CN112999835B - Gypsum quality on-line monitoring method for limestone-gypsum wet desulphurization process - Google Patents
Gypsum quality on-line monitoring method for limestone-gypsum wet desulphurization process Download PDFInfo
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Abstract
The invention discloses a gypsum quality online monitoring method for a limestone-gypsum wet desulphurization process, which is applied to a vacuum belt dehydration system of desulphurization slurry. The image acquisition module captures material images of the tail part of the belt of the vacuum belt dehydrator and the blanking area. The image analysis module analyzes the image captured by the image acquisition module through an image analysis algorithm to obtain the color and the appearance characteristics of the material. The gypsum quality evaluation algorithm module takes the output of the image analysis module as an input condition and outputs the evaluation result of the gypsum quality. The user service module presents the information and the results of the image acquisition module, the image analysis module and the gypsum quality evaluation algorithm module to a user and provides a user interaction function. The invention indirectly judges whether the operation condition in the desulfurizing tower is abnormal or not by monitoring the state of the gypsum product generated by wet desulphurization in real time.
Description
Technical Field
The invention belongs to the field of limestone-gypsum wet flue gas desulfurization, and particularly relates to an online gypsum quality monitoring method for a limestone-gypsum wet flue gas desulfurization process.
Background
At present, most coal-fired power plants adopt a limestone-gypsum wet desulphurization system to remove sulfur dioxide in boiler flue gas. Spraying slurry in the absorption tower to absorb sulfur dioxide in the flue gas, reacting and oxidizing to generate calcium sulfate dihydrate (gypsum) crystals, pumping the slurry containing the gypsum crystals out of the tower by a slurry discharge pump, and delivering the underflow of the slurry after concentration by a cyclone to a vacuum belt dehydrator for dehydration to obtain dehydrated gypsum. The qualified dehydrated gypsum can be sold and continuously utilized, has better recycling economic value, is difficult to continuously utilize the unqualified dehydrated gypsum, loses economic added value, forms solid waste products, and has high environmental protection disposal cost.
As one of the key products of the limestone-gypsum wet desulphurization process system, the state of the gypsum discharged by the vacuum belt dehydrator can also reflect whether the desulphurization system is in normal operation. For example, when the concentration of sulfur dioxide in raw flue gas is significantly increased by coal quality, operators often control the clean flue gas sulfur dioxide not to exceed the standard by increasing the supply of limestone slurry, but this measure is often out of control and results in the slurry "poisoning" (also called limestone blind area), the slurry is difficult to dewater due to the excessive content of calcium sulfite and other components in the slurry, and the discharge of the vacuum belt dewaterer is in a slurry state (normally, the water content of the dewatering product is not more than 10%, and the dewatering product is in a cake or block shape with soft and hard properties). For example, when the slurry retention time is insufficient at a low level in the slurry tank and the gypsum crystals formed are needle-like, flaky or fine particles, the pores of the filter cloth of the vacuum belt dehydrator are easily clogged to cause difficulty in dehydration, and the discharged material is in a slurry state. For example, if the slurry contains a large amount of impurities such as fly ash, metal ions, acid-insoluble substances, and oil stains, the appearance and morphology of the dehydrated gypsum product are also changed, and the slurry shows abnormalities in properties, softness, color, and the like.
At present, the technologies similar to the field are not found through retrieval, and the gypsum quality monitoring of the limestone-gypsum wet desulphurization system of the coal-fired power plant adopts the means of manual on-site inspection visual inspection, remote video monitoring manual visual inspection, on-site sampling and then laboratory testing and the like. The visual inspection means judges whether the quality of the gypsum is normal or not by depending on the experience of people, the judgment process and the result are difficult to quantify, the repeatability is poor, and the method cannot be continuously carried out on line due to the energy and the brain of people. The means of testing after on-site sampling has high accuracy and good repeatability, but consumes long time and can only be implemented discontinuously.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide an on-line gypsum quality monitoring method for a limestone-gypsum wet desulphurization process.
