CN113705916A - Multi-source and multi-target ore blending method for iron ore of strip mine - Google Patents
Multi-source and multi-target ore blending method for iron ore of strip mine Download PDFInfo
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- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 title claims abstract description 119
- 238000002156 mixing Methods 0.000 title claims abstract description 96
- 229910052742 iron Inorganic materials 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 23
- CWYNVVGOOAEACU-UHFFFAOYSA-N Fe2+ Chemical compound [Fe+2] CWYNVVGOOAEACU-UHFFFAOYSA-N 0.000 claims abstract description 25
- 238000004880 explosion Methods 0.000 claims abstract description 22
- RAQDACVRFCEPDA-UHFFFAOYSA-L ferrous carbonate Chemical compound [Fe+2].[O-]C([O-])=O RAQDACVRFCEPDA-UHFFFAOYSA-L 0.000 claims abstract description 22
- 238000013178 mathematical model Methods 0.000 claims abstract description 15
- 238000004519 manufacturing process Methods 0.000 claims description 27
- 238000005065 mining Methods 0.000 claims description 23
- 238000000605 extraction Methods 0.000 claims description 6
- 238000005422 blasting Methods 0.000 claims description 5
- 239000011435 rock Substances 0.000 claims description 5
- YPJCVYYCWSFGRM-UHFFFAOYSA-H iron(3+);tricarbonate Chemical compound [Fe+3].[Fe+3].[O-]C([O-])=O.[O-]C([O-])=O.[O-]C([O-])=O YPJCVYYCWSFGRM-UHFFFAOYSA-H 0.000 claims description 4
- 238000005457 optimization Methods 0.000 abstract description 4
- 241000196324 Embryophyta Species 0.000 description 6
- 230000008901 benefit Effects 0.000 description 2
- 238000004064 recycling Methods 0.000 description 2
- 201000004569 Blindness Diseases 0.000 description 1
- 235000000485 Smilax china Nutrition 0.000 description 1
- 241000533870 Smilax tamnoides Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000009776 industrial production Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
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- G06Q10/06311—Scheduling, planning or task assignment for a person or group
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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Abstract
The invention provides a multi-source multi-target ore blending method for iron ore of strip mine, which comprises the following steps: (1) selecting an electric shovel participating in ore blending and an ore unloading point to be unloaded; (2) acquiring geological data of an explosion area where the electric shovel is located; (3) setting explosion area parameters and unloading point parameters; (4) acquiring the distance between the electric shovel and an ore unloading point; (5) and constructing a multi-source multi-target ore blending linear programming mathematical model of the iron ore of the strip mine, and solving the mathematical model to obtain an ore blending scheme. According to the multi-source multi-target ore blending method for the iron ore of the strip mine, provided by the invention, the multi-source multi-target ore blending optimization of the iron ore of the strip mine is realized by establishing the ore blending model which takes the maximum ore yield and the shortest transport distance as targets, and takes the optional index, the total iron grade, the ferrous iron grade and the content of iron carbonate after ore blending as constraints.
Description
Technical Field
The invention relates to the field of mining, in particular to a multi-source multi-target ore blending method for iron ore of strip mine.
Background
The iron ore blending of strip mines is an important means for guaranteeing ore grade balance and resource recycling in the industrial production of iron mines, and with the comprehensive recycling of resource mining, the multi-source multi-target blending optimization problem becomes one of the focuses of general attention in the mine industry. Scientific and reasonable multi-source multi-target ore blending can effectively reduce the transportation cost of enterprises, ensure that the ore blending grade, the selectable index and the content of iron carbonate meet the requirements of ore dressing plants, and obviously improve the comprehensive utilization rate and the economic benefit of ores. The traditional ore blending is completed manually by ore blending personnel, the operation is complex, the experience requirement on the ore blending personnel is high, the human factors are excessive, certain blindness is realized, and the unreasonable ore blending scheme is easily caused. At present, the gap in research of ore blending theory at home and abroad is not large, and a lot of academic achievements are obtained, but the large scale of the iron ore production of the large-scale strip mine possibly needs to provide ore for a plurality of ore dressing plants, and the requirements of each ore dressing plant on the ore may be different, so that the ore blending difficulty is increased, and the actual requirements of the strip mine iron ore on multi-source and multi-target ore blending can not be met in the actual production of the existing ore blending.
