Multi-objective integrated optimization method for large-scale low-cost dry separation system

Abstract

The invention relates to the technical field of dry separation, and discloses a multi-objective integrated optimization method for a large-scale low-cost dry separation system, which comprises the following steps: analyzing material parameters, and determining the initial air supply quantity of a separator to be monitored and the initial air supply proportion of a separation area based on an analysis result; whether the initial air supply amount is adjusted or not is judged according to the historical sorting record; when it is judged that the initial air supply amount is adjusted, the initial air supply amount is adjusted based on the real-time thickness of the bed layer and the medium density in the bed layer, and the final air supply amount is obtained; whether the initial air supply proportion is adjusted or not is judged according to the real-time material feeding amount; and when it is judged that the initial air supply proportion is adjusted, the initial air supply proportion is adjusted based on the partition pressure drop and the real-time sorting effect index, and the final air supply proportion is obtained. Air supply is reasonably adjusted for materials with different granularities and water contents, efficient separation is achieved, and the product quality is stable and reliable.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of dry sorting, in particular to a multi-objective integrated optimization method of large-scale low-cost dry sorting system. BACKGROUND

[0002] In the traditional dry sorting system, there are many problems. On the one hand, small-scale dry sorting system is inefficient when dealing with large-scale materials, which cannot meet the needs of industrial production. The processing capacity of the equipment is limited, which leads to slow sorting speed of materials, long production cycle and increases the time cost. On the other hand, the existing dry sorting system is often high in cost, which mainly reflects in equipment purchase, operation energy consumption and maintenance. Some advanced sorting equipment is expensive, which is a considerable expense for enterprises. Moreover, in the operation process, in order to ensure the sorting effect, a large amount of energy is consumed, which further increases the production cost.

[0003] In addition, the target of the traditional dry sorting system is relatively single, which can usually only optimize one or a few indicators, and it is difficult to realize the integrated optimization of multiple targets. For example, when pursuing high sorting precision, the sorting efficiency may be sacrificed; while improving the sorting efficiency, the purity of the sorting may be reduced. This single-target optimization method cannot adapt to the complex and changeable production demand, and cannot balance the cost and benefit in large-scale production.

[0004] Therefore, it is necessary to design a multi-objective integrated optimization method of large-scale low-cost dry sorting system to solve the problems existing in the prior art. SUMMARY

[0005] In view of this, the present application provides a multi-objective integrated optimization method of large-scale low-cost dry sorting system, which aims to solve the problem that the target of the traditional dry sorting system is relatively single, which can usually only optimize one or a few indicators, and it is difficult to realize the integrated optimization of multiple targets.

[0006] The present application provides a multi-objective integrated optimization method of large-scale low-cost dry sorting system, comprising the following steps: determining a to-be-monitored sorting machine, a sorting area and a to-be-sorted material, collecting material parameters of the to-be-sorted material, analyzing the material parameters, and determining an initial air supply amount of the to-be-monitored sorting machine and an initial air supply proportion of the sorting area based on the analysis result; collecting historical sorting records of the to-be-monitored sorting machine, and determining whether to adjust the initial air supply amount according to the historical sorting records; When it is determined to adjust the initial air supply amount, the real-time bed thickness and the medium density in the bed of the sorting machine to be monitored are collected, the initial air supply amount is adjusted based on the real-time bed thickness and the medium density in the bed, and a final air supply amount is obtained; The material to be sorted is sorted by the final air supply amount and the initial air supply ratio, the real-time material feeding amount entering each sorting area is collected, and it is determined whether to adjust the initial air supply ratio according to the real-time material feeding amount; When it is determined to adjust the initial air supply ratio, the partition pressure drop and the real-time sorting effect index of each sorting area are collected, the initial air supply ratio is adjusted based on the partition pressure drop and the real-time sorting effect index, and a final air supply ratio is obtained.

[0007] Further, when determining the initial air supply amount of the sorting machine to be monitored and the initial air supply ratio of the sorting area based on the analysis result, the method comprises: analyzing the material parameters to obtain a material particle size characteristic value and a material moisture content characteristic value of the material to be sorted; respectively obtaining a material particle size standard value and a material moisture content standard value corresponding to the material particle size characteristic value and the material moisture content characteristic value; obtaining a ratio of the material particle size characteristic value to the material particle size standard value, denoted as a particle size ratio; obtaining a ratio of the material moisture content characteristic value to the material moisture content standard value, denoted as a moisture content ratio; determining a sorting deviation degree of the sorting machine to be monitored according to the particle size ratio and the moisture content ratio; determining the initial air supply amount of the sorting machine to be monitored and the initial air supply ratio of the sorting area according to the sorting deviation degree.

[0008] Further, when determining the initial air supply amount of the sorting machine to be monitored and the initial air supply ratio of the sorting area according to the sorting deviation degree, the method comprises: comparing the sorting deviation degree with a first sorting deviation degree and a second sorting deviation degree, and determining the initial air supply amount and the initial air supply ratio according to the comparison result; wherein the first sorting deviation degree is smaller than the second sorting deviation degree; when the sorting deviation degree is smaller than or equal to the first sorting deviation degree, determining that the initial air supply amount and the initial air supply ratio are a first air supply amount and a first air supply ratio respectively; when the sorting deviation degree is greater than the first sorting deviation degree and smaller than or equal to the second sorting deviation degree, determining that the initial air supply amount and the initial air supply ratio are a second air supply amount and a second air supply ratio respectively; When the sorting deviation degree is different from the second sorting deviation degree, the initial air supply amount and the initial air supply ratio are determined as a third air supply amount and a third air supply ratio, respectively.

