on household garbage incineration power generation, which comprises an intelligent monitoring module, a data analysis and prediction module, a dynamic regulation and control module and a comprehensive feedback and response module, by planning the layout of sensors of the incinerator, various parameter data of the incinerator are collected and processed, and data support is provided for subsequent data analysis and prediction; a machine learning algorithm is used for predicting the temperature and NOx concentration in the future time, and a decision basis is provided for the dynamic regulation and control module; fuel parameters of the incinerator are automatically adjusted according to the prediction result and the current working condition, and therefore the temperature in the incinerator is accurately controlled; the dynamic regulation and control effect of the incinerator is comprehensively evaluated and fed back to the system in time, so that long-term stable operation and efficient emission management of the incinerator are ensured. The method is used for solving the technical problems that monitoring is not comprehensive and temperature regulation and control are not accurate in current incinerator operation management.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis and prediction, and in particular to a denitration temperature management system based on power generation by incinerating domestic waste. Background Art

[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 results based on this information.

[0003] With the continuous advancement of technology and the continuous expansion of application fields, new progress has been made in the field of environmental protection energy. However, the following problems still exist in the operation and management of incinerators: low data processing efficiency. Traditional incinerator data may rely on manual operation and traditional monitoring tools for collection and processing. Such a process is time-consuming and error-prone, resulting in low data processing efficiency and difficulty in quickly reflecting the actual operation status of the incinerator; lack of real-time monitoring and prediction capabilities. Traditional incinerator management may lack real-time monitoring and prediction capabilities, unable to timely perceive changes in the furnace environment and predict future trends, so it is difficult to make rapid and accurate adjustments; lack of comprehensive evaluation and response feedback lag: traditional response feedback mechanisms often rely on manual judgment and decision-making, resulting in a long response time, unable to timely evaluate and adjust the control effect, thereby limiting the improvement of the incinerator's operating efficiency and environmental protection performance. To this end, the present invention proposes a denitrification temperature management system based on the incineration of domestic waste for power generation. Summary of the invention

[0004] The purpose of the present invention is to solve the problems in the background technology and to propose a denitrification temperature management system based on incineration of domestic waste for 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 incineration of domestic waste for power generation, comprising: 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 environment inside the incinerator, collect and process the parameter data of the incinerator, obtain the monitoring data packet, and upload the monitoring data packet to the data management center in real time through the wireless sensor network, thereby reducing the wiring complexity and improving the 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 through low-power wireless sensor network technology, and performing data analysis, processing and storage;

[0009] The data analysis and prediction module is used to predict the temperature and NOx concentration in the future based on the 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 according to the prediction results and current working conditions, accurately control the temperature in the furnace to keep it in the optimal temperature range of 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 comprehensive feedback and response module is used to comprehensively evaluate the dynamic control effect of the incinerator, provide timely feedback to the system, and continuously optimize the control strategy.

[0012] It should be noted that the application object of a denitrification temperature management system based on incineration of domestic waste for power generation in an embodiment of the present invention can be the operation management or emission control of incinerators in the field of environmental protection energy. Specifically, it can be through the Internet of Things, big data analysis, artificial intelligence and other advanced technologies to conduct a comprehensive analysis and accurate evaluation of the collected temperature data, NOx concentration data, pressure, etc., so as to realize real-time monitoring of the denitrification process of the incinerator and timely regulation of the denitrification temperature. Through data collection, transmission, processing, feedback control and other links, a closed-loop intelligent management system is formed, so that the system can automatically optimize the denitrification reaction conditions, ensure that NOx emissions meet the standards and the temperature is accurately controlled, and at the same time improve the operation efficiency and stability of the incinerator. In addition, the system also promotes the intelligent upgrading of environmental protection technology and meets the urgent needs for energy conservation, emission reduction and environmental protection.

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

[0014] Determine the total number of sensors N, and obtain the installation locations of various sensors in turn according to the sensor types, including the installation location A of the high-precision temperature sensor i 、Flue gas composition analyzer installation location B j , Pressure sensor installation position D b And the flame detector installation position E l ; Wherein, i, j, b, and l represent the indexes of the installation locations of the high-precision temperature sensor, the flue gas composition analyzer, the pressure sensor, and the flame detector, respectively;

[0015] The objective function is used to maximize the coverage of the monitoring area:

[0016]

[0017] In the formula, C m represents the mth key monitoring area; m represents the index of the key monitoring area; M represents the number of key monitoring areas; N A 、N B 、N D 、N E Respectively represent the number of high-precision temperature sensors, flue gas composition analyzers, pressure sensors and flame detectors; Cov (C m ,A i ) represents the coverage rate of the ith high-precision temperature sensor to the mth key monitoring area; Cov(C m ,B j ) represents the coverage rate of the jth flue gas component analyzer to the mth key monitoring area; Cov(C m ,D b ) represents the coverage rate of the bth pressure sensor to the mth key monitoring area; Cov(C m ,E l ) represents the coverage rate of the lth flame detector to the mth key monitoring area;

