A method, system and product for blending multi-source complex copper concentrate

Abstract

The present invention relates to a method, system and product for blending multi-source complex copper concentrate. The method comprises: sampling and analyzing components of various mixed copper concentrates to determine the percentage content of each copper concentrate in various mixed copper concentrates; establishing a linear multi-objective programming batching model with the quality of the mixed copper concentrate as the objective function and the percentage content of impurity elements in the copper concentrate as the constraint function; using the batching model to perform linear multi-objective programming, re-proportioning the percentage content of copper concentrate in the mixed copper concentrate, and determining the proportion of the mixed copper concentrate; combining the slag structure properties, calculating the type and amount of flux, and obtaining the raw materials for the furnace according to the proportion; based on the raw materials for the furnace, analyzing the smelting products using the copper metallurgical principles, and predicting the quality of the smelting products; analyzing the proportion of the raw materials for the furnace according to the quality of the smelting products and combining the production data, adjusting the objective function and the constraint function, and generating the raw materials for the furnace and the batching scheme with the optimal proportion. The present invention can improve the accuracy of ore blending.

Description

Technical Field

[0001] The present invention relates to the technical field of copper ore smelting and processing, and in particular to an ore blending method, system and product for multi-source complex copper concentrate. Background Art

[0002] With the continuous mining of high-quality copper ore resources, low-grade complex copper concentrates have become the main source of raw materials for copper pyrometallurgy. Reasonable ore blending is an important way to improve the utilization rate of mineral resources and enhance the economic benefits of enterprises. It is extremely important to develop accurate ore blending of complex copper raw materials in the copper smelting process. The accuracy of ore blending of complex copper raw materials in the copper smelting process is low, and it mainly relies on manual experience or semi-experience. Although the accuracy of ore blending by manual experience has improved, it still cannot reach the expected accuracy. As the decision-making and guiding basis for actual production, the optimization technology of ore blending plan is gradually developing in the direction of informatization and intelligence. Scientific and effective ore blending provides theoretical guidance for subsequent processes such as furnace smelting.

[0003] Modern information technology and intelligent optimization ore blending technology are gradually introduced into enterprise production, which plays a decisive role in the metallurgical performance complementarity of the chemical composition of complex multi-phase copper ores and the rational use of various raw materials. Under the premise of ensuring the quality and output of copper products and the emission of impurities, the proportion of copper ore entering the furnace is reasonably selected according to the type, grade and performance of raw materials. Combined with the principles of copper smelting and production data, a precise batching system is developed to reduce the introduction of impurities and pollutant emissions and improve product quality. Summary of the invention

[0004] The purpose of the present invention is to provide a method, system and product for blending multi-source complex copper concentrates to improve the accuracy of blending.

[0005] To achieve the above object, the present invention provides the following solutions:

[0006] A method for blending multi-source complex copper concentrates, comprising:

[0007] Sampling and analyzing the components of various mixed copper concentrates to determine the percentage content of each copper concentrate in the various mixed copper concentrates;

[0008] A linear multi-objective programming batching model is established with the quality of the mixed copper concentrate as the objective function and the percentage content of impurity elements in the copper concentrate as the constraint function;

[0009] Using the batching model to perform linear multi-objective programming, re-proportioning the percentage content of the copper concentrate in the mixed copper concentrate, and determining the proportion of the mixed copper concentrate;

[0010] The type and amount of flux are calculated based on the slag structure and properties, and the raw materials for the furnace are obtained according to the proportion; the slag structure and properties include viscosity and copper content; the raw materials for the furnace include mixed copper concentrate and flux after proportioning;

[0011] Based on the raw materials fed into the furnace, the smelting products are analyzed using copper metallurgy principles, and the quality of the smelting products is predicted;

[0012] According to the quality of the smelting product, the proportion of the raw materials entering the furnace is analyzed in combination with production data, the objective function and constraint function of the batching model are adjusted, and the optimal proportion of raw materials entering the furnace and the optimal batching plan are generated; the production data includes slag copper content, copper matte grade, magnetic iron content and viscosity.