[0009] Further, when determining whether to adjust the initial air supply amount according to the historical sorting records, the method comprises: analyzing the historical sorting records to obtain historical sorting records related to abnormal air supply amounts within a preset time window, denoted as historical abnormal sorting records; obtaining the number of all the historical abnormal sorting records, denoted as a historical abnormal number; obtaining a historical sorting effect index corresponding to each of the historical abnormal sorting records, and obtaining a proportion of abnormal sorting effect indexes in all the historical sorting effect indexes, denoted as a historical abnormal index ratio; determining whether to adjust the initial air supply amount according to the historical abnormal number and the historical abnormal index ratio.

[0010] Further, when determining whether to adjust the initial air supply amount according to the historical abnormal number and the historical abnormal index ratio, the method comprises: respectively comparing the historical abnormal number with a historical abnormal number threshold value and comparing the historical abnormal index ratio with a historical abnormal index ratio threshold value, and determining whether to adjust the initial air supply amount according to the comparison results; when the historical abnormal number is less than the historical abnormal number threshold value and the historical abnormal index ratio is less than the historical abnormal index ratio threshold value, determining not to adjust the initial air supply amount; otherwise, determining to adjust the initial air supply amount.

[0011] Further, when adjusting the initial air supply amount based on the real-time bed layer thickness and the medium density in the bed layer, the method comprises: respectively comparing the real-time bed layer thickness with a standard bed layer thickness and comparing the medium density in the bed layer with a standard medium density in the bed layer, and determining an air supply adjustment coefficient of the initial air supply amount according to the comparison results; when the real-time bed layer thickness is greater than or equal to the standard bed layer thickness and the medium density in the bed layer is greater than or equal to the standard medium density in the bed layer, determining that the air supply adjustment coefficient is a first air supply adjustment coefficient; when the real-time bed layer thickness is greater than or equal to the standard bed layer thickness and the medium density in the bed layer is less than the standard medium density in the bed layer, determining that the air supply adjustment coefficient is a second air supply adjustment coefficient; determining the air supply adjustment coefficient as a third air supply adjustment coefficient when the real-time bed thickness is less than the standard bed thickness and the medium density in the bed is greater than or equal to the standard medium density in the bed; determining the air supply adjustment coefficient as a fourth air supply adjustment coefficient when the real-time bed thickness is less than the standard bed thickness and the medium density in the bed is less than the standard medium density in the bed; multiplying the air supply adjustment coefficient by the initial air supply amount to obtain the final air supply amount.

[0012] Further, when determining whether to adjust the initial air supply ratio according to the real-time material feeding amount, the method comprises: obtaining a real-time material feeding amount corresponding to each sorting area; obtaining a preset material feeding amount corresponding to each sorting area; calculating an absolute value of a difference between the real-time material feeding amount and the preset material feeding amount corresponding to each sorting area, and recording the absolute value as an absolute material feeding amount difference; calculating a comprehensive material feeding amount deviation index according to all the absolute material feeding amount differences; determining whether to adjust the initial air supply ratio according to the comprehensive material feeding amount deviation index.

[0013] Further, when determining whether to adjust the initial air supply ratio according to the comprehensive material feeding amount deviation index, the method comprises: comparing the comprehensive material feeding amount deviation index with a comprehensive material feeding amount deviation index threshold value, and determining whether to adjust the initial air supply ratio according to a comparison result; if the comprehensive material feeding amount deviation index exceeds the comprehensive material feeding amount deviation index threshold value, it is determined to adjust the initial air supply ratio; otherwise, it is determined not to adjust the initial air supply ratio.

[0014] Further, when adjusting the initial air supply ratio based on the partition pressure drop and the real-time sorting effect index and obtaining a final air supply ratio, the method comprises: analyzing the real-time sorting effect index to obtain a real-time sorting efficiency, a real-time sorting precision, and a real-time sorting quality; normalizing the real-time sorting efficiency, the real-time sorting precision, and the real-time sorting quality respectively to obtain a normalized real-time sorting efficiency, a normalized real-time sorting precision, and a normalized real-time sorting quality; weighting and summing the normalized real-time sorting efficiency, the normalized real-time sorting precision, and the normalized real-time sorting quality to obtain a comprehensive real-time sorting index. all the partition pressure drops and the comprehensive real-time sorting indexes are constructed into a set of air supply proportion vectors; the set of air supply proportion vectors is compared with a set of historical air supply proportion adjustments, and a proportion adjustment coefficient of the initial air supply proportion is determined according to a comparison result; when there is a historical air supply proportion vector group identical to the set of air supply proportion vectors in the set of historical air supply proportion adjustment groups, a historical air supply proportion adjustment coefficient corresponding to the historical air supply proportion vector group is taken as the proportion adjustment coefficient; when there is no historical air supply proportion vector group identical to the set of air supply proportion vectors in the set of historical air supply proportion adjustment groups, the proportion adjustment coefficient is determined according to the set of air supply proportion vectors; the initial air supply proportion is adjusted according to the proportion adjustment coefficient to obtain the final air supply proportion.

