Optimization method for reducing the unit consumption of flotation reagents in copper smelting slag

Abstract

The present application discloses an optimization method for reducing the unit consumption of copper smelting slag flotation beneficiation reagents, comprising the following steps: step S1: field investigation; step S2: establishing a concentrate grade-dosage and dosing point number model, a concentrate recovery rate-dosage and dosing point number model; step S3: verifying the obtained model; step S4: establishing a reagent addition error value-dosing valve pipeline size and dosing flow rate model, and verifying the obtained model; step S5: obtaining the optimal parameters for each model and running the obtained optimized parameters to evaluate the operation results. The method obtains the optimal dosing amount, the number of dosing points, the dosing valve pipeline size and the dosing flow rate through the obtained model, and optimizes the current reagent addition system according to the above parameters, thereby improving the effect of flotation reagents, reducing the unit consumption of beneficiation reagents, reducing the error between the actual dosing amount and the required dosing amount, stabilizing production indicators, solving the problem of inaccurate dosing on site, and facilitating the optimization of the dosing system on site.

Description

Technical Field

[0001] The present application relates to the technical field of copper smelting, and in particular to an optimization method for reducing the unit consumption of flotation dressing reagents in copper smelting slag. Background Art

[0002] After production and smelting, valuable metals such as copper, gold and silver in the ore are partially enriched in the smelting slag, and most of the slag with a higher copper grade is treated as waste slag.

[0003] The emergence of copper smelting slag flotation technology has eased the pressure on my country's copper resource mining. Slag flotation has a low production cost and can effectively recover valuable metals such as copper, gold and silver from copper smelting slag by secondary separation of high-grade copper slag produced during the copper smelting process.

[0004] However, the actual reagent consumption per unit in current slag concentrators often does not meet the prescribed reagent consumption standards. At the same time, due to the strong subjectivity of the on-site reagent addition process, a large amount of reagent loss occurs, and the reagent addition system is unstable, resulting in large fluctuations in flotation indicators. Summary of the invention

[0005] The present application provides an optimization method for reducing the unit consumption of copper smelting slag flotation beneficiation reagents, which is used to solve the technical problems in the prior art that the actual addition amount of copper slag flotation reagents is greatly affected by the operator, does not meet the prescribed addition amount, increases the amount of reagents used, and is difficult to effectively verify the amount of flotation reagents applied.

[0006] The invention aims to solve the problems of unclear reagent effect, low addition accuracy, poor on-site indicators, high reagent unit consumption and poor stability of the addition system in the existing copper smelting slag flotation beneficiation reagent addition process.

[0007] The present application provides an optimization method for reducing the unit consumption of flotation dressing reagents for copper smelting slag, comprising the following steps:

[0008] Step S1: Obtain the types of added agents in the current production of the plant to be optimized, obtain the addition amount and number of addition points of each agent during the sampling time according to the obtained agent types, and calculate the average value M of the addition amount of each agent during the sampling period;

[0009] Step S2: According to the sampling results in step S1, the number of dosing points is set to X1 = (X 11 ,

[0010] X 21 ,…X n1 ), where n is the nth sampling, X n1 is the number of dosing points counted during the nth sampling period; the dosage of the agent at each dosing point is X 2＝ (X 12 ,X22 ,…X n2 ) where n is the nth sampling, X n2 is the dosage of the reagent calculated during the nth sampling period; the concentrate grade is set to Y1 = (Y 11 ,Y 21 ,…Y i1 , ...Y n1 ), where n is the nth sampling, Y n1 is the weighted average of the concentrate grade calculated during the nth sampling period; the concentrate recovery rate is set to Y2 = (Y 12 ,

[0011] Y 22 ,…Y i2 , ...Y n2 ) where n is the nth sampling, Y n2 is the weighted average of the concentrate recovery rate during the nth sampling period. There is a linear relationship between X1, X2 and Y1, Y2. When the reliability coefficient α = 0.05, the first multivariate linear regression model is established, and the concentrate grade-dosage and dosing point number model is obtained as follows:

[0012] Y1=b0+b1X1+b2X2Formula (1);

[0013] in,

[0014] The model of concentrate recovery rate, dosage and number of dosing points is obtained as follows:

[0015] Y2=c0+c1X1+c2X2 Formula (5);

[0016] in

[0017] Step S3: Verify the significance of the concentrate grade-dosage and dosing point number model and the concentrate recovery rate-dosage and dosing point number model:

[0018] The significance of the above concentrate recovery rate-dosage and dosing point number model is verified by the following formula:

[0019]