[0018] It is understandable that regarding the division of key monitoring areas, since the temperature distribution in the incinerator is usually uneven, and temperature is a key factor affecting the incineration efficiency and denitrification effect, several high-precision temperature sensors are usually required to monitor the temperature of different areas and divide the furnace into multiple temperature monitoring areas; in order to avoid obvious differences in NOx generation and distribution in the incinerator, more sophisticated NOx concentration emission control is required, and it is necessary to divide multiple NOx concentration monitoring areas; because pressure is a relatively uniform physical quantity, and the pressure spatial distribution difference in the incinerator is small, only one or two pressure sensors located in appropriate positions are required; at the same time, in order to ensure comprehensive monitoring of the flame state, the present invention needs to install multiple flame detectors to cover different areas or angles;

[0019] Set constraints for all sensors, including location constraints, coverage constraints, and performance constraints. Location constraints are used to limit the installation location of sensors, that is, they must be located within feasible physical locations and ensure that they do not interfere with each other. Coverage constraints require that each key monitoring area be covered by at least a predetermined number of sensors to ensure comprehensive monitoring. Performance constraints specify that the performance parameters of the sensors must meet the established target requirements, that is, the sensor's measurement range, accuracy, and response time.

[0020] According to the structure and size of the incinerator, high-precision temperature sensors are deployed to monitor the temperature of various points in the furnace to ensure that all key positions in the furnace (such as the combustion zone, high-temperature zone, cooling zone, etc.) are fully covered; high-temperature resistant, high-precision, and fast-response temperature sensors are selected and installed in predetermined locations to ensure that the real-time temperature of various points in the furnace can be accurately measured; the predetermined locations are the combustion zone, high-temperature zone, and any area with significant temperature changes in the furnace;

[0021] Select sampling points in the flue that represent the average composition of the flue gas in the entire flue, and install a flue gas composition analyzer to monitor the NOx concentration in the flue gas. The analyzer must be able to measure the NOx concentration and can be expanded to measure other harmful gases (such as SO2, CO, etc.) as needed; it is understandable that the selection of sampling points should avoid areas with local concentrations that are too high or too low, to ensure that the measurement results of the flue gas composition analyzer can truly reflect the flue gas conditions in the entire flue, and avoid the risk of measurement errors caused by local concentrations that are too high or too low;

[0022] Pressure sensors are installed at key locations in the furnace and flue to monitor pressure changes in the furnace and ensure the safety and stability of the incineration process. It is understandable that key locations in the furnace and flue include the entrance and exit of the furnace, the turning point of the flue or any area sensitive to pressure changes. At the same time, the pressure sensor needs to have high accuracy and fast response capabilities to detect and handle abnormal pressure conditions in a timely manner;

[0023] Flame detectors are installed in the combustion area of ​​the incinerator to monitor the flame status in the furnace in real time, including flame intensity, color, stability, etc.; it is understandable that 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 flame changes in time. The flame detector should have high sensitivity and anti-interference capabilities to ensure that the flame status can still be accurately judged in complex environments.

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

[0025] Each sensor automatically collects various parameter data 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 are preliminarily processed 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); wherein G(t) is the NOx concentration measured by a flue gas component analyzer, and is a function that reflects the law of NOx concentration change over time through calculation and analysis; the flame intensity index data F(t) is a quantitative evaluation value obtained by converting flame characteristics (such as brightness, area, etc.) extracted from flame status by a flame detector in real time;

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

[0028] It is understandable that 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, and parses and stores the data packets that pass the check.

[0029] Furthermore, the data analysis and prediction module uses a machine learning algorithm to predict the temperature and NOx concentration in the future based on the historical time series data of the incinerator. The process includes:

[0030] Extract the incinerator historical time series data from the data management center, including the 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)} in the past time period; where τ represents the historical time offset;

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

[0032]

[0033] Where ψ(t) represents the operating characteristic value of the incinerator; T1 and T2 represent the minimum and maximum values ​​of the temperature in the incinerator, respectively, which are used to normalize the temperature data; G re Indicates the reference value of NOx concentration in the incinerator; P av , P st Respectively represent the mean value and standard deviation of the pressure in the incinerator, used to standardize the pressure data; F baIt represents the reference value of the flame intensity index in the incinerator; α, β, and γ represent the weight coefficients of the temperature, pressure, and flame intensity index data in the incinerator, respectively, and are used to adjust the influence of different parameters on the incinerator operation characteristic value ψ(t);

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

[0035] Collect all the calculated incinerator operation 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.