[0013] Optionally, the objective function is:

[0014]

[0015] Where F(x) is the objective function; m is the total number of impurity elements selected; w i is the weight value of the i-th objective function; f i (x) is the percentage content of the i-th impurity element contained in the mixed copper concentrate; f m (x) is the percentage content of the mth impurity element contained in the mixed copper concentrate; r is the proportion of the rth type of copper concentrate in the mixed copper concentrate; x n is the proportion of the nth copper concentrate in the mixed copper concentrate, n is the total number of copper concentrates; a ir is the percentage content of the i-th impurity element contained in the r-th copper concentrate; a in is the percentage content of the ith impurity element in the nth copper concentrate; a mr is the percentage content of the mth impurity element contained in the rth copper concentrate; a mn It is the percentage content of the mth impurity element contained in the nth copper concentrate.

[0016] Optionally, the constraint function is:

[0017]

[0018] Among them, b i b is the limit value of the percentage content of the i-th impurity element contained in the mixed copper concentrate; m It is the limit value for the percentage content of the mth impurity element contained in the mixed copper concentrate.

[0019] Optionally, the raw materials entering the furnace meet the restriction requirements on the chemical composition of various impurity elements;

[0020] The restrictions are:

[0021]

[0022] Among them, Max i Min is the upper limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate; i It is the lower limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate.

[0023] A multi-source complex copper concentrate blending system, comprising:

[0024] A percentage content determination module is used to perform sampling and component analysis on a plurality of mixed copper concentrates to determine the percentage content of each copper concentrate in the various mixed copper concentrates;

[0025] A batching model establishment module, used to establish a batching model of linear multi-objective programming with the quality of the mixed copper concentrate as the objective function and the percentage content of impurity elements in the copper concentrate as the constraint function;

[0026] A proportion determination module, used to perform linear multi-objective programming using the proportioning model, re-proportion the percentage content of the copper concentrate in the mixed copper concentrate, and determine the proportion of the mixed copper concentrate;

[0027] A furnace raw material acquisition module is used to calculate the type and amount of flux added in combination with the slag structure properties, and obtain the furnace raw materials according to the ratio; the slag structure property characteristics include viscosity and slag copper content; the furnace raw materials include mixed copper concentrate and flux after ratio;

[0028] A prediction module, used to analyze the smelting product based on the raw materials fed into the furnace and use the copper metallurgical principle to predict the quality of the smelting product;

[0029] The optimal batching scheme generation module is used to analyze the proportion of the raw materials entering the furnace according to the quality of the smelting product in combination with production data, adjust the objective function and constraint function of the batching model, and generate the raw materials entering the furnace with the optimal proportion and the optimal batching scheme; the production data includes slag copper content, copper matte grade, magnetic iron content and viscosity.

[0030] Optionally, the objective function is:

[0031]

[0032] Where F(x) is the objective function; m is the total number of impurity elements selected; w i is the weight value of the i-th objective function; f i (x) is the percentage content of the i-th impurity element contained in the mixed copper concentrate; f m (x) is the percentage content of the mth impurity element contained in the mixed copper concentrate; ris the proportion of the rth type of copper concentrate in the mixed copper concentrate; x n is the proportion of the nth copper concentrate in the mixed copper concentrate, n is the total number of copper concentrates; a ir is the percentage content of the i-th impurity element contained in the r-th copper concentrate; a in is the percentage content of the ith impurity element in the nth copper concentrate; a mr is the percentage content of the mth impurity element contained in the rth copper concentrate; a mn It is the percentage content of the mth impurity element contained in the nth copper concentrate.

[0033] Optionally, the constraint function is:

[0034]

[0035] where b i b is the limit value of the percentage content of the i-th impurity element contained in the mixed copper concentrate; m It is the limit value for the percentage content of the mth impurity element contained in the mixed copper concentrate.

[0036] Optionally, the raw materials entering the furnace meet the restriction requirements on the chemical composition of various impurity elements;

[0037] The restrictions are:

[0038]

[0039] Among them, Max i Min is the upper limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate; i It is the lower limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate.

[0040] An electronic device comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program so that the electronic device executes the above-mentioned multi-source complex copper concentrate blending method.

[0041] A computer-readable storage medium, characterized in that it stores a computer program, which, when executed by a processor, implements the above-mentioned multi-source complex copper concentrate blending method.

