Sandstone processing system gradation and energy consumption double target optimization method and system
By establishing a dual-objective optimization model for gradation and energy consumption in a sand and gravel processing system, and using a genetic algorithm to find the optimal configuration scheme, the problems of slow response and low efficiency in existing sand and gravel processing systems are solved. This achieves dual-objective optimization of gradation and energy consumption, and improves system efficiency and decision-making accuracy.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHEJIANG HUADONG ENG CONSTR MANAGEMENT CO LTD
- Filing Date
- 2026-04-10
- Publication Date
- 2026-05-29
AI Technical Summary
In existing technologies, determining sand and gravel processing parameters through manual trial and error results in slow response and low efficiency of the sand and gravel processing system. Furthermore, it fails to simultaneously meet the dual objectives of gradation and energy consumption optimization, leading to poor system efficiency.
A dual-objective optimization method for gradation and energy consumption of sand and gravel processing system is adopted. By acquiring and analyzing sand and gravel processing units, processing unit processes and unit mapping relationships, a general process model is established, the dual-objective optimization model and equipment operating boundary are determined, and the optimal configuration scheme is found by using genetic algorithm and two-stage search strategy to optimize sand and gravel processing parameters.
It improved the efficiency and decision-making efficiency of the sand and gravel processing system, enhanced the accuracy and precision of the processing flow, and achieved dual-objective optimization of gradation and energy consumption.
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Figure CN122114541A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of sand and gravel processing, and in particular to a dual-objective optimization method and system for gradation and energy consumption in sand and gravel processing systems. Background Technology
[0002] Dual-objective optimization of gradation and energy consumption in sand and gravel processing systems refers to the process of optimizing the parameters of sand and gravel processing systems with the goal of minimizing the sum of gradation and energy consumption.
[0003] In related technologies, the sand and gravel processing parameters are usually determined manually, relying on the experience of the operators. When the target gradation changes, the key process parameters such as the crusher speed, screen size and feed rate are repeatedly adjusted by the operators through trial and error, and finally the sand and gravel processing parameters that match the target gradation are determined.
[0004] Regarding the aforementioned technologies, when determining sand and gravel processing parameters through manual trial and error, the process requires repeated adjustments to different parameters before finalizing the processing parameters. This results in a slow response and low efficiency in the sand and gravel processing system. Furthermore, when determining sand and gravel processing parameters manually, the final parameters are usually based on a single optimization objective, which cannot simultaneously satisfy the dual-objective optimization problem of gradation and energy consumption. This leads to poor efficiency of the sand and gravel processing system, leaving room for improvement. Summary of the Invention
[0005] To improve the efficiency of sand and gravel processing systems, this application provides a dual-objective optimization method and system for gradation and energy consumption in sand and gravel processing systems.
[0006] Firstly, this application provides a dual-objective optimization method for gradation and energy consumption in a sand and gravel processing system, employing the following technical solution: A dual-objective optimization method for aggregate processing systems, considering both gradation and energy consumption, includes: Obtain the sand and gravel processing unit, processing unit flow, and unit mapping relationship; The sand and gravel processing unit, processing unit process, and unit mapping relationship are analyzed to determine the general process modeling. Obtain project unit parameters and project optimization requirements; Input the project unit parameters into the general process modeling to determine the dual-objective optimization model and equipment operating boundaries; The dual-objective optimization model and equipment operation boundary are analyzed based on the pre-set dual-objective optimization model to determine the demand weight reorganization. Based on the project's optimization needs, search within the demand weight reorganization to determine the target optimization weight; The dual-objective optimization model is analyzed based on the objective optimization weights and the dual-objective optimization model to determine the optimal configuration scheme.
[0007] Optionally, the steps for analyzing sand and gravel processing units, processing unit flows, and unit mapping relationships to determine the general process modeling include: Integrate the sand and gravel processing units and unit mapping relationships to determine the independent unit model; The processing unit flow and independent unit model are analyzed based on the preset steady-state matrix model to determine the full-process modeling. Analyze the independent unit model and the full-process model to determine the actual equipment utilization rate, actual gradation formula and actual energy consumption formula; The entire process modeling, actual equipment utilization rate, actual gradation formula, and actual energy consumption formula are integrated to determine a general process modeling.
