Monitoring optimization method and system for vibration grate of biomass boiler

Abstract

The invention discloses a monitoring optimization method and system for a vibration grate of a biomass boiler. The method comprises the steps that sensors are arranged in a biomass boiler system, pollutant concentration, temperature and pressure parameters in flue gas are monitored in real time, then collected data are utilized to form a desulfurization facility data curve, an optimization algorithm model is established, and finally, according to output of the optimization algorithm model, the desulfurization facility data curve is calculated. And the intelligent control system automatically adjusts the operation parameters of the desulfurization and dust removal equipment so as to maximize the desulfurization efficiency. The method realizes automation and intelligentization of the desulfurization and dust removal process, effectively reduces environmental pollution, and improves the energy utilization efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring optimization, and in particular to a monitoring optimization method and system for a biomass boiler vibrating grate. Background Art

[0002] With the rapid development of industrialization and urbanization, air pollution has become increasingly prominent, especially the harm caused by sulfur dioxide (SO2) and particulate matter (PM) emissions to the environment and human health. In response to this problem, the government has introduced a series of strict environmental protection policies, requiring all industries to take effective measures to reduce pollutant emissions. As a major energy consumer, the desulfurization and dust removal effect of biomass boilers directly affects the quality of the atmospheric environment.

[0003] Traditional desulfurization and dust removal methods often have problems such as low efficiency, high energy consumption, and high operating costs. In order to improve the efficiency of desulfurization and dust removal, reduce operating costs, and achieve intelligent control. Summary of the invention

[0004] Based on this, the embodiment of the present application provides a monitoring and optimization method and system for the vibrating grate of a biomass boiler. By real-time monitoring of key parameters in the flue gas, such as pollutant concentration, temperature, pressure, etc., the optimization algorithm model is used to automatically adjust parameters such as the dosage of the desulfurizer and the oxygen supply, so as to achieve intelligent control and improve the desulfurization efficiency.

[0005] In a first aspect, a monitoring and optimization method for a biomass boiler vibrating grate is provided, the method comprising:

[0006] By deploying sensors in the biomass boiler system, the pollutant concentration, temperature and pressure parameters in the flue gas can be monitored in real time;

[0007] The collected data is used to form a desulfurization facility data curve and establish an optimization algorithm model; the optimization algorithm model automatically adjusts the dosage of desulfurizer and oxygen supply through an intelligent algorithm according to the preset desulfurization efficiency target;

[0008] According to the output of the optimization algorithm model, the intelligent control system will automatically adjust the operating parameters of the desulfurization and dust removal equipment to maximize the desulfurization efficiency, including adjusting the dosage of the desulfurizer, controlling the oxygen supply, and optimizing the flue gas flow.

[0009] Optionally, by deploying sensors in the biomass boiler system, pollutant concentration, temperature, and pressure parameters in the flue gas are monitored in real time, including sending the sensor data to the control system via wireless transmission.

[0010] Optionally, the collected data is used to form a desulfurization facility data curve, including:

[0011] Preprocess the data collected by the sensor to eliminate noise and outliers; the preprocessing includes exponential moving average of the data and merging the minimum analysis period;

[0012] The pre-processed data is used to form a data curve of the desulfurization facility; wherein the data curve includes a pollutant concentration curve, a temperature curve and a pressure curve that vary with time.

[0013] Optionally, establishing the optimization algorithm model specifically includes:

[0014] The formed data curve is used to establish an optimization algorithm model in combination with the goal of desulfurization efficiency. A deep neural network is used to learn the corresponding relationship in the data curve, and the dropout technology is used in the model training process to prevent overfitting, and the prediction ability of the model is optimized by adjusting the model parameters.

[0015] Optionally, based on the output of the optimization algorithm model, the intelligent control system will automatically adjust the operating parameters of the desulfurization and dust removal equipment to maximize the desulfurization efficiency, including:

[0016] According to the deviation between the inlet NOx concentration and the outlet NOx set value, the required ammonia flow rate is calculated through the PID control algorithm, and the opening of the ammonia flow control valve is adjusted;

[0017] The fan speed is controlled by frequency converters and other devices to ensure that the oxygen supply matches the NOx concentration requirement in the flue gas. The flue gas flow is monitored by a flow meter, and the system can provide real-time feedback and adjust the operating status of the fan.

