Garbage feeding device for incineration power generation and adjustment method

Abstract

The present application relates to the technical field of waste feeding control, in particular to a waste feeding device and adjusting method for incineration power generation. The waste water content characteristics are analyzed through leachate flow time series data, the load coefficient quantifies the density characteristics, and the combustion characteristics are represented by the change of carbon monoxide concentration, thereby constructing a three-dimensional index system of leachate characteristic coefficient, density characteristic coefficient and combustion characteristic coefficient. The cluster algorithm is used for pattern recognition of waste grabbing operation, and a cluster group with similar combustion characteristics is formed. In the current waste grabbing operation belonging to the cluster group, the feeding speed and shaking speed parameters are dynamically optimized based on the carbon monoxide concentration distribution characteristics, replacing the traditional fixed control mode. The method realizes the intelligent matching of waste composition change and process parameters, optimizes the combustion process through leachate management, pile compaction degree and the like, and significantly improves the sufficiency of waste combustion.

Description

Technical Field

[0001] This invention relates to the field of waste feeding control technology, specifically to a waste feeding device and adjustment method for incineration power generation. Background Technology

[0002] Municipal solid waste refers to solid waste generated by humans in daily life, including community waste, waste from public places, and waste from schools and government agencies. Waste incineration is a method of treating waste by burning it at high temperatures in an incinerator, converting the heat energy contained in the high-temperature flue gas into electrical energy, thus realizing the resource recovery of waste through incineration.

[0003] Existing devices and technologies typically use preset fixed operating parameters (waste feeding speed and slamming speed) to control the waste incineration process. However, incomplete waste combustion can affect resource recycling and increase the concentration of harmful substances. Furthermore, waste composition is complex, and different types of waste have significant differences in parameters such as moisture content and density. Therefore, using fixed operating parameters to control the waste incineration process can lead to problems such as unstable calorific value and incomplete combustion. Summary of the Invention

[0004] To address the challenges posed by incomplete waste combustion—which negatively impacts resource recycling and increases the concentration of harmful substances—and the complex composition of waste with varying moisture content and density across different types, controlling the incineration process with fixed operating parameters can lead to unstable calorific value and incomplete combustion. This invention aims to provide a waste feeding device and adjustment method for incineration power generation. The specific technical solution is as follows:

[0005] A method for regulating a waste feeding device for incineration power generation, comprising:

[0006] In each waste dumping batch, time-series data of leachate flow rate, load coefficient corresponding to each waste grabbing, and time-series data of carbon monoxide concentration are acquired; waste grabbing includes historical grabbing and current grabbing;

[0007] Analyze the numerical variation characteristics and cumulative effects of leachate flow rate to determine the leachate characteristic value for each batch of waste dumping; combine the temporal variation characteristics of leachate flow rate for each batch of waste dumping with the load coefficient at each waste grabbing time to determine the waste density characteristic value at each waste grabbing time; analyze the numerical variation characteristics and cumulative effects of carbon monoxide concentration to determine the carbon monoxide characteristic value at each waste grabbing time.

[0008] Based on leachate characteristic values, waste density characteristic values, and carbon monoxide characteristic values, cluster analysis is performed on the number of waste grabbing operations to obtain clusters. By combining the numerical characteristics and distribution of carbon monoxide concentration values ​​from historical grabbing operations within the cluster to which the current grabbing operation belongs, the feeding speed and shaking speed of the waste under the current grabbing operation are controlled.

[0009] Furthermore, the method for obtaining the leachate characteristic values ​​includes:

[0010] In the time series data of landfill leachate flow, the difference between the maximum leachate flow value and the leachate flow value at the first time point is used as the leachate increase index.

[0011] The value of the leachate flow time series data curve for each batch of waste dumping is normalized after definite integral calculation over time to obtain the total leachate index.

[0012] The normalized value of the sum of the leachate increase index and the leachate total amount index is used as the leachate characteristic value for each batch of waste dumping.

[0013] Furthermore, the method for obtaining the waste density feature value includes:

[0014] In the leachate flow time series data of each batch of waste dumping, the leachate flow value at the corresponding time period of each waste grabbing is extracted to obtain the target data segment;

[0015] In the target data segment, the absolute value of the difference between the maximum leachate flow rate and the leachate flow rate at the first moment is negatively correlated with the length of the target data segment to obtain the density factor for each garbage grabbing.

[0016] The normalized value of the product of the density factor and the load coefficient at each garbage grabbing time is used as the garbage density feature value at each garbage grabbing time.

[0017] Furthermore, the method for obtaining the carbon monoxide characteristic value includes:

[0018] In each historical capture of carbon monoxide concentration time series data, the difference between the maximum carbon monoxide concentration value and the carbon monoxide concentration value at the first moment is used as the carbon monoxide concentration increase index.

[0019] The value of the carbon monoxide concentration time series data curve for each historical capture is normalized after definite integral calculation over time to obtain the total carbon monoxide concentration index.

[0020] The normalized value of the product of the carbon monoxide concentration increase index and the total carbon monoxide concentration index is used as the carbon monoxide characteristic value for each historical capture.

[0021] The carbon monoxide characteristic value at the time of capture is a preset value.