[0042] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects: the present invention provides a method, system and product for blending multi-source complex copper concentrates, takes the quality of the mixed copper concentrate as the objective function, and takes the percentage content of the copper concentrate as the constraint function to establish a linear multi-objective programming batching model; uses the batching model to perform linear multi-objective programming, re-proportionates the percentage content of the copper concentrate in the mixed copper concentrate, determines the ratio of the mixed copper concentrate, and obtains the raw materials for the furnace; and predicts the quality of the smelting product to adjust the objective function and constraint function of the batching model, generates the raw materials for the furnace with the optimal ratio and the optimal batching scheme; on the premise of ensuring the quality of copper products and the emission of impurity elements meeting the standards, reasonably selects the ratio of the raw materials for the furnace according to the type, grade and performance of the raw materials, combines the copper smelting principles and production data, achieves the purpose of reducing the introduction of impurity elements and improving product quality, and improves the accuracy of ore blending. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] Figure 1 A flow chart of the ore blending method for multi-source complex copper concentrate provided in Example 1 of the present invention;

[0045] Figure 2 A schematic diagram of the results of ore matching provided in the second embodiment of the present invention;

[0046] Figure 3 It is a GA-BP algorithm prediction process flow chart of the present invention;

[0047] Figure 4 A schematic diagram of the results of ore matching provided in the third embodiment of the present invention;

[0048] Figure 5 This is a flow chart of the ore blending method for multi-source complex copper concentrate provided in Example 3 of the present invention. DETAILED DESCRIPTION

[0049] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0050] The purpose of the present invention is to provide a method, system and product for blending multi-source complex copper concentrates to improve the accuracy of blending.

[0051] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] Embodiment 1

[0053] Figure 1 The flow chart of the ore blending method for multi-source complex copper concentrate provided by the present invention is as follows: Figure 1 As shown, a method for blending a multi-source complex copper concentrate comprises:

[0054] Step 101: Sampling and analyzing the components of a plurality of mixed copper concentrates to determine the percentage content of each copper concentrate in the mixed copper concentrates.

[0055] Step 102: Establishing a linear multi-objective programming batching model with the quality of the mixed copper concentrate as the objective function and the percentage content of the impurity elements in the copper concentrate as the constraint function.

[0056] In practical applications, the objective function is:

[0057]

[0058] Where F(x) is the objective function; m is the total number of impurity elements selected; w i is the weight value of the i-th objective function; f i (x) is the percentage content of the i-th impurity element contained in the mixed copper concentrate; f m (x) is the percentage content of the mth impurity element contained in the mixed copper concentrate; r is the proportion of the rth type of copper concentrate in the mixed copper concentrate; x n is the proportion of the nth copper concentrate in the mixed copper concentrate, n is the total number of copper concentrates; a ir is the percentage content of the i-th impurity element contained in the r-th copper concentrate; a in is the percentage content of the ith impurity element in the nth copper concentrate; a mr is the percentage content of the mth impurity element contained in the rth copper concentrate; a mn It is the percentage content of the mth impurity element contained in the nth copper concentrate.

[0059] f i (x) represents one of the objective functions among multiple objectives, and F(x) represents the function of converting f i (x) The total function after linear weighting.

[0060] In practical applications, the constraint function is:

[0061]

[0062] Among them, b i b is the limit value of the percentage content of the i-th impurity element contained in the mixed copper concentrate; m It is the limit value for the percentage content of the mth impurity element contained in the mixed copper concentrate.

[0063] In practical applications, the BP neural network optimized by genetic algorithm is used to predict the quality of each smelting product, and the BP neural network optimized by genetic algorithm is used to predict product data, which solves the problem of low prediction accuracy of previous single neural network.

[0064] The fitness function, selection operation, crossover operation and mutation operation of the genetic algorithm obtain the optimal individual, which is used to assign weights and thresholds to the input, hidden and output nodes of the BP neural network, thereby reducing the prediction error of the BP neural network for smelting results such as viscosity, matte grade, copper content in slag, and magnetic iron.

[0065] Step 103: using the batching model to perform linear multi-objective programming, re-proportioning the percentage content of the copper concentrate in the mixed copper concentrate, and determining the proportion of the mixed copper concentrate.

[0066] Step 104: Calculate the type and amount of flux added based on the slag structure and properties, and obtain the raw materials for the furnace according to the proportion; the slag structure and properties include viscosity and copper content; the raw materials for the furnace include proportioned mixed copper concentrate and flux.

