Intelligent proportioning method for various copper concentrates

Abstract

The invention discloses an intelligent proportioning method for various copper concentrates. The intelligent proportioning method comprises the following specific steps: S1, acquiring basic data of copper concentrate materials; s2, setting ingredient target information, wherein the ingredient target information comprises various ingredient targets, target upper limits and target lower limits of ingredients; the material upper limit and the material lower limit of each material; the ratio of the maximum usage amount to the minimum usage amount of the materials; target mixed ore weight; s3, establishment of a batching mathematical model: setting proper decision variables, constraint conditions and target functions through known material basic data and target information to establish a mathematical model, and establishing different mathematical models through three dimensions of maximized batching, most economical batching and most matched target batching to realize three different batching modes; the intelligent proportioning method for various copper concentrates disclosed by the invention can achieve the purposes of greatly shortening the proportioning time, improving the proportioning accuracy, saving the material cost and enabling the copper concentrate proportioning of enterprises to be more intelligent.

Description

Technical Field

[0001] The invention relates to the technical field of copper ore smelting and processing, and in particular to a method for intelligent batching of multiple copper concentrates. Background Art

[0002] The smelting batching process is to mix copper concentrate, miscellaneous ores, quartz sand, pulverized coal and other materials from different sources into a mixture that meets the smelting requirements. It is the first step in the smelting process. The traditional batching method is based on the material inspection and testing results, and the formula is edited in the Excel table for calculation. By continuously adjusting the proportion of different minerals, the batching results that meet the production requirements are obtained.

[0003] The proportion of various materials in this traditional batching mode mainly depends on the production scheduling experience accumulated by the dispatchers over the years, and is manually calculated and prepared by comprehensively considering the quantity, composition, output and various production process conditions of the concentrate and miscellaneous ores entering the factory. However, due to the wide range of concentrate sources and large composition deviations, it was difficult to balance the relationship between the various influencing factors by judging and determining the proportions through human subjective consciousness, and it was also difficult to accurately grasp whether the mixed concentrate could meet the process requirements to the maximum extent. Summary of the invention

[0004] The invention discloses a method for intelligent batching of multiple copper concentrates, aiming to solve the problem that due to the wide variety of concentrate sources and large component deviation, it is difficult to balance the relationship between various influencing factors by judging and determining the proportion through human subjective consciousness in the past, and it is also difficult to accurately grasp whether the mixed concentrate can meet the process requirements to the maximal extent.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for intelligent batching of multiple copper concentrates includes the following specific steps:

[0007] S1: Acquisition of basic data of copper concentrate materials: The basic data of copper concentrate materials include the test data of different materials such as Cu, Fe, S, SiO2, CaO, Pb, Zn, As, MgO, Al2O3, Sb, Bi, Ni, Sn, F, Cl, Co, Cd, H2O, etc., the weight and unit price information of different materials;

[0008] S2: Setting batching target information: The batching target information includes: various component targets, target upper limit, target lower limit of the batching; upper and lower limits of each material; the ratio of the maximum and minimum material usage; target mixed ore weight;

[0009] S3: Establishment of mathematical model of batching: Establish mathematical model by setting appropriate decision variables, constraints and objective functions through known basic material data and target information, and establish different mathematical models from three dimensions: maximum batching, most economical batching and most matching target batching, to achieve three different batching methods;

[0010] S4: Input the model into the program for solution.

[0011] By adopting mathematical optimization methods to realize the automatic batching process, the batching time can be greatly shortened, the batching accuracy can be improved, the material cost can be saved, and the copper concentrate batching of the enterprise can be made more intelligent.

[0012] In a preferred solution, in S3, the maximum batching is to determine how many tons of mixed materials that meet the target can be obtained from this batch of materials based on the given multiple materials and batching target information, so as to achieve maximum utilization of materials.

[0013] In a preferred solution, in S3, the most economical batching is based on the given multiple materials and batching target information, so that the mixed ore is prepared with the lowest raw material cost while meeting the target.

[0014] In a preferred solution, in S3, the best matching target batching is based on the given multiple materials and batching target information, so that the mixed ore is prepared with the component content of the mixed ore closest to the target while meeting the target.

[0015] In a preferred solution, in S3, the decision variable is set to x, and x i is the weight of the i-th copper concentrate, where i=1, 2,…, n, and n is the number of types of copper concentrate.

