Thermal power generation fire coal heat loss prediction method, system and device

Abstract

The invention provides a thermal power generation fire coal heat loss prediction method, system and device, and the method comprises the steps: building a fire coal heat distribution model through obtaining a to-be-predicted thermal power generation boiler heat distribution data type, and obtaining boiler parameters and environment temperatures at the same time interval; respectively calculating heat which is not effectively utilized and heat which is effectively utilized to form a preliminary lost heat data set and a preliminary utilized heat data set, calculating a measurement error value according to the total heat input in each time period, calculating a use-loss ratio according to the lost heat and the utilized heat, and calculating a measurement error value according to the measurement error value; correcting the preliminary loss heat to obtain a prediction data set of the coal in each time interval, establishing a fire coal heat loss prediction model through a neural network, establishing an optimization model of the fire coal heat loss prediction model, optimizing parameters of the prediction model, and predicting the fire coal heat loss at the future moment through the optimized prediction model.

Description

Technical Field

[0001] The present invention relates to the technical field of coal-fired heat loss, and in particular to a method, system and device for predicting coal-fired heat loss in thermal power generation. Background Art

[0002] With the transformation of the global energy structure and the improvement of environmental protection requirements, the thermal power generation industry faces increasingly severe challenges in reducing coal heat loss and improving combustion efficiency. Coal heat loss refers to the part of coal that is not effectively converted into heat or electricity during the combustion process in the process of thermal power generation. This part of heat loss not only wastes energy, but also seriously affects the economic benefits of the power plant. Therefore, accurately predicting and optimizing coal heat loss has become a key issue in improving coal utilization efficiency and promoting the sustainable development of the power industry.

[0003] In the prior art, the publication number CN112465241 B discloses a method, system, equipment and storage medium for predicting heat loss of coal-fired power generation, including collecting burnout influencing factors of coal combustion, the burnout influencing factors include coal characteristic parameters, combustion mode parameters, furnace structure parameters and furnace operation parameters; based on the pre-created neural network model and burnout influencing factors, determining the characteristic quantity of coal combustion products; wherein the characteristic quantity includes slag parameters, ash parameters, coal leakage parameters, and fly ash parameters; according to the heat loss relationship and the characteristic quantity, determining the heat loss of coal combustion, the heat loss relationship is the relationship between the heat loss of coal and the characteristic quantity. In this application, by collecting the burnout influencing factors and the neural network model, the content of slag, ash and unburned combustibles in the products after coal combustion is determined, so as to accurately predict the heat loss. However, the invention mainly focuses on the characteristic quantities of combustion products, and pays less attention to dynamic operating conditions such as boiler parameters, ambient temperature, and combustion mode, resulting in the incompleteness of the model's input variables, which limits the accuracy of its predictions. However, it does not model and analyze the dynamic characteristics of heat distribution over time. This method of ignoring the characteristics of time series may result in the inability to capture the regular changes in heat loss in actual operation, thereby reducing the accuracy and practicality of the prediction, and making it difficult to adapt to the dynamic changes and complex operating conditions in the operation of thermal power plants.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0005] The object of the present invention is to provide a method, system and device for predicting heat loss of coal-fired power generation to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for predicting heat loss of coal-fired power generation, comprising the following specific steps:

[0008] Step 1: Obtain the data type of the thermal power boiler heat distribution to be predicted, including boiler input heat, boiler effective utilization heat, exhaust heat loss heat, gas incomplete combustion heat loss heat, solid incomplete combustion heat loss heat, heat loss heat, ash physical heat loss heat, and establish a coal-fired heat distribution model;

[0009] Step 2: According to the coal heat distribution model and the heat distribution data type, by obtaining the boiler parameters and ambient temperature at the same time interval, the heat that is not effectively utilized and the heat that is effectively utilized are calculated respectively to form a preliminary loss heat data set and a preliminary utilization heat data set;

[0010] Step 3: Calculate the measurement error value based on the preliminary heat loss data set, the preliminary heat utilization data set and the boiler input heat in each time interval, calculate the loss-use ratio based on the heat loss and the heat utilization, correct the preliminary heat loss, and obtain the corrected heat loss;

[0011] Step 4: Obtain the coal particle size in each time interval to calculate the coal average particle value and particle deviation, and obtain the unit load, furnace parameters, steam parameters, air volume parameters and flue gas parameters. Through normalization processing, a prediction data set is formed, and a coal heat loss prediction model is established through a neural network, and the model is trained;

[0012] Step 5: Establish an optimization model for the coal-fired heat loss prediction model, optimize the parameters of the prediction model, speed up the training of the model, and use the current prediction data set as the input of the prediction model to predict the coal-fired heat loss in the future.

[0013] Furthermore, the calculation formula of the coal heat distribution model is:

[0014] Q z =Q1+Q2+Q3+Q4+Q5+Q6+Q w

[0015] Among them, Q z is the heat input to the boiler, Q1 is the effective heat utilization of the boiler, Q2 is the heat loss of exhaust gas, Q3 is the heat loss of incomplete combustion of gas, Q4 is the heat loss of incomplete combustion of solid, Q5 is the heat loss of heat dissipation, Q6 is the physical heat loss of ash, Q w is the measurement error value.

