A coal blending optimization system and method suitable for large-scale coal preparation plants

Abstract

The present invention discloses a coal blending optimization system and method suitable for large-scale coal preparation plants, which takes into account the actual scenarios in the actual production process, and can select different models for optimization calculation according to the actual production scenarios, taking into account abnormal situations that may occur in the actual coal blending process, such as bunker overflow, inventory data errors, etc., and performing re-rolling calculations. The optimization model can perform coal blending calculations for multiple coal bunkers at the same time, changing the current situation of only manually blending 2-3 types of coal at a time. It solves the diversified order component ratio requirements, multiple coal bunkers and / or bunker vacancy requirements of large-scale coal preparation plants, and updates the optimal coal blending ratio according to the bunker overflow treatment situation when bunker overflow occurs, and realizes real-time adjustment of the dynamic component ratio of coal blending to be suitable for the actual production status of large-scale coal preparation plants.

Description

Technical Field

[0001] The present application relates to the technical field of coal preparation and blending operations, and more specifically, to the technology for controlling the quality of coal components in coal blending operations in large-scale coal preparation plants, as well as a coal bunker (coal outlet blockage) control system and method. Background Art

[0002] Large coal mining groups generally have multiple sources of coal mines, and the coal quality of each coal mine may vary greatly. Different commercial coal order demand indicators also have different requirements for ash content, sulfur content, moisture, heat, adhesion and other component indicators.

[0003] In order to mix coal products that meet the order's composition indicators, the coal preparation plant will mix the coal in each coal bunker together to ensure that the quality of the mixed coal meets the composition indicator requirements and there is no quality redundancy to avoid waste. Since the mixing ratio and adjustment rely on manual experience, it is generally not necessary to mix more than three different coal qualities at the same time. Since the composition indicators of commercial coal demand also have requirements for sulfur content, moisture content, ash content, etc. At the same time, the existing inventory in the coal bunker and the production scenario must also be considered. For example, in some cases, in order to ensure that production can proceed, a certain proportion of coal in a coal bunker needs to be vacated so that the produced coal can be stored in the bunker.

[0004] Large coal preparation plants have many coal bunkers, and some large coal preparation plants have more than 20 coal bunkers. It is difficult to accurately calculate the composition ratio by relying on manual experience, and to obtain the cost-minimizing coal composition ratio based on the actual operation site.

[0005] In terms of the hardware of the coal blending system, the cluster uses air cannons and other devices to solve the problem of coal bunker blockage during the coal blending process. The existing technology has coal blending optimization models for thermal power plants, which are solved by genetic algorithms and other methods, and mainly calculate the optimal ratio based on the load of the generator set. However, the coal blending scenarios of thermal power plants and large coal preparation plants are different, and the factors considered are also different. In particular, the problem of coal bunker blockage during the coal blending process cannot be properly solved by the thermal power generation model due to its impact on coal blending, so it is difficult to apply directly. Summary of the invention

[0006] The purpose of this application is to provide a coal blending optimization system and method suitable for large-scale coal preparation plants, to solve the diversified order component ratio requirements, multiple coal bunkers and / or bunker emptying requirements of large-scale coal preparation plants, and to update the optimal coal blending ratio according to the bunker emptying treatment situation in the event of bunker emptying, to achieve real-time adjustment of the dynamic component ratio of coal blending, so as to be suitable for the actual production status of large-scale coal preparation plants.

[0007] In order to solve the above technical solution, the present invention adopts the following technical solution:

[0008] A coal blending optimization method applicable to a large coal preparation plant, characterized by comprising the following steps:

[0009] S1: Based on the coal composition and the cost weight coefficients of different components, establish the optimal coal blending ratio control objective function, so that the cumulative value of the product of the proportion of various coals and the unit cost value of various coals or coal bunkers is minimized; the unit cost value of various coals or coal bunkers is set to include: the cumulative sum of the product of the cost coefficient of a certain coal quality index of a certain coal and the actual value of the index of the coal, plus the market price adjustment parameter of the coal;