[0018] In a second aspect, a monitoring and optimization system for a biomass boiler vibrating grate is provided, the system comprising:

[0019] A monitoring module, which is used to monitor the pollutant concentration, temperature and pressure parameters in the flue gas in real time by deploying sensors in the biomass boiler system;

[0020] Establish a module for using the collected data to form a desulfurization facility data curve and establish an optimization algorithm model; wherein the optimization algorithm model automatically adjusts the dosage of the desulfurizer and the oxygen supply through an intelligent algorithm according to a preset desulfurization efficiency target;

[0021] The adjustment module is used to automatically adjust the operating parameters of the desulfurization and dust removal equipment according to the output of the optimization algorithm model, so as to maximize the desulfurization efficiency, including adjusting the dosage of the desulfurizer, controlling the oxygen supply, and optimizing the flue gas flow.

[0022] Optionally, by deploying sensors in the biomass boiler system, pollutant concentration, temperature, and pressure parameters in the flue gas are monitored in real time, including sending the sensor data to the control system via wireless transmission.

[0023] Optionally, the collected data is used to form a desulfurization facility data curve, including:

[0024] Preprocess the data collected by the sensor to eliminate noise and outliers; the preprocessing includes exponential moving average of the data and merging the minimum analysis period;

[0025] The pre-processed data is used to form a data curve of the desulfurization facility; wherein the data curve includes a pollutant concentration curve, a temperature curve and a pressure curve that vary with time.

[0026] Optionally, establishing the optimization algorithm model specifically includes:

[0027] The formed data curve is used to establish an optimization algorithm model in combination with the goal of desulfurization efficiency. A deep neural network is used to learn the corresponding relationship in the data curve, and the dropout technology is used in the model training process to prevent overfitting, and the prediction ability of the model is optimized by adjusting the model parameters.

[0028] Optionally, based on the output of the optimization algorithm model, the intelligent control system will automatically adjust the operating parameters of the desulfurization and dust removal equipment to maximize the desulfurization efficiency, including:

[0029] According to the deviation between the inlet NOx concentration and the outlet NOx set value, the required ammonia flow rate is calculated through the PID control algorithm, and the opening of the ammonia flow control valve is adjusted;

[0030] The fan speed is controlled by frequency converters and other devices to ensure that the oxygen supply matches the NOx concentration requirement in the flue gas. The flue gas flow is monitored by a flow meter, and the system can provide real-time feedback and adjust the operating status of the fan.

[0031] The beneficial effects brought about by the technical solution provided in the embodiment of the present application include at least the ability to monitor and adjust the operating parameters of the desulfurization and dust removal equipment in real time, ensuring that the system operates in an optimal state, thereby reducing energy consumption and material consumption and achieving energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0033] Figure 1 A monitoring optimization flow chart of a biomass boiler vibrating grate provided in an embodiment of the present application;

[0034] Figure 2 A block diagram of a monitoring and optimization system for a biomass boiler vibrating grate provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0036] In the description of the present invention, the terms "comprises", "has" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may also include other steps or units that are not explicitly listed but are inherent to these processes, methods, products or apparatuses, or steps or units added based on further optimization schemes conceived by the present invention.

[0037] With the rapid development of industrialization and urbanization, air pollution has become increasingly prominent, especially the harm caused by sulfur dioxide (SO2) and particulate matter (PM) emissions to the environment and human health. In response to this problem, the government has introduced a series of strict environmental protection policies, requiring all industries to take effective measures to reduce pollutant emissions. As a major energy consumer, the desulfurization and dust removal effect of biomass boilers directly affects the quality of the atmospheric environment.

[0038] Please refer to Figure 1 , which shows a flow chart of a monitoring and optimization method for a biomass boiler vibrating grate provided in an embodiment of the present application, the method may include the following steps:

[0039] S1, by deploying sensors in the biomass boiler system, the pollutant concentration, temperature and pressure parameters in the flue gas are monitored in real time.

