CN114036855A - Dynamic coal blending method, system, equipment and storage medium for thermal power plant - Google Patents

Dynamic coal blending method, system, equipment and storage medium for thermal power plant Download PDF

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CN114036855A
CN114036855A CN202111406842.3A CN202111406842A CN114036855A CN 114036855 A CN114036855 A CN 114036855A CN 202111406842 A CN202111406842 A CN 202111406842A CN 114036855 A CN114036855 A CN 114036855A
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coal
thermal power
power plant
boiler
characteristic index
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马晨曦
李崇晟
陈建平
吴智群
何新
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Xian Thermal Power Research Institute Co Ltd
Xian TPRI Power Station Information Technology Co Ltd
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Xian TPRI Power Station Information Technology Co Ltd
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Abstract

The invention belongs to the field of thermal power plants, and discloses a dynamic coal blending method, a dynamic coal blending system, dynamic coal blending equipment and a dynamic coal blending storage medium for the thermal power plants, wherein the dynamic coal blending method comprises the following steps: acquiring historical data of the change of the characteristic index of the mixed fuel coal of the boiler of the thermal power plant along with time, the requirement of the characteristic index of the mixed fuel coal of the boiler of the thermal power plant and the characteristic index of a single fuel coal; according to historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant changing along with time, the characteristic index requirements of the mixed fuel coal in a future preset time period of the boiler of the thermal power plant are obtained through a preset fuel coal characteristic index prediction model; and taking the characteristic index requirement of the mixed fuel coal of the boiler of the thermal power plant and the characteristic index requirement of the mixed fuel coal in a preset time period as constraint conditions, and obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to the characteristic indexes of the single fuel coal. The dynamic optimization of coal blending is realized, and the coal blending scheme is better adapted to the environment of variable-load operation of a thermal power plant.

Description

Dynamic coal blending method, system, equipment and storage medium for thermal power plant
Technical Field
The invention belongs to the field of thermal power plants, and relates to a dynamic coal blending method, a dynamic coal blending system, dynamic coal blending equipment and a dynamic coal blending storage medium for the thermal power plants.
Background
In the face of the gradual depletion of fossil energy and the increasingly severe global climate problems, renewable energy sources such as wind energy, solar energy and the like are vigorously developed, and the continuous improvement of the utilization efficiency of fossil energy has become a global consensus. In order to actively promote the energy structure transformation of China, the installed capacity of new energy sources such as wind power and photovoltaic power generation is rapidly increased in more than ten years, and the new energy sources must replace thermal power to become a mainstream energy form in the future. However, the generated power of the new energy is unstable and has strong fluctuation characteristics. Therefore, the power generation and network access proportion of a plurality of new energy sources is low, and the electricity abandonment quantity and the electricity abandonment rate are high; meanwhile, unstable new energy power grid access also causes many problems to the safe operation of the whole power grid. Always in order to guarantee that the power grid supplies power continuously and stably and to stabilize the influence of new energy power on the power grid, the power output quick regulation capability of the power generation side needs to be improved urgently. In the energy structure of China, the thermal power generating unit occupies more than six times of the total installed capacity, so that the improvement of the rapid load-variable operation capacity of the thermal power generating unit is a necessary choice for developing new energy.
The capacity of a thermal generator assembling machine in a thermal generator set is large, the consumption of coal is also very large, and the coal blending combustion is widely applied. Although coal resources are abundant, including anthracite, lean coal, lignite and the like, are used in thermal power plants, the storage capacity of low-quality coal accounts for a large proportion, and high-quality coal is often required to be preferentially supplied to the petrochemical metallurgy industry. In addition, the distribution positions of the coal in the thermal power plant are not matched, and one thermal power plant can often purchase coal with different producing areas and different coal qualities for use. Moreover, although the coal-fired boiler is designed and optimized according to the designed coal type at the beginning of the design, the coal type actually applied in the actual operation process of the thermal power plant is often not consistent with the designed coal type. Therefore, the thermal power plant must mix and burn the coal, so that the coal quality of the coal entering the boiler meets the design coal quality of the boiler, and the safe and economic operation of the unit is ensured. The large-scale thermal power plant has great demand on fire coal, and the variety of coal sources is great, so that the method has strong practical significance on how to optimize blending combustion of the blended coal.