[0010] S2: Set general constraints for the coal blending ratio control objective function, including but not limited to:

[0011] Ash content constraint: The product of the ash content and the proportion of various coals is less than or equal to the product of the order demand tonnage and the upper limit of the ash content index required by the order demand;

[0012] Sulfur content constraint: The product of the sulfur content and the proportion of various coals is less than or equal to the product of the tonnage of the order demand and the upper limit of the sulfur content index required by the order demand;

[0013] Cohesion constraint: If a certain type of coal is used in coal blending, then the coal blending variable of this type of coal is =1, otherwise 0, bonding index function for different coal combinations Set to maintain within the upper and lower limits of the adhesion index;

[0014] Demand constraint: The sum of the proportions of various coals is equal to the required output of the order;

[0015] Inventory constraint: the proportion of a certain type of coal must be less than or equal to the inventory of this type of coal in the coal bunker;

[0016] Moisture constraint: The cumulative amount of a certain coal and the moisture content of the coal is less than or equal to the product of the order demand and the upper limit of the moisture content in the order indicator;

[0017] Coal blending constraint: the proportion of a certain coal is set to be less than or equal to the product of the coal blending variable and the positive maximum value;

[0018] Heat constraint: The cumulative sum of the proportion of a certain type of coal and the unit calorific value of the coal is greater than or equal to the number of tons of order demand multiplied by the lower limit of the calorific value index in the order index;

[0019] S3: Set the coal blending variable according to the proportion of a certain coal ; The proportion of this type of coal includes the tonnage and proportion of coal;

[0020] S4: Based on the constraints of step S2 and the coal blending variables of S3, the optimal coal blending ratio control objective function of step S1 is solved.

[0021] In the above technical solution, in step S1, the coal blending ratio control objective function is: , where the decision variables , for i The proportion or tonnage of the coal to be loaded; For the i Coal or i The unit cost value of a coal bunker, ;

[0022] in, For the i The first j The cost coefficient of each coal quality index is For the i The first j The actual value of the indicator, For the i Market price adjustment parameters for different types of coal.

[0023] In the above technical solution, in step S2, different input constraints and matching models are set for different application scenarios, including but not limited to the following application scenario matching models:

[0024] S21: Clean coal average optimal ratio model:

[0025] The coal quality in the coal bunker is regarded as a single coal quality, and the coal quality index is estimated by the overall mean value, which is used as the input of the optimization model to calculate the optimal result;

[0026] S22: Clean coal average optimal ratio model:

[0027] When the produced coal needs to be loaded into a specified coal bunker, and the bunker is already full or has a small available storage capacity, the proportion of the specified coal is greater than or equal to the vacated bunker capacity of the coal;

[0028] S23: Optimal proportioning model with minimum assembly quantity requirement: set a minimum coal quantity, when the coal feeder arrives at a coal bunker, it must work for a set time; and the product of the coal blending variable of the type of coal and the minimum coal quantity must be less than or equal to the proportion of the type of coal;

[0029] S24: Optimal ratio adjustment model for partially assembled:

[0030] If the coal feeder fails or the bunker is filled, it is impossible to load the coal according to the original optimal plan. The coal quality data of the loaded coal and the mixed coal are used as model input, and the optimal ratio under the current given circumstances is calculated again, and the coal is loaded accordingly.

[0031] In the above technical solution, in step S24, the constraint condition added by partial loading is expressed as: ,in For coal that has been blended, The amount of coal that needs to be loaded to complete the entire order task.

[0032] In the above technical solution, in step S24, if the inventory data or warehouse overflow causes recalculation, the inventory needs to be updated.

[0033] In the above technical solution, in step S24, when the material level meter in the silo measures that there is still coal and the coal quantity weighed does not increase over time, it is automatically determined to be a silo full.

[0034] In the above technical solution, in step S24, when it is automatically or manually determined that the warehouse is empty, the air cannon workflow is triggered, and the results after the air cannon works are fed back to the system in time, whether it is effective or not; if it is ineffective, it is necessary to recalculate the optimal coal blending result in real time.