[0015] Further, when the proportion adjustment coefficient is determined according to the set of air supply proportion vectors, the method comprises the following steps: a correlation coefficient of the set of air supply proportion vectors and each historical air supply proportion vector group is calculated one by one, and a maximum correlation coefficient is extracted; the maximum correlation coefficient is compared with a preset proportion adjustment coefficient table, and the proportion adjustment coefficient is determined according to a comparison result.

[0016] Compared with the prior art, the multi-target integrated optimization method of the large-scale low-cost dry separation system provided by the application can accurately control the large-scale low-cost dry separation system. In terms of air supply, the initial air supply is determined according to the material parameters, combined with the historical separation records, the real-time thickness of the bed layer and the medium density, and is adjusted as needed to ensure reasonable supply of air flow and avoid poor separation effect. If the air supply is too small, the material cannot be fully suspended and separated; if the air supply is too large, energy is wasted and the precision is affected. In terms of air supply proportion, whether the initial proportion needs to be adjusted is determined according to the real-time material feeding amount, and the initial proportion is accurately adjusted combined with the partition pressure drop and the separation effect index, so that each separation area obtains appropriate air distribution, improves the overall separation efficiency and quality. From the cost perspective, this method avoids energy consumption and resource waste, accurately adjusts the air supply and proportion to reduce the equipment operation cost, realizes low-cost operation, improves the separation precision and efficiency, reduces the secondary separation, and reduces the comprehensive cost. In terms of separation effect, through multi-target integrated optimization, different material characteristics and working conditions are adapted, the separation precision and quality are improved, the air supply is reasonably adjusted for materials of different particle sizes and moisture contents, high-efficiency separation is realized, the product quality is stable and reliable, and different scene requirements are met. BRIEF DESCRIPTION OF DRAWINGS

[0017] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of a multi-objective integrated optimization method for a large-scale, low-cost dry sorting system provided in an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0019] See Figure 1 As shown in some embodiments of this application, this embodiment provides a multi-objective integrated optimization method for a large-scale, low-cost dry sorting system, including the following steps: S100: Determine the sorting machine to be monitored, the sorting area, and the material to be sorted; collect the material parameters of the material to be sorted; analyze the material parameters; and determine the initial air supply volume of the sorting machine to be monitored and the initial air supply ratio of the sorting area based on the analysis results. S200: Collect the historical sorting records of the sorting machine to be monitored, and determine whether to adjust the initial air supply based on the historical sorting records; S300: When it is determined that the initial air supply volume needs to be adjusted, the real-time thickness of the bed and the density of the medium in the bed of the sorting machine to be monitored are collected, the initial air supply volume is adjusted based on the real-time thickness of the bed and the density of the medium in the bed, and the final air supply volume is obtained. S400: The material to be sorted is sorted using the final air supply volume and the initial air supply ratio, the real-time material feed volume entering each sorting area is collected, and it is determined whether to adjust the initial air supply ratio based on the real-time material feed volume. S500: When it is determined that the initial air supply ratio needs to be adjusted, the zone pressure drop and real-time sorting effect index of each sorting area are collected, the initial air supply ratio is adjusted based on the zone pressure drop and real-time sorting effect index, and the final air supply ratio is obtained.

[0020] In the embodiment, the sorting machine to be monitored is preferably an air dense medium fluidized bed dry sorting machine.

[0021] It can be understood that the multi-objective integrated optimization method of the large-scale low-cost dry sorting system provided in the embodiment can accurately control the large-scale low-cost dry sorting system. In terms of air supply, the material parameters are analyzed, and the initial air supply is determined in combination with historical sorting records, real-time bed thickness, medium density, etc. and adjusted as needed to ensure reasonable air supply and avoid poor sorting effect. For example, if the air supply is too small, the material cannot be fully suspended and separated; if it is too large, energy is wasted and precision is affected. In terms of air supply ratio, whether to adjust the initial ratio is determined according to the real-time material feeding amount, and the initial ratio is accurately adjusted in combination with the partition pressure drop and the sorting effect index, so that each sorting area obtains a suitable air distribution, improves the overall sorting efficiency and quality. From the cost perspective, this method avoids energy consumption and resource waste, accurately adjusts the air supply and ratio to reduce the operating cost of the equipment, realizes low-cost operation, improves the sorting precision and efficiency, reduces secondary sorting, and reduces the comprehensive cost. In terms of sorting effect, through multi-objective integrated optimization, different material characteristics and working conditions are adapted, the sorting precision and quality are improved, the air supply is reasonably adjusted for materials of different particle sizes and moisture contents, efficient separation is realized, the product quality is stable and reliable, and different scene requirements are met.

