A method for preparing dioxin adsorbent by modifying composite clay minerals

Abstract

The present invention belongs to the field of data acquisition and intelligent production technology, and proposes a novel method for preparing a dioxin adsorbent by modifying a composite clay mineral, which is specifically as follows: clay minerals are obtained from several mineral sources for pretreatment, and then the clay minerals are mixed with additives, and then modified to form a second mixture, and the second mixture is transformed in shape and structure to obtain a dioxin adsorbent; then a sample is obtained from the dioxin adsorbent as an adsorbent sample, and the adsorption performance of the adsorbent sample is tested by a hyperspectral imager to obtain a mineral source evaluation; finally, if the mineral source evaluation meets the requirements, the dioxin adsorbent modified by the composite clay mineral is selected, otherwise the mineral source is excluded. Combined with the problem that the adsorbent is prone to failure, the equilibrium loss phenomenon is effectively captured, and the quantitative accuracy of the adsorption performance of the dioxin adsorbent is further improved, and the production efficiency and product quality of the dioxin adsorbent are further improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of data collection and intelligent production, and specifically relates to a method for preparing a dioxin adsorbent by modifying a novel composite clay mineral. Background Art

[0002] The dioxin adsorbent prepared by modifying composite clay minerals is a material used to remove dioxin pollutants. It is usually prepared by modifying natural clay minerals or synthetic clay minerals with some chemical substances (such as organic matter, inorganic matter, etc.). These modified substances can increase the surface activity, specific surface area and adsorption capacity of clay minerals, thereby improving their adsorption effect on dioxins. The dioxin adsorbent prepared by modifying composite clay minerals has the advantages of high adsorption efficiency, high number of reuses, and simple preparation process. Therefore, it has broad application prospects in water treatment, wastewater treatment and other fields.

[0003] However, when making dioxin adsorbents, before the composite clay mineral modification process, since the source of the obtained minerals is uncertain, screening based on a certain chemical property alone is not enough to select the most preferred mineral raw materials, and the performance of the adsorbent is closely related to the natural properties of the minerals. There are more than 200 types of dioxin compounds currently recorded, and adsorbents composed of natural minerals with different structures have different adsorption preferences for different compounds, that is, the selectivity of the adsorbents is different. When mixed with multiple compounds, if the adsorbent cannot efficiently select dioxin compounds for adsorption, the effect of the adsorbent is greatly reduced. At the same time, clay minerals from different mineral sources have different purities, physical structures, and impurity contents. Therefore, it is not possible to simply quantify that some other compounds with similar functionalities to montmorillonite can also have a positive or negative impact on the adsorption effect. Therefore, a set of test methods based on the adsorption effect of dioxin adsorbents is set to intelligently select the mineral raw materials in the preparation of dioxin adsorbents to ensure that the dioxin adsorbents prepared by the modification of new composite clay minerals meet the production standards. Summary of the invention

[0004] The purpose of the present invention is to propose a novel method for preparing a dioxin adsorbent by modifying a composite clay mineral, so as to solve one or more technical problems existing in the prior art and at least provide a beneficial choice or create conditions.

[0005] In order to achieve the above object, according to one aspect of the present invention, a method for preparing a dioxin adsorbent by modifying a novel composite clay mineral is provided, the method comprising the following steps:

[0006] S1, obtaining clay minerals from a plurality of mineral sources, pre-treating the clay minerals; mixing the clay minerals with additives to form a first mixture; modifying the first mixture to form a second mixture; modifying the shape and structure of the second mixture to form dioxin adsorbents according to different mineral sources;

[0007] S2, obtaining a sample from the dioxin adsorbent as an adsorbent sample;

[0008] S21, obtaining an image sequence of the adsorbent sample using a hyperspectral imager, and preprocessing the image sequence;

[0009] S22, calculating the target signal value corresponding to each pixel in the image sequence;

[0010] S23, calculating the target utility value according to the target signal value;

[0011] S24, forming a mineral source evaluation through the target utility value and sending it to the client;

[0012] S3, selecting mineral sources for producing adsorbents based on whether the mineral sources meet the requirements.

[0013] Further, in step S1, clay minerals are obtained from several mineral sources respectively, and the clay minerals are pretreated; the clay minerals are mixed with additives to form a first mixture; the first mixture is modified to form a second mixture; the second mixture is modified in shape and structure, and the method of forming dioxin adsorbents according to different mineral sources is: the clay mineral includes one or more of palygorskite, illite, montmorillonite, kaolinite, and chlorite, and the mineral source is the location or source of obtaining the clay mineral; the clay mineral is dry purified, washed or centrifuged to remove particulate impurities and organic matter therein, and the pretreatment is completed;

[0014] Pre-treating the clay mineral and mixing it with an additive to form a first mixture, wherein the additive includes one or more of activated carbon, coal, calcium, sodium hydroxide, ammonium carbonate, cobalt carbonate, potassium nitrate, permanganate or metal oxide, and the method of mixing the clay mineral and the additive is a sol-gel method or a co-precipitation method;

[0015] In the step of modifying the first mixture to form the second mixture, the modification method includes surface modification or activation treatment, wherein the surface modification process is to introduce one or more of quaternary ammonium salts, organic amines or silane coupling agents into the surface of the clay mineral, and the activation treatment process is one or more of heat treatment, acid washing or alkali washing;

[0016] The method of modifying the shape and structure of the second mixture to form dioxin adsorbents according to different mineral sources is to modify the second mixture into a desired shape or structure, wherein the shape or structure is any one of particles, membranes or fibers.

[0017] Furthermore, in step S2, the method of obtaining samples from the dioxin adsorbent as adsorbent samples is: using the same clay mineral of the same weight from different mineral sources as a variable, keeping other substances and component ratios unchanged, to form several portions of dioxin adsorbent, and obtaining samples of equal weight therefrom as adsorbent samples.

