Furnace bottom corrosion monitoring method and system

Abstract

The invention provides a furnace bottom corrosion monitoring method and system, and the method comprises the steps: building a temperature sensing array, and collecting the temperature data of different depths and different positions of a furnace bottom; establishing an ultrasonic sensing array, and collecting thickness data at the corresponding position; calculating a temperature fluctuation coefficient to reflect the relevance between the fluctuation degree of the furnace bottom temperature and the corrosion condition; calculating a thickness reduction rate index so as to reflect the relationship between the speed condition of reduction of the thickness of the furnace bottom along with time and the corrosion degree; analyzing a position corrosion correlation degree; comprehensively analyzing the temperature fluctuation coefficient, the thickness reduction rate index and the position corrosion correlation degree, and determining a comprehensive evaluation score of the furnace bottom corrosion so as to comprehensively evaluate the furnace bottom corrosion condition; according to the method, temperature and ultrasonic sensing arrays are established to collect data, various calculation models are used for analyzing temperature fluctuation, thickness reduction and position correlation degree, scores are comprehensively evaluated, the corrosion condition of the furnace bottom is accurately monitored, and safe operation of the furnace is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment detection, and in particular to a furnace bottom corrosion monitoring method and system. Background Art

[0002] As a key part of industrial furnaces, the furnace bottom is subjected to a variety of harsh working conditions such as high temperature, material erosion, chemical erosion, etc. The corrosion problem of the furnace bottom will not only affect the normal operation of the furnace and reduce production efficiency, but may also cause equipment damage, safety accidents and huge economic losses. Therefore, timely and accurate monitoring of the corrosion condition of the furnace bottom is of great significance to ensure the safe and stable operation of industrial furnaces, extend the service life of equipment, and improve production efficiency.

[0003] However, traditional furnace bottom corrosion detection methods mainly include regular shutdown inspection and drilling inspection. Regular shutdown inspection requires stopping production and cooling the furnace for manual inspection. This method is time-consuming and labor-intensive, seriously affecting production continuity. Moreover, the inspection can only be carried out during the shutdown period, and the furnace bottom corrosion situation cannot be monitored in real time, making it difficult to detect potential problems in time. Although drilling inspection can obtain local thickness information of the furnace bottom to a certain extent, it is a destructive inspection, which will damage the furnace bottom structure and affect the overall performance of the furnace. Moreover, the inspection results can only reflect the corrosion situation at the drilling position, and cannot comprehensively evaluate the corrosion status of the entire furnace bottom.

[0004] At the same time, most of the existing monitoring technologies fail to effectively integrate and comprehensively analyze the multiple key factors that affect furnace bottom corrosion. Furnace bottom corrosion is a complex process involving the interaction of multiple physical properties such as temperature, thickness, and position. However, there is currently a lack of a method that can comprehensively consider these factors and calculate accurate and quantitative corrosion evaluation indicators through reasonable models, thereby comprehensively evaluating the furnace bottom corrosion status. This makes it difficult to take targeted measures in a timely manner to prevent and deal with furnace bottom corrosion problems based on monitoring results in actual production.

[0005] Therefore, there is an urgent need in the art for a furnace bottom corrosion monitoring method and system to solve the above problems. Summary of the invention

[0006] The present invention provides a furnace bottom corrosion monitoring method and system, aiming to solve the problems existing in the above-mentioned prior art. By establishing a temperature and ultrasonic sensor array to collect data, using a variety of calculation models to analyze temperature fluctuations, thickness thinning and position correlation, and comprehensively evaluating the scores, accurate monitoring of the furnace bottom corrosion condition can be achieved to ensure the safe operation of the furnace.

[0007] In one aspect, the present invention provides a furnace bottom corrosion monitoring method, comprising:

[0008] Step 1: Establish a temperature sensor array to collect temperature data at different depths and locations on the furnace bottom;

[0009] Step 2: Establish an ultrasonic sensor array to collect thickness data at positions corresponding to each temperature data;

[0010] Step 3: Calculate the temperature fluctuation coefficient based on the collected temperature data to reflect the correlation between the fluctuation degree of furnace bottom temperature and the corrosion condition;

[0011] Step 4: Calculate the thickness reduction rate index based on the collected thickness data to reflect the relationship between the speed of the furnace bottom thickness reduction over time and the degree of corrosion;

[0012] Step 5: Analyze the position corrosion correlation by combining the temperature fluctuation coefficient, thickness thinning rate index and the distance between different positions of the furnace bottom and the center of the furnace bottom;

[0013] Step six, comprehensively analyze the temperature fluctuation coefficient, thickness thinning rate index and position corrosion correlation to determine the comprehensive evaluation score of furnace bottom corrosion in order to comprehensively evaluate the furnace bottom corrosion condition.

[0014] According to a furnace bottom corrosion monitoring method provided by the present invention, in step 1, the process of establishing a temperature sensor array includes:

[0015] According to the geometric shape of the furnace bottom, the heat conduction characteristics and the expected distribution of corrosion hot spots, determine the number and type of temperature sensors and the installation position on the furnace bottom, and collect the furnace bottom temperature periodically; ensure that the collected temperature data can fully reflect the temperature changes at different depths and positions of the furnace bottom;

[0016] The temperature sensor should be of high temperature resistance, high precision and stability to adapt to the harsh working environment in the furnace and work stably for a long time.

[0017] According to a furnace bottom corrosion monitoring method provided by the present invention, in step 2, the process of establishing an ultrasonic sensor array includes:

[0018] According to the material properties, structural strength and expected corrosion area of ​​the furnace bottom, an ultrasonic sensor with an appropriate frequency is selected and its installation layout on the furnace bottom is determined so that it corresponds to the position covered by the temperature sensor array. The thickness of the furnace bottom is periodically collected to accurately measure the thickness data at each corresponding position.

