A method for calculating the ratio of mixed coal combustion based on an improved artificial jellyfish search algorithm

Abstract

The present invention relates to a method for calculating the ratio of mixed coal burning based on an improved artificial jellyfish search algorithm, including: selecting different types of coal to be mixed; generating a ratio of mixed coal to be selected; predicting the properties of mixed coal; selecting a coal blending method that meets the constraints and calculating the cost of mixed coal burning; and finding the optimal ratio of mixed coal burning by using an improved artificial jellyfish search algorithm. Compared with the prior art, the algorithm proposed in the present invention introduces a piecewise linear chaotic mapping method in the initialization population, so that the initial value of the population is more evenly distributed in the search space; when the jellyfish moves according to the trend of the ocean current, the cosine adaptive weight is introduced to enhance the global search capability of the algorithm; the sine-cosine strategy and the Cauchy-Gauss strategy are introduced in the stage of jellyfish moving within the group, which enhances the local search capability and avoids falling into the local optimal solution. The final mixed coal burning ratio has a lower cost, and its total calorific value is closest to the planned calorific value, which maximizes the benefits of the power plant and effectively avoids the waste of coal resources.

Description

Technical Field

[0001] The invention relates to a method for calculating a mixed coal combustion ratio, and in particular to a method for calculating a mixed coal combustion ratio based on an improved artificial jellyfish search algorithm. Background Art

[0002] Power plants use a variety of mixed coal combustion methods, which can save fuel costs on the one hand, and improve environmental protection costs, processing costs, etc. on the other hand. Refined mixed coal combustion seeks a balance between reducing fuel price costs and increasing operating costs, and achieves the value optimization of the unit power output cost of the power plant.

[0003] Since actual cost data can only be collected after combustion, traditional cost analysis is a post-analysis. However, procurement and coal blending optimization are both done before combustion, that is, it is necessary to predict the cost based on only the type of coal, medium and planned total calorific value to control the combustion cost, reduce the economic cost, and reduce the waste of coal energy.

[0004] In the prior art, particle swarm optimization, cuckoo search algorithm, artificial jellyfish search algorithm and other methods are often used to calculate coal blending and combustion. For example, China's patent application CN112418664A discloses a partitioned combined blending and combustion method and system based on particle swarm optimization. The partitioned combined blending and combustion method includes the following steps: Step 1, setting a boundary constraint system; Step 2, constructing a mathematical expression of the boundary constraint system; Step 3, determining the objective function, using the particle swarm optimization algorithm to perform a global optimization search on the objective function in the constructed mathematical expression, and obtaining the optimal partitioned combined blending and combustion solution. However, the accuracy of the calculation results obtained by applying the existing optimization method to calculate the mixed coal blending and combustion ratio still needs to be improved. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a method for calculating the mixed coal combustion ratio based on an improved artificial jellyfish search algorithm.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A method for calculating the ratio of mixed coal combustion based on an improved artificial jellyfish search algorithm comprises the following steps:

[0008] S1. Select different types of coal to be blended and retrieve the coal quality characteristics of each type of coal;

[0009] S2, initializing the relevant parameters of the improved artificial jellyfish search algorithm;

[0010] S3, taking the initial position of the jellyfish as the proportion of the mixed coal combustion and predicting the mixed coal characteristics;

[0011] S4, judging whether the mixed coal characteristics meet the constraint conditions, if yes, proceed to S5, otherwise, proceed to S10;

[0012] S5. Establish a fitness function according to the minimum economic cost model, calculate the initial fitness value, and obtain the initial optimal jellyfish position;

[0013] S6, calculating time control parameters and determining the movement mode of the jellyfish;

[0014] S7, based on the time control parameter, introducing cosine adaptive weights according to the type of ocean current trend movement to update the individual position;

[0015] S8. According to the time control parameter, the group moves internally, the position of the type A motion is updated by the sine-cosine strategy, and the position of the type B motion is updated by introducing the Cauchy-Gauss strategy;

[0016] S9, perform boundary processing on the updated position, recalculate the fitness value and update the optimal jellyfish position of the current iteration;

[0017] S10, determine whether the maximum number of iterations has been reached, if so, go to S11, otherwise add 1 to the number of iterations and go to S6;

[0018] S11. Complete the iterative process and output the optimal jellyfish position, mixed coal characteristics and the corresponding minimum economic cost.

[0019] Furthermore, the population position of the improved artificial jellyfish search algorithm is initialized using a piecewise linear chaotic map (PWLCM) method. Step S2 specifically includes the following steps:

[0020] S201, assuming that the number of coal types to be proportioned is M, define the jellyfish population N, the maximum number of iterations n, and the population dimension D in sequence, and the solution space range is b = (b L ,b U ), b L is the lower bound of the solution space, its value is 0; b U is the upper bound of the solution space, and its value is 1;

[0021] S202, using the PWLCM method, randomly generate N initial positions pop of jellyfish in the solution space:

[0022]

[0023] pop i =b L +(b U -b L )*Z i

[0024] In the formula, Z i is the PWLCM logical chaotic value of the i-th jellyfish; P is the adjustment parameter of the PWLCM mapping, and its value range is [0,1]. The value of P determines the distribution characteristics of the generated chaotic sequence; i represents the i-th jellyfish, each jellyfish corresponds to a group of mixed coal ratios, and i=1,2,...,N.