[0016] Further, the boiler parameters include exhaust volume, specific heat capacity of flue gas, flue gas temperature, concentration of combustible gas in flue gas, slag volume, coal content in slag, boiler insulation coefficient, boiler area, boiler surface temperature, specific heat capacity of slag, temperature when slag is discharged, steam production, steam specific enthalpy, feed water specific enthalpy, coal heat;

[0017] The calculation formula of the heat that is not effectively utilized is:

[0018] Q1=Q2+Q3+Q4+Q5+Q6

[0019] Among them, Q1 is the heat that is not effectively utilized, Q2 is the heat loss of flue gas, Q3 is the heat loss of incomplete combustion of gas, Q4 is the heat loss of incomplete combustion of solid, Q5 is the heat loss of heat dissipation, and Q6 is the physical heat loss of ash;

[0020] The calculation formula for exhaust heat loss Q2 is:

[0021] Q2=Gyq*Cyq*(Tyq-Thj)

[0022] Among them, Gyq is the exhaust volume, Cyq is the specific heat of flue gas, Tyq is the flue gas temperature, and Thj is the ambient temperature;

[0023] The calculation formula for the heat loss Q3 due to incomplete combustion of gas is:

[0024] Q3=Gyq*Nrq*Hrq

[0025] Among them, Gyq is the smoke exhaust volume, Nrq is the concentration of combustible gas in the smoke, and Hrq is the heat of the combustible gas;

[0026] The calculation formula for the heat loss Q4 due to incomplete combustion of solids is:

[0027] Q4=Glz*Nc*Hc

[0028] Among them, Glz is the amount of slag, Nc is the coal content in the slag, and Hc is the heat of coal;

[0029] The calculation formula for heat loss Q5 is:

[0030] Q5=Kbw*Sbm*(Tbm-Thj)

[0031] Among them, Kbw is the boiler insulation coefficient, Sbm is the boiler area, Tbm is the boiler surface temperature, and Thj is the ambient temperature;

[0032] The calculation formula of ash physical heat loss heat Q6 is:

[0033] Q6=Glz*Czz*(Tlz-Thj)

[0034] Among them, Glz is the amount of slag, Czz is the specific heat capacity of slag, Tlz is the temperature when slag is discharged, and Thj is the ambient temperature;

[0035] The calculation formula of the effectively utilized heat Q1 is:

[0036] Q1=Mzq*(Pzq-Pjs)

[0037] Among them, Mzq is the steam production, Pzq is the steam specific enthalpy, and Pjs is the feed water specific enthalpy.

[0038] Furthermore, the calculation formula of the boiler input heat is:

[0039] Q z =Mtc*Hc

[0040] Among them, Mtc is the consumption of coal, and Hc is the heat of coal;

[0041] The calculation formula of the measurement error value is:

[0042] Q w =Q z -Q1-Q l

[0043] Among them, Q w is the measurement error value.

[0044] Furthermore, the calculation formula of the use-loss ratio is:

[0045]

[0046] Among them, Pys is the usage-loss ratio, na is the number of data collections, in is the current in-th data collection, Q 1,in is the heat that is effectively utilized for the first time, Q l，in is the heat that is not effectively utilized for the current time;

[0047] The calculation formula for correcting the initial heat loss is:

[0048] Qx l =Pys*Q w +Q2+Q3+Q4+Q5+Q6

[0049] Among them, Qx l is the corrected heat loss.

[0050] Furthermore, the specific steps of calculating the coal average particle value and particle deviation are:

[0051] Take multiple samples of the pulverized coal input into the furnace in each time interval, count the size of the pulverized coal particles in each sample, and calculate the average particle value and particle deviation of the coal in the time interval:

[0052] The calculation formula of coal average particle value is:

[0053]

[0054] Among them, Pmp is the average particle value of coal, nq is the number of sampling times, iq is the iqth sampling, nb is the number of particles in each sampling, MD ib is the size of the ibth coal particle;

[0055] The particle deviation is calculated as:

[0056]

[0057] Among them, Qmp is the particle deviation, nq is the number of sampling times, iq is the iqth sampling, nb is the number of coal particles in each sampling, MD ib is the particle size of the ibth coal.

[0058] Furthermore, the furnace parameters include the amount of coal fed into the furnace and the furnace temperature;

[0059] The steam parameters include feed water flow, main steam flow, main steam pressure, main steam temperature, and reheat steam temperature;

[0060] The air volume parameters include primary air volume, primary air temperature, secondary air volume, secondary air temperature, and burnout air volume;

[0061] The flue gas parameters include flue gas temperature and flue gas flow rate;

[0062] The calculation formula for the normalization process is:

[0063]

[0064] Among them, Xg is the normalized data of each parameter type, Xmax is the largest number in each parameter type, Xmin is the smallest number in each parameter type, and xi id The idth data in each parameter type, and nx is the number of data in each parameter type.

[0065] Furthermore, the specific steps of establishing a coal-fired heat loss prediction model through a neural network and training the model are:

[0066] The neural network is an LSTM neural network prediction model, which consists of three gates: forget gate, input gate, and output gate:

[0067] The equation of the forget gate is:

[0068] f t =δ(W f ·[h t―1 ,x t ]+b f )

[0069] Among them, f t is the current switch state of the forget gate, δ represents the sigmoid function, W f is the forget gate weight, b f is the forget gate bias parameter, h t―1 is the hidden state at the previous moment, x t Input switch for current data;

[0070] Determine how much of the unit state from the previous moment needs to be retained to the current moment;

[0071] The equation for the input gate is:

[0072] i t =δ(W i ·[h t-1 ,x t ]+b i )

[0073] Among them, i t is the current switch state of the input gate, δ represents the sigmoid function, W i is the input gate weight, b i is the input gate bias parameter, h t-1 is the output hidden state of the previous moment, x t is the prediction data set of the previous moment;

[0074]

[0075] in, Candidate cell state, tanh represents the hyperbolic tangent function, W c is the candidate cell weight, b c is the candidate cell state bias parameter, h t-1 is the hidden state at the previous moment, x t Input switch for current data;

[0076] Determine how much of the network's input data needs to be saved to the unit state at the current moment;

[0077] The update equation is:

[0078]

[0079] Among them, C t Current cell state, Ct-1 is the cell state at the previous moment;

[0080] The output gate equation is:

[0081] o t =(W o ·[h t-1 ,x t ]+b o )

[0082] h t =o t *tanh(C t )

[0083] Among them, t is the current switch state of the output gate, δ represents the sigmoid function, W o is the output gate weight, b o is the output gate bias parameter, h t-1 is the hidden state at the previous moment, x t is the current data input switch, h t is the corrected heat loss at the current moment:

[0084] Control how much of the current unit state needs to output the corrected loss heat at the current moment;

[0085] The training steps are:

[0086] The prediction data set at the previous moment is used as the input of the prediction model, and the corrected heat loss at the current moment is used as the output of the model to train the model.