[0016] In a preferred solution, in S3, the maximization batching objective function and constraint conditions are:

[0017] 1) Objective function:

[0018]

[0019] Where: Maxmize:f(x1,x2,...,x n ) means to find the function f(x1,x2,...,x n );i=1,2,…,n,n is the number of types of copper concentrate;

[0020] 2) Constraints:

[0021]

[0022] Where: i = 1, 2, ..., n, n is the number of types of copper concentrate; j = 1, 2, ..., m, m is the number of component types of copper concentrate; t j,min Indicates the lower limit of the percentage of the jth component element in the mixed ore; t j,max represents the upper limit of the percentage of the jth component element in the mixed ore; c ij represents the percentage content of the jth component element in the i-th copper concentrate; W' is the dry weight of the batch weight; w i,min represents the lower limit of the copper concentrate in the ith layer, w i,max is the upper limit of the copper concentrate in the ith layer, satisfying: 0≤w i,min ≤w i,max ;h i It indicates the percentage content of water in the copper concentrate in the i-th layer.

[0023] In a preferred solution, in S3, the most economical batching objective function and constraints are:

[0024] 3) Objective function:

[0025]

[0026] Where: Minmize:f(x1,x2,...,x n ) means to find the function f(x1,x2,...,x n ) minimum value; i = 1, 2, ..., n, n is the number of types of copper concentrate; p i represents the unit price of the i-th copper concentrate;

[0027] 4) Constraints:

[0028]

[0029] Where: i = 1, 2, ..., n, n is the number of types of copper concentrate; j = 1, 2, ..., m, m is the number of component types of copper concentrate; t j,min Indicates the lower limit of the percentage of the jth component element in the mixed ore; t j,max represents the upper limit of the percentage of the jth component element in the mixed ore; c ij represents the percentage content of the jth component element in the i-th copper concentrate; W' is the dry weight of the batching weight; W is the batching target weight constraint; R is the lower limit of the ratio of the minimum amount of material to the maximum amount in the batching result, satisfying 0≤R≤1; w i,min represents the lower limit of the copper concentrate in the ith layer, w i,max is the upper limit of the copper concentrate in the ith layer, satisfying: 0≤w i,min ≤w i,max ;h i It indicates the percentage content of water in the copper concentrate in the i-th layer.

[0030] In a preferred solution, the best matching ore matching objective function and constraint conditions are:

[0031] 3) Objective function:

[0032]

[0033] Where: Minmize:f(x1,x2,...,x n ) means to find the function f(x1,x2,...,x n ) minimum value; i = 1, 2, ..., n, n is the number of types of copper concentrate; λ j represents the weight coefficient of the jth component element; c j is the theoretical percentage content of the jth component element in the mixed copper concentrate; j is the target percentage content of the jth component element in the mixed copper concentrate;

[0034] 4) Constraints:

[0035]

[0036] Where: i = 1, 2, ..., n, n is the number of types of copper concentrate; j = 1, 2, ..., m, m is the number of component types of copper concentrate; t j,min Indicates the lower limit of the percentage of the jth component element in the mixed ore; t j,max represents the upper limit of the percentage of the jth component element in the mixed ore; c ij represents the percentage content of the jth component element in the i-th copper concentrate; W' is the dry weight of the batching weight; W is the batching target weight constraint; R is the lower limit of the ratio of the minimum amount of material to the maximum amount in the batching result, satisfying 0≤R≤1; w i,min represents the lower limit of the copper concentrate in the ith layer, w i,max is the upper limit of the copper concentrate in the ith layer, satisfying: 0≤w i,min ≤w i,max ;h i It indicates the percentage content of water in the copper concentrate in the i-th layer.

[0037] From the above, it can be seen that a method for intelligent batching of multiple copper concentrates includes the following specific steps: S1: Acquisition of basic data of copper concentrate materials: The basic data of copper concentrate materials include the test data of different materials such as Cu, Fe, S, SiO2, CaO, Pb, Zn, As, MgO, Al2O3, Sb, Bi, Ni, Sn, F, Cl, Co, Cd, H2O, etc., the weight of different materials, and unit price information; S2: Setting of batching target information: The batching target information includes: various component targets, target upper limit, target lower limit of batching; upper and lower limits of each material; ratio of maximum material usage to minimum material usage; target mixed ore weight; S3: Establishment of batching mathematical model: Establish a mathematical model by setting appropriate decision variables, constraints, and objective functions through known material basic data and target information, and establish different mathematical models from three dimensions of maximizing batching, most economical batching, and most matching target batching to achieve three different batching methods; S4: Input the model into the program for solution. The various copper concentrate intelligent batching methods provided by the present invention can greatly shorten the batching time, improve the batching accuracy, save material costs and make the copper concentrate batching of the enterprise more intelligent. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 The present invention provides a flow chart of a method for intelligent batching of multiple copper concentrates. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0040] The invention discloses a method for intelligent batching of multiple copper concentrates, which is mainly applied to the scenario of copper ore smelting and processing.