[0019] According to a furnace bottom corrosion monitoring method provided by the present invention, in step three, the process of calculating the temperature fluctuation coefficient based on the collected temperature data includes:

[0020] Determine the temperature data collected at each time in this period at each temperature sensor location; and calculate the average temperature collected by all temperature sensors at each time, as well as the average temperature of all temperature data during the entire measurement period;

[0021] A temperature fluctuation model is established. The temperature fluctuation model is:

[0022]

[0023] Where TFI is the temperature fluctuation coefficient; n is the total number of temperature sensors in the established temperature sensing array; m is the total number of times the temperature data is collected in this cycle; T avg is the average temperature of all temperature data during the entire measurement period, T avg,j is the average temperature collected by all temperature sensors at the jth moment, T i,j is the temperature data collected at the jth moment at the location of the i-th temperature sensor;

[0024] Substitute the acquired data into the model for calculation to determine the temperature fluctuation coefficient TFI of the furnace bottom under the current cycle state.

[0025] According to a furnace bottom corrosion monitoring method provided by the present invention, in step 4, the process of calculating the thickness reduction rate index based on the collected thickness data includes:

[0026] Determine the furnace bottom thickness data obtained at each measurement number at the corresponding position of each ultrasonic sensor in this cycle; and calculate the average value of the thickness data obtained from each measurement;

[0027] The thinning rate model is established, and the thinning rate model is:

[0028]

[0029] Where TRI is the thickness reduction rate index; p is the total number of ultrasonic measurements performed in this cycle; n is the number of ultrasonic sensors in the established ultrasonic sensor array; H i,k is the furnace bottom thickness data obtained at the kth ultrasonic measurement at the corresponding position of the i-th ultrasonic sensor, H avg,k is the average value of the thickness data measured at the corresponding positions of all ultrasonic sensors during the kth ultrasonic measurement;

[0030] Substitute the acquired data into the model for calculation to determine the thickness reduction rate index TRI of the furnace bottom under the current cycle state.

[0031] According to a furnace bottom corrosion monitoring method provided by the present invention, in step 5, the process of analyzing the position corrosion correlation includes:

[0032] Transform the temperature fluctuation model to calculate the temperature fluctuation coefficient corresponding to each position under the current cycle state; transform the thinning rate model to calculate the thickness thinning rate index corresponding to each position under the current cycle state;

[0033] A corrosion correlation model is established, and the corrosion correlation model is:

[0034]

[0035] Among them, PCD is the corrosion correlation degree; TFI is i is the temperature fluctuation coefficient corresponding to the i-th position in the current cycle state, TRI i is the thickness reduction rate index corresponding to the i-th position in the current cycle state; D i is the distance from the ith position to the center of the furnace bottom, and n is the number of positions involved;

[0036] The corrosion correlation degree PCD is used to reflect the differences in corrosion characteristics at different locations on the furnace bottom due to their own environment and different distances from the center.

[0037] Substitute the acquired data into the model accordingly to determine the corrosion correlation degree PCD of the furnace bottom under the current cycle state.

[0038] According to a furnace bottom corrosion monitoring method provided by the present invention, in step six, the process of determining the comprehensive evaluation score of the furnace bottom corrosion includes:

[0039] Determine the total thickness H within this cycle total ,

[0040] Determine the total temperature T during this period total ,

[0041] Establish a comprehensive evaluation model, the comprehensive evaluation model is:

[0042]

[0043] Among them, CES is the comprehensive evaluation score of the furnace bottom corrosion state in the current cycle, TFI is the temperature fluctuation coefficient of the furnace bottom in the current cycle state, TRI is the thickness thinning rate index of the furnace bottom in the current cycle state, and PCD is the corrosion correlation degree of the furnace bottom in the current cycle state; T total is the total temperature in this cycle, H total is the total thickness in this cycle;

[0044] The comprehensive evaluation score CES is used to comprehensively and quantitatively evaluate the furnace bottom corrosion condition. The higher the CES value, the greater the possibility and severity of furnace bottom corrosion.

[0045] A furnace bottom corrosion monitoring method provided by the present invention also includes:

[0046] A comprehensive scoring threshold is preset, and when the comprehensive evaluation score CES exceeds the comprehensive scoring threshold, an alarm signal is triggered.

[0047] On the other hand, the present invention provides a furnace bottom corrosion monitoring system, based on the above method, comprising:

[0048] The temperature data acquisition module is used to establish a temperature sensor array, and accurately collect the temperature data of each point on the furnace bottom through temperature sensors distributed at different depths and positions on the furnace bottom;

[0049] The thickness data acquisition module is used to construct an ultrasonic sensor array and use ultrasonic technology to regularly measure the thickness data of the furnace bottom at the position corresponding to the temperature data;

[0050] A temperature fluctuation coefficient calculation module is connected to the temperature data acquisition module and calculates the temperature fluctuation coefficient of the furnace bottom in the current cycle based on the collected temperature data and a preset temperature fluctuation model;

[0051] A thickness reduction rate index calculation module, which is connected to the thickness data acquisition module, calculates the thickness reduction rate index of the furnace bottom in the current cycle based on the collected thickness data and a preset reduction rate model;

[0052] A position corrosion correlation analysis module is connected to the temperature fluctuation coefficient calculation module and the thickness thinning rate index calculation module, and is used to determine the corrosion correlation of the furnace bottom under the current cycle state by combining the temperature fluctuation coefficient, the thickness thinning rate index and the distance information between different positions of the furnace bottom and the center of the furnace bottom;

[0053] The comprehensive evaluation score determination module is connected to the temperature fluctuation coefficient calculation module, the thickness thinning rate index calculation module and the position corrosion correlation analysis module, and is used to perform a fusion analysis of the temperature fluctuation coefficient, the thickness thinning rate index and the position corrosion correlation to determine the comprehensive evaluation score of the furnace bottom corrosion; and provide an alarm signal when the comprehensive evaluation score of the furnace bottom corrosion exceeds a preset threshold.