[0020] Among them, R 复1 is the multiple correlation coefficient of the Y1 model, Y a is the arithmetic mean value of the copper concentrate grade Y1 obtained from sampling at each sampling point in the plant area to be optimized; Y is the value calculated by the model; Y i1 is the copper concentrate grade at each sampling point;

[0021] The significance of the model of concentrate recovery rate-dosage and number of dosing points is verified by the following formula:

[0022]

[0023] Among them, R 复2 is the multiple correlation coefficient of the Y2 model, Y b is the arithmetic mean value of the concentrate recovery rate Y2 of each sampling point in the plant area to be optimized; Y is the model calculation value; Y i2 is the copper concentrate recovery rate at each sampling point;

[0024] Step S4: respectively calculating the extreme values ​​of the concentrate grade-dosage and dosing point number model, and the extreme values ​​of the concentrate recovery rate-dosage and dosing point number model, comprehensively considering the concentrate grade and concentrate recovery rate indicators, taking the extreme value that can simultaneously meet the requirements of the concentrate grade and the concentrate recovery rate as the optimization standard, and optimizing the number of reagent addition points of the plant and the optimal reagent addition amount at each point according to the obtained optimization standard;

[0025] Step S5: Assume that the dosing flow rate obtained by sampling at each sampling point is X3=(X 13 ,X 23 ,…,X i3 …, X n3 ), the pipe size of the dosing valve is X4=(X 14 ,X 24 ,…,X i4 …X n4 ), the error value of adding reagent is Y3=(Y 13 ,Y 23 ,…,Y i3 …Y n3 ), and there is a linear relationship between X3, X4, and Y3. When the reliability coefficient α = 0.05, the second multivariate linear regression model is established, and the error value of the reagent addition - the pipe size of the dosing valve and the dosing flow rate model is obtained as follows:

[0026] Y3=d0+d1X3+d2X4 Formula (11);

[0027] in,

[0028] The error value of reagent addition - the significance of the dosing valve pipeline size and the dosing flow rate model are verified by the following formula:

[0029]

[0030] Among them, R 复3 is the multiple correlation coefficient of the Y4 model, Y b is the arithmetic mean value of the reagent addition error value Y3 of each sampling point in the plant area to be optimized; Y is the model calculation value; Yi3 The error value of the reagent addition is for each sampling point; the error value of the reagent addition is

[0031] |Unit consumption of reagent at each sampling point - required reagent addition value|÷required reagent addition value×100% Formula (15);

[0032] Step S6: After finding the extreme value of the drug addition error value obtained in step S5 - the drug adding valve pipeline size and the drug adding flow rate model, the corresponding optimal drug valve pipeline size and drug adding flow rate are obtained, and the corresponding parameters of the current drug addition are adjusted according to the obtained optimal drug adding valve pipeline size and drug adding flow rate, and the optimized drug adding system is obtained after running.

[0033] Preferably, the following steps are included: Step S7: operating the copper ore slag dressing process according to the optimal standard parameters obtained in Step S6 and Step S4, obtaining the dosage of each reagent in the copper ore slag dressing process within at least 2 months after optimization, calculating the reagent addition error value, and comparing it with the dosage of each reagent before optimization, and evaluating the optimization result.

[0034] Preferably, step S7 includes: calculating the variance of each drug dosage after optimization and comparing it with the variance of each drug dosage before optimization, and the comparison standard is: when the variance S2 of the drug addition amount is smaller, it means that the addition amount of the drug addition system is more stable.

[0035] Preferably, the sampling time is greater than or equal to 2 months.

[0036] Preferably, the extreme values ​​in step S4 are calculated by respectively taking the derivatives of the concentrate grade-dosage and dosing point number model and the concentrate recovery rate-dosage and dosing point number model, and setting the derivatives to 0 to obtain the corresponding X1 and X2.

[0037] Preferably, the average value M of the amount of agent added is Calculate, where X1, X2, ... X n is the measured amount of the agent added, and n is the nth sampling.

[0038] Preferably, the variance S of the amount of drug added is 2 according to calculate.

[0039] The beneficial effects of this application include:

[0040] 1) The optimization method for reducing the unit consumption of copper smelting slag flotation dressing reagents provided in the present application establishes a concentrate grade-dosage and the number of dosing points model, a concentrate recovery rate-dosage and the number of dosing points model, and a reagent addition error value-dosing valve pipeline size and dosing flow rate model. The optimal dosing amount, the number of dosing points, the dosing valve pipeline size and the dosing flow rate are obtained through the obtained model, and the current reagent addition system is optimized according to the above parameters, thereby improving the effect of the flotation reagent, reducing the unit consumption of the dressing reagent, reducing the error between the actual dosing amount and the required amount, stabilizing the production index, solving the problem of inaccurate on-site dosing, and facilitating on-site optimization of the dosing system.

