Online automatic detection and ash discharge system for low-temperature wide-channel plate-type economizer

Abstract

The invention provides an on-line automatic detection and ash discharge system for a low-temperature wide-channel plate-type economizer, which relates to the technical field of economizer ash discharge and comprises an on-line monitoring module, an automatic detection control module, an economizer ash collection module and a remote monitoring and control module. Data such as temperature, pressure drop and airflow of the economizer are collected in real time through the online monitoring module, an online automatic detection function is achieved, and key indexes such as dust accumulation amount, pressure drop change and temperature change are predicted in combination with a machine learning algorithm. By predicting the ash deposition trend in real time, the system can judge the ash deposition risk in advance, the situation that the equipment efficiency is reduced due to excessive ash deposition is avoided, meanwhile, the system has a self-adaptive ash discharge strategy, the ash removal threshold value and the ash discharge frequency can be automatically adjusted according to real-time data, the ash discharge opportunity, frequency and mode are optimized according to the running state data of the economizer, and the ash discharge efficiency is improved. Therefore, the ash discharge efficiency is improved through intelligent decision making, the ash discharge frequency can be reduced to the maximum extent, and energy is saved.

Description

Technical Field

[0001] This invention relates to the field of economizer ash removal technology, and in particular to an online automatic detection and ash removal system for a low-temperature wide-channel plate economizer. Background Technology

[0002] Low-temperature wide-channel plate economizers are an indispensable component of boiler systems, widely used in thermal power plants, industrial boilers, and other applications. As a key device in the boiler's tail flue, the economizer's main task is to transfer heat from the low-temperature flue gas to the boiler feedwater, raising the feedwater temperature and ultimately heating it to saturation point at the steam drum pressure, thus providing heat to the steam boiler. Through this process, the economizer effectively recovers waste heat from the flue gas, reduces the flue gas exhaust temperature, saves energy, and improves the overall efficiency of the boiler, thus exhibiting significant energy-saving effects.

[0003] Because economizers are installed under flue gas conditions, their working environment contains a large amount of dust and particulate matter, typically composed of coal ash and soot from the combustion process. When flue gas passes through the economizer's heat exchange pipes, these particles gradually accumulate on the pipe surface, forming a thick ash layer that severely affects heat exchange efficiency. As dust accumulates, flue gas flow resistance increases, leading to a gradual increase in the economizer's pressure drop. This phenomenon not only wastes boiler fuel but may also cause the equipment to operate under overload conditions, shortening its service life. Therefore, timely removal of ash buildup inside the economizer to ensure optimal heat exchange efficiency is essential for improving boiler efficiency, reducing energy consumption, and extending equipment life.

[0004] To address the problem of dust accumulation, current ash removal technologies mainly rely on periodic manual inspections, cleaning, and physical dust removal. However, these traditional methods have many problems. For example, while existing automatic ash removal systems can trigger ash removal operations based on timed or quantitative rules in certain situations, most of these systems rely on manually set thresholds and lack real-time dust accumulation prediction capabilities. Operators still need to periodically inspect the system to ensure its normal operation, resulting in low automation and significant reliance on manual intervention. Therefore, this invention proposes an online automatic detection and ash removal system for a low-temperature wide-channel plate economizer to solve the problems existing in the prior art. Summary of the Invention

[0005] To address the aforementioned problems, the present invention aims to propose an online automatic detection and ash removal system for a low-temperature wide-channel plate economizer. This system incorporates intelligent and automated design, enabling real-time monitoring of dust accumulation in the economizer and optimization of the ash removal strategy based on actual operating data, thus solving the problems in the prior art.

[0006] To achieve the objectives of this invention, the invention is implemented through the following technical solution: an online automatic detection and ash removal system for a low-temperature wide-channel plate economizer, including an online monitoring module for real-time acquisition of the operating status of the low-temperature wide-channel plate economizer by deploying different sensors;

[0007] The automatic detection and control module is used to analyze and process the real-time collected operating status data and automatically control the ash removal.

[0008] The economizer ash collection module is used to connect with the low-temperature wide-channel plate economizer to collect the settled dust and discharge it.

