Hazardous waste incinerator exhaust gas purification abnormality detection method based on operation data processing

Abstract

The present application relates to the technical field of data processing, and particularly relates to a hazardous waste incinerator waste gas purification abnormality detection method based on operation data processing, which comprises the following steps: determining the change degree of the rotating speed of an atomizer at each moment during the operation of a hazardous waste incinerator waste gas purification device, so as to screen out a plurality of rotating speed change moments; then determining the associated temperature change value of a semi-dry quench tower at each rotating speed change moment; combining the response time of a PID controller in regulating the rotating speed of the atomizer at each rotating speed change moment and the integral coefficient of the PID controller to determine the stability at each rotating speed change moment, thereby forming a sample point; classifying all sample points to obtain the label value of a suspected abnormal sample point, a normal clustering cluster and a sample point in the normal clustering cluster; and combining the stability and the proportional adjustment coefficient of the PID controller to obtain an abnormal sample point. The present application can improve the accuracy of rotating speed abnormal change detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to a hazardous waste incinerator waste gas purification abnormality detection method based on operation data processing. BACKGROUND

[0002] The waste gas purification equipment of the hazardous waste incinerator has the characteristics of high temperature and high pollutant concentration during the treatment of hazardous waste, which requires the purification equipment to have high stability. The waste gas purification process mainly includes cooling, deacidification, dust removal, etc. In the semi-dry quenching tower of the purification equipment, the alkali slurry is atomized, which simultaneously realizes the cooling and deacidification of the waste gas. The effect of slurry atomization directly affects the purification effect, and the rotation speed of the atomizer determines the atomization effect. Therefore, by detecting the rotation speed of the atomizer during the operation of the waste gas purification equipment, the abnormality detection of the purification equipment operation can be realized.

[0003] The existing problem: for the abnormal detection of the rotation speed of the atomizer, the abnormal change of the rotation speed of the atomizer is mainly detected, and the atomizer often needs to be actively adjusted during actual equipment operation. Therefore, the distinction between the abnormal change and the active adjustment change of the rotation speed of the atomizer is the focus of the current technology. The existing method generally realizes abnormal change recognition by analyzing the difference between the actual rotation speed change of the atomizer and the rotation speed change of the atomizer actively adjusted. However, the change of the rotation speed of the atomizer actively adjusted cannot be directly obtained, and the adjustment strategy of the atomizer is different at different stages of the incinerator, and the specific adjustment effect is directly affected by the PID controller parameters, which leads to the inconsistency of the rotation speed change of the atomizer actively adjusted, thereby reducing the accuracy of directly comparing the actual rotation speed change of the atomizer with the rotation speed change of the atomizer actively adjusted to recognize the abnormal change of the rotation speed. SUMMARY

[0004] The present application provides a hazardous waste incinerator waste gas purification abnormality detection method based on operation data processing to solve the existing problem.

[0005] The hazardous waste incinerator waste gas purification abnormality detection method based on operation data processing provided by the present application adopts the following technical scheme:

[0006] One embodiment of the present application provides a hazardous waste incinerator waste gas purification abnormality detection method based on operation data processing, which comprises the following steps:

[0007] During the operation of the hazardous waste incinerator waste gas purification equipment, the rotation speed of the atomizer, the temperature of the semi-dry quenching tower, the response time of the PID controller regulating the rotation speed of the atomizer, and the integral coefficient and proportional adjustment coefficient of the PID controller are obtained at each moment.

[0008] According to the difference of the rotation speed of the atomizer at adjacent time points, the change degree of the rotation speed of the atomizer at each time point is determined; according to the change degree of the rotation speed, a plurality of rotation speed change time points are screened out; according to the difference of the temperature of the semi-dry quenching tower at adjacent time points before each rotation speed change time point, the associated temperature change value of the semi-dry quenching tower at each rotation speed change time point is determined, and the stability at each rotation speed change time point is determined in combination with the response time of the PID controller for regulating the rotation speed of the atomizer at each rotation speed change time point and the integral coefficient of the PID controller;

[0009] The change degree of the rotation speed of the atomizer at each rotation speed change time point and the associated temperature change value of the semi-dry quenching tower form a sample point, all sample points are classified, and a label value of a suspected abnormal sample point, a normal cluster and a sample point in the normal cluster is obtained;

[0010] According to the difference between the suspected abnormal sample point and the normal cluster, the label value, the stability and the proportional adjustment coefficient of the PID controller are combined to obtain an abnormal sample point.

[0011] Further, the specific steps of determining the change degree of the rotation speed of the atomizer at each time point include the following:

[0012] The rotation speed of the atomizer at all time points is curve-fitted to obtain a fitting error value of the rotation speed of the atomizer at each time point;

[0013] The average value of the absolute value of the difference between the rotation speed of the atomizer at each time point and the rotation speed of the atomizer at the adjacent time point of each time point is obtained, which is recorded as the adjacent difference value of the rotation speed of the atomizer at each time point;

[0014] The normalized value of the product of the fitting error value and the adjacent difference value of the rotation speed of the atomizer at each time point is recorded as the change degree of the rotation speed of the atomizer at each time point.

[0015] Further, the specific steps of screening out a plurality of rotation speed change time points include the following:

[0016] The time point at which the change degree of the rotation speed of the atomizer is greater than a preset rotation speed change threshold value is recorded as a rotation speed change time point.

[0017] Further, the specific steps of determining the associated temperature change value of the semi-dry quenching tower at each rotation speed change time point include the following:

[0018] A reference period corresponding to each rotation speed change time point is obtained, wherein the length of the reference period is a preset time range, and the last time point in the reference period is each rotation speed change time point;

[0019] The absolute value of the difference between the temperature of the semi-dry quenching tower at each time point and the temperature of the semi-dry quenching tower at the previous time point of each time point is obtained, which is recorded as the temperature change value of the semi-dry quenching tower at each time point.