[0022] Specifically, when determining the initial air supply of the sorting machine to be monitored and the initial air supply ratio of the sorting area based on the analysis result, the method comprises the following steps: analyzing the material parameters to obtain a material particle size characteristic value and a material moisture content characteristic value of the material to be sorted; respectively acquiring a material particle size standard value and a material moisture content standard value corresponding to the material particle size characteristic value and the material moisture content characteristic value; acquiring a particle size ratio value of the material particle size characteristic value and the material particle size standard value, denoted as the particle size ratio value; acquiring a moisture content ratio value of the material moisture content characteristic value and the material moisture content standard value, denoted as the moisture content ratio value; determining a sorting deviation degree of the sorting machine to be monitored according to the particle size ratio value and the moisture content ratio value; determining the initial air supply of the sorting machine to be monitored and the initial air supply ratio of the sorting area according to the sorting deviation degree.

[0023] In the embodiment, the sorting deviation degree is obtained by weighted calculation of the particle size ratio value and the moisture content ratio value.

[0024] It can be understood that by comparing the particle size characteristic value and the water content characteristic value of the material with the respective standard values, the particle size ratio and the water content ratio are calculated, and the sorting deviation degree is determined, so as to determine the initial air supply amount and the initial air supply ratio, which can fully consider the characteristics of the material to be sorted. Different particle size and water content of the material can significantly affect the suspension and separation effect of the material in the sorting process. For example, the material with larger particle size or higher water content may need a larger air supply amount to achieve sufficient suspension and separation. Determining the air supply parameters based on the sorting deviation degree can make the sorting system better adapt to different material characteristics, thereby improving the accuracy of sorting.

[0025] Specifically, when determining the initial air supply amount and the initial air supply ratio of the sorting machine to be monitored according to the sorting deviation degree, the method comprises: comparing the sorting deviation degree with a first sorting deviation degree and a second sorting deviation degree, and determining the initial air supply amount and the initial air supply ratio according to the comparison result; wherein the first sorting deviation degree is smaller than the second sorting deviation degree; when the sorting deviation degree is smaller than or equal to the first sorting deviation degree, determining that the initial air supply amount and the initial air supply ratio are a first air supply amount and a first air supply ratio, respectively; when the sorting deviation degree is greater than the first sorting deviation degree and smaller than or equal to the second sorting deviation degree, determining that the initial air supply amount and the initial air supply ratio are a second air supply amount and a second air supply ratio, respectively; when the sorting deviation degree is greater than the second sorting deviation degree, determining that the initial air supply amount and the initial air supply ratio are a third air supply amount and a third air supply ratio, respectively.

[0026] In this embodiment, the size relationship of the initial air supply amount is first air supply amount < second air supply amount < third air supply amount.

[0027] In this embodiment, the sorting region is preferably three, which are respectively referred to as a first sorting region, a second sorting region and a third sorting region. The first air supply ratio of the first sorting region, the second sorting region and the third sorting region is preferably 1:4:5, the second air supply ratio is preferably 2:3:5, and the third air supply ratio is preferably 3:3:4.

[0028] It can be understood that by comparing the sorting deviation degree with the first and second sorting deviation degrees to determine the initial air supply amount and the initial air supply ratio, the hierarchical and accurate regulation of the air supply parameters can be realized. This hierarchical regulation mode considers the actual needs of material sorting under different sorting deviation degrees.

[0029] Specifically, when determining whether to adjust the initial air supply amount according to the historical sorting record, the method comprises: The historical sorting records are parsed to obtain historical sorting records related to the abnormal air supply amount in a preset time window, denoted as historical abnormal sorting records; The number of all the historical abnormal sorting records is obtained, denoted as a historical abnormal number; The historical sorting effect index corresponding to each historical abnormal sorting record is obtained, and the proportion of the abnormal sorting effect index in all the historical sorting effect indexes is obtained, denoted as a historical abnormal index ratio; It is determined whether to adjust the initial air supply amount according to the historical abnormal number and the historical abnormal index ratio.

[0030] In this embodiment, the historical abnormal sorting record refers to a sorting record in which the air supply amount deviates from the normal range (abnormal air supply amount) in a preset time window. The historical sorting effect index refers to various indexes for measuring the sorting effect in the sorting process, such as sorting accuracy and recovery rate. If there is an index abnormality in the historical abnormal sorting record, the historical sorting effect index corresponding to the historical abnormal sorting record is marked as an abnormal sorting effect index, and the historical abnormal index ratio refers to the proportion of the historical abnormal sorting record in which there is an index abnormality in all the historical abnormal sorting records.

[0031] Specifically, when it is determined whether to adjust the initial air supply amount according to the historical abnormal number and the historical abnormal index ratio, the following steps are included: The historical abnormal number and the historical abnormal number threshold value are compared respectively, the historical abnormal index ratio and the historical abnormal index ratio threshold value are compared, and it is determined whether to adjust the initial air supply amount according to the comparison result; When the historical abnormal number is less than the historical abnormal number threshold value, and the historical abnormal index ratio is less than the historical abnormal index ratio threshold value, it is determined that the initial air supply amount is not adjusted; Otherwise, it is determined that the initial air supply amount is adjusted.

