Automatic sampling titrimetric analysis method based on copper smelting acidic wastewater treatment link

Abstract

The invention relates to the technical field of acidic wastewater titrimetric analysis, and discloses an automatic sampling titrimetric analysis method based on a copper smelting acidic wastewater treatment link, which comprises the following steps: collecting waste acid to obtain a waste acid sample; carrying out titration treatment on the waste acid sample to obtain a waste acid sample image after the titration reaction is finished, and carrying out sediment outline extraction on the waste acid sample image to generate sediment coagulation complexity; according to the method, automatic sampling titration is combined with image analysis, precipitation coagulation complexity and arsenic specific hue separation degree are extracted, a precipitation effectiveness index and an arsenic residue risk coefficient are generated through dynamic fusion, arsenic precipitates and other precipitates in similar forms are accurately distinguished by utilizing precipitate contour features and arsenic specific hue in an HSV color space, and the arsenic content is accurately determined. The method has the advantages that spectral signal superposition interference is avoided, and the arsenic residual state can be accurately reflected, so that whether arsenic removal is qualified or not is reliably judged, the accuracy and safety of waste acid treatment are improved, and the quality of water reuse is guaranteed.

Description

Technical Field

[0001] The invention relates to the technical field of titration analysis of dirty acid and wastewater, and in particular to an automatic sampling titration analysis method based on the treatment link of dirty acid and wastewater in copper smelting. Background Art

[0002] Flue gas generated during the smelting process at copper smelters typically contains impurities such as sulfur dioxide, arsenic, and lead, along with varying degrees of pyrolysis. This flue gas is typically fed into an acid production system to produce industrial sulfuric acid. Although this flue gas undergoes dust removal processes such as electrostatic dust collection, it still contains certain impurities. In the acid production system, the flue gas is first purified by circulating sprays to cool and remove dust. The purified washing liquid contains soluble impurities such as arsenic, fluorine, and chlorine, with arsenic levels generally high. It also contains dissolved sulfur trioxide and some sulfur dioxide, resulting in a generally high acidity. Therefore, the portion of purified washing liquid that is regularly discharged is referred to as spent acid, or contaminated acid wastewater.

[0003] The treatment process of waste acid is to remove heavy metal ions such as arsenic, copper, lead, and zinc, especially arsenic. Waste acid treatment usually adopts the method of removing arsenic first and then neutralizing to achieve water reuse.

[0004] At present, when arsenic is removed from waste acid, these ions will react with the treatment agent to generate precipitates of similar form. Spectral analysis is usually used to analyze the arsenic precipitates and the arsenic element remaining in the solution. The arsenic in the waste acid and the precipitates of heavy metal ions such as copper, lead, and zinc have certain similarities in molecular structure. When these precipitates are mixed, the spectral signals will overlap with each other, making it difficult to accurately distinguish arsenic precipitates from other metal precipitates of similar form. This leads to deviations in the detection results of the residual arsenic in the solution, and it is impossible to accurately determine whether the arsenic has been completely removed. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the present invention provides an automatic sampling titration analysis method based on the copper smelting acid and wastewater treatment process, which solves the above problems.

[0006] The above technical objectives of the present invention are achieved through the following technical solutions: An automatic sampling and titration analysis method based on the treatment of copper smelting acid wastewater, comprising: Step S1: collecting waste acid to obtain a waste acid sample; Step S2: performing a titration process on the waste acid sample, obtaining an image of the waste acid sample after the titration reaction is completed, extracting the precipitate contour from the waste acid sample image, and generating a precipitate coagulation complexity; Step S3: converting the waste acid sample image into the HSV color space and segmenting it to generate arsenic-specific color separation; Step S4: fusing the precipitation aggregation complexity and the arsenic specific chromatic phase separation to obtain a precipitation effectiveness index; Step S5: Calculate the precipitation effectiveness index to obtain a dynamic arsenic residual risk coefficient; Step S6: Determine the arsenic removal qualification mark based on the dynamic arsenic residual risk coefficient.

[0007] Furthermore, the spent acid sample is titrated, including: The single sampling volume of the waste acid sample is 50 ml, and sodium hydrosulfide solution is added to carry out titration reaction.

[0008] Furthermore, the sediment contours are extracted from the waste acid sample image to generate the sedimentation complexity, including: The waste acid sample image is segmented and the regional significance coefficient is generated based on the difference in optical properties between the sediment and the background. The edges of the sediments in the waste acid sample images were analyzed to obtain the contour fractal index; Calculate the spatial distribution density of sediment outlines in the waste acid sample image to generate the particle aggregation density; The regional significance coefficient, silhouette fractal index and particle aggregation density are integrated to obtain the precipitation aggregation complexity.

[0009] Furthermore, the waste acid sample image is converted to the HSV color space and segmented to generate arsenic-specific chromatic separation, including: Convert the waste acid sample image from RGB color space to HSV color space, extract hue channel information, and generate original hue channel data; Processing the original hue channel data, retaining effective color information, and obtaining purified hue channel data; Obtaining the characteristic hue range of arsenic precipitates and determining the characteristic hue interval of arsenic; Calculate the proportion of pixels in the purified hue channel data that fall into the arsenic characteristic hue interval and generate the hue interval matching rate.

[0010] Furthermore, the waste acid sample image is converted into the HSV color space and segmented to generate arsenic-specific hue separation, which also includes: According to the hue interval matching rate, the waste acid sample image is threshold segmented to obtain the suspected arsenic precipitation area; Calculate the hue consistency within the suspected area of ​​arsenic precipitation and generate the regional hue uniformity; The hue interval matching rate and regional hue uniformity were combined to obtain the arsenic-specific hue separation.