[0008] Optionally, the steps of analyzing independent unit models and full-process models to determine the actual equipment utilization rate, actual gradation formula, and actual energy consumption formula include: The independent unit model and the full-process model are correlated and calculated to determine the actual equipment utilization rate and the actual gradation formula. The product of the actual utilization rate of the equipment and the preset rated energy consumption of the equipment is used to determine the energy consumption formula for a single piece of equipment; Calculate the sum of the energy consumption formulas for a single device to determine the actual energy consumption formula.
[0009] Optionally, the steps of inputting project unit parameters into the general process model to determine the bi-objective optimization model and equipment operating boundaries include: Data is extracted from the unit parameters of the project to determine the equipment setting parameters and the rated power of the equipment; Input the equipment setting parameters into the general process modeling to determine the project process modeling; Data is extracted from the project process model to determine the unit energy consumption formula and equipment allocation formula; The unit energy consumption formula and equipment allocation formula are weighted and summed according to the preset continuous weighting to determine the optimization objective function; The objective function and project process modeling are integrated to determine a dual-objective optimization model; Input the equipment's rated power into the project process modeling to determine the equipment's operating boundaries.
[0010] Optionally, the steps for determining demand reorganization include analyzing the bi-objective optimization model and equipment operating boundaries based on a pre-defined bi-objective optimization model: The dual-objective optimization model and the equipment operating boundary are input into the dual-objective optimization model to determine the optimal Pareto boundary; The optimal Pareto boundary is divided according to the preset slope division table to determine the energy consumption priority segment, the gradation priority segment, and the balance priority segment. Numerical analysis was performed on the energy consumption priority segment, the gradation priority segment, and the balance priority segment to determine the weights of energy consumption priority, gradation priority, and balance priority. The priority weights of energy consumption, gradation, and balance are integrated to determine the reorganization of demand rights.
[0011] Secondly, this application provides a dual-objective optimization system for sand and gravel processing systems, focusing on gradation and energy consumption, employing the following technical solution: A dual-objective optimization system for sand and gravel processing, comprising: The acquisition module is used to acquire information such as sand and gravel processing units, processing unit processes, unit mapping relationships, project unit parameters, and project optimization requirements. A memory for storing a program for a dual-objective optimization method for gradation and energy consumption of a sand and gravel processing system as described in any of the preceding claims; The processor and the program in the memory can be loaded and executed by the processor to implement a dual-objective optimization method for gradation and energy consumption of a sand and gravel processing system as described in any of the above.
[0012] In summary, this application includes at least one of the following beneficial technical effects: 1. By analyzing the sand and gravel processing units, processing unit processes, and unit mapping relationships, a general process model for the entire sand and gravel processing flow is determined. Then, project unit parameters are input into the general process model to determine the corresponding dual-objective optimization model and equipment operating boundary for each project. Next, the dual-objective optimization model and equipment operating boundary are analyzed based on the dual-objective optimization model to determine demand weight reorganization. Based on the project optimization requirements, the target optimization weights are determined by searching within the demand weight reorganization. Finally, the dual-objective optimization model is analyzed based on the target optimization weights and the dual-objective optimization model to determine the optimal configuration scheme. Thus, the sand and gravel processing system is optimized based on the dual objectives of gradation and energy consumption, thereby improving the efficiency of the sand and gravel processing system. 2. By integrating sand and gravel processing units and unit mapping relationships, an independent unit model is determined. Based on the steady-state matrix model, the processing unit process and the independent unit model are analyzed to determine the full-process model. The independent unit model and the full-process model are analyzed to determine the actual utilization rate, actual gradation formula, and actual energy consumption formula of each device in the unit. The full-process model, actual equipment utilization rate, actual gradation formula, and actual energy consumption formula are integrated to determine the general process model. Thus, the sand and gravel processing process is divided into processing units according to the specific process of sand and gravel processing. The general process model is determined based on the sand and gravel processing units, thereby improving the decision-making efficiency of sand and gravel processing. 3. By inputting the dual-objective optimization model and equipment operation boundary into the dual-objective optimization model, the optimal Pareto boundary corresponding to the continuous weight reorganization is determined according to the dual-objective optimization model. Then, the optimal Pareto boundary is divided according to the slope partitioning table to obtain the energy consumption priority segment, the gradation priority segment, and the balance priority segment. The energy consumption priority segment, the gradation priority segment, and the balance priority segment are analyzed to determine the energy consumption priority weight, the gradation priority weight, and the balance priority weight. Then, the energy consumption priority weight, the gradation priority weight, and the balance priority weight are integrated to determine the demand weight reorganization. Thus, different weights are determined based on different demands, thereby improving the accuracy of the sand and gravel processing flow. Attached Figure Description Figure 1 This is a flowchart of a dual-objective optimization method for gradation and energy consumption of a sand and gravel processing system according to an embodiment of this application.
