CN112837762A - Method for predicting grade of platinum and palladium in flotation concentrate of platinum and palladium paragenic ore - Google Patents
Method for predicting grade of platinum and palladium in flotation concentrate of platinum and palladium paragenic ore Download PDFInfo
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- KDLHZDBZIXYQEI-UHFFFAOYSA-N Palladium Chemical compound [Pd] KDLHZDBZIXYQEI-UHFFFAOYSA-N 0.000 title claims abstract description 125
- BASFCYQUMIYNBI-UHFFFAOYSA-N platinum Chemical compound [Pt] BASFCYQUMIYNBI-UHFFFAOYSA-N 0.000 title claims abstract description 102
- 229910052763 palladium Inorganic materials 0.000 title claims abstract description 74
- 229910052697 platinum Inorganic materials 0.000 title claims abstract description 51
- 239000012141 concentrate Substances 0.000 title claims abstract description 46
- 238000005188 flotation Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000012417 linear regression Methods 0.000 claims abstract description 18
- PXHVJJICTQNCMI-UHFFFAOYSA-N Nickel Chemical compound [Ni] PXHVJJICTQNCMI-UHFFFAOYSA-N 0.000 claims abstract description 16
- RYGMFSIKBFXOCR-UHFFFAOYSA-N Copper Chemical compound [Cu] RYGMFSIKBFXOCR-UHFFFAOYSA-N 0.000 claims abstract description 12
- 229910052802 copper Inorganic materials 0.000 claims abstract description 12
- 239000010949 copper Substances 0.000 claims abstract description 12
- 238000004458 analytical method Methods 0.000 claims abstract description 11
- 238000000611 regression analysis Methods 0.000 claims abstract description 9
- 229910052759 nickel Inorganic materials 0.000 claims abstract description 8
- 239000004088 foaming agent Substances 0.000 claims description 8
- 229910052500 inorganic mineral Inorganic materials 0.000 claims description 7
- 239000011707 mineral Substances 0.000 claims description 7
- CDBYLPFSWZWCQE-UHFFFAOYSA-L Sodium Carbonate Chemical compound [Na+].[Na+].[O-]C([O-])=O CDBYLPFSWZWCQE-UHFFFAOYSA-L 0.000 claims description 6
- 239000010665 pine oil Substances 0.000 claims description 5
- 239000003795 chemical substances by application Substances 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 4
- TUZCOAQWCRRVIP-UHFFFAOYSA-N butoxymethanedithioic acid Chemical compound CCCCOC(S)=S TUZCOAQWCRRVIP-UHFFFAOYSA-N 0.000 claims description 3
- GBCAVSYHPPARHX-UHFFFAOYSA-M n'-cyclohexyl-n-[2-(4-methylmorpholin-4-ium-4-yl)ethyl]methanediimine;4-methylbenzenesulfonate Chemical compound CC1=CC=C(S([O-])(=O)=O)C=C1.C1CCCCC1N=C=NCC[N+]1(C)CCOCC1 GBCAVSYHPPARHX-UHFFFAOYSA-M 0.000 claims description 3
- 230000002000 scavenging effect Effects 0.000 claims description 3
- 229910000029 sodium carbonate Inorganic materials 0.000 claims description 3
- WARIWGPBHKPYON-UHFFFAOYSA-N Ethiolate Chemical compound CCSC(=O)N(CC)CC WARIWGPBHKPYON-UHFFFAOYSA-N 0.000 claims description 2
- -1 butyl propynyl Chemical group 0.000 claims description 2
- 235000010948 carboxy methyl cellulose Nutrition 0.000 claims description 2
- 239000003607 modifier Substances 0.000 claims description 2
- 235000019353 potassium silicate Nutrition 0.000 claims description 2
- 235000017550 sodium carbonate Nutrition 0.000 claims description 2
- GCLGEJMYGQKIIW-UHFFFAOYSA-H sodium hexametaphosphate Chemical compound [Na]OP1(=O)OP(=O)(O[Na])OP(=O)(O[Na])OP(=O)(O[Na])OP(=O)(O[Na])OP(=O)(O[Na])O1 GCLGEJMYGQKIIW-UHFFFAOYSA-H 0.000 claims description 2
- 235000019982 sodium hexametaphosphate Nutrition 0.000 claims description 2
- NTHWMYGWWRZVTN-UHFFFAOYSA-N sodium silicate Chemical compound [Na+].[Na+].[O-][Si]([O-])=O NTHWMYGWWRZVTN-UHFFFAOYSA-N 0.000 claims description 2
- 239000001577 tetrasodium phosphonato phosphate Substances 0.000 claims description 2
- 229910000570 Cupronickel Inorganic materials 0.000 abstract description 11
- YOCUPQPZWBBYIX-UHFFFAOYSA-N copper nickel Chemical compound [Ni].[Cu] YOCUPQPZWBBYIX-UHFFFAOYSA-N 0.000 abstract description 11
- 238000001514 detection method Methods 0.000 abstract description 10
- 238000004519 manufacturing process Methods 0.000 abstract description 10
- 230000007547 defect Effects 0.000 abstract description 5
- 230000000694 effects Effects 0.000 abstract description 4
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 6