[0025] Furthermore, the prediction of mixed coal characteristics is specifically to use linear weighted prediction of mixed coal characteristics, and step S3 specifically includes the following mixed coal characteristics calculation method: linear weighted prediction of calorific value, sulfur content, moisture content, ash content, volatile matter and carbon content of mixed coal:

[0026]

[0027] Where Mix is ​​the calorific value, sulfur content or moisture content of the mixed coal, T j is the corresponding characteristic of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal;

[0028] Furthermore, according to the boiler combustion design requirements and the actual operation conditions of the boiler, the calculated mixed coal characteristics are constrained. Step S4 specifically includes the following constraints:

[0029] S401. The constraints on heat generation are:

[0030]

[0031] Among them, Q j is the calorific value of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, Q L is the lower limit of heat generation, Q U It is the upper limit of heat generation;

[0032] S402. Constraints on volatile matter are:

[0033]

[0034] Among them, V j is the volatile matter of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, V L is the lower limit of volatile matter, V U is the upper limit of volatile matter;

[0035] S403, the constraints on carbon content are:

[0036]

[0037] Among them, C j is the carbon content of the jth raw coal, Xj is the proportion of the jth raw coal in the mixed coal, C L is the lower limit of carbon content, C U is the upper limit of carbon content;

[0038] S404, the constraints for sulfur content are:

[0039]

[0040] Among them, S j is the sulfur content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, S L is the lower limit of sulfur content, S U is the upper limit of sulfur content;

[0041] S405, the ash content constraint is

[0042]

[0043] Among them, A j is the ash content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, A L is the lower limit of ash content, A U is the upper limit of ash content;

[0044] S406, the moisture constraint is

[0045]

[0046] Among them, M j is the moisture content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, M L is the lower limit of moisture, M U It is the upper limit of moisture;

[0047] S407, the proportion constraint condition of each coal is: 0<pop i <1;

[0048] S408. If the purpose is to arrange for storage of coal for mixed combustion, it is necessary to add inventory constraints.

[0049] Furthermore, a minimum economic cost model is established as a fitness function. The fitness function in step S5 is:

[0050] F=C f +C S +C chs

[0051] In the formula, C f represents the fuel cost, C S represents environmental protection cost, Cchs For the ash removal cost.

[0052] Furthermore, in step S6, the calculation formula of the time control parameter is:

[0053] c(t)=|(1-t / n)*(2*r 0 -1)|

[0054] In the formula, t is the current iteration number, n is the maximum iteration number, r 0 is a random number between 0 and 1; when c(t)≥0.5, the jellyfish follows the ocean current; when c(t)<0.5, the jellyfish moves within the population.

[0055] Furthermore, according to the time control parameter jellyfish follows the ocean current, the cosine adaptive weight is introduced to update the position of the jellyfish individual. The specific improved position update formula of step S7 is:

[0056] pop i (t+1)=w*pop i (t)+r 01 *trend

[0057] Where trend = pop * (t)-β*r 02 *μ,w=0.05*cos(pi / 2*(1-t / n)). w represents the cosine adaptive factor; pop * (t) is the optimal position of the jellyfish population in the tth iteration, β is the distribution coefficient, β = 3 in the improved artificial jellyfish, r 01 、r 02 is a random number between 0 and 1, μ is the average position of all jellyfish at the tth iteration; pop i (t) represents the position of the tth iteration of the i-th jellyfish, pop i (t+1) represents the t+1th iteration jellyfish position after the i-th jellyfish update.

[0058] Further, according to the time control parameter, the movement mode of the jellyfish is determined, and the position of the jellyfish is updated by an update strategy. Step S8 specifically includes the following steps:

[0059] S801. By comparison, the jellyfish performs an improved type A motion, that is, the sine-cosine strategy is introduced, and the improved position update formula is:

[0060]

[0061] In the formula, r 1 =-(n / 5)*sinh(0.01*t 3 / n 3)+1,k 1 =0.5*(1-cos(pi*t 5 / n 5 )). pop * (t) is the optimal position of the jellyfish population at the tth iteration, pop i (t) represents the position of the tth iteration of the i-th jellyfish, pop i (t+1) represents the jellyfish position at the t+1th iteration after the i-th jellyfish is updated; r 1 and k 1 is the adaptive factor; r 2 、r 3 、r 4 is a random number between 0 and 1; t is the current iteration number, and n is the maximum iteration number;

[0062] S802. By comparison, the jellyfish performs an improved type B motion and introduces the Cauchy-Gaussian strategy. The improved position update is:

[0063] pop i (t+1) = pop i (t)*(1+λ 1 *cauchy(0,δ 2 ))+pop * (t)*(1+λ 2 *gauss(0,δ 2 ))