[0067] In practical applications, the raw materials entering the furnace meet the restriction requirements on the chemical composition of various impurity elements;

[0068] The restrictions are:

[0069]

[0070] Among them, Max i Min is the upper limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate; i It is the lower limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate.

[0071] In practical applications, the proportion and quality of each ore are calculated by the established multi-objective planning model, and the proportion of flux and other materials required to be added to the raw materials entering the furnace is calculated based on the composition characteristics of the mixed copper concentrate.

[0072] The calculation formula for the amount of flux added is:

[0073]

[0074]

[0075] where x r is the proportion of the rth type of copper concentrate; s r is the content of SiO2 in the rth copper concentrate; c r is the CaO content in the rth copper concentrate.

[0076] Step 105: Based on the raw materials fed into the furnace, the smelting product is analyzed using copper metallurgy principles, and the quality of the smelting product is predicted.

[0077] The present invention utilizes the copper metallurgy principle in combination with the thermodynamic database to predict technical parameters such as viscosity, matte grade, slag copper content, magnetic iron, etc., analyzes the composition of materials such as raw materials and flux entering the furnace with the obtained product prediction data, analyzes the rationality of materials such as raw materials and flux entering the furnace, resets the objective function and constraint conditions of the ore blending model, forms a closed-loop control to optimize the ore blending model, and conforms to actual industrial production.

[0078] Step 106: Analyze the proportion of the raw materials into the furnace according to the quality of the smelting product and the production data, adjust the objective function and constraint function of the proportioning model, and generate the optimal proportion of the raw materials into the furnace and the optimal proportioning scheme; the production data includes the copper content of the slag, the grade of copper matte, the magnetic iron content and the viscosity.

[0079] In practical applications, based on the reaction laws in the copper smelting process, the intrinsic relationship between the process and smelting parameters is analyzed, and technical parameters such as viscosity, matte grade, slag copper content, and magnetic iron are predicted based on the thermodynamic database. Each indicator is analyzed and processed through the genetic algorithm-BP neural network.

[0080] Construction, training and prediction of BP neural network:

[0081] (1) newff: BP neural network parameter setting function, building copper smelting prediction neural network.

[0082] net=newff(inputn,outputn,15);

[0083] Among them, inputn is the input matrix of mixed copper concentrate data; outputn is the output data matrix of viscosity, matte grade, slag copper content, magnetic iron, etc.; 15 is the number of hidden layer nodes, which receives the input of mixed copper concentrate data, performs calculations, and transmits the information to the next layer of nodes.

[0084] (2)train: BP neural network training function, copper smelting prediction neural network training function.

[0085] net.trainParam.lr = 0.001;

[0086] net.trainParam.epochs=1000;

[0087] net.trainParam.goal=0.000001;

[0088] net=train(net,inputn,outputn);

[0089] Where net is the network to be trained; lr is the learning rate of the training function; epochs is the number of iterations of the training function; goal is the set accuracy target value. When the network training reaches the set number of iterations or reaches the set accuracy, the training ends; net is the trained network; inputn is the mixed copper concentrate input data used for training; outputn is the output data such as viscosity, matte grade, slag copper content, magnetic iron, etc. used for training.

[0090] (3) sim: BP neural network prediction function, copper smelting prediction neural network function output.

[0091] Function form: y = sim (net, x);

[0092] Where net is the trained network; x is the mixed copper concentrate input data used for testing; y is the output data such as viscosity, matte grade, slag copper content, magnetic iron, etc. used for testing.

[0093] Genetic algorithm optimizes BP neural network:

[0094] The structure of the BP neural network is determined by the input data parameters of the smelting equilibrium, and the length of the genetic algorithm individual is determined; the fitness value of each individual in the population is calculated by the genetic algorithm, and the individual with the best fitness is found by crossover and mutation operations; the prediction of the BP neural network uses the genetic algorithm to obtain the best individual to assign the weight and threshold of the BP network, and the network is trained to predict the function output. The optimal individual obtained by the genetic algorithm is assigned to the BP neural network, and the optimized network is used to fit the nonlinear function. The obtained product prediction data is used to analyze the composition of materials such as raw materials and flux entering the furnace, reset the objective function and constraints of the ore blending model, and repeat the above steps until the optimal ore blending data is obtained.