- 238000012360 testing method Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000011084 recovery Methods 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 description 3
- 229910052742 iron Inorganic materials 0.000 description 3
- 239000010970 precious metal Substances 0.000 description 3
- 238000000926 separation method Methods 0.000 description 3
- 241000196324 Embryophyta Species 0.000 description 2
- NINIDFKCEFEMDL-UHFFFAOYSA-N Sulfur Chemical compound [S] NINIDFKCEFEMDL-UHFFFAOYSA-N 0.000 description 2
- 238000001479 atomic absorption spectroscopy Methods 0.000 description 2
- 238000004993 emission spectroscopy Methods 0.000 description 2
- AEOCXXJPGCBFJA-UHFFFAOYSA-N ethionamide Chemical compound CCC1=CC(C(N)=S)=CC=N1 AEOCXXJPGCBFJA-UHFFFAOYSA-N 0.000 description 2
- 229960002001 ethionamide Drugs 0.000 description 2
- 238000001036 glow-discharge mass spectrometry Methods 0.000 description 2
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 2
- 229910052737 gold Inorganic materials 0.000 description 2
- 239000010931 gold Substances 0.000 description 2
- 238000001095 inductively coupled plasma mass spectrometry Methods 0.000 description 2
- 238000013178 mathematical model Methods 0.000 description 2
- 238000005272 metallurgy Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 239000000377 silicon dioxide Substances 0.000 description 2
- 238000005245 sintering Methods 0.000 description 2
- 229910052717 sulfur Inorganic materials 0.000 description 2
- 239000011593 sulfur Substances 0.000 description 2
- CWYNVVGOOAEACU-UHFFFAOYSA-N Fe2+ Chemical compound [Fe+2] CWYNVVGOOAEACU-UHFFFAOYSA-N 0.000 description 1
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 1
- CNLWCVNCHLKFHK-UHFFFAOYSA-N aluminum;lithium;dioxido(oxo)silane Chemical compound [Li+].[Al+3].[O-][Si]([O-])=O.[O-][Si]([O-])=O CNLWCVNCHLKFHK-UHFFFAOYSA-N 0.000 description 1
- 238000001636 atomic emission spectroscopy Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 229910052681 coesite Inorganic materials 0.000 description 1
- 229910052906 cristobalite Inorganic materials 0.000 description 1
- 239000003814 drug Substances 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000009616 inductively coupled plasma Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 229910052744 lithium Inorganic materials 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 238000001612 separation test Methods 0.000 description 1
- 235000012239 silicon dioxide Nutrition 0.000 description 1
- 229910052642 spodumene Inorganic materials 0.000 description 1
- 238000013179 statistical model Methods 0.000 description 1
- 229910052682 stishovite Inorganic materials 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 229910052905 tridymite Inorganic materials 0.000 description 1
- 239000012991 xanthate Substances 0.000 description 1
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- G16C—COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
- G16C20/00—Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures
- G16C20/30—Prediction of properties of chemical compounds, compositions or mixtures
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
- B03D1/00—Flotation
- B03D1/001—Flotation agents
- B03D1/018—Mixtures of inorganic and organic compounds
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- B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
- B03D2201/00—Specified effects produced by the flotation agents
- B03D2201/007—Modifying reagents for adjusting pH or conductivity
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
- B03D2201/00—Specified effects produced by the flotation agents
- B03D2201/02—Collectors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B03—SEPARATION OF SOLID MATERIALS USING LIQUIDS OR USING PNEUMATIC TABLES OR JIGS; MAGNETIC OR ELECTROSTATIC SEPARATION OF SOLID MATERIALS FROM SOLID MATERIALS OR FLUIDS; SEPARATION BY HIGH-VOLTAGE ELECTRIC FIELDS
- B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
- B03D2201/00—Specified effects produced by the flotation agents
- B03D2201/04—Frothers
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- B03D—FLOTATION; DIFFERENTIAL SEDIMENTATION
- B03D2203/00—Specified materials treated by the flotation agents; specified applications
- B03D2203/02—Ores
- B03D2203/025—Precious metal ores