[0018] Further, in step S21, a hyperspectral imager is used to obtain an image sequence of the adsorbent sample, and a method for preprocessing the image sequence is: the number of wavelengths collected in the process of acquiring the image by the hyperspectral imager is K, and the resolution of the image is M×N. The preprocessing method is to use filtering and smoothing to process the environmental interference and noise errors of the hyperspectral data.

[0019] Further, in step S22, the method for calculating the target signal value corresponding to each pixel in the image sequence is:

[0020] Select an image from the image sequence as the current image, calculate the target signal value of each pixel in the current image, take the pixel to be calculated as the current pixel, record the light spectrum value of the current pixel as Pn, and record the sequence number of the light spectrum value of the current pixel in the sequence of light spectrum values ​​of all pixels in the current image arranged from small to large as Rk, then the point order value FDG of the current pixel is: FDG = Rk ÷ (M × N);

[0021] The maximum and minimum values ​​of each spectral value in the current image are recorded as Mx and Mn respectively, and the point quantitative value SDG is: SDG = (Pn-Mn) / (Mx-Mn); obtain the corresponding spectral values ​​of each pixel with the same coordinates as the current pixel in the K images of the image sequence and calculate the average value recorded as O_EPn, calculate the simple response ratio O_EPB, O_EPB = Pn / O_EPn; calculate and obtain the target signal value TInf of the current pixel:

[0022] ;

[0023] Where exp(·) is an exponential function with the natural constant e as its base.

[0024] Since the spectral value in the spectrum is simply directly calculated in the above target signal value calculation process, it will lead to a large quantization error when the adsorbent sample is unevenly distributed when acquiring the image. However, the existing technology cannot solve the error problem, which further affects the accuracy of the adsorption capacity of the adsorbent. In order to make the calculation of the target signal value better and solve the problem, and eliminate the error phenomenon, the present invention proposes a more preferred solution as follows:

[0025] Preferably, in step S22, the method for calculating the target signal value corresponding to each pixel of the image in the image sequence is: formatting the obtained original hyperspectral image to obtain a spatial hyperspectral image SIMG, then the size of the spatial hyperspectral image is M×N×K, wherein M×N is the resolution size of the two-dimensional hyperspectral image, and K is the number of wavelengths collected; assuming that the pixel coordinates of SIMG are (x, y, z), SIMG (x, y, z) represents the spectral value of the pixel point with coordinates (x, y) in the two-dimensional hyperspectral image with a sequence number of z in the band; the x value range is [1, M], the y value range is [1, N], and the z value range is [1, K], and the averaged image EIMG is calculated,

[0026] ;

[0027] Where EIMG(x,y) represents the spectral value of the pixel with coordinates (x,y) in the averaged image, and SIMG(x,y,z) represents the spectral value of the pixel with coordinates (x,y,z) in the spatial hyperspectral image; Rak z Represents the spectral representation cardinality of the zth spectrum graph, which is calculated by: recording the median of the spectral values ​​of each pixel of a spectrum graph as VRak, recording the minimum value of the set of VRak of each spectrum graph as VRak_Mn, and recording the ratio of VRak of the zth spectrum graph to VRak_Mn as the spectral representation cardinality Rak of the zth spectrum graph z ;

[0028] Select an image from the image sequence as the current image, calculate the target signal value of each pixel in the current image, take the pixel whose target signal value is to be calculated as the current pixel, record the optical spectrum value of the current pixel as Pn, and record the sequence number of the optical spectrum value of the current pixel in the sequence arranged from small to large composed of the optical spectrum values ​​of all pixels in the current image as Rk, then the point order value FDG of the current pixel is: FDG = Rk ÷ (M×N); record the maximum and minimum values ​​of each optical spectrum value in the current image as Mx and Mn respectively, then the point quantity value SDG is: SDG = (Pn-Mn) / (Mx-Mn); calculate and obtain the target signal value TInf of the current pixel:

[0029] ;

[0030] Where exp(·) is an exponential function with the natural constant e as the base, and EPB represents the response ratio of the current pixel, which is calculated as follows: the spectral value of the pixel whose coordinates in EIMG are the coordinates of the current pixel is recorded as EPn, and the ratio of Pn to EPn is recorded as the sub-response ratio s_EPB of the current pixel; the maximum and minimum values ​​of the sub-response ratios of each pixel in the eight neighborhoods of the current pixel are recorded as s_Mx and s_Mn respectively, where the eight neighborhoods of the current pixel do not include the current pixel, and the response ratio EPB of the current pixel is obtained as:

[0031] .

[0032] Beneficial effect: Since the target signal value is calculated based on the performance of the spectral value of each pixel in the image, and combined with the data of the same position between different wavelengths, it is possible to preliminarily and effectively quantify the adsorption capacity of each position in the current image for dioxin pollutants corresponding to the same wavelength, and at the same time provide data support for secondary detection of positions with adsorption capacity defects in the marked image, thereby improving the ability to further identify invalid blocks for adsorption of dioxin pollutants and enhancing the quantitative accuracy of the adsorption performance of dioxin adsorbents for specific dioxin pollutants.

