A method for estimating volatile components in coal industry analysis based on center-oriented optimization model

Abstract

The present invention discloses a method for estimating the components of volatile components in coal industrial analysis based on a centroid optimization model, comprising the following steps: S1, constructing a component estimation model for coal volatile components; S2, constructing a centroid optimization model; S3, optimizing and solving the decentralized optimization model based on a selected optimization algorithm to obtain optimal related parameters, and substituting the related parameters into the component estimation model for coal volatile components to obtain the optimal content of related components in coal volatile components. The advantage is that by constructing a penalty function associated with the central value in the feasible domain, the optimization result can not only satisfy the calorific value calculation equation but also make the content of each component within a reasonable range, so that the estimated components obtained are more in line with the actual situation. The constructed centroid optimization model has a small amount of calculation, low requirements on the optimization algorithm, and is suitable for calculation requirements in various environments.

Description

Technical Field

[0001] The invention relates to the technical field of coal component estimation and numerical optimization, and in particular to a method for estimating volatile components in coal industrial analysis based on a centroid optimization model. Background Art

[0002] At present, most of the quality inspections or component analyses of coal in actual production are experimentally determined according to the national standard "GB / T212-2008 Industrial Analysis Methods for Coal". Industrial analysis is also called technical analysis or practical analysis. The main quality indicators include the determination of moisture (M), ash (A) and volatile matter (V) in coal and the calculation of fixed carbon (fC). Industrial analysis of coal is the main indicator for understanding the characteristics of coal quality and the basic basis for evaluating coal quality. According to the various test results of industrial analysis, the nature and type of coal and the processing and utilization effects of various coals and their industrial uses can be preliminarily judged.

[0003] Generally speaking, the four components obtained by the coal industry analysis method, combined with the sulfur content (S) obtained by total sulfur analysis and the lower heating value (LHV) obtained by calorific value measurement, can basically meet most application needs in actual production. However, with the improvement of informatization and intelligence, products such as industrial models or systems developed for the industrial field have increasingly higher requirements for data integrity. However, the corresponding testing equipment is not in place in the laboratories of production enterprises. Therefore, a method of estimating more detailed data based on existing measurement data is very necessary at this stage.

[0004] Taking industrial products involving coal combustion as an example, more detailed data on carbon (C), hydrogen (H), oxygen (O) and nitrogen (N) in coal volatile matter (V) are needed to support the calculation of the product model. However, the production company laboratory is unable to measure these components. Therefore, a method is urgently needed to estimate the detailed components in coal volatile matter by building a trend center optimization model based on the corresponding coal type or previous measurement results to meet the data needs of this industrial product. Summary of the invention

[0005] The object of the present invention is to provide a method for estimating volatile components in coal industry analysis based on a centroid optimization model, thereby solving the above-mentioned problems existing in the prior art.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A method for estimating volatile components in coal industry analysis based on a center-trending optimization model comprises the following steps:

[0008] S1. Build a model for estimating components in coal volatiles;

[0009] S2, build a center-oriented optimization model;

[0010] S3. Based on the selected optimization algorithm, the decentralized optimization model is optimized and solved to obtain the optimal relevant parameters, and the relevant parameters are substituted into the component estimation model in the coal volatile matter to obtain the optimal content of the relevant components in the coal volatile matter.

[0011] Preferably, step S1 is specifically as follows: the known quantities in the component estimation model of coal volatile matter are the coal total sulfur analysis results, coal calorific value measurement results and coal industrial analysis results, and the coal industrial analysis results include moisture, ash, volatile matter and fixed carbon; the set value is the set central value of the total carbon content, total hydrogen content and total nitrogen content of the coal type; the output is the carbon content, hydrogen content, nitrogen content and oxygen content in the coal volatile matter; using the coal calorific value prediction formula and the mass conservation equation, the following equation group is established:

[0012] C+H+N+O+S=V

[0013] LHV sim =f(S,M,A,f C +C,H,N,O)

[0014] Wherein, C is the carbon content in coal volatile matter; H is the hydrogen content in coal volatile matter; N is the nitrogen content in coal volatile matter; O is the oxygen content in coal volatile matter; S is the result of coal total sulfur analysis; V is the coal volatile matter in coal industrial analysis result; LHV sim is the predicted value of calorific value, i.e. the predicted result of coal calorific value; M is the moisture content in the coal industry analysis result; A is the ash content in the coal industry analysis result; f C is the fixed carbon in the coal industry analysis results; f is the combination of S, M, A, and f C + Coal calorific value prediction function of all or part of the parameters including C, H, N, and O.

[0015] Preferably, step S2 specifically includes constructing an objective function

[0016] y=a*g(C,H,N)+b*abs(LHV sim -LHV)

[0017] Among them, LHV is the measured value of calorific value, that is, the calorific value of coal; g(C,H,N) is the penalty function caused by the deviation between the variable and the set central value; abs(LHV sim -LHV) is the penalty function for the conversion of the heat value calculation equation; a and b are the weight coefficients of the corresponding penalty function.

[0018] Preferably, step S3 specifically includes selecting a suitable optimization algorithm according to the complexity of the constructed objective function and the calculation speed requirements, optimizing and solving the objective function using the selected optimization algorithm, obtaining optimal related parameters, and substituting the optimal related parameters into the equation group to obtain the optimal output, thereby obtaining the optimal content of the relevant components in the coal volatile matter.

[0019] The beneficial effects of the present invention are as follows: 1. By constructing a penalty function associated with the center value in the feasible domain, the optimization result can satisfy the calorific value calculation equation and make the content of each component within a reasonable range, so that the estimated components obtained are more in line with the actual situation. 2. The constructed center-trending optimization model has a small amount of calculation, low requirements on the optimization algorithm, and is suitable for calculation requirements in various environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a principle flow chart of the method in the embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation methods described herein are only used to explain the present invention and are not used to limit the present invention.

[0022] Embodiment 1

[0023] like Figure 1 As shown, in this embodiment, a method for estimating volatile components in coal industry analysis based on a center-trending optimization model is provided, comprising the following steps:

[0024] S1. Build a model for estimating components in coal volatiles;

[0025] Step S1 is specifically as follows: the known quantities in the component estimation model of coal volatile matter are the coal total sulfur analysis results, coal calorific value measurement results and coal industrial analysis results, and the coal industrial analysis results include moisture, ash, volatile matter and fixed carbon; the set value is the set central value of the total carbon content, total hydrogen content and total nitrogen content of the coal type; the output is the carbon content, hydrogen content, nitrogen content and oxygen content in the coal volatile matter; using the coal calorific value prediction formula and the mass conservation equation, the following equation group is established:

[0026] C+H+N+O+S=V

[0027] LHV sim =f(S,M,A,f C +C,H,N,O)

[0028] Wherein, C is the carbon content in coal volatile matter; H is the hydrogen content in coal volatile matter; N is the nitrogen content in coal volatile matter; O is the oxygen content in coal volatile matter; S is the result of coal total sulfur analysis; V is the coal volatile matter in coal industrial analysis result; LHV sim is the predicted value of calorific value, i.e. the predicted result of coal calorific value; M is the moisture content in the coal industry analysis result; A is the ash content in the coal industry analysis result; f C is the fixed carbon in the coal industry analysis results; f is the combination of S, M, A, and f C + Coal calorific value prediction function of all or part of the parameters including C, H, N, and O.