[0011] Furthermore, the precipitation aggregation complexity and arsenic specific chromatic separation were integrated to obtain the precipitation effectiveness index, including: Analyze the dynamic response characteristics of precipitation aggregation complexity and arsenic specific chromatic phase separation along with the precipitation reaction process, and generate characteristic response difference; Based on the characteristic response difference, the precipitation aggregation complexity and arsenic specific color separation were analyzed to obtain the dynamic adaptation weight. The precipitation aggregation complexity and arsenic specific chromatic separation degree are weightedly fused according to the dynamic adaptation weight to obtain the weighted fusion value; The joint distribution characteristics of precipitation aggregation complexity and arsenic specific chromatic phase separation in the numerical space are calculated to generate the joint contribution of the characteristics.

[0012] Furthermore, the precipitation aggregation complexity and arsenic specific chromatic separation are combined to obtain the precipitation effectiveness index, which also includes: The weighted fusion value and the joint contribution of features are integrated to obtain a preliminary effectiveness index; The preliminary effectiveness index is dynamically calibrated according to the characteristic response difference to obtain the precipitation effectiveness index.

[0013] Furthermore, the precipitation effectiveness index is calculated to obtain the dynamic arsenic residual risk coefficient, including: The relationship between the precipitation effectiveness index and arsenic residue was analyzed to generate the effectiveness-residue correlation; The integrity of arsenic precipitates was analyzed based on the complexity of precipitation aggregation to generate a precipitation integrity index; The effectiveness-residue correlation was processed according to the sedimentation integrity index to obtain the integrity weighted correlation value; Calculate the interference degree of other heavy metal ions in the waste acid sample on arsenic and generate the interference correction factor; The integrity weighted correlation value and the interference correction coefficient are integrated to obtain the dynamic arsenic residual risk coefficient.

[0014] Furthermore, based on the dynamic arsenic residual risk coefficient, the qualified mark of arsenic removal is determined, including: Obtain the emission standard limit of arsenic in the waste acid sample, analyze the emission standard limit, and generate a baseline risk threshold; Analyze the fluctuation range of the sedimentation effectiveness index and calculate the correlation fluctuation coefficient between the fluctuation range and the benchmark risk threshold; The benchmark risk threshold is dynamically adjusted based on the correlation volatility coefficient to obtain a real-time judgment threshold.

[0015] Furthermore, based on the dynamic arsenic residual risk coefficient, the arsenic removal qualification mark is determined, which also includes: The sensitivity coefficient of determination is generated based on the combined stability of arsenic-specific chromatic phase separation and precipitation aggregation complexity. Based on the judgment sensitivity coefficient, the dynamic arsenic residual risk coefficient is compared with the real-time judgment threshold to generate an arsenic removal qualified mark.

[0016] In summary, the present invention mainly has the following beneficial effects: Through multi-dimensional feature extraction and analysis, accurate identification of arsenic precipitates was achieved, effectively solving the problem of signal superposition of precipitates with similar morphologies in traditional spectral analysis. Bubble noise was eliminated through 3×3 median filtering, and the background baseline was determined. The regional significance coefficient was extracted, and the precipitate morphology and distribution were analyzed through the contour fractal index and particle aggregation density. The fused precipitation coagulation complexity can accurately reflect the degree of coagulation of arsenic precipitation. At the same time, the image was converted to the HSV color space, and after purification, the arsenic characteristic hue range was locked. Combined with threshold segmentation and hue uniformity analysis, the generated arsenic-specific hue separation can specifically distinguish arsenic from other heavy metal precipitates. This dual feature extraction mechanism of morphology and color improves the accuracy of arsenic precipitate identification.

[0017] By analyzing the dynamic response characteristics of precipitation aggregation complexity and arsenic-specific chromatic phase separation, dynamic adaptation weights are generated and integrated to obtain the precipitation effectiveness index, which fully considers the correlation changes between the two during the reaction process. The effectiveness-residue correlation is corrected by the precipitation integrity index and interference correction coefficient, combined with the weight distribution of interfering ions such as copper, lead, and zinc. The obtained dynamic arsenic residual risk coefficient can truly reflect the arsenic residue status, making the judgment result more in line with the actual titration reaction situation.

[0018] By generating a benchmark risk threshold based on the arsenic emission standard limit, and dynamically adjusting the fluctuation range of the precipitation effectiveness index to obtain a real-time judgment threshold, the key feature stability is integrated through the judgment sensitivity coefficient, and finally the qualified mark is determined by the risk deviation value. The dynamic fluctuation of the reaction and the synergy of the characteristics are fully considered, effectively avoiding the misjudgment caused by detection deviation. At the same time, the combination of automatic sampling titration and image analysis realizes the automation of the detection process, improves the detection efficiency, and ensures the subsequent reuse of waste acid, enabling copper smelting enterprises to achieve the dual goals of environmental protection compliance and resource recycling. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a step diagram of the automatic sampling titration analysis method for the copper smelting acid and wastewater treatment process of the present invention. DETAILED DESCRIPTION

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0021] refer to Figure 1 , an automatic sampling titration analysis method based on the treatment of copper smelting acid wastewater, including: Step S1: collecting waste acid to obtain a waste acid sample; Step S2: performing a titration process on the waste acid sample, obtaining an image of the waste acid sample after the titration reaction is completed, extracting the precipitate contour from the waste acid sample image, and generating a precipitate coagulation complexity; Step S3: converting the waste acid sample image into the HSV color space and segmenting it to generate arsenic-specific color separation; Step S4: fusing the precipitation aggregation complexity and the arsenic specific chromatic phase separation to obtain a precipitation effectiveness index; Step S5: Calculate the precipitation effectiveness index to obtain a dynamic arsenic residual risk coefficient; Step S6: Determine the arsenic removal qualification mark based on the dynamic arsenic residual risk coefficient.

[0022] Through sediment contour extraction and HSV color space segmentation, the precipitation aggregation complexity and arsenic-specific chromatic phase separation are generated respectively. The dynamic arsenic residual risk coefficient is obtained through fusion calculation, which can accurately determine whether the arsenic is completely removed, avoid the detection bias of traditional spectral analysis, improve the accuracy and reliability of arsenic removal judgment in waste acid treatment, and ensure subsequent water reuse.