[0032] It can be understood that, by comparing the historical abnormal number, the historical abnormal index ratio and the corresponding threshold value to determine whether to adjust the initial air supply amount, the effective information of the historical sorting record can be fully utilized. The historical abnormal number reflects the frequency of air supply amount abnormality, and the historical abnormal index ratio reflects the proportion of the adverse effect of air supply amount abnormality on the sorting effect. If both are less than the threshold value, it indicates that the air supply amount abnormality is less in history and has little effect on the sorting effect, the initial air supply amount is maintained, and system fluctuation is avoided; if any condition is not met, it means that the air supply amount abnormality is serious or has obvious negative impact on the sorting effect, and the initial air supply amount is adjusted, which helps to improve the stability and effect of the sorting system. In actual application, the two threshold values can be flexibly adjusted according to production needs and equipment characteristics to achieve the best sorting effect and cost control.

[0033] Specifically, when adjusting the initial air supply amount based on the real-time bed thickness and the medium density in the bed, the method comprises: respectively comparing the real-time bed thickness with a standard bed thickness and comparing the medium density in the bed with a standard medium density in the bed, and determining an air supply adjustment coefficient of the initial air supply amount according to the comparison results; when the real-time bed thickness is greater than or equal to the standard bed thickness and the medium density in the bed is greater than or equal to the standard medium density in the bed, determining the air supply adjustment coefficient as a first air supply adjustment coefficient; when the real-time bed thickness is greater than or equal to the standard bed thickness and the medium density in the bed is less than the standard medium density in the bed, determining the air supply adjustment coefficient as a second air supply adjustment coefficient; when the real-time bed thickness is less than the standard bed thickness and the medium density in the bed is greater than or equal to the standard medium density in the bed, determining the air supply adjustment coefficient as a third air supply adjustment coefficient; when the real-time bed thickness is less than the standard bed thickness and the medium density in the bed is less than the standard medium density in the bed, determining the air supply adjustment coefficient as a fourth air supply adjustment coefficient; multiplying the air supply adjustment coefficient by the initial air supply amount to obtain the final air supply amount.

[0034] In this embodiment, the real-time bed thickness refers to the actual thickness of the material bed in the sorting machine to be monitored at the current time during the sorting process. The medium density in the bed refers to the actual density of the medium in the material bed in the sorting machine to be monitored during the sorting process.

[0035] It can be understood that the relationship between the air supply adjustment coefficients is first air supply adjustment coefficient > second air supply adjustment coefficient > third air supply adjustment coefficient > fourth air supply adjustment coefficient. This is because when the real-time bed thickness and the medium density in the bed are both large, it means that the bed is relatively dense and the gap between the materials is small, so a larger air supply amount is needed to make the materials fully suspended and separated, so the air supply adjustment coefficient is the largest at this time. When the real-time bed thickness and the medium density in the bed are both small, the bed is relatively loose and the materials are more easily suspended, so the required air supply amount is relatively small, so the air supply adjustment coefficient is the smallest. In this way, the air supply adjustment coefficient is determined according to different combinations of the real-time bed thickness and the medium density in the bed, and then the final air supply amount is obtained, which can realize accurate adjustment of the air supply amount. This accurate adjustment can better adapt to the actual conditions of the bed and ensure that appropriate air force is provided for the materials to be sorted under different bed conditions, so that the materials achieve the best suspension and separation effect during the sorting process.

[0036] Specifically, when judging whether to adjust the initial air supply ratio according to the real-time material feeding amount, the method comprises: obtaining a real-time material feeding amount corresponding to each sorting area; obtaining a preset material feeding amount corresponding to each sorting area; calculating an absolute value of a difference between the real-time material feeding amount and the preset material feeding amount corresponding to each sorting area, and recording the absolute value as an absolute material feeding amount difference; calculating a comprehensive material feeding amount deviation index according to all the absolute material feeding amount differences; judging whether to adjust the initial air supply ratio according to the comprehensive material feeding amount deviation index.

[0037] It can be understood that the comprehensive material feeding amount deviation index is a comprehensive index obtained by weighting calculation of all the absolute material feeding amount differences, which can reflect the overall deviation between the real-time material feeding amount and the preset material feeding amount of each sorting area.

[0038] Specifically, when judging whether to adjust the initial air supply ratio according to the comprehensive material feeding amount deviation index, the method comprises: comparing the comprehensive material feeding amount deviation index with a comprehensive material feeding amount deviation index threshold, and judging whether to adjust the initial air supply ratio according to the comparison result; if the comprehensive material feeding amount deviation index exceeds the comprehensive material feeding amount deviation index threshold, it is determined to adjust the initial air supply ratio; otherwise, it is determined not to adjust the initial air supply ratio.

[0039] It can be understood that by comparing the comprehensive material feeding amount deviation index with the threshold to judge whether to adjust the initial air supply ratio, a reasonable decision can be made according to the actual deviation of the material feeding amount. When the comprehensive material feeding amount deviation index exceeds the threshold, it means that the overall deviation between the real-time material feeding amount and the preset value of each sorting area is large, and at this time the initial air supply ratio may not meet the demand of material sorting. Adjusting the initial air supply ratio can make each sorting area obtain a more suitable air volume, thereby improving the sorting effect. If the comprehensive material feeding amount deviation index does not exceed the threshold, it means that the deviation of the current material feeding amount is within an acceptable range, and maintaining the initial air supply ratio can ensure the stability of the system and avoid unnecessary adjustment to interfere with the sorting process. This judgment method based on the comprehensive material feeding amount deviation index provides a scientific and reasonable basis for the adjustment of the air supply ratio, further improving the accuracy and efficiency of the large-scale low-cost dry method sorting system.

