Method for preparing refined tellurium from copper telluride slag

Abstract

The invention relates to the technical field of tellurium preparation, in particular to a method for preparing refined tellurium from copper telluride slag. Performing time sequence decomposition on the concentration time sequence curve, and obtaining outlier residual points according to discrete characteristics of the residual points in the residual curve; obtaining a slow fluctuation value according to the distance characteristics of the outlier residual points and the adjacent residual points and the data change characteristics of the neighborhood residual points; obtaining a change correlation degree according to the outlier residual points and residual points of other target products; obtaining a suspected oxygen pressure abnormal point according to the slow fluctuation value and the change correlation degree; clustering is carried out according to the distribution characteristics of the suspected oxygen pressure abnormal points, and oxygen pressure abnormal moments are obtained according to the area characteristics of the point clusters and the density characteristics and the distribution characteristics of the residual points in the clusters. The oxygen pressure abnormal trend value is obtained according to the distribution characteristics and the abnormal degree of the oxygen pressure abnormal moment, and the oxygen pressure is adjusted and early warned, so that the timeliness and the accuracy of monitoring are improved, and the tellurium preparation effect is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of tellurium preparation, and in particular to a method for preparing refined tellurium from telluride copper slag. Background Art

[0002] The preparation of refined tellurium from copper telluride slag refers to the process of mixing copper telluride slag with an oxidant and subjecting it to oxygen pressure alkaline leaching to obtain alkaline leaching residue containing sodium tellurate and copper oxide. The alkaline leaching residue is subjected to sulfidation leaching to separate copper and some rare elements, and tellurium is enriched in the sulfidation leaching solution. Finally, the sulfidation leaching solution is reduced to recover refined tellurium. In the preparation process, if the reaction parameters of the oxygen pressure alkaline leaching link are not well controlled, it will directly lead to low leaching rate, impurity dissolution or incomplete tellurium oxidation. Therefore, real-time monitoring is required during the reaction process to ensure the normal progress of the reaction. The oxygen pressure parameter in the oxygen pressure alkaline leaching process has a great influence on the reaction. The oxygen pressure parameter is usually monitored based on the oxygen pressure threshold to see if it is abnormal. However, the range of monitoring through the threshold is large, and the abnormal reaction may have been carried out for a period of time when the threshold is approaching. As a result, the accuracy and timeliness of monitoring the oxygen pressure state through the oxygen pressure threshold are low, which ultimately affects the preparation effect of refined tellurium. Summary of the Invention

[0003] In order to solve the technical problem that the oxygen pressure parameter monitored during alkaline leaching by using the oxygen pressure threshold will result in low monitoring accuracy and timeliness, thus affecting the production of refined tellurium, the present invention aims to provide a method for preparing refined tellurium from copper telluride slag. The technical solution adopted is as follows: Obtain the concentration time series curve of the target product during oxygen pressure alkali leaching; Performing time series decomposition on the concentration time series curve to obtain a residual curve; obtaining an outlier residual point based on the discrete characteristics of the residual points in the residual curve; obtaining a slow fluctuation value based on the distance characteristics between the outlier residual point and adjacent residual points and the data change characteristics of the residual points in the neighborhood of the outlier residual point; obtaining a change correlation degree based on the difference characteristics of the adjacent data changes of the outlier residual point and residual points of other target products at the same time; obtaining a first oxygen pressure anomaly degree of the outlier residual point based on the slow fluctuation value and the change correlation degree; and obtaining a suspected oxygen pressure anomaly point based on the first oxygen pressure anomaly degree; Clustering is performed based on the distribution characteristics of the suspected oxygen pressure anomaly points to obtain different point clusters; a second oxygen pressure anomaly degree of the suspected oxygen pressure anomaly point is obtained based on the area characteristics of the point cluster, the density characteristics and distribution characteristics of the residual points within the cluster; the moment of oxygen pressure anomaly is obtained based on the second oxygen pressure anomaly degree; and an oxygen pressure anomaly trend value is obtained based on the distribution characteristics and the degree of anomaly of the oxygen pressure anomaly moment. The oxygen pressure is adjusted and warned according to the abnormal trend of the oxygen pressure.

[0004] Furthermore, the step of obtaining outlier residual points according to the discrete features of the residual points in the residual curve includes: Construct a residual mean line according to the average value of all residual points in the residual curve; calculate the average value of the distance between all residual points and the residual mean line and distance standard deviation , the distance between the residual point in the residual curve and the residual mean line is not The residual points within the range are regarded as outlier residual points.