[0023] In one aspect of this embodiment, the spent acid sample is titrated, comprising: The single sampling volume of the waste acid sample is 50 ml, and 2-3 drops of sodium hydrosulfide solution are added. After the addition, the solution is allowed to stand for 5 seconds and then stirred. After the stirring is completed, the solution is allowed to stand for 15 seconds to complete the titration reaction.

[0024] In one case of this embodiment, extracting sediment contours from the waste acid sample image to generate sediment coagulation complexity includes: The waste acid sample image is segmented, and the regional significance coefficient is generated according to the difference in optical properties between the precipitate and the background. Specifically, the waste acid sample image is subjected to 3×3 pixel median filtering: each pixel in the waste acid sample image is replaced by the median grayscale value of all pixels within the 3×3 range around it (the median grayscale value of a pixel refers to the grayscale value of all pixels within a certain range (3×3 area) around the specified pixel, which is in the middle position after sorting by size), so as to eliminate noise interference such as bubbles generated by the titration reaction; starting from the upper left corner of the waste acid sample image, with a window of 5×5 pixels, it is moved to the right by 1 pixel each time, and after moving to the right end, it is wrapped and continued, traversing the entire image. For each window, the sum of the grayscale values ​​of 25 pixels is calculated and then divided by 25 to obtain the average grayscale value of the window. The average grayscale values ​​of all windows are collected to form a data set; the data set is processed using K-means clustering (K=2): two initial cluster centers are randomly selected, the distance between the mean of each window and the two initial cluster centers is calculated, and the cluster is classified into the cluster with the closer distance to the initial cluster center; re- Calculate the mean of the two clusters as the new cluster center, repeat the iteration until the new cluster center is stable, count the number of windows contained in the two clusters, and take the cluster with more than 60% of the windows. Its final cluster center value is the background reference grayscale value (because the background usually accounts for a higher proportion in the image); for each pixel in the image, subtract the background reference grayscale value from its grayscale value, take the absolute value and divide it by 255 (the image grayscale value range is 0-255), and normalize the calculation result to 0-1, which is the grayscale difference feature value of each pixel; calculate the gradient of the waste acid sample image Gradient intensity and direction, set the low threshold to 50 and the high threshold to 150, and mark pixels with gradient values ​​higher than 150 as confirmed edges (recorded as 1), and pixels with gradient values ​​lower than 50 as non-edges (recorded as 0), and pixels between the two and connected to the confirmed edge are also recorded as 1; for each pixel, multiply its grayscale difference eigenvalue by 0.4 and its edge eigenvalue (1 or 0) by 0.6, and normalize the calculated result to between 0 and 1, which is the regional significance coefficient of the pixel (range 0-1); the higher the value, the more likely the location is sediment; The edges of the sediment in the waste acid sample image were analyzed to obtain the contour fractal index. Specifically, the following steps were performed: forming a binary image (precipitate is 1, background is 0) for pixels with a regional significance coefficient ≥ 0.6, scanning the binary image pixel by pixel from the upper left corner, finding the first pixel with a value of 1 as the starting point, checking the pixels in the eight directions around the pixel in a clockwise direction, filtering out edge points with a value of 1 that were not recorded and whose neighbors contained a value of 0, recording their coordinates and using this as the new starting point to repeat until returning to the starting point, thus obtaining the coordinates of all pixels of the complete contour; using the box dimension method: taking square boxes with side lengths of 1, 2, 4, 8, 16, and 32 pixels, for each size, counting the minimum number of boxes required to cover the entire contour, calculating the natural logarithm of the side length of each box and the natural logarithm of the corresponding number of boxes, mapping these values ​​to points, and finding a line that is closest to all points (so that the sum of the squares of the perpendicular distances from each point to the line is minimized). The absolute value of the slope of this line (the degree of inclination represents the slope) is the contour fractal index; The spatial distribution density of the sediment outline in the waste acid sample image is calculated to generate the particle aggregation density, specifically including: dividing the number of all sediment pixels with a value of 1 in the binary image by the total number of pixels in the image to obtain the area ratio; in the binary image, scanning pixel by pixel from the upper left corner, when encountering a pixel with a value of 1 and no mark, it is regarded as the starting point of an independent sediment particle, and the 8 adjacent pixels above, below, left, right and four diagonal directions of the pixel are checked, and all pixels connected to the starting point and with a value of 1 are grouped into the same group and marked with the same number. After completing a group of marks, continue scanning the image and repeat the above process. The process is repeated until all pixels with a value of 1 are marked. Each number corresponds to an independent sediment particle, and each sediment particle is regarded as a connected domain. For each connected domain, the average value of all pixel coordinates is calculated as the contour center of the sediment particle. For the contour centers of all sediment particles, the straight-line distance between any two contour centers is calculated one by one (that is, the square root of the square of the difference in the horizontal coordinate plus the square of the difference in the vertical coordinate), and the sum of all distances is divided by the total number of distances to obtain the average distance. The area ratio is divided by the average distance, and the result is normalized to 0-1, which is the particle aggregation density. The regional significance coefficient, contour fractal index and particle aggregation density are integrated to obtain the precipitation coagulation complexity, which specifically includes: calculating the average regional significance coefficient of all sediment pixels, recorded as the significant mean; multiplying the significant mean, contour fractal index and particle aggregation density by weights of 0.3, 0.4 and 0.3 respectively, and then adding the three products and normalizing the result to 0-1, which is the precipitation coagulation complexity. The larger the value, the higher the degree of precipitation coagulation. Among them, in the removal of arsenic in copper smelting waste acid, the precipitation coagulation complexity should give priority to reflecting the morphological integrity and aggregation of the precipitation. The contour fractal index directly describes the complexity of the edge of the sediment. The more complex the edge (such as the irregular agglomeration morphology of arsenic sulfide precipitation), the more complete the reaction is, and the influence on the degree of cohesion is the most core, so the highest weight is 0.4; the regional significance coefficient is the basis for identifying sediments, but it mainly reflects the distinction from the background and is relatively greatly affected by image noise. Therefore, the significant mean is used as an auxiliary indicator with a weight of 0.3; the particle aggregation density reflects the spatial density of the sediment. The denser the aggregation, the better the cohesion effect. Its importance is lower than that of the contour fractal index, so the weight is 0.3.