Disclosure of Invention
In order to solve the technical problems, meet the actual requirements of the current strip mine iron ore on multi-source multi-target ore blending and solve the problem of multi-source multi-target ore blending of the current strip mine iron ore, the invention aims to provide a strip mine iron ore multi-source multi-target ore blending method, and the multi-source multi-target ore blending optimization of the strip mine iron ore is realized by establishing an ore blending model with the maximum ore yield and the shortest transport distance as the target, and with the constraint that the selectable index, the full iron grade, the ferrous grade and the content of iron carbonate meet the requirements after ore blending.
The technical scheme adopted by the invention for solving the technical problems is as follows: a multi-source multi-target ore blending method for iron ore of strip mine comprises the following steps:
(1) selecting an electric shovel participating in ore blending and an ore unloading point to be unloaded;
(2) acquiring geological data of an explosion area where the electric shovel is located;
(3) setting explosion area parameters and unloading point parameters;
(4) acquiring the distance between the electric shovel and an ore unloading point;
(5) and constructing a multi-source multi-target ore blending linear programming mathematical model of the iron ore of the strip mine, and solving the mathematical model to obtain an ore blending scheme.
Further, in the step (1), a plurality of electric shovels and a plurality of ore unloading points are respectively arranged.
Further, the geological data of the explosion area in the step (2) comprise the exploitable amount, the selectable index, the geological total iron grade, the geological ferrous iron grade and the content of iron carbonate of the explosion area.
Further, the parameters of the explosion area in the step (3) comprise the rock content of the explosion area and the production capacity range of the electric shovel in the ore blending time period.
Further, the ore discharge point parameters in the step (3) include an ore yield range, a mining selectable index range, a mining total iron grade range, a mining ferrous iron grade range and a mining ferric carbonate grade range in an ore blending time period of the ore discharge point.
Further, the parameters in the ore blending method are defined as:
n electric shovels are arranged, i is 1,2,3, … and n; m ore unloading points, j is 1,2,3, … and m;
Qithe exploding volume, GP, of the i-th electric shovel isiFor optional indices, GTiFor geological all-iron grade, GFiFor geological ferrous grade, GCiIs the iron carbonate content;
Rithe lithologic rate, SMIN, of the explosion zone where the ith electric shovel is positionediSMAX, the minimum capacity of an electric shovel in a period of ore blendingiThe maximum production capacity of the electric shovel in the ore blending time period;
UMINjthe lower limit of ore production, UMAX, in the ore blending period at the jth ore discharge pointjUpper limit of ore production, PPMINjFor extracting an optional lower exponential limit, PPMAXjFor producing an optional upper exponential limit, PTMINjFor extracting the lower limit of the total iron grade, PTMAXjFor extracting the upper limit of the total iron grade, PFMINjFor taking out the lower limit of ferrous grade, PFMAXjFor the upper limit of ferrous grade, PCMINjFor extracting the lower limit of iron carbonate content, PCMAXjThe upper limit of the content of the extracted iron carbonate is reached;
Dijthe distance from the ith electric shovel to the jth ore discharge point.
Further, in the model in the step (5), let xijThe target and constraint of the strip mine iron ore multi-source multi-target ore blending linear programming mathematical model are that the ore quantity is transported from the ith explosion area to the jth ore unloading point, and the target and constraint of the strip mine iron ore multi-source multi-target ore blending linear programming mathematical model comprise:
the goal of maximum ore yield and shortest transport distance is as follows:
exploitable volume constraints of the blast zone:
electric shovel capacity constraint:
the ore yield requirement of the ore unloading point is as follows:
and (3) optional index constraint of ore unloading point extraction:
and (3) restriction of full iron extraction at an ore unloading point:
and (3) restriction of ferrous iron extracted from an ore unloading point:
and (3) restriction of extracting iron carbonate from ore unloading points:
and solving the equation set to obtain an optimal solution, namely the ore blending scheme.