[0036] Among them, the training process and optimization steps of the time series prediction model LSTM are as follows:

[0037] S1. Assume that there is a time series data set I(x r ,y r )={(x1,y1),(x2,y2),...,(x n ,y n )}; where x r =ψ(t r ) represents the feature vector, y r represents the corresponding target value, i.e., the temperature and NOx concentration at the future time, and r represents the index of the feature vector;

[0038] S2, initialize random values ​​of network weights W and bias q;

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

[0040] S4. Calculate the predicted value using mean square error and the target value y r The difference between them is the loss value Lo:

[0041]

[0042] S5. Use the gradient descent method to update the parameters, namely the network weight W and bias q:

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

[0044] S6, repeating steps S3 to S5 until a preset stop condition is reached; wherein the preset stop condition may be that the loss value is less than a loss threshold, and the loss threshold is used to evaluate whether the performance of the model on the training set has reached a sufficient level;

[0045] Use the optimized time series prediction model LSTM to predict temperature and NOx concentration: output the temperature prediction value of the future time step h and NOx concentration prediction

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

[0047] Furthermore, the dynamic control module automatically adjusts the fuel parameters of the incinerator according to the prediction results and current working conditions, accurately controls the temperature in the furnace, and obtains the optimal reducing agent dosage through quantitative analysis. The process includes:

[0048] Predict results based on temperature and the current temperature in the incinerator Calculating temperature deviation

[0049] The PID controller is used to adjust the fuel parameters and formulate the control law:

[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 fuel supply rate, combustion air volume, and adjustment amount of secondary air configuration) to eliminate the deviation, and the value of u(t) changes with time t; K1, K2, and K3 represent proportional, integral, and differential 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 larger control input to quickly respond to the deviation. K2 is used to deal with the cumulative effect of the deviation, that is, to integrate the historical value of the deviation to consider the impact 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), the combustion air volume RA(t) and the secondary air configuration SA(t) (including wind speed and wind direction) are adjusted to make the temperature deviation ΔT approach 0, thereby accurately controlling the furnace temperature and keeping it in the optimal temperature range for the denitration 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 They represent the adjustment factors of fuel supply rate, combustion air volume and secondary air configuration respectively, and are used to convert the controller output into the actual operating parameter adjustment amount;

[0057] Combined with NOx concentration prediction results And real-time monitoring of NOx concentration, quantitative analysis of the optimal reducing agent dosage, determine the best injection position, and use precision injection technology for injection to achieve precise denitrification: estimate the flue gas flow GC and reducing agent denitrification efficiency DE in the furnace under the current working conditions, and 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] It is understandable that determining the optimal injection position requires the use of computational fluid dynamics simulation or empirical data to understand the flow characteristics of the flue gas in the furnace, including velocity distribution, temperature distribution, etc., consider the reaction kinetics between the reductant and NOx, and make a comprehensive judgment based on the results of flow simulation and reaction kinetics to select the position where the reductant and NOx can be fully mixed and react quickly as the optimal injection position.

[0059] Furthermore, the multi-comprehensive feedback and response module comprehensively evaluates the dynamic control effect of the incinerator and provides timely feedback to the system. The process of continuously optimizing the control strategy includes:

[0060] Compare and evaluate the actual monitoring parameters of the current incinerator with the control target value; if the deviation between the actual monitoring parameters and the control target value is greater than the preset deviation threshold, a signal to be detected is generated, and the PID controller performance is optimized in time or the fuel supply rate, combustion air volume and secondary air configuration are adjusted, and the injection amount and position of the reducing agent are adjusted; if the deviation between the actual monitoring parameters and the control target value is not greater than the preset deviation threshold, a normal operation signal is generated, and the operating status of the incinerator is continuously monitored; wherein, the deviation is the difference between the actual monitoring parameters and the control target value, the actual monitoring parameters include temperature and NOx concentration, and the control target value refers to the optimal denitrification reaction temperature range and NOx concentration emission limit.

[0061] Compared with the existing technology, the present invention provides a denitration temperature management system and method based on incineration of domestic waste for power generation, which has the following advantages:

[0062] 1. The present invention ensures comprehensive coverage of the operating parameters in the incinerator by planning the sensor layout of the incinerator, collects and processes the parameter data of the incinerator, obtains the monitoring data packet, and uploads it in real time using the wireless network, making the system deployment more flexible and easy to adjust and optimize according to the actual situation of the incinerator in the future; based on the historical time series data of the incinerator, the machine learning algorithm is used to predict the temperature and NOx concentration in the future, providing a scientific basis for dynamic regulation;

[0063] 2. The present invention automatically adjusts the fuel parameters of the incinerator according to the prediction results and the current working conditions, accurately controls the temperature in the furnace, keeps it in the optimal temperature range of the denitration reaction, and can respond to the changes in the operating state of the incinerator in real time; obtains the optimal amount of reducing agent through quantitative analysis, realizes accurate denitration, and reduces resource waste;

[0064] 3. The present invention comprehensively evaluates the dynamic control effect of the incinerator and feeds the evaluation results back to the system, thereby providing data support for the continuous optimization of the control strategy and improving the operating efficiency and stability of the incinerator.