[0013] Figure 2 This application embodiment analyzes the sand and gravel processing unit, the processing unit process, and the unit mapping relationship to determine the flowchart for general process modeling.
[0014] Figure 3 This is a flowchart in the embodiments of this application that analyzes the independent unit model and the whole process model to determine the actual utilization rate of the equipment, the actual gradation formula and the actual energy consumption formula.
[0015] Figure 4 This embodiment of the application is a flowchart in which project unit parameters are input into a general process model to determine the dual-objective optimization model and the equipment operating boundary.
[0016] Figure 5 In this embodiment of the application, the dual-objective optimization model and the equipment operating boundary are analyzed according to the preset dual-objective optimization model to determine the flowchart of demand reorganization. Detailed Implementation
[0017] To make the purpose, technical solution, and advantages of this application clearer, the following description is provided in conjunction with the appendix. Figures 1 to 5 The present application will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the application.
[0018] This application discloses a dual-objective optimization method and system for gradation and energy consumption in a sand and gravel processing system. Specifically, it discloses a processing terminal that acquires sand and gravel processing units, processing unit processes, and unit mapping relationships. The processing terminal analyzes these units to determine a general process model for the entire sand and gravel processing flow. Project unit parameters are then input into the general process model to determine the dual-objective optimization model and equipment operating boundary corresponding to each project. The dual-objective optimization model and equipment operating boundary are then analyzed based on the dual-objective optimization model to determine demand weight reorganization. Based on the project optimization requirements, the target optimization weights are determined by searching within the demand weight reorganization. Finally, the dual-objective optimization model is analyzed based on the target optimization weights and the dual-objective optimization model to determine the optimal configuration scheme. This optimizes the sand and gravel processing system based on the dual objectives of gradation and energy consumption, thereby improving the efficiency of the sand and gravel processing system.
[0019] Reference Figure 1 This application discloses a dual-objective optimization method for gradation and energy consumption in a sand and gravel processing system, comprising the following steps: Step S100: Obtain the sand and gravel processing unit, processing unit flow, and unit mapping relationship.
[0020] Among them, the sand and gravel processing unit refers to the functional unit obtained by simplifying the sand and gravel processing process according to the different functions of each device in the sand and gravel processing process, including crushing unit and screening unit, etc. The processing terminal divides and determines the sand and gravel processing process based on the start and end points of each process in the sand and gravel processing process stored in the system.
[0021] The processing unit process refers to the processing unit process formed by combining various sand and gravel processing units. The processing terminal determines the complete unitized processing process by combining the processing units according to the standard sand and gravel processing process based on the feeding requirements and material flow of each unit in the sand and gravel processing unit.
[0022] Unit mapping relationship refers to the physical mapping relationship between the input and output of sand and gravel in each sand and gravel processing unit. For example, the output gradation of the crushing unit is the product of a fixed crushing coefficient determined based on the equipment model and rotation speed and the input gradation. The processing terminal determines the direct mapping relationship between the input and output of sand and gravel in each processing unit by setting the specific equipment parameters to be determined in each unit and taking the input amount of each unit as independent variables, and integrating the known mapping formulas between the equipment and gradation and the material processing performance within the unit.
[0023] Step S101: Analyze the sand and gravel processing unit, processing unit process, and unit mapping relationship to determine the general process model.