[0041] 2) The optimization method for reducing the unit consumption of copper smelting slag flotation dressing reagents provided in this application can optimize and improve the on-site flotation reagent addition system from three aspects: reagent addition point, reagent addition type and reagent addition equipment, so as to maximize the effect of flotation reagents, and at the same time reduce the unit consumption of dressing reagents, reduce production costs, and improve the grade and recovery rate of copper concentrate flotation from copper smelting slag. It can also solve the problems of inaccurate on-site addition and difficult on-site operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic flow chart of an optimization method for reducing the unit consumption of flotation dressing reagents for copper smelting slag provided in this application; DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0044] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] The technical means that are not described in detail in this application and are not used to solve the technical problems of this application are all set according to the common knowledge in the field, and can be implemented in a variety of common knowledge settings.

[0046] See also Figure 1 The optimization method for reducing the unit consumption of copper smelting slag flotation dressing reagents provided in the present application comprises the following steps:

[0047] Step S1: Obtain the types of added agents in the current production of the plant to be optimized, obtain the addition amount and number of addition points of each agent during the sampling time according to the obtained agent types, and calculate the average value M of the addition amount of each agent during the sampling period;

[0048] Step S2: According to the sampling results in step S1, the number of dosing points is set to X1 = (X 11 ,

[0049] X 21 ,…X n1 ), where n is the nth sampling, X n1 is the number of dosing points at the site during the nth sampling; the dosage of the agent at each dosing point is X 2＝ (X 12 ,X 22 ,…X n2 ) where n is the nth sampling, X n2 is the amount of the reagent added during the nth sampling period; the concentrate grade is set to Y1 = (Y 11 ,

[0050] Y 21 ,…Y i1 , ...Y n1 ), where n is the nth sampling, Y n1 is the weighted average of the concentrate grade at the time of the nth sampling; the concentrate recovery rate is set to Y2 = (Y 12 ,Y 22 ,…Y i2 , ...Y n2 ) where n is the nth sampling, Y n2 is the weighted average of the concentrate recovery rate at the nth sampling site. There is a linear relationship between X1, X2 and Y1, Y2. When the reliability coefficient α = 0.05, the first multivariate linear regression model is established, and the concentrate grade-dosage and dosing point number model is obtained as follows:

[0051] Y1=b0+b1X1+b2X2Formula (1);

[0052] in,

[0053] The model of concentrate recovery rate, dosage and number of dosing points is obtained as follows:

[0054] Y2=c0+c1X1+c2X2 Formula (5);

[0055] in

[0056] Step S3: Verify the significance of the concentrate grade-dosage and dosing point number model and the concentrate recovery rate-dosage and dosing point number model:

[0057] The significance of the above concentrate recovery rate-dosage and dosing point number model is verified by the following formula:

[0058]

[0059] Among them, R 复1 is the multiple correlation coefficient of the Y1 model, Y a is the arithmetic mean value of the copper concentrate grade Y1 obtained from sampling at each sampling point in the plant area to be optimized; Y is the value calculated by the model; Y i1 is the copper concentrate grade at each sampling point;

[0060] The significance of the model of concentrate recovery rate-dosage and number of dosing points is verified by the following formula:

[0061]

[0062] Among them, R 复2 is the multiple correlation coefficient of the Y2 model, Y b is the arithmetic mean value of the concentrate recovery rate Y2 of each sampling point in the plant area to be optimized; Y is the model calculation value; Y i2 is the copper concentrate recovery rate at each sampling point;

[0063] Step S4: respectively calculating the extreme values ​​of the concentrate grade-dosage and dosing point number model, and the extreme values ​​of the concentrate recovery rate-dosage and dosing point number model, comprehensively considering the concentrate grade and concentrate recovery rate indicators, taking the extreme value that can simultaneously meet the requirements of the concentrate grade and the concentrate recovery rate as the optimization standard, and optimizing the number of reagent addition points of the plant and the optimal reagent addition amount at each point according to the obtained optimization standard;

[0064] Step S5: Assume that the dosing flow rate obtained by sampling at each sampling point is X3=(X 13 ,X 23 ,…,X i3 …, X n3 ), the pipe size of the dosing valve is X4=(X 14 ,X 24 ,…,X i4 …X n4 ), the error value of adding reagent is Y3=(Y 13 ,Y 23 ,…,Y i3 …Y n3 ), and there is a linear relationship between X3, X4, and Y3. When the reliability coefficient α = 0.05, the second multivariate linear regression model is established, and the error value of the reagent addition - the pipe size of the dosing valve and the dosing flow rate model is obtained as follows:

[0065] Y3=d0+d1X3+d2X4 Formula (11);

[0066] in,

[0067] The error value of reagent addition - the significance of the dosing valve pipeline size and the dosing flow rate model are verified by the following formula:

[0068]

[0069] Among them, R 复3 is the multiple correlation coefficient of the Y4 model, Y b is the arithmetic mean value of the reagent addition error value Y3 of each sampling point in the plant area to be optimized; Y is the model calculation value; Y i3 The error value of the reagent addition is for each sampling point; the error value of the reagent addition is

[0070] |Unit consumption of reagent at each sampling point - required reagent addition value|÷required reagent addition value×100% Formula (15);

[0071] Step S6: After finding the extreme value of the drug addition error value obtained in step S5 - the drug adding valve pipeline size and the drug adding flow rate model, the corresponding optimal drug valve pipeline size and drug adding flow rate are obtained, and the corresponding parameters of the current drug addition are adjusted according to the obtained optimal drug adding valve pipeline size and drug adding flow rate.

[0072] The method provided in the present application can be used to construct the required models for various parameters based on the current drug addition system, and the required optimization indicators can be obtained after finding the extreme values ​​of the obtained models. The obtained optimization indicators can be applied to drug addition to achieve the stability of the drug addition amount, avoid the problem of fluctuations in the main parameter indicators of the product due to inaccurate drug addition by on-site workers, and at the same time reduce the unit consumption of drugs and improve the stability of the obtained products.

[0073] The present application provides a method for optimizing the existing reagent addition system, which can scientifically and accurately reflect the stability of the reagent addition system, and can optimize and improve the on-site flotation reagent addition system in many aspects, improve the effect of the flotation reagent, reduce the unit consumption of mineral processing reagents, reduce the error between the unit consumption of reagents and the standard value, improve the stability of reagent addition, stabilize production indicators, and at the same time solve the problems of inaccurate on-site manual dosing and difficult on-site operation.

[0074] Preferably, the following steps are included: Step S7: operating the copper ore slag dressing process according to the optimal standard parameters obtained in Step S6 and Step S4, obtaining the dosage of each reagent in the copper ore slag dressing process within at least 2 months after optimization, calculating the reagent addition error value, and comparing it with the dosage of each reagent before optimization, and evaluating the optimization result.

[0075] Preferably, step S7 includes: calculating the variance of the dosage of each agent after optimization and comparing it with the variance of the dosage of each agent before optimization, and the comparison standard is: when the variance S of the dosage of the agent added is 2 The smaller it is, the more stable the amount of the drug addition system is.

[0076] When the evaluation conclusion is that the more stable the reagent addition system is, the more the addition system can meet the requirements for copper slag concentrate grade and concentrate recovery rate, and after adopting this addition system, the dosage of reagents is the most reasonable.

[0077] Preferably, the sampling time is greater than or equal to 2 months.

[0078] Preferably, the calculation of the extreme value in step S4 is to respectively obtain the derivatives of the concentrate grade-dosage and dosing point number model and the concentrate recovery rate-dosage and dosing point number model, and set the corresponding X1 and X2 when the derivative is 0. According to this setting method, the optimal value of the required optimization parameter can be quickly obtained, and it can be applied in practice, which has a significant improvement effect on actual production.

[0079] Preferably, the average value M of the amount of the agent added is Calculate, where X1, X2, ... X n is the measured amount of the agent added, and n is the nth sampling.

[0080] Preferably, the variance S of the amount of drug added is 2 according to calculate.

[0081] Calculate the variance S of the amount of added medicine 2 Based on the obtained variance of the reagents in the plant, the stability of the dosing system in the plant area is preliminarily evaluated. The variance of the amount of each reagent added S 2 The smaller it is, the more stable the number of existing addition points and the addition amount of the agent are.

[0082] Example

[0083] Unless otherwise specified, the drugs used in the following examples were obtained from commercial channels.

[0084] Example 1

[0085] The method used in this embodiment includes the following steps:

[0086] Step (1) Obtain the types of added agents in the current production of the plant to be optimized, obtain the amount of each agent added and the number of addition points during the sampling time according to the obtained agent types, and calculate the average value M of the amount of each agent added during the sampling period and the variance S of the amount of each agent added 2 Based on the obtained variance of the reagents in the plant, the stability of the dosing system in the plant area is preliminarily evaluated. The variance of the amount of each reagent added S 2The smaller it is, the more stable the existing addition method, addition point, and addition amount of the agent are;

[0087] Research situation in this example: A five-month on-site research was conducted on a copper smelting slag concentrator in Yunnan.