[0064] In the formula, λ 1 =1-t 2 / n 2 ,λ 2 =t 2 / n 2 ,δ=tanh(2*(1-t / n)).pop * (t) is the optimal position of the jellyfish population at the tth iteration, pop i (t) represents the position of the tth iteration of the i-th jellyfish, pop i (t+1) represents the t+1th iteration jellyfish position after the i-th jellyfish update, cauchy(0,δ 2 ) is a random variable that satisfies the Cauchy distribution, gauss(0,δ 2 ) is a random variable that satisfies Gaussian distribution, λ 1 , 2 It is a dynamic parameter that is adaptively adjusted according to the number of iterations, t is the current number of iterations, and n is the maximum number of iterations.

[0065] Furthermore, the updated jellyfish position is subjected to boundary processing, and the specific formula of step S9 is:

[0066]

[0067] In the formula, pop i,k represents the updated jellyfish position, i refers to the i-th jellyfish, k represents the k-th population, and i=1,2,...,N,k=1,2...,D。

[0068] Compared with the prior art, the present invention has the following beneficial effects:

[0069] 1. The present invention uses an improved artificial jellyfish search algorithm to solve the mixed coal burning ratio and coal purchasing plan, which can perform cost analysis before combustion and provide data support for the blending plan, which not only meets the power generation needs of the power grid, but also minimizes the economic cost and helps to reduce the waste of coal energy. The present invention introduces the PWLCM method into the traditional artificial jellyfish search algorithm to initialize the position of the jellyfish, so that the initial value of the population is more evenly distributed in the search space.

[0070] 2. The improved artificial jellyfish search algorithm proposed in the present invention introduces dynamic adaptive weights in the jellyfish following current stage, thereby enhancing the algorithm's global search capability.

[0071] 3. The improved artificial jellyfish search algorithm proposed in the present invention introduces the sine-cosine strategy and the Cauchy-Gauss strategy when the jellyfish are moving in the group, thereby enhancing the local search capability and avoiding falling into the local optimal solution.

[0072] 4. The present invention aims at the lowest economic cost, automatically finds the optimal coal blending ratio of the power plant's stored coal, collects all coal types and coal qualities on the market, and guides the coal purchase plan with the optimal coal blending method. Compared with the cuckoo search algorithm, the deep search cuckoo search algorithm, the particle swarm optimization algorithm and the artificial jellyfish search algorithm, the method proposed by the present invention can obtain a better coal blending scheme than other methods, so that the economic cost is the lowest. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 It is a schematic diagram of the overall framework structure of the present invention;

[0074] Figure 2 It is a specific implementation flow chart of the present invention. DETAILED DESCRIPTION

[0075] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0076] Example

[0077] like Figure 1As shown, the present invention provides a method for calculating the ratio of mixed coal combustion based on an improved artificial jellyfish search algorithm, which mainly uses the improved artificial jellyfish search algorithm to optimize the optimal ratio of multiple coal mixtures. According to the established coal type and coal quality database, considering all coal mixing methods and satisfying the constraints of coal mixing characteristics, the lowest cost combination of coal mixing methods according to the power of a certain time period of the daily load curve of a certain power plant and the lowest cost combination of coal mixing methods for a certain power plant to achieve the planned total calorific value of a certain month or a certain year are found, thereby guiding the coal purchase plan or coal mixing method.

[0078] Specifically, Figure 2 As shown, the present invention comprises the following steps:

[0079] S1. Select different types of coal to be blended, set the number of types of coal to be blended as M, and retrieve the coal quality characteristics of each type of coal;

[0080] S2, initializing the relevant parameters of the improved artificial jellyfish search algorithm;

[0081] S3. The initial position of the jellyfish is used as the proportion of the mixed coal. The mixed coal characteristics are predicted using linear weighting;

[0082] S4, judging whether the mixed coal characteristics meet the constraint conditions, if yes, proceed to S5, otherwise, proceed to S10;

[0083] S5. Establish a fitness function according to the minimum economic cost model, calculate the initial fitness value, and obtain the initial optimal jellyfish position;

[0084] S6, calculating time control parameters and determining the movement mode of the jellyfish;

[0085] S7, according to the time control parameter, introduce the cosine adaptive weight according to the type of ocean current trend movement to update the individual position;

[0086] S8. According to the time control parameters, the group moves internally, the position of the A-type movement is updated by the sine-cosine strategy, and the position of the B-type movement is updated by introducing the Cauchy-Gauss strategy;

[0087] S9, perform boundary processing on the updated position, recalculate the fitness value and update the optimal jellyfish position of the current iteration;

[0088] S10, determine whether the maximum number of iterations has been reached, if so, go to S11, otherwise add 1 to the number of iterations and go to S6;

[0089] S11. Complete the iterative process and output the optimal jellyfish position, mixed coal characteristics and the corresponding minimum economic cost.