[0033] Further, in step S23, the method for calculating the target utility value according to the target signal value is:

[0034] Perform image erosion on each image in the image sequence, select an image from the image sequence to calculate the target utility value, and use the image whose target utility value is to be calculated as the deconstructed image; the upper quartile and median of the set composed of the target signal value of each pixel are respectively recorded as TInf Fq and TInf Mid ; When the target signal value of a pixel is the minimum value in the eight neighborhoods of the pixel and the value of the target signal value is less than TInf Fq The pixel is marked as a weak point, and the target signal value of the weak point is recorded as TInf Bt ;

[0035] According to the light spectrum value of each pixel, the corresponding fatigue growth area is obtained for each fatigue point through the regional growing algorithm, and the fatigue points except the fatigue point in the fatigue growth area corresponding to a fatigue point are recorded as non-essential fatigue points; the average number of non-essential fatigue points in each fatigue growth area is recorded as L; after obtaining the corresponding fatigue growth area for each fatigue point, the first fatigue reference FLDI of the fatigue growth area is calculated respectively:

[0036] ;

[0037] Where ln(·) is the logarithmic function with the natural constant e as the base, RT_ZLw is the target signal value in the weak growth zone that is less than TInf Fq The ratio of the number of pixels in the weak growth area to the total number of pixels in the weak growth area; the target utility value TEIdx of the image is obtained:

[0038] ;

[0039] Where j1 is the cumulative variable, er_pixel represents the variance of the target signal value of each pixel, UpRnd(·) is the upward rounding function, and the UpRnd(L) weak growth areas with the smallest point domain span with a pixel are used as the weak reference area of ​​the pixel. The point domain span of a pixel is the maximum value in the set of distance values ​​between a pixel and each pixel in a region. Then DM <fldi> j1 It is the ratio of the first fatigue reference of the j1th fatigue reference area of ​​a pixel to the point domain span between the pixel and the j1th fatigue reference area.

[0040] Since the above target utility value calculation method is based on fine acquisition of all pixels, it will lead to data overfitting problem, so that the final obtained value will have different degrees of result deviation. However, the existing technology cannot solve the problem of data overfitting. In order to better solve this problem and improve the accuracy of the quantitative results, the present invention proposes a more preferred solution as follows:

[0041] Preferably, in step S23, the method for calculating the target utility value according to the target signal value is:

[0042] Perform image erosion on each image in the image sequence, select an image from the image sequence to calculate the target utility value, use the image whose target utility value is to be calculated as the deconstructed image, perform superpixel segmentation on the deconstructed image, and use each region obtained by segmentation as a deconstruction unit; obtain the average value of the target signal value of each pixel in the deconstructed unit as the unit target signal level UTI of the deconstructed unit, and record the median of the unit target signal level of each deconstructed unit in the deconstructed image as the image target signal level of the deconstructed image; if the unit target signal level of a deconstructed unit is less than the image target signal level, it is defined that the 0 deconstructed unit meets the macro contrast requirements;

[0043] When the target signal value of a pixel is the minimum value in the eight neighborhoods of the pixel, the pixel is marked as a weak point. If a deconstruction unit contains at least one weak point, the deconstruction unit is defined to meet the minimum value requirement; the weak radius Lwr of a weak point is defined as the distance between the weak point and the other weak point closest to it; a circular area is constructed with the weak point as the center and Lwr as the radius as the weak boundary of the weak point. When the weak boundary of a weak point includes all pixels of the deconstruction unit where the weak point is located, the weak point is defined as a selected weak point. If a deconstruction unit contains at least one selected weak point, the deconstruction unit is defined to meet the unit coverage requirement;

[0044] The deconstructed unit that meets the requirements of macro contrast, minimum value and unit coverage is recorded as a weak unit; the intersection of the weak boundaries of each weak point in the weak unit is taken as the weak mapping domain of the weak unit; a deconstructed unit is included in the weak mapping domains of several weak units, and the number of weak mapping domains to which the deconstructed unit belongs is recorded as kLU, and the number of deconstructed units in the deconstructed image is NBkU; the target utility value TEIdx is obtained:

[0045] ;

[0046] Where i1 is the accumulated variable, UTI i1 is the unit target confidence level of the i1th deconstruction unit, er_pic is the variance of the unit target confidence level of each deconstruction unit, km i1 <ldi>is the fatigue mapping function of the i1th deconstruction unit, and the fatigue mapping function is:

[0047] ;

[0048] Calculate the fatigue reference LDI of the fatigue mapping domain according to the fatigue mapping domain of the fatigue unit:

[0049] ;

[0050] Where EPx is the average target signal value of each pixel in the deconstructed image, MPx is the median target signal value of each pixel in the fatigue mapping domain; ln(·) is the logarithmic function with the natural constant e as the base, and RUd is the proportion of pixels in the fatigue mapping domain whose target signal value is less than EPx.

[0051] Since the adsorption effectiveness of dioxin adsorbents is not only reflected in their adsorption amount, the adsorbents prepared by composite mineral modification are prone to failure problems, that is, the mixtures prepared in the same batch are prone to a sudden drop in the adsorption performance of some materials, and the balance of dioxin adsorption performance does not meet the expected performance. Therefore, the lack of balance phenomenon is effectively quantified through the weak points to improve the quantitative accuracy of the adsorption performance of the dioxin adsorbent.

[0052] Further, in step S24, the method of forming a mineral source evaluation through the target utility value and sending it to the client is: each image in the image sequence is respectively calculated to obtain a target utility value, the arithmetic mean of each target utility value is used as the mineral source evaluation, the sequence of each target utility value is sent to the server for storage, and the mineral source evaluation is sent to the client.

[0053] Further, in step S3, according to whether the mineral source evaluation meets the requirements, the method of selecting the mineral source for producing the adsorbent is: after the client obtains the mineral source evaluation of the adsorbent samples corresponding to all different mineral sources, when the mineral source evaluation value of a mineral source is greater than the set threshold, or when the value of the mineral source evaluation is the maximum value among the mineral source evaluations of each group of adsorbent samples obtained in the same batch, the mineral source corresponding to the mineral source evaluation is selected as the preferred supply point of the clay mineral, and the corresponding clay mineral is collected or obtained from the preferred supply point for preparing the dioxin adsorbent.

[0054] Preferably, all undefined variables in the present invention, if not clearly defined, can be manually set thresholds.