[0009] The remote monitoring and control module is used to achieve remote monitoring and operation control via a cloud platform.

[0010] A further improvement is that the online monitoring module includes a sensor submodule for acquiring operational status data, a data collection submodule for centrally collecting operational status data, and a data transmission submodule for transmitting operational status data.

[0011] Further improvements are made in that the different sensors include a temperature sensor, a pressure drop sensor, and a flow sensor, and the corresponding operating states include the gas temperature inside the low-temperature wide-channel plate economizer, the pressure drop change of the gas flow, and the gas flow rate.

[0012] Further improvements are made in that: the automatic detection and control module includes a data processing submodule for processing operating status data, a model building and management submodule for building an automatic ash removal control model based on a machine learning model, an ash removal strategy optimization submodule for dynamically optimizing based on the analysis results of the automatic ash removal control model, and an ash removal equipment control submodule for controlling ash removal based on the optimization results of the ash removal strategy optimization submodule.

[0013] A further improvement is that the automatic detection and control module also includes a warning issuance submodule for making judgments based on operating status data and issuing warning information.

[0014] A further improvement lies in the following: the model construction method of the model construction management submodule is as follows:

[0015] S1. Based on on-site data acquisition, the operating data is recorded by deploying sensors on the low-temperature wide-channel plate economizer, and a real-time dataset is built through accumulation.

[0016] S2. Based on the decision tree learning algorithm, construct an automatic ash discharge control model and train the automatic ash discharge control model using the constructed real-time dataset;

[0017] S3. Analyze the data using the trained automatic ash removal control model. The input data are gas temperature, pressure drop change and gas flow rate. The output data are dust accumulation prediction, pressure drop change prediction and temperature change prediction.

[0018] The further improvement lies in the following: the specific method of dynamic optimization is as follows:

[0019] SS1. The threshold conditions for dust discharge are preset by the staff. These conditions include dust accumulation exceeding the threshold, pressure drop exceeding the threshold, and temperature exceeding the threshold.

[0020] SS2. Compare the output of the automatic ash removal control model with the threshold conditions to trigger the ash removal operation;

[0021] SS3. Based on the feedback from the operation data after ash removal, use reinforcement learning algorithms to dynamically adjust the ash removal process.

[0022] A further improvement is that the economizer ash collection module includes an ash hopper sub-module for collecting settled dust, an ash discharge control sub-module for discharging dust, and a heating anti-condensation sub-module for preventing dust condensation.

[0023] A further improvement is that the ash removal control submodule includes airflow-assisted ash removal and vibration-assisted ash removal.

[0024] A further improvement is that the remote monitoring and control module includes an interface control submodule for user operation control, a cloud data storage module for cloud storage, and an alarm prompting submodule for issuing alarm prompts.

[0025] The beneficial effects of this invention are as follows:

[0026] (1) This invention collects real-time data such as temperature, pressure drop, and airflow of the economizer through an online monitoring module, and has an online automatic detection function. Combined with machine learning algorithms, it predicts key indicators such as dust accumulation, pressure drop changes, and temperature changes. By predicting the dust accumulation trend in real time, the system can judge the risk of dust accumulation in advance and avoid the situation where excessive dust accumulation leads to a decrease in equipment efficiency.

[0027] (2) The present invention has an adaptive ash removal strategy, which can automatically adjust the ash removal threshold and ash removal frequency according to real-time data, optimize the timing, frequency and method of ash removal according to the operating status data of the economizer, thereby improving the ash removal efficiency through intelligent decision-making, while also minimizing the ash removal frequency and saving energy. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a schematic diagram of the system structure of the present invention. Detailed Implementation

[0030] To enhance understanding of the present invention, the present invention will be further described in detail below with reference to embodiments. These embodiments are only used to explain the present invention and do not constitute a limitation on the scope of protection of the present invention.

[0031] The core working principle of an economizer is to absorb heat from low-temperature flue gas through a plate heat exchanger and transfer this heat to the boiler feedwater. In this process, the heat exchange between the low-temperature flue gas and the feedwater plays a crucial role. However, because the economizer is installed under flue gas conditions, its working environment contains a large amount of dust and particulate matter, typically composed of coal ash and soot from the combustion process. When flue gas passes through the economizer's heat exchange pipes, these particles gradually accumulate on the pipe surface, forming a thick ash layer that severely affects heat exchange efficiency.