[0020] In the reference period corresponding to each rotational speed change moment, the maximum temperature change value in the temperature change values of the semi-dry quench tower at all moments is obtained, and is recorded as the correlation temperature change value of the semi-dry quench tower at each rotational speed change moment.

[0021] Further, the determining the stability at each rotational speed change moment comprises the following specific steps:

[0022] The moment corresponding to the maximum temperature change value is recorded as the temperature change moment at each rotational speed change moment.

[0023] The time interval between each rotational speed change moment and the temperature change moment at each rotational speed change moment is obtained, and is recorded as the temperature-rotational speed response time.

[0024] Taking the response time of the PID controller regulating the rotational speed of the atomizer at each moment as the dependent variable, and taking the integral coefficient of the PID controller at each moment as the independent variable, the integral coefficient-rotational speed response time distribution function is obtained by using the least square method.

[0025] The integral coefficient of the PID controller at each rotational speed change moment is input into the integral coefficient-rotational speed response time distribution function, and the target rotational speed response time at each rotational speed change moment is obtained.

[0026] The reciprocal normalized value of the absolute value of the difference between the temperature-rotational speed response time at each rotational speed change moment and the target rotational speed response time is recorded as the stability at each rotational speed change moment.

[0027] Further, the obtaining the label values of the suspected abnormal sample points, the normal clustering clusters and the sample points in the normal clustering clusters comprises the following specific steps:

[0028] Taking the correlation temperature change value of the semi-dry quench tower at each rotational speed change moment and the change degree of the rotational speed of the atomizer as the horizontal and vertical axes, a temperature-rotational speed characteristic scatter plot is constructed.

[0029] In the temperature-rotational speed characteristic scatter plot, the absolute value of the difference between the horizontal coordinate values of any two sample points is taken as the first iteration clustering distance, and all sample points are clustered to obtain a plurality of first iteration clustering clusters; in the first iteration clustering cluster with the number of sample points greater than 1, the label value of each sample point is 1.

[0030] The difference between the number of all sample points and the number of first iteration clustering clusters is recorded as the second iteration weight coefficient.

[0031] According to the difference between the horizontal and vertical axis coordinate values of the sample points in any two first iteration clustering clusters, combined with the second iteration weight coefficient, the second iteration clustering distance is determined, and all the first iteration clustering clusters are clustered to obtain a plurality of second iteration clustering clusters; in the second iteration clustering cluster with the sample point number greater than 1, the label value of each sample point without a label value is 2;

[0032] Similarly, a plurality of final clustering clusters are obtained;

[0033] The sample points in all the final clustering clusters with the sample point number of 1 are recorded as suspected abnormal sample points;

[0034] All the final clustering clusters with the sample point number greater than 1 are recorded as normal clustering clusters.

[0035] Further, the specific steps of determining the second iteration clustering distance according to the difference between the horizontal and vertical axis coordinate values of the sample points in any two first iteration clustering clusters, combined with the second iteration weight coefficient, include the following steps:

[0036] The horizontal axis coordinate value mean and the vertical axis coordinate value mean of all the sample points in each first iteration clustering cluster are taken as the horizontal axis coordinate value and the vertical axis coordinate value of each first iteration clustering cluster;

[0037] For any two first iteration clustering clusters, the product of the difference absolute value of the horizontal axis coordinate values and the inverse proportional normalized value of the second iteration weight coefficient is taken as a first product, the product of the difference absolute value of the vertical axis coordinate values and the normalized value of the second iteration weight coefficient is taken as a second product, and the sum of the first product and the second product is taken as the second iteration clustering distance.

[0038] Further, the specific steps of obtaining the abnormal sample points include the following steps:

[0039] The vertical axis coordinate value mean of all the sample points in each normal clustering cluster is taken as the vertical axis coordinate value of each normal clustering cluster;

[0040] The normal clustering cluster corresponding to the minimum difference absolute value in the difference absolute values of the vertical axis coordinate values of any one suspected abnormal sample point and all the normal clustering clusters is taken as the dependent clustering cluster of the any one suspected abnormal sample point;

[0041] Any one sample point in the dependent clustering cluster of the hth suspected abnormal sample point is taken as a target point;

[0042] According to the Euclidean distance between the hth suspected abnormal sample point and the target point, and the difference between the proportional adjustment coefficient of the PID controller at the corresponding moment of the hth suspected abnormal sample point and the target point, the temperature-rotational speed feature difference value of the hth suspected abnormal sample point and the target point is determined.

[0043] The product of the inverse proportional normalized value of the label value of the target point and the stability of the target point at the corresponding moment is recorded as the contrast action factor of the target point;

[0044] According to the temperature-speed characteristic difference value of the hth suspected abnormal sample point and the target point and the contrast action factor of the target point, the final abnormal performance factor of the hth suspected abnormal sample point is determined.

[0045] The suspected abnormal sample point with the final abnormal performance factor greater than the preset abnormal threshold is recorded as an abnormal sample point.

[0046] Further, the specific steps of determining the temperature-speed characteristic difference value of the hth suspected abnormal sample point and the target point according to the Euclidean distance between the hth suspected abnormal sample point and the target point and the difference of the proportional adjustment coefficient of the PID controller of the hth suspected abnormal sample point and the target point at the corresponding moment include the following steps:

[0047] The Euclidean distance between the hth suspected abnormal sample point and the target point is obtained and recorded as a first distance, and the absolute value of the difference between the proportional adjustment coefficient of the PID controller of the hth suspected abnormal sample point at the corresponding moment and the proportional adjustment coefficient of the PID controller of the target point at the corresponding moment is obtained and recorded as a first difference value, and the product of the first distance and the first difference value is recorded as the temperature-speed characteristic difference value of the hth suspected abnormal sample point and the target point.