[0055] The present invention also provides a novel composite clay mineral modified dioxin adsorbent preparation system, the novel composite clay mineral modified dioxin adsorbent preparation system comprises: a processor, a memory and a computer program stored in the memory and executable on the processor, the processor implements the steps in the novel composite clay mineral modified dioxin adsorbent preparation method when executing the computer program, the novel composite clay mineral modified dioxin adsorbent preparation system can be run in computing devices such as desktop computers, notebook computers, PDAs and cloud data centers, and the executable system may include, but is not limited to, a processor, a memory, and a server cluster, and the processor executes the computer program in the following system units:

[0056] A spectrum acquisition unit, used for obtaining an image sequence of the adsorbent sample by using a hyperspectral imager, and preprocessing the image sequence;

[0057] A target signal value calculation unit, used to calculate the target signal value corresponding to each pixel in the image sequence;

[0058] A target utility calculation unit, used for calculating the target utility value according to the target signal value;

[0059] The mineral source evaluation unit is used to form a mineral source evaluation through the target utility value and send it to the client.

[0060] The beneficial effects of the present invention are as follows: the present invention provides a novel method for preparing a dioxin adsorbent by modifying a composite clay mineral. Clay minerals from different mineral sources have different purities, physical structures, and impurity contents, so it is not possible to simply quantify that some other compounds with similar functionality to montmorillonite can also have a positive or negative impact on the adsorption effect. The present method can preliminarily and effectively quantify the adsorption capacity of dioxin pollutants corresponding to the same wavelength at each position in the current image, and can also provide data support for secondary detection of adsorption capacity defect positions in the marked image to enhance the quantitative accuracy of the adsorption performance of the dioxin adsorbent for specific dioxin pollutants. Since the adsorption efficacy of the dioxin adsorbent is not only reflected in its adsorption amount, the adsorbent prepared by the composite mineral modification is scientifically quantified to be prone to failure problems, and the equilibrium loss phenomenon is effectively captured, thereby further improving the quantitative accuracy of the adsorption performance of the dioxin adsorbent, providing efficient and accurate data support for the selection of mineral sources for the dioxin adsorbent, and greatly improving the production efficiency and product quality of the novel composite clay mineral modified dioxin adsorbent. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] The above and other features of the present invention will become more obvious by describing in detail the embodiments shown in the accompanying drawings. The same reference numerals in the accompanying drawings of the present invention represent the same or similar elements. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other accompanying drawings can be obtained based on these accompanying drawings without creative work. In the accompanying drawings:

[0062] Figure 1 The figure shows a flow chart of a novel method for preparing a dioxin adsorbent by modifying composite clay minerals;

[0063] Figure 2 The figure shows the comparison of the average monitoring concentration of dioxins in the incineration waste gas after the dioxin adsorbent prepared by modified activated carbon and composite clay minerals;

[0064] Figure 3 Shown is a structural diagram of a new type of composite clay mineral modified dioxin adsorbent system. DETAILED DESCRIPTION

[0065] The following will be combined with the embodiments and drawings to clearly and completely describe the concept, specific structure and technical effects of the present invention, so as to fully understand the purpose, scheme and effect of the present invention. It should be noted that the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0066] like Figure 1 The flowchart of a novel composite clay mineral modification method for preparing dioxin adsorbent is shown below. Figure 1 To illustrate a novel method for preparing a dioxin adsorbent by modifying a composite clay mineral according to an embodiment of the present invention:

[0067] Comparative Example 1

[0068] Activated carbon is used as adsorbent. Process description: A combined flue gas purification process of "SNCR denitrification, rotary spray tower (semi-dry method), activated carbon injection, and bag filter" is used to remove dioxins from incineration flue gas. Activated carbon uses an activated carbon injection system, which is quantitatively fed by a disc feeder and transported to the flue by compressed air to remove dioxins from the flue gas.

[0069] Example 1

[0070] Description of treatment process: The same treatment process as in Comparative Example 1 is used, that is, the dioxin adsorbent prepared by modifying the composite clay mineral is added in the same manner as the activated carbon adsorbent.

[0071] Among them, the dioxin adsorbent prepared by modifying the composite clay mineral of adsorbent type 1 of Example 1,

[0072] S1, obtaining clay minerals from a plurality of mineral sources, pre-treating the clay minerals; mixing the clay minerals with additives to form a first mixture; modifying the first mixture to form a second mixture; modifying the shape and structure of the second mixture to form dioxin adsorbents according to different mineral sources;

[0073] S2, obtaining a sample from the dioxin adsorbent as an adsorbent sample;

[0074] S21, obtaining an image sequence of the adsorbent sample using a hyperspectral imager, and preprocessing the image sequence;

[0075] S22, calculating the target signal value corresponding to each pixel in the image sequence;

[0076] S23, calculating the target utility value according to the target signal value;

[0077] S24, forming a mineral source evaluation through the target utility value and sending it to the client;

[0078] S3, selecting mineral sources for producing adsorbents based on whether the mineral sources meet the requirements.

[0079] Further, in step S1, clay minerals are obtained from several mineral sources respectively, and the clay minerals are pretreated; the clay minerals are mixed with additives to form a first mixture; the first mixture is modified to form a second mixture; the second mixture is transformed in shape and structure, and the method of forming dioxin adsorbents according to different mineral sources is: the combination ratio of clay minerals is 13% chlorite, 15% illite, 19% palygorskite, 3% kaolinite, and 7% montmorillonite; the clay minerals are dry purified, washed or centrifuged to remove particulate impurities and organic matter therein to complete the pretreatment; the key to the dry purification technology lies in the grinding and classification process, using Raymond mill, and air classifier for classification; non-clay impurities such as quartz sand and carbonate that are not easy to grind in the composite clay mineral adsorbent stone are removed by dry purification.

