Automatic coal blending control method and device based on real coal price calculation

Abstract

The invention provides an automatic coal blending control method and device based on real coal price calculation. The method comprises the following specific steps: combining real-time coal prices with boiler operation characteristics under different conditions to establish a dynamic coal quality cost database about boiler efficiency, coal consumption rate, environmental protection cost and heating surface wear cost; taking the blending proportion of coal as a decision variable, establishing a coal blending strategy optimization model taking the power supply comprehensive cost minimization as a target, and determining a model constraint condition; the real-time boiler operation characteristics are input into a coal blending strategy optimization model, and the coal blending strategy optimization model is optimized in a dynamic coal quality cost database based on model constraint conditions to obtain a coal blending scheme with the minimum power supply comprehensive cost; the coal blending scheme is converted into the running time of the coal conveying belt, and automatic coal blending control is achieved. According to the invention, boiler operation characteristics, fuel cost, environmental protection expenditure and equipment maintenance loss are fused, and intelligent fire coal blending decision and execution can be realized.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of coal blending in thermal power plants, and particularly relates to an automatic coal blending control method and device based on real coal price calculation. BACKGROUND

[0002] Currently, there are many common defects in the coal blending management of thermal power plants. The traditional method mainly relies on fixed blending ratio tables and historical experience to manually formulate a static coal blending scheme. However, this scheme cannot dynamically respond to real-time coal price fluctuations, boiler variable condition requirements, and changes in environmental protection constraints. In terms of cost accounting, the dimension is extremely single, only considering the fuel procurement price, but ignoring the environmental protection costs caused by desulfurization and denitrification agent consumption, as well as the maintenance costs of the heating surface wear. This leads to the situation of high-priced and low-efficiency coal being misused in actual operation, or the use of high-wear coal causing equipment life loss, greatly increasing the operating cost.

[0003] The decision-making data basis of the coal blending management of thermal power plants is very weak, and there is a lack of a combustion characteristic database covering full load and multiple air distribution modes. This makes the prediction of coal consumption rate and pollutant generation amount only rely on theoretical calculation or interpolation of a small number of conditions, and the prediction result has a large error, which is easy to exceed the standard. In the optimization link, the existing algorithms have serious shortcomings, most of which focus on single-objective optimization, and are not integrated with real-time databases, which takes too long to search for optimization and is difficult to meet the demand of rapid peak shaving of the unit. Moreover, the generated coal blending scheme needs to be transferred manually to the bucket wheel machine operation, and lacks a real-time calibration mechanism, resulting in a serious disconnection between the optimization link and the execution system.

[0004] There are significant systematic technical defects in the coal blending management of thermal power plants. The traditional static coal blending scheme cannot be dynamically adjusted according to market coal price fluctuations and boiler variable condition requirements, and it is difficult to achieve comprehensive cost optimization due to the lack of a multi-dimensional economic model integrating combustion efficiency, environmental protection cost and equipment wear. Under the conditions of environmental protection constraints (such as ultra-low emission standards) and equipment safety boundaries, the existing methods cannot accurately predict the actual pollutant generation amount and wear risk of different coal blending schemes, and the coordination between optimization decision and execution equipment is broken, resulting in a significant deviation between the theoretical scheme and the field execution. This series of problems ultimately leads to the dilemma of rising operating costs, increasing environmental protection compliance risks and declining equipment reliability. Therefore, the technical problems to be solved are: to build a coal blending scheme that can dynamically adapt to market coal price fluctuations and boiler variable condition requirements, and integrates a multi-dimensional economic model to achieve comprehensive cost optimization; to establish a mechanism for accurately predicting the pollutant generation amount and wear risk of different coal blending schemes; to open up the coordination channel between optimization decision and execution equipment to ensure that the theoretical scheme is highly consistent with the field execution, thereby reducing operating costs, avoiding environmental protection compliance risks, and improving equipment reliability. SUMMARY

[0005] In order to solve the problems in the prior art, the present application provides an automatic coal blending control method and device based on real coal price calculation, which combines boiler operation characteristics, fuel cost, environmental protection expenditure and equipment maintenance loss, and can realize intelligent coal blending decision and execution.