Compared with the prior art, the invention has the following beneficial effects:
(1) aiming at the condition that strip mine iron ore provides ore for a plurality of ore dressing plants, an ore blending model is designed, so that the method can meet the condition of a single ore dressing plant for a single mine and the condition of a plurality of ore dressing plants for a single mine;
(2) the ore blending condition is restrained by specifying whether the electric shovel is ore-blended, the production capacity range of the electric shovel in the ore blending time period, the ore yield range of each ore unloading point, the mining selectable index range of each ore unloading point, the mining full iron grade range, the mining ferrous iron grade range and the mining ferric carbonate content range, so that the ore blending is closer to the actual production;
(3) the ore blending result is generated by taking the selected ore blending time period as a unit, so that the ore blending method is more flexible and meets the ore blending requirements of different time dimensions;
(4) the current production bottleneck can be known according to the ore blending result, and the production potential of the strip mine can be excavated through targeted optimization management.
Drawings
FIG. 1 is a schematic diagram of the ore blending process of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention provides a multi-source multi-target ore blending method for iron ore of strip mine, which comprises the following steps:
step 1, selecting an electric shovel participating in ore blending and an ore unloading point to be unloaded; n electric shovels are arranged, i is 1,2,3, … and n; m ore unloading points, j is 1,2,3, … and m;
step 2, acquiring geological data of an explosion area where the electric shovel is located, and acquiring the exploitable amount, selectable index, geological total iron grade, geological ferrous iron grade and iron carbonate content of the nearby explosion area through a three-dimensional geological software interface according to the electric shovel appointed by a user; wherein Q isiThe exploding volume, GP, of the i-th electric shovel isiFor optional indices, GTiThe method is used for the geological all-iron grade,GFifor geological ferrous grade, GCiIs the iron carbonate content;
step 3, setting the rock content of the blasting area and the production capacity range of the electric shovel in the ore blending time period; wherein R isiThe lithologic rate, SMIN, of the explosion zone where the ith electric shovel is positionediSMAX, the minimum capacity of an electric shovel in a period of ore blendingiThe maximum production capacity of the electric shovel in the ore blending time period;
step 4, setting the ore yield range, the mining selectable index range, the mining total iron grade range, the mining ferrous iron grade range and the mining ferric carbonate grade range of each ore unloading point in the ore blending time period; wherein, UMINjThe lower limit of ore production, UMAX, in the ore blending period at the jth ore discharge pointjUpper limit of ore production, PPMINjFor extracting an optional lower exponential limit, PPMAXjFor producing an optional upper exponential limit, PTMINjFor extracting the lower limit of the total iron grade, PTMAXjFor extracting the upper limit of the total iron grade, PFMINjFor taking out the lower limit of ferrous grade, PFMAXjFor the upper limit of ferrous grade, PCMINjFor extracting the lower limit of iron carbonate content, PCMAXjThe upper limit of the content of the extracted iron carbonate is reached;
step 5, acquiring the distances between all electric shovels and the ore unloading points through the interface of a dispatching system of the strip mine truck; wherein D isijThe distance from the ith electric shovel to the jth ore unloading point;
step 6, constructing a multi-source multi-target ore blending linear programming mathematical model of the strip mine iron ore; and setting xij as the ore transportation amount from the ith explosion area to the jth ore unloading point, wherein the target and constraint of the strip mine iron ore multi-source multi-target ore blending linear programming mathematical model comprise:
the goal of maximum ore yield and shortest transport distance is as follows:
exploitable volume constraints of the blast zone:
electric shovel capacity constraint:
the ore yield requirement of the ore unloading point is as follows:
and (3) optional index constraint of ore unloading point extraction:
and (3) restriction of full iron extraction at an ore unloading point:
and (3) restriction of ferrous iron extracted from an ore unloading point:
and (3) restriction of extracting iron carbonate from ore unloading points:
and 7, solving the mathematical model to obtain a reasonable and feasible ore blending scheme, and outputting the ore blending scheme to an open-pit mine truck dispatching system.
Examples
The method is characterized in that the iron ore of a certain strip mine has 5 electric shovels which can remove the ore, the ore removal is planned to be carried out to 2 ore unloading points, and in order to obtain an ore blending scheme, the method comprises the following steps as shown in figure 1:
step 1, selecting electric shovels 16, 17, 2, 12 and 14 participating in ore blending and ore unloading points to be unloaded to be broken north and broken south.