The technical scheme adopted by the invention for solving the problems is as follows: the gypsum quality on-line monitoring method for the limestone-gypsum wet desulphurization process is applied to a vacuum belt dehydration system of desulphurization slurry, and is characterized in that the monitoring system comprises an image acquisition module, an image analysis module, a gypsum quality evaluation algorithm module and a user service module.
The image acquisition module comprises a camera and a light source and is used for capturing material images of the tail part of the belt of the vacuum belt dehydrator and the blanking area.
The image analysis module analyzes the image captured by the image acquisition module through an image analysis algorithm to obtain the color and the appearance characteristics of the material.
The gypsum quality evaluation algorithm module takes the output of the image analysis module as an input condition and outputs the evaluation result of the gypsum quality.
And the user service module presents the information and the results of the image acquisition module, the image analysis module and the gypsum quality evaluation algorithm module to a user and provides a user interaction function.
The material color and appearance characteristics include (but are not limited to) color, crack length, size of material, slenderness ratio, area.
The online evaluation of the gypsum quality by the gypsum quality evaluation algorithm module comprises the following steps:
1. the method for calculating the water content of the gypsum according to the appearance characteristics of the material is realized by the following steps:
1) under the stable working condition, the image acquisition module captures an image of the material, and the image analysis module analyzes the image to obtain the appearance characteristic of the material;
2) sampling the material, and testing to obtain the water content of the gypsum;
3) adjusting working conditions to change the water content of the gypsum, and repeating the steps 1) to 2) to establish a data set of material appearance characteristics and corresponding water content of the gypsum;
4) based on the data set in the step 3), establishing a mapping model for calculating the water content of the gypsum according to the appearance characteristics of the material by adopting a machine learning method;
5) inputting the shape characteristics of the material under the condition of unknown gypsum water content into the mapping model in the step 4), and outputting the calculated gypsum water content value by the mapping model.
2. The method for evaluating the quality of gypsum according to the color characteristics of the material is realized by the following steps:
1) establishing a correlation data set of the color characteristics and the quality of the gypsum (approach one: the image acquisition module captures a material image in the actual production process, the image analysis module analyzes the image to obtain the color characteristics of the material, and marks that the gypsum quality is normal/abnormal are given according to the production practice experience; and (2) a second way: collecting gypsum pictures and corresponding normal/abnormal quality marks in literature data in the technical field, and analyzing the pictures by an image analysis module to obtain the color characteristics of the materials);
2) based on the data set in the step 1), adopting a machine learning method to establish a mapping model for calculating the normality/abnormality of the gypsum quality according to the color characteristics of the material;
3) inputting the color characteristics of the materials under a certain working condition into the mapping model in the step 2), and outputting an inference result that the gypsum quality is normal/abnormal by the mapping model.
The gypsum quality on-line monitoring method comprises the following steps:
1) the image acquisition module captures a material image, and the image analysis module analyzes the image to acquire color characteristics and appearance characteristics of the material;
2) the gypsum quality evaluation algorithm module gives out a gypsum water content calculation result according to the appearance characteristics of the material and judges whether the gypsum water content is normal or abnormal, the gypsum water content meets or exceeds the related technical standard and the specified limit value, and the gypsum quality is judged to be normal/abnormal;
3) the gypsum quality evaluation algorithm module gives an inference result that the gypsum quality is normal/abnormal according to the color characteristics of the materials;
4) comprehensively evaluating the gypsum quality inference results given in the steps 2) and 3), wherein the gypsum quality inference results are normal, the gypsum quality inference result is normal, otherwise, the gypsum quality inference result is abnormal;
5) the user service module presents information such as material images, color characteristics, appearance characteristics, gypsum quality evaluation results and the like to a user through a graphical interface, and provides user interaction functions including (but not limited to) retrieval, storage and the like of images and data.
When the gypsum quality is abnormal, the user service module reminds the user in a software box, sound, light word plate and other modes.