[0087] The parameters optimized by the prediction model include learning rate, number of hidden units, time step, and regularization parameter;

[0088] The optimization model is based on the sparrow search optimization algorithm. The sparrow population consists of discoverers, joiners and a certain number of scouts. The specific optimization logic is:

[0089] The finder's position update formula is as follows:

[0090]

[0091] Where t is the current iteration number, j is the dimension of the parameter to be optimized, j = 1, 2, ..., d, d is the total number of parameters to be optimized, X i，j is the position information of the i-th sparrow in the j-th dimension; iter max is the maximum number of iterations; α is a random number, α∈(0,1), R2 and ST are the alarm value and safety threshold; L is a 1×d dimensional matrix; Q is a random number that conforms to the normal distribution;

[0092] The position update formula of the joiner is as follows:

[0093]

[0094] Where: X P is the optimal position of the current discoverer; X worst is the current global worst position; A is a 1×d matrix, whose elements are randomly assigned 1 or -1, and ni is the size of the sparrow population;

[0095] The scout position update formula is as follows:

[0096]

[0097] Among them, X best is the global optimal position, β is the iteration step length, f i is the fitness value of the current sparrow individual, f g and f w are the current global optimal and worst fitness values, K is a random number, K∈[-1,1], ε is a constant to prevent the denominator from being 0:

[0098] The root mean square error is used to calculate the fitness, and the calculation formula is as follows:

[0099]

[0100] Among them, n is the number of samples, x i is the prediction data set of the previous moment, y i is the corrected heat loss.

[0101] The present invention also provides a thermal power generation coal-fired heat loss prediction system, the thermal power generation coal-fired heat loss prediction system is used to execute the thermal power generation coal-fired heat loss prediction method, comprising:

[0102] A heat distribution module, which is used to obtain the type of heat distribution data of the thermal power boiler to be predicted, including boiler input heat, boiler effective heat utilization, flue gas heat loss, gas incomplete combustion heat loss, solid incomplete combustion heat loss, heat dissipation loss, ash physical heat loss, and establish a coal-fired heat distribution model;

[0103] A heat calculation module, which is used to calculate the heat that is not effectively utilized and the heat that is effectively utilized respectively according to the coal heat distribution model and the heat distribution data type by obtaining boiler parameters and ambient temperature at the same time interval to form a preliminary loss heat data set and a preliminary utilization heat data set;

[0104] A loss correction module, the loss correction module is used to calculate the measurement error value according to the preliminary heat loss data set, the preliminary heat utilization data set and the boiler input heat in each time interval, calculate the loss ratio according to the heat loss and the heat utilization, correct the preliminary heat loss, and obtain the corrected heat loss;

[0105] A model building module, which is used to obtain the coal particle size in each time interval to calculate the coal average particle value and particle deviation, and obtain the unit load, furnace parameters, steam parameters, air volume parameters and flue gas parameters through normalization processing to form a prediction data set, establish a coal-fired heat loss prediction model through a neural network, and train the model;

[0106] The model optimization module is used to establish an optimization model for the coal-fired heat loss prediction model, optimize the parameters of the prediction model, speed up the training of the model, and use the prediction data set at the current moment as the input of the prediction model to predict the coal-fired heat loss at future moments.

[0107] The present invention also provides a device for predicting heat loss of coal-fired power generation, which includes a processor and a storage medium. The storage medium stores a computer program, and when the computer program is executed by the processor, it can implement the above-mentioned method for predicting heat loss of coal-fired power generation.

[0108] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention establishes a coal heat distribution model by acquiring the data type of the heat distribution of the thermal power boiler to be predicted, and respectively calculates the heat that is not effectively utilized and the heat that is effectively utilized by acquiring the boiler parameters and the ambient temperature at the same time interval, so as to form a preliminary heat loss data set and a preliminary heat utilization data set, and calculates the measurement error value according to the total heat input in each time period, calculates the loss ratio according to the loss heat and the utilized heat, and corrects the preliminary heat loss data set to obtain the predicted data set of coal in each time interval, establishes a coal heat loss prediction model by means of a neural network, establishes an optimization model of the coal heat loss prediction model, optimizes the parameters of the prediction model, and predicts the coal heat loss at future moments by means of the optimized prediction model;

[0109] The present invention calculates the heat that is not effectively utilized and the heat that is effectively utilized separately, and calculates the measurement error value for the total heat in each time period. At the same time, the ratio of lost heat to utilized heat is combined for dynamic correction to more comprehensively reflect the source of heat loss and improve the reliability of the predicted data. The data set at the time interval is used for modeling, and the predicted data set at the current moment is used as input to predict future heat loss. This method fully reflects the dynamic characteristics of time series data, can adapt to real-time changes in power plant operation, and the prediction results are closer to the actual situation. The parameters of the neural network model are optimized by the optimization algorithm, which not only improves the prediction accuracy, but also makes it more efficient and stable when dealing with complex nonlinear relationships. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0111] Figure 2 It is a schematic diagram of the overall system structure of the present invention. DETAILED DESCRIPTION

[0112] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments.

[0113] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should be understood by people with ordinary skills in the field to which the present invention belongs. The words "first", "second" and similar words used in the present invention do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0114] Example:

[0115] See also Figure 1 , the present invention provides a technical solution:

[0116] A method for predicting heat loss of coal-fired power generation, comprising the following specific steps:

[0117] Step 1: Obtain the data type of the thermal power boiler heat distribution to be predicted, including boiler input heat, boiler effective utilization heat, flue gas heat loss, gas incomplete combustion heat loss, solid incomplete combustion heat loss, heat dissipation loss, ash physical heat loss, and establish a coal-fired heat distribution model.