Step 2, acquiring geological data of an explosion area where the electric shovel is located, and acquiring the exploding amount of the explosion area where the electric shovel is located through a three-dimensional geological software interface according to the electric shovel specified by a user, wherein the exploding amount is 25400t, 18600t, 39800t, 53200t and 5900t, the selectable indexes are 50.25, 49.86, 68.80, 49.79 and 48.77, the geological total iron grade is 30.95%, 31.52%, 30.58%, 30.17% and 27.83%, the geological ferrous grade is 11.32%, 12.83%, 16.06%, 5.68% and 8.54%, and the content of iron carbonate is 6.00%, 4.00%, 5.00%, 2.00% and 5.00% respectively, as shown in table 1.
TABLE 1 electric shovel data and parameters setting of explosion area
And 3, inputting the rock containing rate of the appointed explosion area by a user according to the actual situation on site, wherein the rock containing rate is respectively 2%, 3%, 1% and 1%, and the production capacity ranges of the electric shovels are unified to be 5000 t-12000 t, as shown in the table 1.
And 4, inputting the ore yield ranges of 10000 t-25000 t and 8000 t-15000 t in the ore blending time period of each ore unloading point by a user according to the actual situation on site, wherein the ranges of the extracted selectable indexes are unified to be 50-60, the ranges of the extracted total iron grades are unified to be 28.00-35.00, the ranges of the extracted ferrous iron grades are unified to be 8.00-12.00, and the ranges of the extracted iron carbonate grades are unified to be 4.00-6.00, as shown in the table 2.
TABLE 2 Deore Point parameter settings
And 5, acquiring the distances between all electric shovels and the ore unloading point through an interface of a dispatching system of the strip mine truck, wherein the distances are respectively 200m from No. 16 electric shovels to a north breaker, 500m from No. 1800 electric shovels to a south breaker and No. 17 electric shovels to the north breaker, 1500m from the south breaker and No. 2 electric shovels to the north breaker, 1200m from the south breaker and No. 12 electric shovels to the north breaker, 800m from the south breaker and No. 14 electric shovels to the north breaker and 200m from the south breaker as shown in Table 3.
TABLE 3 electric shovel and unloading Point distance
| Electric shovel | Distance north broke (m) | Distance south China root (m) |
| 16 | 200 | 1800 |
| 17 | 500 | 1500 |
| 2 | 800 | 1200 |
| 12 | 1200 | 800 |
| 14 | 1800 | 200 |
And 6, constructing a multi-source multi-target ore blending linear programming mathematical model of the strip mine iron ore.
And 7, solving the multi-source multi-target ore blending linear programming mathematical model of the iron ore of the strip mine to obtain an electric shovel ore blending result shown in the table 3, an ore unloading point ore blending result shown in the table 4 and an ore blending operation result shown in the table 5. The ore distribution results of the electric shovels list the ore transportation ratio of each electric shovel to each ore unloading point, and the concrete data are that the ratio of five electric shovels to the north broke is respectively 13.74%, 20%, 15.94%, 26.72% and 23.6%, and the ratio of five electric shovels to the south broke is respectively 11.10%, 0%, 53.44%, 35.46% and 0%. The ore unloading point ore blending result lists the optional index result, the total iron grade result, the ferrous grade result and the iron carbonate content result which are predicted if each ore unloading point is produced according to the electric shovel ore blending result, wherein the north breaking prediction results are 52.66, 29.59, 10.05 and 4.07, and the south breaking prediction results are 60.00, 30.14, 11.72 and 4.00. And sending the ore blending result of the electric shovel to a dispatching system of the strip mine truck, and organizing production by the dispatching system of the strip mine truck. The result of ore blending operation shows that the production bottleneck is located under the current condition, the situation that the production bottleneck is reached by the limit value is the production bottleneck, the production capacities of the electric shovel 2 and the electric shovel 12 are the production bottleneck, and the yields of the north breaking and south breaking mine points are the production bottleneck.