[0032] As dust accumulates, the resistance to flue gas flow increases, leading to a gradual increase in the pressure drop of the economizer. This phenomenon not only wastes boiler fuel but may also cause the equipment to operate under overload conditions, shortening its service life. Therefore, timely removal of ash buildup inside the economizer to ensure it maintains optimal heat exchange efficiency is a necessary measure to improve boiler efficiency, reduce energy consumption, and extend equipment life.

[0033] While existing automated dust removal systems can trigger dust removal operations based on timed or quantitative rules in certain situations, most of these systems rely on manually set thresholds and lack real-time dust accumulation prediction capabilities. Operators still need to periodically inspect the system to ensure its proper functioning, resulting in a low level of automation and a significant need for manual intervention.

[0034] Meanwhile, most current ash removal systems rely on pressure drop changes and temperature fluctuations as indicators to trigger ash removal, but these data cannot fully reflect the dust accumulation within the economizer. Detection of dust accumulation still depends on manual or indirect methods, failing to provide accurate, real-time ash accumulation data. Furthermore, existing systems typically lack integrated advanced data analysis and prediction functions after data collection, preventing ash removal strategies from adaptively optimizing for different operating conditions.

[0035] Therefore, according to Figure 1 As shown, this embodiment proposes an online automatic detection and ash removal system for a low-temperature wide-channel plate economizer, including:

[0036] The online monitoring module is used to collect real-time data on the operating status of the low-temperature wide-channel plate economizer by deploying various sensors. By monitoring the economizer's operating status, it can assess equipment performance in real time, identify potential problems, and ensure that the system always operates at its optimal state. This module includes:

[0037] The sensor submodule, responsible for acquiring operational status data, is responsible for deploying various sensors to monitor the economizer's operating parameters in real time. These sensors transmit the data to the data centralization submodule for subsequent analysis and control decisions. Specifically, the different sensors include temperature sensors (used to monitor the gas temperature at the economizer inlet, outlet, and other key locations in real time. Temperature changes can reflect heat exchange efficiency and ash accumulation; excessively high temperatures may indicate excessive ash accumulation or decreased heat exchange efficiency), pressure drop sensors (installed at the economizer inlet and outlet to monitor pressure drop changes throughout the airflow path; pressure drop changes can reflect ash accumulation), and flow sensors (used to measure gas flow rate; changes in gas flow rate may reflect boiler load changes or uneven airflow distribution; installed at the economizer inlet and outlet to monitor gas flow rate changes in real time). The corresponding operational statuses include the gas temperature inside the low-temperature wide-channel plate economizer, changes in gas flow pressure drop, and gas flow rate.

[0038] The data centralization submodule is used for centralized collection of operational status data. This submodule is responsible for centrally receiving data from various sensors, storing the data for subsequent transmission. Specifically, it receives data from various sensors through various interfaces (such as Modbus, CAN bus, Ethernet, etc.). These interfaces ensure that sensor data can be transmitted to the data centralization submodule efficiently and stably.

[0039] The data transmission submodule, used for transmitting operational status data, is responsible for transmitting the centrally collected operational status data from the field to the cloud platform for storage, so that the automatic detection and control module can call it. It adopts standard communication protocols (Modbus TCP / IP, MQTT, HTTP / HTTPS, OPC-UA, etc.) to ensure stable data transmission.

[0040] The automatic detection and control module analyzes and processes real-time collected operating status data and automatically controls ash removal. By monitoring and analyzing operating status data in real time, it automates ash removal control. This involves multiple stages, including data acquisition, analysis, model prediction, and ash removal strategy optimization, to ensure the equipment operates at its optimal state and prevent ash accumulation from affecting heat exchange efficiency. It includes:

[0041] The data processing submodule is used to process operational status data. It performs operations such as data cleaning, preprocessing, and formatting, and provides an accurate data foundation for subsequent data analysis and ash removal control.