[0022] Furthermore, the method for obtaining the clusters includes:

[0023] Based on the leachate characteristic value of each waste dumping batch, the waste density characteristic value at each waste grabbing time, and the carbon monoxide characteristic value, the feature vector at each waste grabbing time is determined.

[0024] Calculate the cosine similarity between the feature vectors of any two garbage collections and perform negative correlation mapping as the difference factor;

[0025] Based on the difference factor between the feature vectors during garbage collection and the preset K value, the K-means clustering algorithm is used to perform cluster analysis on all garbage collection times to obtain clusters. The optimal K value is obtained based on the elbow method and is used as the preset K value.

[0026] Furthermore, the method for obtaining the feature vector includes:

[0027] The feature vector for each garbage grab is composed of the garbage density feature value, the carbon monoxide feature value, and the leachate feature value of the garbage dumping batch to which each garbage grab belongs.

[0028] Furthermore, the control of the feeding speed and slugging speed of the waste under the current grab, based on the numerical characteristics and distribution of the carbon monoxide concentration values ​​from historical grabs within the cluster to which the current grab belongs, includes:

[0029] The control coefficient is determined by combining the numerical characteristics and distribution of historical carbon monoxide concentration values ​​captured in the cluster to which the current capture count belongs;

[0030] The value of the control coefficient after negative correlation mapping is used as the feed adjustment factor, and the product of the feed adjustment factor and the preset feed speed is used as the adjusted feed speed during the current gripping.

[0031] The preset shaking speed is adjusted using the control coefficient to obtain the adjusted shaking speed during the current grasping process.

[0032] Furthermore, the method for obtaining the control coefficient includes:

[0033] In the cluster to which the current capture count belongs, the average of the maximum carbon monoxide concentration values ​​in all historical capture time-series data is used as the first control factor.

[0034] The kurtosis of the maximum carbon monoxide concentration value in all historically captured time-series data is normalized to obtain the second regulatory factor.

[0035] Obtain the fitted straight line of the first preset number of carbon monoxide concentration values ​​in the time series data of each historically captured carbon monoxide concentration in the cluster to which the current capture number belongs, and use the normalized value of the slope of the fitted straight line as the second control factor.

[0036] The normalized product of the first, second, and third regulatory factors, plus the sum of the product and a preset parameter, is used as the regulatory coefficient, wherein the preset parameter is set to 0.5.

[0037] Furthermore, the method for obtaining the adjusted shaking speed includes:

[0038] The value of the control coefficient after negative correlation mapping is used as the shake-off adjustment factor, and the product of the shake-off adjustment factor and the preset shake-off speed is used as the adjusted shake-off speed during the current capture.

[0039] A waste feeding device for incineration power generation includes at least a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of a method for regulating the waste feeding device for incineration power generation.

[0040] The present invention has the following beneficial effects:

[0041] Different types of waste have different characteristics. Therefore, in each batch of waste disposal, the moisture content and combustion status of the waste are analyzed to determine the characteristics of the waste in each batch, thereby enabling adaptive and precise control of the waste feeding and discharging speeds. First, time-series data of leachate flow rate for each batch is acquired to reflect the moisture content of the waste. Time-series data of load coefficient and carbon monoxide concentration are also obtained for each waste grabbing operation. The load coefficient reflects density characteristics, and the carbon monoxide concentration reflects combustion status. Further, analyzing the variation characteristics and cumulative effects of leachate flow rate values ​​quantifies the moisture content characteristics of each batch of waste disposal. Through the fusion analysis of leachate flow rate time-series data and load coefficients, the density characteristics of each waste grabbing operation are quantified. Analyzing the numerical variation characteristics and cumulative effects of carbon monoxide concentration values ​​quantifies the combustion characteristics of each waste grabbing operation. Then, based on the indicators characterizing the aforementioned three features, the number of waste grabbing operations is clustered, ensuring that the number of grabbing operations in each cluster has relatively consistent waste characteristics. Finally, when waste combustion is incomplete, the feeding speed and the swaying speed should be reduced to ensure complete combustion. Therefore, by adaptively generating a control strategy based on the carbon monoxide concentration distribution characteristics of the historical grabbing times in the cluster to which the current grabbing time belongs, the feeding speed and swaying speed of the waste under the current grabbing time are adjusted to replace the traditional control mode with fixed operating parameters. This allows the waste feeding device to autonomously adapt to changes in waste composition and effectively improves the completeness of waste combustion. Attached Figure Description

[0042] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is an overall structural diagram of a waste feeding device for incineration power generation according to an embodiment of the present invention;

[0044] Figure 2 This is a flowchart illustrating a method for adjusting a waste feeding device for incineration power generation, provided in one embodiment of the present invention.

[0045] Figure 3 This is a flowchart illustrating a method for obtaining clusters according to an embodiment of the present invention.

[0046] Figure 4 A process flow diagram of a control process provided in one embodiment of the present invention;

[0047] Attached reference numerals: 1-Garbage grab bucket; 2-Dumping port; 3-Dumping platform; 4-Garbage pile; 5-Leachate collection pool; 6-Piston push rod; 7-Garbage temporary placement platform; 8-Reciprocating connecting rod; 9-L-type piston push head; 10-Air inlet module; 11-Air valve; 12-Slag outlet; 13-Ventilation nozzle; 14-Reciprocating grate; 15-Garbage hopper; 16-Flue outlet; 17-Evaporator; 18-Generator set; 19-Flow meter; 20-Electrochemical sensor; 21-Control module. Detailed Implementation

[0048] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a waste feeding device and adjustment method for incineration power generation according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0049] 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 this invention pertains.