[0048] Further, the specific steps of determining the final abnormal performance factor of the hth suspected abnormal sample point according to the temperature-speed characteristic difference value of the hth suspected abnormal sample point and the target point and the contrast action factor of the target point include the following steps:

[0049] The product of the temperature-speed characteristic difference value of the hth suspected abnormal sample point and the target point and the contrast action factor of the target point is obtained and recorded as the fifth product of the hth suspected abnormal sample point and the target point, and the normalized value of the sum of the fifth products of the hth suspected abnormal sample point and all sample points in the subordinate clustering cluster of the hth suspected abnormal sample point is recorded as the final abnormal performance factor of the hth suspected abnormal sample point.

[0050] The beneficial effects of the technical scheme of the present application are:

[0051] In the embodiment of the present application, during the operation of the hazardous waste incinerator waste gas purification equipment, the change degree of the rotation speed of the atomizer at each moment is determined to screen a plurality of rotation speed change moments, and the associated temperature change value of the semi-dry quenching tower at each rotation speed change moment is determined, combined with the response time of the PID controller regulating the rotation speed of the atomizer at each rotation speed change moment and the integral coefficient of the PID controller, the stability at each rotation speed change moment is determined, and the change degree of the rotation speed of the atomizer at each rotation speed change moment and the associated temperature change value of the semi-dry quenching tower constitute sample points, thereby constructing the temperature-rotation speed characteristics according to the change temperature and the change rotation speed, so as to obtain the same rotation speed adjustment based on the basis of rotation speed adjustment, using the same rotation speed adjustment logic for the same temperature change, thereby facilitating the determination of normal atomizer active adjustment. Classify all sample points to obtain suspected abnormal sample points, normal clustering clusters and label values of sample points in the normal clustering clusters, and combine the stability and the proportional adjustment coefficient of the PID controller to obtain abnormal sample points. Thus, when comparing the actual rotation speed change and the active adjustment rotation speed change, the relationship between the active adjustment rotation speed change and the PID controller is considered, and based on the basis of active adjustment, the active adjustment strategy and the result of active adjustment, the actual rotation speed change and the active adjustment rotation speed change are effectively compared in all directions, thereby improving the accuracy of rotation speed abnormal change detection. BRIEF DESCRIPTION OF DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0053] Figure 1 The step flow chart of the hazardous waste incinerator waste gas purification abnormality detection method based on operation data processing of the present application;

[0054] Figure 2 The structure of the hazardous waste incinerator is shown in the schematic diagram.

[0055] Figure 3 The temperature-rotation speed characteristic scatter plot corresponding to the rotation speed change moment. DETAILED DESCRIPTION

[0056] In order to further illustrate the technical means and effects taken by the present application to achieve the predetermined inventive objectives, the specific implementation, structure, features and effects of the hazardous waste incinerator exhaust gas purification abnormality detection method based on operation data processing according to the present application are described in detail as follows in combination with the drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0057] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0058] The specific scheme of the hazardous waste incinerator exhaust gas purification abnormality detection method based on operation data processing provided by the present application is specifically described below in combination with the drawings.

[0059] Please refer to Figure 1 , which shows the step flowchart of the hazardous waste incinerator exhaust gas purification abnormality detection method based on operation data processing provided by one embodiment of the present application. The method comprises the following steps:

[0060] Step S001: During the operation of the hazardous waste incinerator exhaust gas purification equipment, the rotational speed of the atomizer, the temperature of the semi-dry quench tower, the response time of the PID controller regulating the rotational speed of the atomizer, and the integral coefficient and proportional adjustment coefficient of the PID controller at each moment are obtained.

[0061] It should be noted that the hazardous waste incinerator mainly adopts incineration method, that is, a certain amount of excess air is used for oxidation combustion reaction with the treated organic waste in the furnace. Under the action of high temperature, the harmful and toxic substances in the waste are destroyed by oxidation and pyrolysis, so as to realize the harmlessness, reduction and resource utilization of the waste. The structure of the hazardous waste incinerator comprises: (1) rotary kiln: made of steel plate with excellent refractory performance, in the shape of cylinder, with a certain inclination angle, so that different types of waste can be uniformly mixed and gradually separated until burned out, and the working temperature is generally 845 to 905 degrees Celsius. (2) Secondary combustion chamber: further processes the flue gas generated by the rotary kiln to make the incompletely combusted substances completely combusted, and the operating temperature is controlled at about 1100 degrees Celsius, and the flue gas residence time is greater than 2 seconds, so as to ensure that the harmful components are effectively treated. (3) Flue gas purification system: including waste heat boiler, semi-dry quench tower (hereinafter referred to as quench tower), dry deacidification tower, bag-type dust collector, scrubbing tower, etc., used for treating the flue gas generated during the incineration process to remove harmful substances such as dioxin, acid gas and particulate matter. The structure of the hazardous waste incinerator is shown in Figure 2 Figure 2 ​The waste incinerator comprises a rotary kiln, a secondary combustion chamber, a waste heat boiler, a semi-dry quenching tower, a dry deacidification tower and a bag-type dust collector.

[0062] Further, the rotation speed of the atomizer in the semi-dry quenching tower is monitored. First, the rotation speed of the atomizer is obtained by using a sensor. The rotation speed acquisition method of the atomizer is as follows: (1) a sensor is selected, i.e., a magneto-rotational speed sensor; (2) a working principle is that an alternating voltage signal is generated by the gap change between the gear tooth top and the sensor core; (3) a working parameter is that the rotation speed range is usually 0 to 10,000 revolutions per minute; (4) an installation method is that a 60-tooth gear is installed on the atomizer shaft, and the sensor is fixed at a distance of 5 to 10 mm from the gear. The sensor shell needs to be grounded to prevent electromagnetic interference. In the current equipment operation monitoring, the collected data is transmitted to a data processing center, which is connected with the control system of the semi-dry quenching tower, so that the temperature data in the semi-dry quenching tower and the PID parameters for controlling the rotation speed of the atomizer, i.e., the PID parameters for controlling the rotation speed of the atomizer, can be directly read. The PID controller is a very common and well-known controller type used to control industrial processes, mechanical systems and other various systems. PID represents proportional, integral and derivative, which represent the three main parts of the controller.