[0006] To achieve the above object, the present application provides the following technical scheme: an automatic coal blending control method based on real coal price calculation, the specific steps are as follows:

[0007] The real-time coal price is combined with the boiler operation characteristics in different situations to establish a dynamic coal quality cost database about boiler efficiency, coal consumption rate, environmental protection cost and heating surface wear cost;

[0008] The blending ratio of coal is taken as a decision variable, a coal blending strategy optimization model with the minimum power supply comprehensive cost as the target is established, and the model constraint conditions are determined;

[0009] The real-time boiler operation characteristics are input into the coal blending strategy optimization model, and the coal blending strategy optimization model obtains the coal blending scheme with the minimum power supply comprehensive cost in the dynamic coal quality cost database based on the model constraint conditions;

[0010] The coal blending scheme is converted into the running time of the coal conveying belt to realize automatic coal blending control.

[0011] Further, the dynamic coal quality cost database is established according to the combustion adjustment experiment matrix, and specifically:

[0012] The boiler operation characteristics include load conditions, air distribution modes, coal blending schemes and operation parameters, different load conditions, different air distribution modes, different coal blending schemes and different operation parameters are set to carry out combustion experiments, and the boiler efficiency, environmental protection cost and heating surface wear cost data under corresponding conditions are obtained;

[0013] The above data are combined with the real-time coal price to constitute the dynamic coal quality cost database, the dynamic coal quality cost database includes a basic layer, a calculation layer and an economic layer, the basic layer is used for recording experimental original data and contains condition number, load, air distribution mode, coal quality blending ratio and real-time operation parameter; the calculation layer is used for storing combustion experiment results and contains actual boiler thermal efficiency, coal consumption rate, environmental protection cost matrix and wear cost coefficient; and the economic layer is used for integrating the real-time coal price data input from outside.

[0014] Further, the boiler efficiency includes the actual boiler thermal efficiency, the actual boiler thermal efficiency is calculated by using the counterbalance method, and includes the flue gas heat loss calculated by the flue gas temperature and the excess air coefficient, the solid incomplete combustion heat loss calculated by the carbon content of fly ash, and the gas incomplete combustion heat loss calculated by the CO concentration;

[0015] The coal consumption rate is obtained by the ratio of the measured fuel consumption and the power generation;

[0016] The environmental protection cost includes desulfurization cost and denitration cost, the desulfurization cost includes limestone consumption and its purchase unit price, the limestone consumption is obtained according to the measured SO2 concentration and flue gas volume, combined with the design parameters of the desulfurization system; the denitration cost includes liquid ammonia / urea consumption and its purchase unit price, the liquid ammonia / urea consumption is obtained according to the inlet NO x concentration, flue gas flow and ammonia escape rate of the SCR system;

[0017] The heating surface wear cost is obtained according to the heating surface metal wear rate, material, labor and repair cost, the heating surface metal wear rate is obtained according to the coal grinding index, fly ash particle size distribution and flue gas velocity field simulation.

[0018] Further, the coal blending strategy optimization model is as follows:

[0019]

[0020] In the formula, C T represents the comprehensive coal price; n represents the total number of coal types participating in blending; ρ i represents the blending ratio of the i-th coal, 0<ρ i <1, and P i represents the real-time plant price of the i-th coal; g represents the coal consumption rate under the working condition, which is fed back by the database according to the input blending ratio; C S and C N respectively represent the desulfurization cost and the denitration cost, which constitute the environmental protection cost; C W represents the wear cost.

[0021] The desulfurization cost C S is calculated as follows:

[0022] C S = Q y ·[SO2]·η1·M1 / P

[0023] In the formula, Q y is the flue gas flow; [SO2] is the SO2 concentration, η1 is the desulfurization system efficiency; M1 is the total cost of the desulfurization limestone used; P is the power generation;

[0024] The denitration cost C N is calculated as follows:

[0025] C N = Q y ·[NO x ]·η2·M2 / P

[0026] In the formula, [NO x ] is the NO xConcentration, η2 is the denitration system efficiency; M2 is the total cost of denitration ammonia or urea used;

[0027] Wear cost C N The calculation formula is as follows:

[0028]

[0029] In the formula, HGI is the wear coefficient of mixed coal; k, a and b are empirical parameters; v y is the flue gas velocity field;

[0030] The constraint conditions are as follows:

[0031] (1) The load constraint is forced to meet the scheduling demand;

[0032] (2) The environmental protection constraint limits NO x ≤50mg / Nm 3 , SO2≤35mg / Nm 3 ;

[0033] (3) The use amount of each coal does not break through the upper limit of the coal yard inventory.