[0080] Pre-treating the clay mineral and mixing it with an additive to form a first mixture, wherein the additive includes 17% activated carbon, 9% coal, and 17% calcified material, and the method of mixing the clay mineral and the additive is a sol-gel method;

[0081] In the step of modifying the first mixture to form the second mixture, the modification method includes surface modification or activation treatment, wherein the surface modification process is to add an inorganic acid with a concentration of 15% to the crushed clay mineral, stir and activate for 30 minutes, and then wash with water;

[0082] The method of modifying the shape and structure of the second mixture and forming dioxin adsorbents according to different mineral sources is to modify the second mixture into a desired shape or structure, wherein the shape or structure is a particle, dehydrate and dry the second mixture, and then pulverize it.

[0083] Among them, montmorillonite, kaolinite and palygorskite were screened for mineral sources by the following methods:

[0084] Furthermore, in step S2, the method of obtaining samples from the dioxin adsorbent as adsorbent samples is: using the same clay mineral of the same weight in each mineral source as a variable, keeping other substances and component ratios unchanged, to form corresponding portions of dioxin adsorbent, and obtaining samples of equal weight therefrom as adsorbent samples.

[0085] Further, in step S21, a hyperspectral imager is used to obtain an image sequence of the adsorbent sample, and a method for preprocessing the image sequence is: the number of wavelengths collected in the process of acquiring the image by the hyperspectral imager is K, and the resolution of the image is M×N. The preprocessing method is to use filtering and smoothing to process the environmental interference and noise errors of the hyperspectral data.

[0086] A sample is obtained from the dioxin adsorbent as an adsorbent sample, and the adsorption performance of the adsorbent sample is tested by a hyperspectral imager to obtain a method for evaluating the mineral source: clay minerals of the same weight from different mineral sources are used as variables to form a number of dioxin adsorbents, and are used as adsorbent samples respectively. Under the same laboratory environment, each adsorbent sample is used to adsorb dioxin polluted gas by a controlled variable method, and the adsorption performance of each adsorbent sample is tested to obtain a mineral source evaluation. The specific method is as follows:

[0087] S21, obtaining an image sequence of the adsorbent sample using a hyperspectral imager, and preprocessing the image sequence;

[0088] S22, calculating the target signal value corresponding to each pixel in the image sequence;

[0089] S23, calculating the target utility value according to the target signal value;

[0090] S24, forming a mineral source evaluation through the target utility value and sending it to the client;

[0091] In step S21, the number of wavelengths collected in the process of acquiring the image by the hyperspectral imager is K, the resolution of the image is M×N, and the preprocessing method is to use filtering and smoothing to process the environmental interference and noise errors of the hyperspectral data.

[0092] Further, in step S22, the method for calculating the target signal value corresponding to each pixel of the image in the image sequence is: formatting the obtained original hyperspectral image to obtain a spatial hyperspectral image SIMG, then the size of the spatial hyperspectral image is M×N×K, wherein M×N is the resolution size of the two-dimensional hyperspectral image, and K is the number of wavelengths collected; assuming that the pixel coordinates of SIMG are (x, y, z), SIMG (x, y, z) represents the spectral value of the pixel point with coordinates (x, y) in the two-dimensional hyperspectral image with the sequence number z of the band; the x value range is [1, M], the y value range is [1, N], and the z value range is [1, K], and the averaged image EIMG is calculated,

[0093] ;

[0094] Where EIMG(x,y) represents the spectral value of the pixel with coordinates (x,y) in the averaged image, and SIMG(x,y,z) represents the spectral value of the pixel with coordinates (x,y,z) in the spatial hyperspectral image; Rak z Represents the spectral representation cardinality of the zth spectrum graph, which is calculated by: recording the median of the spectral values ​​of each pixel of a spectrum graph as VRak, recording the minimum value of the set of VRak of each spectrum graph as VRak_Mn, and recording the ratio of VRak of the zth spectrum graph to VRak_Mn as the spectral representation cardinality Rak of the zth spectrum graph z ;

[0095] Select an image from the image sequence as the current image, calculate the target signal value of each pixel in the current image, take the pixel whose target signal value is to be calculated as the current pixel, record the optical spectrum value of the current pixel as Pn, and record the sequence number of the optical spectrum value of the current pixel in the sequence arranged from small to large composed of the optical spectrum values ​​of all pixels in the current image as Rk, then the point order value FDG of the current pixel is: FDG = Rk ÷ (M×N); record the maximum and minimum values ​​of each optical spectrum value in the current image as Mx and Mn respectively, then the point quantity value SDG is: SDG = (Pn-Mn) / (Mx-Mn); calculate and obtain the target signal value TInf of the current pixel:

[0096] ;

[0097] Where exp(·) is an exponential function with the natural constant e as the base, and EPB represents the response ratio of the current pixel, which is calculated as follows: the spectral value of the pixel whose coordinates in EIMG are the coordinates of the current pixel is recorded as EPn, and the ratio of Pn to EPn is recorded as the sub-response ratio s_EPB of the current pixel; the maximum and minimum values ​​of the sub-response ratios of each pixel in the eight neighborhoods of the current pixel are recorded as s_Mx and s_Mn respectively, where the eight neighborhoods of the current pixel do not include the current pixel, and the response ratio EPB of the current pixel is obtained as:

[0098] .