[0063] Therefore, during the operation of the waste incinerator exhaust gas purification equipment, the rotation speed of the atomizer at each moment, the temperature of the semi-dry quenching tower at each moment, the response time of the PID controller for regulating the rotation speed of the atomizer at each moment, and the integral coefficient and proportional adjustment coefficient of the PID controller at each moment can be obtained. The sampling frequency is one per second, and this is used as an example for description.

[0064] Step S002: According to the difference in the rotation speed of the atomizer at adjacent moments, the change degree of the rotation speed of the atomizer at each moment is determined; according to the change degree of the rotation speed, a plurality of rotation speed change moments are selected; according to the temperature difference of the semi-dry quenching tower at adjacent moments before each rotation speed change moment, the associated temperature change value of the semi-dry quenching tower at each rotation speed change moment is determined, and the stability at each rotation speed change moment is determined in combination with the response time of the PID controller for regulating the rotation speed of the atomizer at each rotation speed change moment and the integral coefficient of the PID controller.

[0065] It should be noted that the response time of the PID controller for controlling the atomizer speed at each moment is the time required for the atomizer speed to reach the atomizer speed control command after the PID controller outputs an atomizer speed control command at each moment. The integral coefficient of the PID controller at each moment reflects the accumulated errors from all previous moments, primarily affecting the controller's response to past errors and helping to eliminate steady-state errors. The main function of the atomizer is to atomize the liquid slurry. In the semi-dry quench tower, the slurry comes into direct contact with the exhaust gas. This not only rapidly cools the exhaust gas, but also neutralizes the alkaline substances in the slurry with the acidic substances in the exhaust gas, achieving exhaust gas deacidification. The atomization effect directly affects the exhaust gas purification effect, and the atomizer speed directly affects the atomizer atomization effect. Therefore, atomizer speed monitoring is used to monitor the operation of the exhaust gas purification equipment. Abnormal changes in the atomizer speed directly affect the atomization effect. In actual operation, the atomizer speed often requires active adjustment of the atomizer speed. Therefore, it is necessary to extract the atomizer speed and perform abnormal analysis.

[0066] Preferably, in one embodiment of the present invention, the method for obtaining stability at each speed change moment includes:

[0067] During the operation of the hazardous waste incinerator exhaust gas purification equipment, a polynomial fitting method is used to perform curve fitting on the atomizer speed at all times, and the fitting error value of the atomizer speed at each moment is obtained.

[0068] The polynomial fitting method is a well-known technology, and the specific method will not be introduced here.

[0069] Get the The speed of the atomizer at this moment is Moment and The average absolute value of the difference in the rotation speed of the atomizer at the time is recorded as The adjacent difference values ​​of the atomizer rotation speed at the moment.

[0070] If there is only one adjacent moment at any moment, the absolute value of the difference between the rotational speeds of the atomizer at the two moments is used as the adjacent difference value.

[0071] The normalized value of the product of the fitting error value of the rotation speed of the atomizer at each moment and the adjacent difference value is recorded as the degree of change of the rotation speed of the atomizer at each moment.

[0072] In this embodiment, use The linear normalization function normalizes the product of the fitting error value and the adjacent difference value to between 0 and 1.

[0073] The preset speed change threshold is 0.7, which is used as an example for description.

[0074] The moment when the change degree of the rotation speed of the atomizer is greater than the preset rotation speed change threshold is recorded as the rotation speed change moment.

[0075] It should be noted that during the operation of a semi-dry quench tower, a primary task of the atomizer is to reduce the exhaust gas temperature. Therefore, the atomizer speed must be adjusted according to the cooling tower temperature. Therefore, changes in the atomizer speed are correlated with changes in the semi-dry quench tower temperature. Normally, after a temperature change in the semi-dry quench tower, temperature control is achieved by adjusting the atomizer speed. Therefore, the semi-dry quench tower temperature typically changes first, followed by changes in the atomizer speed. Therefore, when analyzing the relationship between speed changes and temperature changes, it is first necessary to search for temperature changes that have an impact on the speed before the speed change occurs.

[0076] A reference time period corresponding to each speed change moment is obtained, wherein the length of the reference time period is a preset time range, and the last moment in the reference time period is each speed change moment.

[0077] It should be noted that the reference period is used to search for temperature changes before each speed change. The length of the reference period is affected by the atomizer speed adjustment response time. In this embodiment, the preset time range is 5 seconds, which is used as an example for description.

[0078] During the operation of the hazardous waste incinerator exhaust gas purification equipment, obtain the Moment and The absolute value of the temperature difference of the semi-dry quench tower at the time is recorded as Temperature change value of the semi-dry quenching tower at time .

[0079] Here, the temperature change value of the semi-dry quenching tower at the first moment is set to the temperature change value of the semi-dry quenching tower at the second moment.

[0080] In the reference time period corresponding to each speed change moment, the maximum temperature change value among the temperature change values ​​of the semi-dry quenching tower at all moments is obtained, and recorded as the associated temperature change value of the semi-dry quenching tower at each speed change moment. The moment corresponding to the maximum temperature change value is recorded as the temperature change moment at each speed change moment.

[0081] The temperature-speed characteristic at each speed change moment is formed by the change degree of the speed of the atomizer and the associated temperature change value of the semi-dry quenching tower at each speed change moment.

[0082] It needs to be explained that in the speed adjustment of the atomizer, the trigger factor of the speed change is the temperature change of the semi-dry quenching tower, and the speed of the atomizer is adjusted by the PID controller, so the direct factor of the speed change is the parameter of the PID controller. In normal speed adjustment, the adjustment basis is consistent, which is manifested in that the temperature change and the speed change are consistent. At this time, the abnormal performance of each temperature-speed characteristic can be analyzed by comparing the normal temperature-speed characteristic. Therefore, the distribution of the corresponding temperature-speed characteristic in the normal speed adjustment needs to be obtained first.