[0034] Further, the real-time boiler operation characteristics are input into the coal blending strategy optimization model, and the coal blending strategy optimization model is based on the model constraint conditions to obtain the coal blending scheme of the minimum power supply comprehensive cost in the dynamic coal quality cost database.

[0035] The particle swarm optimization algorithm is used to obtain the coal blending scheme of the minimum power supply comprehensive cost in the dynamic coal quality cost database.

[0036] Further, the real-time boiler operation characteristics include target load, coal price list, inventory threshold, and the coal blending strategy optimization model outputs the optimal coal blending scheme.

[0037] The application also provides an automatic coal blending control system based on real coal price calculation, comprising:

[0038] A database generation module is used to combine real-time coal prices with different situation boiler operation characteristics to establish a dynamic coal quality cost database about boiler efficiency, coal consumption rate, environmental protection cost and heating surface wear cost;

[0039] A model establishment module is used to establish a coal blending strategy optimization model with the minimum power supply comprehensive cost as the target, with the blending ratio of coal as the decision variable, and to determine the model constraint conditions;

[0040] A model solution module is used to input real-time boiler operation characteristics into the coal blending strategy optimization model, and the coal blending strategy optimization model is based on the model constraint conditions to obtain the coal blending scheme of the minimum power supply comprehensive cost in the dynamic coal quality cost database.

[0041] The coal blending control module is used for converting a coal blending scheme into running time of a coal conveying belt to realize automatic coal blending control.

[0042] The application further provides a terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the automatic coal blending control method based on real coal price calculation.

[0043] The application further provides a computer readable storage medium storing a computer program, wherein the computer program is executable on a processor to implement the steps of the automatic coal blending control method based on real coal price calculation.

[0044] The application further provides a computer program product comprising a computer program, wherein the computer program is executable on a processor to implement the steps of the automatic coal blending control method based on real coal price calculation.

[0045] Compared with the prior art, the application has at least the following beneficial effects:

[0046] The application provides an automatic coal blending control method based on real coal price calculation, which establishes a dynamic coal quality cost database according to a combustion adjustment experiment matrix, records in detail data such as boiler efficiency, environmental protection cost and heating surface wear cost under different load conditions, air distribution modes, coal blending schemes and operation parameters, and combines real-time coal prices to provide rich and accurate information sources for subsequent coal blending strategy optimization. Further, the coal blending ratio is taken as a decision variable, the minimum power supply comprehensive cost is taken as a target, and model constraint conditions including load constraints, environmental protection constraints and coal type consumption constraints are determined, the model can comprehensively consider various limiting factors in the operation of a thermal power plant, and ensures that the generated coal blending scheme meets production requirements, conforms to environmental protection standards and equipment safety requirements. The method realizes complete closed-loop control from data acquisition, model optimization to automatic execution. Real-time boiler operation characteristics are input into a coal blending strategy optimization model, an optimal coal blending scheme is obtained, the coal blending scheme is converted into running time of a coal conveying belt, and automatic coal blending control is realized. The closed-loop control mode can timely respond to changes in the boiler operation state, dynamically adjusts the coal blending scheme, ensures that the thermal power plant is always in an optimal operation state, effectively improves the economic benefit, environmental protection benefit and equipment reliability of the thermal power plant, and realizes the change of coal management from experience driving to data intelligent driving.

[0047] The present application adopts counter-balance method when calculating boiler efficiency, comprehensively considers multiple factors such as exhaust gas heat loss, solid incomplete combustion heat loss and gas incomplete combustion heat loss, so that the calculation of boiler efficiency is more accurate; the calculation of environmental protection cost and heating surface wear cost fully considers various actual operation conditions, and provides reliable basis for comprehensive cost analysis. In the model solving process, the particle swarm optimization algorithm is adopted, the coal blending scheme for minimizing the power supply comprehensive cost can be quickly and accurately found in the dynamic coal quality cost database, and the optimization efficiency and accuracy are improved.