[0035] In the above technical solution, in step S4, the solution is mainly solved by using an open source solver to calculate the result.

[0036] Therefore, the present invention sets different objective functions according to the requirements of different coal blending scenarios, and the coal blending scenarios include multi-coal blending scenarios, multi-coal bunker scenarios, bunker vacancy demand scenarios, and re-coal blending scenarios after bunker emptying judgment and processing.

[0037] At the same time, the present invention also provides a coal blending optimization system suitable for large-scale coal preparation plants, which is characterized by being used to implement the above-mentioned coal blending optimization method suitable for large-scale coal preparation plants, and to achieve coal blending in scenarios where multiple coal blending components are optimally controlled, multiple coal bunker scenarios, bunker vacancy demand scenarios, and bunker emptying determination and re-blending after processing.

[0038] A storage medium stores a computer program, characterized in that when the computer program is executed, it is used to implement the steps of the above-mentioned coal blending optimization method applicable to large-scale coal preparation plants.

[0039] The beneficial effects of this application are:

[0040] In view of the technical defects that the existing technology cannot adapt to the optimal coal blending of multiple types of coal in multiple coal bins and cannot realize the determination and processing of rattan bins and open bins, the present invention takes into account the actual scenarios in the actual production process and can select different models for optimization calculation according to the actual production scenarios.

[0041] Abnormal situations that may occur in the actual coal blending process, such as empty warehouses, incorrect inventory data, etc., are also taken into consideration, and a re-rolling calculation is performed.

[0042] Different objective functions are set according to the requirements of different coal blending scenarios to realize automatic switching operation of multiple coal blending scenarios, multiple coal bunker scenarios, bunker vacancy demand scenarios, and bunker emptying determination and re-blending scenarios after processing. The coal blending process is automated and the blending ratio is accurate.

[0043] The average optimal proportion model of clean coal is accurately set according to the demand of Fujikura, which fully considers the demand of Fujikura and satisfies the proportion control between various coal bunkers in large-scale coal preparation plants.

[0044] It realizes automatic judgment of warehouse overflow and updates inventory and proportion quantity in real time based on warehouse overflow processing results, effectively ensuring the consistency between the proportion quantity of proportioned ingredients and the proportion of ingredients required by the order. The proportion is precise and controllable, close to the actual production process.

[0045] The present invention uses an optimization model to calculate the coal blending ratio, which can better reduce the waste of high-quality coal blending ratio according to cost changes. The optimization model can calculate the coal blending for multiple coal bunkers (multiple bunkers ≥ 2) at the same time, changing the current situation of only blending 2-3 types of coal at a time. It is particularly suitable for large-scale coal preparation plants with up to 20 or more coal bunkers for blending different types of coal, and is particularly suitable for multi-component quality production blending scenarios with different requirements for the quality of the coal blending components. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 The present invention is a flow chart of the coal blending optimization method applicable to large-scale coal preparation plants. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0049] Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application for which protection is sought, but merely represents selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0050] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0051] The features and performance of the present application are further described in detail below in conjunction with the embodiments.

[0052] Example 1

[0053] like Figure 1 As shown, the present invention establishes a coal blending optimization model for different application scenarios, targeting actual application scenarios such as diversified demand contract products (high and low sulfur coal), multiple warehouses (specified multiple warehouses ≥ 2), warehouse vacancy requirements, etc. The model takes cost minimization as the goal, outputs the optimal blending ratio, and the output tonnage of raw coal from the coal bunker. The air cannon is started in the event of a bunker collapse, and the optimal coal blending ratio is recalculated based on the results of the air cannon operation.

[0054] The components of the optimal ratio model are as follows:

[0055] (1) Objective function: By setting different objective functions, the model can meet the different needs of users.

[0056] The model supports custom cost functions, which can adjust the cost weight ratio of each coal quality indicator and modify the cost function according to the market situation. If a certain type of coal is in short supply in the market recently, the cost coefficient corresponding to the product can be appropriately increased.