[0083] It needs to be further explained that when the speed of the atomizer is adjusted, there is a response time from the temperature change of the semi-dry quenching tower to the speed change of the atomizer, and the response time can also reflect the influence relationship between the speed of the atomizer and the temperature change of the quenching tower. The smaller the difference between the response time and the actual response time of the PID controller, the higher the stability of the temperature-speed characteristic corresponding to the speed.

[0084] The time interval between each speed change time and the temperature change time at each speed change time is obtained, which is denoted as the temperature-speed response time.

[0085] In the operation process of the hazardous waste incinerator waste gas purification equipment, the response time of the PID controller for regulating the speed of the atomizer at each time is used as the dependent variable, the integral coefficient of the PID controller at each time is used as the independent variable, and the least squares method is used to obtain the integral coefficient-speed response time distribution function.

[0086] The least squares method is a known technology, and the specific method is not introduced here. The reason for constructing the distribution function is that when the PID controller regulates the speed of the atomizer at the current time, it has not responded yet, so the response time cannot be obtained. Therefore, the relationship between the integral coefficient and the speed response time is determined by constructing the distribution function.

[0087] The integral coefficient of the PID controller at each speed change time is input into the integral coefficient-speed response time distribution function to obtain the target speed response time at each speed change time.

[0088] The absolute value of the difference between the temperature-speed response time at each speed change time and the target speed response time is inversely proportional to the normalized value, which is denoted as the stability at each speed change time.

[0089] It needs to be explained that in this embodiment, is inversely proportional to the normalized value of , and is a linear normalization function for normalizing the data value to between 0 and 1.

[0090] Step S003: forming sample points by the change degree of the rotational speed of the atomizer at each rotational speed change moment and the associated temperature change value of the semi-dry quenching tower, classifying all sample points, and obtaining the label values of the suspected abnormal sample points, the normal clustering clusters, and the sample points in the normal clustering clusters.

[0091] It should be noted that in the obtained temperature-rotational speed characteristics, there are a large number of rotational speed adjustments caused by temperature changes, and at this time, it is necessary to first determine the temperature-rotational speed characteristics corresponding to the normal rotational speed adjustment.

[0092] Preferably, in an embodiment of the present application, the method for obtaining the label values of the suspected abnormal sample points, the normal clustering clusters, and the sample points in the normal clustering clusters comprises:

[0093] The temperature-rotational speed characteristics at each rotational speed change moment are taken as a sample point, the associated temperature change value of the semi-dry quenching tower at each rotational speed change moment is taken as the horizontal axis, and the change degree of the rotational speed of the atomizer at each rotational speed change moment is taken as the vertical axis, to construct the temperature-rotational speed characteristic scatter plot corresponding to each rotational speed change moment.

[0094] It should be noted that in this embodiment, the minimum maximum normalization method is used to normalize the horizontal and vertical axis coordinate values in the temperature-rotational speed characteristic scatter plot, for unified dimension, wherein the minimum maximum normalization method is a known technology, and the specific method is not introduced here.

[0095] The temperature-rotational speed characteristic scatter plot corresponding to the rotational speed change moment is as shown in Figure 3 , wherein the horizontal axis Figure 3 is the associated temperature change value of the semi-dry quenching tower at each rotational speed change moment, and the vertical axis is the change degree of the rotational speed of the atomizer at each rotational speed change moment.

[0096] It should be noted that in the normal rotational speed adjustment, similar temperature changes have similar adjustment strategies, so similar rotational speed changes are obtained. Therefore, the normal rotational speed distribution can be determined by the similarity of the temperature-rotational speed characteristics. All changes are caused by temperature changes, so the first condition for the same temperature-rotational speed characteristics is the similarity of the temperature changes.

[0097] The preset merging cluster threshold is 0.6, and this is described as an example.

[0098] ​In the temperature-rotation speed characteristic scatter plot, the absolute value of the difference of the horizontal axis coordinate values of any two sample points is taken as the first iteration clustering distance, and a hierarchical clustering algorithm is used for first iteration clustering operation on all sample points to obtain a plurality of first iteration clustering clusters. The mean value of the horizontal axis coordinate values and the mean value of the vertical axis coordinate values of all sample points in each first iteration clustering cluster are taken as the horizontal axis coordinate value and the vertical axis coordinate value of each first iteration clustering cluster. In the first iteration clustering cluster with the number of sample points greater than 1, the label value of each sample point is assigned as 1.

[0099] The difference between the number of all sample points and the number of first iteration clustering clusters is denoted as the second iteration weight coefficient . For any two first iteration clustering clusters, the product of the absolute value of the difference of the horizontal axis coordinate values and the inverse proportional normalized value of the second iteration weight coefficient is denoted as the first product, and the product of the absolute value of the difference of the vertical axis coordinate values and the normalized value of the second iteration weight coefficient is denoted as the second product. The sum of the first product and the second product is taken as the second iteration clustering distance, and a hierarchical clustering algorithm is used for second iteration clustering operation on all first iteration clustering clusters to obtain a plurality of second iteration clustering clusters. The mean value of the horizontal axis coordinate values and the mean value of the vertical axis coordinate values of all sample points in each second iteration clustering cluster are taken as the horizontal axis coordinate value and the vertical axis coordinate value of each second iteration clustering cluster. In the second iteration clustering cluster with the number of sample points greater than 1, the label value of each sample point without a label value is assigned as 2.

[0100] The difference between the number of all sample points and the number of second iteration clustering clusters is denoted as the third iteration weight coefficient . For any two second iteration clustering clusters, the product of the absolute value of the difference of the horizontal axis coordinate values and the inverse proportional normalized value of the third iteration weight coefficient is denoted as the third product, and the product of the absolute value of the difference of the vertical axis coordinate values and the normalized value of the third iteration weight coefficient is denoted as the fourth product. The sum of the third product and the fourth product is taken as the third iteration clustering distance, and a hierarchical clustering algorithm is used for third iteration clustering operation on all second iteration clustering clusters to obtain a plurality of third iteration clustering clusters. In the third iteration clustering cluster with the number of sample points greater than 1, the label value of each sample point without a label value is assigned as 3.