The technical principle of the invention is as follows:
(1) the material appearance characteristics at the tail end of the vacuum belt conveyor are directly related to the water content of the material. When the quality of the dehydrated slurry is normal and the vacuum dehydration system works normally, the moisture in the slurry is gradually removed along with the movement on the belt of the vacuum dehydrator. When the material is run to the end of the vacuum dewatering machine, the water content of the material on the belt is very low (generally not more than 10%) and is in a 'filter cake' state. As the filter cake travels closer to the end roll of the belt, cracks develop as the curvature of the belt increases. The cracks are generally longer and, as the movement on the belt continues, the cracks extend from the upper surface to the lower surface of the filter cake, with increasingly sharp boundaries between filter cake pieces until they are no longer connected. After the filter cake continuously moves to the lower edge of the slope at the tail end of the belt, the filter cake blocks are separated from the belt and freely fall down, the filter cake blocks are in a cake shape or a strip shape with larger size and thinner thickness, and the edges are irregular. When slurry poisoning or abnormal work of a vacuum dehydration system occurs, the slurry is difficult to dehydrate, and the slurry still presents a slurry shape and has high water content when moving to the tail end of the dehydrator, so that the slurry still approaches a fluid state, the surface of the material does not have cracks, and the falling objects present a bulk or drop shape and have small size. Therefore, the moisture content of the material at the tail end of the vacuum belt conveyor can be inferred by monitoring the shape characteristics of the material. The traditional water content depends on laboratory manual test, usually takes hours, and is difficult to implement continuously due to manpower limitation.
(2) The color of the normal dehydrated gypsum product is close to earthy yellow, and the abnormal color usually represents the quality of the product and the abnormal process in the absorption tower of wet desulphurization. For example, when the concentration of fly ash in flue gas entering the absorption tower is high and the carbon content of fly ash is high, the dehydrated gypsum product has gray or even black color due to the high carbon content, and when the flue gas contains oil, the dehydrated gypsum product can also have black color. When the limestone raw material used for reaction contains non-ferrous metal ion impurities and the fly ash impurity components in the slurry pool are more, the dehydrated gypsum product can also present the difference of color or brightness. The abnormal color gypsum affects its sale and secondary use due to appearance, composition factors.
Compared with the prior art, the invention has the following advantages and effects: according to the method, a quantitative model is established according to the characteristics such as color, appearance and the like which can be quickly obtained and the relation between the characteristics and the gypsum quality, whether the quality of the dehydrated gypsum of the limestone-gypsum wet desulphurization system is normal or not is judged, and the problems that the prior art means mainly depends on manual implementation and can not give consideration to both quantification and online monitoring are solved. The quality of the gypsum can also reflect whether the integral operation state of the limestone-gypsum wet desulphurization system is normal or not, so the invention also provides an auxiliary means for monitoring the integral operation state of the limestone-gypsum wet desulphurization system on line, which is beneficial for operators to find out the abnormal operation in the upstream link of the gypsum dehydration of the wet desulphurization system in time.
Drawings
Fig. 1 is a schematic diagram of an industrial camera arrangement.
Fig. 2 is a system work flow diagram.
FIG. 3 is a comparison graph of normal (upper) and abnormal (lower) water content images of gypsum.
FIG. 4 is a graph showing the relationship between the water content of gypsum and the length of cracks.
FIG. 5 is a schematic representation of the relationship between the moisture content of the gypsum and the size of the falling material.
FIG. 6 is a schematic diagram of the principle of regression of the relationship between water content and fracture length and size of falling material using a Back Propagation Neural Network (BPNN).
Detailed Description
The present invention will be described in further detail below by way of examples with reference to the accompanying drawings, which are illustrative of the present invention and are not to be construed as limiting the present invention.
Example 1.
Coal quality of a certain plant is changed frequently, load is lifted frequently, the phenomenon of sudden jump of the sulfur dioxide in the raw flue gas occurs, and the slurry poisoning of the absorption tower occurs frequently. In this example, the length of the crack of the dehydrated product at the end of the vacuum belt dehydrator and the size of the falling product were monitored to calculate the water content of the dehydrated gypsum.