[0065] In summary, the present invention realizes precise control of the temperature in the furnace and optimizes the combustion process by taking advantage of multiple aspects such as planning sensor layout, predicting temperature changes in the future, and automatically adjusting 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, thereby improving the continuity and stability of the incinerator temperature management and the overall performance of the system, and ensuring the efficient and stable operation of a subsequent denitrification temperature management system based on the incineration of domestic waste for power generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a module diagram of a denitrification temperature management system based on incineration of domestic waste for power generation proposed by the present invention. DETAILED DESCRIPTION

[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the implementation regulations described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0068] Reference Figure 1 , a denitrification temperature management system based on incineration of domestic waste for 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 environment inside the incinerator, collect and process the parameter data of the incinerator, obtain the monitoring data packet, and upload the monitoring data packet to the data management center in real time through the wireless sensor network, thereby reducing the wiring complexity and improving the 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 through low-power wireless sensor network technology, and performing data analysis, processing and storage;

[0071] The data analysis and prediction module is used to predict the temperature and NOx concentration in the future based on the 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 according to the prediction results and current working conditions, accurately control the temperature in the furnace to keep it in the optimal temperature range of 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 comprehensive feedback and response module is used to comprehensively evaluate the dynamic control effect of the incinerator, provide timely feedback to the system, and continuously optimize the control strategy.

[0074] It should be noted that the application object of a denitrification temperature management system based on incineration of domestic waste for power generation in an embodiment of the present invention can be the operation management or emission control of incinerators in the field of environmental protection energy. Specifically, it can be through the Internet of Things, big data analysis, artificial intelligence and other advanced technologies to conduct a comprehensive analysis and accurate evaluation of the collected temperature data, NOx concentration data, pressure, etc., so as to realize real-time monitoring of the denitrification process of the incinerator and timely regulation of the denitrification temperature. Through data collection, transmission, processing, feedback control and other links, a closed-loop intelligent management system is formed, so that the system can automatically optimize the denitrification reaction conditions, ensure that NOx emissions meet the standards and the temperature is accurately controlled, and at the same time improve the operation efficiency and stability of the incinerator. In addition, the system also promotes the intelligent upgrading of environmental protection technology and meets the urgent needs for energy conservation, emission reduction and environmental protection.

[0075] The intelligent monitoring module plans the sensor layout of the incinerator, collects and processes the parameter data of the incinerator, obtains the monitoring data packet, and uploads the monitoring data packet to the data management center in real time through the wireless sensor network. The steps include:

[0076] Step 101: Determine the total number of sensors N, and obtain the installation locations of various sensors in turn according to the sensor types, including the installation location A of the high-precision temperature sensor. i、Flue gas composition analyzer installation location B j , Pressure sensor installation position D b And the flame detector installation position E l ; Wherein, i, j, b, and l represent the indexes 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 represents the mth key monitoring area; m represents the index of the key monitoring area; M represents the number of key monitoring areas; N A 、N B 、N D 、N E Respectively represent the number of high-precision temperature sensors, flue gas composition analyzers, pressure sensors and flame detectors; Cov (C m ,A i ) represents the coverage rate of the ith high-precision temperature sensor to the mth key monitoring area; Cov(C m ,B j ) represents the coverage rate of the jth flue gas component analyzer to the mth key monitoring area; Cov(C m ,D b ) represents the coverage rate of the bth pressure sensor to the mth key monitoring area; Cov(C m ,E l ) represents the coverage rate of the lth flame detector to the mth key monitoring area;

[0080] In step 102, regarding the division of key monitoring areas, since the temperature distribution in the incinerator is usually uneven, and temperature is a key factor affecting the incineration efficiency and denitrification effect, a number of high-precision temperature sensors are usually required to monitor the temperature of different areas and divide the furnace into multiple temperature monitoring areas; in order to avoid obvious differences in NOx generation and distribution in the incinerator, more sophisticated NOx concentration emission control is required, and it is necessary to divide multiple NOx concentration monitoring areas; because pressure is a relatively uniform physical quantity, and the pressure spatial distribution difference in the incinerator is small, only one or two pressure sensors located at appropriate positions are required; at the same time, in order to ensure comprehensive monitoring of the flame state, the present invention needs to install 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. The location constraints are used to limit the installation locations of sensors, i.e., they must be located within feasible physical locations and must not interfere with each other. The coverage constraints require that each key monitoring area be covered by at least a predetermined number of sensors to ensure comprehensive monitoring. The performance constraints specifically stipulate that the performance parameters of the sensors must meet the established target requirements, i.e., the measurement range, accuracy, and response time of the sensors.