[0054] Compared with the prior art, the beneficial effects of this application are:

[0055] This application establishes a temperature sensor array and an ultrasonic sensor array to continuously collect temperature and thickness data of the furnace bottom, overcoming the shortcomings of traditional shutdown inspections and regular inspection methods that cannot be monitored in real time, and can grasp the corrosion dynamics of the furnace bottom at any time, providing a guarantee for timely discovery of problems;

[0056] Comprehensively considering the temperature fluctuation coefficient, thickness thinning rate index and position corrosion correlation, it avoids the limitations of single sensor monitoring or only considering a single factor evaluation, and can more accurately reflect the corrosion status of the furnace bottom;

[0057] Each analysis model conducts precise quantitative analysis of each factor, reducing misjudgment and making corrosion assessment results more reliable;

[0058] Collecting and analyzing data from different depths and positions of the furnace bottom can not only obtain the overall corrosion situation, but also clarify the differences in corrosion characteristics at different positions, providing a basis for targeted maintenance;

[0059] The comprehensive evaluation score comprehensively integrates various key indicators, clearly presents the possibility and severity of furnace bottom corrosion in a quantitative manner, and facilitates operators to intuitively judge and make decisions;

[0060] Unlike destructive methods such as drilling detection, the sensor array monitoring method of the present application does not need to cause damage to the furnace bottom structure, will not affect the overall performance and service life of the furnace, and ensures the integrity and stability of the furnace equipment during the monitoring process;

[0061] The pre-set threshold triggers the alarm signal, which can promptly remind the staff when the furnace bottom corrosion reaches a certain level, effectively prevent equipment damage and safety accidents caused by severe corrosion, ensure the safe and stable operation of industrial furnaces, and reduce potential economic losses.

[0062] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0063] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0065] Figure 1 It is a flow chart of a furnace bottom corrosion monitoring method provided by an embodiment of the present invention;

[0066] Figure 2 It is a structural schematic diagram of a furnace bottom corrosion monitoring system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0068] Embodiment 1:

[0069] The present invention provides a furnace bottom corrosion monitoring method. Figure 1 ,include:

[0070] Step 1: Establish a temperature sensor array to collect temperature data at different depths and locations on the furnace bottom;

[0071] Step 2: Establish an ultrasonic sensor array to collect thickness data at positions corresponding to each temperature data;

[0072] Step 3: Calculate the temperature fluctuation coefficient based on the collected temperature data to reflect the correlation between the fluctuation degree of furnace bottom temperature and the corrosion condition;

[0073] Step 4: Calculate the thickness reduction rate index based on the collected thickness data to reflect the relationship between the speed of the furnace bottom thickness reduction over time and the degree of corrosion;

[0074] Step 5: Analyze the position corrosion correlation by combining the temperature fluctuation coefficient, thickness thinning rate index and the distance between different positions of the furnace bottom and the center of the furnace bottom;

[0075] Step six, comprehensively analyze the temperature fluctuation coefficient, thickness thinning rate index and position corrosion correlation to determine the comprehensive evaluation score of furnace bottom corrosion in order to comprehensively evaluate the furnace bottom corrosion condition.

[0076] The principle and beneficial effects of this embodiment are as follows: the temperature change and thickness information of different positions of the furnace bottom are respectively obtained by the temperature sensor array and the ultrasonic sensor array, so as to realize the detailed record of the furnace bottom status from the two dimensions of time and space; the temperature fluctuation coefficient is calculated by analyzing the collected temperature data, which helps to determine how the temperature change affects the corrosion process; based on the thickness data obtained by the ultrasonic sensor array, the thickness thinning rate index that changes with time can be calculated; this directly reflects the degree of influence of corrosion on the furnace bottom material and can predict the future corrosion trend; combined with the temperature fluctuation coefficient, the thickness thinning rate index and the position of each measuring point relative to the center of the furnace bottom, the distribution law of corrosion at different positions can be analyzed, so as to identify high-risk areas; finally, by integrating all the above parameters, a comprehensive evaluation score that can comprehensively reflect the corrosion status of the furnace bottom is formed; this method not only takes into account the degree of corrosion, but also takes into account the development trend and distribution characteristics of corrosion, providing maintenance personnel with a more scientific and reasonable decision-making basis.

[0077] In order to further optimize the above technical solution, in step 1, the process of establishing the temperature sensing array includes:

[0078] According to the geometric shape of the furnace bottom, the heat conduction characteristics and the expected distribution of corrosion hot spots, determine the number and type of temperature sensors and the installation position on the furnace bottom, and collect the furnace bottom temperature periodically; ensure that the collected temperature data can fully reflect the temperature changes at different depths and positions of the furnace bottom;

[0079] The temperature sensor should be of high temperature resistance, high precision and stability to adapt to the harsh working environment in the furnace and work stably for a long time.

[0080] It should be noted that for furnace bottoms of regular shapes (such as circular, rectangular, etc.), sensors can be arranged at certain intervals at key locations (such as the center of the circle, the central axis, corners, etc.) and along the radius or side length direction according to the principle of geometric symmetry to ensure uniform coverage; for example, for a circular furnace bottom, sensors are arranged equidistantly along different radial directions with the center of the circle as the center, and the number can be determined according to the size of the furnace bottom. Generally, a small furnace bottom can be arranged in 3-5 radial directions, with 2-3 sensors in each direction; a large furnace bottom can be increased to 5-8 radial directions, with 3-5 sensors in each direction;

[0081] For irregularly shaped furnace bottoms, it is necessary to divide them into different areas first, such as convex, concave, corners and other special areas, and analyze each area separately; appropriately increase the density of sensors in areas where heat flow may be concentrated or the structure is complex, such as using a denser arrangement at corners than in flat areas, and determine the heat flow distribution through finite element analysis and other methods, and then determine the sensor position. The number depends on the complexity of the area and the size of the overall furnace bottom. Generally, the number of sensors in complex areas can account for 30%-50% of the total number;

[0082] For furnace bottom materials with good thermal conductivity (such as metal materials), heat propagation is fast, and the sensor spacing can be appropriately increased, but to ensure that the temperature gradient changes can be captured, the general spacing can be 10-20 cm; for materials with poor thermal conductivity (such as ceramics, etc.), the heat distribution is relatively uneven, and the sensor spacing should be reduced to 5-10 cm to accurately monitor temperature changes; at the same time, sensors must be arranged at key nodes of the heat conduction path (such as heat flow inlet and outlet positions) to monitor the impact of heat flow changes on the furnace bottom temperature;

[0083] According to the operating process of the kiln, the direction of material flow and other factors, estimate the areas where corrosion hot spots may occur, such as areas with severe material erosion and areas with frequent temperature changes. Focus on arranging sensors in these areas, with the number of sensors being 20%-30% more than other areas, and use higher-precision sensor types to more accurately monitor temperature changes and detect corrosion signs in a timely manner.