[0057] The specific formula is:

[0058] in For the i Coal i The unit cost of a coal bunker, For the i The first j The cost coefficient of each coal quality index is For the i The first j The actual value of an indicator. For the i The market price adjustment parameters of the coal species can be adjusted according to the actual market conditions.

[0059] The objective function can be expressed as: ,in is the decision variable, i The proportion of coal or the number of tons of coal to be loaded.

[0060] (2) General constraints

[0061] Ash content constraint: The overall weighted average ash content shall not exceed the required product specification requirements.

[0062] It can be expressed as: , in For the i The ash content of the coal, D is the order quantity in tons, H The upper limit of the ash content requirement for the order is set.

[0063] Sulfur content constraint: The overall weighted average sulfur content shall not exceed the required product specification requirements.

[0064] It can be expressed as: , in For the i The sulfur content of the coal, D is the order quantity in tons, F The upper limit of the contract ash content requirement is set.

[0065] Cohesion constraint: Since there is no ordinary weighted average relationship for cohesion, it is necessary to build a set of historical experience of cohesion. When matching products that need to meet the cohesion requirements, it is necessary to identify the cohesion constraints from the set that meets the cohesion requirements. Based on the existing cohesion knowledge base set, the model will automatically select the warehouse that meets the cohesion requirements for assembly.

[0066] It can be expressed as: , where the variable of whether a certain coal is blended is is a 0 / 1 variable, a and b are the upper and lower limit parameters of the adhesion index. i Seed coal participates in coal blending, =1, otherwise 0. It is the bonding index of different coal combinations.

[0067] Demand constraint: The tonnage of assembled products must be equal to the required output of the order.

[0068] It can be expressed as: ,in D Request product tonnage for the contract.

[0069] Inventory constraint: The assembly quantity of a certain coal quality must be less than or equal to the existing inventory.

[0070] It can be expressed as: ,in For the i The inventory of the type of coal in the coal bunker.

[0071] Moisture constraint: The moisture content of the package must meet the quality requirement range.

[0072] It can be expressed as: ,in W This is the upper limit of moisture requirement in commercial coal contract indicators. It is the unit moisture content of a certain type of coal.

[0073] Participation in coal blending constraint: Only the i If you plant coal, there will be a specific amount of coal to be blended. If you do not participate, the amount of coal to be blended will be 0.

[0074] It can be expressed as: , where Q is a positive maximum number.

[0075] Heat constraint: The heat content of the blended coal must meet the lower limit of the heat index required by the commercial coal contract.

[0076] It can be expressed as: R, where For the i Unit calorific value of coal, R It is the lower limit of the calorific value requirement in the commercial coal order index.

[0077] (3) Decision variables

[0078] The decision variable is i Coal proportion of each type of coal (tons and proportions), the decision variable is greater than or equal to 0; i Whether the coal is involved in coal blending .

[0079] (4) Model establishment and solution: Solve the optimal coal blending problem based on constraints, business objectives, coal quality data, etc. The solution mainly uses open source solvers to calculate the results.

[0080] In the above embodiments, the difference between the models for different application scenarios mainly lies in the difference in input constraints. The main models include:

[0081] (1) Average optimal proportion model of clean coal

[0082] The coal quality in the coal bunker is regarded as a single coal quality, and the coal quality index is estimated by the overall mean value, which is used as the input of the optimization model to calculate the optimal result. The coal quality data can be accessed from the coal quality monitoring data in the data platform.

[0083] (2) Optimal Allocation Model with Liquidation Demand

[0084] In some production situations, the coal produced needs to be loaded into a designated coal bunker, but the bunker is already full or has a small available capacity. In order to ensure that the coal produced can be loaded into the bunker in time, it is necessary to specify that certain bunkers must vacate a certain amount of capacity during coal blending operations and load them first to ensure normal production. This condition is used as the input of the model to calculate the optimal result.

[0085] This condition can be expressed as: ,in For the i The amount of coal vacated in storage.

