CN103723967A - Mining filling cementing material ratio decision-making method - Google Patents
Mining filling cementing material ratio decision-making method Download PDFInfo
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- CN103723967A CN103723967A CN201310733426.3A CN201310733426A CN103723967A CN 103723967 A CN103723967 A CN 103723967A CN 201310733426 A CN201310733426 A CN 201310733426A CN 103723967 A CN103723967 A CN 103723967A
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Abstract
The invention discloses a mining filling cementing material ratio decision-making method, relating to the field of mining technology. A uniform design experiment method and an orthogonal experiment are adopted to obtain orthogonal experiment data that the strength of a filling body is changed along with the change of ratio of gelatinizing agent; an artificial neural network model is adopted to perform information extension on finite orthogonal experiment data; a statistical regression and parameter fitting method is adopted to establish the function relationship of the cementing material ratio and the strength of the filling body, the strength of the filling body is used as an optimization object and the value range of the gelatinizing agent material is used as a constraint condition to establish a cementing material ratio optimization model; the optimization model is solved to obtain the optimal cementing material ratio scheme. The decision-making method can utilize the finite experiment data to rapidly and accurately obtain the optimal ratio of the cementing material meeting the requirement of the strength of the filling body.
Description
Technical field
Dig up mine by a decision-making technique for the proportioning of gelling material, be specifically related to a kind of decision-making technique of proportioning of the mining of being prepared by industrial residue gelling material.
Background technology
The discharge of a large amount of industrial residue and solid waste is followed in industrial production and development of resources.Industrial residue and solid waste are stacked in a large number, not only take a large amount of soils, and environment has been caused to pollution.
In recent years, being mainly used in the filling method of non-ferrous metal deposit exploitation, in iron mine, more and more being applied at present, is also the only way of Future exploitation.Stowing method mining relates generally to selection, material mixture ratio and the Pulp preparation of filling aggregate and gelling material.The main cement that adopts of stowing method mining is at present as gelling material, and its cost accounts for 1/2 ~ 1/3 of mining cost.When utilizing full tailings or barren rock to do casting resin, the ratio regular meeting of the shared mining cost of cement cementitious material is higher.Adopt industrial residue to prepare gelling material, in mining process, replace cement, can greatly reduce mining cost.As can be seen here, utilize solid waste to do casting resin, utilize industrial residue to prepare jelling agent, not only can reduce mining with stowing cost, meanwhile, refuse and waste residue are landfilled in down-hole, have avoided it to stack pollution on the environment.
Because the solid waste on exploitation ground is different with industrial residue kind, for guaranteeing that the intensity of the obturator obtaining can meet the needs of mining, needed to carry out a large amount of experiments before exploitation to determine the proportioning of gelling material, this process length that expends time in, efficiency is low.
Summary of the invention
The invention provides a kind of decision-making technique of proportioning of the use gelling material of digging up mine, can, through the test of limited number of time, obtain fast and accurately the proportioning of the gelling material that meets strength of filling mass demand.
Above-mentioned purpose realizes by following proposal:
Gelling material proportioning decision-making technique for a kind of mining, is characterized in that, said method comprising the steps of:
(1) solid waste is carried out to the analysis and test of physicochemical characteristic and grain composition; According to test result, in conjunction with the utilizable industrial residue in location, filling mine, select gelling material kind;
(2) according to above-mentioned gelling material kind, adopt Uniform Design method, carry out the investigative test of the multifactor and multilevel strength of filling mass of the proportioning of gelling material;
(3), according to the strength of filling mass test-results of Uniform Design, select kind and the proportioning of gelling material corresponding to obturator that some groups of intensity is the highest;
(4) according to the proportioning of some groups of gelling material in step (3), carry out orthogonal test, obtain the orthogonal experiment data that strength of filling mass changes with the proportioning of jelling agent;
(5) take above-mentioned orthogonal experiment data as learning sample, set up the artificial nerve network model of the proportioning of gelling material, learning sample is learnt and trained, and by the adjustment to training parameter, make the predicated error of neural network model reach the precision of permission;
(6) utilize the artificial nerve network model having trained, carry out the strength of filling mass prediction of the different proportionings of gelling material, thereby the orthogonal experiment data of limited number of time is carried out to information extension, obtain the test sample that meets mathematical statistics needs;
(7) utilize the extension information of orthogonal experiment data and artificial nerve network model, adopt the method for statistical regression and parameter fitting, set up the proportioning of gelling material and the funtcional relationship of strength of filling mass, take strength of filling mass as optimization aim, take the span of gellant material as constraint condition, set up the Optimized model of the proportioning of gelling material;
(8) solve above-mentioned Optimized model, obtain the optimal proportion scheme of gelling material.