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Abstract
The invention relates to a method for predicting the grade of platinum and palladium in flotation concentrate of platinum and palladium paragenic ore, which comprises the following steps: s1: determining the grade sum of copper and nickel in the flotation concentrate of the platinum-palladium paragenetic ore, and recording as x percent; correspondingly measuring the grade sum of platinum and palladium in the flotation concentrate, and recording the grade sum as y, g/t; s2: using the linear regression equation y ═ eta0+η1x)/10000 regression analysis of x and y measured in S1 to get eta0And η1(ii) a S3: and (3) determining the grades and x of copper and nickel in the flotation concentrate sample to be detected, and calculating the grades and y of platinum and palladium according to a linear regression equation. The prediction method provided by the invention is based on the rapidity of the detection of the copper-nickel element, the interdependence correlation among the elements is fully considered, the grade of the copper-nickel element is taken as an independent variable factor, and the defects that the analysis and detection of the grade of the platinum and the palladium are difficult, the feedback period is long, and the guidance effect on the production practice is seriously influenced are overcome; the linear regression equation obtained by the prediction method has high fitting degree, and the platinum and palladium grade can be effectively and accurately predicted.
Description
Technical Field
The invention belongs to the technical field of ore dressing, and particularly relates to a method for predicting the grade of platinum and palladium in flotation concentrate of platinum and palladium paragenic ore.
Technical Field
Grade refers to the content of useful components or useful minerals in the ore (or beneficiation products), is the main quality index of the ore and beneficiation products, and directly influences beneficiation efficiency. At present, the grade of the mineral separation product is generally obtained by a sampling and testing method on a production site, the detection means is various but real-time monitoring cannot be provided, and data is usually lagged relative to the process flow, so that the prediction of the mineral separation index by establishing a linear regression equation model by using mathematical thinking is more and more emphasized by mineral processing engineers of all countries in the world.
Yellow orange (establishment and application of prediction model of copper and sulfur grade and gold and copper recovery rate of raw ore, & lt & gt modern mining industryAnd 2018, (3) 168-171) utilizing a statistical regression analysis method to establish a multivariate linear regression model of the recovery rates of gold and copper and the grades of sulfur and copper in the raw ore, and predicting and guiding the production index rule under the fluctuation of the grade of the raw ore. Zhao le (determination of the correlation between iron grade and silicon dioxide content in iron ore concentrate by regression analysis, metallurgy standardization and quality 2006, 44(1), 17-21) utilizes statistical technique to establish TFe grade and SiO2Determining TFe grade and SiO of iron ore concentrate of Anemata major-lonely-selected factory, Dongshan sintering factory and Qida selection factory by regression analysis and significance test2Correlation of contents and (TFe + SiO)2) The% is a constant relation and has important significance for mineral separation and sintering production. Bright (regression analysis of recovery and grade of spodumene lean ore dressing process, non-ferrous Xinjiang, 1995, 21-28) applies regression analysis method to establish mathematical model of multiple linear regression of concentrate grade of lithium and medicament system, and uses the model to realize prediction and control of dressing production. However, the research on the mathematical model mainly focuses on the ore dressing recovery rate, is relatively mature, but is rarely reported in the aspect of concentrate grade prediction, and particularly no relevant report is found on precious metals such as platinum, palladium and the like.