[0090] Step S2 includes the following steps:

[0091] S201, define the jellyfish population N, the maximum number of iterations n, the population dimension D, and the range of the solution space is b = (b L ,b U ), b L is the lower bound of the solution space, its value is 0; b U is the upper bound of the solution space, and its value is 1.

[0092] S202, using the piecewise linear chaotic mapping method, randomly generating N jellyfish initial positions pop in the solution space:

[0093]

[0094] pop i =b L +(b U -b L )*Z i

[0095] In the formula, Z i is the PWLCM logical chaotic value of the i-th jellyfish; P is the adjustment parameter of the PWLCM mapping, with a value range of [0,1]. The value of P determines the distribution characteristics of the generated chaotic sequence; b L is the lower bound of the solution space, b U is the upper bound of the solution space, i represents the i-th jellyfish, each jellyfish corresponds to a set of mixed coal ratios, and i = 1, 2, ..., N.

[0096] Step S3 specifically includes the following mixed coal characteristic calculation method:

[0097] Linear weighting is used to predict the calorific value, sulfur content, moisture content, ash content, volatile matter and carbon content of mixed coal: Where Mix is ​​the calorific value, sulfur, moisture, ash, volatile matter or carbon content of the mixed coal, T j is the corresponding characteristic of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal;

[0098] Step S4 specifically includes the following constraints:

[0099] S401. The constraints on heat generation are: Among them, Q j is the calorific value of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, Q L is the lower limit of heat generation, Q U It is the upper limit of heat generation;

[0100] S402. Constraints on volatile matter are: Among them, V jis the volatile matter of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, V L is the lower limit of volatile matter, V U is the upper limit of volatile matter;

[0101] S403, the constraints on carbon content are: Among them, C j is the carbon content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, C L is the lower limit of carbon content, C U is the upper limit of carbon content;

[0102] S404, the constraints for sulfur content are: Among them, S j is the sulfur content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, S L is the lower limit of sulfur content, S U is the upper limit of sulfur content;

[0103] S405. The constraints for ash content are: Among them, A j is the ash content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, A L is the lower limit of ash content, A U is the upper limit of ash content;

[0104] S406. The constraints of moisture are: Among them, M j is the moisture content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, M L is the lower limit of moisture, M U It is the upper limit of moisture;

[0105] S407, the proportion constraint condition of each coal is: 0<pop i <1;

[0106] In this embodiment, the fitness function in step S5 is: F = C f +C S +C chs , where C f represents the fuel cost, C S represents environmental protection cost, C chs For the ash removal cost.

[0107] In step S6, the time control parameters are updated, and the time control formula is as follows:

[0108] c(t)=|(1-t / n)*(2*r0 -1)|

[0109] Where t is the current number of iterations, n is the maximum number of iterations, and r 0 is a random number between 0 and 1. When c(t)≥0.5, the jellyfish follows the ocean current; when c(t)<0.5, the jellyfish moves within the population.

[0110] Step S7 specifically involves introducing cosine adaptive weights to update the position of individual jellyfish according to the time control parameter jellyfish follow the ocean current. The improved position update formula is as follows:

[0111] pop i (t+1)=w*pop i (t)+r 01 *trend

[0112] Where trend = pop * (t)-β*r 02 *μ,w=0.05*cos(pi / 2*(1-t / n)), w represents the cosine adaptive factor; pop * (t) is the optimal position of the jellyfish population in the tth iteration, β is the distribution coefficient, β = 3 in the improved artificial jellyfish, r 01 、r 02 is a random number between 0 and 1, μ refers to the average position of all jellyfish in the tth iteration; pop i (t) represents the position of the tth iteration of the i-th jellyfish, pop i (t+1) represents the t+1th iteration jellyfish position after the i-th jellyfish update.

[0113] Step S8 specifically determines the movement mode of the jellyfish according to the time control parameter.

[0114] S801. By comparison, the jellyfish performs an improved type A motion and introduces a sine-cosine strategy. The improved position update is as follows:

[0115]

[0116] In the formula, r 1 =-(n / 5)*sinh(0.01*t 3 / n 3 )+1,k 1 =0.5*(1-cos(pi*t 5 / n 5 )). pop * (t) is the optimal position of the jellyfish population at the tth iteration, pop i (t) represents the position of the tth iteration of the i-th jellyfish, popi (t+1) represents the jellyfish position at the t+1th iteration after the i-th jellyfish is updated; r 1 and k 1 is the adaptive factor; r 2 、r 3 、r 4 is a random number between 0 and 1; t is the current iteration number, and n is the maximum iteration number.

[0117] S802, the jellyfish performs an improved type B motion, introducing the Cauchy-Gaussian strategy. The improved position update is as follows:

[0118] pop i (t+1) = pop i (t)*(1+λ 1 *cauchy(0,δ 2 ))+pop * (t)*(1+λ 2 *gauss(0,δ 2 ))

[0119] In the formula, λ 1 =1-t 2 / n 2 ,λ 2 =t 2 / n 2 ,δ=tanh(2*(1-t / n)).pop * (t) is the optimal position of the jellyfish population at the tth iteration, pop i (t) represents the position of the tth iteration of the i-th jellyfish, pop i (t+1) represents the t+1th iteration jellyfish position after the i-th jellyfish update, cauchy(0,δ 2 ) is a random variable that satisfies the Cauchy distribution, gauss(0,δ 2 ) is a random variable that satisfies Gaussian distribution, λ 1 , 2 It is a dynamic parameter that is adaptively adjusted according to the number of iterations, t is the current number of iterations, and n is the maximum number of iterations.