[0088] The original method of adding reagents in the plant area to be optimized is: mechanical stirring - height difference direct current - ball valve flow control addition.

[0089] The reagents used are: foaming agent 2# oil, collecting agent butyl xanthate, and adjusting agent sodium sulfide. The operating procedure is to manually check on site after adding the reagents.

[0090] according to Calculate the average value M of the amount of each agent added during the survey period, where X1, X2, ... X n is the measured addition amount of each agent, and n is the nth sampling.

[0091] In this embodiment, the average amount of the drug added each month is calculated first, and after the average amount of each drug added each month is obtained, the average addition value of the drug within 5 months is calculated.

[0092] The average added values ​​of 2# oil, butyl xanthate and sodium sulfide were 557.432 g / t, 303.022 g / t and 276.76 g / t respectively.

[0093] according to Calculate the variance S of the amount of each agent added during the survey period 2 .

[0094] In this embodiment, X1, X2, ..., X in the above variance formula n is the measured addition amount of each agent at each sampling, n = 5. The addition variances of 2# oil, butyl xanthate, and sodium sulfide are 1673.19 g respectively. 2 / t 2 、732.99g 2 / t 2 、148.90g 2 / t 2 .

[0095] According to the obtained reagent addition amount variance value, determine the reagent addition stability of the plant area to be optimized: the variance of each reagent addition amount S 2 Value S 2 The smaller the value, the more stable it is. From the above, we can see that the existing addition system of sodium sulfide in the plant area to be optimized has the highest stability, followed by butyl xanthate, and the addition system of 2# oil has the worst stability.

[0096] Since the processes, reagent addition points, reagent addition amounts and reagent addition types of each concentrator are different, the optimal range of the variance cannot be determined, and the calculation of this value only provides a comparison parameter for the comprehensive evaluation of the subsequent step 7, indicating the reagent addition situation after the invention is carried out.

[0097] During the investigation, it was learned that the average monthly copper concentrate copper recovery rate of the slag concentrator was 93.67%, the average monthly copper concentrate copper grade was 21.37%, and the average monthly tailings copper grade was 0.217%.

[0098] Step (2) according to the existing process on site, the reagent addition position is reasonably designed, and according to the design results, a model of the number of reagent addition points on site and the amount of reagent added at each point and the flotation index is established;

[0099] Among them, optimizing the reagent addition point mainly achieves the purpose of fully stirring and dispersing the flotation reagent and prolonging the action time of the reagent and the slurry by adjusting the reagent addition position.

[0100] Step (2) combines the existing process on site to reasonably design the reagent addition location, and according to the design results, establish a model of the number of reagent addition points on site and the amount of reagent added at each point and the flotation index; based on the large amount of data obtained in step 1), the following model is obtained:

[0101] The following model corresponding to any one of 2# oil, butyl xanthate and sodium sulfide is obtained:

[0102] The flotation index model is established based on the number of on-site reagent addition points and the amount of reagent added at each point as follows:

[0103] Set the number of dosing points measured on site to X1 = (X 11 ,X 21 ,…X n1 ), where n represents the nth sampling, X n1 is the number of dosing points for the nth sampling. For example, in this embodiment, X 11 The number of dosing points actually measured in the plant for the first sampling, and so on;

[0104] The dosage of the agent at each dosing point is X 2＝ (X 12 ,X 22 ,…X n2 ) where n represents the nth sampling, X n2 is the dosage of the agent sampled for the nth time. For example, in this embodiment, X 12 It is the average amount of the drug added during the first sampling period, and so on;

[0105] The concentrate grade is Y1=(Y 11 ,Y 21 ,…Yi1 , ...Y n1 ), where n represents the nth sampling, Y n1 is the weighted average of the concentrate grade obtained during the nth sampling period. For example, in this embodiment, Y 11 It is the weighted average of the copper grade of the copper concentrate produced by the plant during the first sampling period, and so on;

[0106] The concentrate recovery rate is Y2 = (Y 12 ,Y 22 ,…Y i2 , ...Y n2 ) where n represents the nth sampling, Y n2 is the weighted average of the copper recovery rate of the copper concentrate of the plant during the nth sampling period. For example, taking this embodiment as an example, Y 12 is the weighted average of the copper recovery rates of the copper concentrate produced by the plant during the first sampling period, and so on;

[0107] According to the above data obtained from the field investigation, it is found that there is a linear relationship between X1, X2 and Y1, Y2. When the reliability coefficient α=0.05, the first multivariate linear regression model is established. The concentrate grade-dosage and dosing point number model is:

[0108] Y1=b0+b1X1+b2X2Formula (1);

[0109] in,

[0110] The model of concentrate recovery rate, dosage and number of dosing points is:

[0111] Y2=c0+c1X1+c2X2 Formula (5);

[0112] in

[0113] Step (3) verifying the significance of the number of on-site reagent addition points and the flotation index model;

[0114] The significance of the above concentrate recovery rate-dosage and dosing point number model is verified by the following formula:

[0115]

[0116] Among them, R 复1 is the multiple correlation coefficient of the Y1 model, Y a is the arithmetic mean value of copper grade Y1 of copper concentrate obtained from sampling at each sampling point in the plant area to be optimized; Y is the value calculated by the model; Y i1 is the copper grade of copper concentrate at each sampling point;

[0117] The significance of the model of concentrate recovery rate-dosage and number of dosing points is verified by the following formula:

[0118]

[0119] Among them, R 复2 is the multiple correlation coefficient of the Y2 model, Y b is the arithmetic mean value of the concentrate copper recovery rate Y2 obtained from sampling at each sampling point in the plant area to be optimized; Y is the model calculation value; Y i2 is the copper recovery rate of copper concentrate at each sampling point.

[0120] According to the complex correlation coefficient of reagent addition obtained from the above formula, the reliability of the model built for concentrate recovery rate and concentrate grade is judged, and the obtained R 复 The closer it is to 1, the higher the significance of the model is, and the more reliable the model is. 复1 is 0.423; R 复2 It is 0.706.

[0121] By calculating the extreme value of the relationship, the optimal agent addition point and agent addition amount can be obtained.

[0122] Step (4) calculates the extreme values ​​of the concentrate grade-dosage and number of dosing points model, and the extreme values ​​of the concentrate recovery-dosage and number of dosing points model, respectively, and takes the extreme value that can simultaneously meet the requirements of concentrate grade and concentrate recovery as the optimization standard based on the index of concentrate grade and concentrate recovery. According to the obtained optimization standard, the number of reagent addition points of the plant and the optimal reagent addition amount at each point are optimized. The calculation of the extreme value is to take the derivative of each formula, and let the corresponding X1 and X2 when the derivative is 0.

[0123] After obtaining the extreme values, calculate the concentrate grade and concentrate recovery rate corresponding to each group of extreme values, and judge whether the concentrate grade and concentrate recovery rate corresponding to the extreme values ​​meet the requirements. If they meet the requirements at the same time, use this group of extreme values ​​for optimization operation.

[0124] Step (5) For the reagent adding device used on site, establish and analyze the influence model of the on-site reagent adding time interval and the reagent adding amount on the reagent adding error value, and determine the optimal reagent adding system. Among them, the model for analyzing the influence of the on-site reagent flow rate and the pipe size of the dosing valve on the reagent adding error value is as follows;

[0125] Assume that the dosing flow rate obtained at each sampling time point in the plant area to be optimized obtained from the on-site investigation is X3=(X 13 ,X 23 ,…,X i3 …, X n3 ), the pipe size of the dosing valve is X4=(X 14 ,X 24 ,…,Xi4 …X n4 ), the error value of adding reagent is Y3=(Y 13 ,Y 23 ,…,Y i3 …Y n3 ), and there is a linear relationship between X3, X4, and Y3. When the reliability coefficient α = 0.05, a multiple linear regression model is established, and a second multiple linear regression model is established to obtain the error value of the reagent addition-the size of the dosing valve pipeline and the dosing flow rate model:

[0126] Y3=d0+d1X3+d2X4 formula (11);

[0127] in,

[0128] The error value of reagent addition - the significance of the dosing valve pipeline size and the dosing flow rate model are verified by the following formula:

[0129]

[0130] Among them, R 复3 is the multiple correlation coefficient of the Y4 model, Y b is the arithmetic mean value of the reagent addition error value Y3 of each sampling point in the plant area to be optimized; Y is the model calculation value; Y i3 The error value of the reagent addition is for each sampling point; the error value of the reagent addition is

[0131] |Unit consumption of reagent at each sampling point - required reagent addition value| ÷ required reagent addition value × 100% Formula (15).

[0132] The R obtained in this example 复3 The value is 0.548. The corresponding optimal medicine valve pipeline size and dosing flow rate are obtained by finding the extreme value of the obtained medicine addition error value-dosing valve pipeline size and dosing flow rate model. The method of finding the extreme value is the same as the above step 2.

[0133] In this embodiment, when the reagent addition system is not optimized according to the method provided in this application, the average monthly reagent addition error value of each reagent is: the average monthly addition error of butyl xanthate is 6.97%, the average monthly addition error of 2# oil is 4.61%, and the average monthly addition error of sodium sulfide is 5.77%.