[0005] Furthermore, the step of obtaining the slow fluctuation value according to the distance characteristics between the outlier residual point and the adjacent residual points and the data change characteristics of the residual points in the neighborhood of the outlier residual point includes: Calculate the sum of the Euclidean distances between the outlier residual point and the two adjacent residual points before and after to obtain a comprehensive distance; calculate the slope of the line connecting any residual point and the previous adjacent residual point to obtain the degree of change of the arbitrary residual point; calculate the average of the absolute values ​​of the differences in the degrees of change between all adjacent residual points within a preset front adjacent range of the outlier residual point to obtain a first change difference value; calculate the average of the absolute values ​​of the differences in the degrees of change between all adjacent residual points within a preset rear adjacent range of the outlier residual point to obtain a second change difference value; calculate the reciprocal of the sum of the first change difference value and the second change difference value to obtain a third value; calculate the product of the reciprocal of the comprehensive distance and the third value to obtain a slow fluctuation value of the outlier residual point.

[0006] Furthermore, the step of obtaining the degree of change correlation based on the adjacent data change difference characteristics between the outlier residual point and the residual points of other target products at the same time includes: Normalize the comprehensive distance of the residual points of other target generated objects at the moment when the outlier residual point is located to obtain a fourth value; normalize the comprehensive distance of the outlier residual point and calculate the inverse of the absolute value of the difference with the fourth value to obtain the degree of correlation of the change of the outlier residual point.

[0007] Furthermore, the step of obtaining the first oxygen pressure anomaly degree of the outlier residual point according to the slow fluctuation value and the change correlation degree includes: The slow fluctuation value and the change correlation degree are normalized respectively, and the normalized average values ​​are calculated to obtain the first oxygen pressure anomaly degree of the outlier residual point.

[0008] Furthermore, the step of obtaining a suspected oxygen pressure abnormal point according to the first oxygen pressure abnormality degree includes: The outlier residual point whose first oxygen pressure anomaly degree exceeds a preset first threshold is taken as the suspected oxygen pressure anomaly point.

[0009] Furthermore, the step of obtaining a second oxygen pressure anomaly degree of the suspected oxygen pressure anomaly point based on the area characteristics of the point cluster, the density characteristics and the distribution characteristics of the residual points within the cluster includes: The area of ​​the minimum circumscribed circle of the point cluster is calculated and normalized to obtain an area eigenvalue; the average value of the nearest neighbor distances of all suspected oxygen pressure anomaly points in the point cluster is calculated and normalized to obtain an interval eigenvalue; the standard deviation of the nearest neighbor distances of all suspected oxygen pressure anomaly points in the point cluster is calculated and normalized to obtain a distribution discrete eigenvalue; the average value of the area eigenvalue, the interval eigenvalue, and the distribution discrete eigenvalue is calculated to obtain a second oxygen pressure anomaly degree of all suspected oxygen pressure anomaly points in the point cluster.

[0010] Furthermore, the step of obtaining the oxygen pressure abnormality time according to the second oxygen pressure abnormality degree includes: The time at which the second oxygen pressure abnormality degree exceeds the preset second threshold value and the suspected oxygen pressure abnormality point occurs is used as the oxygen pressure abnormality time.

[0011] Furthermore, the step of obtaining an abnormal trend value of oxygen pressure according to the distribution characteristics and abnormal degree of the abnormal oxygen pressure at the time of abnormality includes: Calculate the number ratio of the oxygen pressure abnormal moments after the middle moment and before the middle moment of the concentration time series curve to obtain the number ratio; calculate the average value of the distances between the residual points at the oxygen pressure abnormal moment after the middle moment and the residual mean line to obtain a first distance; calculate the average value of the distances between the residual points at the oxygen pressure abnormal moment before the middle moment and the residual mean line to obtain a second distance; calculate the ratio of the first distance to the second distance to obtain the abnormal ratio; calculate the product of the number ratio and the abnormal ratio to obtain the oxygen pressure abnormal trend value.

[0012] Furthermore, the step of adjusting and warning the oxygen pressure according to the abnormal trend of the oxygen pressure includes: The sum of the oxygen pressure abnormal trend value and a constant 1 is calculated to obtain an adjustment coefficient; the product of the adjustment coefficient and a preset basic oxygen pressure is calculated to obtain an adjusted oxygen pressure value; when the oxygen pressure abnormal trend value exceeds a preset abnormal trend threshold, an early warning is issued for the oxygen pressure alkali leaching process.