[0042] The model building and management submodule, used to construct an automatic ash removal control model based on machine learning models, utilizes real-time collected data for model training and prediction. This allows it to predict dust accumulation, pressure drop changes, and temperature changes, thus supporting ash removal decisions. The model building method of the model building and management submodule is as follows:

[0043] S1. Based on on-site data acquisition, operational data is recorded by deploying sensors on the low-temperature wide-channel plate economizer. This process continuously accumulates real-time datasets, forming high-quality training datasets. As the amount of data increases, the system's predictive ability gradually improves.

[0044] S2. Based on the decision tree learning algorithm (decision trees can predict the output target by progressively splitting the input data), an automatic ash discharge control model is constructed. The model is trained using the constructed real-time dataset to learn the relationship between factors such as dust accumulation, pressure drop changes and airflow temperature. After training, cross-validation is used to avoid overfitting of the model and improve its generalization ability.

[0045] S3. Use the trained automatic ash removal control model for data analysis. The input data are gas temperature, pressure drop change and gas flow rate. The output data are dust accumulation prediction (predicting the current and future dust accumulation), pressure drop change prediction (predicting the trend of pressure drop change to help determine whether there is a risk of dust accumulation), and temperature change prediction (predicting the trend of temperature change to help assess heat exchange efficiency).

[0046] The ash removal strategy optimization submodule is used to dynamically optimize the ash removal strategy based on the analysis results of the automatic ash removal control model. The specific method of dynamic optimization is as follows:

[0047] SS1. The threshold conditions for triggering ash removal are preset by the staff. These conditions include dust accumulation exceeding a threshold (the ash removal operation is triggered when the dust accumulation exceeds a certain threshold), pressure drop exceeding a threshold (the ash removal operation is triggered when the pressure drop changes beyond a set threshold), and temperature exceeding a threshold (the ash removal operation is triggered when the temperature changes beyond a preset threshold).

[0048] SS2 compares the results output by the automatic ash removal control model with threshold conditions to trigger ash removal operation. When the dust accumulation, pressure drop change or temperature change predicted by the model exceeds the threshold set by the staff, the ash removal operation is automatically triggered. This allows for comprehensive judgment under multiple threshold conditions to make the optimal ash removal decision.

[0049] SS3. Based on the operational data feedback after ash removal, the system dynamically adjusts the ash removal process using a reinforcement learning algorithm. After each ash removal operation, the system collects relevant feedback data (i.e., subsequent real-time operational status data), such as temperature recovery and pressure drop recovery. Based on the reinforcement learning algorithm (Q-learning), the system dynamically adjusts the ash removal strategy according to the evaluation of the ash removal effect (such as system efficiency and energy consumption). Through continuous iterative training, the ash removal decision is optimized.

[0050] The ash removal equipment control submodule is used to control ash removal based on the optimization results of the ash removal strategy optimization submodule. According to the optimized ash removal strategy, it controls the start, stop and operation parameter adjustment of the ash removal equipment (corresponding to the ash removal control submodule). Specifically, it sends specific instructions to the ash removal equipment through a PLC or embedded system to perform the operation.

[0051] This submodule, called the Early Warning Issuance Submodule, is used to make judgments based on operational status data and issue early warning information. Specifically, based on the analysis results of the automatic ash removal control model, this system will determine whether any abnormal conditions have occurred (such as excessively high temperature, excessive pressure drop, or excessively rapid dust accumulation), and issue an early warning. It interfaces with the Alarm Notification Submodule to display the early warning information. Correspondingly, it has a set of alarm rules (such as dust accumulation exceeding a threshold, or pressure drop change exceeding a threshold), which are preset by the staff.

[0052] The economizer ash collection module is used to connect to the low-temperature wide-channel plate economizer, collect the settled dust, and discharge it. The goal of the economizer ash collection module is to ensure that the accumulated ash inside the economizer can be effectively collected, removed, and discharged, maintaining the high-efficiency operation of the economizer. It includes:

[0053] The ash collection hopper submodule is responsible for collecting dust that settles during economizer operation due to airflow deceleration and temperature drop. Specifically, it corresponds to the hopper device, which has an ash discharge port at the bottom. This port is connected to external dust collection equipment via an external pipe. The bottom of the hopper device is designed with an inclined structure, which helps dust slide naturally along the inclined surface to the discharge port, preventing ash accumulation and clogging of the hopper. The ash discharge port connects to an external ash discharge pipe.