[0041] Reference Figure 1 A method for intelligent batching of multiple copper concentrates comprises the following steps:

[0042] S1: Acquisition of basic data of copper concentrate materials: The basic data of copper concentrate materials include the test data of different materials such as Cu, Fe, S, SiO2, CaO, Pb, Zn, As, MgO, Al2O3, Sb, Bi, Ni, Sn, F, Cl, Co, Cd, H2O, etc., the weight and unit price information of different materials;

[0043] S2: Setting batching target information: The batching target information includes: various component targets, target upper limit, target lower limit of the batching; upper and lower limits of each material; the ratio of the maximum and minimum material usage; target mixed ore weight;

[0044] S3: Establishment of mathematical model of batching: Establish mathematical model by setting appropriate decision variables, constraints and objective functions through known basic material data and target information, and establish different mathematical models from three dimensions: maximum batching, most economical batching and most matching target batching, to achieve three different batching methods;

[0045] S4: Input the model into the program for solution.

[0046] Implementation use case:

[0047] The basic information of a batch of copper concentrate is shown in Table 1.

[0048] Table 1: Some basic information of a batch of copper concentrate

[0049]

[0050] The target component content percentage target, upper limit and lower limit information of the mixed ore are shown in Table 2.

[0051] Table 2: Ingredient target ingredient content percentage target, upper limit, lower limit partial information

[0052] name Cu / % Fe / % S / ％ SiO2 / % CaO / % Pb / % Zn / % As / % MgO / % ...... F / ％ Cl / ％ Co / % Cd / % Ingredient Target_Upper Limit 14.97 22.49 17.74 13.88 4.14 1.61 4.52 1.33 1.74 ...... 0.18 0.26 0.07 0.06 Ingredients Target_Target 14.47 21.49 16.74 11.88 2.64 1.11 1.52 1.13 1.24 ...... 0.08 0.16 0.02 0.02 Ingredient target_lower limit 13.97 20.49 15.74 9.88 1.14 0.61 0 0.93 0.74 ...... 0 0.06 0 0

[0053] The total amount of mixed ore is set to 1,600 tons, and the lower limit of the ratio of the minimum amount to the maximum amount of a single copper concentrate in the batching result is set to 0.1. The upper and lower limit information of the amount of various copper concentrates are shown in Table 3.

[0054] Table 3: Information on upper and lower limits of copper concentrate usage

[0055] name Inventory (tons) Minimum dosage Usage Limit Mine-1 660 50 660 Mine-2 130 50 130 Mine-3 113 50 113 Mine-4 320 50 320 Mine-5 420 50 420

[0056] The objective function is defined based on the material information and target information as follows:

[0057] 1) Maximize ingredients:

[0058] Maxmize:f(x1,x2,...,x n )=x1+x2+x3+x4+x5

[0059] Where: Maxmize:f(x1,x2,...,x n ) means to find the function f(x1,x2,...,x n )

[0060] 2) The most economical ingredients:

[0061] Minmize:f(x1,x2,...,x n )=α(12058x1+11858x2+13960x3+12178x4+

[0062] 12058x5)

[0063] Where: Minmize:f(x1,x2,...,x n ) means to find the function f(x1,x2,...,x n ) minimum value; α is a constant coefficient.

[0064] 3) Best matching target ingredients:

[0065]

[0066]

[0067] Where: Minmize:f(x1,x2,...,x n ) means to find the function f(x1,x2,...,x n ). Set the constraints as follows:

[0068] Constraint function of percentage content range of component elements:

[0069]

[0070] Ingredient target weight constraint function:

[0071] x1+x2+x3+x4+x5=1600

[0072] Material usage range constraint function:

[0073]

[0074] Material usage ratio constraint function:

[0075]

[0076] After determining the objective function and constraints, the program is used to solve the problem. The results are shown in Tables 4 and 5:

[0077] Table 4: Material usage of batching results

[0078]

[0079] Table 5: Ingredients results Theoretical percentage content of each ingredient

[0080]

[0081] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A method for intelligent batching of multiple copper concentrates, characterized in that: The specific steps include: S1: Acquisition of basic data of copper concentrate materials: The basic data of copper concentrate materials include the test data of different materials such as Cu, Fe, S, SiO2, CaO, Pb, Zn, As, MgO, Al2O3, Sb, Bi, Ni, Sn, F, Cl, Co, Cd, H2O, etc., the weight and unit price information of different materials; S2: Setting batching target information: The batching target information includes: various component targets, target upper limit, target lower limit of the batching; upper and lower limits of each material; the ratio of the maximum and minimum material usage; target mixed ore weight; S3: Establishment of mathematical model of batching: Establish mathematical model by setting appropriate decision variables, constraints and objective functions through known basic material data and target information, and establish different mathematical models from three dimensions: maximum batching, most economical batching and most matching target batching, to achieve three different batching methods; S4: Input the model into the program for solution.