[0013] The present invention has the following beneficial effects: In the present invention, obtaining the residual curve can determine the degree to which each moment in the concentration time series curve does not conform to the periodic characteristics and trend characteristics, and obtaining the outlier residual point can determine the moment that obviously does not conform to the periodic characteristics and trend characteristics, and then judge the oxygen pressure anomaly in the oxygen pressure alkali leaching process through the outlier residual point. Since there is noise data in the concentration collection process of the target product, the outlier residual points caused by the noise data and the outlier residual points caused by the reaction anomaly have obvious characteristic differences in the local area, so obtaining the slow fluctuation value and the degree of correlation of the change can characterize whether the outlier residual point is caused by noise data; obtaining the first oxygen pressure anomaly degree can characterize the possibility that the outlier residual point is caused by noise data; obtaining the suspected oxygen pressure anomaly point can determine the outlier residual point caused by non-noise data, and preliminarily improve the accuracy of reaction monitoring. Obtaining different point clusters can further determine the cause of the suspected oxygen pressure anomaly point; obtaining the second oxygen pressure anomaly degree can characterize the possibility that the suspected oxygen pressure anomaly point is caused by oxygen pressure anomaly, and obtaining the oxygen pressure anomaly moment can improve the accuracy of oxygen pressure monitoring. Obtaining the trend value of oxygen pressure anomaly can determine whether the oxygen pressure anomaly has a trend of gradually becoming more serious, and ultimately adjust the oxygen pressure and issue an early warning based on the trend of oxygen pressure anomaly. Compared with threshold monitoring, it improves the timeliness and accuracy of reaction monitoring and ensures the preparation effect of refined tellurium. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0015] Figure 1 A flow chart of a method for preparing refined tellurium from telluride copper slag provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0016] To further illustrate the technical means and effectiveness of the present invention in achieving its intended objectives, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effectiveness of a method for producing refined tellurium from telluride copper slag according to the present invention. In the following description, references to different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0017] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0018] A specific scheme of a method for preparing refined tellurium from telluride copper slag provided by the present invention is described in detail below with reference to the accompanying drawings.

[0019] See also Figure 1 , which shows a flow chart of a method for preparing refined tellurium from telluride copper slag provided by one embodiment of the present invention, the method comprising the following steps: Step S1, obtaining a concentration time series curve of a target product during oxygen pressure alkali leaching.

[0020] First, the concentration time series curve of the target product during oxygen pressure alkaline leaching is obtained. The reaction process of copper telluride slag mixed with oxidant for oxygen pressure alkaline leaching is: 、 , from the beginning to the end of the oxygen pressure alkali leaching reaction, the target product was monitored by spectrophotometry Tellurite ions and The concentration of copper oxide is monitored and collected at a frequency of 10 Hz. The concentration time series curve of the target product is updated in real time according to the collection sequence. In the embodiment of the present invention, the concentration time series segment 60 seconds before the current moment in the reaction process is used as the latest concentration time series curve, which can be determined by the implementer according to the implementation scenario.

[0021] Step S2, performing time series decomposition on the concentration time series curve to obtain a residual curve; obtaining an outlier residual point based on the discrete characteristics of the residual point in the residual curve; obtaining a slow fluctuation value based on the distance characteristics between the outlier residual point and the adjacent residual points, and the data change characteristics of the residual points in the neighborhood of the outlier residual point; obtaining a change correlation degree based on the difference characteristics of the adjacent data changes of the outlier residual point and the residual points of other target products at the same time; obtaining a first oxygen pressure anomaly degree of the outlier residual point based on the slow fluctuation value and the change correlation degree; obtaining a suspected oxygen pressure anomaly point based on the first oxygen pressure anomaly degree.

[0022] During oxygen-pressure alkali leaching, oxygen pressure affects the concentration of the target product during both reactions. Therefore, the concentration time-series curve of any target product can be analyzed to determine whether the oxygen pressure is abnormal. As oxygen-pressure alkali leaching proceeds, the concentration of the target product gradually increases, with the concentration value exhibiting a trend-like, slow change. Furthermore, the reaction rate will not remain consistent throughout the reaction. Because oxygen-pressure alkali leaching typically utilizes automatic alkali replenishment or intermittent heating, fluctuations in liquid phase conditions can cause periodic fluctuations in the concentration of the target product. Therefore, the concentration time-series curve of the target product exhibits certain trends and periodicity. Time-series decomposition can then be performed on the concentration time-series curve with periodicity and trend, yielding a residual curve. Time-series decomposition is a well-known technique that can yield the trend term, period term, and residual term of the original data. The specific decomposition steps will not be detailed here. The residual curve represents the residual term corresponding to the concentration time-series curve. Since the residual term is obtained by subtracting the period term and trend term from the original curve, the residual point in the residual curve reflects the degree of deviation from the period and trend at that moment in the concentration time-series curve. The more discrete the residual degree of the residual point in the residual curve is, the more the concentration at that moment deviates from the normal periodic characteristics and trend characteristics, and the more likely the oxygen pressure is abnormal. Therefore, the outlier residual point can be obtained according to the discrete characteristics of the residual point in the residual curve.