The coal quality of the coal as fired can reach or approach the requirement of the designed coal quality of the boiler as much as possible by blending and burning the coal, thereby improving the combustion efficiency and ensuring the safe operation of the unit; coal resources are fully utilized, and particularly, the coal produced in the local area is fully combusted, so that the transportation cost is saved, and the power generation cost is reduced; and certain anthracite, high-sulfur-content coal, low-heat coal and the like which can not be independently combusted are effectively utilized, and the electric coal is saved. However, the problems with coal blending optimization in engineering practice are as follows: the relationship between the characteristic indexes of the mixed coal and the single coal is unclear. The existing optimization means adopts a linear optimization model, although the solution is convenient, the calculation precision is not enough, and the adoption of a nonlinear model often faces the difficulty of high difficulty in model construction. Meanwhile, the existing coal blending optimization mostly adopts a single-target optimization model, is difficult to consider the relationship between economic indexes of coal blending and various characteristic indexes of fire coal, is usually established based on a static objective function and boundary conditions, and cannot well adapt to the characteristic of variable load operation of a thermal power plant.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a dynamic coal blending method, a system, equipment and a storage medium for a thermal power plant.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
in a first aspect of the invention, a dynamic coal blending method for a thermal power plant comprises the following steps:
acquiring historical data of the change of the characteristic index of the mixed fuel coal of the boiler of the thermal power plant along with time, the requirement of the characteristic index of the mixed fuel coal of the boiler of the thermal power plant and the characteristic index of a single fuel coal;
according to historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant changing along with time, the characteristic index requirements of the mixed fuel coal in a future preset time period of the boiler of the thermal power plant are obtained through a preset fuel coal characteristic index prediction model;
and taking the characteristic index requirement of the mixed fuel coal of the boiler of the thermal power plant and the characteristic index requirement of the mixed fuel coal in a preset time period as constraint conditions, and obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to the characteristic indexes of the single fuel coal.
Optionally, the characteristic index includes one or more of volatile components, calorific value, ash content, moisture content, sulfur content, coal ash melting temperature, and harderian wear resistance index.
Optionally, the fire coal characteristic index prediction model is as follows:
y(t)＝g(t)+s(t)+h(t)+ε(t)
wherein, y (t) is the time sequence of the variation trend of the characteristic indexes of the mixed fire coal; g (t) is the non-periodic variation trend of the time series; s (t) is a periodic variation trend of the time series; h (t) is the effect of holidays on the time series; epsilon (t) is the error generated by the time series fitting process;
g(t)＝(k+α(t)T·δ)·t+(m+α(t)T·γ)
wherein k represents an initial growth rate of the time series, α (t) is a preset indicator function, δ represents a variation of the growth rate of the time series, m represents an offset of the time series, γ ═ s δ, and s is a time stamp of the time series;
Figure BDA0003372558290000031
wherein, P represents the period of the time series, and N represents the expansion dimension of the Fourier series; a isn、bnIs a fitting coefficient;
Figure BDA0003372558290000032
wherein, κiRepresenting the range of influence of holidays, DiRepresenting the duration of the holiday.
Optionally, the characteristic index requirement of the mixed coal includes a predicted value of the characteristic index of the mixed coal, a predicted upper limit of the characteristic index of the mixed coal, and a lower limit of the characteristic index of the mixed coal.
Optionally, the specific method for obtaining the dynamic coal blending scheme of the boiler of the thermal power plant through the preset multi-objective optimization coal blending model according to the characteristic indexes of the single coal is as follows:
according to the characteristic indexes of the single fire coal, a plurality of mixed fire coal proportioning schemes which meet the characteristic index requirements of the mixed fire coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed fire coal in a preset time period are obtained through a preset mixed fire coal characteristic index prediction model; and obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to a plurality of mixed coal blending schemes which meet the characteristic index requirements of the mixed coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed coal in a preset time period.