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

[0083]

[0084]

[0085] Where FL A represents the feasible installation location set of high-precision temperature sensors, A e represents any high-precision temperature sensor except the i-th high-precision temperature sensor, where e represents the index of the high-precision temperature sensor; J min Indicates the minimum allowable distance between two sensors; MinD(A i ,A e ) represents high-precision temperature sensor A i And high-precision temperature sensor A e The shortest distance between

[0086] The coverage constraint is specifically expressed as:

[0087]

[0088] Where Thr m Indicates the minimum number of sensors required to cover each key monitoring area to ensure that each key monitoring area C m By at least Thre m Covered by sensors;

[0089] The performance constraint is expressed as:

[0090]

[0091] Where Per(A i ) represents the performance parameters of the high-precision temperature sensor, R A Indicates the target requirements for high-precision temperature sensors;

[0092] Step 104: According to the structure and size of the incinerator, high-precision temperature sensors are deployed to monitor the temperature of various points in the furnace to ensure that all key positions in the furnace (such as the combustion zone, high-temperature zone, cooling zone, etc.) are fully covered; high-temperature resistant, high-precision, and fast-response temperature sensors are selected and installed at predetermined locations to ensure that the real-time temperature of various points in the furnace can be accurately measured; wherein the predetermined locations are the combustion zone, high-temperature zone, and any area with significant temperature changes in the furnace;

[0093] Select sampling points in the flue that represent the average composition of the flue gas in the entire flue, and install a flue gas composition analyzer to monitor the NOx concentration in the flue gas. The analyzer must be able to measure the NOx concentration and can be expanded to measure other harmful gases (such as SO2, CO, etc.) as needed; the sampling points should be selected to avoid areas with excessively high or low local concentrations to ensure that the measurement results of the flue gas composition analyzer can truly reflect the flue gas conditions in the entire flue and avoid the risk of measurement errors caused by excessively high or low local concentrations;

[0094] Pressure sensors are installed at key locations in the furnace and flue to monitor pressure changes in the furnace and ensure the safety and stability of the incineration process. Key locations in the furnace and flue include the entrance and exit of the furnace, the turning point of the flue or any area sensitive to pressure changes. At the same time, the pressure sensor must have high accuracy and fast response capabilities to detect and handle abnormal pressure conditions in a timely manner.

[0095] Flame detectors are installed in the combustion area of ​​the incinerator to monitor the flame status in the furnace in real time, including flame intensity, color, stability, etc. The flame detector should be installed away from 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 flame changes in time. The flame detector should have high sensitivity and anti-interference capabilities to ensure that the flame status can be accurately judged in complex environments.

[0096] Step 105, each sensor automatically collects various parameter data of the furnace environment, namely, temperature, NOx concentration, pressure and flame state according to the preset sampling frequency and accuracy;

[0097] Step 106: preliminarily process 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 a function of NOx concentration measured by a flue gas component analyzer and reflecting the law of NOx concentration changing over time through calculation and analysis. The flame intensity index data F(t) is a quantitative evaluation value obtained by converting flame characteristics (such as brightness, area, etc.) extracted by real-time monitoring of the flame state through a flame detector;

[0099] Step 107: The processed data is packaged in 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)}; the monitoring data packet is transmitted to the data management center in real time using wireless sensor network technology, and the upload frequency is 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, and parses and stores the data packet that passes the check.

[0101] The data analysis and prediction module uses machine learning algorithms to predict the temperature and NOx concentration in the future based on the historical time series data of the incinerator. The steps include:

[0102] Step 201, extracting the incinerator historical time series data from the data management center, including the 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)} in the past time period; wherein 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] Where ψ(t) represents the operating characteristic value of the incinerator; T1 and T2 represent the minimum and maximum values ​​of the temperature in the incinerator, respectively, which are used to normalize the temperature data; G re Indicates the reference value of NOx concentration in the incinerator; P av , P st Respectively represent the mean value and standard deviation of the pressure in the incinerator, used to standardize the pressure data; F ba It represents the reference value of the flame intensity index in the incinerator; α, β, and γ represent the weight coefficients of the temperature, pressure, and flame intensity index data in the incinerator, respectively, and are used to adjust the influence of different parameters on the incinerator operation characteristic value ψ(t);

[0106] Step 203: construct a prediction model using a machine learning algorithm; wherein, the time series prediction model LSTM is selected, and the number of network layers and the number of hidden units in each layer of the time series prediction model LSTM are marked as L and H respectively, where L is a structural parameter used to define the depth of the model;