[0023] Preferably, in an embodiment of the present invention, the step of obtaining outlier residual points includes: constructing a residual mean line according to the average value of all residual points in the residual curve; when the residual point is farther away from the residual mean line, it means that the distribution of the residual point is more discrete, and the degree of deviation from the cycle and trend in the concentration time series curve at that moment is greater. Calculate the average value of the distance of all residual points from the residual mean line and distance standard deviation , the distance between the residual point in the residual curve and the residual mean line is not The residual points within the range are regarded as outlier residual points. The acquisition of outlier residual points is based on the existing According to the criterion, outlier residual points are more outliers than other residual points. The time when the outlier residual points are located represents the periodic and trend characteristics of the concentration that deviate from the normal ones, and there may be a risk of abnormal reaction.

[0024] Furthermore, since there are many factors that cause outlier residual points, in order to improve monitoring accuracy, it is necessary to determine the causes of outlier residual points. In actual monitoring, noise data will inevitably exist during the acquisition process, which may cause some outlier residual points to be generated by noise data. The main anomaly in the oxygen pressure alkali leaching reaction is oxygen pressure anomaly. The uneven oxidant supply rate caused by low or intermittent oxygen pressure will cause the oxidation of tellurium to be interrupted or repeated, resulting in concentration fluctuations. The effect of oxygen pressure on concentration is gradual, and the waveform is gentle. Therefore, in the residual curve, the outlier residual point caused by oxygen pressure anomaly is close to the adjacent residual point; and because it shows a gradual deviation feature, the change characteristics of other residual points in its neighborhood are similar. Noise data is usually a sudden event with strong randomness, and the appearance of outlier residual points caused by noise data is more abrupt. Therefore, the slow fluctuation value can be obtained based on the distance characteristics between the outlier residual point and the adjacent residual point, and the data change characteristics of the residual points in the neighborhood of the outlier residual point.

[0025] Preferably, in an embodiment of the present invention, the step of obtaining a slow fluctuation value includes: calculating the sum of the Euclidean distances between the outlier residual point and its two preceding and succeeding adjacent residual points to obtain a comprehensive distance; a smaller comprehensive distance indicates a closer distance between the residual points, making the outlier residual point more likely to be caused by an oxygen pressure anomaly and less likely to be caused by noise. The slope of the line connecting any residual point and its preceding adjacent residual point is calculated to obtain the degree of variation of any residual point; the degree of variation reflects the magnitude of variation of the residual point. A first variation difference value is obtained by calculating the average absolute value of the difference in degree of variation between all adjacent residual points within a preset preceding adjacent range of the outlier residual point. In this embodiment of the present invention, the preset preceding adjacent range refers to the range including the outlier residual point and the three preceding adjacent residual points. The implementer can determine this value based on the implementation scenario. A smaller first variation difference value indicates a more similar magnitude of variation between the adjacent residual points preceding the outlier residual point, making the slow variation more likely to be caused by an oxygen pressure anomaly. A larger first variation difference value indicates a greater difference in magnitude of variation, making the significant fluctuation more likely to be caused by noise data. Calculate the average of the absolute values ​​of the differences in the degree of change between all adjacent residual points within the preset adjacent range of the outlier residual point to obtain a second change difference value; in an embodiment of the present invention, the preset adjacent range refers to the range including the outlier residual point and the three adjacent residual points, which can be determined by the implementer according to the implementation scenario; similarly, when the second change difference value is larger, it is more likely that the slow change is caused by abnormal oxygen pressure. Calculate the reciprocal of the sum of the first change difference value and the second change difference value to obtain a third value; when the first change difference value and the second change difference value are smaller, the larger the third value is, which means that the outlier residual point is less likely to be caused by noise data. Calculate the product of the reciprocal of the comprehensive distance and the third value to obtain the slow fluctuation value of the outlier residual point; when the comprehensive distance is larger and the third value is smaller, the slow fluctuation value is smaller, and it is more likely that the outlier residual point is caused by noise data. The formula for obtaining the slow fluctuation value includes:

[0026] Where R represents the slow fluctuation value, D represents the comprehensive distance, and M represents the number of residual points in the adjacent range before the preset. Indicates the degree of change of the mth residual point, Indicates the The degree of change of the residual point, represents the first change difference value, Indicates the number of residual points within the preset adjacent range, Indicates the degree of change of the nth residual point, Indicates the The degree of change of the residual point, represents the second change difference value, Indicates the third value.