[0025] Bubble noise is eliminated through 3×3 median filtering, and the background baseline grayscale value is determined. The significant coefficient of the sediment area is accurately extracted to effectively eliminate interference. Through the calculation of the contour fractal index and the analysis of the particle aggregation density, the sediment morphology and distribution characteristics can be understood in detail, which solves the problem that traditional methods are difficult to distinguish sediments of similar morphology, improves the specificity of arsenic sediment identification, and generates precipitation aggregation complexity through weighted fusion of the significant mean, contour fractal index and particle aggregation density, highlighting the core role of the contour fractal index, accurately reflecting the reaction sufficiency and aggregation effect of arsenic precipitation, and specifically solving the detection deviation caused by the superposition of spectral signals, thereby improving the accuracy of waste acid arsenic removal judgment.

[0026] In one aspect of this embodiment, the waste acid sample image is converted into the HSV color space and segmented to generate arsenic-specific chromatic separation, including: The waste acid sample image is converted from RGB color space to HSV color space, and the hue channel information is extracted to generate the original hue channel data, specifically including: for the RGB value (red, green, blue, range 0-255) of each pixel in the image, the maximum, minimum and difference of the three are first calculated; if the maximum value is red, the hue value is (green-blue) divided by the difference and multiplied by 60; if the hue value exceeds 0, 360 is added to it; if the maximum value is green, the hue value is (blue-red) divided by the difference and multiplied by 60 and then added to 120; if the maximum value is blue, the hue value is (red-green) divided by the difference and multiplied by 60 and then added to 240; if the difference is 0, the hue value is 0; extract the hue values ​​of all pixels to form the original hue channel data; The original hue channel data is processed to retain valid color information to obtain purified hue channel data, specifically including: calculating the first quartile (the value at the 25% position after all data are sorted in ascending order) and the third quartile (the value at the 75% position) of the original hue channel data, with the difference between the two being the interquartile range; removing data that is less than the first quartile minus 1.5 times the interquartile range and greater than the third quartile plus 1.5 times the interquartile range; for each remaining pixel, taking the sum of the hue values ​​of its eight surrounding pixels and the pixel itself, a total of nine pixels, and dividing the sum by 9; the calculated result is used as the purified hue value of the pixel; the complete data formed after all pixels are processed in this way is the purified hue channel data; Obtaining the characteristic hue range of arsenic precipitates and determining the characteristic hue interval of arsenic includes: based on the typical hue characteristics of known arsenic precipitates (arsenic sulfide), filtering out pixels whose median value is 1 and whose hue meets the typical characteristics from the purified hue channel data, extracting the hue values ​​of these pixels, calculating the minimum value as the lower limit and the maximum value as the upper limit, and this range is the characteristic hue interval of arsenic; Calculate the proportion of pixels in the purified hue channel data that fall into the arsenic characteristic hue interval to generate the hue interval matching rate, specifically including: counting the total number of pixels in the purified hue channel data whose hue values ​​are greater than or equal to the lower limit and less than or equal to the upper limit of the arsenic characteristic hue interval, and at the same time counting the total number of pixels in the purified hue channel data; divide the total number of pixels by the total number of pixels, and normalize the calculation result to between 0 and , which is the hue interval matching rate, used to represent the proportion of matching pixels.

[0027] By converting the waste acid sample image from RGB to HSV color space, extracting the original hue channel data by accurately calculating the hue value, combining the interquartile range to filter outliers and smoothing with the neighborhood mean, the purified hue channel data is obtained, which effectively eliminates the interference of noise and retains the true color information, providing a color basis for distinguishing similar morphological sediments. At the same time, based on the typical hue characteristics of known arsenic sediments, the arsenic characteristic hue interval is determined, and the hue interval matching rate is generated by calculating the proportion of pixels falling into this interval in the purified data. This can specifically identify arsenic sediments and significantly improve their distinction from heavy metal sediments such as copper, lead, and zinc, avoiding misjudgment due to similar morphology.

[0028] In one aspect of this embodiment, the waste acid sample image is converted into the HSV color space and segmented to generate arsenic-specific hue separation, further comprising: Based on the hue interval matching rate, the waste acid sample image is threshold segmented to obtain the suspected arsenic precipitate area. Specifically, the following steps are performed: 0.5 times the hue interval matching rate is used as the segmentation threshold, and the purified hue channel data is processed pixel by pixel to determine whether its hue value is within the arsenic characteristic hue interval. If so, the hue matching degree is set to 1, otherwise it is set to 0; the product of the regional significance coefficient of the pixel and the hue matching degree is calculated. If the product result is greater than the segmentation threshold, it is marked as 1 (suspected arsenic precipitate area), otherwise it is marked as 0. After all pixels are processed, a binary image consisting of 0 and 1 is formed, where the area with 1 is the suspected arsenic precipitate area; Calculate the hue consistency within the suspected arsenic deposit area to generate the regional hue uniformity. Specifically, the following steps are performed: calculate the mean of the purified hue values ​​of all pixels within the suspected arsenic deposit area, calculate the absolute difference between the hue value of each pixel and the mean, sum all the differences and divide them by the total number of pixels to obtain the average deviation. Subtract the ratio of the average deviation to the width of the arsenic characteristic hue interval from 1, and normalize the calculated result to a range of 0-1 to obtain the regional hue uniformity. The hue interval matching rate and the regional hue uniformity were integrated to obtain the arsenic-specific hue separation degree, which specifically included: assigning a weight of 0.6 to the hue interval matching rate and a weight of 0.4 to the regional hue uniformity. The hue interval matching rate and the regional hue uniformity were multiplied by their corresponding weights and then added together. The obtained result was normalized to 0-1, which is the arsenic-specific hue separation degree. Among them, the hue interval matching rate is the core basis for identifying arsenic precipitation, directly reflecting the degree of fit between the pixel and the characteristic hue, and is the prerequisite for regional judgment. Therefore, it is given a higher weight (0.6); the regional hue uniformity is used to verify the hue stability within the region and is a quality correction for the matching result, so it has a slightly lower weight (0.4).