[0101] By analogy, a plurality of final clustering clusters and the label value of each sample point in the final clustering cluster with the number of sample points greater than 1 are obtained. The sample points in all final clustering clusters with the number of sample points being 1 are all denoted as suspected abnormal sample points. All final clustering clusters with the number of sample points greater than 1 are all denoted as normal clustering clusters.

[0102] It should be noted that the hierarchical clustering algorithm is a well-known technology. In this embodiment, agglomerative hierarchical clustering is used, which first takes each sample point as an independent cluster, obtains the cluster distance between clusters, merges the two most similar clusters into a new cluster, and the merged cluster is also regarded as a new cluster. Then the cluster distance between the new clusters is obtained, and the two most similar new clusters are merged into an updated cluster, and iterates in this way until the iterative stopping condition is reached. The stopping condition of the iterative clustering operation in this embodiment is: the preset stopping threshold is 0.6. In all cluster clusters after any iterative clustering operation, if the mean of the Euclidean distance between all two sample points in any cluster is greater than the preset stopping threshold, the subsequent iterative clustering operation is stopped, and the preset merging cluster threshold is assigned to each iterative clustering operation in the hierarchical clustering algorithm. The label value of the above sample point indicates the number of times the cluster of a single sample point is merged in the iterative clustering. In this embodiment, the label value of the above sample point indicates the number of times the cluster of the single sample point is merged ... as well as As the second iteration weight coefficient The normalized value and the inverse normalized value of as well as As the third iteration weight coefficient The normalized value and the inverse normalized value of , where It is a linear normalization function used to normalize the data value to between 0 and 1. The sample point in the final cluster with a sample point number of 1 indicates that it has no similar sample points.

[0103] Step S004: according to the difference between the suspected abnormal sample point and the normal cluster, the abnormal sample point is obtained in combination with the label value, the stability and the proportional adjustment coefficient of the PID controller.

[0104] What needs to be explained is that in the actual incinerator exhaust gas purification, due to the differences in the types of gases produced by incineration or the different stages of incineration, there may be differences in the adjustment of the semi-dry quenching tower temperature and the atomizer speed, that is, there are slight differences in the adjustment of the atomizer speed corresponding to the same temperature change. In the above-mentioned clustering of normal temperature-speed characteristic distribution, it is determined only by a small number of temperature-speed characteristics with high similarity, and more consideration is given to the consistency of the changing temperature. Therefore, the clustering of normal temperature-speed characteristic distribution obtained by the above operation is still credible. At this time, for the suspected abnormal temperature-speed characteristics obtained above, it is necessary to further determine the final abnormal temperature-speed characteristics by comparing them with the consistent relationship of the clustering of normal temperature-speed characteristic distribution.

[0105] Further need to be explained: after the temperature changes, the atomizer speed is adjusted by the PID controller, wherein the proportional coefficient of the PID controller directly causes the speed change, and the integral coefficient and the differential coefficient have a continuous output, which causes the speed change to be accumulated over time, so the speed change is not obvious. Accordingly, by comparing the clustering clusters of single suspected abnormal temperature-speed and normal temperature-speed characteristics, the final abnormal performance of single suspected abnormal temperature-speed is analyzed.

[0106] Preferably, in one embodiment of the application, the method for obtaining abnormal sample points comprises:

[0107] Obtain the mean value of the longitudinal axis coordinate values of all sample points in each normal clustering cluster as the longitudinal axis coordinate value of each normal clustering cluster.

[0108] Obtain the minimum difference absolute value of the difference absolute values between any one suspected abnormal sample point and the longitudinal axis coordinate values of all normal clustering clusters, and record it as the dependent clustering cluster of the any one suspected abnormal sample point.

[0109] It needs to be explained that the change degree of the atomizer speed of the suspected abnormal sample point and the sample points in its dependent clustering cluster is similar. Therefore, the similarity of the associated temperature change value of the suspected abnormal sample point and the sample points in its dependent clustering cluster is further analyzed, and since the speed change caused by actual speed adjustment is first affected by the proportional coefficient of the PID control, the similarity also shows the consistency of the proportional coefficient.

[0110] Any one sample point in the dependent clustering cluster of the hth suspected abnormal sample point is recorded as the target point.

[0111] Obtain the Euclidean distance between the hth suspected abnormal sample point and the target point, and record it as the first distance. Then obtain the difference absolute value between the proportional adjustment coefficient of the PID controller corresponding to the hth suspected abnormal sample point and the proportional adjustment coefficient of the PID controller corresponding to the target point, and record it as the first difference value. The product of the first distance and the first difference value is recorded as the temperature-speed characteristic difference value of the hth suspected abnormal sample point and the target point.

[0112] It needs to be explained that for a single suspected abnormal temperature-speed characteristic, its final abnormal performance is the difference with the corresponding dependent clustering cluster. And the corresponding dependent clustering cluster contains multiple temperature-speed characteristics, and each temperature-speed characteristic has different comparison effects. The stronger the stability of the temperature-speed characteristics in the dependent clustering cluster, the more obvious the comparison effect. And the closer to the front the merging order of the temperature-speed characteristics in the dependent clustering cluster, the more obvious the comparison effect.

[0113] Obtain the label value of the target point The product of the inverse proportional normalized value of the temperature-speed characteristic of the hth suspected abnormal sample point and the stability of the target point at the corresponding moment of the target point is recorded as the contrast action factor of the hth suspected abnormal sample point and the target point.

[0114] The linear normalization function is used to normalize the data value to 0 to 1. The label value of the target point The inverse proportional normalized value of the temperature-speed characteristic of the hth suspected abnormal sample point, The linear normalization function is used to normalize the data value to 0 to 1.