[0029] S2, build a center-oriented optimization model;

[0030] Step S2 specifically includes constructing the objective function

[0031] y=a*g(C,H,N)+b*abs(LHV sim -LHV)

[0032] Among them, LHV is the measured value of calorific value, that is, the calorific value of coal; g(C,H,N) is the penalty function caused by the deviation between the variable and the set central value; abs(LHV sim -LHV) is the penalty function for the conversion of the heat value calculation equation; a and b are the weight coefficients of the corresponding penalty function;

[0033] For example, g(C,H,N) is set as follows:

[0034] g(C,H,N)=k C *dC 2 *10 abs(dC) +k H *dH 2 *10 abs(dH) +k H *dN 2 *10 abs(dN)

[0035] dC=CC t +f C

[0036] dH=H–H t

[0037] dN=N–N t

[0038] Where dC, dH, and dN are the deviations between the calculated values ​​of C, H, and N and the set values ​​respectively; k C , k H , k N are the weight coefficients corresponding to C, H, and N respectively; C t , Ht 、N t They are the set center values ​​for total carbon, total hydrogen, and total nitrogen respectively.

[0039] S3. Based on the selected optimization algorithm, the decentralized optimization model is optimized and solved to obtain the optimal relevant parameters, and the relevant parameters are substituted into the component estimation model in the coal volatile matter to obtain the optimal content of the relevant components in the coal volatile matter.

[0040] Step S3 specifically includes selecting a suitable optimization algorithm according to the complexity of the constructed objective function and the calculation speed requirements, optimizing and solving the objective function using the selected optimization algorithm, obtaining the optimal relevant parameters, and substituting the optimal relevant parameters into the equation group to obtain the optimal output, thereby obtaining the optimal content of the relevant components in the coal volatile matter.

[0041] In this embodiment, the estimation method of the present invention is based on the traditional optimization algorithm, and a related center-trending optimization model is built by improving the algorithm's objective function calculation method within the feasible domain, so that it can calculate the optimal solution that is closest to the reasonable value of each variable in the case of multiple optimization solutions. Center-trending optimization means that when each optimization variable is within a set value or range, the impact on the objective function is minimal. As the distance from the set value or range increases, the impact of the optimization variable on the objective function also increases. Among them, the penalty function caused by the deviation of the variable from the central value in the center-trending optimization model can be constructed according to the variable weight and the actual problem, and the choice of optimization algorithm can be determined according to the actual calculation requirements.

[0042] Embodiment 2

[0043] In this embodiment, the execution process of the method of the present invention is described in detail in combination with specific data examples.

[0044] 1. Build a model for estimating components in coal volatile matter.

[0045] The air-dry basis data is used as the calculation data. In the model,

[0046] Known quantities: coal total sulfur analysis results - sulfur content (Sad), coal calorific value determination results - calorific value (LHV) and coal industrial analysis results, coal industrial analysis results include moisture (Mad), ash (Aad), volatile matter (Vad) and fixed carbon (f Cad ).

[0047] Setting value: Calculate the total carbon content of coal (C t ), total hydrogen content (H t ) and total nitrogen (N t ).

[0048] Output: Carbon content (Cad), hydrogen content (Had), nitrogen content (Nad) and oxygen content in coal volatile matter (Vad).

[0049] Using the coal calorific value prediction formula (for example, the second formula is a coal calorific value prediction regression relationship) and the mass conservation equation, the following equations are established:

[0050] Cad+Had+Nad+Oad+Sad=Vad

[0051] LHV sim =339*(Cad+f Cad )+1030*Had–109*(Oad-Sad)-25*Mad

[0052] 2. Build a center-oriented optimization model.

[0053] Constructing the objective function

[0054] y=a*g(Cad,Had,Nad)+b*abs(LHVsim-LHV)

[0055] The objective function consists of two parts: g(Cad, Had, Nad) is the penalty function caused by the deviation between the variable and the set central value, 339*(Cad+f Cad )+1030*Had–109*(Oad-Sad)-25*Mad-LHV is the penalty function for the conversion of the heat calculation equation. g(Cad,Had,Nad) can be set according to the needs, for example:

[0056] g(C,H,N)=k C *dC 2 *10 abs(dC) +k H *dH 2 *10 abs(dH) +k N *dN 2 *10 abs(dN)

[0057] dC=CC t +f C

[0058] dH=H–H t

[0059] dN=N–N t .