[0095] Embodiment 2

[0096] The ore blending method of multi-source complex copper concentrate based on Example 1 has the following steps:

[0097] (1) Sampling and component analysis of various copper concentrates.

[0098] (2) The objective function is set as the quality of the mixed copper concentrate, and the impurity element content of the mixed copper concentrate is set as the constraint.

[0099] The objective function is as follows:

[0100]

[0101]

[0102] Where F(x) is the objective function; m is the total number of impurity elements selected; w i is the weight value of the i-th objective function; f i (x) is the percentage content of the i-th impurity element contained in the mixed copper concentrate; f m (x) is the percentage content of the mth impurity element contained in the mixed copper concentrate; r is the proportion of the rth type of copper concentrate in the mixed copper concentrate; x n is the proportion of the nth copper concentrate in the mixed copper concentrate, n is the total number of copper concentrates; a ir is the percentage content of the i-th impurity element contained in the r-th copper concentrate; a in is the percentage content of the ith impurity element in the nth copper concentrate; a mr is the percentage content of the mth impurity element contained in the rth copper concentrate; a mn It is the percentage content of the mth impurity element contained in the nth copper concentrate.

[0103] The constraint function is as follows:

[0104]

[0105] Among them, b i b is the limit value of the percentage content of the i-th impurity element contained in the mixed copper concentrate; m It is the limit value for the percentage content of the mth impurity element contained in the mixed copper concentrate.

[0106] (3) The ingredients of each complex copper concentrate into the furnace meet the restriction requirements on the chemical composition of the impurity elements As, Sb, Bi, Pb, Zn, etc. of the copper ore. That is:

[0107]

[0108] Max i It is the upper limit of the percentage content of a certain impurity element i (As, Sb, Bi, Pb, Zn, etc.) contained in the mixed copper concentrate; Min i It is the lower limit of the percentage content of a certain impurity element i (As, Sb, Bi, Pb, Zn, etc.) contained in the mixed copper concentrate. The ranges of Cu, SiO2, As, Pb, and Sb are 17%-21%, 10%-13%, 0.5%-0.9%, 1.1%-1.4%, and 0.2%-0.3%, respectively. The content of each element in the ore is shown in Table 1, and the ore blending results are shown in Figure 2 shown.

[0109] Table 1 Schematic table of the content of each element in the ore

[0110]

[0111] The established multi-objective planning model is used to calculate the proportion and quality of each ore, and the proportion of flux and other materials required to be added to the raw materials entering the furnace is calculated based on the composition characteristics of the mixed copper concentrate.

[0112] (4) Based on the elemental characteristics of the target product molten matte, the composition ratio of flux and other materials is calculated to obtain the raw material ratio entering the furnace.

[0113] The calculation formula for the amount of flux added is:

[0114]

[0115]

[0116] where x r is the proportion of the rth type of copper concentrate; s r is the content of SiO2 in the rth copper concentrate; c r is the CaO content in the rth copper concentrate.

[0117] (5) The program completes the fitness function, selection operation, crossover operation, and mutation operation of the genetic algorithm to obtain the optimal individual for assigning the weights and thresholds of the BP neural network. Figure 3 shown.

[0118] (6) According to the reaction rules in the copper smelting process, the intrinsic relationship between the process and smelting parameters is analyzed, and the viscosity, matte grade, slag copper content, magnetic iron and other technical parameters are predicted based on the thermodynamic database. The genetic algorithm-BP neural network is used to analyze and process each indicator.

[0119] (7) The obtained viscosity accuracy can reach 99%, and the prediction accuracy of copper matte can reach 94.6% after testing.

[0120] (8) Use the predicted data to re-analyze the composition ratio of the raw materials and flux and other materials entering the furnace. When the viscosity data obtained is very high, FeO-containing ore can be appropriately added to the raw materials entering the furnace to guide the setting of ore blending parameters, so as to obtain better smelting products.

[0121] Embodiment 3

[0122] The ore blending method of multi-source complex copper concentrate based on Example 1 has the following steps:

[0123] (1) Sampling and analysis of copper concentrates from 9 different mineral species.

[0124] (2) The objective function is set as the quality of the mixed copper concentrate, and the impurity element content of the mixed copper concentrate is set as the constraint.

