Method and device for optimizing coal blending combustion

Abstract

The embodiment of the invention relates to the field of coal blending combustion, and discloses a coal blending combustion optimization method and device, and the method comprises the steps: obtaining the unit price of each coal type added into each raw coal bunker; determining a calculation formula of the fire coal cost according to the first decision variable, the second decision variable and the unit price by taking whether each coal type is added into each raw coal bunker or not as each first decision variable and taking the proportion of each coal type added into each raw coal bunker as a second decision variable; and constraining the first decision variable and the second decision variable by using a preset raw coal bunker index constraint condition, a coal type index constraint condition and a unit index constraint condition, and minimizing the fire coal cost calculated by the calculation formula of the fire coal cost as a target. And calculating an optimization value of the first decision variable and an optimization value of the second decision variable.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of coal blending and combustion, and in particular to an optimization method and device for coal blending and combustion. Background Art

[0002] As my country's basic energy source, coal is an important pillar supporting my country's rapid economic development, and coal-fired power generation is also one of my country's important power generation methods. However, due to the uneven quality of coal used in my country's coal-fired power generation and limited high-quality coal, it is still the norm for coal-fired power plants to mix high-quality coal with other coal. If too little high-quality coal is used, it will not be able to meet the power generation demand, and if too much high-quality coal is used, the cost of coal will increase.

[0003] At present, the research on coal blending and combustion schemes in coal-fired power plants mainly focuses on how to ensure that the coal used in the unit meets the coal combustion indicators and how to monitor the combustion effect of the existing coal blending and combustion schemes in real time through sensors. However, there is a lack of research on how to find the coal blending and combustion scheme that can best reduce costs while ensuring that the coal used in the unit meets the indicators. Summary of the invention

[0004] The purpose of the present invention is to at least provide a method and device for optimizing coal blending and combustion, which can at least solve the problem of finding a coal blending and combustion scheme that can best reduce costs, and at least achieve the effect of reducing costs.

[0005] In order to solve the above technical problems, at least one embodiment of the present application provides a method for optimizing coal blending and combustion, comprising:

[0006] Get the unit price of each type of coal added to each raw coal bin;

[0007] Whether each raw coal bin is added with each coal type is used as each first decision variable, and the proportion of each coal type added with each raw coal bin is used as the second decision variable. According to the first decision variable, the second decision variable and the unit price, a calculation formula for coal burning cost is determined;

[0008] The first decision variable and the second decision variable are constrained by the preset raw coal bunker index constraint conditions, coal type index constraint conditions, and unit index constraint conditions, and the optimized value of the first decision variable and the optimized value of the second decision variable are calculated with the goal of minimizing the coal cost calculated by the calculation formula of the coal cost.

[0009] At least one embodiment of the present application further provides a device for optimizing coal blending and combustion, comprising:

[0010] The data collection module is used to obtain the unit price of each type of coal added to each raw coal bin;

[0011] A coal burning cost module, for determining a calculation formula for coal burning cost based on whether each raw coal bin is added with each coal type as each first decision variable, and the proportion of each coal type added with each raw coal bin as a second decision variable, and based on the first decision variable, the second decision variable and the unit price;

[0012] A variable calculation module is used to constrain the first decision variable and the second decision variable with preset raw coal bunker index constraints, coal type index constraints, and unit index constraints, and calculate the optimized value of the first decision variable and the optimized value of the second decision variable with the goal of minimizing the coal cost calculated by the calculation formula of the coal cost.

[0013] At least one embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned coal blending optimization method.

[0014] At least one embodiment of the present application further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned method for optimizing coal blending and combustion when executed by a processor.

[0015] The embodiments of the present application provide an optimization method and device for coal blending and combustion, which obtain the unit price of each type of coal added to each raw coal bin. The unit price required to add the same type of coal to different raw coal bins may be different, and the unit price required to add the same raw coal bin to different raw coal bins may be different. Whether each raw coal bin adds each type of coal is used as the first decision variable. There is a first decision variable corresponding to the addition of a raw coal bin to a type of coal. If a raw coal bin adds a type of coal, the first decision variable between the raw coal bin and the type of coal can be determined as 1. If a raw coal bin does not add a type of coal, the first decision variable between the raw coal bin and the type of coal can be determined as 0. The proportion of each type of coal added to the raw coal bin is used as the second decision variable. The second decision variable is used to determine The content of a certain type of coal added to a raw coal bin is determined. The larger the second decision variable is, the greater the content of the coal type added is. The calculation formula of the coal burning cost is determined according to the first decision variable, the second decision variable and the unit price. The first decision variable and the second decision variable are constrained by the preset raw coal bin index constraint conditions, coal type index constraint conditions and unit index constraint conditions. With the goal of minimizing the coal burning cost calculated by the calculation formula of the coal burning cost, a mathematical programming model is constructed. The optimized value of the first decision variable and the optimized value of the second decision variable are calculated by the COPT solver, so that coal blending can be carried out according to the optimized value of the first decision variable and the optimized value of the second decision variable, so as to reduce the cost of coal blending and combustion. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.

[0017] Figure 1 It is a flow chart of a method for optimizing coal blending and combustion provided by an embodiment of the present application. DETAILED DESCRIPTION

[0018] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined and referenced with each other under the premise of no contradiction.

[0019] In order to facilitate understanding of the embodiments of the present application, relevant contents about coal blending and combustion are first introduced here.

[0020] As my country's basic energy source, coal is an important pillar supporting my country's rapid economic development, and coal-fired power generation is also one of my country's important power generation methods. However, due to the uneven quality of coal used in my country's coal-fired power generation and limited high-quality coal, it is still normal for coal-fired power plants to mix high-quality coal with other coal. If too little high-quality coal is used, it will not be able to meet the power generation demand, and if too much high-quality coal is used, the cost of coal will increase. Therefore, how coal-fired power plants can use various types of coal as efficiently as possible while meeting daily power generation needs, reduce the supply pressure of high-quality coal, and reduce coal costs is still an important issue in the operation of coal-fired power plants.