The process of this embodiment is:
(1) shooting photo images of a filter cake at the tail end of the vacuum belt dehydrator and a falling object by an industrial camera of the image acquisition module;
(2) the image acquisition module transmits the image to the image analysis module, and the image analysis module respectively acquires the length of a filter cake crack and the size data of a falling object through an image recognition algorithm and counts to obtain respective average values;
(3) sampling the dehydrated gypsum manually on site, and testing to obtain the water content of the gypsum;
(4) adjusting the working condition to change the water content of the gypsum, repeating the steps (1) to (3), and establishing a data set (the change relationship of the data set is shown in figures 4 and 5) of the crack length, the size of the falling object and the corresponding water content of the gypsum;
(5) based on the data set in the step (3), performing nonlinear regression (as shown in fig. 6) on the quantitative relation among the water content, the crack length and the size of the falling material by adopting a Back Propagation Neural Network (BPNN), so as to establish a mapping model for calculating the water content of the gypsum according to the crack length and the size of the falling material, and integrating the mapping model into a gypsum quality evaluation algorithm module;
(6) the system enters a continuous monitoring state, namely, the steps (1) to (2) are executed, the crack length and the size of the falling material obtained in the step (2) are input into a gypsum quality evaluation algorithm module, the latter calculates a gypsum water content value, and compares the gypsum water content value with a qualified gypsum water content limit value, the gypsum water content is higher than the qualified gypsum limit value, the gypsum is unqualified in water content (or called abnormal), otherwise, the gypsum is qualified (normal);
(7) the user service module reads an original image from the image acquisition module, reads a post-processing image marked with cracks and falling materials from the image analysis module, reads a gypsum water content calculation value and a water content normal/abnormal state identifier from the gypsum quality evaluation algorithm module, presents the gypsum water content calculation value and the water content normal/abnormal state identifier to operating personnel through a webpage interface, and provides gypsum quality monitoring information for the operating personnel.
Example 2.
Referring to example 1, the difference is that a support vector machine model, a random forest model or a multi-independent variable nonlinear fitting method is adopted to perform nonlinear regression on the quantitative relation between the water content and the crack length and the size of the falling material, so as to establish a mapping model for calculating the water content of the gypsum according to the crack length and the size of the falling material.
Example 3.
Referring to example 1, the difference is that this example monitors the color of the dehydrate at the end of the vacuum belt dehydrator to estimate whether the dehydrated gypsum is abnormal.
The process of this embodiment is:
(1) shooting photo images of a filter cake at the tail end of the vacuum belt dehydrator and a falling object by an industrial camera of the image acquisition module;
(2) the image acquisition module transmits the image to the image analysis module, and the image analysis module respectively acquires color RGB data of a filter cake through an image recognition algorithm, converts the RGB format into an HSL format, and counts to acquire an average value of respective components;
(3) sampling the dehydrated gypsum manually on site, and judging whether the gypsum is abnormal or not through testing or according to production practice experience;
(4) adjusting working conditions to change the gypsum components, repeating the steps (1) to (3), and establishing a data set of HSL components and corresponding gypsum water content;
(5) based on the data set in the step (3), an automatic machine learning tool/software is adopted to carry out regression on the quantitative relation between the impurity concentration and the type, so that a mapping model for calculating the normal/abnormal gypsum quality according to the color component value is established, and the mapping model is integrated into a gypsum quality evaluation algorithm module;
(6) the system enters a continuous monitoring state, namely, the steps (1) to (2) are executed, the HSL obtained in the step (2) is input into a gypsum quality evaluation algorithm module, the latter calculates the failure type and the strength of the gypsum, compares the failure type and the strength with a qualified gypsum limit value, and sets a qualified threshold value; if the gypsum content is higher than the qualified limit value, the gypsum is abnormal, otherwise, the gypsum is qualified (normal);
(7) the user service module reads the original image from the image acquisition module, reads the post-processing image marked with normal/abnormal gypsum quality from the image analysis module, and presents the post-processing image to the operator through a webpage interface to provide gypsum quality monitoring information for the operator.
Example 4.