[0050] The following description, in conjunction with the accompanying drawings, details a specific scheme for a waste feeding device and adjustment method for incineration power generation provided by the present invention.

[0051] Please see Figure 1 The diagram shows an overall structural diagram of a waste feeding device for incineration power generation according to an embodiment of the present invention.

[0052] The workflow of the waste feeding device used for incineration power generation is briefly described below:

[0053] The garbage truck stops at the dumping platform 3 and dumps the garbage into the garbage pile 4 through the dumping port 2. The garbage undergoes dry and wet separation in the garbage pile 4, and the leachate enters the leachate collection tank 5. A flow meter 19 is installed in the leachate collection tank 5 to acquire the leachate flow time-series data. The garbage grab bucket 1 grabs the drained garbage into the garbage hopper 15. The load power is recorded once each time the garbage grab bucket 1 grabs garbage. The garbage falls from the garbage hopper 15 onto the garbage temporary placement platform 7. The piston push rod 6 pushes the garbage on the garbage temporary placement platform 7 onto the reciprocating grate 14. At the same time, the L-shaped piston push head 9 connected to the piston push rod 6 seals the garbage hopper 15 to prevent garbage in the garbage hopper 15 from falling into the L-shaped piston. At the connection point between the pusher head 9 and the piston push rod 6; after the waste falls onto the reciprocating grate 14, the air intake module 10 provides air, the air valve 11 opens, and air is sprayed onto the reciprocating grate 14 through the ventilation nozzle 13, thereby providing the oxygen required for the waste to burn on the reciprocating grate 14. After the waste burns on the reciprocating grate 14, the reciprocating connecting rod 8 drives the reciprocating grate 14 to move, shaking the waste downwards to the slag outlet 12. The hot air generated by combustion heats the evaporator 17, generating high-temperature steam that enters the worm gear generator set in the generator set 18 to generate electricity. The air cooled by the evaporator 17 is discharged from the flue outlet 16. An electrochemical sensor 20 is installed in the flue outlet 16 to obtain carbon monoxide concentration time-series data.

[0054] Since the water in the garbage needs to be drained before it is incinerated, in each batch of garbage dumping, after the garbage is poured into the garbage pile 4 from the dumping port 2, it is necessary to wait for a period of time before the garbage grab bucket 1 sends the garbage into the garbage hopper 15. Therefore, the leachate flow rate value at each moment is recorded by the flow monitoring meter 19 from the start of garbage dumping (each moment in the embodiment of the present invention is every second). Furthermore, since the waste hopper 15 is used to catch waste falling from the waste grabber 1, it does not have the effect of storing large amounts of waste. If the waste on the waste temporary storage platform 7 is not promptly pushed into the reciprocating grate 14 by the piston pusher 6 before the next grabbing, it will cause the piston pusher 6 to be obstructed by waste and the waste hopper 15 to become clogged. Therefore, one push of the piston pusher 6 corresponds to one grabbing action of the waste grabber 1. Thus, the normalized load power of each grabbing action of the waste grabber 1 is used as the load coefficient for each grabbing action. During the time interval between two adjacent grabbing actions, the carbon monoxide concentration time-series data obtained by the electrochemical sensor 20 is used as the carbon monoxide concentration time-series data corresponding to the latter grabbing action. It should be noted that the carbon monoxide concentration time-series data for the first grabbing action is set as the carbon monoxide concentration time-series data between the start of waste dumping and the first grabbing action; and waste grabbing is divided into historical grabbing and current grabbing.

[0055] A control module 21 is also provided on the waste feeding device for incineration power generation. It includes a memory, a processor, and a computer program (not shown in the figure) stored in the memory and capable of running on the processor. The control module 21 can control the advancing speed of the piston push rod 6 and the shaking speed of the reciprocating grate 14 during the current grabbing by using the leachate flow time-series data, the load coefficient during waste grabbing, and the carbon monoxide concentration time-series data, thereby realizing the steps in a waste feeding device adjustment method for incineration power generation.

[0056] Please see Figure 2 The diagram illustrates a method flow chart of a waste feeding device adjustment method for incineration power generation according to an embodiment of the present invention, the method comprising the following steps:

[0057] Step S1: In each batch of waste dumping, obtain the time-series data of leachate flow, the load coefficient corresponding to each waste grabbing, and the time-series data of carbon monoxide concentration; waste grabbing includes historical grabbing and current grabbing.

[0058] Urban waste exhibits distinct classification characteristics, including waste from residential life, commercial activities, government offices, and industrial waste. Residential waste, collected by garbage trucks along the streets, mainly consists of paper, plastics, and kitchen waste, which contains a high amount of moisture. When the waste enters the landfill, more leachate is collected. Commercial activities use banners, decorative fabrics, etc., which are mostly high-density composite materials with higher calorific value and are prone to incomplete combustion. Waste from government offices is mainly paper, which is easily combustible and has a high calorific value. Industrial waste often contains a large amount of non-combustible materials, such as waste ceramics, glass, and metals, which produce lower calorific value when burned.