[0115] The product of the temperature-speed characteristic difference value of the hth suspected abnormal sample point and the target point and the contrast action factor of the target point is recorded as the fifth product of the hth suspected abnormal sample point and the target point, and the normalized value of the sum of the fifth products of the hth suspected abnormal sample point and all sample points in the dependent clustering cluster of the hth suspected abnormal sample point is recorded as the final abnormal performance factor of the hth suspected abnormal sample point.

[0116] The linear normalization function is used to normalize the data value to 0 to 1. The linear normalization function is used to normalize the data value to 0 to 1.

[0117] According to the above manner, the final abnormal performance factor of each suspected abnormal sample point is obtained.

[0118] It should be noted that in the actual operation of the exhaust gas purification equipment, the suspected abnormal temperature-speed characteristic is first identified, then the final abnormal performance of each suspected abnormal temperature-speed characteristic is determined, and finally the abnormal temperature-speed characteristic is determined through the final abnormal performance, that is, the abnormal speed of the atomizer is determined.

[0119] The preset abnormal threshold is 0.8, and this is described as an example.

[0120] The suspected abnormal sample point with the final abnormal performance factor greater than the preset abnormal threshold is recorded as an abnormal sample point.

[0121] It should be noted that after the detection platform identifies the abnormal sample point (abnormal temperature-speed characteristic), a warning is directly issued to notify the technical personnel to check the atomizer speed control system and the quench tower control system.

[0122] Thus, the present application is completed.

[0123] To sum up, in the embodiment of the present application, during the operation of the hazardous waste incinerator waste gas purification equipment, the change degree of the rotation speed of the atomizer at each moment is determined to screen a plurality of rotation speed change moments, the associated temperature change value of the semi-dry quench tower at each rotation speed change moment is determined, the response time of the PID controller in regulating the rotation speed of the atomizer at each rotation speed change moment and the integral coefficient of the PID controller are combined to determine the stability at each rotation speed change moment, the change degree of the rotation speed of the atomizer at each rotation speed change moment and the associated temperature change value of the semi-dry quench tower constitute sample points, all sample points are classified to obtain suspected abnormal sample points, normal clustering clusters and label values of sample points in the normal clustering clusters, the stability and the proportional adjustment coefficient of the PID controller are combined to obtain abnormal sample points. The present application can improve the accuracy of rotation speed abnormal change detection.

[0124] The above merely describes preferred embodiments of the present application and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. within the principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for detecting abnormalities in the exhaust gas purification of hazardous waste incinerators based on operation data processing, characterized in that: The method comprises the following steps: During the operation of the hazardous waste incinerator exhaust gas purification equipment, obtain the atomizer speed, semi-dry quench tower temperature, PID controller response time for controlling the atomizer speed, and the PID controller's integral coefficient and proportional adjustment coefficient at each moment; Determine the degree of change of the atomizer speed at each moment based on the speed difference of the atomizer at adjacent moments; select several speed change moments based on the speed change degree; determine the associated temperature change value of the semi-dry quenching tower at each speed change moment based on the temperature difference of the semi-dry quenching tower at adjacent moments before each speed change moment, and determine the stability at each speed change moment in combination with the response time of the PID controller for controlling the atomizer speed and the integral coefficient of the PID controller at each speed change moment; The change in the atomizer speed at each speed change moment and the associated temperature change value of the semi-dry quenching tower are used to form sample points. All sample points are classified to obtain the label values ​​of suspected abnormal sample points and normal clusters, as well as the sample points in the normal clusters. According to the difference between the suspected abnormal sample point and the normal cluster, the abnormal sample point is obtained in combination with the label value, the stability and the proportional adjustment coefficient of the PID controller.

2. The method for detecting abnormalities in exhaust gas purification of hazardous waste incinerators based on operation data processing according to claim 1 is characterized in that: The specific steps of determining the degree of change of the rotation speed of the atomizer at each moment are as follows: Perform curve fitting on the rotation speed of the atomizer at all times to obtain the fitting error value of the rotation speed of the atomizer at each time; Obtain the average of the absolute values ​​of the differences between the rotation speeds of the atomizer at each moment and its adjacent moments, and record this as the adjacent difference value of the rotation speed of the atomizer at each moment; The normalized value of the product of the fitting error value of the rotation speed of the atomizer at each moment and the adjacent difference value is recorded as the degree of change of the rotation speed of the atomizer at each moment.

3. The method for detecting abnormalities in exhaust gas purification of hazardous waste incinerators based on operation data processing according to claim 1 is characterized in that: The specific steps of screening out a number of speed change moments are as follows: The moment when the change degree of the rotation speed of the atomizer is greater than the preset rotation speed change threshold is recorded as the rotation speed change moment.

4. The method for detecting abnormalities in exhaust gas purification of hazardous waste incinerators based on operation data processing according to claim 1 is characterized in that: The specific steps of determining the associated temperature change value of the semi-dry quenching tower at each speed change moment are as follows: Obtain a reference period corresponding to each speed change moment, wherein the length of the reference period is a preset time range, and the last moment in the reference period is each speed change moment; Obtain the absolute value of the difference between the temperature of the lower semi-dry quench tower at each moment and the moment before each moment, and record it as the temperature change value of the lower semi-dry quench tower at each moment; In the reference period corresponding to each speed change moment, the maximum temperature change value among the temperature change values ​​of the semi-dry quenching tower at all moments is obtained, and recorded as the associated temperature change value of the semi-dry quenching tower at each speed change moment.