[0048] The database generation module in the system can construct a dynamic coal quality cost database covering key information such as boiler efficiency, coal consumption rate, environmental protection cost and heating surface wear cost according to real-time coal prices and various boiler operation characteristics, and provide solid data support for subsequent coal blending decision. The model establishment module scientifically constructs a coal blending strategy optimization model and determines reasonable constraint conditions with the minimization of power supply comprehensive cost as the target, and fully considers various actual demands and limitations in the operation of thermal power plants. The model solving module can quickly and accurately find the optimal coal blending scheme in the complex database by using an advanced algorithm, and effectively improves the decision efficiency and accuracy. The coal blending control module accurately converts the optimized coal blending scheme into the running time of the coal conveying belt, realizes automatic coal blending control, and reduces the errors and uncertainties caused by manual intervention.

[0049] The system can dynamically adjust the coal blending strategy according to the real-time changing boiler operation characteristics such as target load, coal price list and inventory threshold, and ensure that the optimal coal blending effect can be realized under different working conditions. At the same time, the modular design of the system is also convenient for subsequent function expansion and upgrading, can adapt to the development needs of thermal power plants, and provides a strong guarantee for the long-term stable operation and continuous optimization of thermal power plants. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The flowchart of the automatic coal blending control method based on real coal price calculation of the present application. DETAILED DESCRIPTION

[0051] The present application will be further described below in combination with the drawings and specific embodiments.

[0052] The present application constructs an intelligent coal blending decision and execution system integrating boiler operation characteristics, fuel cost, environmental protection expenditure and equipment maintenance loss, provides an automatic coal blending control method based on real coal price calculation, which comprises the following steps:

[0053] (1) Economic parameter calculation and database establishment based on combustion adjustment experiment: basic data are obtained through systematic combustion experiment, and a coal quality cost database integrating multiple economic parameters is established;

[0054] (2) Database optimization-based blending strategy development: The core goal of database optimization-based blending strategy development is to establish a quantitative coal blending strategy optimization model, and to solve the comprehensive cost lowest coal blending scheme under multiple constraints through intelligent algorithms;

[0055] (3) Closed-loop intelligent coal feeding control of blending strategy: Relying on the existing digital coal yard system and unmanned bucket wheel machine equipment of the power plant, a closed-loop intelligent coal feeding control system based on database and strategy rules is constructed.

[0056] Step (1) includes the following sub-steps:

[0057] S1: First, design a combustion adjustment experiment matrix covering typical boundary conditions of boiler operation: under four stable load conditions (such as 100% / 75% / 50% / 30% rated load), implement a variety of differentiated air distribution modes such as equal air distribution, beam waist air distribution, upper and lower staged air distribution, side air enhanced air distribution, etc., and carry out blending combination for more than eight basic coal types, set ≥10 groups of blending ratio, cover typical coal blending schemes such as low calorific value high volatile matter coal, high sulfur coal, high ash melting point coal, etc. During the experiment, key parameters such as furnace temperature field distribution, flue gas composition (including O2, CO, NO x , SO2 concentration) at the outlet of the economizer, air preheater exhaust gas temperature, fly ash carbon content, and slag sample of the slag extractor are collected in real time through fixed measuring points, and DCS operation data are recorded synchronously to form a complete working condition data set. The stable operation time for each working condition is not less than 2 hours, and a total of not less than 50 groups of effective experimental data sets are completed.

[0058] S2: Based on the data obtained from the experiment, the mature economic parameter quantification modeling process is used. In the economic parameter quantification modeling process, the boiler thermal efficiency is calculated strictly in accordance with the relevant standards, and the actual boiler thermal efficiency η is calculated by using the counterbalance method: the exhaust gas temperature and the excess air coefficient are used to calculate the exhaust gas heat loss, the fly ash carbon content is used to calculate the solid incomplete combustion heat loss, and the CO concentration is used to calculate the gas incomplete combustion heat loss.

[0059] The coal consumption rate is directly obtained by the ratio of the measured fuel consumption to the power generation (g / kWh).

[0060] The environmental protection cost accounting focuses on the desulfurization and denitrification links: the desulfurization cost is calculated according to the measured SO2 concentration and the flue gas flow, combined with the design parameters of the desulfurization system (calcium-sulfur ratio, desulfurization efficiency, power consumption, etc.) to calculate the limestone consumption and its procurement unit price; the denitrification cost is based on the inlet NO x concentration, flue gas flow, and SCR system ammonia escape rate to derive the liquid ammonia / urea consumption and relate to the market price.

[0061] The heat surface wear cost innovation adopts the equivalent conversion model: according to the coal quality grinding index (HGI) and the flue gas velocity field simulation, the metal wear rate of the heat surface is calculated, the material and labor prices are comprehensively considered, and the unit area heat exchange pipe maintenance cost is converted into the equivalent degree electric cost.