[0118] The thermal power generation process involves a large amount of heat transfer and conversion. The heat distribution of the boiler directly determines the effective utilization efficiency of thermal energy and the overall performance of the system. The heat distribution model is to realize the prediction of boiler heat loss. The establishment of the model can provide a basis for subsequent prediction and control strategy formulation. The heat distribution model integrates the input heat and various heat loss data, and can intuitively show the flow direction and loss ratio of heat during boiler operation.

[0119] The input heat of the boiler is the calorific value of the pulverized coal, which also includes the physical sensible heat of the fuel, the heat carried by the heat entering the furnace and the heat brought in by the atomized fuel oil vapor. The effective utilization heat of the boiler is the heat absorbed by the water and steam in the steam drum and the heat absorbed by the boiler steam when it flows through various heating surfaces. The exhaust heat loss of the boiler is the heat carried by the hot flue gas discharged by the boiler when it passes through the economizer, air preheater, etc. and is finally discharged into the atmosphere. This is the largest item of boiler heat loss. The heat loss of incomplete combustion of gas is the heat loss caused by the incomplete combustion of combustible gases in the furnace during the combustion of pulverized coal. The heat loss of incomplete combustion of solids refers to the loss caused by the incomplete combustion of carbon in the slag, fly ash and pulverized coal. The heat dissipation loss is the heat loss dissipated by the boiler and its connected pipes because the temperature is higher than the external air temperature. The physical sensible heat loss of ash is the heat carried by the high-temperature slag when it is discharged from the furnace.

[0120] In this embodiment, the calculation formula of the coal heat distribution model is:

[0121] Q z =Q1+Q2+Q3+Q4+Q5+Q6+Q w

[0122] Among them, Q z is the heat input to the boiler, Q1 is the effective heat utilization of the boiler, Q2 is the heat loss of exhaust gas, Q3 is the heat loss of incomplete combustion of gas, Q4 is the heat loss of incomplete combustion of solid, Q5 is the heat loss of heat dissipation, Q6 is the physical heat loss of ash, Q w is the measurement error value.

[0123] Step 2: According to the coal heat distribution model and heat distribution data type, by obtaining the boiler parameters and ambient temperature at the same time interval, the heat that is not effectively utilized and the heat that is effectively utilized are calculated respectively to form a preliminary loss heat data set and a preliminary utilization heat data set.

[0124] By acquiring time interval data, the dynamic changes in the boiler operation process can be captured, providing a more accurate description of the operating conditions. At the same time, it can adapt to the dynamic load requirements of boiler operation, such as changes in heat distribution when the power generation load fluctuates, reduce the impact of random measurement noise on heat distribution analysis, and provide more stable input data.

[0125] In this embodiment, the boiler parameters include exhaust volume, specific heat of flue gas, flue gas temperature, concentration of combustible gas in flue gas, slag volume, coal content in slag, boiler insulation coefficient, boiler area, boiler surface temperature, specific heat of slag, temperature when slag is discharged, steam production, steam specific enthalpy, feed water specific enthalpy, and coal heat;

[0126] The calculation formula of the heat that is not effectively utilized is:

[0127] Q l =Q2+Q3+Q4+Q5+Q6

[0128] Among them, Q l is the heat that is not effectively utilized, Q2 is the heat loss due to exhaust gas, Q3 is the heat loss due to incomplete combustion of gas, Q4 is the heat loss due to incomplete combustion of solid, Q5 is the heat loss due to heat dissipation, and Q6 is the physical heat loss due to ash;

[0129] The calculation formula for exhaust heat loss Q2 is:

[0130] Q2=Gyq*Cyq*(Tyq-Thj)

[0131] Among them, Gyq is the exhaust volume, Cyq is the specific heat of flue gas, Tyq is the flue gas temperature, and Thj is the ambient temperature;

[0132] The calculation formula for the heat loss Q3 due to incomplete combustion of gas is:

[0133] Q3=Gyq*Nrq*Hrq

[0134] Among them, Gyq is the smoke exhaust volume, Nrq is the concentration of combustible gas in the smoke, and Hrq is the heat of the combustible gas;

[0135] The calculation formula for the heat loss q4 of incomplete combustion of solid is:

[0136] Q4=Glz*Nc*Hc

[0137] Among them, Glz is the amount of slag, Nc is the coal content in the slag, and Hc is the heat of coal;

[0138] The calculation formula for heat loss Q5 is:

[0139] Q5=Kbw*Sbm*(Tbm-Thj)

[0140] Among them, Kbw is the boiler insulation coefficient, Sbm is the boiler area, Tbm is the boiler surface temperature, and Thj is the ambient temperature;

[0141] The calculation formula of ash physical heat loss heat Q6 is:

[0142] Q6=Glz*Czz*(Tlz-Thj)

[0143] Among them, Glz is the amount of slag, Czz is the specific heat capacity of slag, Tlz is the temperature when slag is discharged, and Thj is the ambient temperature;

[0144] The calculation formula of the effectively utilized heat Q1 is:

[0145] Q1=Mzq*(Pzq-Pjs)

[0146] Among them, Mzq is the steam production, Pzq is the steam specific enthalpy, and Pjs is the feed water specific enthalpy.

[0147] Step 3: Calculate the measurement error value based on the preliminary heat loss data set, the preliminary heat utilization data set and the boiler input heat in each time interval, calculate the loss-use ratio based on the heat loss and the heat utilization, correct the preliminary heat loss, and obtain the corrected heat loss.

[0148] During the operation and calculation of the boiler, when calculating the ineffectively utilized heat and the effectively utilized heat respectively, errors caused by measurement and calculation may occur, such as insufficient sensor accuracy or data acquisition delay, some small influencing factors ignored by the coal heat distribution model, and fluctuations in boiler operating load may lead to uneven heat distribution in a short period of time.

[0149] The total boiler input heat is used as a reference value to ensure that the sum of lost heat and utilized heat is equal to the input heat. If the error is not corrected, the calculation results may deviate from the actual operating conditions. The corrected data set is more in line with the actual operating status of the boiler and provides high-quality input for the prediction model.