[0084] Select the sensor according to the working temperature range of the furnace to ensure that it can work normally under the highest temperature environment in the furnace and has a certain temperature margin; for example, for a furnace with an operating temperature of 800-1000℃, select a sensor that can withstand a maximum temperature of more than 1200℃ to ensure long-term stable operation under high temperature environment and prevent the sensor from being damaged by high temperature and affecting the accuracy and continuity of monitoring data;

[0085] Choose sensors with a measurement accuracy of ±0.5℃ or even higher to accurately capture small temperature changes at the furnace bottom. Especially when the temperature fluctuation is small but related to corrosion, high-precision sensors can provide more reliable data, help accurately analyze the correlation between temperature fluctuation and corrosion, and improve the accuracy of corrosion monitoring;

[0086] Priority should be given to sensor models that have been verified by long-term stability tests. During long-term operation, the measurement error should be kept within a very small range. For example, after continuous operation for more than 1,000 hours, the measurement error fluctuation should not exceed ±0.2°C. At the same time, the sensor should have good anti-interference ability and be able to resist the influence of factors such as electromagnetic fields and airflow fluctuations in the furnace on the measurement results, ensuring that the collected temperature data is true and reliable.

[0087] Determine the collection cycle according to the operation characteristics of the kiln. For kilns that are in continuous operation and have a faster corrosion rate, the collection cycle should be shorter, such as collecting temperature data every 5-10 minutes; for kilns that are in intermittent operation or have a slower corrosion rate, the collection cycle can be appropriately extended to collect data every 30 minutes to 1 hour; during the stage of drastic temperature changes such as when the kiln is started or stopped, the collection frequency can be temporarily increased, such as shortened to collecting data every 1-2 minutes, to fully record the temperature change process;

[0088] Use automated data acquisition equipment, such as a programmable logic controller (PLC) or a data acquisition card, to connect the temperature sensor and automatically collect temperature data according to the set collection cycle; the collection equipment should have a data storage function and be able to cache the collected data for a certain period of time to prevent data loss; at the same time, it can be equipped with a data transmission module (such as wireless transmission or wired Ethernet transmission) to transmit the collected data to the monitoring center or data processing equipment in real time for timely subsequent analysis and processing.

[0089] In order to further optimize the above technical solution, in step 2, the process of establishing the ultrasonic sensor array includes:

[0090] According to the material properties, structural strength and expected corrosion area of ​​the furnace bottom, an ultrasonic sensor with an appropriate frequency is selected and its installation layout on the furnace bottom is determined so that it corresponds to the position covered by the temperature sensor array. The thickness of the furnace bottom is periodically collected to accurately measure the thickness data at each corresponding position.

[0091] It should be noted that for furnace bottoms made of metal, due to their relatively low acoustic impedance and good sound wave propagation performance, higher frequency ultrasonic sensors, such as 5-10MHz, can be selected; higher frequencies can provide higher resolution, more accurately measure furnace bottom thickness, and accurately detect small thickness changes caused by corrosion; for example, in the monitoring of steel furnace bottoms, 8MHz sensors can clearly distinguish thickness differences of several millimeters or even smaller, and effectively monitor the thickness reduction in the early stages of corrosion;

[0092] For furnace bottom materials with high acoustic impedance and large sound wave attenuation, such as ceramics and refractory materials, a lower frequency ultrasonic sensor, such as 1-3MHz, should be used; lower frequency sound waves can propagate farther in such materials and can effectively penetrate the materials to reach deeper levels of the furnace bottom, ensuring the accuracy of the measurement; for example, in a refractory brick furnace bottom, a 2MHz sensor can better obtain information within the entire furnace bottom thickness range, avoiding the problem of being unable to measure the bottom thickness due to excessive sound wave attenuation;

[0093] For furnace bottoms with high structural strength and large thickness, in order to ensure that the ultrasonic wave can penetrate the entire furnace bottom and obtain a clear echo signal, if the material allows, a sensor with a lower frequency is preferred to ensure sufficient penetration ability; at the same time, the transmission power of the sensor should be considered to ensure that it can generate an ultrasonic signal of sufficient strength. Generally, the transmission power should be between 100-500mW to meet the measurement requirements of thicker furnace bottoms;

[0094] For furnace bottoms with relatively thin structures or low strength, excessive ultrasonic energy may damage the furnace bottom structure. Therefore, comprehensive considerations should be made when selecting the frequency. Under the premise of ensuring measurement accuracy, try to choose sensors with lower transmission power (such as 50-150mW) and moderate frequency (such as 3-5MHz), which can accurately measure the thickness without affecting the integrity of the furnace bottom structure.

[0095] First, ensure that an ultrasonic sensor is installed at the corresponding position of each temperature sensor to achieve a one-to-one correspondence, so as to obtain temperature and thickness data at the same position and provide accurate correlation data for subsequent comprehensive analysis; for example, at the bottom of the furnace where the temperature sensors are arranged in a matrix, the ultrasonic sensors are also installed according to the same matrix layout to ensure that the temperature and thickness information of each data collection point can be effectively matched;

[0096] For sensors in special positions in the temperature sensing array (such as near the furnace wall, heat flux concentration area, etc.), when installing the ultrasonic sensor, reasonable adjustments should be made according to the furnace bottom structure and space limitations to ensure that the thickness of the corresponding position can be accurately measured without affecting the normal operation and maintenance of the furnace; in areas with narrow space, miniaturized and customized ultrasonic sensor mounting brackets can be used to ensure that the sensor can be stably installed and work normally;

[0097] In addition to the correspondence with the temperature sensor array, the density of ultrasonic sensors should be appropriately increased in key areas (such as inlets and outlets, heat exchange areas and other areas prone to corrosion) according to the overall shape and structural characteristics of the furnace bottom. For example, near the inlets and outlets, due to frequent material scouring and high corrosion risk, the sensors can be densely arranged along the material flow direction in this area, and the spacing can be reduced to 1 / 2-1 / 3 of the conventional area, so as to more accurately monitor the thickness changes in this area and detect corrosion signs in time.