[0125] The objective function is as follows:

[0126]

[0127] Where F(x) is the objective function; m is the total number of impurity elements selected; w i is the weight value of the i-th objective function; f i (x) is the percentage content of the i-th impurity element contained in the mixed copper concentrate; f m (x) is the percentage content of the mth impurity element contained in the mixed copper concentrate; r is the proportion of the rth type of copper concentrate in the mixed copper concentrate; x n is the proportion of the nth copper concentrate in the mixed copper concentrate, n is the total number of copper concentrates; a ir is the percentage content of the i-th impurity element contained in the r-th copper concentrate; a in is the percentage content of the ith impurity element in the nth copper concentrate; a mr is the percentage content of the mth impurity element contained in the rth copper concentrate; a mn It is the percentage content of the mth impurity element contained in the nth copper concentrate.

[0128] The constraint function is as follows:

[0129]

[0130] Among them, b i b is the limit value of the percentage content of the i-th impurity element contained in the mixed copper concentrate; m It is the limit value for the percentage content of the mth impurity element contained in the mixed copper concentrate.

[0131] (3) The ingredients of each complex copper concentrate into the furnace meet the restriction requirements on the chemical composition of the impurity elements As, Sb, Bi, Pb, Zn, etc. of the copper ore. That is:

[0132]

[0133] Max i It is the upper limit of the percentage content of a certain impurity element i (As, Sb, Bi, Pb, Zn, etc.) contained in the mixed copper concentrate; Min iIt is the lower limit of the percentage content of a certain impurity element i (As, Sb, Bi, Pb, Zn, etc.) contained in the mixed copper concentrate. The ranges of Cu, SiO2, As, Pb, and Sb are 18%-21%, 11%-13%, 0.4%-0.85%, 1.0%-1.35%, and 0.15%-0.25%, respectively. The ore blending results are as follows Figure 4 .

[0134] (4) Based on the elemental characteristics of the target product molten matte, the composition ratio of flux and other materials is calculated to obtain the raw material ratio entering the furnace.

[0135] The calculation formula for the amount of flux added is:

[0136]

[0137]

[0138] where x r is the proportion of the rth type of copper concentrate; s r is the content of SiO2 in the rth copper concentrate; c r is the CaO content in the rth copper concentrate. (The SiO2 content obtained by blending is 11.50% and the CaO content is 2.42%). An additional 1.02% CaO needs to be added.

[0139] The program completes the fitness function, selection operation, crossover operation, and mutation operation of the genetic algorithm to obtain the optimal individual for assigning weights and thresholds to the BP neural network.

[0140] According to the reaction laws in the copper smelting process, the intrinsic relationship between the process and smelting parameters is analyzed. The viscosity, matte grade, slag copper content, magnetic iron and other technical parameters are predicted based on the thermodynamic database. The genetic algorithm-BP neural network is used to analyze and process each indicator.

[0141] (7) The obtained viscosity accuracy can reach 99%, and the prediction accuracy of copper matte, smelting slag, flue gas volume, spinel, fayalite, etc. can reach 94.6% after testing.

[0142] (8) Use the predicted data to reanalyze the composition ratio of the raw materials and fluxes. When the copper matte data is low, the raw materials with high content of FeO, CaO, etc. can be appropriately increased to guide the setting of ore blending parameters, so as to obtain better smelting products. Figure 5 .

[0143] Embodiment 4

[0144] In order to execute the method corresponding to the above-mentioned embodiment 1 and realize the corresponding functions and technical effects, a multi-source complex copper concentrate blending system is provided below.

[0145] A multi-source complex copper concentrate blending system, comprising:

[0146] The percentage content determination module is used to perform sampling and component analysis on a plurality of mixed copper concentrates to determine the percentage content of each copper concentrate in the various mixed copper concentrates.

[0147] The batching model establishment module is used to establish a batching model of linear multi-objective programming with the quality of the mixed copper concentrate as the objective function and the percentage content of impurity elements in the copper concentrate as the constraint function.

[0148] The proportion determination module is used to use the proportioning model to perform linear multi-objective programming, re-proportion the percentage content of the copper concentrate in the mixed copper concentrate, and determine the proportion of the mixed copper concentrate.