[0021] At present, the coal blending schemes of most coal-fired power plants still rely on manual experience, presenting an extensive and short-sighted management model. The formulated coal blending schemes can basically guarantee normal operation, but there is often an excessive use of high-quality coal. Therefore, coal-fired power plants often have problems such as high coal costs, low-priced coal that should be used in combination with high-quality coal cannot be fully utilized, and high-quality coal supply pressure is too high. The current research on coal blending and combustion schemes for coal-fired power plants mainly focuses on how to ensure that the coal used by the unit meets the coal combustion index and how to monitor the combustion effect of the existing coal blending and combustion schemes in real time through sensors. However, there is a lack of research on how to find the coal blending and combustion scheme that can reduce costs the most while ensuring that the coal used by the unit meets the index. In order to reduce coal costs, coal-fired power plants are in urgent need of new decision-making methods to support the formulation of coal blending and combustion schemes for daily power generation.

[0022] In order to solve the above-mentioned technical problem of high coal combustion cost, the present invention proposes an optimization method for coal blending and combustion. The implementation details of the optimization method for coal blending and combustion of this embodiment are specifically described below. The following content is only the implementation details provided for the convenience of understanding and is not necessary for the implementation of this solution.

[0023] Embodiment 1:

[0024] The optimization method of coal blending and combustion in this embodiment can be applied to electronic devices with communication, computing and data storage capabilities. The specific process can be as follows: Figure 1 As shown, including:

[0025] Step 110, obtaining the unit price of each type of coal added to each raw coal bin.

[0026] Specifically, the unit price of each type of coal added to each raw coal bunker is obtained in the information system of the coal-fired power plant. i.k . Unit price i.k The cost required to add each unit of coal type k to the raw coal bin i, i represents the serial number of the raw coal bin, and k represents the serial number of the coal type. 1 and raw coal bunker 2 , the corresponding unit price and The same raw coal bunker i may have different coal types k 1 and coal type k 2 , the corresponding unit price and may be different, so it is necessary to obtain the unit price of each type of coal added to each raw coal bin i.k , in order to determine the costs required under different coal blending schemes.

[0027] Step 120, taking whether each raw coal bin is added with each of the coal types as the first decision variable, taking the proportion of each of the coal types added to each of the raw coal bins as the second decision variable, and determining the calculation formula for coal burning cost based on the first decision variable, the second decision variable and the unit price.

[0028] Specifically, in order to construct the objective function of the mixed integer programming model, the first decision variable and the second decision variable related to the coal blending and combustion decision are determined.

[0029] Take whether each raw coal bin is added with each type of coal as the first decision variable x i，k , each raw coal bin has a first decision variable with each coal type, and whether the coal type is added to the raw coal bin is determined according to the first decision variable. The first decision variable is a discrete variable with a value of 0 or 1. When the value of the first decision variable is 0, it means that the corresponding coal type is not added to the corresponding raw coal bin. When the value of the first decision variable is 1, it means that the corresponding coal type is added to the corresponding raw coal bin. For example, when x i，k When the value of is 0, it means that the coal type k is not added to the raw coal bin i. When the value of the first decision variable is 1, it means that the corresponding coal type k is added to the corresponding raw coal bin i.

[0030] The proportion of each type of coal added to each raw coal bin is taken as the second decision variable. Each raw coal bin has a second decision variable with each type of coal. The proportion of coal added to the raw coal bin is determined according to the second decision variable. The second decision variable is a continuous variable with a value range of [0, 1]. For example, when y i，k When the value of is a, it means that the proportion of coal type k in raw coal bin i is a.

[0031] In order to implement the mixed integer programming model, the calculation formula of coal cost is shown in formula (1):

[0032]

[0033] i represents the serial number of the raw coal bin, I represents the set of raw coal bins, I includes the serial numbers of all raw coal bins, k represents the serial number of the coal type, ω i，k represents the cost required to add each unit proportion of coal type k to the raw coal bunker i, x i，k represents the first decision variable, i.e., whether to add coal type k to raw coal bin i, y i，k The second decision variable is the proportion of coal type k added to raw coal bin i. The calculation formula of coal cost is used as the objective function of the mixed integer programming model. In the iterative optimization calculation of the first decision variable and the second decision variable, the objective function of formula (1) is minimized, that is, the coal cost is used as the objective for iterative calculation. Formula (1) can be set so that when mixed coal cannot be added to raw coal bin i, the corresponding term in the objective function of raw coal bin i is ∑ k ωi，k ·x i，k , when mixed coal can be added to the raw coal bin i, the corresponding term of the raw coal bin i in the objective function is ∑ k ω i，k ·y i，k , thus avoiding the x in the objective function i，k and i，k The quadratic term formed by multiplication increases the difficulty of solving the problem, and at the same time accurately calculates the cost of the type of coal added to the raw coal bin i in the objective function.

[0034] Step 130, constraining the first decision variable and the second decision variable with the preset raw coal bunker index constraint condition, coal type index constraint condition, and unit index constraint condition, with the goal of minimizing the coal cost calculated by the calculation formula of the coal cost, calculate the optimized value of the first decision variable and the optimized value of the second decision variable.

[0035] Specifically, the raw coal bunker index constraint is used to constrain the first decision variable and the second decision variable, so that the coal type determined by the first decision variable and the second decision variable added to the raw coal bunker meets the coal blending requirements of the raw coal bunker. The coal type index constraint is used to constrain the first decision variable and the second decision variable, so that the coal type index under the coal blending and combustion determined by the first decision variable and the second decision variable is within the required range. The unit index constraint is used to constrain the first decision variable and the second decision variable, so that the unit index under the coal blending and combustion determined by the first decision variable and the second decision variable is within the required range. The first decision variable and the second decision variable are constrained by the preset raw coal bunker index constraint conditions, coal type index constraint conditions, and unit index constraint conditions. The goal is to minimize the coal cost calculated by the calculation formula of the coal cost, build a mixed integer programming model, call the COPT solver to solve the mixed integer programming model, and obtain the optimized value of the first decision variable and the optimized value of the second decision variable. According to the optimized value of the first decision variable, the type of coal required to be added to each raw coal bunker can be determined, and according to the optimized value of the second decision variable, the proportion of coal required to be added to the raw coal bunker can be determined.