Use decision-making technique provided by the invention to determine the proportioning of filling by gelling material in the shorter time, the intensity of the obturator obtaining can meet the needs of mining, has realized the recycling of industrial residue and refuse, and workload is little, efficiency is high.
Accompanying drawing explanation
Fig. 1 is the artificial neural network training error procedure chart of embodiment 1.
Fig. 2 is the artificial neural network training error procedure chart of embodiment 2.
Embodiment
Gelling material proportioning decision-making technique for a kind of mining, is characterized in that, said method comprising the steps of:
(1) solid waste is carried out to the analysis and test of physicochemical characteristic and grain composition; According to test result, in conjunction with the utilizable industrial residue in location, filling mine, select gelling material kind.
The industrial development type difference of different areas, utilizable industrial residue kind is also different, in conjunction with the kind of industrial residue, selects the kind of gelling material.Conventionally, the raw material of gelling material is more than three kinds or three kinds.
(2) according to gelling material raw material, adopt Uniform Design method, carry out the investigative test of the multifactor and multilevel strength of filling mass of the proportioning of gelling material.
Through Uniform Design, can obtain in the ratio range of the gelling material wider at, along with the variation of the proportioning of gelling material, the variation of strength of filling mass.
(3), according to the strength of filling mass test-results of Uniform Design, select kind and the proportioning of gelling material corresponding to obturator that many group intensity is the highest.
This step can be dwindled the range of observation of kind and the proportioning of gelling material, in follow-up test, realizes with test number (TN) still less, obtains the preferably mix proportion scheme of gelling material.
(4) according to the proportioning of some groups of gelling material in step (3), carry out orthogonal test, obtain the orthogonal experiment data that strength of filling mass changes with the proportioning of jelling agent.
The number of levels of orthogonal test is the group number of the proportioning of the gelling material of selecting in step (3).
(5) take above-mentioned orthogonal experiment data as learning sample, set up the artificial nerve network model of the proportioning of gelling material, learning sample is learnt and trained, and by the adjustment to training parameter, make the predicated error of neural network model reach the precision of permission.
(6) utilize the artificial nerve network model having trained, carry out the strength of filling mass prediction of the different proportionings of gelling material, thereby the orthogonal experiment data of limited number of time is carried out to information extension, obtain the test sample that meets mathematical statistics needs.
(7) utilize the extension information of orthogonal experiment data and artificial nerve network model, adopt the method for statistical regression and parameter fitting, set up the proportioning of gelling material and the funtcional relationship of strength of filling mass, take strength of filling mass as optimization aim, take the span of the proportioning of gellant material as constraint condition, set up the Optimized model of the proportioning of gelling material.
(8) solve above-mentioned Optimized model, obtain the optimal proportion scheme of gelling material.
According to the optimal proportion scheme of above-mentioned gelatinous material, can carry out the proof test of the proportioning of gelling material.
Below in conjunction with specific embodiment, the present invention will be described.
the gelling material proportioning decision-making for iron ore mining of embodiment 1 battalion of man of department
A department man battalion mining area is located in Ji and conquers east formerly, and not only Tailings Dam expropriation of land difficulty, and earth's surface does not allow depression, and therefore resource exploitation all adopts complete tailing-filled method.In order to solve the economical efficiency of the complete tailing-filled exploitation of iron ore, urgently carry out the optimum decision of the proportioning of the complete tailing-filled gelling material of iron ore.For this reason, adopt the inventive method to carry out the decision-making of the proportioning of the full tailings filling of battalion of man of department iron ore mining gelling material, concrete implementation step is as follows:
(1) man of department battalion's iron ore unclassified tailing filling materials and industrial residue investigation and physicochemical characteristic analysis, and the selection of gelling material kind.
The full tailings prose style free from parallelism of man of department battalion's iron ore density is 2.38t/m
3, specific surface area reaches 2130cm
2/ g, full tailings size-grade distribution is:
d 10=200-270 μ m,
d 50=30-33 μ m,
d 60=45-50 μ m,
d 90=53-60 μ m,
d p=200-270 μ m, silt content (m) 26%-28% of 20 μ.The chemical composition of the full tailings of man of department battalion's iron ore: CaO=2.90%, Fe
2o
3=9.27%, Al
2o
3=7.3%, MgO=3.88%, SiO
2=69.88%, loss on ignition is 3.76%.The pH=7 of full tailings is neutral casting resin.Si Jia campsite district can utilize slag micropowder that has Steel Plant, power plant desulfurization gypsum of industrial residue etc.Complete tailing-filled gelling material also relate to unslaked lime and water glass, water glass, sodium sulfate etc.