[0040] High-precision sensors are deployed in the flue gas emission path of the biomass boiler system. These sensors can monitor and record key parameters such as pollutant concentration (such as sulfur dioxide, nitrogen oxides), temperature and pressure in the flue gas in real time. The sensor data is sent to the central control system via wired or wireless transmission. To ensure the accuracy of the data, the sensors need to be calibrated and maintained regularly. In addition, the data acquisition system should have fault diagnosis and abnormal alarm functions to respond in time when the sensor fails.

[0041] S2, using the collected data, forms a desulfurization facility data curve and establishes an optimization algorithm model.

[0042] Among them, the optimization algorithm model automatically adjusts the dosage of desulfurizer and oxygen supply according to the preset desulfurization efficiency target through intelligent algorithm.

[0043] The collected flue gas parameter data are used to form data curves of the desulfurization facilities, which reflect the real-time status and historical trends of the desulfurization process. Based on these data, an optimization algorithm model is established, which combines the desulfurization efficiency target and the changes in flue gas composition, and automatically calculates and adjusts the dosage of desulfurizer and oxygen supply through intelligent algorithms (such as genetic algorithms, neural networks or machine learning models). The model needs to be updated regularly to adapt to changes in fuel composition and changes in boiler operating conditions.

[0044] In this step, first, the pollutant concentration, temperature, pressure and other parameters in the flue gas are monitored in real time through sensors deployed in the biomass boiler system. The data collected by these sensors need to be preprocessed to eliminate noise and outliers and improve the accuracy and reliability of the data. The preprocessing step may include techniques such as exponential moving average of data and merging minimum analysis cycles to reduce data volatility and highlight its trends and patterns. This step ensures the quality and availability of the data and provides a solid foundation for subsequent data analysis and model building.

[0045] Data curve formation: The pre-processed data is used to form data curves for the desulfurization facility. These curves usually include pollutant concentration curves, temperature curves, and pressure curves that change over time. The generation of data curves can be achieved through professional data analysis software or customized algorithms, which can intuitively show the changing trends of various parameters in the desulfurization process. These curves not only help to understand the dynamic characteristics of the desulfurization process, but can also be used for subsequent model building and performance analysis.

[0046] Establish an optimization algorithm model: Use the data curve formed and combine it with the goal of desulfurization efficiency to establish an optimization algorithm model. This model may use machine learning algorithms, such as deep neural networks, to learn complex patterns and relationships in the data curve. During the model training process, dropout technology may be used to prevent overfitting, and the model's predictive ability may be optimized by adjusting model parameters.

[0047] Model verification and application: The established optimization algorithm model needs to be verified by actual operation data to ensure its prediction accuracy and reliability. The verification process may involve the comparison between the model output and the actual desulfurization efficiency, as well as the adaptability test of the model under different working conditions. Once the model is verified, it can be applied to the real-time monitoring system to automatically adjust the desulfurization agent dosage, oxygen supply and other parameters according to the preset desulfurization efficiency target to achieve intelligent control and optimized operation.

[0048] S3, according to the output of the optimization algorithm model, the intelligent control system will automatically adjust the operating parameters of the desulfurization and dust removal equipment to maximize the desulfurization efficiency.

[0049] These include adjusting the dosage of desulfurizer, controlling the oxygen supply, and optimizing the flue gas flow.

[0050] The intelligent control system automatically adjusts the operating parameters of the desulfurization and dust removal equipment based on the output results of the optimization algorithm model. This includes but is not limited to:

[0051] Adjust the dosage of desulfurizer to ensure that it matches the pollutant concentration in the flue gas and achieve the best desulfurization effect.

[0052] Control the oxygen supply to optimize the combustion process and desulfurization reaction and reduce unnecessary energy consumption.

[0053] Optimize flue gas flow to ensure full contact between flue gas and desulfurizer and improve desulfurization efficiency. The intelligent control system should have a user interface that enables operators to monitor the operating status of the system and perform manual intervention when necessary. In addition, the system should also have a log recording function to facilitate tracking of operation history and subsequent performance analysis.