[0027] Furthermore, since the abnormal oxygen pressure during the oxygen pressure alkali leaching process will cause the concentration changes of the two target products to be similarly affected at the same time, the concentration changes are similar, and the neighborhood differences at the same residual point are similar. Therefore, the degree of change correlation can be obtained based on the difference characteristics of the adjacent data changes of the residual points of the outlier residual point and the other target products at the same time; preferably, in an embodiment of the present invention, the step of obtaining the degree of change correlation includes: normalizing the comprehensive distance of the residual points of other target products at the time of the outlier residual point to obtain a fourth value; normalizing the comprehensive distance of the outlier residual point and calculating the inverse of the absolute value of the difference with the fourth value to obtain the degree of change correlation of the outlier residual point; in the normalization step in the embodiment of the present invention, a linear normalization method is used, and the comprehensive distances corresponding to different target products are normalized in order to unify the magnitude. When the comprehensive distances of two target products at the same time are relatively close, it means that the concentration change trends are relatively similar near that time. The greater the degree of change correlation, the greater the possibility of oxygen pressure anomaly. The smaller the degree of change correlation, the greater the difference in comprehensive distances. The concentration change trends of the two target products at the same time are relatively different, and the outlier residual point is more likely to be caused by noise data.

[0028] Then, the first oxygen pressure anomaly degree of the outlier residual point can be obtained based on the slow fluctuation value and the degree of correlation between the changes; preferably, in an embodiment of the present invention, the step of obtaining the first oxygen pressure anomaly degree includes: normalizing the slow fluctuation value and the degree of correlation between the changes and calculating the normalized average value to obtain the first oxygen pressure anomaly degree of the outlier residual point; when the first oxygen pressure anomaly degree is greater, it means that the outlier residual point is more likely to have an oxygen pressure anomaly; when the first oxygen pressure anomaly degree is smaller, it means that the outlier residual point is more likely to be caused by noise data. Therefore, according to the first oxygen pressure anomaly degree, a suspected oxygen pressure anomaly point is obtained, specifically including: taking the outlier residual point whose first oxygen pressure anomaly degree exceeds a preset first threshold as a suspected oxygen pressure anomaly point; in an embodiment of the present invention, since the first oxygen pressure anomaly degree is the normalized average value, the result corresponding to the true anomaly will tend to 1, and the result corresponding to the noise data will tend to 0, and the two are distributed at both ends of the value range, so the preset first threshold is 0.5. By calculating the first oxygen pressure anomaly degree, the suspected oxygen pressure anomaly point in the outlier residual point can be determined, and the monitoring accuracy can be preliminarily improved.

[0029] Step S3: cluster the suspected oxygen pressure anomaly points according to their distribution characteristics to obtain different point clusters; obtain the second oxygen pressure anomaly degree of the suspected oxygen pressure anomaly points according to the area characteristics of the point clusters, the density characteristics and distribution characteristics of the residual points within the clusters; obtain the oxygen pressure anomaly moment according to the second oxygen pressure anomaly degree; and obtain the oxygen pressure anomaly trend value according to the distribution characteristics and anomaly degree of the oxygen pressure anomaly moment.

[0030] During oxygen-pressure alkali leaching, alkali leaching is primarily a solid-liquid contact reaction. Uneven stirring can easily lead to precipitation and agglomeration during stirring, resulting in a localized reaction that initially accelerates and then slows down. This is typically manifested as fluctuations in the concentration curve, which can be difficult to distinguish from oxygen pressure anomalies on the waveform. However, uneven stirring is temporary and disappears with continued stirring, so it should not be analyzed as abnormal reaction data. Therefore, further analysis is needed to determine whether suspected oxygen pressure anomalies are caused by uneven stirring. Abnormal concentration changes caused by uneven stirring are short-lived and disappear with further stirring. Therefore, suspected oxygen pressure anomalies caused by such abnormalities have a concentrated distribution and similar values ​​in the time series. However, uneven oxidant supply rates caused by low or intermittent oxygen pressure can interrupt or repeat the oxidation of tellurium and copper, resulting in an uneven distribution in the time series. Therefore, clustering can be performed according to the distribution characteristics of the suspected oxygen pressure anomaly points to obtain different point clusters; in an embodiment of the present invention, the existing K-means clustering algorithm is used to cluster all the suspected oxygen pressure anomaly points in the residual curve, the horizontal axis of the clustering space is time, and the vertical axis is the residual value. The number of clusters is obtained by the elbow method. The K-means clustering algorithm belongs to the existing technology, and the specific clustering steps are not repeated here; through clustering, the suspected oxygen pressure anomaly points with close time and residual values ​​can be clustered into one cluster.