[0040] Specifically, the initial air supply ratio is adjusted based on the partition pressure drop and the real-time sorting effect index, and a final air supply ratio is obtained, comprising: The real-time sorting efficiency, real-time sorting accuracy and real-time sorting quality are obtained by analyzing the real-time sorting effect index; The real-time sorting efficiency, real-time sorting accuracy and real-time sorting quality are normalized respectively to obtain normalized real-time sorting efficiency, normalized real-time sorting accuracy and normalized real-time sorting quality; The normalized real-time sorting efficiency, normalized real-time sorting accuracy and normalized real-time sorting quality are weighted and summed to obtain a comprehensive real-time sorting index; All the partition pressure drops and comprehensive real-time sorting indexes are constructed into an air supply ratio vector group; The air supply ratio vector group is compared with the historical air supply ratio adjustment group, and the proportion adjustment coefficient of the initial air supply ratio is determined according to the comparison result; When there is a historical air supply ratio vector group in the historical air supply ratio adjustment group that is the same as the air supply ratio vector group, the historical air supply ratio adjustment coefficient corresponding to the historical air supply ratio vector group is taken as the proportion adjustment coefficient; When there is no historical air supply ratio vector group in the historical air supply ratio adjustment group that is the same as the air supply ratio vector group, the proportion adjustment coefficient is determined according to the air supply ratio vector group; The initial air supply ratio is adjusted according to the proportion adjustment coefficient to obtain the final air supply ratio.

[0041] It can be understood that by constructing the air supply ratio vector group from the partition pressure drop and the real-time sorting effect index, and comparing it with the historical air supply ratio adjustment group to determine the proportion adjustment coefficient, and then adjusting the initial air supply ratio to obtain the final air supply ratio, the historical experience can be fully used to optimize the air supply ratio. Different combinations of partition pressure drop and real-time sorting effect index correspond to different material sorting states, and the historical air supply ratio adjustment group records the adjustment experience under similar states in the past. When there is a same historical air supply ratio vector group, the corresponding historical air supply ratio adjustment coefficient is directly used to quickly and accurately adjust the air supply ratio; when there is no same, the proportion adjustment coefficient is determined according to the air supply ratio vector group, which can also be reasonably adjusted according to the current actual situation. This way takes into account the partition pressure drop and the real-time sorting effect, so that the final air supply ratio can better adapt to the actual needs in the sorting process, further improving the sorting efficiency and quality of the large-scale low-cost dry sorting system, while reducing unnecessary resource waste and reducing production costs, achieving multi-objective integrated optimization.

[0042] Specifically, when the proportion adjustment coefficient is determined according to the air supply ratio vector group, it comprises: correlation coefficients between the current air supply ratio vector group and each of the historical air supply ratio vector groups are calculated one by one, and the maximum correlation coefficient is extracted; The maximum correlation coefficient is compared with a preset air supply ratio adjustment coefficient table, and the air supply ratio adjustment coefficient is determined according to the comparison result.

[0043] In this embodiment, the correlation coefficient is calculated by the Euclidean distance.

[0044] It can be understood that the preset air supply ratio adjustment coefficient table is a table that is set in advance, which records the air supply ratio adjustment coefficients corresponding to different correlation coefficients. The table is obtained based on a large amount of historical sorting data and experimental results. In a large-scale low-cost dry separation system, different correlation coefficients reflect the similarity between the current air supply ratio vector group and the historical air supply ratio vector group. When the correlation coefficient is larger, it means that the current situation is more similar to a certain historical situation, and at this time, the corresponding historical air supply ratio adjustment coefficient can be referred to for adjusting the current air supply ratio. By comparing the maximum correlation coefficient with the preset air supply ratio adjustment coefficient table, the air supply ratio adjustment coefficient suitable for the current sorting state can be quickly and accurately determined. In this way, the system can timely and reasonably adjust the air supply ratio according to the actual situation, so that the air supply ratio is more matched with the sorting demand of the material.

[0045] It can be understood that when the initial air supply ratio of the first sorting area, the second sorting area and the third sorting area is 1:5:8, the maximum correlation coefficient is 0.8, and the corresponding ratio adjustment coefficients a1, a2 and a3 are preferably 0.9, 1.1 and 1.2 respectively. Then, when the initial air supply ratio is adjusted, the air supply ratio of the first sorting area is adjusted to 1 x 0.9 = 0.9, the air supply ratio of the second sorting area is adjusted to 5 x 1.1 = 5.5, and the air supply ratio of the third sorting area is adjusted to 8 x 1.2 = 9.6. After such adjustment, the air supply ratio of each sorting area can better adapt to the real-time sorting demand of the material. In the actual sorting process, the characteristics and feeding conditions of the material may change at any time. For example, when the particle size distribution of the material changes or the moisture content of the material changes, the sorting effect of the material will be affected. At this time, through the above adjustment method, the system can dynamically optimize the air supply ratio. Assuming that during the subsequent sorting process, the particle size of the material in the first sorting area suddenly increases, resulting in an increase in the sorting difficulty of the region. The system re-constructs the air supply ratio vector group by monitoring the real-time sorting effect indicators and the partition pressure drop, and compares it with the historical air supply ratio adjustment group. If the new maximum correlation coefficient is 0.85, and the corresponding ratio adjustment coefficients a1, a2 and a3 are 1.0, 0.9 and 0.8 respectively. Then, the air supply ratio is adjusted again, the air supply ratio of the first sorting area is adjusted to 0.9 x 1.0 = 0.9, the air supply ratio of the second sorting area is adjusted to 5.5 x 0.9 = 4.95, and the air supply ratio of the third sorting area is adjusted to 9.6 x 0.8 = 7.68. This dynamic adjustment of the air supply ratio enables the large-scale low-cost dry sorting system to maintain high sorting efficiency and quality under different working conditions. At the same time, by making full use of historical sorting data and experimental results, the system can avoid blindly adjusting the air supply ratio and reduce unnecessary resource waste, truly realizing integrated optimization of multiple targets. Moreover, this adjustment method has strong flexibility and adaptability, and can flexibly adjust the air supply parameters according to different production requirements and equipment characteristics to achieve the best sorting effect and cost control.