[0024] Among them, general process modeling refers to the complete process modeling of a sand and gravel processing system without specific processing parameters. This modeling is determined by the processing terminal through analysis of sand and gravel processing units, processing unit processes, and unit mapping relationships. The specific analysis process is described in [reference needed]. Figure 2 The steps in the process.
[0025] Step S102: Obtain project unit parameters and project optimization requirements.
[0026] Among them, the project unit parameters refer to the specific equipment parameters in each sand and gravel processing unit of the construction project, including the model and quantity of sand and gravel processing equipment in each sand and gravel processing unit. The processing terminal first obtains all equipment information of the construction project, and then determines the corresponding equipment information according to the unit classification based on the processing equipment in each sand and gravel processing unit.
[0027] Project optimization requirements refer to the target optimization focus requirements of the construction project, including energy consumption priority requirements, gradation priority requirements, and energy consumption gradation balance requirements. These requirements are determined by the processing terminal by directly obtaining the optimization focus options selected by the user on the sand and gravel processing system interface or by the operator directly inputting them into the system.
[0028] Step S103: Input the project unit parameters into the general process modeling to determine the dual-objective optimization model and the equipment operating boundary.
[0029] The dual-objective optimization model refers to the specific sand and gravel processing model whose optimal processing parameters are to be determined, along with the model's optimization objective formula. The equipment operating boundary refers to the upper limit boundary of the sand and gravel input volume for each piece of equipment during the sand and gravel processing process. Both are determined by the processing terminal through analysis and input of project unit parameters into the general process modeling. Specific analysis steps are detailed in [reference needed]. Figure 4 The steps in the process.
[0030] Step S104: Analyze the dual-objective optimization model and equipment operation boundary according to the preset dual-objective optimization model to determine the demand weight reorganization.
[0031] The dual-objective optimization model refers to an algorithmic model that optimizes sand and gravel processing parameters based on genetic algorithms and a two-stage search strategy. This model uses specific sand and gravel processing parameters such as the opening options of the crushing equipment, the rotation speed options of the shaping equipment, and the diversion ratio as the final optimization output. In the first stage, the genetic algorithm first performs a global coarse search for the optimal solution of the dual-objective optimization objective function in the dual-objective optimization model, and quickly locates the interval where the optimal solution is located by taking a large parameter step size. Then, the genetic algorithm takes a small step size to perform a fine search for the interval where the optimal solution is located, and finally locates the optimal solution. The parameters corresponding to the optimal solution are then fine-tuned, thereby improving the accuracy of the sand and gravel processing system.
[0032] Demand weight reconfiguration refers to a weighted reconfiguration that corresponds one-to-one with the optimization demands of three types of projects. It is used to calculate system energy consumption and system configuration, and to determine the optimization objective function by weighting system energy consumption and system configuration. This allows for different weight reconfigurations to be determined based on the different demands of the construction project, improving the optimization effect and robustness of the decision-making model. The processing terminal analyzes and determines this by inputting the bi-objective optimization model and equipment operating boundaries into the bi-objective optimization model. Specific analysis steps are detailed below. Figure 5 The steps in the process.
[0033] Step S105: Based on the project optimization requirements, search in the demand weight reorganization to determine the target optimization weight.
[0034] Among them, the target optimization weight refers to the target optimization weight corresponding to the project optimization needs of the construction project. The processing terminal first analyzes the project optimization needs to determine whether the project optimization needs are energy consumption priority needs, gradation priority needs, or energy consumption gradation balance needs, and then determines the corresponding energy consumption priority weight, gradation priority weight, or balance priority weight based on the specific project optimization needs.
[0035] Step S106: Analyze the dual-objective optimization model based on the objective optimization weights and the dual-objective optimization model to determine the optimal configuration scheme.
[0036] The optimal configuration scheme refers to the optimal sand and gravel processing parameters that meet the project's optimization requirements. These parameters include specific processing parameters such as the opening options of the crushing equipment, the rotation speed options of the shaping equipment, and the diversion ratio. The processing terminal first analyzes the dual-objective optimization model to determine the processing flow model of the construction project, as well as the gradation formula and energy consumption formula. Then, it performs a weighted summation of the gradation formula and energy consumption formula according to the objective optimization weights to determine the dual-objective optimization function of the problem to be optimized. Finally, it inputs the processing flow model into the dual-objective optimization model with the goal of minimizing this function for iteration. The solution that minimizes the objective function is the optimal configuration scheme.