[0031] Furthermore, if the range of the point cluster is small and the distribution of points within the cluster is dense and uniform, the point cluster is more likely to be a cluster of suspected oxygen pressure anomaly points caused by uneven stirring; if the range of the point cluster is large and the distribution of points within the cluster is discrete and uneven, the point cluster is more likely to be a cluster of suspected oxygen pressure anomaly points caused by oxygen pressure anomaly. Therefore, the second oxygen pressure anomaly degree of the suspected oxygen pressure anomaly point is obtained based on the area characteristics of the point cluster and the density and distribution characteristics of the residual points within the cluster. Preferably, in this embodiment of the present invention, the step of obtaining the second oxygen pressure anomaly degree includes: calculating the area of ​​the minimum circumscribed circle of the point cluster and normalizing it to obtain an area characteristic value; the larger the area characteristic value, the more likely the point cluster is a cluster of suspected oxygen pressure anomaly points caused by oxygen pressure anomaly. Calculate the average nearest neighbor distances of all suspected oxygen pressure anomaly points within a cluster and normalize them to obtain the interval eigenvalue. The nearest neighbor distance is the Euclidean distance between any suspected oxygen pressure anomaly point and the nearest other suspected oxygen pressure anomaly point within the cluster. A larger interval eigenvalue indicates a more dispersed distribution of suspected oxygen pressure anomaly points within the cluster, making the cluster more likely to be a cluster of suspected oxygen pressure anomaly points caused by an oxygen pressure anomaly. Calculate the standard deviation of the nearest neighbor distances of all suspected oxygen pressure anomaly points within the cluster and normalize them to obtain the distribution dispersion eigenvalue. A larger distribution dispersion eigenvalue indicates a more uneven distribution of suspected oxygen pressure anomaly points within the cluster, making the cluster more likely to be a cluster of suspected oxygen pressure anomaly points caused by an oxygen pressure anomaly. Calculate the average of the area eigenvalue, interval eigenvalue, and distribution dispersion eigenvalue to obtain the second oxygen pressure anomaly degree for all suspected oxygen pressure anomaly points within the cluster. A larger second oxygen pressure anomaly degree indicates a more likely oxygen pressure anomaly point within the cluster is caused by an oxygen pressure anomaly.

[0032] Furthermore, the moment of oxygen pressure anomaly can be obtained based on the second oxygen pressure anomaly degree, specifically including: taking the moment when the second oxygen pressure anomaly degree exceeds the suspected oxygen pressure anomaly point at the preset second threshold as the moment of oxygen pressure anomaly; in an embodiment of the present invention, since the second oxygen pressure anomaly degree is the average value of each feature after normalization, the second oxygen pressure anomaly degree caused by uneven stirring tends to 0, and the second oxygen pressure anomaly degree caused by oxygen pressure anomaly tends to 1, and the two are distributed at both ends of the value range, so the preset second threshold is 0.5. The moment of oxygen pressure anomaly characterizes that the oxygen pressure at that moment does not meet the requirements of normal oxygen pressure alkali leaching. If the number of moments of oxygen pressure anomaly gradually increases, and the corresponding residual values ​​are relatively discrete, it means that the oxygen pressure anomaly has a tendency to become increasingly serious, so the oxygen pressure anomaly trend value is obtained based on the distribution characteristics and the degree of anomaly of the oxygen pressure anomaly moment.

[0033] Preferably, in an embodiment of the present invention, the step of obtaining an oxygen pressure anomaly trend value includes: calculating the ratio of the number of oxygen pressure anomaly moments after the middle moment to the number of oxygen pressure anomaly moments before the middle moment of the concentration time series curve to obtain a number ratio; a larger number ratio indicates that the closer the number ratio is to the current moment, the greater the number of oxygen pressure anomaly moments, and the oxygen pressure anomaly is tending to become increasingly severe. calculating the average distance between the residual points at the oxygen pressure anomaly moments after the middle moment and the residual mean line to obtain a first distance; calculating the average distance between the residual points at the oxygen pressure anomaly moments before the middle moment and the residual mean line to obtain a second distance; and calculating the ratio of the first distance to the second distance to obtain an anomaly ratio; a larger anomaly ratio indicates that the closer the number ratio is to the current moment, the more discrete the residual values ​​corresponding to the oxygen pressure anomaly moments are, and the more significantly the concentration time series curve deviates from the normal cycle and trend characteristics, indicating that the oxygen pressure anomaly is tending to become increasingly severe. calculating the product of the number ratio and the anomaly ratio to obtain an oxygen pressure anomaly trend value; a larger oxygen pressure anomaly trend value indicates that the closer the number ratio is to the current moment, the more obvious and severe the oxygen pressure anomaly is. By analyzing the changing trend of oxygen pressure anomalies, abnormal oxygen pressure conditions can be quickly discovered, and the accuracy of oxygen pressure anomaly monitoring is improved.

[0034] Step S4: adjusting the oxygen pressure and issuing an early warning according to the abnormal trend of the oxygen pressure.