[0062] S3: A dynamic coal quality cost database is constructed, and three types of core information are stored by using a hierarchical data structure: the basic layer records the experimental raw data, including: working condition number, load, air distribution mode, coal blending ratio, real-time operating parameters; the calculation layer stores the output results of the thermal model, including: actual boiler thermal efficiency η, coal consumption rate g, environmental protection cost matrix, wear cost coefficient; the economic layer integrates the real-time coal price data input from the outside. Considering the real-time coal price, boiler efficiency, coal consumption rate, environmental protection cost and heat surface wear and other factors, a real coal price calculation model is established and stored in the database.

[0063] Intelligent association rules are set in the database: when a new coal blending scheme and target load are input, the system automatically retrieves the matching working condition or outputs the corresponding economic parameter package through the interpolation algorithm to form a coal blending scheme cost panoramic view. The database supports API interface and real-time interaction with power plant SIS system and fuel management system to ensure dynamic updating of price data and coal quality characteristics, providing data-driven basis for subsequent optimization models. During operation, the database supports self-optimization sub-update function.

[0064] Step (2) includes the following sub-steps:

[0065] S1: A coal blending strategy optimization model is constructed with the minimum unit kilowatt-hour power supply comprehensive cost as the target, and the objective function is defined as:

[0066]

[0067] In the formula, C T represents the comprehensive coal price, unit yuan / kWh; n represents the total number of coal species participating in blending; ρ i represents the blending ratio of the i-th coal, (0<ρ i <1, and ); P i represents the real-time factory price (including freight) of the i-th coal; g represents the coal consumption rate under this working condition, which is fed back by the database according to the input blending ratio, unit g / kWh; C S and C N represent desulfurization cost and denitration cost, respectively; C W represents wear cost.

[0068] The desulfurization cost C S is calculated as follows:

[0069] C S = Q y·[SO2]·η1·M1 / P (2)

[0070] In the formula, Q y is the flue gas flow; [SO2] is the SO2 concentration, η1 is the desulfurization system efficiency; M1 is the total cost of desulfurization limestone used; and P is the power generation capacity.

[0071] The desulfurization cost C N is calculated as follows:

[0072] C N = Q y ·[NO x ]·η2·M2 / P (3)

[0073] In the formula, [NO x ] is the NO x concentration, η2 is the denitration system efficiency; and M2 is the total cost of denitration ammonia or urea used.

[0074] The wear cost C N is calculated as follows:

[0075]

[0076] In the formula, HGI is the coal quality grinding index of the mixed coal; and k, a, and b are empirical parameters.

[0077] S2: Set multiple physical constraint boundaries:

[0078] (1) The load constraint is forced to meet the scheduling demand;

[0079] (2) The environmental protection constraint limits NO x ≤ 50 mg / Nm 3 , SO2≤ 35 mg / Nm 3 , in accordance with the national ultra-low emission standard;

[0080] (3) The use amount of each coal does not exceed the upper limit of the coal yard inventory.

[0081] A mixed intelligent optimization algorithm is developed. A constraint processing mechanism is introduced based on a particle swarm optimization (PSO) framework. In the case of meeting all constraint conditions, an optimization algorithm is used based on a dynamic coal quality cost database to obtain an optimal coal blending scheme. A visual decision platform is constructed. After the target load, coal price list, inventory threshold, and other parameters are input by the operating personnel, the coal blending strategy optimization model automatically outputs the optimal coal blending scheme. After the coal blending scheme is obtained, it can be weighted according to the properties of the coal, related national and industry standards are calculated to obtain key parameter prediction results, including coal consumption rate prediction, NO x trend generation, wear risk level, and the formation of a quantifiable and verifiable coal blending decision verification, which provides a basis for closed-loop control.

[0082] Step (3) includes the following sub-steps:

[0083] S1: When the unit is operated to a specific load condition, the system automatically retrieves the historical optimal blending strategy data in the dynamic coal quality cost database of the load point, combines the blending priority rules preset according to the actual needs of the user, and generates a precise coal blending scheme in real time. The scheme is directly issued to the fuel management system in the form of structured instructions through a standard interface, and the core instructions include target blending ratio, required coal type area, and corresponding total fuel amount.