[0054] The ash discharge control submodule is responsible for discharging the collected dust from the ash hopper, ensuring that ash accumulation in the economizer is cleaned promptly during operation and preventing dust buildup from affecting heat exchange efficiency. Specifically, ash discharge methods include airflow-assisted ash discharge and vibration-assisted ash discharge. Airflow-assisted ash discharge uses high-pressure airflow to blow dust from the hopper into the discharge pipe. Vibration-assisted ash discharge uses a vibrator installed at the bottom of the ash hopper to push the accumulated ash to the discharge port. This method is suitable for situations where the ash is light and does not easily accumulate.

[0055] The heating anti-condensation submodule is designed to prevent dust from condensing. Under low-temperature flue gas conditions, dust may clump together due to excessively low temperatures, leading to difficulties in ash removal and system blockage. The heating anti-condensation submodule incorporates a heating device, such as an electric heating belt, within the hopper equipment to maintain the dust within a certain temperature range and prevent dust from clumping.

[0056] The remote monitoring and control module is used to realize remote monitoring and operation control through a cloud platform. It includes an interface control submodule for user operation control, a cloud data storage module for storing data in the cloud, and an alarm prompting submodule for issuing alarm prompts.

[0057] Specifically, sensors are deployed at key locations of the economizer to collect the following data in real time:

[0058] Gas temperature: Temperature sensors are installed at the economizer inlet and outlet to collect gas temperature data, i.e., the gas temperature at the economizer inlet is 180℃ and the gas temperature at the outlet is 120℃.

[0059] Pressure drop: Install pressure drop sensors at the inlet and outlet of the economizer to monitor the pressure drop data, i.e., the initial pressure drop is 250 Pa;

[0060] Gas flow rate: The gas flow rate entering the economizer is monitored in real time by a flow sensor and is 100,000 Nm³ / h.

[0061] Using the trained automatic ash removal control model, based on real-time collected data, the dust accumulation, pressure drop, and temperature changes are predicted. The predicted dust accumulation is 5 kg / m², which exceeds the preset threshold (4 kg / m²), triggering the ash removal operation.

[0062] The predicted pressure drop change is 350 Pa, which exceeds the set threshold (300 Pa), triggering ash discharge.

[0063] The predicted temperature change is 125℃ for the outlet gas, which is within the normal range of 120℃ to 130℃, with a temperature fluctuation range of 5℃, which meets the requirements.

[0064] Therefore, based on the real-time predicted dust accumulation, pressure drop, and temperature changes, a threshold comparison is performed. Since both the dust accumulation and pressure drop exceed the threshold, the dust removal operation is initiated.

[0065] Furthermore, due to the large amount of dust accumulation and significant pressure drop changes, the system automatically adjusts the dust removal frequency. The system is initially set to remove dust once per hour, but when dust accumulation is rapid, the system increases the dust removal frequency to once every 30 minutes.

[0066] For situations with significant ash accumulation and increased pressure drop, the system employs airflow-assisted ash removal, using high-pressure airflow to rapidly clear the ash. Specifically, a compressed air system is used, with the airflow pressure set to 6 bar to ensure sufficient force for ash removal. Each pulse ash removal lasts for 5 seconds to avoid excessive impact on the economizer structure. The pulse ash removal interval is adjusted based on real-time monitoring data. When the system predicts severe ash accumulation, the ash removal interval is reduced; conversely, the interval is appropriately increased.

[0067] After ash removal, the monitoring system collects new operating data, namely, the gas temperature recovers from 125℃ to 120℃ and the pressure drop decreases from 350 Pa to 280 Pa. These changes indicate that the ash removal operation is effective.