Optionally, the optimization targets of the multi-target optimization coal blending model include an economic target, a stability target and an environmental protection target;
among these, economic objectives are:
Figure BDA0003372558290000041
in the formula, XiThe coal blending ratio of the ith single coal, PiThe price of the ith single coal is Yuan/ton;
the stability targets were:
Figure BDA0003372558290000042
in the formula, aQ、aVIs a preset weight coefficient; qmixThe calorific value of the mixed fuel coal; qdThe calorific value of the fire coal is designed for the boiler; vmixIs the volatile component of the mixed fuel coal; vdDesigning the volatile component of the fire coal for the boiler;
the environmental protection targets are:
Figure BDA0003372558290000043
in the formula, aS、aM、aAIs a preset weight coefficient; smixIs the total sulfur of the mixed fuel coal; smax、SminMaximum and minimum values for total sulfur in all individual coals; mmixIs the total water of mixed fire coal; mmax、MminMaximum and minimum of total water in all individual coals; a. themixAsh content of mixed fuel coal; a. themax、AminThe maximum and minimum ash values in all individual coals.
Optionally, when the dynamic coal blending scheme of the boiler of the thermal power plant is obtained through the preset multi-objective optimization coal blending model, the multi-objective optimization coal blending model is solved by adopting a multi-objective optimization algorithm based on multi-particle swarm cooperation.
In a second aspect of the present invention, a dynamic coal blending system for a thermal power plant includes:
the data acquisition module is used for acquiring historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant along with the change of time, the characteristic index requirements of the mixed fuel coal of the boiler of the thermal power plant and the characteristic indexes of single fuel coal;
the demand determination module is used for obtaining the demand of the characteristic indexes of the mixed fuel coal in the boiler of the thermal power plant in the future preset time period through a preset fuel coal characteristic index prediction model according to the historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant changing along with time;
and the coal blending module is used for obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to the characteristic indexes of single coal by taking the characteristic index requirements of the mixed coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed coal in a preset time period as constraint conditions.
In a third aspect of the present invention, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned method for dynamic coal blending of a thermal power plant when executing the computer program.
In a fourth aspect of the present invention, a computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the dynamic coal blending method for a thermal power plant.
Compared with the prior art, the invention has the following beneficial effects:
the invention relates to a dynamic coal blending method for a thermal power plant, which is characterized in that based on historical data of the change of characteristic indexes of mixed fuel coal of a boiler of the thermal power plant along with time, the characteristic index requirements of the mixed fuel coal in a future preset time period of the boiler of the thermal power plant are obtained through a preset fuel coal characteristic index prediction model, and then the dynamic single coal blending scheme of the boiler of the thermal power plant is obtained through a preset multi-target optimization coal blending model according to the characteristic indexes of the fuel coal by taking the characteristic index requirements of the mixed fuel coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed fuel coal in the preset time period as constraint conditions. The dynamic optimization of the boiler coal blending of the thermal power plant is realized by predicting the characteristic index requirements of the mixed coal in a future preset time period, so that the coal blending scheme is better adapted to the variable-load operation environment of the thermal power plant, the real-time coal blending scheme is determined, and the safe, economic and environment-friendly operation of a thermal power generating unit is ensured.
Drawings
FIG. 1 is a flow chart of a dynamic coal blending method of a thermal power plant according to the present invention;
FIG. 2 is a schematic diagram illustrating the principle of the dynamic coal blending method of the thermal power plant according to the present invention;
FIG. 3 is a graph of predicted results of dry ashless-based volatiles over time for the present invention;
FIG. 4 is a flow chart of a multi-objective optimization algorithm based on multi-particle cluster cooperation according to the present invention;
FIG. 5 is a schematic diagram of an optimal coal blending solution set according to the present invention;
fig. 6 is a block diagram of a dynamic coal blending system of a thermal power plant according to the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1 and 2, the dynamic coal blending method for the thermal power plant is used for meeting the dynamic requirements of a boiler on the characteristic indexes of fire coal under the variable load operation condition of the thermal power plant, determining a real-time coal blending combustion scheme by considering the cost of the fire coal, and ensuring the safe, economic and environment-friendly operation of a thermal power generating unit.