[0120] Step S9 specifically performs boundary processing on the updated jellyfish position, and the formula is as follows:

[0121]

[0122] In the formula, pop i,k represents the updated jellyfish position, i refers to the i-th jellyfish, k represents the k-th population, and i=1,2,...,N,k=1,2...,D。

[0123] (1) Improved artificial jellyfish search algorithm

[0124] This application improves the traditional artificial jellyfish search algorithm in three aspects. First, the PWLCM method is introduced to initialize the position of the jellyfish, so that the initial value of the population is more evenly distributed in the search space; second, dynamic adaptive weights are introduced in the stage of jellyfish following the ocean current, which enhances the algorithm's global search ability; third, the sine-cosine strategy and Cauchy-Gaussian strategy are introduced in the stage of jellyfish moving in the group, which enhances the local search ability and avoids falling into the local optimal solution.

[0125] (2) Establishment of economic cost model

[0126] The economic cost is composed of fuel cost, environmental protection cost and ash discharge cost.

[0127] Fuel cost: C f =P A X 1 +P B X 2 +...+P l X l ,l=1,2,...,M,

[0128] Among them, C f is the fuel cost, P A ,P B ,...P l is the unit price of raw coal in different calorific value ranges, X 1 ,X 2 ,...X l is the ratio of mixed coal.

[0129] Environmental protection costs include sewage discharge costs and limestone costs. Sewage discharge costs mainly consider SO 2 Sewage charges: in, For SO 2 Sewage charges, SO generated by mixed coal combustion 2 Quality, is the desulfurization efficiency, P S For SO 2 National emission charging standard (yuan / ton). Limestone cost calculation considers treating one ton of SO 2 It takes 1.56 tons of limestone to convert into 25% limestone slurry. The slurry cost for burning one ton of sulfur is:

[0130]

[0131] in, is the limestone slurry cost, SO generated by mixed coal combustion2 The quality of JY is the market price of slurry (yuan / ton).

[0132] In summary, the environmental protection cost mainly considers the cost of limestone used to absorb sulfur dioxide in the cooling tower and the environmental protection cost caused by sulfur dioxide discharged into the air:

[0133]

[0134] The ash removal cost is the cost of ash flushing water under the condition of known coal consumption and ash content: C chs =13*G*A*P chs , where C chs is the ash flushing water fee (yuan), G is the amount of coal burned (tons), A is the ash content (%), P chs The unit price of ash flushing water (yuan / ton).

[0135] In summary, the economic cost model is: F = C f +C S +C chs ,Right now:

[0136]

[0137] (3) In this embodiment, the linear weighted method is used to predict the calorific value, sulfur content, moisture content, ash content, volatile matter and carbon content of the mixed coal. According to the coal type data, boiler combustion design requirements and actual boiler operation site conditions, the coal quality constraint conditions are set as follows:

[0138] The upper and lower limits of the calorific value of mixed coal:

[0139]

[0140] Upper and lower limits of volatile matter in mixed coal:

[0141]

[0142] Upper and lower limits of carbon content in mixed coal:

[0143]

[0144] The upper and lower limits of sulfur content in mixed coal:

[0145]

[0146] Upper and lower limits of mixed coal ash content:

[0147]

[0148] Upper and lower limits of mixed coal moisture:

[0149]

[0150] In order to verify the effectiveness of the method proposed in the present invention for blending coal in the inventory of power plant coal, this embodiment first establishes a database of coal types and coal quality in the inventory of a power plant. The power plant has 3 types of high calorific value coal and 6 types of low calorific value coal in stock. The characteristics and prices of each type of coal are shown in Table 1. Under the condition of meeting the constraints of the mixed coal characteristics, the power of a certain time period according to the daily load curve of a power plant is taken, and the calorific value selection range is about 4500kcal / kg. A high calorific value coal and a low calorific value coal are selected for mixed combustion, and the mixed coal combustion method is selected with the lowest cost as the goal.

[0151] In the experiment, the parameters of the improved artificial jellyfish search algorithm are set as follows: jellyfish population size N = 25, population dimension D = 1, maximum number of iterations n = 200, solution space range b = (0, 1); and the settings of these parameters in the cuckoo search, deep search cuckoo search, particle swarm optimization algorithm, and artificial jellyfish search algorithm are consistent with the improved artificial jellyfish search algorithm. Other parameters of the particle swarm algorithm are set as follows: self-learning factor is 1.49445, group learning factor is 1.49445, maximum speed is 2, minimum speed is -2, the starting value of inertia weight is 0.9, and the ending value of inertia weight is 0.4. The coal blending scheme obtained is shown in Table 2.