[0150] In this embodiment, the calculation formula of the boiler input heat is:

[0151] Q z =Mtc*Hc

[0152] Among them, Mtc is the consumption of coal, and Hc is the heat of coal;

[0153] The calculation formula of the measurement error value is:

[0154] Q w =Q z -Q1-Q l

[0155] Among them, Q w is the measurement error value.

[0156] During boiler operation, the ratio of heat loss to heat utilization will change with operating conditions such as load fluctuations and ambient temperature changes. The correction method for the ratio can dynamically reflect these changes. At the same time, it can reduce the cumulative deviation caused by the initial calculation error and ensure the stability and reliability of the final data.

[0157] In this embodiment, the calculation formula of the use-loss ratio is:

[0158]

[0159] Among them, Pys is the usage-loss ratio, na is the number of data collections, in is the current in-th data collection, Q1, in is the heat that is effectively utilized for the first time, Q l，in The amount of heat that is not effectively utilized for the current time

[0160] The calculation formula for correcting the initial heat loss is:

[0161] Qx l =Pys*Q w +Q2+Q3+Q4+Q5+Q6

[0162] Among them, Qx l is the corrected heat loss.

[0163] Step 4: Obtain the coal particle size in each time interval to calculate the average coal particle value and particle deviation, and obtain the unit load, furnace parameters, steam parameters, air volume parameters and flue gas parameters. Through normalization processing, a prediction data set is formed. A coal heat loss prediction model is established through a neural network, and the model is trained.

[0164] The size of coal particles directly affects the completeness of combustion. The smaller the particles, the more complete the combustion and the smaller the heat loss. Particles that are too large may lead to increased incomplete combustion losses. Large deviations in particle size indicate uneven particle distribution, which may lead to incomplete local combustion and increased heat losses. By obtaining the mean and deviation of particle size, the impact of coal powder particles on combustion effects can be quantified, improving the ability to describe combustion losses.

[0165] In this embodiment, the specific steps of calculating the coal average particle value and particle deviation are:

[0166] Take multiple samples of the pulverized coal input into the furnace in each time interval, count the size of the pulverized coal particles in each sample, and calculate the average particle value and particle deviation of the coal in the time interval:

[0167] The calculation formula of coal average particle value is:

[0168]

[0169] Among them, Pmp is the average particle value of coal, nq is the number of sampling times, iq is the iqth sampling, nb is the number of particles in each sampling, MD ib is the size of the ibth coal particle;

[0170] The particle deviation is calculated as:

[0171]

[0172] Among them, Qmp is the particle deviation, nq is the number of sampling times, iq is the iqth sampling, nb is the number of coal particles in each sampling, MD ib is the particle size of the ibth coal.

[0173] In this embodiment, the furnace parameters include the amount of coal fed into the furnace and the furnace temperature;

[0174] The steam parameters include feed water flow, main steam flow, main steam pressure, main steam temperature, and reheat steam temperature;

[0175] The air volume parameters include primary air volume, primary air temperature, secondary air volume, secondary air temperature, and burnout air volume;

[0176] The flue gas parameters include flue gas temperature and flue gas flow rate.

[0177] Unit load reflects the output level of boiler operation and directly affects heat distribution and loss. Furnace parameters reflect the combustion conditions in the combustion chamber and have a significant impact on heat loss. Steam parameters are directly related to the thermal efficiency of the boiler and affect the calculation of heat utilization and heat loss. Air volume parameters determine the supply of oxygen during combustion, affecting the completeness of combustion and heat loss from exhaust gas. Flue gas parameters reflect incomplete combustion losses and heat loss from exhaust gas. By comprehensively collecting these parameters, a multi-dimensional coal-fired heat loss prediction data set can be formed, covering all key influencing factors in the combustion process.

[0178] In this embodiment, the calculation formula for the normalization process is:

[0179]

[0180] Among them, Xg is the normalized data of each parameter type, Xmax is the largest number in each parameter type, Xmin is the smallest number in each parameter type, and xi id The idth data in each parameter type, and nx is the number of data in each parameter type.

[0181] The numerical ranges of different parameters (such as particle size, temperature, pressure, etc.) may vary greatly. Directly inputting them into the neural network may cause the influence of certain parameters to be amplified or reduced, affecting the model training effect. Normalization standardizes the data to the same range [-1, 1] or, to ensure that the model can learn the contribution of each parameter fairly. At the same time, normalization can reduce the impact of numerical differences on gradient descent, avoid gradient explosion or disappearance, and thus improve the convergence speed of neural network training.

[0182] The core advantage of establishing a coal-fired heat loss prediction model through LSTM lies in its powerful modeling ability for time series data, accurate capture of multivariate nonlinear relationships, and adaptability to complex working conditions. Compared with traditional methods, LSTM can better improve prediction accuracy, reduce reliance on feature engineering, and meet the requirements for real-time and robustness under actual working conditions.

[0183] LSTM avoids information loss over long time spans by recording important information. The input gate, forget gate, and output gate mechanisms are used to dynamically control the information flow, thereby extracting and retaining features that are important to the prediction target and ignoring irrelevant information. Therefore, the long-term dependency of historical information can be captured in the prediction of coal-fired heat loss. For example, short-term changes in furnace parameters and loads can affect current heat loss, but steam parameters and coal particle characteristics may have an impact over a longer time range. Retaining multi-period features helps to extract dynamic change features in past time intervals, thereby improving the accuracy of predictions.