[0098] The determination of the collection cycle should comprehensively consider the corrosion rate of the furnace bottom, the operation cycle of the furnace, and the requirements for real-time data. For furnace bottoms with faster corrosion rates (such as furnaces operating in corrosive media environments), the collection cycle should be shortened, and the thickness data can be collected every 1-2 hours. For furnace bottoms with relatively slow corrosion rates, the collection cycle can be appropriately extended to every 4-6 hours. When the furnace is started, stopped, or the operating conditions change significantly (such as drastic temperature and pressure fluctuations), the collection frequency should be temporarily increased, such as shortened to every 30 minutes to 1 hour, to capture the possible rapid changes in the thickness of the furnace bottom under these special conditions.

[0099] Establish an automated data collection system similar to temperature data collection, and use an ultrasonic flaw detector or a special ultrasonic data collection device to connect the ultrasonic sensor; the collection equipment must have an accurate time synchronization function to ensure that the thickness data collected each time accurately matches the corresponding temperature data in time, which is convenient for subsequent comprehensive analysis; the data collection equipment will pre-process the collected thickness data (such as filtering, amplification, etc.), and then transmit it to the data processing center in real time through a reliable data transmission method (such as industrial Ethernet, wireless communication, etc.), and integrate and store it with the temperature data to provide complete data support for subsequent corrosion analysis and calculation.

[0100] In order to further optimize the above technical solution, in step 3, the process of calculating the temperature fluctuation coefficient based on the collected temperature data includes:

[0101] Determine the temperature data collected at each time in this period at each temperature sensor location; and calculate the average temperature collected by all temperature sensors at each time, as well as the average temperature of all temperature data during the entire measurement period;

[0102] A temperature fluctuation model is established. The temperature fluctuation model is:

[0103]

[0104] Where TFI is the temperature fluctuation coefficient; n is the total number of temperature sensors in the established temperature sensing array; m is the total number of times the temperature data is collected in this cycle; T avgis the average temperature of all temperature data during the entire measurement period, T avg,j is the average temperature collected by all temperature sensors at the jth moment, T i,j is the temperature data collected at the jth moment at the location of the i-th temperature sensor;

[0105] Substitute the acquired data into the model for calculation to determine the temperature fluctuation coefficient TFI of the furnace bottom under the current cycle state.

[0106] It should be noted that T i,j It reflects the actual temperature value of each specific position of the furnace bottom at different times; T avg,j It is used to measure the deviation of different locations from the overall average temperature at the same time; n determines the number of samples involved in temperature data collection, which affects the comprehensiveness of the overall temperature fluctuation statistics; m reflects the frequency and duration of temperature data collection over a period of time. More time points help to more accurately capture the long-term characteristics of temperature fluctuations; T avg It is a comprehensive indicator of the overall temperature level of the furnace bottom during the entire monitoring period. Based on this, combined with the deviation of each moment and position from the average temperature, the above formula can accurately quantify the degree of fluctuation of the furnace bottom temperature. The degree of temperature fluctuation is closely related to the corrosion of the furnace bottom. Larger temperature fluctuations often indicate that the furnace bottom structure has undergone uneven changes due to corrosion and other factors, which helps to judge the corrosion condition of the furnace bottom.

[0107] In order to further optimize the above technical solution, in step 4, the process of calculating the thickness reduction rate index based on the collected thickness data includes:

[0108] Determine the furnace bottom thickness data obtained at each measurement number at the corresponding position of each ultrasonic sensor in this cycle; and calculate the average value of the thickness data obtained from each measurement;

[0109] The thinning rate model is established, and the thinning rate model is:

[0110]

[0111] Where TRI is the thickness reduction rate index; p is the total number of ultrasonic measurements performed in this cycle; n is the number of ultrasonic sensors in the established ultrasonic sensor array; H i,k is the furnace bottom thickness data obtained at the kth ultrasonic measurement at the corresponding position of the i-th ultrasonic sensor, H avg,k is the average value of the thickness data measured at the corresponding positions of all ultrasonic sensors during the kth ultrasonic measurement;

[0112] Substitute the acquired data into the model for calculation to determine the thickness reduction rate index TRI of the furnace bottom under the current cycle state.

[0113] It should be noted that H i,k It directly reflects the actual thickness of each specific position of the furnace bottom at different measurement times and is the basic data source for analyzing thickness changes; avg,k It can reflect the overall thickness level of the furnace bottom in the same measurement batch, which is convenient for measuring the difference between each specific position and the overall average thickness; n determines the number of sample points involved in the thickness data collection, which plays a key role in comprehensively measuring the thickness of each position of the furnace bottom and affects the representativeness of the thickness thinning rate statistics; p reflects the frequency of furnace bottom thickness measurement within a certain time period. More measurements mean that the changes in furnace bottom thickness over time can be tracked more carefully, which helps to more accurately reflect the dynamic process of thickness thinning;

[0114] Through the above formula, the difference between each position and the average thickness at the corresponding measurement time is accumulated, and combined with factors such as the total average thickness of each measurement, the number of sensors and the number of measurements, the thickness thinning rate index is finally obtained; this index can intuitively reflect the speed of the furnace bottom thickness thinning over time, and the speed of thickness thinning is closely related to the degree of corrosion of the furnace bottom. The faster the thinning rate, the more serious the degree of corrosion of the furnace bottom, thereby providing a key quantitative reference basis for evaluating the corrosion condition of the furnace bottom.

[0115] In order to further optimize the above technical solution, in step 5, the process of analyzing the position corrosion correlation includes:

[0116] Transform the temperature fluctuation model to calculate the temperature fluctuation coefficient corresponding to each position under the current cycle state; transform the thinning rate model to calculate the thickness thinning rate index corresponding to each position under the current cycle state;

[0117] A corrosion correlation model is established, and the corrosion correlation model is:

[0118]

[0119] Among them, PCD is the corrosion correlation degree; TFI is i is the temperature fluctuation coefficient corresponding to the i-th position in the current cycle state, TRI i is the thickness reduction rate index corresponding to the i-th position in the current cycle state; D i is the distance from the ith position to the center of the furnace bottom, and n is the number of positions involved;

[0120] The corrosion correlation degree PCD is used to reflect the differences in corrosion characteristics at different locations on the furnace bottom due to their own environment and different distances from the center.

[0121] Substitute the acquired data into the model accordingly to determine the corrosion correlation degree PCD of the furnace bottom under the current cycle state.