[0037] Reference Figure 2 The steps for general process modeling include analyzing the sand and gravel processing units, processing unit processes, and unit mapping relationships to determine the general process modeling steps: Step S200: Integrate the sand and gravel processing units and unit mapping relationships to determine the independent unit model.
[0038] Among them, the independent unit model refers to the complete processing mapping model of each sand and gravel processing unit. The processing terminal determines the complete processing model by integrating the sand and gravel processing units and the unit mapping relationship to generate a unit, unit energy consumption, unit gradation and unit input-output relationship.
[0039] Step S201: Analyze the processing unit flow and independent unit model according to the preset steady-state matrix model to determine the full process modeling.
[0040] Among them, the steady-state matrix model refers to a steady-state flow model constructed in matrix form based on the principle of material conservation of input and output. This model quantifies the material transfer relationship into matrix form, characterizes the transfer mapping relationship between the flow rate and gradation ratio of materials in each unit, transforms the constraint relationship between upstream and downstream of the sand and gravel processing process into matrix equations, and finally determines the steady-state material distribution law of the entire process in the sand and gravel processing system.
[0041] Full-process modeling refers to the steady-state operation model of the entire sand and gravel processing system. By inputting specific construction parameters and sand and gravel processing parameters into this model, the steady-state operation solution of the entire sand and gravel processing system under these parameters can be obtained. This solution includes data such as the gradation, energy consumption, and power utilization rate of each piece of equipment. The processing terminal first arranges and integrates the independent unit models according to the processing unit process, and then inputs the arranged independent unit models into the steady-state matrix model for determination.
[0042] Step S202: Analyze the independent unit model and the full process model to determine the actual equipment utilization rate, actual gradation formula and actual energy consumption formula.
[0043] Among them, the actual equipment utilization rate refers to the correlation formula between the utilization rate of each piece of equipment and specific construction parameters and sand and gravel processing parameters. For example, if a project includes A units of m equipment and B units of n equipment, and the rotational speed of the equipment is c, the utilization rate of equipment k is w times the rotational speed. The actual gradation formula refers to the total sand and gravel processing gradation formula in the sand and gravel processing flow, with specific construction parameters and sand and gravel processing parameters as independent variables. The actual energy consumption formula refers to the total sand and gravel processing energy consumption formula in the sand and gravel processing flow, with specific construction parameters and sand and gravel processing parameters as independent variables. All three are determined by the processing terminal through analysis of independent unit models and full-process modeling. The specific analysis formula steps refer to [reference needed]. Figure 3 The steps in the process.
[0044] Step S203: Integrate the whole process modeling, actual equipment utilization rate, actual gradation formula and actual energy consumption formula to determine the general process modeling.
[0045] Among them, the general process modeling is consistent with the general process modeling in step S101. The processing terminal integrates and determines the general process modeling, actual equipment utilization rate, actual gradation formula and actual energy consumption formula after determining the overall process modeling, actual equipment utilization rate, actual gradation formula and actual energy consumption formula.
[0046] Reference Figure 3The steps for analyzing independent unit models and full-process models to determine the actual equipment utilization rate, actual gradation formula, and actual energy consumption formula include: Step S300: Perform correlation calculations between the independent unit model and the full-process model to determine the actual equipment utilization rate and the actual gradation formula.
[0047] The actual utilization rate is consistent with the actual utilization rate in step S202. The processing terminal first analyzes the model of the entire process to determine the relationship between the steady-state input and output of each independent unit. Then, it substitutes the input and output relationship into the independent unit model for inverse solution to determine the corresponding mapping relationship between the steady-state input and output and the equipment utilization rate. Based on the mapping relationship between the steady-state input and output and the specific construction parameters and sand and gravel processing parameters, it further determines the relationship between the specific construction parameters, the sand and gravel processing parameters and the equipment utilization rate, which is the actual utilization rate.