[0046] Those skilled in the art will appreciate that embodiments of the application can be provided as methods, systems or computer program products. Accordingly, the application can be embodied in the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the application can be embodied in the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage media, etc.) having computer usable program code embodied therein.

[0047] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program goods according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0048] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0049] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0050] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A multi-objective integrated optimization method for large scale low cost dry sorting system, characterized in that, The method comprises the following steps: determining a sorting machine to be monitored, a sorting area and material to be sorted, collecting material parameters of the material to be sorted, analyzing the material parameters, determining an initial air supply of the sorting machine to be monitored and an initial air supply ratio of the sorting area based on the analysis result; collecting historical sorting records of the sorting machine to be monitored, and determining whether to adjust the initial air supply according to the historical sorting records; when it is determined to adjust the initial air supply, collecting real-time bed thickness and medium density in the bed of the sorting machine to be monitored, adjusting the initial air supply based on the real-time bed thickness and medium density in the bed, and obtaining a final air supply; sorting the material to be sorted with the final air supply and the initial air supply ratio, collecting real-time material feeding amount entering each sorting area, and determining whether to adjust the initial air supply ratio according to the real-time material feeding amount; when it is determined to adjust the initial air supply ratio, collecting partition pressure drop and real-time sorting effect index of each sorting area, adjusting the initial air supply ratio based on the partition pressure drop and real-time sorting effect index, and obtaining a final air supply ratio.

2. The multi-objective integrated optimization method of large scale low cost dry sorting system according to claim 1, characterized in that, When determining the initial air supply of the sorting machine to be monitored and the initial air supply ratio of the sorting area based on the analysis result, the method comprises the following steps: analyzing the material parameters to obtain material particle size characteristic value and material moisture content characteristic value of the material to be sorted; respectively obtaining material particle size standard value and material moisture content standard value corresponding to the material particle size characteristic value and the material moisture content characteristic value; obtaining a ratio of the material particle size characteristic value to the material particle size standard value, denoted as a particle size ratio; obtaining a ratio of the material moisture content characteristic value to the material moisture content standard value, denoted as a moisture content ratio; determining a sorting deviation degree of the sorting machine to be monitored according to the particle size ratio and the moisture content ratio; determining the initial air supply of the sorting machine to be monitored and the initial air supply ratio of the sorting area according to the sorting deviation degree.

3. The multi-objective integrated optimization method of large scale low cost dry sorting system according to claim 2, characterized in that, When determining the initial air supply of the sorting machine to be monitored and the initial air supply ratio of the sorting area according to the sorting deviation degree, the method comprises the following steps: comparing the sorting deviation degree with a first sorting deviation degree and a second sorting deviation degree, and determining the initial air supply and the initial air supply ratio according to the comparison result; wherein the first sorting deviation degree is smaller than the second sorting deviation degree; when the sorting deviation degree is smaller than or equal to the first sorting deviation degree, determining that the initial air supply and the initial air supply ratio are a first air supply and a first air supply ratio respectively; when the sorting deviation degree is greater than the first sorting deviation degree and smaller than or equal to the second sorting deviation degree, determining that the initial air supply and the initial air supply ratio are a second air supply and a second air supply ratio respectively; when the sorting deviation degree is greater than the second sorting deviation degree, determining that the initial air supply and the initial air supply ratio are a third air supply and a third air supply ratio respectively.

4. The multi-objective integrated optimization method of large scale low cost dry sorting system according to claim 3, characterized in that, When determining whether to adjust the initial air supply according to the historical sorting records, the method comprises the following steps: analyzing the historical sorting records to obtain historical sorting records related to the abnormal air supply amount within a preset time window, denoted as historical abnormal sorting records; obtaining the number of all the historical abnormal sorting records, denoted as a historical abnormal number; obtaining a historical sorting effect index corresponding to each of the historical abnormal sorting records, and obtaining a proportion of an abnormal sorting effect index in all the historical sorting effect indexes, denoted as a historical abnormal index ratio; determining whether to adjust the initial air supply amount according to the historical abnormal number and the historical abnormal index ratio.