Specifically, the dynamic coal blending method for the thermal power plant comprises the following steps:
s1: historical data of the change of the characteristic indexes of the mixed fire coal of the boiler of the thermal power plant along with time, the requirement of the characteristic indexes of the mixed fire coal of the boiler of the thermal power plant and the characteristic indexes of single fire coal are obtained.
The characteristic indexes comprise one or more of volatile components, calorific value, ash content, moisture content, sulfur content, coal ash melting temperature and Hardgrove abrasion resistance index, specifically, the volatile components are dry ash-free-base volatile components, the calorific value is low-grade calorific value, the ash content is dry-base ash content, the moisture content is total moisture content, the sulfur content is dry-base total sulfur content, and the coal ash melting temperature is coal ash melting softening temperature.
S2: and according to historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant changing along with time, the characteristic index requirements of the mixed fuel coal in the future preset time period of the boiler of the thermal power plant are obtained through a preset fuel coal characteristic index prediction model.
The fire coal characteristic index prediction model comprises the following steps:
y(t)＝g(t)+s(t)+h(t)+ε(t)
wherein, y (t) is the time sequence of the variation trend of the characteristic indexes of the mixed fire coal; g (t) is the non-periodic variation trend of the time series; s (t) is a periodic variation trend of the time series; h (t) is the effect of holidays on the time series; ε (t) is the error generated by the time series fitting process.
Specifically, for the trend term g (t), S change points are selected in the range of the known time series to divide the time series into a plurality of sections with gentle trends. The position of the change point being at the time stamp sjThe variation of the growth rate of the time-series value at any variable point position is deltaj. Secondly, fitting a trend term by adopting a piecewise linear function:
g(t)＝(k+α(t)T·δ)·t+(m+α(t)T·γ)
wherein k represents the time series initial growth rate; α (t) is an indicator function, which is related to the time stamp of the change point; δ represents the amount of change in the growth rate of the time series; m represents a time series offset; time stamp of gamma and change point sjRelated to the variation delta, gamma, of the growth rate of the time seriesj＝-sjδj。
For the periodic term s (t), fitting is performed using a fourier series:
Figure BDA0003372558290000081
wherein, P represents the period of time series, and for the time series with the period of year, P is 365.25; for a time series with a cycle of weeks, P ═ 7; sequentially pushing in a row; n represents the expansion dimension of a Fourier series; a isn、 bnThe fitting coefficient is obtained by historical data fitting.
For the holiday term h (t), the influence of the holiday on the time series is represented by setting a window period for the holidays without festivals. The fitting function for the vacation term h (t) is:
Figure BDA0003372558290000082
wherein, κiRepresenting the influence range of holidays; diRepresenting the duration of the holiday.
The change trend of the corresponding characteristic indexes in the future time is predicted by taking the historical data of the change of the characteristic indexes of the mixed fire coal of the boiler of the thermal power plant along with the time as a time sequence, and the requirement of the characteristic indexes of the mixed fire coal in a certain time period in the future is expressed in the form of the time sequence.
Specifically, firstly, according to historical data of the change of the characteristic indexes of the mixed fire coal of the boiler of the thermal power plant along with time, a time stamp and a corresponding value of a historical time sequence are generated, then, the length of the time sequence needing to be predicted is determined, and then, the change trend of the future time sequence of the characteristic indexes of each mixed fire coal is predicted based on a constructed fire coal characteristic index prediction model. The time series can be in units of months, weeks, days or hours, the characteristic index requirements of the mixed fuel coal include a predicted value of the characteristic index of the mixed fuel coal, a predicted upper limit of the characteristic index of the mixed fuel coal and a lower limit of the characteristic index of the mixed fuel coal, and referring to fig. 3, a requirement curve of the dry ash-free-based volatile matter in the characteristic index of the mixed fuel coal in an actual application process is shown.
S3: and taking the characteristic index requirement of the mixed fuel coal of the boiler of the thermal power plant and the characteristic index requirement of the mixed fuel coal in a preset time period as constraint conditions, and obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to the characteristic indexes of the single fuel coal.
The characteristic index requirements of the mixed fuel coal comprise a characteristic index predicted value of the mixed fuel coal, a characteristic index prediction upper limit of the mixed fuel coal and a characteristic index lower limit of the mixed fuel coal.