[0184] In this embodiment, the specific steps of establishing a coal-fired heat loss prediction model through a neural network and training the model are:

[0185] The neural network is an LSTM neural network prediction model, which consists of three gates: forget gate, input gate, and output gate:

[0186] The equation of the forget gate is:

[0187] f t =δ(W f ·[h t-1 , x t ]+b f )

[0188] Among them, f t is the current switch state of the forget gate, δ represents the sigmoid function, W f is the forget gate weight, b f is the forget gate bias parameter, h t-1 is the hidden state at the previous moment, x t Input switch for current data;

[0189] Determine how much of the unit state from the previous moment needs to be retained to the current moment;

[0190] The equation for the input gate is:

[0191] i t =δ(W i ·[h t-1 ,x t ]+b i )

[0192] Among them, i t is the current switch state of the input gate, δ represents the sigmoid function, W i is the input gate weight, b i is the input gate bias parameter, h t-1 is the output hidden state of the previous moment, x t is the prediction data set of the previous moment;

[0193]

[0194] in, Candidate cell state, tanh represents the hyperbolic tangent function, W c is the candidate cell weight, b c is the candidate cell state bias parameter, h t-1 is the hidden state at the previous moment, x t Input switch for current data;

[0195] Determine how much of the network's input data needs to be saved to the unit state at the current moment;

[0196] The update equation is:

[0197]

[0198] Among them, C t Current cell state, C t-1 is the cell state at the previous moment;

[0199] The output gate equation is:

[0200] o t =(W o ·[h t-1 ,x t ]+b o )

[0201] h t =o t *tanh(C t )

[0202] Among them, tis the current switch state of the output gate, δ represents the sigmoid function, W o is the output gate weight, b o is the output gate bias parameter, h t-1 is the hidden state at the previous moment, x t is the current data input switch, h t is the corrected heat loss at the current moment;

[0203] Control how much of the current unit state needs to output the corrected loss heat at the current moment;

[0204] The training steps are:

[0205] The prediction data set at the previous moment is used as the input of the prediction model, and the corrected heat loss at the current moment is used as the output of the model to train the model.

[0206] Step 5: Establish an optimization model for the coal-fired heat loss prediction model, optimize the parameters of the prediction model, speed up the training of the model, and use the current prediction data set as the input of the prediction model to predict the coal-fired heat loss in the future.

[0207] In the prediction of coal-fired heat loss in thermal power generation, building an optimization model based on the sparrow search optimization algorithm to optimize the prediction model can significantly improve the performance and practical value of the prediction model. Coal-fired heat loss prediction involves complex nonlinear and multivariate relationships. The core challenge lies in how to efficiently and accurately optimize the hyperparameters of the neural network, such as the learning rate, number of hidden layers, regularization coefficient, etc., to improve the prediction accuracy and robustness of the model.

[0208] During the optimization process, the sparrow search optimization algorithm initializes the population with random distribution, sets the parameters of the neural network to the individual positions, and uses discoverers, joiners, and a certain number of scouts to iteratively optimize the parameters. By updating the position and speed of the individual, it can quickly explore globally and effectively jump out of the local optimal trap, ensuring the global optimality of the parameter optimization results.

[0209] In this embodiment, the parameters optimized by the prediction model include learning rate, number of hidden units, time step, and regularization parameter;

[0210] The optimization model is based on the sparrow search optimization algorithm. The sparrow population consists of discoverers, joiners and a certain number of scouts. The specific optimization logic is:

[0211] The finder's position update formula is as follows:

[0212]

[0213] Where t is the current iteration number, j is the dimension of the parameter to be optimized, j = 1, 2, ..., d, d is the total number of parameters to be optimized, X i，j is the position information of the i-th sparrow in the j-th dimension; iter max is the maximum number of iterations; α is a random number, α∈(0,1), R2 and ST are the alarm value and safety threshold; L is a 1×d dimensional matrix; Q is a random number that conforms to the normal distribution;

[0214] The position update formula of the joiner is as follows:

[0215]

[0216] Where: X P is the optimal position of the current discoverer; X worst is the current global worst position; A is a 1×d matrix, whose elements are randomly assigned 1 or -1, and ni is the size of the sparrow population;

[0217] The scout position update formula is as follows:

[0218]

[0219] Among them, X best is the global optimal position, β is the iteration step length, f i is the fitness value of the current sparrow individual, f g and f w is the current global optimal and worst fitness value, K is a random number, K∈[-1,1], ε is a constant to prevent the denominator from being 0;

[0220] The root mean square error is used to calculate the fitness, and the calculation formula is as follows:

[0221]

[0222] Among them, n is the number of samples, x i is the prediction data set of the previous moment, y i is the corrected heat loss.

[0223] See also Figure 2 The present invention also provides a thermal power generation coal-fired heat loss prediction system, the thermal power generation coal-fired heat loss prediction system is used to execute the thermal power generation coal-fired heat loss prediction method, comprising:

[0224] A heat distribution module, which is used to obtain the type of heat distribution data of the thermal power boiler to be predicted, including boiler input heat, boiler effective heat utilization, flue gas heat loss, gas incomplete combustion heat loss, solid incomplete combustion heat loss, heat dissipation loss, ash physical heat loss, and establish a coal-fired heat distribution model;

[0225] A heat calculation module, which is used to calculate the heat that is not effectively utilized and the heat that is effectively utilized respectively according to the coal heat distribution model and the heat distribution data type by obtaining boiler parameters and ambient temperature at the same time interval to form a preliminary loss heat data set and a preliminary utilization heat data set;

[0226] A loss correction module, the loss correction module is used to calculate the measurement error value according to the preliminary heat loss data set, the preliminary heat utilization data set and the boiler input heat in each time interval, calculate the loss ratio according to the heat loss and the heat utilization, correct the preliminary heat loss, and obtain the corrected heat loss;

[0227] A model building module, which is used to obtain the coal particle size in each time interval to calculate the coal average particle value and particle deviation, and obtain the unit load, furnace parameters, steam parameters, air volume parameters and flue gas parameters through normalization processing to form a prediction data set, establish a coal-fired heat loss prediction model through a neural network, and train the model;

[0228] The model optimization module is used to establish an optimization model for the coal-fired heat loss prediction model, optimize the parameters of the prediction model, speed up the training of the model, and use the prediction data set at the current moment as the input of the prediction model to predict the coal-fired heat loss at future moments.

[0229] The present invention also provides a device for predicting heat loss of coal-fired power generation, which includes a processor and a storage medium. The storage medium stores a computer program, and when the computer program is executed by the processor, it can implement the above-mentioned method for predicting heat loss of coal-fired power generation.