[0035] After obtaining the oxygen pressure anomaly trend corresponding to the current moment, the oxygen pressure can be adjusted and an early warning issued based on the anomaly trend. This includes calculating the sum of the oxygen pressure anomaly trend value and a constant of 1 to obtain an adjustment coefficient. The larger the oxygen pressure anomaly trend value, the greater the need to increase the oxygen pressure, and thus the larger the adjustment coefficient. The product of the adjustment coefficient and a preset base oxygen pressure is calculated to obtain an adjusted oxygen pressure value. The larger the oxygen pressure anomaly trend value, the larger the adjusted oxygen pressure value, thereby ensuring the normal progress of the oxygen pressure alkali leaching process and improving the production of refined tellurium. When the oxygen pressure anomaly trend value exceeds a preset anomaly trend threshold, an early warning is issued for the oxygen pressure alkali leaching process, indicating that the oxygen pressure anomaly trend value is too high and requires manual inspection. In this embodiment of the present invention, the preset anomaly trend threshold is 0.6, which can be determined by the implementer based on the implementation scenario. It should be noted that the oxygen pressure is adjusted and an early warning is issued based on the maximum value of the oxygen pressure anomaly trend corresponding to all target products. Thus, monitoring the oxygen pressure during the oxygen pressure alkali leaching process using the oxygen pressure anomaly trend value is more accurate and timely than monitoring using the oxygen pressure threshold.

[0036] In summary, an embodiment of the present invention provides a method for preparing refined tellurium from telluride copper slag; performing time series decomposition on a concentration time series curve, obtaining outlier residual points based on the discrete characteristics of the residual points in the residual curve; obtaining slow fluctuation values ​​based on the distance characteristics between the outlier residual points and adjacent residual points, and the data change characteristics of the neighborhood residual points; obtaining the degree of change correlation based on the outlier residual points and the residual points of other target products; obtaining suspected oxygen pressure anomaly points based on the slow fluctuation values ​​and the degree of change correlation; clustering based on the distribution characteristics of the suspected oxygen pressure anomaly points, and obtaining the oxygen pressure anomaly moment based on the area characteristics of the point cluster, the density characteristics of the residual points within the cluster, and the distribution characteristics. The present invention obtains an oxygen pressure anomaly trend value based on the distribution characteristics and degree of anomaly at the oxygen pressure anomaly moment and adjusts and warns the oxygen pressure, thereby improving the timeliness and accuracy of monitoring and ensuring the tellurium preparation effect.

[0037] It should be noted that the order in which the embodiments of the present invention are described above is for illustrative purposes only and does not necessarily represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0038] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on the differences from other embodiments.

Claims

1. A method for preparing refined tellurium from copper telluride slag, characterized in that: The method comprises the following steps: Obtain the concentration time series curve of the target product during oxygen pressure alkali leaching; Performing time series decomposition on the concentration time series curve to obtain a residual curve; obtaining an outlier residual point based on the discrete characteristics of the residual points in the residual curve; obtaining a slow fluctuation value based on the distance characteristics between the outlier residual point and adjacent residual points and the data change characteristics of the residual points in the neighborhood of the outlier residual point; obtaining a change correlation degree based on the difference characteristics of the adjacent data changes of the outlier residual point and residual points of other target products at the same time; obtaining a first oxygen pressure anomaly degree of the outlier residual point based on the slow fluctuation value and the change correlation degree; and obtaining a suspected oxygen pressure anomaly point based on the first oxygen pressure anomaly degree; Clustering is performed based on the distribution characteristics of the suspected oxygen pressure anomaly points to obtain different point clusters; a second oxygen pressure anomaly degree of the suspected oxygen pressure anomaly point is obtained based on the area characteristics of the point cluster, the density characteristics and distribution characteristics of the residual points within the cluster; the moment of oxygen pressure anomaly is obtained based on the second oxygen pressure anomaly degree; and an oxygen pressure anomaly trend value is obtained based on the distribution characteristics and the degree of anomaly of the oxygen pressure anomaly moment. The oxygen pressure is adjusted and warned according to the abnormal trend of the oxygen pressure.

2. The method for preparing refined tellurium from copper telluride slag according to claim 1, characterized in that: The step of obtaining outlier residual points according to the discrete features of the residual points in the residual curve comprises: Construct a residual mean line according to the average value of all residual points in the residual curve; calculate the average value of the distance between all residual points and the residual mean line and distance standard deviation , the distance between the residual point in the residual curve and the residual mean line is not The residual points within the range are regarded as outlier residual points.