[0107] Step 204: collect all calculated incinerator operation characteristic values ​​and divide them into a training set and a 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 that there is a time series data set I(x r ,y r )={(x1,y1),(x2,y2),...,(x n ,y n )}; where x r =ψ(t r ) represents the feature vector, y r represents the corresponding target value, i.e., the temperature and NOx concentration at the future time, and r represents the index of the feature vector;

[0110] S2, initialize random values ​​of network weights W and bias q;

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

[0112] S4. Calculate the predicted value using mean square error and the target value y r The difference between them is the loss value Lo:

[0113]

[0114] S5. Use the gradient descent method to update the parameters, namely the network weight W and bias q:

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

[0116] S6, repeating steps S3 to S5 until a preset stop condition is reached; wherein the preset stop condition may be that the loss value is less than a loss threshold, and the loss threshold is used to evaluate whether the performance of the model on the training set has reached a sufficient level;

[0117] Step 205: Use the optimized time series prediction model LSTM to predict the temperature and NOx concentration: output the predicted temperature value of the future time step h and NOx concentration prediction

[0118] Step 206: The prediction result and Transmitted to the dynamic control module.

[0119] The dynamic control module automatically adjusts the fuel parameters of the incinerator according to the prediction results and current working conditions, accurately controls the temperature inside the furnace, and obtains the optimal reducing agent dosage through quantitative analysis. The steps include:

[0120] Step 301: Predict the temperature and the current temperature in the incinerator Calculating 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 fuel supply rate, combustion air volume, and adjustment amount of secondary air configuration) to eliminate the deviation, and the value of u(t) changes with time t; K1, K2, and K3 represent proportional, integral, and differential 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 larger control input to quickly respond to the deviation. K2 is used to deal with the cumulative effect of the deviation, that is, to integrate the historical value of the deviation to consider the impact 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), the combustion air volume RA(t) and the secondary air configuration SA(t) (including wind speed and wind direction) to make the temperature deviation ΔT approach 0, thereby accurately controlling the temperature in the furnace and maintaining it in the optimal temperature range for the denitration 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 SAThey represent the adjustment factors of fuel supply rate, combustion air volume and secondary air configuration respectively, and are used to convert the controller output into the actual operating parameter adjustment amount;

[0129] Step 304: Combine the NOx concentration prediction results And real-time monitoring of NOx concentration, quantitative analysis of the optimal reducing agent dosage, determine the best injection position, and use precision injection technology for injection to achieve precise denitrification: estimate the flue gas flow GC and reducing agent denitrification efficiency DE in the furnace under the current working conditions, and 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 the use of computational fluid dynamics simulation or empirical data to understand the flow characteristics of the flue gas in the furnace, including velocity distribution, temperature distribution, etc., and consider the reaction kinetics between the reductant and NOx. A comprehensive judgment is made based on the results of flow simulation and reaction kinetics to select the position where the reductant and NOx can be fully mixed and react quickly as the optimal injection position.

[0131] The comprehensive feedback and response module comprehensively evaluates the dynamic control effect of the incinerator and provides timely feedback to the system. The steps of continuously optimizing the control strategy include:

[0132] Step 401, compare and evaluate the actual monitoring parameters of the current incinerator with the control target value; if the deviation between the actual monitoring parameters and the control target value is greater than the preset deviation threshold, a signal to be detected is generated, and the PID controller performance is optimized in time or the fuel supply rate, the amount of combustion air and the secondary air configuration are adjusted, and the injection amount and position of the reducing agent are adjusted; if the deviation between the actual monitoring parameters and the control target value is not greater than the preset deviation threshold, a normal operation signal is generated, and the operating status of the incinerator is continuously monitored; wherein the deviation is the difference between the actual monitoring parameters and the control target value, the actual monitoring parameters include temperature and NOx concentration, and the control target value refers to the optimal denitrification reaction temperature range and NOx concentration emission limit.

[0133] In the embodiment of the present invention, by planning the sensor layout, comprehensive monitoring of multiple parameters such as temperature, pressure, gas concentration, etc. in the incinerator is achieved to ensure the comprehensiveness and accuracy of the data. By collecting sensor data and performing preliminary processing to form a monitoring data packet, a high-quality data source is provided for subsequent analysis. The monitoring data is uploaded using wireless network technology, which greatly reduces the wiring requirements and reduces the complexity of the system. By using a machine learning algorithm to analyze the historical time series data of the incinerator, the temperature and NOx concentration change trends in the future can be accurately predicted, providing a scientific basis for dynamic regulation. According to the prediction results and the current working conditions, the fuel parameters (such as fuel supply rate, combustion air volume, secondary air configuration) are automatically adjusted to achieve precise control of the temperature in the furnace, ensuring that the denitration reaction is carried out within the optimal temperature range. The optimal amount of reducing agent is obtained through quantitative analysis, unnecessary waste is reduced, operating costs are reduced, and pollutant emissions are reduced at the same time. The dynamic regulation can respond to the changes in the operating state of the incinerator in real time, keep the system in the optimal operating state, and improve the processing efficiency and stability of the incinerator. By comprehensively evaluating the dynamic regulation effect of the incinerator, the evaluation results are fed back to the system to provide data support for the continuous optimization of the control strategy. In summary, the example of the present invention solves the problems of incomplete monitoring and inaccurate temperature control in the current incinerator operation management. In actual situations, more data and contextual information may be needed to make specific decisions and optimization plans.

