Strip mine equipment type selection method and system

Abstract

The invention discloses a strip mine equipment type selection method and system. The method comprises the following steps: selecting scoring indexes of strip mine mining equipment; converting the scoring indexes into extremely large indexes, and carrying out standardization processing on the extremely large indexes to obtain a standardization matrix; determining a maximum value and a minimum value by using the standardized matrix; calculating the distance between column vectors formed by each column of elements and the maximum value of the standardized matrix and the distance between column vectors formed by each column of elements and the minimum value of the standardized matrix to obtain the distance between the mining equipment of each model and the maximum value and the distance between the mining equipment of each model and the minimum value, and determining the comprehensive score of the mining equipment of each model by using the distances. According to the method, the problem of index numerical value distortion caused by pure dependence on objective data can be avoided, and errors possibly generated due to excessive dependence on individual subjective judgment are reduced.

Description

Technical Field

[0001] The invention relates to the field of equipment selection for open-pit metal mines and non-metal mines, and in particular to an open-pit mine equipment selection method and system. Background Art

[0002] With the deep integration of information technology and automation technology in the mining industry, the production scale of open-pit mines and the trend of large-scale mining equipment are becoming more and more obvious. According to the "Metallurgical Mining Design Code" (GB50830-2013), when the annual ore output exceeds 15 million tons or the total amount of excavated ore exceeds 60 million tons, the open-pit mine is defined as an extra-large open-pit mine. In order to meet the mining needs of such extra-large mines, the specifications and models of mining equipment have also increased accordingly: the bucket capacity of large shovel loading equipment can reach 55m 3 The load capacity of mining trucks can reach 400t. In the total investment structure of mining projects, equipment investment accounts for about 50%, and in the production process, the total equipment operation and maintenance costs account for about 30-40% of the daily production costs of mines.

[0003] In summary, for super-large open-pit mines, the huge initial investment makes the selection and configuration of equipment directly related to the overall economic benefits of the mine. Traditional equipment selection method: The selection method based on analogy and experience usually makes decisions by analyzing the matching data of shovel equipment in most existing mines. However, in the context of the diversified development of open-pit mine equipment at this stage, this traditional method that relies on past experience has gradually revealed its limitations and can no longer meet the needs of modern mines for refined management. In addition, considering the poor operating environment of open-pit mines and the heavy production tasks, the reasonable selection of production equipment plays a key role in the production efficiency and economic benefits of open-pit mines, involving the influence of multiple factors. Therefore, in order to achieve a more economical and efficient equipment combination solution, it is urgent to explore and develop a new and more sophisticated equipment selection method to optimize its equipment configuration, improve overall operational efficiency, and reduce unnecessary energy consumption and cost expenditure. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an open-pit mine equipment selection method and system in view of the deficiencies in the existing technology, so as to avoid the problem of index value distortion caused by relying solely on objective data and reduce the mistakes that may be caused by over-reliance on personal subjective judgment.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: a method for selecting open-pit mine equipment, comprising the following steps:

[0006] S1. Select scoring indicators for open-pit mining equipment;

[0007] S2, converting the scoring index into an extremely large index, and performing standardization on the extremely large index to obtain a standardized matrix;

[0008] S3, using the standardized matrix to determine the maximum and minimum values;

[0009] S4. Calculate the distance between each column element of the standardized matrix and the column vector composed of the maximum value, and the distance between each column element and the column vector composed of the minimum value, that is, obtain the distance between each model of mining equipment and the maximum value and the minimum value, and use the distance to determine the comprehensive score of each model of mining equipment.

[0010] The present invention uses a method combining objective calculation formulas with subjective engineering experience to determine the specific values ​​of various selection indicators of target equipment. By scientifically and rationally balancing the influence of objective and subjective factors, it avoids the problem of indicator value distortion caused by relying solely on objective data and reduces the mistakes that may be caused by over-reliance on personal subjective judgment.

[0011] In step S1, the scoring index includes working capacity, economic index, and carbon emission index; the economic index includes equipment price and operation and maintenance cost. The present invention improves the conventional open-pit mine equipment selection index system and introduces the carbon emission index of the target equipment as one of the consideration factors, thereby making the equipment selection result more comprehensive, giving the selection result green and low-carbon attributes, and to a certain extent helping to reduce carbon emissions from the source of open-pit mine production equipment.