5. The method for detecting abnormalities in exhaust gas purification of hazardous waste incinerators based on operation data processing according to claim 4 is characterized in that: The specific steps of determining the stability at each speed change moment are as follows: The time corresponding to the maximum temperature change value is recorded as the temperature change time at each speed change moment; Obtain the time interval between each speed change moment and the temperature change moment at each speed change moment, and record it as the temperature-speed response time; The response time of the PID controller to control the atomizer speed at each moment is taken as the dependent variable, and the integral coefficient of the PID controller at each moment is taken as the independent variable. The least squares method is used to obtain the integral coefficient-speed response time distribution function. The integral coefficient of the PID controller at each speed change moment is input into the integral coefficient-speed response time distribution function to obtain the target speed response time at each speed change moment; The inversely proportional normalized value of the absolute value of the difference between the temperature-speed response time and the target speed response time at each speed change moment is recorded as the stability at each speed change moment.

6. The method for detecting abnormalities in exhaust gas purification of hazardous waste incinerators based on operation data processing according to claim 1 is characterized in that: The specific steps of obtaining the label values ​​of the suspected abnormal sample points, the normal clusters, and the sample points in the normal clusters are as follows: A temperature-speed characteristic scatter plot was constructed with the temperature change value of the semi-dry quench tower and the degree of change in the speed of the atomizer at each speed change moment as the horizontal and vertical axes; In the temperature-speed characteristic scatter plot, the absolute value of the difference between the horizontal axis coordinate values ​​of any two sample points is used as the first iteration clustering distance, and all sample points are clustered to obtain several first iteration clusters; In the first iterative clustering where the number of sample points is greater than 1, the label value of each sample point is assigned to 1; The difference between the number of all sample points and the number of clusters in the first iteration is recorded as the weight coefficient of the second iteration; According to the difference in the horizontal and vertical coordinate values ​​of the sample points in any two first-iteration clusters, combined with the second-iteration weight coefficient, the second-iteration cluster distance is determined, and all first-iteration clusters are clustered to obtain several second-iteration clusters; In the second iterative clustering where the number of sample points is greater than 1, the label value assigned to each sample point without a label value is 2; And so on, several final clusters are obtained; All sample points in the final cluster with a sample point number of 1 are recorded as suspected abnormal sample points; All final clusters with the number of sample points greater than 1 are recorded as normal clusters.

7. The method for detecting abnormalities in exhaust gas purification of hazardous waste incinerators based on operation data processing according to claim 6 is characterized in that: The specific steps of determining the second iteration clustering distance based on the difference in the horizontal and vertical coordinate values ​​of the sample points in any two first iteration clusters and combining the second iteration weight coefficient are as follows: The mean of the horizontal axis coordinate values ​​and the mean of the vertical axis coordinate values ​​of all sample points in each first iteration cluster are used as the horizontal axis coordinate value and the vertical axis coordinate value of each first iteration cluster; For any two first-iteration clusters, obtain the product of the absolute value of the difference between the horizontal axis coordinate values ​​and the inversely proportional normalized value of the second-iteration weight coefficient, recorded as the first product, obtain the product of the absolute value of the difference between the vertical axis coordinate values ​​and the normalized value of the second-iteration weight coefficient, recorded as the second product, and the sum of the first product and the second product is used as the second-iteration cluster distance.

8. The method for detecting abnormalities in exhaust gas purification of hazardous waste incinerators based on operation data processing according to claim 6 is characterized in that: The specific steps of obtaining abnormal sample points are as follows: Obtain the mean of the vertical axis coordinate values ​​of all sample points in each normal cluster as the vertical axis coordinate value of each normal cluster; Obtain the normal cluster corresponding to the minimum absolute value of the difference between the vertical axis coordinate values ​​of any suspected abnormal sample point and all normal clusters, and record it as the subordinate cluster of the any suspected abnormal sample point; Any sample point in the subordinate cluster of the h-th suspected abnormal sample point is recorded as the target point; According to the Euclidean distance between the hth suspected abnormal sample point and the target point, and the difference in the proportional adjustment coefficient of the PID controller at the corresponding time between the hth suspected abnormal sample point and the target point, the temperature-speed characteristic difference value between the hth suspected abnormal sample point and the target point is determined; Obtain the product of the inversely proportional normalized value of the target point's label value and the stability of the target point at the corresponding moment, and record it as the contrast factor of the target point; Determine the final abnormal performance factor of the hth suspected abnormal sample point based on the temperature-speed characteristic difference value between the hth suspected abnormal sample point and the target point and the comparative effect factor of the target point; The suspected abnormal sample points whose final abnormal performance factor is greater than the preset abnormal threshold are recorded as abnormal sample points.

9. The method for detecting abnormalities in exhaust gas purification of hazardous waste incinerators based on operation data processing according to claim 8 is characterized in that: The method of determining the temperature-speed characteristic difference value between the hth suspected abnormal sample point and the target point based on the Euclidean distance between the hth suspected abnormal sample point and the target point, and the difference in the proportional adjustment coefficient of the PID controller at the corresponding time between the hth suspected abnormal sample point and the target point, includes the following specific steps: Obtain the Euclidean distance between the hth suspected abnormal sample point and the target point, which is recorded as the first distance. Then obtain the absolute value of the difference between the proportional adjustment coefficient of the PID controller at the time corresponding to the hth suspected abnormal sample point and the proportional adjustment coefficient of the PID controller at the time corresponding to the target point, which is recorded as the first difference value. The product of the first distance and the first difference value is recorded as the temperature-speed characteristic difference value between the hth suspected abnormal sample point and the target point.

10. The method for detecting abnormalities in exhaust gas purification of hazardous waste incinerators based on operation data processing according to claim 8, characterized in that: The method of determining the final abnormal performance factor of the hth suspected abnormal sample point based on the temperature-speed characteristic difference value between the hth suspected abnormal sample point and the target point and the comparative effect factor of the target point includes the following specific steps: Obtain the product of the temperature-speed characteristic difference value between the h-th suspected abnormal sample point and the target point and the contrast effect factor of the target point, and record it as the fifth product of the h-th suspected abnormal sample point and the target point. Take the normalized value of the sum of the fifth products of the h-th suspected abnormal sample point and all sample points in the subordinate cluster of the h-th suspected abnormal sample point, and record it as the final abnormal performance factor of the h-th suspected abnormal sample point.

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