[0099] Further, in step S23, the method for calculating the target utility value according to the target signal value is:

[0100] Perform image erosion on each image in the image sequence, select an image from the image sequence to calculate the target utility value, and use the image whose target utility value is to be calculated as the deconstructed image; the upper quartile and median of the set composed of the target signal value of each pixel are respectively recorded as TInf Fq and TInf Mid ; When the target signal value of a pixel is the minimum value in the eight neighborhoods of the pixel and the value of the target signal value is less than TInf Fq The pixel is marked as a weak point, and the target signal value of the weak point is recorded as TInf Bt ;

[0101] According to the light spectrum value of each pixel, the corresponding fatigue growth area is obtained for each fatigue point through the regional growing algorithm, and the fatigue points except the fatigue point in the fatigue growth area corresponding to a fatigue point are recorded as non-essential fatigue points; the average number of non-essential fatigue points in each fatigue growth area is recorded as L; after obtaining the corresponding fatigue growth area for each fatigue point, the first fatigue reference FLDI of the fatigue growth area is calculated respectively:

[0102] ;

[0103] Where ln(·) is the logarithmic function with the natural constant e as the base, RT_ZLw is the target signal value in the weak growth zone that is less than TInf Fq The ratio of the number of pixels in the weak growth area to the total number of pixels in the weak growth area; the target utility value TEIdx of the image is obtained:

[0104] ;

[0105] Where j1 is the cumulative variable, er_pixel represents the variance of the target signal value of each pixel, UpRnd(·) is the upward rounding function, and the UpRnd(L) weak growth areas with the smallest point domain span with a pixel are used as the weak reference area of ​​the pixel. The point domain span of a pixel is the maximum value in the set of distance values ​​between a pixel and each pixel in a region. Then DM <fldi> j1 It is the ratio of the first fatigue reference of the j1th fatigue reference area of ​​a pixel to the point domain span between the pixel and the j1th fatigue reference area.

[0106] Further, in step S24, the method of forming a mineral source evaluation through the target utility value and sending it to the client is: each image in the image sequence is respectively calculated to obtain a target utility value, the arithmetic mean of each target utility value is used as the mineral source evaluation, the sequence of each target utility value is sent to the server for storage, and the mineral source evaluation is sent to the client.

[0107] Further, in step S3, according to whether the mineral source evaluation meets the requirements, the method of selecting the mineral source for producing the adsorbent is: after the client obtains the mineral source evaluation of the adsorbent samples corresponding to all different mineral sources, when the mineral source evaluation value of a mineral source is greater than the set threshold, or when the value of the mineral source evaluation is the maximum value among the mineral source evaluations of each group of adsorbent samples obtained in the same batch, the mineral source corresponding to the mineral source evaluation is selected as the preferred supply point of the clay mineral, and the corresponding clay mineral is collected or obtained from the preferred supply point for preparing the dioxin adsorbent.

[0108] Example 2

[0109] On the basis of preparing tannin by the method of Example 1, adsorbent type 2 is prepared by the method of Example 1, wherein the difference between Example 2 and Example 1 is that in step S22, the method for calculating the target signal value corresponding to each pixel of the image in the image sequence is: formatting the obtained original hyperspectral image to obtain a spatial hyperspectral image SIMG, and the size of the spatial hyperspectral image is M×N×K, wherein M×N is the resolution size of the two-dimensional hyperspectral image, and K is the number of wavelengths collected; assuming that the pixel coordinates of SIMG are (x, y, z), SIMG (x, y, z) represents the spectral value of the pixel point with coordinates (x, y) in the two-dimensional hyperspectral image with the sequence number of z in the band; the x value ranges from [1, M], the y value ranges from [1, N], and the z value ranges from [1, K], and the averaged image EIMG is obtained by calculation,

[0110] ;

[0111] Where EIMG(x,y) represents the spectral value of the pixel with coordinates (x,y) in the averaged image, and SIMG(x,y,z) represents the spectral value of the pixel with coordinates (x,y,z) in the spatial hyperspectral image; Rak z Represents the spectral representation cardinality of the zth spectrum graph, which is calculated by: recording the median of the spectral values ​​of each pixel of a spectrum graph as VRak, recording the minimum value of the set of VRak of each spectrum graph as VRak_Mn, and recording the ratio of VRak of the zth spectrum graph to VRak_Mn as the spectral representation cardinality Rak of the zth spectrum graph z ;

[0112] Select an image from the image sequence as the current image, calculate the target signal value of each pixel in the current image, take the pixel whose target signal value is to be calculated as the current pixel, record the optical spectrum value of the current pixel as Pn, and record the sequence number of the optical spectrum value of the current pixel in the sequence arranged from small to large composed of the optical spectrum values ​​of all pixels in the current image as Rk, then the point order value FDG of the current pixel is: FDG = Rk ÷ (M×N); record the maximum and minimum values ​​of each optical spectrum value in the current image as Mx and Mn respectively, then the point quantity value SDG is: SDG = (Pn-Mn) / (Mx-Mn); calculate and obtain the target signal value TInf of the current pixel:

[0113] ;

[0114] Where exp(·) is an exponential function with the natural constant e as the base, and EPB represents the response ratio of the current pixel, which is calculated as follows: the spectral value of the pixel whose coordinates in EIMG are the coordinates of the current pixel is recorded as EPn, and the ratio of Pn to EPn is recorded as the sub-response ratio s_EPB of the current pixel; the maximum and minimum values ​​of the sub-response ratios of each pixel in the eight neighborhoods of the current pixel are recorded as s_Mx and s_Mn respectively, where the eight neighborhoods of the current pixel do not include the current pixel, and the response ratio EPB of the current pixel is obtained as:

[0115] .

[0116] The difference between Example 2 and Example 1 is that, in step S23, the method for calculating the target utility value according to the target signal value is:

[0117] Perform image erosion on each image in the image sequence, select an image from the image sequence to calculate the target utility value, use the image whose target utility value is to be calculated as the deconstructed image, perform superpixel segmentation on the deconstructed image, and use each region obtained by segmentation as a deconstruction unit; obtain the average value of the target signal value of each pixel in the deconstructed unit as the unit target signal level UTI of the deconstructed unit, and record the median of the unit target signal level of each deconstructed unit in the deconstructed image as the image target signal level of the deconstructed image; if the unit target signal level of a deconstructed unit is less than the image target signal level, it is defined that the 0 deconstructed unit meets the macro contrast requirements;