(2) adopt uniform experiment design, carried out the investigative test of the multiple gelling material such as slag micropowder, desulfurated plaster, unslaked lime, water glass, sodium sulfate, water glass and multilevel proportioning.
(3) according to the investigative test result of homogeneous design, determine kind and the proportioning of three groups of gelling material that the obturator that intensity is the highest is corresponding, as shown in table 1.
Table 1 gelling material kind and proportioning
Select thus slag micropowder, unslaked lime, desulfurated plaster as gelling material, wherein the proportioning of unslaked lime is respectively 3.5%, 4.0%, 4.5% by percentage to the quality, the proportioning of desulfurated plaster is respectively 16%, 17%, 18% by percentage to the quality, and slag micropowder is surplus.
(4) on the constant basis of other test conditions, according to the kind of selected gelling material and proportioning, carry out orthogonal test.Owing to having selected the proportioning of three groups of gelling material that the obturator that intensity is the highest is corresponding, therefore the number of levels of orthogonal test is 3, select the standard orthogonal test table of four factor three levels, the factor of determining orthogonal test is respectively slag micropowder, unslaked lime, desulfurated plaster and cement clinker, wherein, wherein cement clinker content is 0, the proportioning of unslaked lime is respectively 3.5%, 4.0%, 4.5% by percentage to the quality, the proportioning of desulfurated plaster is respectively 16%, 17%, 18% by percentage to the quality, and slag micropowder is surplus.
The orthogonal experiment data that strength of filling mass changes with the proportioning of gelling material is as table 2.
The orthogonal experiment data of table 2 strength of filling mass and gelling material proportioning
(5) take above-mentioned 9 orthogonal experiment data as learning sample, set up the artificial nerve network model of the proportioning of gelling material, 9 learning samples are learnt and trained.As shown in Figure 1, target setting error E is 0.001, through 297 step training computings, reaches optimal fitting, and network training error is that the dotted line in 0.00098479, Fig. 1 is target error standard lines 0.001.
(6) utilize trained artificial nerve network model, the different proportionings of gelling material are carried out to strength of filling mass prediction, obtained thus full tailings and filled the extension information of the proportioning test of gelling material, expanded the sample of orthogonal test.
(7) utilize the extension information of the orthogonal experiment data of step (4) and the artificial nerve network model of step (6), adopt the method for statistical regression and parameter fitting, set up the proportioning of gelling material and the funtcional relationship of strength of filling mass, take strength of filling mass as optimization aim, take the span of gellant material as constraint condition, set up the Optimized model of the proportioning of gelling material.
Objective function:
constraint condition:
.
(8) solve above-mentioned Optimized model, the optimal proportion of the complete tailing-filled gelling material of man of acquisition department battalion's iron ore is thus
.
According to the optimization proportioning of the complete tailing-filled gelling material of man of department battalion iron ore that adopts above-mentioned implementation step decision-making, carry out proof test, and carry out the simultaneous test of cement cementitious material simultaneously, thus obtainedly the results are shown in Table 3.
The full tailings gelling material 28d strength comparison test result of table 3
the embodiment full tailings of 2 Jinchuan Nickel Mine and the decision-making of rod milling sand mixed filling material jelling agent proportioning
The exploitation of Jinchuan Nickel Mine cemented-method adopts rod milling sandfilling material always, and mining with stowing cost is high, and the full tailings of ore dressing is not used, and stacks in a large number.The mixed filling material that adopts rod milling sand and full tailings, can address the above problem.For this reason, adopt the inventive method to carry out the decision-making of the proportioning of the full tailings of Jinchuan Nickel Mine and rod milling sand mixed filling material filling mining gelling material, concrete implementation step is as follows:
(1) the full tailings of Jinchuan Nickel Mine and rod milling sand mixed filling material and industrial residue investigation and physicochemical characteristic analysis, and the selection of gelling material kind.