[0012] The work capability determination process includes:

[0013] For transport vehicles: A 运 =T×G×K 1 ×K 2 / t 1 Among them, A 运 is the transport vehicle's shift carrying capacity, T is the transport vehicle's shift working hours, G is the transport vehicle's rated load, K is 1 , K 2 are the load utilization coefficient and time utilization coefficient during the operation of the transport vehicle, respectively. The load utilization coefficient is the ratio of the actual load of the transport vehicle to the rated load, that is, K 1 =G 实际 / G, time utilization coefficient K 2 =0.75-0.80, t 1 The time required for one turnover;

[0014] For shovel loaders: Among them, A 铲 is the working capacity of the shovel loading equipment, T * is the working time of the shovel loading equipment per shift, V *The single bucket capacity of the shovel loading equipment. K 3 They are the time utilization coefficients of the shovel loading equipment during operation. Full bucket coefficient K 3 =0.85-0.90, e is the loose coefficient, e is 1.5, t 2 It is the cycle time of a single bucket.

[0015] The economic indicator determination process includes:

[0016] For fuel oil equipment: E 燃油 =AD×CC×OF×a; where E 燃油 For fuel oil equipment, tons of CO 2 Emissions, AD is the fuel consumption of the equipment to complete one ton of ore work, CC is the carbon content of the fuel, OF is the carbon oxidation rate of the fuel, a is the conversion coefficient of the molecular weight of carbon dioxide and carbon;

[0017] For electrical equipment: E 用电 =AD 电力 ×EF 电力 ; Among them, E 用电 Tons of CO for electrical equipment 2 Emissions, AD 电力 EF is the power consumption of the equipment to complete a ton of mining work. 电力 is the CO2 emission factor for electricity supply.

[0018] In the present invention, CC = 0.863; OF = 0.98; a = 44 / 12; EF 电力 =0.5568kg CO 2 / kWh.

[0019] In step S2, the calculation formula of the extremely large index is: X i =(x max +x min )-x i ; Xi is the i-th extremely large index, x max 、x min are the maximum and minimum values ​​of the i-th index, respectively, x i It is the indicator value before conversion.

[0020] In step S3:

[0021] Maximum Z + The calculation formula is:

[0022]

[0023] Minimum Z - The calculation formula is:

[0024]

[0025] Among them, z nj is the element in the nth row and jth column of the normalized matrix, 1≤j≤4, is the maximum value corresponding to each row of the standardized matrix, is the minimum value corresponding to each row in the normalized matrix.

[0026] The comprehensive score S of the i-th model of mining equipment i The calculation formula is: is the distance between the i-th model of mining equipment and the maximum value, is the i-th model of mining equipment and its minimum value.

[0027] The method of the present invention further comprises:

[0028] S5. Sort the comprehensive scores of various types of mining equipment, and determine the selection plan based on the sorted comprehensive scores.

[0029] As an inventive concept, the present invention also provides an open-pit mine equipment selection system, including a memory, a processor, and a computer program stored in the memory; the processor executes the computer program to implement the steps of the above method.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. Improvements have been made to the conventional open-pit mine equipment selection index system, and the carbon emission index of the target equipment has been introduced as one of the considerations, making the equipment selection results more comprehensive and giving the selection results green and low-carbon attributes, which is beneficial to reducing carbon emissions from the source of open-pit mine production equipment to a certain extent.

[0032] 2. Use a method that combines objective calculation formulas with subjective engineering experience to determine the specific values ​​of each selection index of the target equipment. By scientifically and rationally balancing the influence of objective and subjective factors, we can avoid the problem of index value distortion caused by relying solely on objective data and reduce the mistakes that may be caused by over-reliance on personal subjective judgment.