3. The method for preparing refined tellurium from copper telluride slag according to claim 1, characterized in that: The step of obtaining a slow fluctuation value according to the distance characteristics between the outlier residual point and the adjacent residual points and the data change characteristics of the residual points in the neighborhood of the outlier residual point comprises: Calculate the sum of the Euclidean distances between the outlier residual point and the two adjacent residual points before and after to obtain a comprehensive distance; calculate the slope of the line connecting any residual point and the previous adjacent residual point to obtain the degree of change of the arbitrary residual point; calculate the average of the absolute values ​​of the differences in the degrees of change between all adjacent residual points within a preset front adjacent range of the outlier residual point to obtain a first change difference value; calculate the average of the absolute values ​​of the differences in the degrees of change between all adjacent residual points within a preset rear adjacent range of the outlier residual point to obtain a second change difference value; calculate the reciprocal of the sum of the first change difference value and the second change difference value to obtain a third value; calculate the product of the reciprocal of the comprehensive distance and the third value to obtain a slow fluctuation value of the outlier residual point.

4. The method for preparing refined tellurium from copper telluride slag according to claim 3, characterized in that: The step of obtaining the degree of change correlation based on the adjacent data change difference characteristics of the outlier residual point and the residual points of other target products at the same time includes: Normalize the comprehensive distance of the residual points of other target generated objects at the moment when the outlier residual point is located to obtain a fourth value; normalize the comprehensive distance of the outlier residual point and calculate the inverse of the absolute value of the difference with the fourth value to obtain the degree of correlation of the change of the outlier residual point.

5. The method for preparing refined tellurium from copper telluride slag according to claim 1, characterized in that: The step of obtaining the first oxygen pressure anomaly degree of the outlier residual point according to the slow fluctuation value and the change correlation degree includes: The slow fluctuation value and the change correlation degree are normalized respectively, and the normalized average values ​​are calculated to obtain the first oxygen pressure anomaly degree of the outlier residual point.

6. The method for preparing refined tellurium from copper telluride slag according to claim 1, characterized in that: The step of obtaining a suspected oxygen pressure abnormal point according to the first oxygen pressure abnormality degree includes: The outlier residual point whose first oxygen pressure anomaly degree exceeds a preset first threshold is taken as the suspected oxygen pressure anomaly point.

7. The method for preparing refined tellurium from copper telluride slag according to claim 1, characterized in that: The step of obtaining the second oxygen pressure anomaly degree of the suspected oxygen pressure anomaly point according to the area characteristics of the point cluster, the density characteristics and the distribution characteristics of the residual points in the cluster comprises: The area of ​​the minimum circumscribed circle of the point cluster is calculated and normalized to obtain an area eigenvalue; the average value of the nearest neighbor distances of all suspected oxygen pressure anomaly points in the point cluster is calculated and normalized to obtain an interval eigenvalue; the standard deviation of the nearest neighbor distances of all suspected oxygen pressure anomaly points in the point cluster is calculated and normalized to obtain a distribution discrete eigenvalue; the average value of the area eigenvalue, the interval eigenvalue, and the distribution discrete eigenvalue is calculated to obtain a second oxygen pressure anomaly degree of all suspected oxygen pressure anomaly points in the point cluster.

8. The method for preparing refined tellurium from copper telluride slag according to claim 1, characterized in that: The step of obtaining the oxygen pressure abnormality time according to the second oxygen pressure abnormality degree comprises: The time at which the second oxygen pressure abnormality degree exceeds the preset second threshold value and the suspected oxygen pressure abnormality point occurs is used as the oxygen pressure abnormality time.

9. The method for preparing refined tellurium from copper telluride slag according to claim 2, characterized in that: The step of obtaining an abnormal trend value of oxygen pressure according to the distribution characteristics and abnormal degree of the abnormal oxygen pressure at the time of abnormality includes: Calculate the number ratio of the oxygen pressure abnormal moments after the middle moment and before the middle moment of the concentration time series curve to obtain the number ratio; calculate the average value of the distances between the residual points at the oxygen pressure abnormal moment after the middle moment and the residual mean line to obtain a first distance; calculate the average value of the distances between the residual points at the oxygen pressure abnormal moment before the middle moment and the residual mean line to obtain a second distance; calculate the ratio of the first distance to the second distance to obtain the abnormal ratio; calculate the product of the number ratio and the abnormal ratio to obtain the oxygen pressure abnormal trend value.

10. The method for preparing refined tellurium from copper telluride slag according to claim 1, characterized in that: The step of adjusting and warning the oxygen pressure according to the abnormal trend of the oxygen pressure includes: The sum of the oxygen pressure abnormal trend value and a constant 1 is calculated to obtain an adjustment coefficient; the product of the adjustment coefficient and a preset basic oxygen pressure is calculated to obtain an adjusted oxygen pressure value; when the oxygen pressure abnormal trend value exceeds a preset abnormal trend threshold, an early warning is issued for the oxygen pressure alkali leaching process.

Images