[0060] 3. Select an optimization algorithm.

[0061] According to the complexity of the constructed model and the requirements for calculation speed and accuracy, select an appropriate optimization algorithm with good performance. The optimization model in the current scenario can be solved by most traditional optimization algorithms.

[0062] 4. Optimize the solution.

[0063] Optimize the solution based on the selected optimization algorithm.

[0064] 5. Calculation details

[0065] The volatile components of coal industry analysis are estimated by the method of the present invention. Specific relevant data are shown in Table 1. It can be seen that the deviation between the data estimated by the method of the present invention and the real data is very small, and the estimated components are more in line with the actual situation.

[0066] Table 1 Specific calculation data

[0067]

[0068] By adopting the above technical solution disclosed in the present invention, the following beneficial effects are obtained:

[0069] The present invention provides a method for estimating volatile components of coal industry analysis based on a centroid optimization model. By constructing a penalty function associated with the center value in a feasible domain, the optimization result can satisfy the calorific value calculation equation and make the content of each component within a reasonable range, so that the estimated components are more in line with the actual situation. The centroid optimization model has a small amount of calculation and low requirements on the optimization algorithm, and is suitable for calculation requirements in various environments.

[0070] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A method for estimating volatile components in coal industry analysis based on a centroid optimization model, characterized in that: The following steps are included: S1. Build a model for estimating the components in coal volatiles; specifically, step S1 includes: the known quantities in the model for estimating the components in coal volatiles are the results of total sulfur analysis of coal, the results of calorific value determination of coal and the results of coal industrial analysis, and the results of coal industrial analysis include moisture, ash, volatile matter and fixed carbon; the set values ​​are the set central values ​​of the total carbon content, total hydrogen content and total nitrogen content of the coal; the output is the carbon content, hydrogen content, nitrogen content and oxygen content in the coal volatiles; using the coal calorific value prediction formula and the mass conservation equation, the following set of equations is established: C+H+N+O+S=V LHV sim =f(S,M,A,f C +C,H,N,O) Wherein, C is the carbon content in coal volatile matter; H is the hydrogen content in coal volatile matter; N is the nitrogen content in coal volatile matter; O is the oxygen content in coal volatile matter; S is the result of coal total sulfur analysis; V is the coal volatile matter in coal industrial analysis result; LHV sim is the predicted calorific value, i.e. the predicted calorific value of coal; M is the moisture in the coal industry analysis result; A is the ash in the coal industry analysis result; f C is the fixed carbon in the coal industry analysis results; f is the combination of S, M, A, and f C + Coal calorific value prediction function of all or part of the parameters of C, H, N, O; S2, build a center-oriented optimization model; step S2 specifically includes: constructing the objective function y=a*g(C,H,N)+b*abs(LHV sim -LHV) Among them, LHV is the measured value of calorific value, that is, the calorific value of coal; g(C,H,N) is the penalty function caused by the deviation between the variable and the set central value; abs(LHV sim -LHV) is the penalty function for the conversion of the heat value calculation equation; a and b are the weight coefficients of the corresponding penalty function; S3. Based on the selected optimization algorithm, the centroid optimization model is optimized and solved to obtain the optimal related parameters, and the related parameters are substituted into the component estimation model of coal volatile matter to obtain the optimal content of the related components in the coal volatile matter; step S3 is specifically to select a suitable optimization algorithm according to the complexity of the constructed objective function and the calculation speed requirements, and use the selected optimization algorithm to optimize and solve the objective function to obtain the optimal related parameters, and substitute the optimal related parameters into the equation group to obtain the optimal output, and then obtain the optimal content of the related components in the coal volatile matter.

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