[0134] In addition, the formulas involved in the above are all calculated by removing dimensions and taking their numerical values. They are a formula that is closest to the actual situation obtained by collecting a large amount of data and performing software simulation. The proportional coefficient in the formula and the various preset thresholds in the analysis process are set by technical personnel in this field according to actual conditions or obtained by simulating a large amount of data; the size of the proportional coefficient is to quantify each parameter to obtain a specific value for subsequent comparison. The size of the proportional coefficient depends on the amount of sample data and the preliminary setting of the corresponding processing coefficient for each group of sample data by technical personnel in this field; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0135] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically based on the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0136] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0137] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, 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 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 flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, 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 flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0139] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A 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 instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0141] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0142] Finally: The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A denitrification temperature management system based on incineration of domestic waste for power generation, characterized by: It includes intelligent monitoring module, data analysis and prediction module, dynamic control module and comprehensive feedback and response module; The intelligent monitoring module is used to plan the sensor layout of the incinerator, collect and process the parameter data of the incinerator, obtain the monitoring data packet, and upload the monitoring data packet to the data management center in real time through the wireless sensor network; wherein, the intelligent monitoring module includes a sensor layout planning unit and a data collection and processing unit; The data analysis and prediction module is used to predict the temperature and NOx concentration in the future based on the historical time series data of the incinerator using machine learning algorithms; The dynamic control module is used to automatically adjust the fuel parameters of the incinerator according to the prediction results and current working conditions, accurately control the temperature inside the furnace, and obtain the optimal reducing agent dosage through quantitative analysis; The comprehensive feedback and response module is used to comprehensively evaluate the dynamic control effect of the incinerator, provide timely feedback to the system, and continuously optimize the control strategy.

2. A denitrification temperature management system based on incineration of domestic waste for power generation according to claim 1, characterized in that: The process used by the sensor layout planning unit to plan the sensor layout for the incinerator includes: Determine the total number of sensors N, and obtain the installation locations of various sensors in turn according to the sensor types, including the installation location A of the high-precision temperature sensor i 、Flue gas composition analyzer installation location B j , Pressure sensor installation position D b And the flame detector installation position E l ; Wherein, i, j, b, and l represent the indexes of the installation locations of the high-precision temperature sensor, the flue gas composition analyzer, the pressure sensor, and the flame detector, respectively; The objective function is used to maximize the coverage of the monitoring area: In the formula, C m represents the mth key monitoring area; m represents the index of the key monitoring area; M represents the number of key monitoring areas; N A 、N B 、N D 、N E Respectively represent the number of high-precision temperature sensors, flue gas composition analyzers, pressure sensors and flame detectors; Cov (C m ,A i ) represents the coverage rate of the ith high-precision temperature sensor to the mth key monitoring area; Cov(C m ,B j ) represents the coverage rate of the jth flue gas component analyzer to the mth key monitoring area; Cov(C m ,D b ) represents the coverage rate of the bth pressure sensor to the mth key monitoring area; Cov(C m ,E l ) represents the coverage rate of the lth flame detector to the mth key monitoring area; Set constraints for all sensors, including location constraints, coverage constraints, and performance constraints. Location constraints are used to limit the installation location of sensors, that is, they must be located within feasible physical locations and ensure that they do not interfere with each other. Coverage constraints require that each key monitoring area is covered by at least a predetermined number of sensors. Performance constraints specify that the performance parameters of the sensors must meet the established target requirements, that is, the sensor's measurement range, accuracy, and response time. According to the structure and size of the incinerator, high-precision temperature sensors are deployed to monitor the temperature of various points in the furnace; sampling points representing the average composition of the flue gas in the entire flue are selected in the flue, and a flue gas composition analyzer is installed to monitor the NOx concentration in the flue gas; pressure sensors are installed at key locations in the furnace and the flue to monitor pressure changes in the furnace, where key locations in the furnace and the flue include the entrance and exit of the furnace, the turning point of the flue or any area that is sensitive to pressure changes; flame detectors are installed in the combustion area of ​​the incinerator to monitor the flame status in the furnace in real time.