The entity density of the full tailings of Jinchuan Nickel Mine is 2.77-2.97t/m
3, aerated density is 1.2-1.3t/m
3, porosity is 54%-58%; Size-grade distribution is d
10=0.0015-0.0017mm, d
50=0.03-0.05mm, d
90=0.1-0.2mm, d
v=0.03-0.04mm, nonuniformity coefficient is 20-23.Chemical composition is SiO
2=33-36%, Al
2o
3=3%-5%, MgO=28%-33%, CaO=2.6%-3.9%, S<0.7%.Rod milling sandfilling material particle diameter is-3mm that physical property is: entity density is 2.6-2.7t/m
3, aerated density is 1.5-1.6t/m
3, porosity is 40%-42%; Size-grade distribution is d
10=0.13-0.16mm, d
50=0.8-1.0mm, d
60=1.0-1.5mm, d
90=2.9-3.3mm, d
v=0.8-1.2mm, nonuniformity coefficient=7-9.Chemical composition is calculated in mass percent as SiO
2=64%-76%, Al
2o
3=5%-8%, MgO=3%-6%, CaO=2%-5%, Fe
2o
3=3%-5%, S<0.1%.
Jinchuan area is utilizable the industrial residues such as slag micropowder, power plant desulfurization lime-ash, phosphogypsum.Cementing filling material also relates to unslaked lime, desulfurization ash and saltcake, caustic soda, calcium chloride, sodium-chlor etc.
(2) adopt uniform experiment design, carried out the investigative test of the multiple gelling material such as slag micropowder, desulfurization ash, unslaked lime, phosphogypsum, saltcake, caustic soda, calcium chloride, sodium-chlor and multilevel proportioning.
(3) according to the investigative test result of homogeneous design, for the different proportionings of rod milling sand and full tailings, determine kind and the proportioning of 4 groups of gelling material that the obturator that intensity is the highest is corresponding, as shown in table 4.
Table 4 gelling material kind and proportioning
Select thus slag micropowder, unslaked lime, desulfurization ash, saltcake and caustic soda as gelling material, wherein the proportioning of unslaked lime is respectively 6%, 7%, 8%, 9% by percentage to the quality, the proportioning of desulfurization ash is respectively 14%, 16%, 18%, 20% by percentage to the quality, the proportioning of saltcake is respectively 2%, 3%, 4%, 5% by percentage to the quality, the proportioning of caustic soda is respectively 0,0.5%, 1.0%, 1.5% by percentage to the quality, slag micropowder is surplus, and the mass ratio of full tailings and rod milling sand is 0:1,2:8,3:7,4:6.
(4) on the constant basis of other test conditions, according to the kind of selected gelling material and proportioning, carry out orthogonal test.Owing to having selected the proportioning of four groups of gelling material that the obturator that intensity is the highest is corresponding, therefore the number of levels of orthogonal test is 4, select the standard orthogonal test table of five factor four levels, the factor of determining orthogonal test is respectively full tailings and rod milling sand proportioning, slag micropowder, unslaked lime, desulfurization ash, saltcake and caustic soda, wherein, the mass ratio of full tailings and rod milling sand is 0:1, 2:8, 3:7, 4:6, the proportioning of unslaked lime is respectively 6% by percentage to the quality, 7%, 8%, 9%, the proportioning of desulfurization ash is respectively 14% by percentage to the quality, 16%, 18%, 20%, the proportioning of saltcake is respectively 2% by percentage to the quality, 3%, 4%, 5%, the proportioning of caustic soda is respectively 0.0% by percentage to the quality, 0.5%, 1.0%, 1.5%, slag micropowder is surplus.
The orthogonal experiment data that strength of filling mass changes with the proportioning of gelling material is as shown in table 5.
The full tailings of table 5 and the novel cementing filling material orthogonal experiments of rod milling sand mixed filling material
(5) take above-mentioned 16 orthogonal experiment data as learning sample, set up the artificial nerve network model of the proportioning of gelling material, 16 learning samples are learnt and trained.As shown in Figure 2, it is 0.0001 that target setting error E changes into, through 92 step training computings, reaches optimal fitting, and network training error is 9.82292e-005, and the solid line in Fig. 2 is target error standard lines 0.0001.
(6) utilize trained artificial nerve network model, different proportionings to gelling material are carried out strength of filling mass prediction, obtain thus the extension information of full tailings and rod milling sand mixed filling material gelling material proportioning test, expanded the sample of orthogonal test.
(7) utilize the extension information of the orthogonal experiment data of step (4) and the artificial nerve network model of step (6), adopt the method for statistical regression and parameter fitting, set up the proportioning of gelling material and the funtcional relationship of strength of filling mass, take strength of filling mass as optimization aim, take the span of gellant material as constraint condition, set up the Optimized model of the proportioning of gelling material.
Objective function:
Constraint condition:
.
(8) solve above-mentioned Optimized model, the optimal proportion that obtains thus the full tailings of Jinchuan Nickel Mine and rod milling sandfilling material gelling material is
.