Referring to embodiment 3, the difference is that RGB components are directly used for image analysis, a gypsum state picture under normal/abnormal gypsum quality is obtained through scientific literature, and machine learning is performed on the picture and an abnormal type by using cloud commercial deep learning software, so that a mapping model for estimating the normal/abnormal gypsum quality according to color is established.
Those not described in detail in this specification are well within the skill of the art.
Although the present invention has been described with reference to the above embodiments, it should be understood that the scope of the present invention is not limited thereto, and that various changes and modifications can be made by those skilled in the art without departing from the spirit and scope of the present invention.
Claims (2)
1. A gypsum quality on-line monitoring method for limestone-gypsum wet desulphurization process is applied to a vacuum belt dehydration system of desulphurization slurry, and is characterized in that the monitoring system comprises an image acquisition module, an image analysis module, a gypsum quality evaluation algorithm module and a user service module;
the image acquisition module comprises a camera and a light source and is used for capturing material images of the tail part of the belt of the vacuum belt dehydrator and the blanking area;
the image analysis module analyzes the image captured by the image acquisition module through an image analysis algorithm to obtain the color and the appearance characteristics of the material;
the gypsum quality evaluation algorithm module takes the output of the image analysis module as an input condition and outputs the evaluation result of the gypsum quality;
the user service module presents the information and the results of the image acquisition module, the image analysis module and the gypsum quality evaluation algorithm module to a user and provides a user interaction function;
the online evaluation of the gypsum quality by the gypsum quality evaluation algorithm module comprises the following steps:
1. the method for calculating the water content of the gypsum according to the appearance characteristics of the material is realized by the following steps:
1) under the stable working condition, the image acquisition module captures an image of the material, and the image analysis module analyzes the image to obtain the appearance characteristic of the material;
2) sampling the material, and testing to obtain the water content of the gypsum;
3) adjusting working conditions to change the water content of the gypsum, and repeating the steps 1) to 2) to establish a data set of material appearance characteristics and corresponding water content of the gypsum;
4) based on the data set in the step 3), establishing a mapping model for calculating the water content of the gypsum according to the appearance characteristics of the material by adopting a machine learning method;
5) inputting the shape characteristics of the material under the condition of unknown gypsum water content into the mapping model in the step 4), and outputting the calculated gypsum water content value by the mapping model;
2. the method for evaluating the quality of gypsum according to the color characteristics of the material is realized by the following steps:
1) establishing a correlation data set of the color characteristics and the quality of the gypsum: the first way is as follows: the image acquisition module captures a material image in the actual production process, the image analysis module analyzes the image to obtain the color characteristics of the material, and marks that the gypsum quality is normal/abnormal are given according to the production practice experience; and (2) a second way: collecting gypsum pictures and corresponding normal/abnormal quality marks in literature data in the technical field, and analyzing the pictures by an image analysis module to obtain the color characteristics of the materials;
2) based on the data set in the step 1), adopting a machine learning method to establish a mapping model for calculating the normality/abnormality of the gypsum quality according to the color characteristics of the material;
3) inputting the color characteristics of the materials under a certain working condition into the mapping model in the step 2), and outputting an inference result that the gypsum quality is normal/abnormal by the mapping model;
the gypsum quality online monitoring method comprises the following steps:
1) the image acquisition module captures a material image, and the image analysis module analyzes the image to acquire color characteristics and appearance characteristics of the material;
2) the gypsum quality evaluation algorithm module gives out a gypsum water content calculation result according to the appearance characteristics of the material and judges whether the gypsum water content is normal or abnormal, the gypsum water content meets or exceeds the related technical standard and the specified limit value, and the gypsum quality is judged to be normal/abnormal;
3) the gypsum quality evaluation algorithm module gives an inference result that the gypsum quality is normal/abnormal according to the color characteristics of the materials;
4) comprehensively evaluating the gypsum quality inference results given in the steps 2) and 3), wherein the gypsum quality inference results are normal, the gypsum quality inference result is normal, otherwise, the gypsum quality inference result is abnormal;
5) the user service module presents the information to the user through a graphical interface and provides a user interaction function.
2. The method of claim 1, wherein the physical characteristics of the material include crack length, and size, slenderness ratio, and area of the material.
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