[0068] This invention uses a Q-learning algorithm to adaptively adjust the ash removal strategy. By analyzing the feedback effects after each ash removal (such as temperature recovery, pressure drop changes, etc.), the system adjusts model parameters, such as the ash removal interval and airflow pressure, to optimize the overall ash removal efficiency. A reward value is calculated based on the effect after each ash removal operation (e.g., pressure drop recovery speed, equipment efficiency improvement, etc.). Therefore, higher efficiency and energy saving will result in higher rewards, thereby prompting the system to gradually optimize the ash removal strategy.

[0069] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A low-temperature wide-channel plate economizer online automatic detection and ash removal system, characterized in that: Includes an online monitoring module, used to collect the real-time operating status of the low-temperature wide-channel plate economizer by deploying different sensors; The automatic detection and control module is used to analyze and process the real-time collected operating status data and automatically control the ash removal. The economizer ash collection module is used to connect with the low-temperature wide-channel plate economizer to collect the settled dust and discharge it. The remote monitoring and control module is used to achieve remote monitoring and operation control via a cloud platform.

2. The online automatic detection and ash removal system for a low-temperature wide-channel plate economizer according to claim 1, characterized in that: The online monitoring module includes a sensor submodule for acquiring operational status data, a data collection submodule for centrally collecting operational status data, and a data transmission submodule for transmitting operational status data.

3. The online automatic detection and ash removal system for a low-temperature wide-channel plate economizer according to claim 1, characterized in that: The different sensors include temperature sensors, pressure drop sensors, and flow sensors, and the corresponding operating states include the gas temperature, pressure drop changes of gas flow, and gas flow rate inside the low-temperature wide-channel plate economizer.

4. The online automatic detection and ash removal system for a low-temperature wide-channel plate economizer according to claim 1, characterized in that: The automatic detection and control module includes a data processing submodule for processing operating status data, a model building and management submodule for building an automatic ash removal control model based on a machine learning model, an ash removal strategy optimization submodule for dynamically optimizing based on the analysis results of the automatic ash removal control model, and an ash removal equipment control submodule for controlling ash removal based on the optimization results of the ash removal strategy optimization submodule.

5. The online automatic detection and ash removal system for a low-temperature wide-channel plate economizer according to claim 1, characterized in that: The automatic detection and control module also includes an early warning issuance submodule for making judgments based on operating status data and issuing early warning information.

6. The online automatic detection and ash removal system for a low-temperature wide-channel plate economizer according to claim 4, characterized in that: The model building management submodule uses the following model building method: S1. Based on on-site data acquisition, the operating data is recorded by deploying sensors on the low-temperature wide-channel plate economizer, and a real-time dataset is built through accumulation. S2. Based on the decision tree learning algorithm, construct an automatic ash discharge control model and train the automatic ash discharge control model using the constructed real-time dataset; S3. Analyze the data using the trained automatic ash removal control model. The input data are gas temperature, pressure drop change and gas flow rate. The output data are dust accumulation prediction, pressure drop change prediction and temperature change prediction.

7. The online automatic detection and ash removal system for a low-temperature wide-channel plate economizer according to claim 4, characterized in that: The specific method of dynamic optimization is as follows: SS1. The threshold conditions for ash removal are preset by the staff. These conditions include ash accumulation exceeding the threshold, pressure drop exceeding the threshold, and temperature exceeding the threshold. SS2. Compare the output of the automatic ash removal control model with the threshold conditions to trigger the ash removal operation; SS3. Based on the feedback from the operation data after ash removal, use reinforcement learning algorithms to dynamically adjust the ash removal process.

8. The online automatic detection and ash removal system for a low-temperature wide-channel plate economizer according to claim 1, characterized in that: The economizer ash collection module includes an ash hopper sub-module for collecting settled dust, an ash discharge control sub-module for discharging dust, and a heating anti-condensation sub-module for preventing dust condensation.

9. The online automatic detection and ash removal system for a low-temperature wide-channel plate economizer according to claim 8, characterized in that: The ash removal control submodule includes airflow-assisted ash removal and vibration-assisted ash removal.

10. The online automatic detection and ash removal system for a low-temperature wide-channel plate economizer according to claim 1, characterized in that: The remote monitoring and control module includes an interface control submodule for user operation control, a cloud data storage module for cloud storage, and an alarm notification submodule for issuing alarm prompts.

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