5. The multi-objective integrated optimization method of large scale low cost dry sorting system according to claim 4, characterized in that, When determining whether to adjust the initial air supply amount according to the historical abnormal number and the historical abnormal index ratio, the method comprises: respectively comparing the historical abnormal number with a historical abnormal number threshold value and comparing the historical abnormal index ratio with a historical abnormal index ratio threshold value, and determining whether to adjust the initial air supply amount according to a comparison result; when the historical abnormal number is less than the historical abnormal number threshold value and the historical abnormal index ratio is less than the historical abnormal index ratio threshold value, determining not to adjust the initial air supply amount; otherwise, determining to adjust the initial air supply amount.

6. The multi-objective integrated optimization method of large scale low cost dry sorting system according to claim 5, characterized in that, When adjusting the initial air supply amount based on the real-time bed thickness and the medium density in the bed, the method comprises: respectively comparing the real-time bed thickness with a standard bed thickness and comparing the medium density in the bed with a standard medium density in the bed, and determining an air supply adjustment coefficient of the initial air supply amount according to a comparison result; when the real-time bed thickness is greater than or equal to the standard bed thickness and the medium density in the bed is greater than or equal to the standard medium density in the bed, determining that the air supply adjustment coefficient is a first air supply adjustment coefficient; when the real-time bed thickness is greater than or equal to the standard bed thickness and the medium density in the bed is less than the standard medium density in the bed, determining that the air supply adjustment coefficient is a second air supply adjustment coefficient; when the real-time bed thickness is less than the standard bed thickness and the medium density in the bed is greater than or equal to the standard medium density in the bed, determining that the air supply adjustment coefficient is a third air supply adjustment coefficient; when the real-time bed thickness is less than the standard bed thickness and the medium density in the bed is less than the standard medium density in the bed, determining that the air supply adjustment coefficient is a fourth air supply adjustment coefficient; multiplying the air supply adjustment coefficient by the initial air supply amount to obtain the final air supply amount.

7. The multi-objective integrated optimization method of large scale low cost dry sorting system according to claim 6, characterized in that, When determining whether to adjust the initial air supply proportion according to the real-time material feeding amount, the method comprises: obtaining a real-time material feeding amount corresponding to each of the sorting areas; obtaining a preset material feeding amount corresponding to each of the sorting areas; calculating an absolute value of a difference value of the material feeding amount corresponding to each of the sorting areas according to the real-time material feeding amount and the preset material feeding amount, and denoting the absolute value as an absolute material feeding amount difference value; calculating a comprehensive material feeding amount deviation index according to all the absolute material feeding amount difference values; determining whether to adjust the initial air supply proportion according to the comprehensive material feeding amount deviation index.

8. The multi-objective integrated optimization method of large scale low cost dry sorting system according to claim 7, characterized in that, When determining whether to adjust the initial air supply ratio according to the comprehensive material feeding amount deviation index, the method comprises: comparing the comprehensive material feeding amount deviation index with a comprehensive material feeding amount deviation index threshold value, and determining whether to adjust the initial air supply ratio according to the comparison result; if the comprehensive material feeding amount deviation index exceeds the comprehensive material feeding amount deviation index threshold value, it is determined that the initial air supply ratio is adjusted; otherwise, it is determined that the initial air supply ratio is not adjusted.

9. The multi-objective integrated optimization method of large scale low cost dry sorting system according to claim 8, characterized in that, When adjusting the initial air supply ratio based on the partition pressure drop and the real-time sorting effect index and obtaining the final air supply ratio, the method comprises: analyzing the real-time sorting effect index to obtain a real-time sorting efficiency, a real-time sorting precision and a real-time sorting quality; normalizing the real-time sorting efficiency, the real-time sorting precision and the real-time sorting quality respectively to obtain a normalized real-time sorting efficiency, a normalized real-time sorting precision and a normalized real-time sorting quality; weighting and summing the normalized real-time sorting efficiency, the normalized real-time sorting precision and the normalized real-time sorting quality to obtain a comprehensive real-time sorting index; constructing all the partition pressure drops and the comprehensive real-time sorting index into an air supply ratio vector group; comparing the air supply ratio vector group with a historical air supply ratio adjustment group, and determining a proportion adjustment coefficient of the initial air supply ratio according to the comparison result; when there is a historical air supply ratio vector group identical to the air supply ratio vector group in the historical air supply ratio adjustment group, a historical air supply ratio adjustment coefficient corresponding to the historical air supply ratio vector group is taken as the proportion adjustment coefficient; when there is no historical air supply ratio vector group identical to the air supply ratio vector group in the historical air supply ratio adjustment group, the proportion adjustment coefficient is determined according to the air supply ratio vector group; adjusting the initial air supply ratio according to the proportion adjustment coefficient to obtain the final air supply ratio.

10. The multi-objective integrated optimization method of large scale low cost dry sorting system according to claim 9, wherein, When determining the proportion adjustment coefficient according to the air supply ratio vector group, the method comprises: calculating the correlation coefficient of the air supply ratio vector group and each historical air supply ratio vector group one by one, and extracting the maximum correlation coefficient; comparing the maximum correlation coefficient with a preset proportion adjustment coefficient table, and determining the proportion adjustment coefficient according to the comparison result.

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