[0118] When the target signal value of a pixel is the minimum value in the eight neighborhoods of the pixel, the pixel is marked as a weak point. If a deconstruction unit contains at least one weak point, the deconstruction unit is defined to meet the minimum value requirement; the weak radius Lwr of a weak point is defined as the distance between the weak point and the other weak point closest to it; a circular area is constructed with the weak point as the center and Lwr as the radius as the weak boundary of the weak point. When the weak boundary of a weak point includes all pixels of the deconstruction unit where the weak point is located, the weak point is defined as a selected weak point. If a deconstruction unit contains at least one selected weak point, the deconstruction unit is defined to meet the unit coverage requirement;

[0119] The deconstructed unit that meets the requirements of macro contrast, minimum value and unit coverage is recorded as a weak unit; the intersection of the weak boundaries of each weak point in the weak unit is taken as the weak mapping domain of the weak unit; a deconstructed unit is included in the weak mapping domains of several weak units, and the number of weak mapping domains to which the deconstructed unit belongs is recorded as kLU, and the number of deconstructed units in the deconstructed image is NBkU; the target utility value TEIdx is obtained:

[0120] ;

[0121] Where i1 is the accumulated variable, UTI i1 is the unit target confidence level of the i1th deconstruction unit, er_pic is the variance of the unit target confidence level of each deconstruction unit, km i1 <ldi>is the fatigue mapping function of the i1th deconstruction unit,

[0122] The fatigue mapping function is:

[0123] ;

[0124] The fatigue reference LDI of the fatigue mapping domain is:

[0125] ;

[0126] Where EPx is the average target signal value of each pixel in the deconstructed image, MPx is the median target signal value of each pixel in the fatigue mapping domain; in the formula, ln(·) is a logarithmic function with the natural constant e as the base, and RUd is the proportion of pixels in the fatigue mapping domain whose target signal value is less than EPx.

[0127] Table 1: Comparison of the effects of activated carbon and composite clay mineral modified dioxin adsorbents on dioxins in incineration waste gas

[0128]

[0129] Combine Table 1 Figure 2 It can be concluded that the dioxin adsorbent prepared by the modification of the composite clay mineral can meet the implementation standard for the adsorption of dioxins. In addition, the dioxin adsorbent prepared by the modification of the composite clay mineral obtained by the scheme of Example 2 has better adsorption effect on dioxins than the traditional activated carbon adsorbent. This shows that when a more sophisticated mineral source screening strategy is adopted, the functionality of the dioxin adsorbent prepared by the modification of the composite clay mineral will have a more considerable effect. It also indirectly reflects that the influence of the mineral source on the performance of the dioxin adsorbent prepared by the modification of the composite clay mineral cannot be ignored, and it also reflects the superiority and necessity of this method.

[0130] The embodiment of the present invention provides a novel composite clay mineral modified dioxin adsorbent system, such as Figure 3 The figure shows a structural diagram of a novel composite clay mineral modified dioxin adsorbent system of the present invention. The novel composite clay mineral modified dioxin adsorbent system of this embodiment comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the novel composite clay mineral modified dioxin adsorbent system embodiment are implemented.

[0131] The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to run in the following units of the system:

[0132] A spectrum acquisition unit, used for obtaining an image sequence of the adsorbent sample by using a hyperspectral imager, and preprocessing the image sequence;

[0133] A target signal value calculation unit, used to calculate the target signal value corresponding to each pixel in the image sequence;

[0134] A target utility calculation unit, used for calculating the target utility value according to the target signal value;

[0135] The mineral source evaluation unit is used to form a mineral source evaluation through the target utility value and send it to the client.

[0136] The novel composite clay mineral modified dioxin adsorbent system can be run in computing devices such as desktop computers, laptops, PDAs, and cloud servers. The novel composite clay mineral modified dioxin adsorbent system can include, but is not limited to, processors and memories. Those skilled in the art can understand that the example is only an example of a novel composite clay mineral modified dioxin adsorbent system, and does not constitute a limitation on a novel composite clay mineral modified dioxin adsorbent system. It can include more or fewer components than the example, or a combination of certain components, or different components. For example, the novel composite clay mineral modified dioxin adsorbent system can also include input and output devices, network access devices, buses, etc.

[0137] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the operation system of the novel composite clay mineral modified dioxin adsorbent system, and uses various interfaces and lines to connect the various parts of the entire novel composite clay mineral modified dioxin adsorbent system.

[0138] The memory can be used to store the computer program and / or module, and the processor realizes various functions of the novel composite clay mineral modified dioxin adsorbent system by running or executing the computer program and / or module stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data created according to the use of the mobile phone (such as audio data, a phone book, etc.), etc. In addition, the memory can include a high-speed random access memory, and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage devices.

[0139] Although the description of the present invention has been quite detailed and has been described in particular with respect to several described embodiments, it is not intended to be limited to any of these details or embodiments or any particular embodiment, so as to effectively cover the intended scope of the present invention. In addition, the present invention is described above with the embodiments foreseeable by the inventors, and its purpose is to provide a useful description, and those non-substantial changes to the present invention that are not currently foreseen may still represent equivalent changes of the present invention.< / ldi> < / fldi> < / ldi> < / fldi>