[0059] Therefore, in each batch of waste dumping, the leachate flow time series data from the start of waste dumping to the current moment can be obtained through the flow monitoring meter 19. During this period, there will be multiple waste grabs, and the carbon monoxide concentration time series data corresponding to each waste grab can be obtained by the electrochemical sensor 20. At the same time, each waste grab will also correspond to a load coefficient.

[0060] In each waste dumping batch, waste grabbing includes historical grabbing and current grabbing. In this embodiment of the invention, based on the time-series data of leachate flow rate in the waste dumping batch, as well as the time-series data of carbon monoxide concentration and load coefficient corresponding to each waste grabbing, the characteristics of the waste are analyzed, thereby controlling the feeding speed and shaking speed of the waste during the current waste grabbing, in order to improve the completeness of waste combustion.

[0061] It should be noted that the time series data is collected once per second. In other embodiments of the present invention, the collection frequency may be adjusted according to the implementation scenario, and is not limited here.

[0062] Step S2: Analyze the numerical variation characteristics and cumulative effect of leachate flow rate to determine the leachate characteristic value for each batch of waste dumping; combine the time variation characteristics of leachate flow rate for each batch of waste dumping with the load coefficient at each waste grabbing time to determine the waste density characteristic value at each waste grabbing time; analyze the numerical variation characteristics and cumulative effect of carbon monoxide concentration to determine the carbon monoxide characteristic value at each waste grabbing time.

[0063] In this embodiment of the invention, it is assumed that the types of waste delivered in the same batch are roughly fixed. Therefore, parameters such as the feeding rate of subsequent waste to be incinerated can be adjusted based on the characteristic conditions of the waste during combustion. Since the above-mentioned data is the time series data of leachate flow rate of the same batch of waste from the start of dumping to the current moment, analyzing the variation characteristics and cumulative effect of the leachate flow rate value of this batch of waste can reflect the physicochemical characteristics of this batch of waste, such as moisture content and organic matter content, and quantify them as leachate characteristic values, which serve as an indicator to characterize the waste.

[0064] Preferably, in one embodiment of the present invention, the method for obtaining leachate characteristic values ​​includes:

[0065] The moisture content in waste is reflected in the increase and total flow of leachate. Therefore, in the time series data of leachate flow, the difference between the maximum leachate flow value and the leachate flow value at the first moment is used as the leachate increase index. The larger the leachate increase index, the greater the moisture content in the waste.

[0066] Then, the total leachate flow rate is analyzed. In this embodiment of the invention, the concept of definite integral is used to normalize the value of the leachate flow rate time series data curve for each batch of waste dumping after performing definite integral calculation over time, thus obtaining the total leachate index. The upper limit of the definite integral is the current time, and the lower limit is the initial time (i.e., time 0). This normalization can be performed using the time length; that is, the value calculated by the definite integral is divided by the time length. A larger total leachate index indicates a higher moisture content in the waste.

[0067] Given that both the leachate increase index and the total leachate volume index are positively correlated with the waste moisture content, the normalized sum of these two indices is used as the leachate characteristic value for each batch of waste dumping. Based on the aforementioned analysis, a larger leachate characteristic value indicates a higher waste moisture content; therefore, the leachate characteristic value serves as an indicator of waste moisture content. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. Specific normalization methods are not limited here.

[0068] Furthermore, since the density and volume of garbage vary, larger garbage has larger pores. The lower the density (after draining), the larger the gaps between them, and the easier it is for water to drain out. Therefore, the leachate flow rate is higher. The density also affects the load power of the subsequent garbage grab bucket 1. Therefore, by combining the leachate flow rate of each garbage dumping batch with the load coefficient at each garbage grab, the garbage density characteristic value at each garbage grab was determined, which also serves as an indicator reflecting the characteristics of the garbage.

[0069] Preferably, in one embodiment of the present invention, the method for obtaining the waste density feature value includes:

[0070] In the leachate flow time-series data of each batch of waste dumping, the leachate flow value corresponding to the time period of each waste grabbing is extracted to obtain the target data segment. In this embodiment of the invention, the time period corresponding to each waste grabbing is also the time period corresponding to its carbon monoxide concentration time-series data.

[0071] The reason for extracting the target data segment is to perform spatiotemporal alignment, that is, to precisely bind the physical operation (grabbing) and the leachate response (flow change) on the time axis.

[0072] Then, within the target data segment, the absolute value of the difference between the maximum leachate flow rate and the leachate flow rate at the first time point is calculated, and the ratio of this ratio to the length of the target data segment is used. This ratio characterizes the maximum rate of change of the leachate flow rate; a larger value indicates a stronger response intensity, higher water content, greater porosity, and lower density. Therefore, this ratio is subjected to negative correlation mapping to correct the logical relationship, thereby obtaining the density factor at each waste grabbing time. A larger density factor indicates lower porosity and higher density in the waste. This negative correlation mapping can be achieved using the formula... ,in, Let x represent an exponential function with the natural constant e as the base, and let x represent the independent variable.