[0230] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0231] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. Those skilled in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0232] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0233] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A method for predicting heat loss of coal-fired power generation, characterized in that: The specific steps include: Step 1: Obtain the data type of the thermal power boiler heat distribution to be predicted, including boiler input heat, boiler effective utilization heat, exhaust heat loss heat, gas incomplete combustion heat loss heat, solid incomplete combustion heat loss heat, heat loss heat, ash physical heat loss heat, and establish a coal-fired heat distribution model; Step 2: According to the coal heat distribution model and the heat distribution data type, by obtaining the boiler parameters and ambient temperature at the same time interval, the heat that is not effectively utilized and the heat that is effectively utilized are calculated respectively to form a preliminary loss heat data set and a preliminary utilization heat data set; Step 3: Calculate the measurement error value based on the preliminary heat loss data set, the preliminary heat utilization data set and the boiler input heat in each time interval, calculate the loss-use ratio based on the heat loss and the heat utilization, correct the preliminary heat loss, and obtain the corrected heat loss; Step 4: Obtain the coal particle size in each time interval to calculate the coal average particle value and particle deviation, and obtain the unit load, furnace parameters, steam parameters, air volume parameters and flue gas parameters. Through normalization processing, a prediction data set is formed, and a coal heat loss prediction model is established through a neural network, and the model is trained; Step 5: Establish an optimization model for the coal-fired heat loss prediction model, optimize the parameters of the prediction model, speed up the training of the model, and use the current prediction data set as the input of the prediction model to predict the coal-fired heat loss in the future.

2. A method for predicting heat loss of coal-fired power generation according to claim 1, characterized in that: The calculation formula of the coal heat distribution model is: Q z =Q1+Q2+Q3+Q4+Q5+Q6+Q w Among them, Q z is the heat input to the boiler, Q1 is the effective heat utilization of the boiler, Q2 is the heat loss of exhaust gas, Q3 is the heat loss of incomplete combustion of gas, Q4 is the heat loss of incomplete combustion of solid, Q5 is the heat loss of heat dissipation, Q6 is the physical heat loss of ash, Q w is the measurement error value.

3. A method for predicting heat loss of coal-fired power generation according to claim 1, characterized in that: The boiler parameters include exhaust volume, specific heat of flue gas, flue gas temperature, concentration of combustible gas in flue gas, slag volume, coal content in slag, boiler insulation coefficient, boiler area, boiler surface temperature, specific heat of slag, temperature when slag is discharged, steam production, steam specific enthalpy, feed water specific enthalpy, and coal heat; The calculation formula of the heat that is not effectively utilized is: Q l =Q2+Q3+Q4+Q5+Q6 Among them, Q l is the heat that is not effectively utilized, Q2 is the heat loss due to exhaust gas, Q3 is the heat loss due to incomplete combustion of gas, Q4 is the heat loss due to incomplete combustion of solid, Q5 is the heat loss due to heat dissipation, and Q6 is the physical heat loss due to ash; The calculation formula for exhaust heat loss Q2 is: Q2=Gyq*cyq*(Tyq―Thj) Among them, Gyq is the exhaust volume, Cyq is the specific heat of flue gas, Tyq is the flue gas temperature, and Thj is the ambient temperature; The calculation formula for the heat loss Q3 due to incomplete combustion of gas is: Q3=Gyq*Nrq*Hrq Among them, Gyq is the smoke exhaust volume, Nrq is the concentration of combustible gas in the smoke, and Hrq is the heat of the combustible gas; The calculation formula for the heat loss Q4 due to incomplete combustion of solids is: Q4=Glz*Nc*Hc Among them, Glz is the amount of slag, Nc is the coal content in the slag, and Hc is the heat of coal; The calculation formula for heat loss Q5 is: Q5=Kbw*Sbm*(Tbm―Thj) Among them, Kbw is the boiler insulation coefficient, Sbm is the boiler area, Tbm is the boiler surface temperature, and Thj is the ambient temperature; The calculation formula of ash physical heat loss heat Q6 is: Q6=Glz*Gzz*(Tlz―Thj) Among them, Glz is the amount of slag, Czz is the specific heat capacity of slag, Tlz is the temperature when slag is discharged, and Thj is the ambient temperature; The calculation formula of the effectively utilized heat Q1 is: Q1=Mzq*(Pzq―Pjs) Among them, Mzq is the steam production, Pzq is the steam specific enthalpy, and Pjs is the feed water specific enthalpy.

4. A method for predicting heat loss of coal-fired power generation according to claim 1, characterized in that: The calculation formula of the boiler input heat is: Q z =Mtc*Hc Among them, Mtc is the consumption of coal, and Hc is the heat of coal; The calculation formula of the measurement error value is: Q w =Q z ―Q1―Q l Among them, Q w is the measurement error value.

5. A method for predicting heat loss of coal-fired power generation according to claim 1, characterized in that: The calculation formula of the use-loss ratio is: Among them, Pys is the usage-loss ratio, na is the number of data collections, in is the current in-th data collection, Q 1,in is the heat that is effectively utilized for the first time, Q l,in is the heat that is not effectively utilized for the current time; The calculation formula for correcting the initial heat loss is: Qx l =Pys*Q w +Q2+Q3+Q4+Q5+Q6 Among them, Qx l is the corrected heat loss.

6. A method for predicting heat loss of coal-fired power generation according to claim 1, characterized in that: The specific steps for calculating the coal average particle value and particle deviation are: Take multiple samples of the pulverized coal input into the furnace in each time interval, count the size of the pulverized coal particles in each sample, and calculate the average particle value and particle deviation of the coal in the time interval: The calculation formula of coal average particle value is: Among them, Pmp is the average particle value of coal, nq is the number of sampling times, iq is the iqth sampling, nb is the number of particles in each sampling, MD ib is the size of the ibth coal particle; The particle deviation is calculated as: Among them, Qmp is the particle deviation, nq is the number of sampling times, iq is the iqth sampling, nb is the number of coal particles in each sampling, MD ib is the particle size of the ibth coal.