Claims

1. A composite clay mineral modified dioxin adsorbent system, characterized in that: The composite clay mineral modified dioxin adsorbent preparation system comprises: a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program including a composite clay mineral modified dioxin adsorbent preparation method, and the composite clay mineral modified dioxin adsorbent preparation system runs on a desktop computer, a laptop computer, a PDA, and a computing device in a cloud data center; The method for preparing a dioxin adsorbent by modifying a composite clay mineral comprises the following steps: S1, obtaining clay minerals from a plurality of mineral sources, pre-treating the clay minerals; mixing the clay minerals with additives to form a first mixture; modifying the first mixture to form a second mixture; modifying the shape and structure of the second mixture to form dioxin adsorbents according to different mineral sources; S2, obtaining a sample from the dioxin adsorbent as an adsorbent sample; S21, obtaining an image sequence of the adsorbent sample using a hyperspectral imager, and preprocessing the image sequence; S22, calculating the target signal value corresponding to each pixel in the image sequence; S23, calculating the target utility value according to the target signal value; S24, forming a mineral source evaluation through the target utility value and sending it to the client; S3, selecting a mineral source for producing an adsorbent based on whether the mineral source meets the requirements; Wherein, in step S22, the method for calculating the target signal value corresponding to each pixel in the image sequence is: Select an image from the image sequence as the current image, calculate the target signal value of each pixel in the current image, take the pixel whose target signal value is to be calculated as the current pixel, record the spectral value of the current pixel as Pn, and record the sequence number of the spectral value of the current pixel in the sequence arranged from small to large composed of the spectral values ​​of all pixels in the current image as Rk, then the point order value FDG of the current pixel is: FDG = Rk ÷ (M × N); The maximum and minimum values ​​of each spectral value in the current image are recorded as Mx and Mn respectively, and the point quantitative value SDG is: SDG = (Pn-Mn) / (Mx-Mn); obtain the corresponding spectral values ​​of each pixel with the same coordinates as the current pixel in the K images of the image sequence and calculate the average value recorded as O_EPn, calculate the simple response ratio O_EPB, O_EPB = Pn / O_EPn; obtain the target signal value TInf of the current pixel through the simple response ratio calculation: ； Where exp(·) is an exponential function with the natural constant e as the base; Wherein, in step S23, the method for calculating the target utility value according to the target signal value is: Perform image erosion on each image in the image sequence, select an image from the image sequence to calculate the target utility value, and use the image whose target utility value is to be calculated as the deconstructed image; the upper quartile and median of the set composed of the target signal value of each pixel are respectively recorded as TInf Fq and TInf Mid ; When the target signal value of a pixel is the minimum value in the eight neighborhoods of the pixel and the value of the target signal value is less than TInf Fq The pixel is marked as a weak point, and the target signal value of the weak point is recorded as TInf Bt ; According to the spectral value of each pixel, the corresponding fatigue growth area is obtained for each fatigue point through the regional growing algorithm, and the fatigue points except the fatigue point in the fatigue growth area corresponding to a fatigue point are recorded as non-essential fatigue points; the average number of non-essential fatigue points in each fatigue growth area is recorded as L; after obtaining the corresponding fatigue growth area for each fatigue point, the first fatigue reference FLDI of the fatigue growth area is calculated by the target signal value, and then the target utility value TEIdx of the image is calculated according to FLDI.

2. The system for preparing dioxin adsorbent by modifying composite clay minerals according to claim 1, characterized in that: In step S1, clay minerals are obtained from several mineral sources respectively, and the clay minerals are pretreated; the clay minerals are mixed with additives to form a first mixture; the first mixture is modified to form a second mixture; the second mixture is modified in shape and structure, and the method of forming dioxin adsorbents according to different mineral sources is: the clay mineral includes one or more of palygorskite, illite, montmorillonite, kaolinite, and chlorite, and the mineral source is the location or source of obtaining the clay mineral; the clay mineral is dry purified, washed or centrifuged to remove particulate impurities and organic matter therein, and the pretreatment is completed; Pre-treating the clay mineral and mixing it with an additive to form a first mixture, wherein the additive includes one or more of activated carbon, coal, calcium, sodium hydroxide, ammonium carbonate, cobalt carbonate, potassium nitrate, permanganate or metal oxide, and the method of mixing the clay mineral and the additive is a sol-gel method or a co-precipitation method; In the step of modifying the first mixture to form the second mixture, the modification method includes surface modification or activation treatment, wherein the surface modification process is to introduce one or more of quaternary ammonium salts, organic amines or silane coupling agents into the surface of the clay mineral, and the activation treatment process is one or more of heat treatment, acid washing or alkali washing; The method of modifying the shape and structure of the second mixture to form dioxin adsorbents according to different mineral sources is to modify the second mixture into a desired shape or structure, wherein the shape or structure is any one of particles, membranes or fibers.

3. The system for preparing dioxin adsorbent by modifying composite clay minerals according to claim 1, characterized in that: In step S2, the method of obtaining samples from the dioxin adsorbent as adsorbent samples is: using the same clay mineral of the same weight from different mineral sources as a variable, keeping other substances and component ratios unchanged, to form several portions of dioxin adsorbent, and obtaining samples of equal weight therefrom as adsorbent samples.

4. The system for preparing dioxin adsorbent by modifying composite clay minerals according to claim 1, characterized in that: In step S21, a hyperspectral imager is used to obtain an image sequence of the adsorbent sample, and a method for preprocessing the image sequence is: the number of wavelengths collected in the process of obtaining the image by the hyperspectral imager is K, and the resolution of the image is M×N. The preprocessing method is to use filtering and smoothing to process the environmental interference and noise errors of the hyperspectral data.

5. The system for preparing dioxin adsorbent by modifying composite clay minerals according to claim 1, characterized in that: In step S24, the method of forming a mineral source evaluation through the target utility value and sending it to the client is: each image in the image sequence is respectively calculated to obtain a target utility value, the arithmetic mean of each target utility value is used as the mineral source evaluation, the sequence of each target utility value is sent to the server for storage, and the mineral source evaluation is sent to the client.

6. The composite clay mineral modified dioxin adsorbent system according to claim 1, characterized in that: In step S3, according to whether the mineral source evaluation meets the requirements, the method of selecting the mineral source for producing the adsorbent is: after the client obtains the mineral source evaluation of the adsorbent samples corresponding to all different mineral sources, when the mineral source evaluation value of a mineral source is greater than the set threshold, or when the value of the mineral source evaluation is the maximum value among the mineral source evaluations of each group of adsorbent samples obtained in the same batch, the mineral source corresponding to the mineral source evaluation is selected as the preferred supply point of the clay mineral, and the corresponding clay mineral is collected or obtained from the preferred supply point for preparing the dioxin adsorbent.

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