[0029] By using 0.5 times the hue interval matching rate as the segmentation threshold and combining the product of the regional significance coefficient and the hue matching degree for threshold segmentation, the suspected arsenic precipitate area is accurately locked, effectively reducing the interference of similar morphological precipitates such as copper, lead, and zinc, solving the problem of difficulty in distinguishing due to similar morphology in traditional methods, and improving the targeted identification of arsenic precipitates.

[0030] By calculating the regional hue uniformity to reflect the hue stability in the suspected area, and fusing the hue interval matching rate and the regional hue uniformity with weights of 0.6 and 0.4, the arsenic-specific hue separation degree is generated, which significantly improves the accuracy of distinguishing arsenic precipitates from other heavy metal precipitates and avoids detection bias.

[0031] In one case of this embodiment, the precipitation aggregation complexity and the arsenic specific chromatic phase separation are integrated to obtain the precipitation effectiveness index, including: The dynamic response characteristics of precipitation aggregation complexity and arsenic-specific chromatic phase separation as the precipitation reaction progresses were analyzed to generate characteristic response differences. Specifically, the following steps were performed: first, the precipitation aggregation complexity and arsenic-specific chromatic phase separation at different reaction times were recorded to form two sets of time series data (containing values ​​at n and m reaction times, respectively); then, a local distance matrix with n rows and m columns was constructed, in which each element was the absolute difference between the two series values ​​at the corresponding time (i.e., the absolute difference between the precipitation aggregation complexity at time i and the arsenic-specific chromatic phase separation at time j); then, path constraints were set (limiting the path slope to between 0.5 and 2 to avoid excessive distortion). Starting from the upper left corner of the matrix (the initial time), a path to the lower right corner (the end time) was found according to the rule that "each step can only move rightward, downward, or diagonally to the lower right" to minimize the sum of all local distances along the path (the cumulative distance); then, the minimum cumulative distance was divided by the total number of steps in the path and normalized to between 0 and 1. The normalized result was then subtracted from 1 to obtain the characteristic response difference within the range of 0-1. Based on the characteristic response difference, the precipitation aggregation complexity and the arsenic specific chromatic phase separation were analyzed to obtain the dynamic adaptation weight. Specifically, the weights of the precipitation aggregation complexity and the arsenic specific chromatic phase separation were allocated according to a fixed proportion based on the characteristic response difference. When the characteristic response difference = 0 (the precipitation aggregation complexity and the arsenic specific chromatic phase separation are completely synchronized), the precipitation aggregation complexity weight is set to 0.5, and the arsenic specific chromatic phase separation weight is 0.5; when the characteristic response difference = 1 (the precipitation aggregation complexity and the arsenic specific chromatic phase separation are completely synchronized), the precipitation aggregation complexity weight is set to 0.5, and the arsenic specific chromatic phase separation weight is 0.5. When the separation response is completely out of sync), the precipitation aggregation complexity weight is set to 0.6, and the arsenic specific chromatic phase separation weight is 0.4; when the characteristic response difference is between 0 and 1, the precipitation aggregation complexity weight = 0.5 + 0.1 × characteristic response difference, and the arsenic specific chromatic phase separation weight = 0.5 - 0.1 × characteristic response difference, ensuring that the weight is continuously adjusted linearly with the change of characteristic response difference, and the sum of the two weights is always 1, thereby obtaining the dynamic adaptive weight of precipitation aggregation complexity and arsenic specific chromatic phase separation; The precipitation coagulation complexity and arsenic-specific chromatic phase separation are weightedly fused according to the dynamic adaptation weight to obtain a weighted fusion value, specifically including: dividing the numerical range of precipitation coagulation complexity (0-1) and arsenic-specific chromatic phase separation (0-1) into 10 intervals (each interval width is 0.1) to form a 10×10 two-dimensional grid (a total of 100 units), counting the number of two-dimensional data points of the waste acid sample at different times during the titration reaction (each two-dimensional data point is the precipitation coagulation complexity and arsenic-specific chromatic phase separation at the corresponding time) that fall into each grid unit, dividing the number of each unit by the total number of dimensional data points to obtain the unit proportion; calculating the interval median of the precipitation coagulation complexity and arsenic-specific chromatic phase separation of each unit (for example, the median of the first interval is 0.05, the second interval is 0.15, and so on), multiplying the unit proportion by the product of the two medians, and then summing the results of 100 units and normalizing them to 0-1, which is the feature joint contribution.

[0032] By analyzing the dynamic response characteristics of precipitation and aggregation complexity and arsenic-specific chromatic phase separation, constructing a local distance matrix and finding the minimum cumulative distance path, the characteristic response difference is generated to determine the dynamic adaptation weight. This dynamic adjustment method can accurately adapt to the synchronous changes of the two in the reaction process, avoiding the deviation caused by fixed weights, solving the problem of traditional methods that it is difficult to take into account the dynamic correlation between morphology and color characteristics, and improving the flexibility and accuracy of the analysis.

[0033] The distribution of statistical data points is recorded through a two-dimensional grid, and the joint contribution of features is calculated to achieve weighted fusion. This process comprehensively considers the synergistic effect of precipitation coagulation and chromatic separation, highlights the joint impact of the two on the effectiveness of arsenic precipitation, effectively distinguishes arsenic from other heavy metal precipitates, and facilitates the subsequent accurate judgment of whether the arsenic has been completely removed.