7. A method for predicting heat loss of coal-fired power generation according to claim 1, characterized in that: The furnace parameters include the amount of coal fed into the furnace and the furnace temperature; The steam parameters include feed water flow, main steam flow, main steam pressure, main steam temperature, and reheat steam temperature; The air volume parameters include primary air volume, primary air temperature, secondary air volume, secondary air temperature, and burnout air volume; The flue gas parameters include flue gas temperature and flue gas flow rate; The calculation formula for the normalization process is: Among them, Xg is the normalized data of each parameter type, Xmax is the largest number in each parameter type, Xmin is the smallest number in each parameter type, and xi id The idth data in each parameter type, and nx is the number of data in each parameter type.

8. A method for predicting heat loss of coal-fired power generation according to claim 1, characterized in that: The specific steps of establishing a coal-fired heat loss prediction model through a neural network and training the model are as follows: The neural network is an LSTM neural network prediction model, which consists of three gates: forget gate, input gate, and output gate: The equation of the forget gate is: f t =δ(W f ·[h t―1 ,x t ]+b f ) Among them, f t is the current switch state of the forget gate, δ represents the sigmoid function, W f is the forget gate weight, b f is the forget gate bias parameter, h t―1 is the hidden state at the previous moment, x t Input switch for current data; Determine how much of the unit state at the previous moment needs to be retained to the current moment; The equation for the input gate is: i t =δ(W i ·[h t―1 ,x t ]+b i ) Among them, i t is the current switch state of the input gate, δ represents the sigmoid function, W i is the input gate weight, b i is the input gate bias parameter, h t―1 is the output hidden state of the previous moment, x t is the prediction data set of the previous moment; in, Candidate cell state, tanh represents the hyperbolic tangent function, W c is the candidate cell weight, b c is the candidate cell state bias parameter, h t―1 is the hidden state at the previous moment, x t Input switch for current data; Determine how much of the network's input data needs to be saved to the unit state at the current moment; The update equation is: Among them, C t Current cell state, C t―1 is the cell state at the previous moment; The output gate equation is: o t =(W o ·[h t―1 ,x t ]+b o ) h t =o t *tanh(C t ) Among them, t is the current switch state of the output gate, δ represents the sigmoid function, W o is the output gate weight, b o is the output gate bias parameter, h t―1 is the hidden state at the previous moment, x t is the current data input switch, h t is the corrected heat loss at the current moment; Control how much of the current unit state needs to output the corrected loss heat at the current moment; The training steps are: The prediction data set of the previous moment is used as the input of the prediction model, and the corrected heat loss at the current moment is used as the output of the model to train the model; The parameters optimized by the prediction model include learning rate, number of hidden units, time step, and regularization parameter; The constructed optimization model is based on the sparrow search optimization algorithm. The sparrow population consists of discoverers, joiners, and a certain number of scouts. The specific optimization logic is: The finder's position update formula is as follows: Where t is the current iteration number, j is the dimension of the parameter to be optimized, j = 1, 2, ..., d, d is the total number of parameters to be optimized, X i,j is the position information of the i-th sparrow in the j-th dimension; iter max is the maximum number of iterations; α is a random number, α∈(0,1), R2 and ST are the alarm value and safety threshold; L is a 1×d dimensional matrix; Q is a random number that conforms to the normal distribution; The position update formula of the joiner is as follows: Where: X P is the optimal position of the current discoverer; X worst is the current global worst position; A is a 1×d matrix, whose elements are randomly assigned 1 or -1, and ni is the size of the sparrow population; The scout position update formula is as follows: Among them, X best is the global optimal position, β is the iteration step length, f i is the fitness value of the current sparrow individual, f g and f w is the current global optimal and worst fitness value, K is a random number, K∈[―1,1], ε is a constant to prevent the denominator from being 0; The root mean square error is used to calculate the fitness, and the calculation formula is as follows: Among them, n is the number of samples, x i is the prediction data set of the previous moment, y i is the corrected heat loss.

9. A thermal power generation coal-fired heat loss prediction system, characterized in that: The thermal power generation coal-fired heat loss prediction system is used to execute the thermal power generation coal-fired heat loss prediction method according to any one of claims 1 to 8, comprising: A heat distribution module, which is used to obtain the type of heat distribution data of the thermal power boiler to be predicted, including boiler input heat, boiler effective heat utilization, flue gas heat loss, gas incomplete combustion heat loss, solid incomplete combustion heat loss, heat dissipation loss, ash physical heat loss, and establish a coal-fired heat distribution model; A heat calculation module, which is used to calculate the heat that is not effectively utilized and the heat that is effectively utilized respectively according to the coal heat distribution model and the heat distribution data type by obtaining boiler parameters and ambient temperature at the same time interval to form a preliminary loss heat data set and a preliminary utilization heat data set; A loss correction module, the loss correction module is used to calculate the measurement error value according to the preliminary heat loss data set, the preliminary heat utilization data set and the boiler input heat in each time interval, calculate the loss ratio according to the heat loss and the heat utilization, correct the preliminary heat loss, and obtain the corrected heat loss; A model building module, which is used to obtain the coal particle size in each time interval to calculate the coal average particle value and particle deviation, and obtain the unit load, furnace parameters, steam parameters, air volume parameters and flue gas parameters through normalization processing to form a prediction data set, establish a coal-fired heat loss prediction model through a neural network, and train the model; The model optimization module is used to establish an optimization model for the coal-fired heat loss prediction model, optimize the parameters of the prediction model, speed up the training of the model, and use the prediction data set at the current moment as the input of the prediction model to predict the coal-fired heat loss at future moments.

10. A device for predicting heat loss of coal-fired power generation, characterized in that: The device includes a processor and a storage medium, wherein the storage medium stores a computer program. When the computer program is executed by the processor, the method for predicting heat loss of coal-fired power generation according to any one of claims 1 to 8 can be implemented.

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