[0152] Table 1 Coal inventory of power plants

[0153]

[0154] Table 2 Optimal coal blending scheme

[0155]

[0156] In Table 2, the cuckoo algorithm and particle swarm algorithm select coal types 2 and 4, and the other algorithms select coal types 2 and 5. The improved artificial jellyfish search algorithm has the best mixed coal ratio and the lowest economic cost. It can be seen from Table 2 that the method proposed in the present invention can obtain the lowest cost mixed coal combustion plan compared with other methods.

[0157] In order to verify the effectiveness of the proposed method for the power plant coal purchase plan, firstly, according to the load forecast result of a power plant, the power generation of a certain month or a whole year is calculated. For example, the power generation of August, December and the whole year with peak power consumption is selected, and the power generation is 69.54, 82.64 and 633.09 million kWh respectively. The coal conversion efficiency of the power plant is 40%. Therefore, according to the conversion formula of power generation and calorific value, the planned total calorific value required for coal combustion is 1.4958054*10 12 kcal, 1.7775864*10 12kcal and 1.3617766*10 13 kcal, and the coal purchasing plan for August, December and the whole year can be formulated based on the planned total calorific value.

[0158] Specifically, a market coal type and coal quality database is established to collect 8 types of high calorific value coal and 15 types of low calorific value coal on the market. The characteristics and prices of each type of coal are shown in Table 3. A high calorific value coal and a low calorific value coal are selected for blending. The purchase plan is formulated based on the premise of meeting the constraints and achieving the mixed coal closest to the planned total calorific value to achieve the minimum purchase cost. The improved artificial jellyfish search algorithm is used to solve the lowest cost mixed coal, and it is compared with cuckoo search, deep search cuckoo search, particle swarm optimization algorithm, and artificial jellyfish search algorithm.

[0159] Considering the stability of the swarm intelligence evolutionary algorithm, each algorithm was run independently for 20 times. The parameters of the improved artificial jellyfish search algorithm were set as follows: the number of coal types to be proportioned M = 23, the size of the jellyfish population N = 25, the population dimension D = 1, the maximum number of iterations n = 200, the solution space range b = (0, 1), the adjustment parameter of the PWLCM mapping p = rand (0, 1), and the distribution coefficient β = 3. The number of coal types, population size and dimension, maximum number of iterations, and solution space range of the cuckoo search, deep search cuckoo search, particle swarm optimization algorithm, and artificial jellyfish search algorithm compared with them are consistent with the improved artificial jellyfish search algorithm. The other parameters of the particle swarm algorithm are set as follows: the self-learning factor is 1.49445, the group learning factor is 1.49445, the maximum speed is 2, the minimum speed is -2, the starting value of the inertia weight is 0.9, and the ending value of the inertia weight is 0.4. The optimal coal purchase plan is shown in Tables 4, 5, and 6.

[0160] Table 3 Market coal list

[0161]

[0162]

[0163] In order to obtain the coal purchase plan for August, the deep search cuckoo, particle swarm, artificial jellyfish and improved artificial jellyfish algorithms were independently run 20 times, and the results were all coal types 7 and 20. The average values ​​of 20 times were taken for comparison, and the results are shown in Table 4, where the cost refers to the economic cost per ton, and the total cost refers to the economic cost required for the planned power generation in the purchased month. Both are composed of fuel cost (referring to the cost of two mixed coals), environmental protection cost and ash removal cost. As can be seen from Table 4, the improved jellyfish algorithm saves 13691.82 yuan compared with the jellyfish algorithm, 48908.3 yuan compared with the deep search cuckoo search algorithm, and 97493.26 yuan compared with the particle swarm optimization algorithm. Therefore, the coal purchase plan obtained by the method proposed in this patent is the best.

[0164] Table 4 Coal purchasing plan for a power plant in August

[0165]

[0166] In addition, in the 20 independent runs of the cuckoo search algorithm, only 13 times did it obtain coal type 7 and coal type 20, another 6 times it obtained coal type 7 and coal type 17, and 1 time it obtained coal type 2 and coal type 20, indicating that the optimization performance in solving the optimal coal purchase plan is unstable, so it is not given in Table 4.

[0167] Table 5 Coal purchasing plan for a power plant in December

[0168]

[0169] Table 5 shows the coal purchase plan for December. The four algorithms were run independently 20 times and all obtained coal type 7 and coal type 20. The average values ​​were taken for comparison. As can be seen from Table 5, the improved jellyfish algorithm saves 16,271.1 yuan compared with the jellyfish algorithm, 58,121.69 yuan compared with the deep search cuckoo algorithm, and 115,859.11 yuan compared with the particle swarm optimization algorithm. In addition, since the cuckoo search algorithm only obtained coal type 7 and coal type 20 13 times out of 20 independent runs, the optimization performance is unstable, so it is not given in Table 5.