[0034] In one case of this embodiment, the precipitation aggregation complexity and the arsenic specific chromatic separation degree are integrated to obtain the precipitation effectiveness index, which also includes: The weighted fusion value and the joint contribution of features are integrated to obtain a preliminary effectiveness index. Specifically, when integrating the weighted fusion value and the joint contribution of features, a weighted summation algorithm is used, and a weight of 0.6 is assigned to the weighted fusion value and a weight of 0.4 to the joint contribution of features. The two are multiplied by the corresponding weights and then added together. The sum is normalized to the range of 0-1, which is the preliminary effectiveness index. The preliminary effectiveness index is dynamically calibrated according to the characteristic response difference to obtain the precipitation effectiveness index, specifically: when the characteristic response difference is 0 (complete synchronization), the calibration coefficient is 0.8; when the characteristic response difference is 1 (completely out of sync), the calibration coefficient is 1; when the characteristic response difference is between 0-1, the calibration coefficient = 0.8 + 0.2 × characteristic response difference; the preliminary effectiveness index is multiplied by the calibration coefficient, and the calculated result is normalized to 0-1, which is the precipitation effectiveness index.

[0035] By fusing the weighted fusion value and the joint contribution of the features, a preliminary effectiveness index is obtained, and then the calibration coefficient is dynamically adjusted according to the difference in feature responses to generate a precipitation effectiveness index. This process not only highlights the dominant role of the core features, but also adapts to the response differences between the two through dynamic calibration, accurately reflecting the true effectiveness of arsenic precipitation, and effectively solving the judgment bias caused by the superposition of similar precipitate signals in traditional methods, further ensuring the accuracy of waste acid arsenic removal detection.

[0036] In one case of this embodiment, the precipitation effectiveness index is calculated to obtain a dynamic arsenic residual risk coefficient, including: Analyze the relationship between the precipitation effectiveness index and arsenic residue to generate the effectiveness-residue correlation, specifically including: obtaining the arsenic residue from historical titration analysis, subdividing the precipitation effectiveness index (0-1) into 100 scales at intervals of 0.01, recording the historical maximum and minimum arsenic residue corresponding to each scale, and calculating the difference between the historical maximum and minimum values, which is the residual fluctuation amplitude of that scale; taking the largest fluctuation amplitude among all scales as the baseline value, dividing the residual fluctuation amplitude of the scale corresponding to the current precipitation effectiveness index by the baseline value to obtain the fluctuation ratio, subtracting the fluctuation ratio from 1, and normalizing to 0-1, which is the effectiveness-residue correlation; The integrity of the arsenic precipitates was analyzed based on the complexity of precipitation and coagulation to generate a precipitation integrity index. Specifically, the maximum historical value of precipitation and coagulation complexity was set to 1 (completely intact) and the minimum value was set to 0 (no precipitation). The ratio of the current precipitation and coagulation complexity to the historical maximum value was used as the base value. The base value was multiplied by 0.9 and a baseline compensation value of 0.1 was added. The calculated result was normalized to 0-1 to obtain the precipitation integrity index. The higher the value of the precipitation integrity index, the more complete the arsenic precipitate. The effectiveness-residue correlation is processed according to the sedimentation integrity index to obtain an integrity weighted correlation value, specifically including: taking the sedimentation integrity index as a benchmark, calculating the absolute difference between the sedimentation integrity index and 0.5 to obtain the integrity deviation; subtracting the integrity deviation from 1 to obtain a correction coefficient, multiplying the effectiveness-residue correlation by the correction coefficient, and normalizing the calculated result to 0-1 to obtain an integrity weighted correlation value, wherein the closer the integrity weighted correlation value is to 0.5, the smaller the impact of the correction coefficient on the correlation; Calculate the degree of interference of other heavy metal ions on arsenic in the waste acid sample and generate an interference correction coefficient, specifically including: taking copper, lead, and zinc as the main interfering ions, obtaining the historical maximum interference concentrations of copper, lead, and zinc, setting their historical maximum interference concentrations as the standard threshold, obtaining the concentration of each ion in the waste acid sample, calculating the ratio of each ion concentration to its corresponding standard threshold, and obtaining the single ion interference ratio; assigning weights of 0.4 for copper, 0.3 for lead, and 0.3 for zinc, multiplying the single ion interference ratios of copper, lead, and zinc by their corresponding weights and adding them together to obtain the total interference ratio; subtracting the total interference ratio from 1 and normalizing the result to 0-1, which is the interference correction coefficient. The higher the interference correction coefficient value, the smaller the interference; among them, in the titration reaction, copper ions usually interfere most significantly with arsenic, so the weight is higher (0.4), while lead and zinc have similar interference degrees on arsenic, so they are assigned the same weight (0.3); The integrity weighted correlation value and the interference correction coefficient are integrated to obtain the dynamic arsenic residual risk coefficient, which specifically includes: setting the integrity weighted correlation value weight to 0.6 and the interference correction coefficient weight to 0.4, calculating the sum of the products of the integrity weighted correlation value and the interference correction coefficient with the corresponding weights, and then dividing it by the total weight (1), and normalizing the result to 0-1, which is the dynamic arsenic residual risk coefficient. Among them, the lower the value of the dynamic arsenic residual risk coefficient, the higher the arsenic residual risk; Among them, in the arsenic removal of copper smelting dirty acid, the integrity weighted correlation value directly reflects the reliable correlation between precipitation effectiveness and arsenic residue, and the precipitation integrity is the core basis for the effective removal of arsenic. The more complete the precipitate, the less likely arsenic will remain. This is the core basis for judging the arsenic removal effect, so it is given a higher weight (0.6); and the interference correction coefficient reflects the interference of other heavy metals on arsenic, but interference is a secondary influence and is not the fundamental factor in determining the qualification of arsenic removal, so the weight is lower (0.4).