[0033] 3. Apply the superiority and inferiority solution distance method to optimize equipment selection. Conventional selection methods focus on economic indicators and are highly dependent on the personal experience of designers, which to a certain extent limits the scientificity and rationality of the selection work. The superiority and inferiority solution distance rule is a method based on multi-criteria decision analysis, which can systematically evaluate and compare the advantages and disadvantages of different alternatives, thereby helping decision makers make more informed choices. The present invention overcomes the limitations of traditional selection methods, provides new ideas for open-pit mine equipment selection, and improves the efficiency and quality of selection work. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a flowchart of an open-pit mine equipment selection method based on a superior-inferior solution distance method according to an embodiment of the present invention.

[0035] Figure 2 It is a bar chart comparing the target equipment indicator effect values ​​(standardized) in an embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are 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.

[0037] Example 1

[0038] This embodiment provides a method for selecting equipment for an open pit mine, and the main implementation process is as follows:

[0039] S1. Equipment index selection and effect value calculation

[0040] According to the technical and economic indicators and selection process of the main mining equipment in open-pit mines, and taking into account the quantifiability of the indicators and the main parameters in the current open-pit mining equipment selection work, the three main indicators of technology (working capacity), economy (investment and operation and maintenance costs) and low carbon of the target equipment are selected for comparison.

[0041] Table 1 Comprehensive scoring indicators for open-pit mine equipment selection

[0042]

[0043] The working capacity in Table 1 is selected as the scoring index based on the target equipment. For transport vehicles, the working capacity is calculated by the following formula:

[0044] A 运 =T×G×K 1 ×K 2 / t 1

[0045] Where: A 运 is the transport vehicle's shift carrying capacity; T is the transport vehicle's shift working hours; G is the transport vehicle's rated load; K 1 , K 2 are the load utilization coefficient and time utilization coefficient of the transport vehicle during operation; t 1 The time required for one turnover.

[0046] For shovel loaders, the work capacity of a shift is calculated using the following formula:

[0047] A 铲 =T×V×K 2 ×K 3 / (e×t 2 )

[0048] Where: A 铲 is the working capacity of the shovel loading equipment per shift; T is the working time of the shovel loading equipment per shift; V is the bucket capacity of the shovel loading equipment; K 2 , K 3 are the time utilization coefficient and full bucket coefficient during the operation of the shovel loading equipment; e is the loose coefficient; t 2 It is the cycle time of a single bucket.

[0049] The economic indicators (investment and operation and maintenance) in Table 1 are determined by the unit price, maintenance cost, and consumable spare parts cost of the target equipment.

[0050] The carbon emission index in Table 1 is calculated for fuel-fired equipment using the following formula:

[0051] E 燃油 =AD×CC×OF×a

[0052] Where: E 燃油 For fuel oil equipment, tons of CO 2 Emissions; AD is the fuel consumption of the equipment to complete a ton of ore work; CC is the carbon content of the fuel, liquid fuel is measured in tons of carbon / ton of fuel, CC=0.863; OF is the carbon oxidation rate of the fuel, OF=0.98; a is the molecular weight conversion coefficient of carbon dioxide and carbon, a=44 / 12.

[0053] For electrical equipment, the following formula is used for calculation:

[0054] E 用电 =AD 电力 ×EF 电力

[0055] Where: E 用电 Tons of CO for electrical equipment 2 Emissions; AD 电力 The power consumption of the equipment to complete the work of one ton of ore; EF 电力 is the carbon dioxide emission factor for electricity supply, taking EF 电力 =0.5568kg CO 2 / kWh.

[0056] S2. Positive index processing

[0057] Convert each sub-item index in Table 1 into a very large index, that is, perform positive processing on the index. The economic and low-carbon sub-item indexes in Table 1 are all very small and need to be positively processed. The formula for converting very small indexes into very large indexes is:

[0058] X i =(x max +x min )-x i

[0059] Where, X i is the positive indicator value; x max 、x min are the maximum and minimum values ​​of the i-th index respectively; x i It is the indicator value that has not been positively converted.