Specifically, on a certain optimized timestamp, a constraint function of the multi-objective optimization coal blending model is composed of two parts: one part is determined by the requirement of the boiler of the thermal power plant on the characteristic index of the mixed fuel coal; the other part is determined by the upper predicted value limit and the lower predicted value limit of the characteristic index of the mixed fuel coal on the time stamp.
The specific method for obtaining the dynamic coal blending scheme of the boiler of the thermal power plant through the preset multi-objective optimization coal blending model according to the characteristic indexes of the single fire coal comprises the following steps: according to the characteristic indexes of the single fire coal, a plurality of mixed fire coal proportioning schemes which meet the characteristic index requirements of the mixed fire coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed fire coal in a preset time period are obtained through a preset mixed fire coal characteristic index prediction model; and obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to a plurality of mixed coal blending schemes which meet the characteristic index requirements of the mixed coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed coal in a preset time period. Wherein, the mixed coal proportioning plan can be given in the form that the mass of each single coal accounts for the mass proportion of the mixed coal.
In this embodiment, the relationship between the characteristic index of the single fire coal and the characteristic index of the mixed fire coal is predicted by a neural network algorithm. Specifically, a BP neural network algorithm is adopted to predict the mixed fire coal characteristic indexes, an input layer is a coal blending scheme, namely the ratio of the mass of each single fire coal to the mass of the mixed fire coal, an output layer is the mixed fire coal characteristic indexes, and the historical coal blending data of the boiler of the thermal power plant are collected by training and verification.
The optimization targets of the multi-target optimization coal blending model comprise an economic target, a stability target and an environmental protection target;
among these, economic objectives are:
Figure BDA0003372558290000101
in the formula, XiThe coal blending ratio of the ith single coal, PiThe price of the ith single coal is Yuan/ton;
the stability targets were:
Figure BDA0003372558290000102
in the formula, aQ、aVIs a preset weight coefficient; qmixThe calorific value of the mixed fuel coal; qdThe calorific value of the fire coal is designed for the boiler; vmixIs the volatile component of the mixed fuel coal; vdDesigning the volatile component of the fire coal for the boiler;
the environmental protection targets are:
Figure BDA0003372558290000103
in the formula，aS、aM、aAIs a preset weight coefficient; smixIs the total sulfur of the mixed fuel coal; smax、SminMaximum and minimum values for total sulfur in all individual coals; mmixIs the total water of mixed fire coal; mmax、MminMaximum and minimum of total water in all individual coals; a. themixAsh content of mixed fuel coal; a. themax、AminThe maximum and minimum ash values in all individual coals.
And when the dynamic coal blending scheme of the boiler of the thermal power plant is obtained through the preset multi-objective optimization coal blending model, solving the multi-objective optimization coal blending model by adopting a multi-objective optimization algorithm based on multi-particle group cooperation.
Specifically, the existing coal blending method mostly adopts a single-target optimization coal blending model, only the economic index of price is considered, so that the optimal coal blending scheme is single, other important indexes in the operation of the boiler cannot be considered, and the result of low comprehensive economic benefit is caused. By adopting a pareto frontier based multi-objective optimization algorithm, the number of optimal solutions can be greatly increased. Therefore, in this embodiment, a multi-objective optimization algorithm based on multi-particle swarm cooperation is adopted to obtain the multi-objective optimization coal blending model.
And taking the single coal blending ratio as a particle swarm state space of the multi-objective optimization coal blending model, and optimizing by adopting a multi-objective optimization algorithm based on multi-particle swarm cooperation. Specifically, referring to fig. 4, the multi-objective optimization algorithm based on multi-particle cluster cooperation flows as follows:
(1) initializing a particle swarm and a non-dominated solution set; (2) dividing the particle swarm into a plurality of small groups; (3) determining optimal solution lbest in each small population group and historical optimal solution pbest of each particle; (4) updating the speed and position of each particle; (5) updating each particle fitness, the optimal solution lbest in each small group and the historical optimal solution pbest of each particle; (6) judging whether the conditions for re-dividing the small population are met, and if so, jumping to the step (2); (7) and (4) judging whether a convergence condition is met, if not, jumping to the step (4), and if so, stopping calculation to obtain a non-dominated solution set.