[0073] The load factor reflects the weight or volume of waste during each grab and can be used for mass compensation. The larger the load factor, the greater the impact on the density factor, which is considered to be the greater the degree of compaction of the waste, the smaller the pores, and the greater the density.

[0074] Finally, the normalized value of the product of the density factor and the load coefficient at each garbage collection point is used as the garbage density characteristic value for each collection point. Based on the foregoing analysis, a larger garbage density characteristic value indicates lower porosity and higher density in the garbage. Therefore, the garbage density characteristic value serves as an indicator of garbage density characteristics. Normalization is a technique well-known to those skilled in the art, and the normalization function can be linear normalization or standard normalization, etc. Specific normalization methods are not limited here.

[0075] Furthermore, each garbage grabbing corresponds to one push of the piston rod 6, which in turn corresponds to a carbon monoxide concentration time series data. When the garbage is pushed from the garbage temporary placement platform 7 onto the reciprocating grate 14, the carbon monoxide content will remain at an extremely low value when it is close to complete combustion. When it is not completely burned, the carbon monoxide concentration will increase, and the magnitude and concentration of the increase in carbon monoxide concentration can show its characteristics. Therefore, the numerical change and cumulative effect of carbon monoxide concentration value can be analyzed to determine the carbon monoxide characteristic value at each garbage grabbing time.

[0076] Preferably, in one embodiment of the present invention, the method for obtaining the characteristic value of carbon monoxide includes:

[0077] The combustion characteristics of waste can be characterized by the increase in carbon monoxide concentration and the total concentration. Waste collection is divided into historical collection and current collection. Since there is no corresponding time series data of carbon monoxide concentration for current collection, the time series data of carbon monoxide concentration for historical collection is analyzed first.

[0078] In the time series data of carbon monoxide concentration captured each time in history, the difference between the maximum carbon monoxide concentration value and the carbon monoxide concentration value at the first moment is used as the carbon monoxide concentration increase index. The larger the carbon monoxide concentration increase index, the greater the degree of increase in carbon monoxide concentration, indicating that there are more undegraded substances inside the garbage and that the combustion is not complete.

[0079] Then, the total carbon monoxide concentration is analyzed. The value obtained by performing a definite integral over time on the time-series data curve of carbon monoxide concentration for each historical capture is normalized to obtain a total carbon monoxide concentration index. The upper limit of the definite integral is the last moment of the carbon monoxide concentration time-series data, and the lower limit is the first moment. This normalization can also be performed using the time length; that is, the value calculated by the definite integral is divided by the time length. A higher total carbon monoxide concentration index indicates less complete waste combustion.

[0080] Finally, the product of the carbon monoxide concentration increase index and the total carbon monoxide concentration index is normalized and used as the carbon monoxide characteristic value for each historical capture. Based on the aforementioned analysis, it is known that the larger the carbon monoxide characteristic value, the less complete the waste combustion. In this case, the carbon monoxide characteristic value serves as an indicator characterizing the waste combustion characteristics. Normalization is a technique well-known to those skilled in the art. The normalization function can be linear normalization or standard normalization, etc., and the specific normalization method is not limited here.

[0081] It should be noted that the carbon monoxide characteristic value at the time of capture is a preset value. In this embodiment of the present invention, since there is no time series data of carbon monoxide concentration at the time of capture, the preset value is 0.

[0082] Thus, in this step, by analyzing the time series data of leachate flow, load coefficient, and carbon monoxide concentration, three indicators characterizing the waste characteristics (moisture content, density, and combustion characteristics) can be obtained, namely leachate characteristic value, waste density characteristic value, and carbon monoxide characteristic value.

[0083] Step S3: Based on leachate characteristic values, waste density characteristic values, and carbon monoxide characteristic values, perform cluster analysis on the number of waste grabbing times to obtain clusters; combine the numerical characteristics and distribution of carbon monoxide concentration values ​​from historical grabbing in the cluster to which the current grabbing time belongs to control the feeding speed and shaking speed of the waste under the current grabbing.

[0084] The incineration characteristics of the same batch of waste are similar. However, in order to improve the completeness of waste combustion, the characteristics of the waste can be used to perform cluster analysis on the number of times the waste is grabbed. This allows for more detailed classification of the same batch of waste, which in turn allows for more precise control of the feeding speed and shaking speed of the waste during the current grabbing process. This reduces the probability of problems such as unstable calorific value and incomplete combustion during the waste incineration process.

[0085] Since the three indicators describing the characteristics of the waste have been obtained in the aforementioned steps, cluster analysis can be performed on the number of waste collections using leachate characteristic values, waste density characteristic values, and carbon monoxide characteristic values ​​to obtain clusters.

[0086] Preferably, in one embodiment of the present invention, the method for obtaining clusters includes:

[0087] Please see Figure 3 The diagram illustrates a method flowchart for obtaining clusters according to an embodiment of the present invention, which includes the following steps:

[0088] Step S301: Based on the leachate characteristic value of each batch of waste dumping, the waste density characteristic value at each waste grabbing time, and the carbon monoxide characteristic value, determine the feature vector at each waste grabbing time.