[0048] The actual gradation formula is consistent with the actual gradation formula in step S202. The processing terminal analyzes the model of the entire process to determine the relationship between the steady-state input and output corresponding to each independent unit. Then, the input-output relationship is substituted into the independent unit model for inverse solution to determine the corresponding mapping relationship between the steady-state input and output and the equipment gradation. Based on the mapping relationship between the steady-state input and output and the specific construction parameters and sand and gravel processing parameters, the relationship between the specific construction parameters, the sand and gravel processing parameters and the equipment gradation is further determined. Finally, the sum of all equipment gradation formulas is calculated, which is the actual gradation formula.
[0049] Step S301: Calculate the product of the actual utilization rate of the equipment and the preset rated energy consumption of the equipment to determine the energy consumption formula for a single piece of equipment.
[0050] Among them, the rated energy consumption of the equipment refers to the maximum energy consumption of the equipment. The rated energy consumption of the equipment here is an independent variable, which will be determined by the processing terminal through data extraction of project construction parameters.
[0051] The single-device energy consumption formula refers to the energy consumption of a single device, which is determined by the processing terminal by multiplying the actual utilization rate of the device by the rated energy consumption of the device.
[0052] Step S302: Calculate the sum of the energy consumption formulas for a single device to determine the actual energy consumption formula.
[0053] The actual energy consumption formula is consistent with the actual energy consumption formula in step S202, and is determined by the processing terminal by calculating the sum of the energy consumption formulas for a single device.
[0054] Reference Figure 4 The steps for inputting project unit parameters into the general process model to determine the bi-objective optimization model and equipment operating boundaries include: Step S400: Extract data from the project unit parameters to determine the equipment setting parameters and the equipment rated power.
[0055] Among them, equipment setting parameters refer to the specific layout parameters of each sand and gravel processing equipment in the construction project, including the model and number of each piece of equipment in each sand and gravel processing unit. Rated power of the equipment refers to the maximum operating power of the equipment. Both are determined by the processing terminal through data extraction from the project unit parameters.
[0056] Step S401: Input the equipment setting parameters into the general process modeling to determine the project process modeling.
[0057] Among them, project process modeling refers to general process modeling after determining specific construction parameters, which is calculated and determined by the processing terminal by inputting equipment setting parameters into the general process modeling.
[0058] Step S402: Extract data from the project process model to determine the unit energy consumption formula and equipment gradation formula.
[0059] The unit energy consumption formula refers to the mapping formula between the unit energy consumption of the sand and gravel processing system and the sand and gravel input. The equipment gradation formula refers to the gradation formula of the equipment in the sand and gravel processing system. These are determined by the processing terminal through data extraction based on project process modeling.
[0060] Step S403: Based on the preset continuous weighted recombination, the unit energy consumption formula and the equipment gradation formula are weighted and summed to determine the optimization objective function.
[0061] Among them, continuous weight reorganization refers to the weight reorganization of energy consumption and power set with step size. For example, the weight reorganization for weighting energy consumption is set with a step size of 0.1 in the range of 0.2-0.8. Correspondingly, the weight reorganization for weighting gradation is set with a step size of 0.1 in the range of 0.8-0.2. It is used to input into the dual-objective optimization model one by one to determine the demand weight reorganization that matches the three project requirements. The operator first sets the upper and lower boundaries of the weight group to avoid weight imbalance, and then determines the traversal step size of the weight according to the accuracy of the project requirements.
[0062] The optimization objective function refers to the objective optimization function of the sand and gravel processing system. It is determined by the processing terminal through weighted summation of the unit energy consumption formula and the equipment gradation formula according to the continuous weighted reorganization, and the optimization objective is to minimize the weighted sum.
[0063] Step S404: Integrate the optimization objective function and project process modeling to determine the bi-objective optimization model.
[0064] The dual-objective optimization model is consistent with the dual-objective optimization model in step S103, and is determined by the processing terminal by integrating the optimization objective function and project process modeling.
[0065] Step S405: Input the rated power of the equipment into the project process modeling to determine the equipment operating boundary.
[0066] The equipment operating boundary is consistent with the equipment operating boundary in step S103. The processing terminal determines the material input quantity corresponding to the equipment rated power by substituting the equipment rated power into the project process model, which is the equipment operating boundary.