Specifically, the process of the multi-objective optimization coal blending model of the invention can be described as follows: forecasting the change trend of the characteristic indexes of the mixed fuel coal according to the historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant changing along with time, and constructing a multi-objective optimization constraint condition by combining the requirements of the boiler on the characteristic indexes of the coal as fired; then, dividing an objective function for evaluating the multi-objective optimization effect into an economic objective, a stability objective and an environmental protection objective; then, optimizing the single coal blending ratio as a state space of the particle swarm multi-target optimization model, calculating the characteristic index of the mixed coal from the single coal blending ratio by means of a BP (Back propagation) neural network, and participating in the calculation of a constraint function and a target function of a particle swarm multi-target optimization algorithm; and finally, obtaining an optimal coal blending scheme set according to the particle swarm cooperative multi-objective optimization algorithm process, as shown in fig. 5.
In summary, according to the dynamic coal blending method for the thermal power plant, based on historical data of the change of the characteristic index of the mixed fuel coal of the boiler of the thermal power plant along with time, the characteristic index requirement of the mixed fuel coal in the boiler of the thermal power plant in the future preset time period is obtained through the preset fuel coal characteristic index prediction model, and then the dynamic coal blending scheme of the boiler of the thermal power plant is obtained through the preset multi-objective optimization coal blending model according to the characteristic index of the single fuel coal by taking the characteristic index requirement of the mixed fuel coal of the boiler of the thermal power plant and the characteristic index requirement of the mixed fuel coal in the preset time period as constraint conditions. The dynamic optimization of the boiler coal blending of the thermal power plant is realized by predicting the characteristic index requirements of the mixed coal in a future preset time period, so that the coal blending scheme is better adapted to the variable-load operation environment of the thermal power plant, the real-time coal blending scheme is determined, and the safe, economic and environment-friendly operation of a thermal power generating unit is ensured.
Meanwhile, according to the characteristic indexes of the single fire coal, a plurality of mixed fire coal proportioning schemes which meet the characteristic index requirements of the mixed fire coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed fire coal in a preset time period are obtained through a preset mixed fire coal characteristic index prediction model, wherein the mixed fire coal characteristic index prediction model is built based on a neural network, the neural network is adopted to predict the relation between the single fire coal and the mixed fire coal characteristic indexes, and the calculation precision is improved.
Meanwhile, the multi-objective optimization coal blending model gives consideration to an economic target, a stability target and an environmental protection target, and gives consideration to the stability and the environmental protection of the combustion of the coal in the boiler while considering the economic performance.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details not disclosed in the device embodiments, reference is made to the method embodiments of the invention.
Referring to fig. 6, in a further embodiment of the present invention, a dynamic coal blending system of a thermal power plant is provided, which can be used to implement the above dynamic coal blending method of the thermal power plant.
The data acquisition module is used for acquiring historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant changing along with time, the characteristic index requirements of the mixed fuel coal of the boiler of the thermal power plant and the characteristic indexes of single fuel coal; the demand determination module is used for obtaining the demand of the characteristic indexes of the mixed fuel coal in the boiler of the thermal power plant in the future preset time period through a preset fuel coal characteristic index prediction model according to the historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant changing along with time; the coal blending module is used for obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to the characteristic indexes of single coal by taking the characteristic index requirements of the mixed coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed coal in a preset time period as constraint conditions.
In a possible implementation manner, the specific method for obtaining the dynamic coal blending scheme of the boiler of the thermal power plant by the coal blending module according to the characteristic indexes of the single coal through a preset multi-objective optimization coal blending model comprises the following steps: according to the characteristic indexes of the single fire coal, a plurality of mixed fire coal proportioning schemes which meet the characteristic index requirements of the mixed fire coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed fire coal in a preset time period are obtained through a preset mixed fire coal characteristic index prediction model; and obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to a plurality of mixed coal blending schemes which meet the characteristic index requirements of the mixed coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed coal in a preset time period.
In a possible implementation manner, when the coal blending module obtains a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model, the multi-objective optimization coal blending model is solved by using a multi-objective optimization algorithm based on multi-particle group cooperation.