[0089] The feature vector for each garbage grab is composed of the garbage density feature value, the carbon monoxide feature value, and the leachate feature value of the garbage dumping batch to which each garbage grab belongs.

[0090] Step S302: Determine the difference factor based on the similarity between the feature vectors of any two garbage collection operations.

[0091] Calculate the cosine similarity between feature vectors from any two spam crawls. The cosine similarity value ranges from -1 to 1; a higher value indicates higher similarity, and vice versa. For ease of subsequent calculation, the cosine similarity is negatively correlated to ensure all values ​​are non-negative, resulting in a difference factor. A larger difference factor indicates lower similarity between the feature vectors from the two spam crawls. This negative correlation mapping can be performed using the formula... , where x represents the independent variable.

[0092] Step S303: Perform cluster analysis on all garbage grabbing times based on the difference factor to obtain clusters.

[0093] Based on the difference factor between feature vectors during spam crawling and a preset K value, the K-means clustering algorithm is used to perform cluster analysis on all spam crawling times to obtain clusters. The optimal K value is obtained using the elbow method as the preset K value. It should be noted that both the elbow method and the K-means clustering algorithm are well-known techniques, and their specific processes will not be elaborated here.

[0094] At this point, cluster analysis can be performed on all the garbage grabbing times to obtain all the clusters, and the characteristics of the garbage grabbed in each cluster have a higher consistency.

[0095] When the piston pusher 6 is pushed during each incineration of the same batch of waste, the feeding speed and the dropping speed of the waste can be effectively adjusted by analyzing the characteristics of carbon monoxide during combustion. For example, a sharp increase in carbon monoxide concentration indicates that the feeding is too fast and the amount of combustion gas is insufficient. When the carbon monoxide concentration is too high, it is necessary to slow down the pushing speed of the piston pusher 6 to suppress the feeding speed and slow down the reciprocating motion speed of the reciprocating grate 14, thereby increasing the residence time of the waste on the reciprocating grate 14 so that it can be fully combusted. Furthermore, if it is necessary to control the feeding speed and dropping speed of the waste during the current waste grabbing process, the numerical characteristics and distribution of carbon monoxide concentration values ​​in historical grabbing processes with the same waste characteristics as the current waste grabbing process can be analyzed to adjust and control the feeding speed and dropping speed of the current grabbing process.

[0096] Preferably, in one embodiment of the present invention, the feeding speed and dropping speed of the waste under the current grab are controlled by comprehensively considering the numerical characteristics and distribution of carbon monoxide concentration values ​​from historical grabs within the cluster to which the current grab belongs, including:

[0097] Please see Figure 4 It illustrates a process flowchart of a control process in one embodiment of the present invention, which includes the following steps:

[0098] Step S311: Determine the control coefficient by comprehensively considering the numerical characteristics and distribution of historical carbon monoxide concentration values ​​captured in the cluster to which the current capture count belongs.

[0099] Based on the foregoing analysis, the higher the carbon monoxide concentration, the less complete the combustion, and the more necessary it is to adjust the feeding speed and the shaking speed. Therefore, in the cluster to which the current grabbing number belongs, the maximum value is selected from the time series data of carbon monoxide concentration in each historical grabbing, and the average of the maximum values ​​of carbon monoxide concentration in all historical grabbing time series data is used as the first control factor. The higher the first control factor, the less complete the waste combustion.

[0100] Then, the kurtosis of the maximum carbon monoxide concentration value in all historically captured time-series carbon monoxide concentration data is calculated. The kurtosis value is normalized to obtain the second control factor. The kurtosis value is used to represent the degree of data clustering. The larger the value, the higher the degree of clustering. Therefore, the larger the kurtosis value, the larger the second control factor, indicating that the carbon monoxide in the waste incineration in this cluster is more singular and the characteristics are more obvious.

[0101] Obtain the fitted straight line of the first preset number of carbon monoxide concentration values ​​in the time series data of each historically captured carbon monoxide concentration in the cluster to which the current capture number belongs. Then, normalize the slope value of the fitted straight line and use it as the second control factor. The larger the slope value of the fitted straight line, the more likely the carbon monoxide concentration will increase in the early stage of combustion. That is, the waste will not be completely burned in the early stage of combustion, so more control is needed.

[0102] Finally, the product of the first, second, and third control factors is normalized to obtain the control parameter. The value range of the control parameter is 0 to 1. In this embodiment of the invention, the sum of the control parameter and the preset parameter is used as the control coefficient. The preset parameter is set to 0.5. At this time, the value range of the control coefficient is 0.5 to 1.5. When the control coefficient is 1, it is considered that the degree of waste combustion is in an intermediate state, and the subsequent feeding speed and shaking speed are moderately controlled. The larger the control coefficient, the less complete the waste combustion.

[0103] It should be noted that the least squares method can be used to obtain the fitted line, which is a well-known technique, and the specific process will not be elaborated here. In this embodiment of the invention, the preset first quantity is 20, and the specific value can be adjusted according to the implementation scenario, and is not limited here.

[0104] Step S312: Adjust the preset feeding speed using the control coefficient to obtain the adjusted feeding speed for the current gripping operation.