[0134] The models obtained in this embodiment using the method provided by this application are:

[0135] Y1=21.94+0.52X1-0.0038X2;

[0136] Y2=92.44-0.73X1+0.0072X2;

[0137] Y3=0.3-4.25X3+0.0252X4.

[0138] According to the dosage of reagents, number of dosing points, flow rate of reagents, and pipe size of dosing valves obtained from the above models, the reagent addition system of the factory was optimized, and the accuracy of the addition of each reagent was calculated by sampling within 2 months of production. The results were as follows:

[0139] After optimization according to the above model, the average monthly error value of butyl xanthate was 3.24%, the average monthly error value of 2# oil was 2.24%, and the average monthly error value of sodium sulfide was 2.05%.

[0140] By using the model formula provided in this application and optimizing the relevant parameters of the existing reagent addition, production can be carried out again, which can effectively reduce the error between the actual addition value and the required addition amount. This shows that the model obtained by the method provided in this application can effectively solve the various problems existing in the existing reagents in copper slag smelting and effectively improve the accuracy of the reagent addition amount.

[0141] After running the optimized reagent addition system, the variance of 2# oil addition was 575.70g 2 / t 2 , the variance of butyl xanthate addition is 254.27g 2 / t 2 , the variance of sodium sulfide addition is 100.28g 2 / t 2 , compared with the variance of reagent addition amount before optimization, it is smaller, indicating that the reagent addition system can be effectively optimized by the method provided by the present application, and the stability of reagent addition can be improved. It indicates that the reagent addition system optimized by the method provided by the present application can effectively improve the stability of reagent addition, effectively correct the reagent dosage to reduce the error, and obtain the copper smelting slag flotation concentrate that meets the production requirements.

[0142] Example 2

[0143] A field survey was conducted on a copper smelting slag concentrator in Fujian. The on-site frother used 2# oil, the collector used ethylthiocarbamate, and the adjuster used sodium sulfide. Manual verification was performed on-site. Under this condition, the average monthly copper recovery rate was 89.60%, and the average monthly concentrate copper grade was 22.82%. The variance of reagent addition before the improvement was calculated. The variance of 2# oil, ethylthiocarbamate, and sodium sulfide addition was 209.78g, respectively. 2 / t 2 、1759.56g 2 / t 2 、2316.44g 2 / t 2 .

[0144] Before optimization, the average monthly error of ethionamide addition was 5.49%, the average monthly error of 2# oil addition was 7.31%, and the average monthly error of sodium sulfide addition was 6.44%.

[0145] The pharmaceutical data of the factory was obtained according to the method in Example 1 to build a model as follows:

[0146] Y1=5.22X1+0.12X2-21.94;

[0147] Y2=0.02-20.35X1+0.49X2;

[0148] Y3=0.36+1.35X3+0.0207X4

[0149] After using the above model to optimize the various chemical addition parameters of the plant, the measured data within 5 months of operation according to the optimized data are: the average monthly chemical addition error value of ethionamide is 3.46%, the average monthly chemical addition error value of 2# oil is 3.74%, and the average monthly chemical addition error value of sodium sulfide is 2.26%.