[0170] Table 6 Three optimization methods guide a power plant's one-year coal purchase plan

[0171]

[0172] Table 6 is a solution to the coal purchase plan for a power plant in one year. The improved artificial jellyfish algorithm, particle swarm algorithm and artificial jellyfish algorithm all independently selected coal types 7 and 20 for 20 times, so their average values ​​were selected for comparison. As can be seen from Table 6, the improved jellyfish algorithm saves 75255.25 yuan compared with the jellyfish algorithm and 1226500.22 yuan compared with the particle swarm algorithm.

[0173] Since the deep search cuckoo algorithm was run independently 20 times, it obtained coal type 7 and coal type 20 19 times, coal type 8 and coal type 20 once, and the optimization performance was not stable, which is not shown in Table 5. The cuckoo search algorithm obtained coal type 7 and coal type 20 only 13 times, coal type 7 and coal type 17 6 ​​times, and coal type 2 and coal type 20 once, and the optimization performance was unstable, which is also not shown in Table 5.

[0174] From the solution of the coal allocation plan for a certain period of time of the daily load curve of a power plant and the coal purchasing plan for August, December and the whole year, it can be seen that the improved artificial jellyfish algorithm proposed in the present invention has better stability than other algorithms, and the solved coal allocation and coal purchasing plans can not only meet the power generation demand of the power grid, but also minimize the economic cost, which in a sense reduces the waste of coal energy.

[0175] In summary, the present invention uses an improved artificial jellyfish search algorithm to find the optimal mixed coal blending ratio, establishes a power plant coal inventory database, solves the optimal value of the mixed coal blending ratio, and simultaneously establishes a market coal database. According to the planned calorific value of coal for the next month predicted by the load of a certain power plant, the optimal coal mixing method and blending ratio are found, and a coal purchasing plan is formulated to achieve a total calorific value that is closest to the planned calorific value, thereby maximizing the benefits of the power plant and effectively avoiding the waste of coal resources.

[0176] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for calculating the ratio of mixed coal combustion based on an improved artificial jellyfish search algorithm, characterized in that: The following steps are involved: S1. Select different types of coal to be blended and retrieve the coal quality characteristics of each type of coal; S2, initializing the relevant parameters of the improved artificial jellyfish search algorithm; S3, taking the initial position of the jellyfish as the proportion of the mixed coal combustion and predicting the mixed coal characteristics; S4, judging whether the mixed coal characteristics meet the constraint conditions, if yes, proceed to S5, otherwise, proceed to S10; S5. Establish a fitness function according to the minimum economic cost model, calculate the initial fitness value, and obtain the initial optimal jellyfish position; S6, calculating time control parameters and determining the movement mode of the jellyfish; S7, based on the time control parameter, introducing cosine adaptive weights according to the type of ocean current trend movement to update the individual position; S8. Perform internal movement of the group according to the time control parameter, perform position update of type A motion by using the sine-cosine strategy, and perform position update of type B motion by using the Cauchy-Gauss strategy; S9, perform boundary processing on the updated position, recalculate the fitness value and update the optimal jellyfish position of the current iteration; S10, determine whether the maximum number of iterations has been reached, if so, go to S11, otherwise add 1 to the number of iterations and go to S6; S11, completing the iteration process, outputting the optimal jellyfish position, mixed coal characteristics and the corresponding minimum economic cost; The population position of the improved artificial jellyfish search algorithm is initialized using a piecewise linear chaotic mapping method. Step S2 specifically includes the following steps: S201, assuming that the number of coal types to be proportioned is M, define the jellyfish population N, the maximum number of iterations n, and the population dimension D in sequence, and the solution space range is b=(b L ,b U ), b L is the lower bound of the solution space; b U is the upper bound of the solution space; S202, using piecewise linear chaotic mapping method, randomly generate N initial positions pop of jellyfish in the solution space: pop i =b L +(b U -b L )*Z i In the formula, Z i is the PWLCM logical chaotic value of the i-th jellyfish; P is the adjustment parameter of the PWLCM mapping, and the value of P determines the distribution characteristics of the generated chaotic sequence; i represents the i-th jellyfish, each jellyfish corresponds to a group of mixed coal ratios, and i=1,2,...,N; The prediction of mixed coal characteristics is specifically to use linear weighted prediction of mixed coal characteristics. Step S3 specifically includes the following mixed coal characteristics calculation method: linear weighted prediction of calorific value, sulfur content, moisture content, ash content, volatile matter and carbon content of mixed coal: Where Mix is ​​the calorific value, sulfur, moisture, ash, volatile matter or carbon content of the mixed coal, T j is the corresponding characteristic of the jth raw coal, X j is the proportion of the jth type of raw coal in the mixed coal.