[0037] By analyzing the historical data of precipitation effectiveness index and arsenic residue, the effectiveness-residue correlation was generated, and the integrity weighted correlation value was obtained by combining the precipitation integrity index correction. The correlation between precipitation effectiveness and arsenic residue was accurately analyzed, and the influence of the integrity of arsenic precipitates on residues was fully considered. The correlation deviation caused by the traditional method of ignoring the integrity of precipitation morphology was solved. At the same time, based on the influence of interfering ions such as copper, lead, and zinc, different weights were assigned according to the degree of interference to calculate the interference correction coefficient. The integrity weighted correlation value and the interference correction coefficient were then combined to obtain the dynamic arsenic residue risk coefficient, which effectively offset the interference of similar heavy metal precipitates and avoid the misjudgment caused by the superposition of spectral signals, making the arsenic residue risk assessment more in line with reality and significantly improving the accuracy of waste acid arsenic removal detection.

[0038] In one case of this embodiment, determining the arsenic removal qualification mark based on the dynamic arsenic residual risk coefficient includes: Obtain the emission standard limit for arsenic in the waste acid sample, analyze the emission standard limit, and generate a baseline risk threshold. Specifically, the following steps are performed: obtain the emission standard limit for arsenic, set the precipitation effectiveness index to 1.0 (theoretical optimal state), and determine that the corresponding risk factor should match the emission standard limit. Use the emission standard limit as the baseline reference value, divide the baseline reference value by the historical maximum arsenic residue, and normalize the resulting ratio to 0-1, which is the baseline risk threshold. Analyze the fluctuation range of the sedimentation effectiveness index and calculate the correlation fluctuation coefficient between the fluctuation range and the benchmark risk threshold. Specifically, based on the current sedimentation effectiveness index, set its theoretical fluctuation range to ±0.1 (i.e., the current value minus 0.1 to plus 0.1). The difference between the intervals is the fluctuation range. Calculate the ratio of the fluctuation range to the benchmark risk threshold to obtain the original coefficient. Divide the original coefficient by 0.2 (the theoretical maximum fluctuation range) and normalize the calculated result to 0-1, which is the correlation fluctuation coefficient. The benchmark risk threshold is dynamically adjusted based on the correlation fluctuation coefficient to obtain the real-time judgment threshold, specifically including: multiplying the correlation fluctuation coefficient by 0.2 (the upper limit of the adjustment range) to obtain the threshold correction amount, adding the threshold correction amount to the benchmark risk threshold, and normalizing the result to between 0-1, which is the real-time judgment threshold.

[0039] By taking the arsenic emission standard limit as a benchmark, combining the theoretical optimal state of the precipitation effectiveness index and the historical maximum arsenic residual, a benchmark risk threshold is generated, so that the benchmark risk threshold setting is closely linked to the actual emission requirements, solving the problem of insufficient applicability caused by the traditional judgment standard being separated from the emission standard, and providing a benchmark that meets the requirements for arsenic removal qualification judgment, ensuring that the judgment results meet environmental protection standards.

[0040] By analyzing the fluctuation range of the precipitation effectiveness index and calculating the associated fluctuation coefficient, the baseline risk threshold is dynamically adjusted to obtain a real-time judgment threshold. This adjustment mechanism fully considers the actual fluctuations in precipitation effectiveness, avoids the drawback of fixed thresholds that are difficult to adapt to dynamic changes in response, and effectively offsets the deviation caused by interference from similar sediments, making the determination of arsenic removal qualification more accurate and improving the accuracy of judgment on whether waste acid treatment meets standards.

[0041] In one case of this embodiment, determining the arsenic removal qualification mark based on the dynamic arsenic residual risk coefficient further includes: Based on the joint stability of the arsenic-specific chromatic phase separation and the precipitation and aggregation complexity, a determination sensitivity coefficient is generated, specifically including: calculating the absolute difference between the arsenic-specific chromatic phase separation and the precipitation and aggregation complexity, and calculating the mean of the sum of the two absolute differences, subtracting 1 (the ratio of the absolute difference to the mean), and normalizing the calculation result to 0-1, which is the determination sensitivity coefficient; Based on the judgment sensitivity coefficient, the dynamic arsenic residual risk coefficient is compared with the real-time judgment threshold to generate an arsenic removal qualified mark, specifically including: calculating the difference between the dynamic arsenic residual risk coefficient and the real-time judgment threshold, multiplying the difference by the judgment sensitivity coefficient, and obtaining the risk deviation value; if the risk deviation value ≥ 0, it is judged to be qualified, then the arsenic removal qualified mark is 1, indicating that the arsenic removal is qualified; if the risk deviation value < 0, it is judged to be unqualified, then the arsenic removal qualified mark is 0, indicating that the arsenic removal is unqualified, and an alarm is triggered.

[0042] By calculating the absolute difference and related mean between the arsenic-specific chromatic phase separation and the precipitation coagulation complexity, a judgment sensitivity coefficient is generated, which can accurately reflect the joint stability of the two, solves the problem of ignoring the synergy of key features in traditional judgment, provides a sensitive feature stability basis for qualified judgment, and enhances the ability to distinguish interference from similar precipitates. At the same time, combined with the judgment sensitivity coefficient, the dynamic arsenic residual risk coefficient is compared with the real-time judgment threshold, and the arsenic removal qualified mark is determined by the risk deviation value. When the risk deviation value is ≥0, it is judged to be qualified, otherwise it is unqualified and an alarm is triggered. It fully utilizes the feature stability information, effectively avoids misjudgment caused by detection deviation, and improves the accuracy and timeliness of arsenic removal qualified judgment.