According to adopting the full tailings of Jinchuan Nickel Mine of above-mentioned implementation step decision-making and the optimization proportioning of rod milling sand mixed filling jelling agent to carry out proof test, and carry out the simultaneous test of cement cementitious material simultaneously, thus obtainedly the results are shown in Table 6.
The full tailings in table 6 Jinchuan and rod milling sand epoxy glue gel material proof test result
Claims (1)
1. a gelling material proportioning decision-making technique for mining, is characterized in that, said method comprising the steps of:
(1) solid waste is carried out to the analysis and test of physicochemical characteristic and grain composition; According to test result, in conjunction with the utilizable industrial residue in location, filling mine, select the raw material type of gelling material;
(2) according to the raw material type of described gelling material, adopt Uniform Design method, carry out the investigative test of the multifactor and multilevel strength of filling mass of the proportioning of gelling material;
(3), according to the strength of filling mass test-results of Uniform Design, select kind and the proportioning of gelling material corresponding to obturator that many group intensity is the highest;
(4) according to the proportioning of many group gelling material in step (3), carry out orthogonal test, obtain the orthogonal experiment data that strength of filling mass changes with the proportioning of jelling agent;
(5) take described orthogonal experiment data as learning sample, set up the artificial nerve network model of the proportioning of gelling material, learning sample is learnt and trained, and by the adjustment to training parameter, make the predicated error of neural network model reach the precision of permission;
(6) utilize the artificial nerve network model having trained, carry out the strength of filling mass prediction of the different proportionings of gelling material, thereby the orthogonal experiment data of limited number of time is carried out to information extension, obtain the test sample that meets mathematical statistics needs;
(7) utilize the extension information of orthogonal experiment data and artificial nerve network model, adopt the method for statistical regression and parameter fitting, set up the proportioning of gelling material and the funtcional relationship of strength of filling mass, take strength of filling mass as optimization aim, take the span of gellant material as constraint condition, set up the Optimized model of the proportioning of gelling material;
(8) solve above-mentioned Optimized model, obtain the optimal proportion scheme of gelling material.
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| CN107311582A (en) * | 2017-06-19 | 2017-11-03 | 金川集团股份有限公司 | A kind of early strong rubber gel material proportioning decision-making technique of low cost |
| CN108229062A (en) * | 2018-01-31 | 2018-06-29 | 西安科技大学 | Method based on sensibility micro-parameter prediction cemented fill mechanical response characteristic |
| CN109523069A (en) * | 2018-11-01 | 2019-03-26 | 中南大学 | A method of filler intensive parameter is predicted using machine learning |
| CN110723952A (en) * | 2019-10-12 | 2020-01-24 | 北京科技大学 | Phosphogypsum-based all-solid waste filler proportioning optimization method for improving filling roof contact rate |
| CN111191387A (en) * | 2020-02-12 | 2020-05-22 | 河北钢铁集团矿业有限公司 | Phosphogypsum-based cementing material optimization method for improving filling roof contact rate |
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| CN106746946B (en) * | 2016-11-16 | 2019-05-31 | 玉溪矿业有限公司 | A kind of method of Optimization Packing material proportion |
| CN106746946A (en) * | 2016-11-16 | 2017-05-31 | 玉溪矿业有限公司 | A kind of method of Optimization Packing material proportioning |
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| CN107117888B (en) * | 2017-06-19 | 2019-08-06 | 金川集团股份有限公司 | A kind of mining mixing aggregate filling slurry proportion decision-making technique |
| CN107117888A (en) * | 2017-06-19 | 2017-09-01 | 金川集团股份有限公司 | One kind mining mixing aggregate filling slurry proportioning decision-making technique |
| CN108229062A (en) * | 2018-01-31 | 2018-06-29 | 西安科技大学 | Method based on sensibility micro-parameter prediction cemented fill mechanical response characteristic |
| CN108229062B (en) * | 2018-01-31 | 2019-03-01 | 西安科技大学 | Method based on sensibility micro-parameter prediction cemented fill mechanical response characteristic |
| CN109523069A (en) * | 2018-11-01 | 2019-03-26 | 中南大学 | A method of filler intensive parameter is predicted using machine learning |
| CN110723952A (en) * | 2019-10-12 | 2020-01-24 | 北京科技大学 | Phosphogypsum-based all-solid waste filler proportioning optimization method for improving filling roof contact rate |
| CN110723952B (en) * | 2019-10-12 | 2020-07-24 | 北京科技大学 | Phosphogypsum-based all-solid waste filler proportioning optimization method for improving filling roof contact rate |
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