[0150] Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An optimization method for reducing the unit consumption of flotation agents for copper smelting slag, characterized in that: The following steps are involved: Step S1: Obtain the types of added agents in the current production of the plant to be optimized, obtain the addition amount and number of addition points of each agent during the sampling time according to the obtained agent types, and calculate the average value M of the addition amount of each agent during the sampling period; Step S2: According to the sampling results in step S1, the number of dosing points is set to X1 = (X 11 ,X 21 ,…X n1 ), where n is the nth sampling, X n1 is the number of dosing points counted during the nth sampling period; the dosage of the agent at each dosing point is X 2＝ (X 12 ,X 22 ,…X n2 ) where n is the nth sampling, X n2 is the dosage of the reagent calculated during the nth sampling period; the concentrate grade is set to Y1 = (Y 11 ,Y 21 ,…Y i1 , ...Y n1 ), where n is the nth sampling, Y n1 is the concentrate grade calculated during the nth sampling period; the concentrate recovery rate is set to be Y2=(Y 12 ,Y 22 ,…Y i2 , ...Y n2 ) where n is the nth sampling, Y n2 is the concentrate recovery rate during the nth sampling period. There is a linear relationship between X1, X2 and Y1, Y2. When the reliability coefficient α = 0.05, the first multivariate linear regression model is established, and the concentrate grade-dosage and dosing point number model is obtained as follows: Y1=b0+b1X1+b2X2Formula (1); in, The model of concentrate recovery rate, dosage and number of dosing points is obtained as follows: Y2=c0+c1X1+c2X2 Formula (5); in Step S3: Verify the significance of the concentrate grade-dosage and dosing point number model and the concentrate recovery rate-dosage and dosing point number model: The significance of the above concentrate recovery rate-dosage and dosing point number model is verified by the following formula: Among them, R 复1 is the multiple correlation coefficient of the Y1 model, Y a is the arithmetic mean value of the copper concentrate grade Y1 obtained from sampling at each sampling point in the plant area to be optimized; Y is the value calculated by the model; Y i1 is the copper concentrate grade of the i-th sampling point; The significance of the model of concentrate recovery rate-dosage and number of dosing points is verified by the following formula: Among them, R 复2 is the multiple correlation coefficient of the Y2 model, Y b is the arithmetic mean value of the concentrate recovery rate Y2 of each sampling point in the plant area to be optimized; Y is the model calculation value; Y i2 is the copper concentrate recovery rate at the i-th sampling point; Step S4: respectively calculating the extreme values ​​of the concentrate grade-dosage and dosing point number model, and the extreme values ​​of the concentrate recovery rate-dosage and dosing point number model, comprehensively considering the concentrate grade and concentrate recovery rate indicators, taking the extreme value that can simultaneously meet the requirements of the concentrate grade and the concentrate recovery rate as the optimization standard, and optimizing the number of reagent addition points of the plant and the optimal reagent addition amount at each point according to the obtained optimization standard; Step S5: Assume that the dosing flow rate obtained by sampling at each sampling point is X3=(X 13 ,X 23 ,…,X i3 …, X n3 ), the pipe size of the dosing valve is X4=(X 14 ,X 24 ,…,X i4 …X n4 ), the error value of adding reagent is Y3=(Y 13 ,Y 23 ,…,Y i3 …Y n3 ), and there is a linear relationship between X3, X4, and Y3. When the reliability coefficient α = 0.05, the second multivariate linear regression model is established, and the error value of the reagent addition - the pipe size of the dosing valve and the dosing flow rate model is obtained as follows: Y3=d0+d1X3+d2X4 Formula (11); in, The error value of reagent addition - the significance of the dosing valve pipeline size and the dosing flow rate model are verified by the following formula: Among them, R 复3 is the multiple correlation coefficient of the Y4 model, Y b is the arithmetic mean value of the reagent addition error value Y3 of each sampling point in the plant area to be optimized; Y is the model calculation value; Y i3 is the error value of the reagent addition at the i-th sampling point; the error value of the reagent addition is |the reagent unit consumption of each sampling point - the required reagent addition value|÷the required reagent addition value×100% formula (15); Step S6: After finding the extreme value of the drug addition error value obtained in step S5 - the drug adding valve pipeline size and the drug adding flow rate model, the corresponding optimal drug valve pipeline size and drug adding flow rate are obtained, and the corresponding parameters of the current drug addition are adjusted according to the obtained optimal drug adding valve pipeline size and drug adding flow rate, and the optimized drug adding system is obtained after running.

2. The optimization method for reducing the unit consumption of flotation agents for copper smelting slag according to claim 1 is characterized in that: The following steps are included: S7: Run the copper ore slag dressing process according to the optimal standard parameters obtained in step S6 and step S4, obtain the dosage of each reagent in the copper ore slag dressing process after at least 2 months of optimization, calculate the reagent addition error value, and compare it with the dosage of each reagent before optimization to evaluate the optimization result.

3. The optimization method for reducing the unit consumption of copper smelting slag flotation dressing reagents according to claim 2 is characterized in that: Step S7 includes: calculating the variance of each agent dosage after optimization and comparing it with the variance of each agent dosage before optimization, and the comparison standard is: when the variance S2 of the agent addition amount is smaller, it means that the addition amount of the agent addition system is more stable.

4. The optimization method for reducing the unit consumption of flotation dressing reagents for copper smelting slag according to claim 1 is characterized in that: The sampling period is greater than or equal to 2 months.

5. The optimization method for reducing the unit consumption of copper smelting slag flotation dressing reagents according to claim 1, characterized in that: The calculation of the extreme values ​​in step S4 is to respectively take the derivatives of the concentrate grade-dosage and dosing point number model and the concentrate recovery rate-dosage and dosing point number model, and set the derivatives to 0 to obtain the corresponding X1 and X2.

6. The optimization method for reducing the unit consumption of copper smelting slag flotation dressing reagents according to claim 1, characterized in that: The average amount of agent added M Calculate, where X1, X2, ... X n is the measured amount of the agent added, and n is the nth sampling.

7. The optimization method for reducing the unit consumption of flotation agents for copper smelting slag according to claim 6 is characterized in that: Variance S of the amount of added agent 2 according to calculate.

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