[0036] The optimization method for blending coal in this embodiment obtains the unit price of each type of coal added to each raw coal bin. The unit price required for adding the same type of coal to different raw coal bins may be different, and the unit price required for adding the same raw coal bin to different raw coal bins may be different. Whether each raw coal bin adds each type of coal is used as the first decision variable. There is a first decision variable corresponding to the addition of a raw coal bin to a type of coal. If a raw coal bin adds a type of coal, the first decision variable between the raw coal bin and the type of coal can be determined as 1. If a raw coal bin does not add a type of coal, the first decision variable between the raw coal bin and the type of coal can be determined as 0. The proportion of each type of coal added to each raw coal bin is used as the second decision variable. The second decision variable is used to determine the The content of a type of coal added to the warehouse, the larger the second decision variable is, the greater the content of the coal type added is; the calculation formula of the coal burning cost is determined according to the first decision variable, the second decision variable and the unit price; the first decision variable and the second decision variable are constrained by the preset raw coal warehouse index constraint conditions, coal type index constraint conditions, and unit index constraint conditions, and the mathematical programming model is constructed with the goal of minimizing the coal burning cost calculated by the calculation formula of the coal burning cost, and the optimized value of the first decision variable and the optimized value of the second decision variable are calculated by the COPT solver, so that coal can be blended according to the optimized value of the first decision variable and the optimized value of the second decision variable, so as to reduce the cost of coal blending and combustion.

[0037] In one embodiment, the raw coal bunker index constraint conditions include:

[0038] Taking the fact that only one type of coal is added to the raw coal bin of each pure coal type as a constraint condition, each of the first decision variables is constrained.

[0039] In this embodiment, for the set I of raw coal bins into which pure coal is added, pure To constrain, I pure Includes the serial number identification of all raw coal bins used to add pure coal, constraining each raw coal bin set I pure Only one type of coal is added to the raw coal bunker, and the constraints are shown in formula (2):

[0040]

[0041] In the formula, i represents the serial number of the raw coal bin, k represents the serial number of the coal type, and x i，k represents the first decision variable, y i，k represents the second decision variable, Ki represents the set of available coal types in raw coal bunker i, and I pure Represents the collection of raw coal bins into which pure coal is added.

[0042] In one embodiment, the raw coal bunker index constraint conditions include:

[0043] The first decision variable is constrained by taking the various types of coal required to be added to the raw coal bin in each given proportion as a constraint condition.

[0044] In this embodiment, for the set I of raw coal bins into which mixed coal is added, mix To constrain, I pure Including the serial number identification of all raw coal bins used to add mixed coal, for the raw coal bins that need to add mixed coal and the proportion of the added coal is given, constraining the raw coal bins to add two types of coal, and the raw coal bin i that adds coal according to the given proportion, constraining the raw coal bin i from the mixed coal proportion α i，1 Select one type of coal from the available coal type set and add it to the raw coal bunker i, and select another type of coal from it and add it to the raw coal bunker i. The constraints are shown in equations (3) and (4):

[0045]

[0046] In the formula, i represents the serial number of the raw coal bin, k represents the serial number of the coal type, and x i，k represents the first decision variable, y i，k represents the second decision variable, Indicates that the mixed coal ratio of raw coal bin i is α i，1 The set of available coal types, Indicates that the mixed coal ratio of raw coal bin i is α i，2 The available coal type set, I mix Represents the collection of raw coal bins that are added to the mixed coal.

[0047] In one embodiment, the raw coal bunker index constraint conditions include:

[0048] The second decision variable is constrained by taking the proportion range of the coal types added to the raw coal bin with each proportion to be determined as a constraint condition.

[0049] In this embodiment, for the set I of raw coal bins into which mixed coal is added, mix Constraints are imposed on the raw coal bunkers that need to add mixed coal and whose proportion is to be determined. The raw coal bunkers whose proportion is to be determined are raw coal bunkers whose proportion needs to be determined by the optimization value of the second decision variable. Constraints are imposed on the raw coal bunkers that need to add mixed coal and whose proportion is to be determined. Constraints are imposed on the first type of coal types available from the mixed coal in the raw coal bunker i. Select a type of coal to add to the raw coal bunker i, and constrain the second type of coal set available from the mixed coal in the raw coal bunker i Select a type of coal from the first type of coal and add it to the raw coal bin i. Constrain the proportion of the coal selected from the first type of coal set within the proportion range, and constrain the proportion of the first type of coal selected from the first type of coal set to the lower limit of the proportion of the first type of coal when the raw coal bin i is added with mixed coal and upper limit The proportion of the coal selected from the first coal type set is constrained within the proportion range, and the proportion of the coal selected from the first coal type set is constrained to the lower limit of the proportion of the second type of coal when the mixed coal is added to the raw coal bin i. and upper limit The raw coal bunker index constraint conditions for the raw coal bunker that needs to add mixed coal and the proportion to be determined are shown in formulas (5) to (8):

[0050]

[0051] In the formula, i represents the serial number of the raw coal bin, k represents the serial number of the coal type, and x i，k represents the first decision variable, y i，k represents the second decision variable, I mix represents the set of raw coal bunkers that are added to the mixed coal, K represents the set of available coal types, It indicates the lower limit of the proportion of the first type of coal when the raw coal bin i is added to the mixed coal. It indicates the upper limit of the proportion of the first type of coal when the raw coal bin i is added into the mixed coal. It indicates the lower limit of the proportion of the second type of coal when the raw coal bin i is added to the mixed coal. It indicates the upper limit of the proportion of the second type of coal when the raw coal bin i is added to the mixed coal. represents the first type of coal set available for mixed coal in raw coal bunker i, It represents the set of second-class coal types available for mixed coal in raw coal bunker i. The constraint in formula (5) has two functions: one is to ensure that when x is 0, y must be 0; the other is to ensure that when x is 1, y is not greater than 1.

[0052] In one embodiment, the coal type index constraint condition includes:

[0053] The first decision variable is constrained by taking the fact that the combination of the various types of coal added to the mixed type raw coal bin does not belong to the range of infeasible combinations as a constraint condition.

[0054] In this embodiment, constraints are imposed on the infeasible set of mixed coal, and the infeasible set of mixed coal for raw coal bunkers is pre-constructed. The infeasible set of mixed coal is the combination of coal types that cannot be mixed into the same raw coal bunker at the same time. For raw coal bunker i that needs to add mixed coal, the combination of coal types added to raw coal bunker i is constrained not to be in the infeasible set of mixed coal. The constraint is shown in formula (9):

[0055]

[0056] In the formula, i represents the serial number of the raw coal bunker, k 1 , k 2 Indicates the serial number of the coal type. represents the first decision variable, I mix Indicates adding mixed coal raw coal bunker collection, It represents the infeasible set of mixed coal of the raw coal bunker that needs to be added with mixed coal. The mixed type of raw coal bunker is the raw coal bunker that needs to be added with mixed coal. It constrains the combination of coal types added to the raw coal bunker i. Not in the mixed coal infeasible set. Formula (9) shows that if k1 and k2 are both in the mixed coal infeasible set (model input), they cannot be added to the raw coal bin i at the same time.