[0086] (3) Optimal matching model with minimum assembly quantity requirement

[0087] In some scenarios, the result of the optimal model calculation will result in a smaller amount of a certain type of coal, such as 0.2 tons. The coal feeder will unload 0.2 tons of coal at the coal bunker and then go to other coal bunkers to unload coal. However, this operation is not adopted in actual operation because it is too inefficient. Operators generally set a minimum amount of coal to be unloaded. When the coal feeder arrives at a coal bunker, it must work for a certain period of time, that is, the coal bunker must unload a certain amount of coal before it can be operated.

[0088] This condition can be expressed as: , where O is the minimum coal loading amount.

[0089] (4) Optimal ratio adjustment model for partially assembled

[0090] In the actual loading process, due to various working conditions, such as coal feeder failure or bunker clogging, it is impossible to load coal according to the original optimal solution. The coal already loaded and the coal quality data of the mixed coal are used as model inputs to recalculate the optimal ratio under the current given conditions and load coal accordingly. In this case, other input parameters of the model need to be modified. For example, the inventory data or bunker clogging may cause a recalculation, and the inventory needs to be updated. .

[0091] The constraints added by partial loading can be expressed as: ,in It is coal that has been blended. The amount of coal that needs to be loaded to complete the entire order task.

[0092] Special cases, abnormal conditions in the warehouse:

[0093] When the system or manual judgment is that the warehouse is empty, the air cannon workflow is triggered. The results of the air cannon operation need to be fed back to the system in a timely manner, whether it is effective or not. In the case of no effect, the optimal coal blending result needs to be recalculated in real time. When the system determines that the warehouse is empty, refer to the following judgment criteria: there is still coal measured by the material level meter in the warehouse, and the coal quantity does not increase over time. Or manual judgment of the warehouse emptying occurs through video monitoring.

[0094] Example 2

[0095] An example of the proportion of washed mixed coal (without vacating the warehouse and without minimum assembly quantity requirement) is as follows:

[0096] The coal bunker storage inventory and coal quality data are shown in Table 1. The units of ash content and sulfur content in the table are percentages; the unit of heat is kilocalories, and the inventory is tons.

[0097] Table 1 Coal bunker storage inventory and coal quality data detection

[0098]

[0099] The product demand is 500 tons, and the product quality requirements are: ash content <55, calories >3000; the optimal ratio is shown in Table 2 below; the ash content unit in the table is percentage; the calories unit is kilocalorie.

[0100] Table 2 Product demand 500 tons of blended coal

[0101]

[0102] The product demand is 1,000 tons, and the optimal ratio is shown in Table 3 below; the ash content in the table is in percentage; the heat unit is kilocalorie.

[0103] Table 3 Product demand 1000 tons of blended coal

[0104]

[0105] From the above-mentioned embodiments of the optimal ratio of components under different coal blending requirements, it can be seen that the coal blending optimization model that copes with multiple actual scenarios on site can realize different coal blending controls according to different application scenarios, and at the same time can realize abnormal situation processing such as warehouse bloating according to changing on-site working conditions.

[0106] The embodiments described above are part of the embodiments of the present application, rather than all of the embodiments. The detailed description of the embodiments of the present application is not intended to limit the scope of the present application for protection, but merely represents the selected embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