[0149] The module for acquiring raw materials for the furnace is used to calculate the type and amount of flux added in combination with the structural properties of the slag, and to acquire the raw materials for the furnace according to the proportion; the structural properties of the slag include viscosity and copper content in the slag; the raw materials for the furnace include proportioned mixed copper concentrate and flux.

[0150] The prediction module is used to analyze the smelting product based on the raw materials entering the furnace and use the copper metallurgical principles to predict the quality of the smelting product.

[0151] The optimal batching scheme generation module is used to analyze the proportion of the raw materials entering the furnace according to the quality of the smelting product in combination with production data, adjust the objective function and constraint function of the batching model, and generate the raw materials entering the furnace with the optimal proportion and the optimal batching scheme; the production data includes slag copper content, copper matte grade, magnetic iron content and viscosity.

[0152] Embodiment 5

[0153] An embodiment of the present invention provides an electronic device including a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the ore blending method for multi-source complex copper concentrate of embodiment one.

[0154] In practical applications, the above electronic device may be a server.

[0155] In practical applications, an electronic device includes: at least one processor, a memory, a bus and a communications interface.

[0156] Wherein: the processor, the communication interface, and the memory communicate with each other via a communication bus.

[0157] Communication interface, used to communicate with other devices.

[0158] The processor is used to execute programs, and specifically can execute the ore blending method of multi-source complex copper concentrate described in the above embodiment.

[0159] Specifically, the program may include program codes including computer operation instructions.

[0160] The processor may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present invention. The one or more processors included in the electronic device may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs.

[0161] The memory is used to store programs. The memory may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0162] Based on the description of the above embodiments, the embodiments of the present application provide a storage medium on which computer program instructions are stored, and the computer program instructions can be executed by a processor to implement the ore blending method of multi-source complex copper concentrate described in any embodiment.

[0163] The ore blending system of the multi-source complex copper concentrate provided in the embodiments of the present application exists in various forms, including but not limited to:

[0164] (1) Mobile communication devices: These devices are characterized by their mobile communication functions and their main purpose is to provide voice and data communications. These terminals include: smart phones (such as iPhone), multimedia phones, functional phones, and low-end phones.

[0165] (2) Ultra-mobile personal computer devices: These devices fall into the category of personal computers, have computing and processing capabilities, and generally also have mobile Internet access capabilities. These terminals include: PDA, MID and UMPC devices, such as iPad.

[0166] (3) Portable entertainment devices: These devices can display and play multimedia content. They include audio and video players (such as iPods), handheld game consoles, e-books, smart toys, and portable car navigation devices.

[0167] (4) Other electronic devices with data interaction functions.

[0168] Thus far, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired results. Additionally, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing may be advantageous.

[0169] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0170] For the convenience of description, the above device is described by being divided into various units according to their functions and described separately. Of course, when implementing the present application, the functions of each unit can be implemented in one or more software and / or hardware. It should be understood by those skilled in the art that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0171] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0172] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0173] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0174] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0175] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0176] Computer-readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM),

[0177] Digital Versatile Disc (DVD) or other optical storage, magnetic cassette, magnetic tape, magnetic disk storage or other magnetic storage device

[0178] Or any other non-transmission medium that can be used to store information that can be accessed by a computing device. According to the definition in this article, computer-readable media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0179] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0180] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0181] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific transactions or implement specific abstract data types. The present application may also be practiced in distributed computing environments where transactions are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0182] The present invention forms a computer program for a method for blending multi-source complex copper concentrates, thereby solving the problem of complex manual calculations.