[0057] In one embodiment, the coal type index constraint condition includes:

[0058] The coal type index of the raw coal bin is constrained by taking the coal type index of the raw coal bin within the coal type index range of the coal bin as a constraint condition; wherein the coal type index of the raw coal bin is constructed using the first decision variable and / or the second decision variable.

[0059] In this embodiment, the coal type index of the raw coal bunker For example, the sulfur content, ash content, calorific value, ash melting point or grindability coefficient, calculate the coal type index of each raw coal bin, use the first decision variable and / or the second decision variable to construct the coal type index of the raw coal bin, the formula for constructing the coal type index of the raw coal bin for the pure coal type and the mixed coal type is different, and then the constructed coal type index of the raw coal bin is used. Constrained between the upper and lower limits of the coal type index of raw coal bin i.

[0060] For the raw coal bunker i that adds pure coal, the coal type index of the raw coal bunker i The constructed formula is shown in formula (10):

[0061]

[0062] In the formula, i represents the serial number of the raw coal bin, k represents the serial number of the coal type, and x i，k represents the first decision variable, It represents the coal type index of the raw coal bunker i with pure coal added under the coal blending condition determined by the first decision variable and the second decision variable. It can be sulfur content, ash content, calorific value, ash melting point or grindability coefficient, q k The coal type index of coal type k, I pure Indicates adding the pure coal raw coal bin set, K i represents the set of available coal types in raw coal bin i. Formula (10) is used to constrain all coal types K added to raw coal bin i i Coal type index q k The sum is equal to This constraint will only take effect on the k combinations of coal types in raw coal bin i whose first decision variable is 1, that is, the coal types not selected by the model will not be calculated when calculating the index.

[0063] For a given proportion of raw coal bunker i with mixed coal added, the coal type index of raw coal bunker i is The constructed formula is shown in formula (11):

[0064]

[0065] In the formula, i represents the serial number of the raw coal bin, k represents the serial number of the coal type, and x i，k represents the first decision variable, y i，k represents the second decision variable, represents the coal type index of raw coal bunker i under the condition of coal blending determined by the first decision variable and the second decision variable, q k represents the coal type index of coal type k, α i，1 Indicates that raw coal bin i needs to add mixed coal, the mixed coal ratio of the first type of coal, Indicates that the mixed coal ratio of raw coal bin i is α i，1 The set of available coal types, α i，2 Indicates that raw coal bin i needs to add mixed coal, the mixed coal ratio of the second type of coal, Indicates that the mixed coal ratio of raw coal bin i is α i，2 The available coal type set, I mix Indicates the addition of mixed coal to the original coal bunker set. For the mixed coal bunker, the calculation formula of the index is weighted average, that is, the index in the bunker = coal type 1 mixing ratio * coal type 1 index + coal type 2 mixing ratio * coal type 2 index. The first type of coal is the coal with a mixed coal ratio of α i，1 The second type of coal is the mixed coal ratio of α i，2 Available coal types.

[0066] For the raw coal bunker i that is added with mixed coal in a proportion to be determined, the coal type index of the raw coal bunker i The constructed formula is shown in formula (12):

[0067]

[0068] In the formula, i represents the serial number of the raw coal bin, k represents the serial number of the coal type, and x i，k represents the first decision variable, y i，k represents the second decision variable, represents the coal type index of raw coal bunker i under the condition of coal blending determined by the first decision variable and the second decision variable, q k represents the coal type index of coal type k, α i，1 Indicates that raw coal bin i needs to add mixed coal, K mix represents the set of available coal types with a yet-to-be-determined mixed coal ratio in raw coal bunker i, I mix Indicates adding mixed coal raw coal bunker collection. It represents the coal type index of raw coal bunker i under the condition of coal blending determined by the first decision variable and the second decision variable, and the coal type index of raw coal bunker i For example, sulfur content, ash content, calorific value, ash melting point, grindability coefficient, q k represents the coal type index of coal type k, coal type index q of coal type k k For example, sulfur content, ash content, calorific value, ash melting point, grindability coefficient, coal type index of raw coal bunker i and coal type index q of coal type k k Corresponding to the same type of indicators, such as the coal type indicator of raw coal bunker i is the sulfur content of raw coal bunker i, coal type index q of coal type k k is the sulfur content of coal type k.

[0069] The constraints on the coal type index of the raw coal bunker are shown in formula (13):

[0070]

[0071] In the formula, i represents the serial number of the raw coal bin, k represents the serial number of the coal type, and x i，k represents the first decision variable, y i，k represents the second decision variable, It represents the coal type index of raw coal bin i under the coal blending and combustion conditions determined by the first decision variable and the second decision variable. Raw coal bin i can be a raw coal bin with pure coal, a raw coal bin with mixed coal added in a given proportion, or a raw coal bin with mixed coal added in a to-be-determined proportion. It represents the lower bound of the coal type index of raw coal bunker i, represents the upper bound of the coal type index of raw coal bin i, and I represents the set of raw coal bins. They represent the lower and upper limits of the raw coal bin i on the index. If there is no upper or lower limit, the above constraints can be changed to unilateral constraints.

[0072] Under normal business conditions, the raw coal bin has lower limit constraints on the calorific value and ash melting point of coal types. By bringing the lower limit values ​​of the corresponding indicators into the constraints, the boundary constraints of the raw coal bin on the coal type indicators can be achieved.

[0073] In one embodiment, the unit index constraint conditions include:

[0074] Based on a preset index formula, according to the type of the coal type layer of the raw coal bin, the coal type index of the coal type layer of the raw coal bin is determined; wherein the preset index formula is used to determine different coal type indexes for different types of coal type layers, and the coal type index is determined according to the index of the original coal type layer of the raw coal bin and the index of the coal type added to the raw coal bin, and the coal type added to the raw coal bin is determined according to the first decision variable;

[0075] Based on the relationship between the unit index and the coal type index, the unit index is determined using the coal type index of each coal type layer in the raw coal bunker;

[0076] The unit index is constrained within a unit index range.