[0060] The constructed forward matrix is ​​as follows:

[0061]

[0062] S3. Standardization

[0063] In order to eliminate the influence of different indicator dimensions, it is necessary to standardize the indicator values ​​that have been normalized. Assuming that the target open-pit mine equipment has n comparison models, the standardized matrix formed by the corresponding 4 evaluation indicators (positive processing has been completed) is as follows:

[0064]

[0065] The standardized matrix is ​​Z, and the elements in Z are:

[0066]

[0067] S4. Comprehensive score of indicators

[0068] Based on the normalized matrix Z, the maximum value is defined as:

[0069]

[0070] Define the minimum value:

[0071]

[0072] Define the distance between the target equipment of the i-th (i=1,2,...,n) model and the maximum value:

[0073]

[0074] Define the distance between the target equipment of the i-th (i=1,2,...,n) model and the minimum value:

[0075]

[0076] The unnormalized comprehensive score of the target equipment of the i-th (i=1, 2, ..., n) model can be calculated as:

[0077]

[0078] That is: take the maximum value of each indicator to form a column vector Z+, take the minimum value of each indicator to form a column vector Z-, calculate the distance D+ between each column element and the column vector Z+ composed of the maximum value, and the distance D- between each column element and the column vector Z- composed of the minimum value, and then score each model of target equipment according to the scoring formula.

[0079] S5. Data Analysis

[0080] According to the comprehensive scores of each type of target equipment, data analysis and sorting are carried out to provide a basis for determining the selection plan for the corresponding open-pit mining equipment.

[0081] The following example illustrates the specific implementation process of this implementation:

[0082] S1. Equipment index selection and effect value calculation

[0083] According to the production and technical conditions of a domestic open-pit mine, the mine design uses two specifications of mining trucks with a load capacity of 220t and 100t. For the 100t-load mining truck, there are two models available: pure electric drive and fuel drive. According to the mining truck technology, economic indicators and selection process, and taking into account the quantifiability of indicators and the main parameters in the current open-pit mining design equipment selection work, the mining truck's shift carrying capacity, equipment price, energy consumption cost (ton-kilometer) and low-carbon indicators are selected for comparison. The indicator effect value formula is used to calculate the indicator effect value of the two models with a load capacity of 100t.

[0084] S2. Use the conversion formula to convert the investment, operation and maintenance costs and low-carbon indicators in step S1 into extremely large indicators, that is, to process the indicators positively.

[0085] S3, standardize the indicator effect values ​​that have been positively converted in step S2, and the resulting standardized matrix is ​​as follows:

[0086]

[0087] The comparison of the four indicators of the two types of mining trucks after standardization is shown in the bar chart below: Figure 2 shown.

[0088] S4. According to the standardized matrix in step S3, define the maximum and minimum values:

[0089] Z +=(0.7177 0.7372 0.7672 0.7599)

[0090] Z - =(0.69640.67570.64140.6500)

[0091] By calculating the distance between various indicators of two models of 100t-load mining trucks and the maximum and minimum values, the comprehensive scores of the two models of mining trucks are obtained: the comprehensive score of 100t-class mining truck-pure electric is 0.806, and the comprehensive score of 100t-class mining truck-fuel is 0.194.

[0092] S5. Data Analysis

[0093] According to the comprehensive scores of the two types of mining trucks in step S4, it can be concluded that the 100t-class mining truck-pure electric has more advantages.

[0094] Example 2

[0095] Embodiment 2 of the present invention provides a selection system corresponding to the above-mentioned embodiment 1, including a memory, a processor and a computer program stored in the memory; the processor executes the computer program in the memory to implement the steps of the method in the above-mentioned embodiment 1.

[0096] In some implementations, the memory may be a high-speed random access memory (RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0097] In some other implementations, the processor may be a central processing unit (CPU), a digital signal processor (DSP), or other general-purpose processors of various types, which are not limited herein.

[0098] Example 3

[0099] Embodiment 3 of the present invention provides a computer-readable storage medium corresponding to the above embodiment 1, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, the steps of the method of the above embodiment 1 are implemented.

[0100] Computer readable storage media can be tangible devices that hold and store instructions used by instruction execution devices. Computer readable storage media can be, for example, but not limited to, electronic storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any combination thereof.

[0101] Those skilled in the art will appreciate 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 complete hardware embodiments, complete software embodiments, or embodiments in combination with 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.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0102] 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.

[0103] 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.