[0105] A larger control coefficient indicates lower waste combustion sufficiency, thus requiring a reduction in the feed rate. Therefore, the value of the control coefficient after negative correlation mapping is used as the feed adjustment factor. In this embodiment of the invention, given that the control coefficient ranges from 0.5 to 1.5, the negative correlation mapping here uses the formula... , where x represents the independent variable, and the number 2 is used to prevent over-adjustment from occurring later.

[0106] If the control coefficient is greater than 1, the feeding speed needs to be reduced. The value of the feeding adjustment factor after negative correlation mapping ranges from 0.5 to 1. The greater the control coefficient is greater than 1, the greater the reduction in feeding speed, and the closer the value of the feeding adjustment factor is to 0.5. Conversely, if the control coefficient is less than 1, the feeding speed can be appropriately increased. The value of the feeding adjustment factor after negative correlation mapping ranges from 1 to 1.5. The greater the control coefficient is less than 1, the greater the increase in feeding speed, and the closer the value of the feeding adjustment factor is to 1.5. Therefore, the product of the feeding adjustment factor and the preset feeding speed is used as the adjusted feeding speed for the current grabbing. This adjusted feeding speed is more suitable for the characteristics of the garbage during the current garbage grabbing, thereby effectively improving combustion completeness.

[0107] Step S313: Adjust the preset shaking speed using the control coefficient to obtain the adjusted shaking speed during the current grasping process.

[0108] A larger control coefficient indicates lower waste combustion sufficiency, necessitating a reduction in the shoveling speed to increase the waste's combustion time on the reciprocating grate 14. Therefore, the value of the control coefficient after negative correlation mapping is used as the shoveling adjustment factor. In this embodiment of the invention, given that the control coefficient ranges from 0.5 to 1.5, the negative correlation mapping here still uses the formula... Here, x represents the independent variable. The use of the number 2 is to prevent over-adjustment. If the adjustment coefficient is greater than 1, the shaking speed needs to be reduced. The shaking adjustment factor after negative correlation mapping ranges from 0.5 to 1. The greater the degree of the adjustment coefficient being greater than 1, the greater the degree of reduction in the shaking speed, and the closer the value of the shaking adjustment factor is to 0.5. Conversely, if the adjustment coefficient is less than 1, the shaking speed can be appropriately increased to increase combustion efficiency. The shaking adjustment factor after negative correlation mapping ranges from 1 to 1.5. The greater the degree of the adjustment coefficient being less than 1, the greater the degree of increase in the shaking speed, and the closer the value of the shaking adjustment factor is to 1.5. Therefore, the product of the shaking adjustment factor and the preset shaking speed is used as the adjusted shaking speed during the current grabbing. This adjusted shaking speed is more suitable for the characteristics of the garbage during the current garbage grabbing, thereby effectively improving combustion completeness.

[0109] It should be noted that the preset feeding speed and preset dropping speed can be obtained according to the implementation scenario, and specific values ​​will not be given as examples here.

[0110] In summary, different types of waste have different characteristics. Therefore, analyzing the moisture content and combustion status of waste in each batch of waste disposal allows for adaptive and precise control of the feeding and discharging speeds. First, time-series data of leachate flow rate for each batch is acquired to reflect the moisture content. Load coefficient and carbon monoxide concentration time-series data for each waste grabbing operation are also obtained; the load coefficient reflects density characteristics, and the carbon monoxide concentration reflects combustion status. Further, analyzing the variation characteristics and cumulative effects of leachate flow rate quantifies the moisture content characteristics of each batch. By fusing leachate flow rate time-series data with the load coefficient, the density characteristics of each grabbing operation are quantified. Analyzing the numerical variation characteristics and cumulative effects of carbon monoxide concentration quantifies the combustion characteristics of each grabbing operation. Then, based on the indicators characterizing these three features, the number of grabbing operations is clustered, ensuring that each cluster has a relatively consistent waste characteristic. Finally, when waste combustion is incomplete, the feeding speed and the swaying speed should be reduced to ensure complete combustion. Therefore, by adaptively generating a control strategy based on the carbon monoxide concentration distribution characteristics of the historical grabbing times in the cluster to which the current grabbing time belongs, the feeding speed and swaying speed of the waste under the current grabbing time are adjusted to replace the traditional control mode with fixed operating parameters. This allows the waste feeding device to autonomously adapt to changes in waste composition and effectively improves the completeness of waste combustion.

[0111] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. A method for adjusting a refuse feed device for incineration power generation, characterized by, The method comprises: In each garbage dumping batch, obtain garbage leachate flow time series data, load coefficient corresponding to each garbage grabbing, and carbon monoxide concentration time series data; garbage grabbing includes historical grabbing and current grabbing; Analyze the numerical change characteristics and cumulative effect of the leachate flow value, determine the leachate characteristic value of each garbage dumping batch; combine the change characteristics of the leachate flow value in time with the load coefficient at each garbage grabbing, determine the garbage density characteristic value at each garbage grabbing; analyze the numerical change characteristics and cumulative effect of the carbon monoxide concentration value, determine the carbon monoxide characteristic value at each garbage grabbing; Based on the leachate characteristic value, the garbage density characteristic value and the carbon monoxide characteristic value, cluster analysis is performed on the garbage grabbing times to obtain a cluster; the numerical characteristics and distribution of the carbon monoxide concentration value of the historical grabbing in the cluster to which the current grabbing times belong are comprehensively considered to control the feeding speed and shaking-off speed of the garbage in the current grabbing.