In the process of ground mining, dressing and metallurgy, the precious metals such as platinum and palladium have the characteristics of low content, large component difference, non-uniformity, complex occurrence state and the like, and the requirement on detection technology is high. At present, the detection and analysis technologies of platinum and palladium mainly comprise: inductively coupled plasma emission spectrometry (ICP-AES), Atomic Absorption Spectrometry (AAS), inductively coupled plasma mass spectrometry (ICP-MS), Arc emission spectrometry (Arc-OES), glow discharge mass spectrometry (GD-MS), and the like. Although the technologies have high precision, the technologies have the defects of long analysis period and great difficulty, and the requirements of rapidly guiding mineral separation tests and industrial production are difficult to meet. Therefore, the research on the prediction analysis calculation method has important practical significance for quickly and accurately judging the technical indexes and the trends.
Disclosure of Invention
The invention aims to overcome the blank of research on the aspect of concentrate grade prediction of precious metals such as platinum, palladium and the like in the prior art, and provides a method for predicting the platinum and palladium grade in flotation concentrate of platinum-palladium paragenic ore. The prediction method provided by the invention is based on the rapidity of the detection of the copper-nickel element, the interdependence correlation among the elements is fully considered, the grade of the copper-nickel element is taken as an independent variable factor, and the defects that the analysis and detection of the grade of the platinum and the palladium are difficult, the feedback period is long, and the guidance effect on the production practice is seriously influenced are overcome; the linear regression equation obtained by the prediction method has high fitting degree, and the platinum and palladium grade can be effectively and accurately predicted.
In order to realize the purpose of the invention, the invention adopts the following scheme:
a prediction method for platinum and palladium grade in flotation concentrate of platinum and palladium paragenetic ore comprises the following steps:
s1: determining the grade sum of copper and nickel in the flotation concentrate of the platinum-palladium paragenetic ore, and recording as x percent; correspondingly measuring the grade sum of platinum and palladium in the flotation concentrate, and recording the grade sum as y, g/t;
s2: using the linear regression equation y ═ eta0+η1x)/10000 regression analysis of x and y measured in S1 to get eta0And η1；
S3: and (3) determining the grades and x of copper and nickel in the flotation concentrate sample to be detected, and calculating the grades and y of platinum and palladium according to a linear regression equation.
The inventor of the invention discovers through repeated research that high linear correlation (correlation coefficient is above 0.7) exists between the grade sum of copper and nickel in flotation concentrate of platinum-palladium paragenic ore and the grade sum of platinum and palladium, and the grade sum of platinum and palladium can be predicted by rapidly measuring the grade of copper-nickel element, so that the defects that the platinum-palladium grade analysis and detection is difficult, the feedback period is long, and the guiding effect on production practice is seriously influenced are overcome.
The linear regression equation obtained by the prediction method has high fitting degree, and the platinum and palladium grade can be effectively and accurately predicted.
The number of samples can be optimized according to the accuracy requirements of the prediction.
Preferably, at least 15 sets of corresponding x and y values are determined in S1.
More preferably, the x and y values for 15 to 25 groups are measured in S1.
Preferably, η is obtained by least squares analysis in S20And η1。
More preferably, S2 further includes calculating the correction coefficient γsThe step (2).
Preferably, the flotation concentrate of the platinum-palladium paragenic ore in S1 is obtained by: grinding ore until the ore is ground to-0.074 mm and accounts for 60% -72%, adding a regulator, a collector and a foaming agent for primary roughing, grinding the roughed tailings to-0.043 mm and accounts for 65% -75%, adding the regulator, the collector and the foaming agent for secondary roughing, combining the roughed concentrates twice, performing blank concentration for three times to obtain flotation concentrates, and returning the middlings in the concentration in sequence; and adding a collecting agent and a foaming agent into the secondary rougher tailings for secondary scavenging, returning scavenged middlings in sequence, and obtaining secondary scavenged tailings which are flotation concentrates.