All relevant contents of each step related to the embodiment of the dynamic coal blending method for the thermal power plant can be cited to the functional description of the functional module corresponding to the dynamic coal blending system for the thermal power plant in the embodiment of the present invention, and are not described herein again. The division of the modules in the embodiments of the present invention is schematic, and only one logical function division is provided, and in actual implementation, there may be another division manner, and in addition, each functional module in each embodiment of the present invention may be integrated in one processor, or may exist alone physically, or two or more modules are integrated in one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode.
In yet another embodiment of the present invention, a computer device is provided that includes a processor and a memory for storing a computer program comprising program instructions, the processor for executing the program instructions stored by the computer storage medium. The Processor may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable gate array (FPGA) or other Programmable logic device, a discrete gate or transistor logic device, a discrete hardware component, etc., which is a computing core and a control core of the terminal, and is specifically adapted to load and execute one or more instructions in a computer storage medium to implement a corresponding method flow or a corresponding function; the processor provided by the embodiment of the invention can be used for operating the dynamic coal blending method of the thermal power plant.
In yet another embodiment of the present invention, the present invention further provides a storage medium, specifically a computer-readable storage medium (Memory), which is a Memory device in a computer device and is used for storing programs and data. It is understood that the computer readable storage medium herein can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space storing an operating system of the terminal. Also, one or more instructions, which may be one or more computer programs (including program code), are stored in the memory space and are adapted to be loaded and executed by the processor. It should be noted that the computer-readable storage medium may be a high-speed RAM memory, or may be a non-volatile memory (non-volatile memory), such as at least one disk memory. One or more instructions stored in the computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the method for dynamically blending coal in a thermal power plant in the above-described embodiments.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (10)

1. A dynamic coal blending method for a thermal power plant is characterized by comprising the following steps:
acquiring historical data of the change of the characteristic index of the mixed fuel coal of the boiler of the thermal power plant along with time, the requirement of the characteristic index of the mixed fuel coal of the boiler of the thermal power plant and the characteristic index of a single fuel coal;
according to historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant changing along with time, the characteristic index requirements of the mixed fuel coal in a future preset time period of the boiler of the thermal power plant are obtained through a preset fuel coal characteristic index prediction model;
and taking the characteristic index requirement of the mixed fuel coal of the boiler of the thermal power plant and the characteristic index requirement of the mixed fuel coal in a preset time period as constraint conditions, and obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to the characteristic indexes of the single fuel coal.
2. The dynamic coal blending method of the thermal power plant according to claim 1, wherein the characteristic indexes comprise one or more of volatile components, calorific value, ash content, moisture content, sulfur content, coal ash melting temperature and Hardgrove abrasion resistance index.
3. The dynamic coal blending method of the thermal power plant according to claim 1, wherein the fire coal characteristic index prediction model is as follows:
y(t)＝g(t)+s(t)+h(t)+ε(t)
wherein, y (t) is the time sequence of the variation trend of the characteristic indexes of the mixed fire coal; g (t) is the non-periodic variation trend of the time series; s (t) is a periodic variation trend of the time series; h (t) is the effect of holidays on the time series; epsilon (t) is the error generated by the time series fitting process;
g(t)＝(k+α(t)T·δ)·t+(m+α(t)T·γ)
wherein k represents an initial growth rate of the time series, α (t) is a preset indicator function, δ represents a variation of the growth rate of the time series, m represents an offset of the time series, γ ═ s δ, and s is a time stamp of the time series;
Figure FDA0003372558280000021
wherein, P represents the period of the time series, and N represents the expansion dimension of the Fourier series; a isn、bnIs a fitting coefficient;
Figure FDA0003372558280000022
wherein, κiRepresenting the range of influence of holidays, DiRepresenting the duration of the holiday.
4. The dynamic coal blending method of the thermal power plant according to claim 1, wherein the characteristic index demand of the blended coal includes a predicted value of a characteristic index of the blended coal, an upper limit of a predicted characteristic index of the blended coal, and a lower limit of a characteristic index of the blended coal.