[0043] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An automatic sampling and titration analysis method based on the treatment of copper smelting acid and wastewater, characterized in that: include: Step S1: collecting waste acid to obtain a waste acid sample; Step S2: performing a titration process on the waste acid sample, obtaining an image of the waste acid sample after the titration reaction is completed, extracting the precipitate contour from the waste acid sample image, and generating a precipitate coagulation complexity; Step S3: converting the waste acid sample image into the HSV color space and segmenting it to generate arsenic-specific color separation; Step S4: fusing the precipitation aggregation complexity and the arsenic specific chromatic phase separation to obtain a precipitation effectiveness index; Step S5: Calculate the precipitation effectiveness index to obtain a dynamic arsenic residual risk coefficient; Step S6: Determine the arsenic removal qualification mark based on the dynamic arsenic residual risk coefficient.

2. The automatic sampling titration analysis method based on the treatment of copper smelting acid and wastewater according to claim 1 is characterized in that: Titration of spent acid samples including: The single sampling volume of the waste acid sample is 50 ml, and sodium hydrosulfide solution is added to carry out titration reaction.

3. The automatic sampling titration analysis method based on the treatment of copper smelting acid and wastewater according to claim 1 is characterized in that: Extract sediment contours from waste acid sample images and generate sediment coagulation complexity, including: The waste acid sample image is segmented and the regional significance coefficient is generated based on the difference in optical properties between the sediment and the background. The edges of the sediments in the waste acid sample images were analyzed to obtain the contour fractal index; Calculate the spatial distribution density of sediment outlines in the waste acid sample image to generate the particle aggregation density; The regional significance coefficient, silhouette fractal index and particle aggregation density are integrated to obtain the precipitation aggregation complexity.

4. The automatic sampling titration analysis method based on the treatment of copper smelting acid and wastewater according to claim 1 is characterized in that: The waste acid sample image is converted to the HSV color space and segmented to generate arsenic-specific color separation, including: Convert the waste acid sample image from RGB color space to HSV color space, extract hue channel information, and generate original hue channel data; Processing the original hue channel data, retaining effective color information, and obtaining purified hue channel data; Obtaining the characteristic hue range of arsenic precipitates and determining the characteristic hue interval of arsenic; Calculate the proportion of pixels in the purified hue channel data that fall into the arsenic characteristic hue interval and generate the hue interval matching rate.

5. The automatic sampling titration analysis method based on the treatment of copper smelting acid and wastewater according to claim 4 is characterized in that: The waste acid sample image is converted to HSV color space and segmented to generate arsenic-specific color separation, including: According to the hue interval matching rate, the waste acid sample image is threshold segmented to obtain the suspected arsenic precipitation area; Calculate the hue consistency within the suspected area of ​​arsenic precipitation and generate the regional hue uniformity; The hue interval matching rate and regional hue uniformity were combined to obtain the arsenic-specific hue separation.

6. The automatic sampling titration analysis method based on the treatment of copper smelting acid and wastewater according to claim 5 is characterized in that: The precipitation effectiveness index is obtained by integrating the precipitation aggregation complexity and the arsenic specific chromatic separation, including: Analyze the dynamic response characteristics of precipitation aggregation complexity and arsenic specific chromatic phase separation along with the precipitation reaction process, and generate characteristic response difference; Based on the characteristic response difference, the precipitation aggregation complexity and arsenic specific color separation were analyzed to obtain the dynamic adaptation weight. The precipitation aggregation complexity and arsenic specific chromatic separation degree are weightedly fused according to the dynamic adaptation weight to obtain the weighted fusion value; The joint distribution characteristics of precipitation aggregation complexity and arsenic specific chromatic phase separation in the numerical space are calculated to generate the joint contribution of the characteristics.

7. The automatic sampling titration analysis method based on the treatment of copper smelting acid and wastewater according to claim 6 is characterized in that: The precipitation effectiveness index is obtained by combining the precipitation aggregation complexity and the arsenic specific chromatic separation, which also includes: The weighted fusion value and the joint contribution of features are integrated to obtain a preliminary effectiveness index; The preliminary effectiveness index is dynamically calibrated according to the characteristic response difference to obtain the precipitation effectiveness index.

8. The automatic sampling titration analysis method based on the treatment of copper smelting acid and wastewater according to claim 7 is characterized in that: The precipitation effectiveness index is calculated to obtain the dynamic arsenic residual risk coefficient, including: The relationship between the precipitation effectiveness index and arsenic residue was analyzed to generate the effectiveness-residue correlation; The integrity of arsenic precipitates was analyzed based on the complexity of precipitation aggregation to generate a precipitation integrity index; The effectiveness-residue correlation was processed according to the sedimentation integrity index to obtain the integrity weighted correlation value; Calculate the interference degree of other heavy metal ions in the waste acid sample on arsenic and generate the interference correction factor; The integrity weighted correlation value and the interference correction coefficient are integrated to obtain the dynamic arsenic residual risk coefficient.

9. The automatic sampling titration analysis method based on the treatment of copper smelting acid and wastewater according to claim 8 is characterized in that: Based on the dynamic arsenic residual risk coefficient, the qualified mark of arsenic removal is determined, including: Obtain the emission standard limit of arsenic in the waste acid sample, analyze the emission standard limit, and generate a baseline risk threshold; Analyze the fluctuation range of the sedimentation effectiveness index and calculate the correlation fluctuation coefficient between the fluctuation range and the benchmark risk threshold; The benchmark risk threshold is dynamically adjusted based on the correlation volatility coefficient to obtain a real-time judgment threshold.

10. The automatic sampling titration analysis method based on the treatment of copper smelting acid and wastewater according to claim 9 is characterized in that: Based on the dynamic arsenic residual risk coefficient, the arsenic removal qualification mark is determined, which also includes: The sensitivity coefficient of determination is generated based on the combined stability of arsenic-specific chromatic phase separation and precipitation aggregation complexity. Based on the judgment sensitivity coefficient, the dynamic arsenic residual risk coefficient is compared with the real-time judgment threshold to generate an arsenic removal qualified mark.

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