More preferably, the collector is one or more of propynyl butyl xanthate, ethiopropyl ethiolate, ethionamide or butyl xanthate.
More preferably, the using amount of the collecting agent is 10-300 g/t.
More preferably, the modifier is one or more of sodium carbonate, water glass, CMC or sodium hexametaphosphate.
More preferably, the dosage of the regulator is 200-3000 g/t.
More preferably, the foaming agent is pine oil.
More preferably, the amount of the foaming agent is 5-80 g/t.
The prediction method has good applicability to the existing conventional platinum-palladium paragenic ore (the grade y of platinum-palladium is generally between 1 and 100 g/t)
Preferably, the grades of platinum and palladium in the flotation concentrate and y are 1-100 g/t.
Compared with the prior art, the invention has the following beneficial effects:
the prediction method provided by the invention is based on the rapidity of the detection of the copper-nickel element, the interdependence correlation among the elements is fully considered, the grade of the copper-nickel element is taken as an independent variable factor, and the defects that the analysis and detection of the grade of the platinum and the palladium are difficult, the feedback period is long, and the guidance effect on the production practice is seriously influenced are overcome; the linear regression equation obtained by the prediction method has high fitting degree, and the platinum and palladium grade can be effectively and accurately predicted.
Figures and description
FIG. 1 is a graph of the regression results of Excel software in example 1 of the present invention;
fig. 2 is a flowchart of a prediction method according to embodiment 1 of the present invention.
Detailed Description
The invention is further illustrated by the following examples. These examples are intended to illustrate the invention and are not intended to limit the scope of the invention. Experimental procedures without specific conditions noted in the examples below, generally according to conditions conventional in the art or as suggested by the manufacturer; the raw materials, reagents and the like used are, unless otherwise specified, those commercially available from the conventional markets and the like. Any insubstantial changes and substitutions made by those skilled in the art based on the present invention are intended to be covered by the claims.
The platinum-palladium ore selected in the embodiment 1 of the invention is platinum-palladium paragenic ore, and flotation is carried out on the platinum-palladium paragenic ore to obtain flotation concentrate, and the specific process is as follows: grinding ores until the ore size is-0.074 mm and accounts for 72 percent, adding 1000g/t of sodium carbonate, 100g/t of ethionamide and 30g/t of pine oil to perform primary roughing, grinding rougher tailings until the ore size is-0.043 mm and accounts for 72 percent, adding 300g/t of CMC, 30g/t of ethionamine and 10g/t of pine oil to perform secondary roughing, combining two rougher concentrates to perform three-time blank concentration to obtain flotation concentrate, and returning concentrated middlings in sequence; and adding 20g/t of butyl xanthate and 10g/t of pine oil into the secondary roughing tailings to perform secondary scavenging, wherein the scavenged middlings are returned sequentially, and the secondary scavenged tailings are final tailings.
Example 1
The embodiment provides a method for predicting the grade of platinum and palladium in flotation concentrate of platinum and palladium paragenic ore, as shown in fig. 2, which includes the following steps:
(1) collecting sample survey values
The copper and nickel grades in the flotation concentrate are used as sample investigation values, the sample values are 19 groups, and the concrete numerical values of the 19 groups of sample values are shown in table 1.
Table 119 sets of sample values
(2) Establishing a linear regression model
Assuming that the platinum-palladium grade (y) and the copper-nickel grade (x) are linearly related, establishing a linear regression analysis model: y ═ η0+η1x)/10000, using Excel software to input sample value data, selecting least square method to estimate model parameters, obtaining model estimation result (shown in figure 1) y ═ (16.267+6.9235x)/10000, sample correlation coefficient (R)20.7112), correction coefficient (γ)s0.963974), find η0、η1To obtain a linear regression analysis model: y ═ η0+η1x)/10000, as shown in FIG. 1.
(3) Prediction of platinum palladium grade
And (3) bringing the copper-nickel grade value (shown in table 2) corresponding to the platinum-palladium grade to be predicted into a regression analysis model to obtain the platinum-palladium grade (shown in table 2).