[0077] In this embodiment, the coal type indexes of different types of coal layers in the raw coal bunker i are The calculation method is different. The coal type index of the raw coal bunker i when the cth coal type layer exists According to the original coal layer index of the raw coal bunker and the index of the coal type added to the raw coal bunker Determine, under any combination of coal type layers, the coal type index of the original coal bunker i when the cth coal type layer exists The calculation formula of is shown in the preset indicator formulas of formulas (14) to (16):

[0078]

[0079]

[0080] In the formula, i represents the serial number of the raw coal bunker, x i，k represents the first decision variable, γ i It represents the coal layer burning in the original coal bunker i, and the γ of the original coal layer i The value is 1, the γ of the mixed layer i The value is 2, the γ of the coal type to be added i The value is 3. Indicates that when the cth coal layer exists, according to the coal layer γ i The value of is the coal type index of the raw coal bunker i, Indicates the original coal layer in the original coal bunker i The coal type index, worse (·) indicates Take the worst value. When the coal index is sulfur or ash, Take the maximum value. When the coal index is calorific value and grindability coefficient, Take the minimum value, Represents the coal type index of the original coal type layer of the original coal bunker i, represents the coal type index of the raw coal bunker i under the coal blending condition determined by the first decision variable, that is, the coal type index of the original layer, that is, the coal type index of the coal added to the raw coal bunker determined by the first decision variable, that is, the coal type index of the added layer, C = {c|c = (γ 1 , ..., γ i ), i∈I, γ i ∈{1, 2, 3}} represents the set of combinations of different coal layers between raw coal bins, that is, the combination set of coal layers, where γi It represents the coal type layer burned in the raw coal bunker i, and the γ of the raw coal type layer i The value is 1, the γ of the mixed layer i The value is 2, the γ of the coal type to be added i The value is 3, and I represents the raw coal bin set.

[0081] The coal type index of raw coal bunker i is calculated using the preset index formulas of formulas (14) to (16) when the cth coal type layer exists: It can be the sulfur content of raw coal bunker i in the presence of the cth coal layer Ash Grindability coefficient or calorific value index

[0082] In this embodiment, when the unit index is the unit sulfur content, the unit sulfur content is constrained within the unit index range so that the sulfur content of the unit does not exceed the design value. The constraint is shown in formula (17):

[0083]

[0084] In the formula, is the sulfur content of the unit, P standard ·Sulfur max ·N sulfur is the unit index range of the unit sulfur content, i represents the serial number of the raw coal bunker, x i，k represents the first decision variable, C={c|c=(γ 1 , ..., γ i ), i∈I, γ i ∈{1, 2, 3}} represents the set of different coal type layer combinations between raw coal bins, that is, the coal type layer combination set, where γ i It represents the coal type layer burned in the raw coal bunker i, and the γ of the raw coal type layer i The value is 1, the γ of the mixed layer i The value is 2, the γ of the coal type to be added i The value is 3. It represents the grindability coefficient of raw coal bunker i in the case of the cth coal layer, P i represents the output of the coal mill associated with raw coal bunker i, represents the sulfur content of raw coal bunker i in the cth coal layer, P standard Indicates the total designed coal output at rated load of the unit. max Indicates the maximum designed sulfur content percentage of the unit under rated load, N sulfur Indicates the correction factor when the computer assembles the sulfur content in the warehouse.

[0085] When the unit index is the unit ash content, the unit ash content is constrained within the unit index range so that the ash content of the unit distribution bin does not exceed the design value. The constraint is shown in formula (18):

[0086]

[0087] In the formula, is the ash content of the unit, P standard .Ash max .N ash is the unit index range of the unit ash content, i represents the serial number of the raw coal bunker, C = {c|c = (γ 1 , ..., γ i ), i∈I, γ i ∈{1, 2, 3}} represents the set of different coal type layer combinations between raw coal bins, that is, the coal type layer combination set, where γ i It represents the coal type layer burned in the raw coal bunker i, and the γ of the raw coal type layer i The value is 1, the γ of the mixed layer i The value is 2, the γ of the coal type to be added i The value is 3. It represents the grindability coefficient of raw coal bunker i in the case of the cth coal layer, P i represents the output of the coal mill associated with raw coal bunker i, represents the ash content of raw coal bunker i in the case of the cth coal layer, P standard Indicates the designed total coal output at rated load of the unit, Ash max Indicates the maximum design ash percentage of the unit under rated load, N ash Indicates the correction factor when calculating the ash content in the computer assembly bin.

[0088] When the unit index is the maximum load of the unit, the maximum load of the unit is constrained within the unit index range so that the maximum load of the unit is not less than the set maximum load of the unit. The constraint is shown in formula (19):

[0089]

[0090] In the formula, is the maximum load of the unit, i is the serial number of the raw coal bunker, x i，k represents the first decision variable, C={c|c=(γ 1 , ..., γ i ), i∈I, γ i ∈{1, 2, 3}} represents the set of different coal type layer combinations between raw coal bins, that is, the coal type layer combination set, where γ i It represents the coal type layer burned in the raw coal bunker i, and the γ of the raw coal type layer i The value is 1, the γ of the mixed layer iThe value is 2, the γ of the coal type to be added i The value is 3. represents the grindability coefficient of raw coal bunker i in the case of the cth coal layer, Pi represents the output of the coal mill associated with raw coal bunker i, Heat represents the calorific value of raw coal bunker i in the possible case of the cth coal layer. standard represents the calorific value of standard coal, Transfer represents the coal consumption of the unit for power generation, Power max Indicates the maximum load setting of the unit.

[0091] When the unit index is the unit hedge combustion calorific value ratio, the unit hedge combustion calorific value ratio is constrained within the unit index range, so that the ratio of high calorific coal in the front and rear walls is within a certain range. If the unit is a second-phase unit, the constraint is as shown in formula (20):

[0092]

[0093] In the formula, i represents the serial number of the raw coal bunker, x i，k represents the first decision variable, C={c|c=(γ 1 , ..., γ i ), i∈I, γ i ∈{1, 2, 3}} represents the set of different coal type layer combinations between raw coal bins, that is, the coal type layer combination set, where γ i It represents the coal type layer burned in the raw coal bunker i, and the γ of the raw coal type layer i The value is 1, the γ of the mixed layer i The value is 2, the γ of the coal type to be added i The value is 3, indicating that λ up Indicates the upper limit of the heat value ratio between the front wall and the rear wall of the second phase unit, λ low Indicates the lower limit of the heat value ratio between the front wall and the rear wall of the second phase unit, I front Indicates the raw coal bunker set on the front wall of the unit, I behind Represents the raw coal bunker set on the rear wall of the unit, K i represents the set of available coal types in raw coal bunker i, It represents the grindability coefficient of raw coal bunker i in the case of the cth coal layer, P i represents the output of the coal mill associated with raw coal bunker i, It represents the calorific value of raw coal bunker i in the possible case of the cth coal layer.