TABLE 3 electric shovel ore blending results
| Electric shovel | Broken root of Beicui proportioning | Proportioning of radix seu folium Cayratiae Oligocarpae |
| 16 | 13.74 | 11.10 |
| 17 | 20 | 0.00 |
| 2 | 15.94 | 53.44 |
| 12 | 26.72 | 35.46 |
| 14 | 23.6 | 0.00 |
TABLE 4 ore discharge site ore blending results
| Numbering | Unloading point | Optional exponential results | Total iron grade results | Ferrous grade results | Results of iron carbonate content |
| 1 | North broken | 52.66 | 29.59 | 10.05 | 4.07 |
| 2 | Root of common Nanchong | 60.00 | 30.14 | 11.72 | 4.00 |
TABLE 5 results of ore blending operations
| Name (R) | Set value | Ore blending results | Status of state | Difference value |
| Production capacity of electric shovel 16: | 25400 | 5100 | not reaching the limit value | 20300 |
| Mining volume of the shovel 17: | 18600 | 5000 | not reaching the limit value | 13600 |
| Mining volume of the shovel 2: | 39800 | 12000 | not reaching the limit value | 27800 |
| Mining amount of electric shovel 12: | 53200 | 12000 | not reaching the limit value | 41200 |
| Mining amount of electric shovel 14: | 5900 | 5900 | reach limit value | 0 |
| Capacity of electric shovel 16: | 5000～12000 | 5100 | not reaching the limit value | 6900 |
| Capacity of the shovel 17: | 5000～12000 | 5000 | not reaching the limit value | 7000 |
| Capacity of shovel 2: | 5000～12000 | 12000 | reach limit (highest) | 0 |
| Electric shovel 12 productivity: | 5000～12000 | 12000 | reach limit (highest) | 0 |
| Capacity of electric shovel 14: | 5000～12000 | 5900 | not reaching the limit value | 6100 |
| Yield at north break: | 10000～25000 | 25000 | reach limit (highest) | 0 |
| Yield of south China broke: | 8000～15000 | 15000 | reach limit (highest) | 0 |
The technical idea of the present invention is described in the above technical solutions, and the protection scope of the present invention is not limited thereto, and any changes and modifications made to the above technical solutions according to the technical essence of the present invention belong to the protection scope of the technical solutions of the present invention.
Claims (7)
1. A multi-source multi-target ore blending method for iron ore of strip mine is characterized by comprising the following steps:
(1) selecting an electric shovel participating in ore blending and an ore unloading point to be unloaded;
(2) acquiring geological data of an explosion area where the electric shovel is located;
(3) setting explosion area parameters and unloading point parameters;
(4) acquiring the distance between the electric shovel and an ore unloading point;
(5) and constructing a multi-source multi-target ore blending linear programming mathematical model of the iron ore of the strip mine, and solving the mathematical model to obtain an ore blending scheme.
2. The multi-source and multi-target iron ore blending method for the strip mine according to claim 1, characterized in that the electric shovel and the ore unloading point in the step (1) are respectively multiple.
3. The multi-source multi-target ore blending method for the iron ore of the strip mine according to claim 1, wherein the geological data of the blasting area in the step (2) comprises the exploitable amount, the optional index, the geological total iron grade, the geological ferrous iron grade and the content of iron carbonate of the blasting area.
4. The multi-source multi-target ore blending method for the iron ore of the strip mine according to claim 3, wherein the parameters of the blasting area in the step (3) comprise the rock content of the blasting area and the production capacity range of the electric shovel in the ore blending time period.
5. The multi-source multi-target ore blending method for the iron ore of the strip mine according to claim 4, wherein the ore unloading point parameters in the step (3) comprise an ore yield range, a mining selectable index range, a mining full iron grade range, a mining ferrous iron grade range and a mining ferric carbonate grade range in an ore blending time period of the ore unloading point.
6. The multi-source and multi-target ore blending method for the iron ore of the strip mine according to claim 5, characterized in that the parameters in the ore blending method are defined as follows:
n electric shovels are arranged, i is 1,2,3, … and n; m ore unloading points, j is 1,2,3, … and m;
Qithe exploding volume, GP, of the i-th electric shovel isiFor optional indices, GTiFor geological all-iron grade, GFiFor geological ferrous grade, GCiIs the iron carbonate content;
Rithe lithologic rate, SMIN, of the explosion zone where the ith electric shovel is positionediSMAX, the minimum capacity of an electric shovel in a period of ore blendingiIs prepared forThe maximum production capacity of the electric shovel in a mining time period;
UMINjthe lower limit of ore production, UMAX, in the ore blending period at the jth ore discharge pointjUpper limit of ore production, PPMINjFor extracting an optional lower exponential limit, PPMAXjFor producing an optional upper exponential limit, PTMINjFor extracting the lower limit of the total iron grade, PTMAXjFor extracting the upper limit of the total iron grade, PFMINjFor taking out the lower limit of ferrous grade, PFMAXjFor the upper limit of ferrous grade, PCMINjFor extracting the lower limit of iron carbonate content, PCMAXjThe upper limit of the content of the extracted iron carbonate is reached;
Dijthe distance from the ith electric shovel to the jth ore discharge point.