[0122] It should be noted that TFI i It reflects the degree of temperature fluctuation at a specific location on the furnace bottom, and temperature fluctuation is often associated with corrosion at that location; TRI i It reflects the speed at which the thickness of the specific position of the furnace bottom decreases over time; D i represents the distance between the ith position and the center of the furnace bottom. Due to the different distances between different positions of the furnace bottom and the center, they are affected differently by factors such as heat flux distribution, material scouring, and stress distribution during operation. n is the total number of furnace bottom positions covered by the temperature sensor array and the ultrasonic sensor array for analysis. It determines the data range involved in the calculation of the corrosion correlation of the positions, ensuring that the corrosion correlation characteristics of all relevant positions on the furnace bottom can be comprehensively considered.

[0123] Through the above formula, the temperature fluctuation coefficient, thickness thinning rate index and the distance from the center of the furnace bottom at each position are combined, and their products are summed and corresponding operations are performed to analyze the correlation between temperature fluctuations, thickness thinning and other conditions at different positions and position factors, that is, the position corrosion correlation. This index fully takes into account the differences in corrosion characteristics at different positions of the furnace bottom due to their own environment and distance from the center, and thus provides a strong basis for more accurate evaluation of the overall corrosion status of the furnace bottom, helping to fully grasp the corrosion status of different parts of the furnace bottom and their mutual relationship.

[0124] In order to further optimize the above technical solution, in step six, the process of determining the comprehensive evaluation score of furnace bottom corrosion includes:

[0125] Determine the total thickness H within this cycle total ,

[0126] Determine the total temperature T during this period total ,

[0127] Establish a comprehensive evaluation model, the comprehensive evaluation model is:

[0128]

[0129] Among them, CES is the comprehensive evaluation score of the furnace bottom corrosion state in the current cycle, TFI is the temperature fluctuation coefficient of the furnace bottom in the current cycle state, TRI is the thickness thinning rate index of the furnace bottom in the current cycle state, and PCD is the corrosion correlation degree of the furnace bottom in the current cycle state; T total is the total temperature in this cycle, H total is the total thickness in this cycle;

[0130] The comprehensive evaluation score CES is used to comprehensively and quantitatively evaluate the furnace bottom corrosion condition. The higher the CES value, the greater the possibility and severity of furnace bottom corrosion.

[0131] It should be noted that TFI reflects the impact of the overall temperature fluctuation of the furnace bottom on corrosion. Temperature fluctuations may cause changes in the internal structural stress of the furnace bottom material, accelerate the corrosion process, and reflect the degree of correlation between thermal factors and corrosion; TRI characterizes the rate of reduction of the furnace bottom thickness over time, which is directly related to the speed of corrosion loss of the furnace bottom material. It is an intuitive indicator for evaluating the degree of corrosion and reflects the relationship between physical loss of materials and corrosion; PCD takes into account the special corrosion characteristics of different positions of the furnace bottom. Due to the different working environments and stress conditions in different areas of the furnace bottom, the corrosion conditions are different. This indicator takes this position factor into comprehensive consideration to make the evaluation more comprehensive and accurate; H total It reflects the overall status information of the furnace bottom in terms of thickness. The thickness data at different positions and different measurement times can reflect the overall picture of the thickness change of the furnace bottom. total It presents the overall temperature change of the furnace bottom. The temperature data at different positions and times can reflect the comprehensive information of the thermal state of the furnace bottom.

[0132] By performing specific weighted operations on the temperature fluctuation coefficient, thickness thinning rate index, position corrosion correlation, thickness data sum, temperature data sum, and the square root of their product, the introduction of difficult-to-determine additional constants is avoided, and comprehensive calculations are performed only based on data closely related to the physical properties of the furnace bottom. The higher the comprehensive evaluation score, the greater the possibility and severity of furnace bottom corrosion, thereby being able to comprehensively and quantitatively evaluate the furnace bottom corrosion status and provide a reliable decision-making basis for subsequent furnace bottom maintenance, operating parameter adjustment, and other operations.

[0133] In order to further optimize the above technical solution, it also includes:

[0134] A comprehensive scoring threshold is preset, and when the comprehensive evaluation score CES exceeds the comprehensive scoring threshold, an alarm signal is triggered.

[0135] It should be noted that the long-term accumulated temperature, thickness and other monitoring data are obtained from industrial furnaces of the same type or similar working conditions, covering the different stages of furnace bottoms in good condition, slight corrosion, moderate corrosion to severe corrosion;

[0136] Use the monitoring method of this application to process historical data, calculate the corresponding comprehensive evaluation score, and analyze the concentration range and change trend of the score under different corrosion degrees;

[0137] The comprehensive score threshold is set by referring to the score change point when the furnace bottom begins to show obvious signs of corrosion from the normal state, combined with the safety margin; for example, if historical data shows that the comprehensive evaluation score begins to rise significantly when the furnace bottom is about to show moderate corrosion, a certain percentage (such as 10%-20%) can be appropriately increased based on this score as the threshold to ensure that an alarm is issued before the corrosion problem may have a significant impact on the safe operation of the furnace;

[0138] Analyze the design parameters of the furnace bottom, such as material type, thickness, and strength. For furnace bottoms with high structural strength and corrosion-resistant materials, the threshold can be appropriately increased, because such furnace bottoms may still maintain a certain degree of safety under high corrosion levels; conversely, for furnace bottoms with relatively weak structures or made of ordinary materials, the threshold can be lowered to detect corrosion problems earlier;

[0139] Consider the operating parameters of the furnace, such as the working temperature, pressure, material flow, and operating cycle. For example, the corrosion rate of a furnace that operates continuously at high temperature and pressure may be faster, so the threshold should be lowered. For a furnace that operates intermittently and has relatively mild operating conditions, the threshold can be appropriately increased. According to the expected rate and risk level of furnace bottom corrosion under different operating conditions, adjust the threshold to suit the actual situation.