[0094] Embodiment 2:

[0095] The optimization method for blending coal and burning of this embodiment can be applied to electronic devices with communication, computing and data storage capabilities, and its specific flow includes:

[0096] Step 1: Acquire data in the information system of the coal-fired power plant and read it into the algorithm module of the present invention through the database used by the information system.

[0097] Step 2: Based on the read data, establish the mixed integer programming model required for the decision-making of coal blending and combustion scheme, and fill the read data into the model as the parameters required for each objective, variable, and constraint.

[0098] Step 3: Call the COPT solver to solve the mathematical model established in step 2. After necessary sorting, processing, and visualization conversion of the mathematical optimal solution through the program, it is output as a coal blending and combustion plan.

[0099] The mixed integer programming model involved in the above steps is as follows:

[0100] The parameters and sets required for modeling obtained from the data are as follows:

[0101]

[0102]

[0103]

[0104]

[0105] The decision variables are as follows:

[0106] x i,k : 0-1 variable, whether the raw coal bin i chooses to add coal type k, if it chooses to add, the value is 1, otherwise the value is 0

[0107] y i,k : Continuous variable, the proportion of coal type k added to raw coal bin i, the value range is [0,1]

[0108] The objective function is as follows:

[0109]

[0110] In order to implement the model, the objective function is: When mixed coal cannot be added to the raw coal bin i, the objective function expression of this project for the raw coal bin i is ∑ k ω i,k ·x i,k , and when mixed coal can be added to the raw coal bin i, the target function expression of this project for the raw coal bin i is ∑ k ω i,k ·y i,k This avoids the increased difficulty of solving the problem caused by the quadratic term in the objective function, and accurately adds the cost of the coal type in the warehouse to the objective function. i,k *ω i.j represents the cost of coal type k in bin i, ωi.j is the cost of coal type k in raw coal bin i at each unit ratio.

[0111] The constraints are as follows:

[0112] Coal type blending constraints

[0113] For the raw coal bunker that needs to be added with pure coal, one type of coal is added into the raw coal bunker.

[0114]

[0115] For the raw coal bunker that needs to add mixed coal and has a given ratio, two kinds of mixed coal are added to the raw coal bunker, and according to the given mixed coal ratio α i,1 , α i,2 , one of the two types of coal is selected and added from each optional coal type.

[0116]

[0117] For raw coal bunkers that need to add mixed coal and the proportion needs to be decided, the raw coal bunker is added with mixed coal, and one of the two types of coal is selected from the respective optional coal types. If there is an upper and lower limit on the proportion of a certain type of coal, the proportion of this type of coal should meet the limit.

[0118]

[0119] The constraint in equation (5) has two functions: one is to ensure that when x is 0, y must be 0; the other is to ensure that when x is 1, y is not greater than 1. Equation (7) is Formula (8) is

[0120] Constraints on the use of mixed coal

[0121] For the raw coal bin that needs to add mixed coal, the coal types used will not appear in the infeasible set of mixed coal.

[0122]

[0123] Formula (9) shows that if k1 and k2 are both in the infeasible set of mixed coal (model input), raw coal bin i cannot be added at the same time.

[0124] Coal type index boundary constraints

[0125] The raw coal bunker can be filled with pure coal or mixed coal according to different labor requirements. After determining the type of coal to be added, the calculation method of indicators (sulfur content, ash content, calorific value, ash melting point, grindability coefficient) is different. The calculation formula is as follows:

[0126]

[0127] Formula (10) is used to constrain all coal types K in raw coal bunker ii The indicator q k The sum is equal to This constraint will only take effect on the combination of coal type k in bin i where x=1, that is, the coal types not selected by the model will not be counted when calculating the indicators.

[0128] Compared with the fixed proportion of the mixed coal bunker,

[0129]

[0130] Formula (11) For mixed coal bunkers, the index calculation formula is weighted average, that is, bunker index = coal type 1 mixing ratio * coal type 1 index + coal type 2 mixing ratio * coal type 2 index. The first type of coal is the coal with a mixed coal ratio of α i,1 The second type of coal is the mixed coal ratio of α i,2 Available coal types.

[0131] Compare the proportion of mixed coal silos to be decided.

[0132]

[0133] in It indicates the index of the coal type added to the raw coal bunker i in the current coal blending and combustion plan (sulfur content, ash content, calorific value, ash melting point, grindability coefficient), q k Indicates the index of coal type k (sulfur content, ash content, calorific value, ash melting point, grindability coefficient). k By replacing the corresponding parameters of sulfur content, ash content, calorific value, ash melting point and grindability coefficient, the sulfur content, ash content, grindability coefficient, ash melting point and calorific value indicators of raw coal bin i can be calculated.

[0134] The raw coal bunker has upper and lower limits on the coal types added to it. The general limits are as follows:

[0135]

[0136] in They represent the lower and upper limits of the raw coal bin i on the index. If there is no upper or lower limit, the above constraints can be changed to unilateral constraints.

[0137] Under normal business conditions, the raw coal bin has lower limit constraints on the calorific value and ash melting point of coal types. By bringing the lower limit values ​​of the corresponding indicators into the constraints, the boundary constraints of the raw coal bin on the coal type indicators can be achieved.

[0138] Unit indicator boundary constraints

[0139] Under any combination of coal types and layers, the general calculation method for the sulfur content, ash content, grindability coefficient and calorific value of the raw coal bin i is:

[0140]

[0141] in Indicates the original coal layer in the original coal bunker i Indicators (sulfur content, ash content, grindability coefficient, calorific value); Indicates that in the case of the cth coal layer, according to γ i The indexes (sulfur content, ash content, grindability coefficient, calorific value) calculated for raw coal bin i are different in value; worse (·) means taking the worst value for the indexes (sulfur content, ash content, grindability coefficient, calorific value), taking the maximum for sulfur content and ash content, and taking the minimum for calorific value and grindability coefficient; It indicates the index (sulfur content, ash content, grindability coefficient, calorific value) of the coal type added to the raw coal bunker i in the current coal blending and combustion plan decision. The calculation formula is different depending on whether pure coal or mixed coal is added. For the unit boundary conditions, q k Replace with the corresponding parameters of sulfur, ash, grindability coefficient and calorific value, and you can calculate the sulfur, ash, grindability coefficient and calorific value index of raw coal bunker i in the case of the cth coal layer. After that, the above variables are used to participate in the boundary condition constraints. Because when it is necessary to check different coal layers and their boundaries, the formulas for calculating whether different indicators of coal in bin i meet the boundary values ​​are different, such as: when the calorific value boundary decreases, the calorific value indicator takes the original layer value; when the calorific value / ash melting point boundary does not decrease, the worst value between the original layer and the added layer is taken; when the calorific value boundary increases, the added layer value is taken. represents the value of the grindability coefficient calculated by equations (14), (15), and (16), It is a variable obtained by constraints (14), (15), and (16). The calculation of different indicators can be obtained by replacing q in the formula with the desired indicator value according to actual needs. Here, because the indicator value calculation formula is relatively repetitive, it is expressed through a unified symbol.