3. A denitrification temperature management system based on incineration of domestic waste for power generation according to claim 1, characterized in that: The data acquisition and processing unit is used to collect and process the incinerator parameter data. The process of obtaining the monitoring data packet includes: Each sensor automatically collects various parameter data of the furnace environment, namely temperature, NOx concentration, pressure and flame status, according to the preset sampling frequency and accuracy; The collected raw data is preliminarily processed 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 component analyzer, and is a function that reflects the law of NOx concentration change over time through calculation and analysis. The flame intensity index data F(t) is a quantitative evaluation value obtained by converting flame characteristics after real-time monitoring of the flame state by a flame detector; The processed data will be packaged according to a predetermined format, where the predetermined format is a monitoring data packet containing a timestamp t, i.e., {T(t), G(t), P(t), F(t)}. The monitoring data packet will be transmitted to the data management center in real time using wireless sensor network technology, and the upload frequency is set to once every Δt seconds, thereby forming continuous time series data.

4. A denitrification temperature management system based on incineration of domestic waste for power generation according to claim 1, characterized in that: The data analysis and prediction module uses machine learning algorithms to predict the temperature and NOx concentration in the future based on the historical time series data of the incinerator. The process includes: Extract the incinerator historical time series data from the data management center, including the 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)} in the past time period; where τ represents the historical time offset; Based on the historical time series data of the incinerator, the operating characteristics of the incinerator are calculated: Where ψ(t) represents the operating characteristic value of the incinerator; T1 and T2 represent the minimum and maximum values ​​of the temperature in the incinerator, respectively, which are used to normalize the temperature data; G re Indicates the reference value of NOx concentration in the incinerator; P av , P st Respectively represent the mean value and standard deviation of the pressure in the incinerator, used to standardize the pressure data; F ba It represents the reference value of the flame intensity index in the incinerator; α, β, and γ represent the weight coefficients of the temperature, pressure, and flame intensity index data in the incinerator, respectively, and are used to adjust the influence of different parameters on the incinerator operation characteristic value ψ(t); A prediction model is constructed using a machine learning algorithm; the time series prediction model LSTM is selected, and the number of LSTM network layers and the number of hidden units in each layer of the time series prediction model are marked as L and H respectively. L is a structural parameter used to define the depth of the model; Collect all the calculated incinerator operation 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. Use the optimized time series prediction model LSTM to predict temperature and NOx concentration: output the temperature prediction value of the future time step h and NOx concentration prediction The predicted results and Transmitted to the dynamic control module.

5. A denitrification temperature management system based on power generation by incinerating domestic waste according to claim 1, characterized in that: The dynamic control module automatically adjusts the fuel parameters of the incinerator according to the prediction results and current working conditions, accurately controls the temperature inside the furnace, and obtains the optimal reducing agent dosage through quantitative analysis. The process includes: Predict results based on temperature and the current temperature in the incinerator Calculate temperature deviation The PID controller is used to adjust the fuel parameters and formulate the control law: In the formula, u(t) represents the control output of the PID controller, which is used to adjust the controlled object (such as fuel supply rate, combustion air volume, and adjustment amount of secondary air configuration) to eliminate deviation, and the value of u(t) changes with time t; K1, K2, and K3 represent proportional, integral, and differential gains, respectively; Based on the PID controller output u(t), the fuel supply rate FR(t), the combustion air volume RA(t) and the 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 They represent the adjustment factors of fuel supply rate, combustion air volume and secondary air configuration respectively, and are used to convert the controller output into the actual operating parameter adjustment amount; Combined with NOx concentration prediction results And real-time monitoring of NOx concentration, quantitative analysis of the optimal reducing agent dosage, determine the optimal injection position, and then use precision injection technology for injection: estimate the flue gas flow GC and reducing agent denitrification efficiency DE in the furnace under the current working conditions, and obtain the optimal reducing agent dosage Where 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.

6. A denitrification temperature management system based on power generation by incinerating domestic waste according to claim 1, characterized in that: The comprehensive feedback and response module comprehensively evaluates the dynamic control effect of the incinerator and provides timely feedback to the system. The process of continuously optimizing the control strategy includes: Compare and evaluate the actual monitoring parameters of the current incinerator with the control target value; if the deviation between the actual monitoring parameters and the control target value is greater than the preset deviation threshold, a signal to be detected is generated, and the PID controller performance is optimized in time or the fuel supply rate, combustion air volume and secondary air configuration are adjusted, and the injection amount and position of the reducing agent are adjusted; if the deviation between the actual monitoring parameters and the control target value is not greater than the preset deviation threshold, a normal operation signal is generated, and the operating status of the incinerator is continuously monitored; wherein, the deviation is the difference between the actual monitoring parameters and the control target value, the actual monitoring parameters include temperature and NOx concentration, and the control target value refers to the optimal denitrification reaction temperature range and NOx concentration emission limit.

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