And (3) checking the linear regression model in the step (2):
firstly, detecting the goodness of fit
From the model estimation result in step (2), the correction coefficient γ is knowns0.963974, the copper nickel grade explains 96.4% of the platinum palladium grade float. This also indicates that the model fits well to this sample.
Second, the practical significance of the model is tested
The coefficients in the linear regression equation of the platinum-palladium grade show that when the copper-nickel grade is improved by 0.1 percent, the platinum-palladium grade can be increased by 0.7 percent, which is identical with the current production index of a certain foreign plant, and the production index of the plant selected in the industrial test period is shown in the following table 2.
TABLE 2 results of model testing
As can be seen from Table 2, in the industrial test period, the error between the actual value and the calculated value of the grade of platinum and palladium in the obtained concentrate is 0.35%, and the relative error is 0.5%, which shows that the grade of platinum and palladium in the concentrate calculated according to the model is basically consistent with the actual grade, and also shows that the regression statistical model has certain practical significance.
It will be appreciated by those of ordinary skill in the art that the examples provided herein are intended to assist the reader in understanding the principles of the invention and are to be construed as being without limitation to such specifically recited examples and embodiments. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (10)
1. A prediction method for platinum-palladium grade in flotation concentrate of platinum-palladium paragenetic ore is characterized by comprising the following steps:
s1: determining the grade sum of copper and nickel in the flotation concentrate of the platinum-palladium paragenetic ore, and recording as x percent; correspondingly measuring the grade sum of platinum and palladium in the flotation concentrate, and recording the grade sum as y, g/t;
s2: using the linear regression equation y ═ eta0+η1x)/10000 regression analysis of x and y measured in S1 to get eta0And η1；
S3: and (3) determining the grades and x of copper and nickel in the flotation concentrate sample to be detected, and calculating the grades and y of platinum and palladium according to a linear regression equation.
2. The method of predicting the grade of platinum and palladium in a flotation concentrate of platinum and palladium paragenic ore according to claim 1, wherein at least 15 sets of corresponding x and y values are determined in S1.
3. The method for predicting the grade of platinum and palladium in the flotation concentrate of the platinum and palladium paragenetic ore according to claim 1, wherein the x and y values corresponding to 15-25 groups are measured in S1.
4. The method for predicting the grade of platinum and palladium in the flotation concentrate of platinum-palladium paragenic ore according to claim 1, wherein the eta is obtained by the least square analysis in S20And η1。
5. The method for predicting the grade of platinum and palladium in the flotation concentrate of platinum and palladium paragenic ore according to claim 1, wherein S2 further comprises calculating a correction coefficient γsThe step (2).
6. The method for predicting the grade of platinum and palladium in the flotation concentrate of platinum and palladium paragenic ore according to claim 1, wherein the flotation concentrate of platinum and palladium paragenic ore in S1 is obtained by the following steps: grinding ore until the ore is ground to-0.074 mm and accounts for 60% -72%, adding a regulator, a collector and a foaming agent for primary roughing, grinding the roughed tailings to-0.043 mm and accounts for 65% -75%, adding the regulator, the collector and the foaming agent for secondary roughing, combining the roughed concentrates twice, performing blank concentration for three times to obtain flotation concentrates, and returning the middlings in the concentration in sequence; and adding a collecting agent and a foaming agent into the secondary rougher tailings for secondary scavenging, returning scavenged middlings in sequence, and obtaining secondary scavenged tailings which are flotation concentrates.
7. The method for predicting the grade of platinum and palladium in the flotation concentrate of the platinum and palladium paragenetic mineral as claimed in claim 6, wherein the collecting agent is one or more of butyl propynyl xanthogenate, ethionamynyl ethiolate, ethioamidite and butyl xanthate.
8. The method of claim 6, wherein the modifier is one or more of sodium carbonate, water glass, CMC, and sodium hexametaphosphate.
9. The method of predicting platinum-palladium grade in a flotation concentrate of platinum-palladium paragenic ore according to claim 6, wherein the frother is pine oil.
10. The method for predicting the grade of platinum and palladium in the flotation concentrate of the platinum-palladium paragenetic ore according to claim 1, wherein the sum y of the grades of platinum and palladium is 1-100 g/t.
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