[0140] At regular intervals (such as monthly or quarterly), review and analyze the monitoring data of the furnace bottom to observe the changing trend of the comprehensive evaluation score and the actual corrosion status of the furnace bottom (such as through regular shutdown inspections or other auxiliary detection methods to verify);

[0141] If false alarms or missed alarms are found frequently under the current threshold, or the furnace bottom corrosion situation is inconsistent with expectations (such as the actual corrosion rate is faster or slower than the estimate based on the threshold setting), the threshold will be adjusted accordingly based on new monitoring data and actual experience; for example, if it is found that the comprehensive evaluation score has not exceeded the threshold for multiple consecutive cycles but the furnace bottom corrosion has actually developed, the threshold can be appropriately lowered; conversely, if the data shows that the furnace bottom state is stable and the score is far below the threshold, the threshold can be appropriately increased within a reasonable range to balance the sensitivity and accuracy of the early warning.

[0142] Embodiment 2:

[0143] The present invention provides a furnace bottom corrosion monitoring system. Figure 2 ,include:

[0144] The temperature data acquisition module is used to establish a temperature sensor array, and accurately collect the temperature data of each point on the furnace bottom through temperature sensors distributed at different depths and positions on the furnace bottom;

[0145] The thickness data acquisition module is used to construct an ultrasonic sensor array and use ultrasonic technology to regularly measure the thickness data of the furnace bottom at the position corresponding to the temperature data;

[0146] A temperature fluctuation coefficient calculation module is connected to the temperature data acquisition module and calculates the temperature fluctuation coefficient of the furnace bottom in the current cycle based on the collected temperature data and a preset temperature fluctuation model;

[0147] A thickness reduction rate index calculation module, which is connected to the thickness data acquisition module, calculates the thickness reduction rate index of the furnace bottom in the current cycle based on the collected thickness data and a preset reduction rate model;

[0148] A position corrosion correlation analysis module is connected to the temperature fluctuation coefficient calculation module and the thickness thinning rate index calculation module, and is used to determine the corrosion correlation of the furnace bottom under the current cycle state by combining the temperature fluctuation coefficient, the thickness thinning rate index and the distance information between different positions of the furnace bottom and the center of the furnace bottom;

[0149] The comprehensive evaluation score determination module is connected to the temperature fluctuation coefficient calculation module, the thickness thinning rate index calculation module and the position corrosion correlation analysis module, and is used to perform a fusion analysis of the temperature fluctuation coefficient, the thickness thinning rate index and the position corrosion correlation to determine the comprehensive evaluation score of the furnace bottom corrosion; and provide an alarm signal when the comprehensive evaluation score of the furnace bottom corrosion exceeds a preset threshold.

[0150] The principle and beneficial effects of this embodiment are:

[0151] The temperature data acquisition module distributes temperature sensors according to the characteristics of the furnace bottom to obtain comprehensive temperature data. These data reflect the changes in thermal state at different positions and depths of the furnace bottom and are the basis for subsequent temperature fluctuation analysis.

[0152] The thickness data acquisition module uses an ultrasonic sensor array to accurately measure the thickness data at the corresponding position, which directly reflects the physical loss of the furnace bottom material and provides a basis for the evaluation of the thickness reduction rate.

[0153] The temperature fluctuation coefficient calculation module uses the collected temperature data and the preset model to calculate the temperature fluctuation coefficient and quantify the degree of temperature fluctuation, because temperature fluctuation is related to structural changes caused by corrosion;

[0154] The thickness reduction rate index calculation module calculates the thickness reduction rate index based on the thickness data and model, and determines how fast the thickness reduces over time, which is closely related to the degree of corrosion;

[0155] The position corrosion correlation analysis module combines the temperature fluctuation coefficient, thickness thinning rate index and position information to analyze the differences in corrosion characteristics at different positions, and considers the impact of environmental factors at each position of the furnace bottom on corrosion;

[0156] The comprehensive evaluation score determination module integrates the above indicators and obtains a comprehensive evaluation score through a specific algorithm to comprehensively evaluate the corrosion status of the furnace bottom. The higher the score, the greater the possibility and severity of corrosion.

[0157] When the comprehensive evaluation score exceeds the preset threshold, the system triggers an alarm signal to achieve real-time monitoring and timely warning of the furnace bottom corrosion condition so that operators can take corresponding measures;

[0158] The system starts with two key physical quantities, temperature and thickness, and takes into account factors at different locations on the furnace bottom, thus achieving multi-dimensional and comprehensive monitoring of the corrosion status of the furnace bottom, overcoming the limitations of traditional methods that only focus on a single factor or a local area.

[0159] The calculation model established based on physical characteristics can accurately calculate various indicators, reduce misjudgment, accurately reflect the actual situation of furnace bottom corrosion, and provide a reliable basis for maintenance decisions;

[0160] Real-time monitoring and timely alarm signals enable workers to quickly learn the severity of furnace bottom corrosion, which helps prevent equipment failures and safety accidents caused by corrosion in advance and ensure production continuity and safety;

[0161] The sensor array is used to collect data without damaging the furnace bottom or interfering with the normal operation of the furnace, ensuring the structural integrity and operational stability of the furnace, while providing long-term and stable monitoring data;

[0162] The comprehensive evaluation score presents the corrosion status in a quantitative form, which is convenient for operators to understand intuitively. They can quickly formulate reasonable maintenance, inspection or operation adjustment strategies based on the score and improve production management efficiency.

[0163] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A furnace bottom corrosion monitoring method, characterized in that: include: Step 1: Establish a temperature sensor array to collect temperature data at different depths and locations on the furnace bottom; Step 2: Establish an ultrasonic sensor array to collect thickness data at positions corresponding to each temperature data; Step 3: Calculate the temperature fluctuation coefficient based on the collected temperature data to reflect the correlation between the fluctuation degree of furnace bottom temperature and the corrosion condition; Step 4: Calculate the thickness reduction rate index based on the collected thickness data to reflect the relationship between the speed of the furnace bottom thickness reduction over time and the degree of corrosion; Step 5: Analyze the position corrosion correlation by combining the temperature fluctuation coefficient, thickness thinning rate index and the distance between different positions of the furnace bottom and the center of the furnace bottom; Step six, comprehensively analyze the temperature fluctuation coefficient, thickness thinning rate index and position corrosion correlation to determine the comprehensive evaluation score of furnace bottom corrosion in order to comprehensively evaluate the furnace bottom corrosion condition.