2. The method for calculating the ratio of mixed coal combustion based on the improved artificial jellyfish search algorithm according to claim 1 is characterized in that: The calculated mixed coal characteristics are constrained according to the conditions given by the boiler. Step S4 specifically includes the following constraints: S401. The constraints on heat generation are: Among them, Q j is the calorific value of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, Q L is the lower limit of heat generation, Q U It is the upper limit of heat generation; S402. Constraints on volatile matter are: Among them, V j is the volatile matter of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, V L is the lower limit of volatile matter, V U is the upper limit of volatile matter; S403, the constraints on carbon content are: Among them, C j is the carbon content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, C L is the lower limit of carbon content, C U is the upper limit of carbon content; S404, the constraints for sulfur content are: Among them, S j is the sulfur content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, S L is the lower limit of sulfur content, S U is the upper limit of sulfur content; S405. The constraints for ash content are: Among them, A j is the ash content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, A L is the lower limit of ash content, A U is the upper limit of ash content; S406. The constraints of moisture are: Among them, M j is the moisture content of the jth raw coal, X j is the proportion of the jth raw coal in the mixed coal, M L is the lower limit of moisture, M U It is the upper limit of moisture; S407, the proportion constraint condition of each coal is: 0<pop i <1; S408. If the purpose is to arrange for storage of coal for mixed combustion, it is necessary to add inventory constraints.

3. The method for calculating the ratio of mixed coal combustion based on the improved artificial jellyfish search algorithm according to claim 1 is characterized in that: The minimum economic cost model is established as the fitness function. The fitness function in step S5 is: F=C f +C S +C chs In the formula, C f represents the fuel cost, C S represents the environmental cost, C chs For the ash removal cost.

4. The method for calculating the ratio of mixed coal combustion based on the improved artificial jellyfish search algorithm according to claim 1 is characterized in that: In step S6, the calculation formula of the time control parameter is: c(t)=|(1-t / n)*(2*r0-1)| Where t is the current iteration number, n is the maximum iteration number, and r0 is a random number between 0 and 1. When c(t)≥0.5, the jellyfish follows the ocean currents. When c(t)<0.5, the jellyfish move within the population.

5. The method for calculating the ratio of mixed coal combustion based on the improved artificial jellyfish search algorithm according to claim 1 is characterized in that: According to the time control parameter, jellyfish follow the ocean current, and the cosine adaptive weight is introduced to update the position of the jellyfish individual. The specific improved position update formula of step S7 is: pop i (t+1)=w*pop i (t)+r 01 *trend Where trend = pop * (t)-β*r 02 *μ,w=0.05*cos(pi / 2*(1-t / n)), w represents the cosine adaptive factor; pop * (t) is the optimal position of the jellyfish population at the tth iteration, β is the distribution coefficient, r 01 、r 02 is a random number between 0 and 1, μ is the average position of all jellyfish at the tth iteration; pop i (t) represents the position of the tth iteration of the i-th jellyfish, pop i (t+1) represents the t+1th iteration jellyfish position after the i-th jellyfish update.

6. The method for calculating the ratio of mixed coal combustion based on the improved artificial jellyfish search algorithm according to claim 1, characterized in that: According to the time control parameter, the movement mode of the jellyfish is determined, and the position of the jellyfish is updated by the update strategy. Step S8 specifically includes the following steps: S801. By comparison, the jellyfish performs an improved type A motion, that is, the sine-cosine strategy is introduced, and the improved position update formula is: In the formula, r1=-(n / 5)*sinh(0.01*t 3 / n 3 )+1, k1=0.5*(1-cos(pi*t 5 / n 5 )), pop * (t) is the optimal position of the jellyfish population at the tth iteration, pop i (t) represents the position of the tth iteration of the i-th jellyfish, pop i (t+1) represents the position of the jellyfish at the t+1th iteration after the i-th jellyfish is updated; r1 and k1 are adaptive factors; r2, r3, and r4 are random numbers between 0 and 1; t is the current iteration number, and n is the maximum iteration number; S802. By comparison, the jellyfish performs an improved type B motion and introduces the Cauchy-Gaussian strategy. The improved position update is: pop i (t+1)=pop i (t)*(1+λ1*cauchy(0,δ 2 ))+pop * (t)*(1+λ2*gauss(0,δ 2 )) Where λ1=1-t 2 / n 2 ,λ2=t 2 / n 2 ,δ=tanh(2*(1-t / n)),pop * (t) is the optimal position of the jellyfish population at the tth iteration, pop i (t) represents the position of the tth iteration of the i-th jellyfish, pop i (t+1) represents the t+1th iteration jellyfish position after the i-th jellyfish update, cauchy(0,δ 2 ) is a random variable that satisfies the Cauchy distribution, gauss(0,δ 2 ) is a random variable that satisfies the Gaussian distribution, λ1 and λ2 are dynamic parameters that are adaptively adjusted according to the number of iterations, t is the current number of iterations, and n is the maximum number of iterations.

7. The method for calculating the ratio of mixed coal combustion based on the improved artificial jellyfish search algorithm according to claim 1 is characterized in that: The updated jellyfish position is processed by boundary processing, and the specific formula of step S9 is: In the formula, pop i,k represents the updated jellyfish position, i refers to the i-th jellyfish, k represents the k-th population, and i=1,2,...,N, k=1,2,...,D.

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