[0067] Reference Figure 5 The steps for demand reorganization, including analyzing the dual-objective optimization model and equipment operating boundaries based on the pre-defined dual-objective optimization model, are as follows: Step S500: Input the dual-objective optimization model and the equipment operating boundary into the dual-objective optimization model to determine the optimal Pareto boundary.
[0068] Among them, the optimal Pareto boundary refers to the polyline formed by connecting the coordinates of the optimal solutions corresponding to different weightings. The processing terminal first inputs the dual-objective optimization model and the equipment operation boundary into the dual-objective optimization model for optimization, and after determining each optimal solution, the optimal solutions are uniformly mapped to the coordinate system and connected to determine the optimal solution.
[0069] Step S501: Divide the optimal Pareto boundary according to the preset slope division table to determine the energy consumption priority segment, the gradation priority segment, and the balance priority segment.
[0070] The slope partitioning table refers to a partitioning table that divides the optimal solution within the optimal Pareto boundary based on the slope of the optimal Pareto boundary. For example, when the slope is greater than 3, the corresponding segment is the gradation priority segment; when the slope is greater than 1 but less than 3, the corresponding segment is the balance priority segment; and when the slope is less than 1, the corresponding segment is the energy consumption priority segment. Operators divide the optimal Pareto boundary by setting different partitioning tables, and then perform a full-process simulation of the sand and gravel processing system based on the partitioning results. The partitioning table that provides the best simulation effect and the greatest benefit is the slope partitioning table.
[0071] The energy-priority segment refers to the segment within the optimal Pareto boundary that prioritizes reducing energy consumption. The gradation-priority segment refers to the segment within the optimal Pareto boundary that prioritizes reducing gradation. The equilibrium finite segment refers to the segment within the optimal Pareto boundary that prioritizes optimizing the balance between energy consumption and gradation. All three are determined by the processing terminal through partitioning the optimal Pareto boundary based on the partitioning rules in the slope partitioning table.
[0072] Step S502: Perform numerical analysis on the energy consumption priority segment, gradation priority segment, and balance priority segment to determine the energy consumption priority weight, gradation priority weight, and balance priority weight.
[0073] Among them, the energy consumption priority weight refers to the weighted weight corresponding to the optimal solution with the minimum unit energy consumption in the energy consumption priority segment. The processing terminal determines the optimal solution with the minimum energy consumption by performing numerical analysis on the energy consumption priority segment, and then determines the weight corresponding to the optimal solution based on the optimal solution, which is the energy consumption priority weight.
[0074] The gradation priority weight refers to the weighted weight corresponding to the optimal solution with the smallest gradation error in the energy consumption priority segment. The processing terminal determines the optimal solution with the smallest gradation error by performing data analysis on the gradation priority segment, and then determines the weight corresponding to the optimal solution based on the optimal solution, which is the gradation priority weight.
[0075] The balance priority weight refers to the optimization weight that minimizes the deviation between energy consumption and gradation. The processing terminal first calculates the absolute deviation between energy consumption and gradation corresponding to each optimal solution within the balance priority segment, and then performs numerical analysis on the absolute deviation to determine the weighted weight corresponding to the smallest absolute deviation, which is the balance priority weight.
[0076] Step S503: Integrate the energy consumption priority weight, gradation priority weight and balance priority weight to determine the demand weight reorganization.
[0077] Among them, the demand right reorganization is consistent with the demand right reorganization in step S104, and is determined by the processing terminal by integrating the energy consumption priority weight, the gradation priority weight and the balance priority weight.
[0078] Based on the same inventive concept, embodiments of this application provide a dual-objective optimization system for sand and gravel processing systems, encompassing: The acquisition module is used to acquire information such as sand and gravel processing units, processing unit processes, unit mapping relationships, project unit parameters, and project optimization requirements. The memory is used to store a program for a dual-objective optimization method for gradation and energy consumption in a sand and gravel processing system; The processor and memory can load and execute programs to implement a dual-objective optimization method for gradation and energy consumption in a sand and gravel processing system.
[0079] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.
[0080] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.