Claims

1. A method for optimizing coal blending in multiple coal bunkers and / or empty bunkers applicable to large-scale coal preparation plants, characterized in that The steps include: S1: Based on the coal composition and the cost weight coefficients of different components, the optimal coal blending ratio control objective function is established to minimize the cumulative value of the product of the proportion of various coals and the unit cost value of various coals or coal bunkers; S2: Set general constraints for the coal blending ratio control objective function, including: Ash content constraint and sulfur content constraint: the product of the ash content or sulfur content of various coals and the proportion of the coals is less than or equal to the product of the tonnage of the order demand and the upper limit of the ash content or sulfur content of the order demand; Cohesion constraint: If a certain type of coal is used in coal blending, then the coal blending variable of this type of coal is =1, otherwise 0, bonding index function for different coal combinations Set to maintain within the upper and lower limits of the adhesion index; Demand constraint: The sum of the proportions of various coals is equal to the required output of the order; Inventory constraint: the proportion of a certain type of coal must be less than or equal to the inventory of this type of coal in the coal bunker; Moisture constraint: The cumulative amount of a certain coal blend and the moisture content of the coal is less than or equal to the product of the order demand and the upper limit of the moisture content in the order indicator; Heat constraint: The cumulative sum of the proportion of a certain type of coal and the unit calorific value of the coal is greater than or equal to the number of tons of order demand multiplied by the lower limit of the calorific value index in the order index; Finally, the coal blending variables are set according to the proportion of a certain type of coal; the proportion of the coal includes the tonnage and proportion of the coal; based on the constraints and the coal blending variables, the optimal coal blending proportion control objective function is solved; Different input constraints and matching models are set for different application scenarios, including the following application scenario matching models: S21: Clean coal average optimal ratio model: The coal quality in the coal bunker is regarded as a single coal quality, and the coal quality index is estimated by the overall mean value, which is used as the input of the optimization model to calculate the optimal result; S22: Clean coal average optimal ratio model: When the produced coal needs to be loaded into a specified coal bunker, and the bunker is already full or has a small available storage capacity, the proportion of the specified coal is greater than or equal to the vacated bunker capacity of the coal; S23: Optimal proportioning model with minimum assembly quantity requirement: set a minimum coal quantity, when the coal feeder arrives at a coal bunker, it must work for a set time; and the product of the coal blending variable of the type of coal and the minimum coal quantity must be less than or equal to the proportion of the type of coal; S24: Optimal ratio adjustment model for partially assembled: If the coal feeder fails or the bunker is emptied, the coal cannot be loaded according to the original optimal solution. The coal quality data of the loaded coal and the mixed coal are used as model input to calculate the optimal ratio under the current given situation and load the coal accordingly. When it is automatically or manually determined that the warehouse is empty, the air cannon workflow is triggered, and the results of the air cannon operation are fed back to the system in a timely manner, with or without effect; if there is no effect, the optimal coal blending result needs to be recalculated in real time.

2. The method for optimizing coal blending of multiple coal bunkers and / or empty bunkers applicable to large-scale coal preparation plants according to claim 1, characterized in that In step S1, the coal blending ratio control objective function is: , where the decision variables , for i The proportion or tonnage of the coal to be loaded; For the i Coal or i The unit cost value of a coal bunker, ; in, For the i The first j The cost coefficient of each coal quality index is For the i The first j The actual value of the indicator, For the i Market price adjustment parameters for different types of coal.

3. The method for optimizing coal blending of multiple coal bunkers and / or empty bunkers applicable to large-scale coal preparation plants according to claim 2, characterized in that In model S24, the constraints added by partial loading are expressed as: ,in For coal that has been blended, The amount of coal that needs to be loaded to complete the entire order task.

4. The method for optimizing coal blending of multiple coal bunkers and / or empty bunkers applicable to large-scale coal preparation plants according to claim 2, characterized in that In model step S24, if the inventory data or warehouse overflow causes recalculation, the inventory needs to be updated.

5. The method for optimizing coal blending of multiple coal bunkers and / or empty bunkers applicable to large-scale coal preparation plants according to claim 2, characterized in that In model S24, when the material level meter in the silo measures that there is still coal and the coal quantity weighing does not increase over time, it is automatically determined to be a full silo.

6. The method for optimizing coal blending of multiple coal bunkers and / or empty bunkers applicable to large-scale coal preparation plants according to claim 1, characterized in that The model is mainly solved using open source solvers for result calculation.

7. A multi-coal bunker and / or coal blending optimization system suitable for large-scale coal preparation plants, characterized in that A method for optimizing coal blending applicable to large-scale coal preparation plants is used to implement any one of claims 1-6.

8. A storage medium storing a computer program, characterized in that When the computer program is executed, it is used to implement the steps of the multi-coal bunker and / or coal blending optimization method applicable to a large-scale coal preparation plant as described in any one of claims 1-6.

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