[0183] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0184] The principles and implementation methods of the present invention are described in this article using specific examples. The description of the above embodiments is only used to help understand the method and core idea of ​​the present invention. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for blending multi-source complex copper concentrates, characterized in that: include: Sampling and analyzing the components of various mixed copper concentrates to determine the percentage content of each copper concentrate in the various mixed copper concentrates; A linear multi-objective programming batching model is established with the quality of the mixed copper concentrate as the objective function and the percentage content of impurity elements in the copper concentrate as the constraint function; the objective function is: Where F(x) is the objective function; m is the total number of impurity elements selected; w i is the weight value of the i-th objective function; f i (x) is the percentage content of the i-th impurity element contained in the mixed copper concentrate, 0≤i≤m; x r is the proportion of the rth type of copper concentrate in the mixed copper concentrate; x n is the proportion of the nth copper concentrate in the mixed copper concentrate, n is the total number of copper concentrates; a ir is the percentage content of the i-th impurity element contained in the r-th copper concentrate, 0≤r≤n; Using the batching model to perform linear multi-objective programming, re-proportioning the percentage content of the copper concentrate in the mixed copper concentrate, and determining the proportion of the mixed copper concentrate; The type and amount of flux are calculated based on the slag structure and properties, and the raw materials for the furnace are obtained according to the proportion; the slag structure and properties include viscosity and copper content; the raw materials for the furnace include mixed copper concentrate and flux after proportioning; Based on the raw materials fed into the furnace, the smelting products are analyzed using copper metallurgy principles, and the quality of the smelting products is predicted; According to the quality of the smelting product, the proportion of the raw materials entering the furnace is analyzed in combination with production data, the objective function and constraint function of the batching model are adjusted, and the optimal proportion of raw materials entering the furnace and the optimal batching plan are generated; the production data includes slag copper content, copper matte grade, magnetic iron content and viscosity.

2. The ore blending method for multi-source complex copper concentrate according to claim 1, characterized in that: The constraint function is: Among them, b i It is the limit value of the percentage content of the i-th impurity element contained in the mixed copper concentrate.

3. The ore blending method for multi-source complex copper concentrate according to claim 2, characterized in that: The raw materials entering the furnace meet the restriction requirements on the chemical composition of various impurity elements; The restrictions are: Wherein, Maxi is the upper limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate; Min i It is the lower limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate.

4. A multi-source complex copper concentrate blending system, characterized in that: include: A percentage content determination module is used to perform sampling and component analysis on a plurality of mixed copper concentrates to determine the percentage content of each copper concentrate in the various mixed copper concentrates; The batching model establishment module is used to establish a batching model of linear multi-objective programming with the quality of the mixed copper concentrate as the objective function and the percentage content of impurity elements in the copper concentrate as the constraint function; the objective function is: Where F(x) is the objective function; m is the total number of impurity elements selected; w i is the weight value of the i-th objective function; f i (x) is the percentage content of the i-th impurity element contained in the mixed copper concentrate, 0≤i≤m; x r is the proportion of the rth type of copper concentrate in the mixed copper concentrate; x n is the proportion of the nth copper concentrate in the mixed copper concentrate, n is the total number of copper concentrates; a ir is the percentage content of the i-th impurity element contained in the r-th copper concentrate, 0≤r≤n; A proportion determination module, used to perform linear multi-objective programming using the proportioning model, re-proportion the percentage content of the copper concentrate in the mixed copper concentrate, and determine the proportion of the mixed copper concentrate; A furnace raw material acquisition module is used to calculate the type and amount of flux added in combination with the slag structure properties, and obtain the furnace raw materials according to the ratio; the slag structure property characteristics include viscosity and slag copper content; the furnace raw materials include mixed copper concentrate and flux after ratio; A prediction module, used to analyze the smelting product based on the raw materials fed into the furnace and use the copper metallurgical principle to predict the quality of the smelting product; The optimal batching scheme generation module is used to analyze the proportion of the raw materials entering the furnace according to the quality of the smelting product in combination with production data, adjust the objective function and constraint function of the batching model, and generate the raw materials entering the furnace with the optimal proportion and the optimal batching scheme; the production data includes slag copper content, copper matte grade, magnetic iron content and viscosity.

5. The ore blending system for multi-source complex copper concentrate according to claim 4, characterized in that: The constraint function is: Among them, b i It is the limit value of the percentage content of the i-th impurity element contained in the mixed copper concentrate.

6. The ore blending system for multi-source complex copper concentrate according to claim 5, characterized in that: The raw materials entering the furnace meet the restriction requirements on the chemical composition of various impurity elements; The restrictions are: Among them, Max i Min is the upper limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate; i It is the lower limit of the percentage content of the i-th impurity element contained in the mixed copper concentrate.

7. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store a computer program, and the processor runs the computer program to enable the electronic device to execute the ore blending method for multi-source complex copper concentrate as described in any one of claims 1-3.

8. A computer-readable storage medium, characterized in that: It stores a computer program, which, when executed by a processor, implements the ore blending method for multi-source complex copper concentrate as described in any one of claims 1-3.

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