Claims
1. A dual-objective optimization method for gradation and energy consumption in a sand and gravel processing system, characterized in that, include: Obtain the sand and gravel processing unit, processing unit flow, and unit mapping relationship; The sand and gravel processing unit, processing unit process, and unit mapping relationship are analyzed to determine the general process modeling. Obtain project unit parameters and project optimization requirements; Input the project unit parameters into the general process modeling to determine the dual-objective optimization model and equipment operating boundaries; The dual-objective optimization model and equipment operation boundary are analyzed based on the pre-set dual-objective optimization model to determine the demand weight reorganization. Based on the project's optimization needs, search within the demand weight reorganization to determine the target optimization weight; The dual-objective optimization model is analyzed based on the objective optimization weights and the dual-objective optimization model to determine the optimal configuration scheme.
2. The dual-objective optimization method for gradation and energy consumption of a sand and gravel processing system according to claim 1, characterized in that, The steps for general process modeling include analyzing the sand and gravel processing units, processing unit flows, and unit mapping relationships to determine the general process modeling steps: Integrate the sand and gravel processing units and unit mapping relationships to determine the independent unit model; The processing unit flow and independent unit model are analyzed based on the preset steady-state matrix model to determine the full-process modeling. Analyze the independent unit model and the full-process model to determine the actual equipment utilization rate, actual gradation formula and actual energy consumption formula; The entire process modeling, actual equipment utilization rate, actual gradation formula, and actual energy consumption formula are integrated to determine a general process modeling.
3. The dual-objective optimization method for gradation and energy consumption of a sand and gravel processing system according to claim 2, characterized in that, The steps for analyzing independent unit models and full-process models to determine the actual equipment utilization rate, actual gradation formula, and actual energy consumption formula include: The independent unit model and the full-process model are correlated and calculated to determine the actual equipment utilization rate and the actual gradation formula. The product of the actual utilization rate of the equipment and the preset rated energy consumption of the equipment is used to determine the energy consumption formula for a single piece of equipment; Calculate the sum of the energy consumption formulas for a single device to determine the actual energy consumption formula.
4. The dual-objective optimization method for gradation and energy consumption of a sand and gravel processing system according to claim 1, characterized in that, The steps involved in inputting project unit parameters into the general process model to determine the dual-objective optimization model and equipment operating boundaries include: Data is extracted from the unit parameters of the project to determine the equipment setting parameters and the rated power of the equipment; Input the equipment setting parameters into the general process modeling to determine the project process modeling; Data is extracted from the project process model to determine the unit energy consumption formula and equipment allocation formula; The unit energy consumption formula and equipment allocation formula are weighted and summed according to the preset continuous weighting to determine the optimization objective function; The objective function and project process modeling are integrated to determine a dual-objective optimization model; Input the equipment's rated power into the project process modeling to determine the equipment's operating boundaries.
5. The dual-objective optimization method for gradation and energy consumption of a sand and gravel processing system according to claim 1, characterized in that, The steps for demand reorganization include analyzing the dual-objective optimization model and equipment operating boundaries based on the pre-defined dual-objective optimization model: The dual-objective optimization model and the equipment operating boundary are input into the dual-objective optimization model to determine the optimal Pareto boundary; The optimal Pareto boundary is divided according to the preset slope division table to determine the energy consumption priority segment, the gradation priority segment, and the balance priority segment. Numerical analysis was performed on the energy consumption priority segment, the gradation priority segment, and the balance priority segment to determine the weights of energy consumption priority, gradation priority, and balance priority. The priority weights of energy consumption, gradation, and balance are integrated to determine the reorganization of demand rights.
6. A dual-objective optimization system for aggregate processing, focusing on gradation and energy consumption, characterized in that... include: The acquisition module is used to acquire information such as sand and gravel processing units, processing unit processes, unit mapping relationships, project unit parameters, and project optimization requirements. A memory for storing a program for a dual-objective optimization method for gradation and energy consumption of a sand and gravel processing system as described in any one of claims 1 to 5; The processor and the program in the memory can be loaded and executed by the processor to implement the dual-objective optimization method for gradation and energy consumption of a sand and gravel processing system as described in any one of claims 1 to 5.