[0104] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0105] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for selecting equipment for an open-pit mine, characterized in that: The following steps are involved: S1. Select scoring indicators for open-pit mining equipment; S2, converting the scoring index into an extremely large index, and performing standardization on the extremely large index to obtain a standardized matrix; S3, using the standardized matrix to determine the maximum and minimum values; S4. Calculate the distance between each column element of the standardized matrix and the column vector composed of the maximum value, and the distance between each column element and the column vector composed of the minimum value, that is, obtain the distance between each model of mining equipment and the maximum value and the minimum value, and use the distance to determine the comprehensive score of each model of mining equipment.

2. The method for selecting open-pit mine equipment according to claim 1, characterized in that: In step S1, the scoring indicators include work capacity, economic indicators, and carbon emission indicators; the economic indicators include equipment prices and operation and maintenance costs.

3. The method for selecting open-pit mine equipment according to claim 2, characterized in that: The work capability determination process includes: For transport vehicles: A 运 =T×G×K1×K2 / t1; where A 运 is the transport vehicle's shift carrying capacity, T is the transport vehicle's shift working time, G is the transport vehicle's rated load, K1 and K2 are the load utilization coefficient and time utilization coefficient during the transport vehicle's operation, respectively. The load utilization coefficient is the actual load G of the transport vehicle. 实际 Ratio to rated load, i.e. K1 = G 实际 / G, t1 is the time required for one turnover; preferably, the time utilization coefficient K2 = 0.75-0.80; For shovel loaders: Among them, A 铲 is the working capacity of the shovel loading equipment, T * is the working time of the shovel loading equipment per shift, V * The single bucket capacity of the shovel loading equipment. K3 is the time utilization coefficient and full bucket coefficient during the operation of the shovel loading equipment, e is the loose coefficient, and t2 is the cycle time of a single bucket; preferably, the time utilization coefficient The full bucket coefficient K3 = 0.85 ~ 0.90, e is 1.

5.

4. The method for selecting open-pit mine equipment according to claim 2, characterized in that: The economic indicator determination process includes: For fuel oil equipment: E 燃油 =AD×CC×OF×a; where E 燃油 is the CO2 emission per ton of ore of fuel equipment, AD is the fuel consumption of the equipment to complete the work of one ton of ore, CC is the carbon content of the fuel, OF is the carbon oxidation rate of the fuel, and a is the conversion coefficient of the molecular weight of carbon dioxide and carbon; For electrical equipment: E 用电 =AD 电力 ×EF 电力 ; Among them, E 用电 AD is the CO2 emission per ton of electricity used by the equipment. 电力 EF is the power consumption of the equipment to complete a ton of mining work. 电力 is the CO2 emission factor for electricity supply.

5. The method for selecting open-pit mine equipment according to claim 4, characterized in that: CC=0.863;OF=0.98; a=44 / 12;EF 电力 =0.5568kg CO2 / kWh。 6. The method for selecting open-pit mine equipment according to claim 1, characterized in that: In step S2, the calculation formula of the extremely large index is: X i =(x max +x min )-x i ; Xi is the i-th extreme index, xmax and xmin are the maximum and minimum values ​​of the i-th index respectively, x i It is the indicator value before conversion.

7. The method for selecting open-pit mine equipment according to claim 1, characterized in that: In step S3: Maximum Z + The calculation formula is: Minimum Z - The calculation formula is: Among them, z nj is the element in the nth row and jth column of the normalized matrix, 1≤j≤4, is the maximum value corresponding to each row of the standardized matrix, is the minimum value corresponding to each row in the normalized matrix.

8. The method for selecting open-pit mine equipment according to claim 1, characterized in that: The comprehensive score S of the i-th model of mining equipment i The calculation formula is: is the distance between the i-th model of mining equipment and the maximum value, is the i-th model of mining equipment and its minimum value.

9. The method for selecting open-pit mine equipment according to any one of claims 1 to 8, characterized in that: Also includes: S5. Sort the comprehensive scores of various types of mining equipment, and determine the selection plan based on the sorted comprehensive scores.

10. An open-pit mine equipment selection system, comprising a memory, a processor and a computer program stored in the memory; characterized in that: The processor executes the computer program to implement the steps of the method according to any one of claims 1 to 9.

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