[0054] The adjustment process includes the following details: Adjust the dosage of desulfurizer: The dosage of desulfurizer is usually controlled by an ammonia flow control valve. According to the chemical equation of the denitrification reaction, the ratio of NH3 to NOx mass should theoretically be 1. In actual operation, the system calculates the required ammonia flow rate through the PID control algorithm based on the deviation between the inlet NOx concentration and the outlet NOx set value. By adjusting the opening of the ammonia flow control valve, the system can accurately control the amount of ammonia sprayed to ensure that the denitrification efficiency reaches the predetermined target. The optimized control system can quickly respond and adjust the dosage of ammonia by real-time monitoring of NOx concentration changes to avoid excessive or insufficient ammonia, thereby improving denitrification efficiency and reducing ammonia escape.

[0055] Controlling oxygen supply: The control of oxygen supply is mainly achieved by adjusting the speed of the fan and the air intake valve. The speed of the fan is proportional to the oxygen supply. The higher the speed, the greater the airflow, which can inject more oxygen. The speed of the fan can be controlled by devices such as frequency converters to ensure that the oxygen supply matches the concentration requirements of NOx in the flue gas. In addition, the system can also automatically adjust the opening of the air intake valve according to the real-time monitored oxygen concentration and NOx concentration changes to maintain the optimal oxygen supply, thereby optimizing the denitrification reaction conditions.

[0056] Optimize flue gas flow: Optimizing flue gas flow usually involves improving the fan design and adjusting the fan speed. By optimizing the fan design, the appropriate airflow speed is ensured to effectively capture particulate matter. At the same time, the system can dynamically adjust the fan speed based on real-time data analysis to maintain a suitable flue gas flow. In addition, by using a flow meter to monitor the flue gas flow, the system can provide real-time feedback and adjust the operating status of the fan to ensure that the residence time of the flue gas in the desulfurization reactor is optimal, thereby improving the desulfurization efficiency.

[0057] like Figure 2 The present application also provides a monitoring and optimization system for a biomass boiler vibrating grate. The system includes:

[0058] A monitoring module, which is used to monitor the pollutant concentration, temperature and pressure parameters in the flue gas in real time by deploying sensors in the biomass boiler system;

[0059] Establish a module for using the collected data to form a desulfurization facility data curve and establish an optimization algorithm model; wherein the optimization algorithm model automatically adjusts the dosage of the desulfurizer and the oxygen supply through an intelligent algorithm according to a preset desulfurization efficiency target;

[0060] The adjustment module is used to automatically adjust the operating parameters of the desulfurization and dust removal equipment according to the output of the optimization algorithm model, so as to maximize the desulfurization efficiency, including adjusting the dosage of the desulfurizer, controlling the oxygen supply, and optimizing the flue gas flow.

[0061] The monitoring and optimization system for the vibrating grate of a biomass boiler provided in the embodiment of the present application is used to implement the monitoring and optimization method for the vibrating grate of a biomass boiler. For the specific definition of the monitoring and optimization system for the vibrating grate of a biomass boiler, please refer to the definition of the monitoring and optimization method for the vibrating grate of a biomass boiler in the above text, which will not be repeated here. Each part of the monitoring and optimization system for the vibrating grate of a biomass boiler can be implemented in whole or in part by software, hardware and a combination thereof. The above modules can be embedded in or independent of the processor in the device in the form of hardware, or can be stored in the memory in the device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0062] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0063] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A monitoring and optimization method for a biomass boiler vibrating grate, characterized in that: The method comprises: By deploying sensors in the biomass boiler system, the pollutant concentration, temperature and pressure parameters in the flue gas can be monitored in real time; The collected data is used to form a desulfurization facility data curve and establish an optimization algorithm model; the optimization algorithm model automatically adjusts the dosage of desulfurizer and oxygen supply through an intelligent algorithm according to the preset desulfurization efficiency target; According to the output of the optimization algorithm model, the intelligent control system will automatically adjust the operating parameters of the desulfurization and dust removal equipment to maximize the desulfurization efficiency, including adjusting the dosage of the desulfurizer, controlling the oxygen supply, and optimizing the flue gas flow.