7. The multi-source multi-target iron ore blending method for the strip mine according to claim 6, wherein in the model in the step (5), x is setijThe target and constraint of the strip mine iron ore multi-source multi-target ore blending linear programming mathematical model are that the ore quantity is transported from the ith explosion area to the jth ore unloading point, and the target and constraint of the strip mine iron ore multi-source multi-target ore blending linear programming mathematical model comprise:
the goal of maximum ore yield and shortest transport distance is as follows:
exploitable volume constraints of the blast zone:
electric shovel capacity constraint:
the ore yield requirement of the ore unloading point is as follows:
and (3) optional index constraint of ore unloading point extraction:
and (3) restriction of full iron extraction at an ore unloading point:
and (3) restriction of ferrous iron extracted from an ore unloading point:
and (3) restriction of extracting iron carbonate from ore unloading points:
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1601539A (en) * | 2004-10-15 | 2005-03-30 | 清华大学 | Optimization control method of open ore car flow |
| CN104929687A (en) * | 2015-07-02 | 2015-09-23 | 中国黄金集团内蒙古矿业有限公司 | Mine digitlization production management and control system and method |
| CN107145970A (en) * | 2017-04-18 | 2017-09-08 | 东北大学 | One kind is based on the maximized milling ore Optimization Ore Matching method of Utilization Rate of Mineral Resources |
| CN110348036A (en) * | 2019-04-12 | 2019-10-18 | 长沙迪迈数码科技股份有限公司 | Surface mine mass balance control method, device and storage medium |
| CN111122383A (en) * | 2019-12-23 | 2020-05-08 | 鞍钢集团矿业有限公司 | Open stope fine acceptance method based on ore type and grade division |
| CN111401607A (en) * | 2020-02-26 | 2020-07-10 | 榆林学院 | Multi-objective optimization method for multi-metal ore blending of strip mine |
| CN112085617A (en) * | 2020-08-12 | 2020-12-15 | 河北钢铁集团矿业有限公司 | Optimal ore blending method for surface mine |
| CN112488441A (en) * | 2020-10-23 | 2021-03-12 | 湖南大学 | Intelligent dispatching method and system for strip mine truck |
| US20210208305A1 (en) * | 2018-05-28 | 2021-07-08 | The University Of Melbourne | Mine planning method and system |
-
2021
- 2021-09-01 CN CN202111019587.7A patent/CN113705916B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1601539A (en) * | 2004-10-15 | 2005-03-30 | 清华大学 | Optimization control method of open ore car flow |
| CN104929687A (en) * | 2015-07-02 | 2015-09-23 | 中国黄金集团内蒙古矿业有限公司 | Mine digitlization production management and control system and method |
| CN107145970A (en) * | 2017-04-18 | 2017-09-08 | 东北大学 | One kind is based on the maximized milling ore Optimization Ore Matching method of Utilization Rate of Mineral Resources |
| US20210208305A1 (en) * | 2018-05-28 | 2021-07-08 | The University Of Melbourne | Mine planning method and system |
| CN110348036A (en) * | 2019-04-12 | 2019-10-18 | 长沙迪迈数码科技股份有限公司 | Surface mine mass balance control method, device and storage medium |
| CN111122383A (en) * | 2019-12-23 | 2020-05-08 | 鞍钢集团矿业有限公司 | Open stope fine acceptance method based on ore type and grade division |
| CN111401607A (en) * | 2020-02-26 | 2020-07-10 | 榆林学院 | Multi-objective optimization method for multi-metal ore blending of strip mine |
| CN112085617A (en) * | 2020-08-12 | 2020-12-15 | 河北钢铁集团矿业有限公司 | Optimal ore blending method for surface mine |
| CN112488441A (en) * | 2020-10-23 | 2021-03-12 | 湖南大学 | Intelligent dispatching method and system for strip mine truck |
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