2. The method according to claim 1, characterized in that In step one, the process of building a temperature sensing array includes: According to the geometric shape of the furnace bottom, the heat conduction characteristics and the expected distribution of corrosion hot spots, determine the number and type of temperature sensors and the installation position on the furnace bottom, and collect the furnace bottom temperature periodically; ensure that the collected temperature data can fully reflect the temperature changes at different depths and positions of the furnace bottom; The temperature sensor should be of high temperature resistance, high precision and stability to adapt to the harsh working environment in the furnace and work stably for a long time.

3. The method according to claim 2, characterized in that In step 2, the process of establishing an ultrasonic sensor array includes: According to the material properties, structural strength and expected corrosion area of ​​the furnace bottom, an ultrasonic sensor with an appropriate frequency is selected and its installation layout on the furnace bottom is determined so that it corresponds to the position covered by the temperature sensor array. The thickness of the furnace bottom is periodically collected to accurately measure the thickness data at each corresponding position.

4. The method according to claim 3, characterized in that In step 3, the process of calculating the temperature fluctuation coefficient based on the collected temperature data includes: Determine the temperature data collected at each time in this period at each temperature sensor location; and calculate the average temperature collected by all temperature sensors at each time, as well as the average temperature of all temperature data during the entire measurement period; A temperature fluctuation model is established. The temperature fluctuation model is: Where TFI is the temperature fluctuation coefficient; n is the total number of temperature sensors in the established temperature sensing array; m is the total number of times the temperature data is collected in this cycle; T avg is the average temperature of all temperature data during the entire measurement period, T avg,j is the average temperature collected by all temperature sensors at the jth moment, T i,j is the temperature data collected at the jth moment at the location of the i-th temperature sensor; Substitute the acquired data into the model for calculation to determine the temperature fluctuation coefficient TFI of the furnace bottom under the current cycle state.

5. The method according to claim 4, characterized in that In step 4, the process of calculating the thickness reduction rate index based on the collected thickness data includes: Determine the furnace bottom thickness data obtained at each measurement number at the corresponding position of each ultrasonic sensor in this cycle; and calculate the average value of the thickness data obtained from each measurement; The thinning rate model is established, and the thinning rate model is: Where TRI is the thickness reduction rate index; p is the total number of ultrasonic measurements performed in this cycle; n is the number of ultrasonic sensors in the established ultrasonic sensor array; H i,k is the furnace bottom thickness data obtained at the kth ultrasonic measurement at the corresponding position of the i-th ultrasonic sensor, H avg,k is the average value of the thickness data measured at the corresponding positions of all ultrasonic sensors during the kth ultrasonic measurement; Substitute the acquired data into the model for calculation to determine the thickness reduction rate index TRI of the furnace bottom under the current cycle state.

6. The method according to claim 5, characterized in that In step 5, the process of analyzing the location corrosion correlation includes: Transform the temperature fluctuation model to calculate the temperature fluctuation coefficient corresponding to each position under the current cycle state; transform the thinning rate model to calculate the thickness thinning rate index corresponding to each position under the current cycle state; A corrosion correlation model is established, and the corrosion correlation model is: Among them, PCD is the corrosion correlation degree; TFI is i is the temperature fluctuation coefficient corresponding to the i-th position in the current cycle state, TRI o is the thickness reduction rate index corresponding to the i-th position in the current cycle state; D i is the distance from the ith position to the center of the furnace bottom, and n is the number of positions involved; The corrosion correlation degree PCD is used to reflect the differences in corrosion characteristics at different locations on the furnace bottom due to their own environment and different distances from the center. Substitute the acquired data into the model accordingly to determine the corrosion correlation degree PCD of the furnace bottom under the current cycle state.

7. The method according to claim 6, characterized in that In step six, the process of determining the comprehensive evaluation score of furnace bottom corrosion includes: Determine the total thickness H within this cycle total , Determine the total temperature T during this period total , Establish a comprehensive evaluation model, the comprehensive evaluation model is: Among them, CES is the comprehensive evaluation score of the furnace bottom corrosion state in the current cycle, TFI is the temperature fluctuation coefficient of the furnace bottom in the current cycle state, TRI is the thickness thinning rate index of the furnace bottom in the current cycle state, and PCD is the corrosion correlation degree of the furnace bottom in the current cycle state; T total is the total temperature in this cycle, H total is the total thickness in this cycle; The comprehensive evaluation score CES is used to comprehensively and quantitatively evaluate the furnace bottom corrosion condition. The higher the CES value, the greater the possibility and severity of furnace bottom corrosion.

8. The method according to claim 7, characterized in that Also includes: A comprehensive scoring threshold is preset, and when the comprehensive evaluation score CES exceeds the comprehensive scoring threshold, an alarm signal is triggered.

9. A furnace bottom corrosion monitoring system, based on the method of claims 1-8, characterized in that: include: The temperature data acquisition module is used to establish a temperature sensor array, and accurately collect the temperature data of each point on the furnace bottom through temperature sensors distributed at different depths and positions on the furnace bottom; The thickness data acquisition module is used to construct an ultrasonic sensor array and use ultrasonic technology to regularly measure the thickness data of the furnace bottom at the position corresponding to the temperature data; A temperature fluctuation coefficient calculation module is connected to the temperature data acquisition module and calculates the temperature fluctuation coefficient of the furnace bottom in the current cycle based on the collected temperature data and a preset temperature fluctuation model; A thickness reduction rate index calculation module, which is connected to the thickness data acquisition module, calculates the thickness reduction rate index of the furnace bottom in the current cycle based on the collected thickness data and a preset reduction rate model; A position corrosion correlation analysis module is connected to the temperature fluctuation coefficient calculation module and the thickness thinning rate index calculation module, and is used to determine the corrosion correlation of the furnace bottom under the current cycle state by combining the temperature fluctuation coefficient, the thickness thinning rate index and the distance information between different positions of the furnace bottom and the center of the furnace bottom; The comprehensive evaluation score determination module is connected to the temperature fluctuation coefficient calculation module, the thickness thinning rate index calculation module and the position corrosion correlation analysis module, and is used to perform a fusion analysis of the temperature fluctuation coefficient, the thickness thinning rate index and the position corrosion correlation to determine the comprehensive evaluation score of the furnace bottom corrosion; and provide an alarm signal when the comprehensive evaluation score of the furnace bottom corrosion exceeds a preset threshold.

Images