[0142] Unit sulfur content boundary constraints

[0143] For each coal type and layer combination, the sulfur content of the unit shall not exceed the design value.

[0144]

[0145] Unit ash boundary constraints

[0146] For each coal type and layer combination, the ash content of the unit shall not exceed the design value.

[0147]

[0148] Maximum load boundary constraint of unit

[0149] For each coal type and layer combination, the calculated maximum load of the unit shall not be less than the set maximum load of the unit.

[0150]

[0151] Unit hedging combustion calorific value ratio constraint

[0152] If the unit is a Phase II unit, the requirements are: the high and low heat distribution of the front and rear walls is equal, and the proportion of high heat coal is within a certain range

[0153]

[0154] In this embodiment, the COPT solver is a normative analytical solution and is software used to solve mathematical programming problems. It supports the use of mathematics and constraint programming to quickly develop and deploy decision optimization models, and can express complex business problems as mathematical programming models. Advanced optimization algorithms can quickly find solutions to these models. In this solution, this method uses the COPT solver to solve the mathematical programming model of the corresponding problem established based on the operations research optimization method in order to minimize the coal cost objective function based on the real-time coal inventory, power generation plan and coal index requirements of the coal-fired power plant, obtain the optimal solution to the corresponding combinatorial optimization problem, and obtain the final scheduling result, that is, the coal blending and combustion plan.

[0155] In this embodiment, a method for calculating coal blending and combustion schemes for thermal power plants based on operations research methods and combinatorial optimization methods is used, so that it can comprehensively consider the equipment parameters, power generation demand, and inventory coal information of the coal-fired power plant, and the problem of finding a coal blending and combustion scheme from the existing coal that can meet the power generation demand and equipment coal usage standards while optimizing the coal usage cost as much as possible is converted into a quantitative mathematical programming model, so that the business problem of finding an excellent solution can be converted into a mathematical problem of finding a mathematical optimal solution. The mathematical optimal solution found can be strictly guaranteed by mathematical theory, and the corresponding coal blending and combustion scheme can be quantitatively evaluated, so as to maximize the purpose of optimizing the coal blending and combustion scheme while meeting strict business constraints.

[0156] In this embodiment, a method for calculating the coal blending scheme of a thermal power plant based on operations research methods and combinatorial optimization methods is used to solve the problems raised in the above invention objectives. The technical solution adopted by the present invention is: first, in a coal-fired power plant that needs to use this method to calculate the corresponding coal blending scheme, the data required for decision-making is collected and integrated into the database used by the information system of the power plant. The main data to be collected include:

[0157] (1) It is necessary to calculate the required range of data for the unit's combustion calorific value in the power generation and heating plan corresponding to the coal blending scheme.

[0158] (2) The upper and lower limit data of the operating indicators of the existing units. The operating indicators that need to be considered include combustion calorific value, coal sulfur content, and coal ash content.

[0159] (3) Raw coal bunker parameters of existing units, including the output of coal mills corresponding to the raw coal bunker, the capacity of the raw coal bunker, the range requirements of the raw coal bunker for coal calorific value, ash melting point, ash content, sulfur content and other indicators, the types of coal available in the raw coal bunker, and the amount of coal available in the raw coal bunker

[0160] (4) Coal inventory data of coal-fired power plants, including the inventory of all types of coal, availability, and specific data on all indicators of each type of coal that need to be considered, such as calorific value, ash content, sulfur content, and ash melting point.

[0161] (5) Operational requirements for power generation, including the types of coal that need to be given priority for combustion, whether a raw coal bunker needs to maintain the existing type of coal, and whether a raw coal bunker needs to add multiple types of coal for blending.

[0162] After collecting the required data, this technical solution organizes the coal blending and combustion plan decision problem into a mixed integer programming problem that includes optimization objectives, constraints, and decision variables, so that it can be solved using mathematical programming software in subsequent steps. The mixed integer programming problem considered.

[0163] The optimization goal is:

[0164] (1) The cost of the blending solution should be as low as possible

[0165] (2) Try to use coal that needs to be used more frequently according to the power plant inventory management rules, such as coal that has been stored for a longer time.

[0166] The decision variables are:

[0167] (1) A 0-1 variable related to the raw coal bunker and the type of coal, indicating whether the raw coal bunker chooses to add the type of coal. If it chooses to add, the value is 1, otherwise the value is 0.

[0168] (2) A continuous variable related to the raw coal bin and coal type, indicating the proportion of coal type added to the raw coal bin, with a value range between 0 and 1.

[0169] The constraints are:

[0170] (1) Raw coal bunker index constraints: The specific types of coal or mixed coal added to each raw coal bunker and the combustion indexes must comply with the operating requirements of the raw coal bunker.

[0171] (2) Unit index constraints: The combustion index calculated based on the coal added to each raw coal bin of a unit and the coal loading speed should comply with the unit's constraints on each combustion index.

[0172] (3) Constraints on coal type availability: The coal types used in the blending scheme should be ensured to have sufficient inventory in the current coal yard and not be stored in a space that cannot be directly accessed, so they can be directly used for additional combustion.

[0173] In this embodiment, after the above-mentioned mixed integer programming problem is established and converted into a mathematical programming problem that can be recognized by the solver through computer language, a commercial solver will be used to solve this mathematical programming problem. A commercial solver is a commercial software that encapsulates many algorithms and techniques specifically used to solve mathematical programming problems, and calls corresponding algorithms and techniques to solve mathematical problems according to the mathematical structure and data characteristics of the problem to be processed. The mathematical result solved by the solver corresponds to a coal blending and combustion plan that satisfies the above-mentioned constraints and optimizes the business indicators referred to by the objective function. After converting this mathematical result into an output form that can be understood by business personnel, this method provides a specific plan that can be directly used to guide coal blending and combustion work.