2. The method for adjusting the waste feed device for incineration power generation according to claim 1, characterized in that, The method for obtaining the leachate characteristic value comprises: In the garbage leachate flow time series data, the difference between the maximum leachate flow value and the leachate flow value at the first time is taken as a leachate increase amplitude index; The value obtained by performing definite integral calculation on the curve of the garbage leachate flow time series data of each garbage dumping batch in time is normalized to obtain a leachate total amount index; The value obtained by normalizing the sum of the leachate increase amplitude index and the leachate total amount index is taken as the leachate characteristic value of each garbage dumping batch.

3. The method of claim 1, wherein the method further comprises: The method for obtaining the garbage density characteristic value comprises: In the garbage leachate flow time series data of each garbage dumping batch, the garbage leachate flow value at the corresponding time period of each garbage grabbing is intercepted to obtain a target data segment; In the target data segment, the absolute value of the difference between the maximum leachate flow value and the garbage leachate flow value at the first time is negatively correlated with the length of the target data segment to obtain a density factor at each garbage grabbing; The product of the density factor at each garbage grabbing and the load coefficient is normalized to obtain the garbage density characteristic value at each garbage grabbing.

4. The method of claim 1, wherein the method further comprises: The method for obtaining the carbon monoxide characteristic value comprises: In the carbon monoxide concentration time series data at each historical grabbing, the difference between the maximum carbon monoxide concentration value and the carbon monoxide concentration value at the first time is taken as a carbon monoxide concentration increase amplitude index; The value obtained by performing definite integral calculation on the curve of the carbon monoxide concentration time series data at each historical grabbing in time is normalized to obtain a carbon monoxide concentration total amount index; The product of the carbon monoxide concentration increase amplitude index and the carbon monoxide concentration total amount index is normalized to obtain the carbon monoxide characteristic value at each historical grabbing; The carbon monoxide characteristic value at the current grabbing is a preset value.

5. The method of claim 1, wherein the method further comprises: The method for obtaining the cluster comprises: Based on the leachate characteristic value of each garbage dumping batch, the garbage density characteristic value at each garbage grabbing and the carbon monoxide characteristic value, a feature vector at each garbage grabbing is determined; Calculate the cosine similarity between the feature vectors at any two times of garbage grabbing and perform negative correlation mapping processing as a difference factor; Based on the difference factor between the feature vectors at the time of garbage grabbing and the preset K value, the K-means clustering algorithm is used for clustering analysis of all garbage grabbing times to obtain a clustering cluster, wherein the optimal K value obtained based on the elbow method is used as the preset K value.

6. A method of adjusting a waste feed arrangement for incineration power generation according to claim 5, wherein, The feature vector acquisition method comprises: The feature vector at each garbage grabbing time is composed of the corresponding garbage density feature value, carbon monoxide feature value, and leachate feature value of the garbage dumping batch to which each garbage grabbing belongs.

7. The method of claim 1, wherein the method further comprises: The numerical characteristics and distribution of the historical carbon monoxide concentration values in the clustering cluster to which the current grabbing number belongs are integrated to control the feeding speed and shaking-off speed of the garbage under the current grabbing, including: The numerical characteristics and distribution of the historical carbon monoxide concentration values in the clustering cluster to which the current grabbing number belongs are integrated to determine a regulation coefficient; The value of the regulation coefficient after negative correlation mapping is used as a feeding adjustment factor, and the product of the feeding adjustment factor and the preset feeding speed is used as the adjusted feeding speed at the current grabbing time. The preset shaking-off speed is adjusted using the regulation coefficient to obtain the adjusted shaking-off speed at the current grabbing time.

8. A method of adjusting a waste feed arrangement for incineration power generation according to claim 7, characterised in that, The regulation coefficient acquisition method comprises: In the clustering cluster to which the current grabbing number belongs, the mean value of the maximum value of the carbon monoxide concentration in all historical carbon monoxide concentration time series data is used as a first regulation factor; The kurtosis value of the maximum value of the carbon monoxide concentration value in all historical carbon monoxide concentration time series data is normalized to obtain a second regulation factor; The fitting straight line of the first preset number of carbon monoxide concentration values on the time series in each historical carbon monoxide concentration time series data in the clustering cluster to which the current grabbing number belongs is obtained, and the value of the slope of the fitting straight line after normalization is used as a second regulation factor; The product of the first regulation factor, the second regulation factor, and the third regulation factor after normalization is used as the sum of the preset parameters, which is used as the regulation coefficient, wherein the preset parameter is set to 0.

5.

9. The method of claim 7, wherein the method further comprises: The adjustment shaking-off speed acquisition method comprises: The value of the regulation coefficient after negative correlation mapping is used as a shaking-off adjustment factor, and the product of the shaking-off adjustment factor and the preset shaking-off speed is used as the adjusted shaking-off speed at the current grabbing time.

10. A waste feed arrangement for incineration power generation, comprising at least a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the garbage feeding device adjustment method for waste incineration power generation according to any one of claims 1-9.

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