5. The dynamic coal blending method of the thermal power plant according to claim 1, wherein the specific method for obtaining the dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to the characteristic indexes of single coal is as follows:
according to the characteristic indexes of the single fire coal, a plurality of mixed fire coal proportioning schemes which meet the characteristic index requirements of the mixed fire coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed fire coal in a preset time period are obtained through a preset mixed fire coal characteristic index prediction model; and obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to a plurality of mixed coal blending schemes which meet the characteristic index requirements of the mixed coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed coal in a preset time period.
6. The dynamic coal blending method of the thermal power plant according to claim 1, wherein the optimization objectives of the multi-objective optimization coal blending model include an economic objective, a stability objective, and an environmental protection objective;
among these, economic objectives are:
Figure FDA0003372558280000031
in the formula, XiThe coal blending ratio of the ith single coal, PiThe price of the ith single coal is Yuan/ton;
the stability targets were:
Figure FDA0003372558280000032
in the formula, aQ、aVIs a preset weight coefficient; qmixThe calorific value of the mixed fuel coal; qdThe calorific value of the fire coal is designed for the boiler; vmixIs the volatile component of the mixed fuel coal; vdDesigning the volatile component of the fire coal for the boiler;
the environmental protection targets are:
Figure FDA0003372558280000033
in the formula, aS、aM、aAIs a preset weight coefficient; smixIs the total sulfur of the mixed fuel coal; smax、SminMaximum and minimum values for total sulfur in all individual coals; mmixIs the total water of mixed fire coal; mmax、MminMaximum and minimum of total water in all individual coals; a. themixAsh content of mixed fuel coal; a. themax、AminThe maximum and minimum ash values in all individual coals.
7. The dynamic coal blending method of the thermal power plant according to claim 1, wherein when the dynamic coal blending scheme of the boiler of the thermal power plant is obtained through a preset multi-objective optimization coal blending model, the multi-objective optimization coal blending model is solved by using a multi-objective optimization algorithm based on multi-particle cluster cooperation.
8. A dynamic coal blending system of a thermal power plant is characterized by comprising:
the data acquisition module is used for acquiring historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant along with the change of time, the characteristic index requirements of the mixed fuel coal of the boiler of the thermal power plant and the characteristic indexes of single fuel coal;
the demand determination module is used for obtaining the demand of the characteristic indexes of the mixed fuel coal in the boiler of the thermal power plant in the future preset time period through a preset fuel coal characteristic index prediction model according to the historical data of the characteristic indexes of the mixed fuel coal of the boiler of the thermal power plant changing along with time;
and the coal blending module is used for obtaining a dynamic coal blending scheme of the boiler of the thermal power plant through a preset multi-objective optimization coal blending model according to the characteristic indexes of single coal by taking the characteristic index requirements of the mixed coal of the boiler of the thermal power plant and the characteristic index requirements of the mixed coal in a preset time period as constraint conditions.
9. Computer arrangement comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor when executing said computer program performs the steps of the method for dynamic coal blending of a thermal power plant according to any of the claims 1 to 7.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for dynamic coal blending of a thermal power plant according to any of the claims 1 to 7.
CN202111406842.3A 2021-11-24 2021-11-24 Dynamic coal blending method, system, equipment and storage medium for thermal power plant Pending CN114036855A (en)

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CN115310885A (en) * 2022-10-12 2022-11-08 中国电建集团山东电力建设第一工程有限公司 BIM-based fire coal conveying system and method for thermal power plant
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CN116307513A (en) * 2023-02-01 2023-06-23 华能国际电力股份有限公司上海石洞口第二电厂 Thermal power plant coal blending scheme optimization method based on improved MOEA/D algorithm
CN116307513B (en) * 2023-02-01 2023-12-22 华能国际电力股份有限公司上海石洞口第二电厂 Thermal power plant coal blending scheme optimization method based on improved MOEA/D algorithm
CN116777537A (en) * 2023-05-26 2023-09-19 华能国际电力股份有限公司上海石洞口第二电厂 Method and system for calculating and optimizing electricity-measuring coal cost and blending coal based on coal quality characteristics
CN116777537B (en) * 2023-05-26 2024-04-12 华能国际电力股份有限公司上海石洞口第二电厂 Method and system for calculating and optimizing electricity-measuring coal cost and blending coal based on coal quality characteristics
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