2. The monitoring optimization method according to claim 1, characterized in that: By deploying sensors in the biomass boiler system, the pollutant concentration, temperature, and pressure parameters in the flue gas are monitored in real time, including sending the sensor data to the control system via wireless transmission.

3. The monitoring optimization method according to claim 1, characterized in that: Using the collected data, a desulfurization facility data curve is formed, including: Preprocess the data collected by the sensor to eliminate noise and outliers; the preprocessing includes exponential moving average of the data and merging the minimum analysis period; The pre-processed data is used to form a data curve of the desulfurization facility; wherein the data curve includes a pollutant concentration curve, a temperature curve and a pressure curve that vary with time.

4. The monitoring optimization method according to claim 1, characterized in that: Establishing an optimization algorithm model specifically includes: An optimization algorithm model is established by using the formed data curve and combining it with the goal of desulfurization efficiency. A deep neural network is used to learn the corresponding relationship in the data curve, and dropout technology is used during model training to prevent overfitting, and the prediction ability of the model is optimized by adjusting the model parameters.

5. The monitoring optimization method according to claim 1, characterized in that: Based on the output of the optimization algorithm model, the intelligent control system will automatically adjust the operating parameters of the desulfurization and dust removal equipment to maximize the desulfurization efficiency, including: According to the deviation between the inlet NOx concentration and the outlet NOx set value, the required ammonia flow rate is calculated through the PID control algorithm, and the opening of the ammonia flow control valve is adjusted; The fan speed is controlled by frequency converters and other devices to ensure that the oxygen supply matches the NOx concentration requirement in the flue gas. The flue gas flow is monitored by a flow meter, and the system can provide real-time feedback and adjust the operating status of the fan.

6. A monitoring and optimization system for a biomass boiler vibrating grate, characterized in that: The system comprises: A monitoring module, which is used to monitor the pollutant concentration, temperature and pressure parameters in the flue gas in real time by deploying sensors in the biomass boiler system; Establish a module for using the collected data to form a desulfurization facility data curve and establish an optimization algorithm model; wherein the optimization algorithm model automatically adjusts the dosage of the desulfurizer and the oxygen supply through an intelligent algorithm according to the preset desulfurization efficiency target; The adjustment module is used to automatically adjust the operating parameters of the desulfurization and dust removal equipment according to the output of the optimization algorithm model, so as to maximize the desulfurization efficiency, including adjusting the dosage of the desulfurizer, controlling the oxygen supply, and optimizing the flue gas flow.

7. The monitoring and optimization system according to claim 6, characterized in that: By deploying sensors in the biomass boiler system, the pollutant concentration, temperature, and pressure parameters in the flue gas are monitored in real time, including sending the sensor data to the control system via wireless transmission.

8. The monitoring and optimization system according to claim 6, characterized in that: Using the collected data, a desulfurization facility data curve is formed, including: Preprocess the data collected by the sensor to eliminate noise and outliers; the preprocessing includes exponential moving average of the data and merging the minimum analysis period; The pre-processed data is used to form a data curve of the desulfurization facility; wherein the data curve includes a pollutant concentration curve, a temperature curve and a pressure curve that vary with time.

9. The monitoring and optimization system according to claim 6, characterized in that: Establishing an optimization algorithm model specifically includes: An optimization algorithm model is established by using the formed data curve and combining it with the goal of desulfurization efficiency. A deep neural network is used to learn the corresponding relationship in the data curve, and dropout technology is used during model training to prevent overfitting, and the prediction ability of the model is optimized by adjusting the model parameters.

10. The monitoring and optimization system according to claim 6, characterized in that: Based on the output of the optimization algorithm model, the intelligent control system will automatically adjust the operating parameters of the desulfurization and dust removal equipment to maximize the desulfurization efficiency, including: According to the deviation between the inlet NOx concentration and the outlet NOx set value, the required ammonia flow rate is calculated through the PID control algorithm, and the opening of the ammonia flow control valve is adjusted; The fan speed is controlled by frequency converters and other devices to ensure that the oxygen supply matches the NOx concentration requirement in the flue gas. The flue gas flow is monitored by a flow meter, and the system can provide real-time feedback and adjust the operating status of the fan.

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