2. The intelligent batching method for multiple copper concentrates according to claim 1, characterized in that: In S3, the maximum batching is to determine how many tons of mixed materials that meet the target can be obtained from this batch of materials based on the given multiple materials and batching target information, so as to achieve maximum utilization of materials.

3. The intelligent batching method for multiple copper concentrates according to claim 1, characterized in that: In S3, the most economical batching is to make the mixed ore with the lowest raw material cost under the premise of meeting the target according to the given multiple materials and batching target information.

4. The intelligent batching method for multiple copper concentrates according to claim 1, characterized in that: In S3, the best matching target batching is based on the given multiple materials and batching target information, so that the mixed ore is prepared with the component content of the mixed ore closest to the target while meeting the target.

5. The intelligent batching method for multiple copper concentrates according to claim 1, characterized in that: In S3, the decision variable is set to x, and x i is the weight of the i-th copper concentrate, where i=1, 2,…, n, and n is the number of types of copper concentrate.

6. The intelligent batching method for multiple copper concentrates according to claim 2, characterized in that: In S3, the objective function and constraints for maximizing batching are: 1) Objective function: Where: Maxmize:f(x1,x2,...,x n ) means to find the function f(x1,x2,...,x n );i=1,2,…,n,n is the number of types of copper concentrate; 2) Constraints: Where: i = 1, 2, ..., n, n is the number of types of copper concentrate; j = 1, 2, ..., m, m is the number of component types of copper concentrate; t j,min Indicates the lower limit of the percentage of the jth component element in the mixed ore; t j,max represents the upper limit of the percentage of the jth component element in the mixed ore; c ij represents the percentage content of the jth component element in the i-th copper concentrate; W' is the dry weight of the batch weight; w i,min represents the lower limit of the copper concentrate in the ith layer, w i,max is the upper limit of the copper concentrate in the ith layer, satisfying: 0≤w i,min ≤w i,max ;h i It indicates the percentage content of water in the copper concentrate in the i-th layer.

7. The intelligent batching method for multiple copper concentrates according to claim 3, characterized in that: In S3, the most economical batching objective function and constraints are: 1) Objective function: Where: Minmize:f(x1,x2,...,x n ) means to find the function f(x1,x2,...,x n ) minimum value; i = 1, 2, ..., n, n is the number of types of copper concentrate; p i represents the unit price of the i-th copper concentrate; 2) Constraints: Where: i = 1, 2, ..., n, n is the number of types of copper concentrate; j = 1, 2, ..., m, m is the number of component types of copper concentrate; t j,min Indicates the lower limit of the percentage of the jth component element in the mixed ore; t j,max represents the upper limit of the percentage of the jth component element in the mixed ore; c ij represents the percentage content of the jth component element in the i-th copper concentrate; W' is the dry weight of the batching weight; W is the batching target weight constraint; R is the lower limit of the ratio of the minimum amount of material to the maximum amount in the batching result, satisfying 0≤R≤1; w i,min represents the lower limit of the copper concentrate in the ith layer, w i,max is the upper limit of the copper concentrate in the ith layer, satisfying: 0≤w i,min ≤w i,max ;h i It indicates the percentage content of water in the copper concentrate in the i-th layer.

8. The intelligent batching method for multiple copper concentrates according to claim 4, characterized in that: The best matching ore matching objective function and constraint conditions are as follows: 1) Objective function: Where: Minmize:f(x1,x2,...,x n ) means to find the function f(x1,x2,...,x n ) minimum value; i = 1, 2, ..., n, n is the number of types of copper concentrate; λ j represents the weight coefficient of the jth component element; c j is the theoretical percentage content of the jth component element in the mixed copper concentrate; j is the target percentage content of the jth component element in the mixed copper concentrate; 2) Constraints: Where: i = 1, 2, ..., n, n is the number of types of copper concentrate; j = 1, 2, ..., m, m is the number of component types of copper concentrate; t j,min Indicates the lower limit of the percentage of the jth component element in the mixed ore; t j,max represents the upper limit of the percentage of the jth component element in the mixed ore; c ij represents the percentage content of the jth component element in the i-th copper concentrate; W' is the dry weight of the batching weight; W is the batching target weight constraint; R is the lower limit of the ratio of the minimum amount of material to the maximum amount in the batching result, satisfying 0≤R≤1; w i,min represents the lower limit of the copper concentrate in the ith layer, w i,max is the upper limit of the copper concentrate in the ith layer, satisfying: 0≤w i,min ≤w i,max ;h i It indicates the percentage content of water in the copper concentrate in the i-th layer.

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