[0174] In this embodiment, the actual coal inventory of the coal-fired power plant, coal-fired plans such as power generation and heating, parameter requirements of the generating units for coal, coal use rules and other business information are built into a corresponding mixed integer programming model, and the COPT solver is called to solve the combinatorial optimization problem corresponding to this model, so that its mathematical optimal solution can correspond to an optimized coal blending and combustion scheme, and the coal blending and combustion scheme obtained by the solution is used as the actual scheme for coal-fired power generation of the unit, which solves the problems of long time consumption, high corresponding cost of the scheme, and poor response flexibility to special occasions when the coal blending and combustion scheme was originally manually formulated by planners. In this embodiment, the problem of formulating coal blending and combustion scheme from a business perspective is modeled by combining business rules with real-time data of coal-fired power plants, converted into a certain mathematical programming problem, and a commercial solver with accurate solving capability is called to solve it, so that the coal blending and combustion scheme calculation method provided by the present invention can calculate the optimal coal blending and combustion scheme guaranteed by mathematical theory, and optimize the coal cost while ensuring the normal operation of the coal-fired power plant to the greatest extent; this method only enters necessary data and business requirements in the modeling process, and does not rely on the calculation experience of specific business personnel. Therefore, the coal blending and combustion scheme calculation capability of this method can quickly calculate the required coal blending and combustion scheme when the operating conditions of new power plants, new equipment, new coal types or coal-fired power plants suddenly change due to failures or maintenance, and has good adaptability to emergencies and unfamiliar scenarios.

[0175] Embodiment three:

[0176] Another embodiment of the present application relates to an optimization device for coal blending and combustion. The implementation details of the optimization device for coal blending and combustion of this embodiment are specifically described below. The following content is only for the convenience of understanding the implementation details, and is not necessary for the implementation of this solution. The optimization device for coal blending and combustion of this embodiment includes a data acquisition module, a coal combustion cost module and a variable calculation module.

[0177] The data collection module is used to obtain the unit price of each type of coal added to each raw coal bin;

[0178] A coal burning cost module, for determining a calculation formula for coal burning cost based on whether each raw coal bin is added with each coal type as each first decision variable, and the proportion of each coal type added with each raw coal bin as a second decision variable, and based on the first decision variable, the second decision variable and the unit price;

[0179] A variable calculation module is used to constrain the first decision variable and the second decision variable with preset raw coal bunker index constraints, coal type index constraints, and unit index constraints, and calculate the optimized value of the first decision variable and the optimized value of the second decision variable with the goal of minimizing the coal cost calculated by the calculation formula of the coal cost.

[0180] It is worth mentioning that the coal blending and combustion optimization device described in this embodiment can be used to execute any step of the embodiment of the above-mentioned coal blending and combustion optimization method. The modules involved in this embodiment are all logical modules. In practical applications, a logical unit can be a physical unit, or a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0181] Embodiment 4:

[0182] Another embodiment of the present application relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the optimization method for coal blending and combustion in the above-mentioned embodiments.

[0183] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0184] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0185] Embodiment five:

[0186] Another embodiment of the present application relates to a computer-readable storage medium storing a computer program, which implements the above method embodiment when executed by a processor.

[0187] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as: ROM), random access memory (Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program codes.

[0188] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A method for optimizing coal blending and combustion, characterized in that: include: Get the unit price of each type of coal added to each raw coal bin; Whether each raw coal bin is added with each coal type is used as each first decision variable, and the proportion of each coal type added with each raw coal bin is used as the second decision variable. According to the first decision variable, the second decision variable and the unit price, a calculation formula for coal burning cost is determined; The first decision variable and the second decision variable are constrained by the preset raw coal bunker index constraint conditions, coal type index constraint conditions, and unit index constraint conditions, and the optimized value of the first decision variable and the optimized value of the second decision variable are calculated with the goal of minimizing the coal cost calculated by the calculation formula of the coal cost.

2. The optimization method according to claim 1, characterized in that: The raw coal bunker index constraint conditions include: Taking the fact that only one type of coal is added to the raw coal bin of each pure coal type as a constraint condition, each of the first decision variables is constrained.

3. The optimization method according to claim 1, characterized in that: The raw coal bunker index constraint conditions include: The first decision variable is constrained by taking the various types of coal required to be added to the raw coal bin in each given proportion as a constraint condition.

4. The optimization method according to claim 1, characterized in that: The raw coal bunker index constraint conditions include: The second decision variable is constrained by taking the proportion range of the coal types added to the raw coal bin with each proportion to be determined as a constraint condition.

5. The optimization method according to claim 1, characterized in that: The coal type index constraints include: The first decision variable is constrained by taking the fact that the combination of the various types of coal added to the mixed type raw coal bin does not belong to the range of infeasible combinations as a constraint condition.

6. The optimization method according to claim 1, characterized in that: The coal type index constraints include: The coal type index of the raw coal bin is constrained by taking the coal type index of the raw coal bin within the coal type index range of the coal bin as a constraint condition; wherein the coal type index of the raw coal bin is constructed using the first decision variable and / or the second decision variable.

7. The optimization method according to claim 1, characterized in that: The unit index constraints include: Based on a preset index formula, according to the type of the coal type layer of the raw coal bin, the coal type index of the coal type layer of the raw coal bin is determined; wherein the preset index formula is used to determine different coal type indexes for different types of coal type layers, and the coal type index is determined according to the index of the original coal type layer of the raw coal bin and the index of the coal type added to the raw coal bin, and the coal type added to the raw coal bin is determined according to the first decision variable; Based on the relationship between the unit index and the coal type index, the unit index is determined using the coal type index of each coal type layer in the raw coal bunker; The unit index is constrained within a unit index range.

8. An optimization device for coal blending, characterized in that: include: The data collection module is used to obtain the unit price of each type of coal added to each raw coal bin; A coal burning cost module, for determining a calculation formula for coal burning cost based on whether each raw coal bin is added with each coal type as each first decision variable, and the proportion of each coal type added with each raw coal bin as a second decision variable, and based on the first decision variable, the second decision variable and the unit price; A variable calculation module is used to constrain the first decision variable and the second decision variable with preset raw coal bunker index constraints, coal type index constraints, and unit index constraints, and calculate the optimized value of the first decision variable and the optimized value of the second decision variable with the goal of minimizing the coal cost calculated by the calculation formula of the coal cost.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the coal blending and combustion optimization method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for optimizing coal blending and combustion according to any one of claims 1 to 7 is implemented.

Images