[0084] S2: The digital coal yard three-dimensional map automatically analyzes the coal type demand in the coal blending scheme, matches the current position of the bucket wheel machine with the target coal pile coordinates to generate an optimal material taking path. After receiving the path instructions, the unattended bucket wheel machine moves to the specified coal area through the built-in positioning module, and the adaptive material taking model converts the coal blending ratio into equipment operation parameters. The system pre-sets the material taking roller speed and pitch angle based on the characteristics of the coal pile to ensure that the unit time coal taking amount meets the subsequent proportional blending requirements.

[0085] S3: The coal blending execution is realized through precise timing control. The system directly converts the coal blending ratio into a running time allocation scheme of each coal type on the coal conveying belt. The closed-loop calibration is completed based on database prediction and real-time metering. The system continuously compares the deviation of the actual blending ratio from the target value, and dynamically adjusts the running time allocation of the subsequent period when the predicted value of the key parameter exceeds the threshold or the metering deviation exceeds the limit. All execution data are automatically archived to the historical strategy library, and are analyzed in association with the actual combustion performance of the unit to continuously optimize the decision accuracy of the strategy rule library. Finally, a full-process autonomous operation mechanism is formed, including load condition triggering, strategy automatic generation, equipment direct execution, and data feedback optimization.

[0086] In summary, the present application improves the economy, environmental protection and reliability of coal blending in thermal power plants by constructing a technical system of "combustion characteristic database-multi-dimensional cost model-intelligent optimization algorithm-automatic execution closed loop". The dynamic coal quality cost database established based on full-condition combustion experiments first realizes the fusion and quantification of boiler thermal efficiency, coal consumption rate, desulfurization and denitrification agent consumption cost, and equivalent cost of heating surface wear, providing accurate data basis for comprehensive cost optimization; the intelligent optimization algorithm dynamically generates a coal blending scheme under multiple constraint boundaries, simultaneously predicts the pollutant emission trend and equipment wear risk, and ensures that the scheme meets the ultra-low emission standard and equipment safety threshold at the same time; relying on the millisecond-level instruction execution closed loop established based on the digital coal yard and unmanned bucket wheel machine, the manual operation deviation is completely eliminated, seamless cooperation from optimization decision to precise coal feeding is realized, and finally the paradigm shift from experience-driven to data-intelligent-driven in coal management is achieved.

[0087] Example 1

[0088] The flowchart of the method of the present application is as followsFigure 1 Systematic combustion experiments were carried out in a 600 MW supercritical unit, covering four load conditions of 100%, 75%, 50%, 30% and three modes of equal distribution, beam-waist distribution and upper-lower staged distribution. Eight typical coal types were selected, including high-sulfur Shanxi coal, high-volatile Mongolian coal and low-calorific value Indonesian coal, etc. Combustion was carried out in combination with the preset ten groups of blending ratio. Each group of working condition was stably operated for two hours, the combustion state was monitored in real time by the furnace temperature field scanner, the SO2, NO x concentration of flue gas analyzer was synchronously collected at the outlet of economizer, the carbon content of fly ash was tested by sampling, and the fuel consumption and power generation of DCS system were recorded. After 52 groups of effective experiments were completed, the original data were stored in the database basic layer to provide multi-dimensional combustion characteristic atlas for subsequent modeling.

[0089] Based on the experimental data, a multi-dimensional economic calculation process was started. Taking the "50% Mongolian coal + 50% Indonesian coal" scheme under 75% load as an example: the actual thermal efficiency was calculated to be 92.1% by using the counterbalance method, and the coal consumption rate was derived to be 285 g / kWh; according to the measured SO2 concentration of 320 mg / Nm 3 and the calcium-sulfur ratio of 1.03 in the desulfurization system, the limestone consumption per hour was calculated to be 42.3 tons, and the desulfurization cost was converted to be 0.015 yuan / kWh combined with the market unit price; the wear equivalent model was innovatively introduced, by substituting the coal quality grinding index HGI = 58 and the flue gas velocity 12 m / s into the calibration formula C v , the wear cost was obtained to be 0.008 yuan / kWh. The above parameters are automatically associated with real-time updated coal price data (such as Mongolian coal 650 yuan / ton, Indonesian coal 580 yuan / ton), forming a dynamic database containing four-dimensional tags of thermal efficiency, coal consumption rate, environmental protection cost and wear cost.

[0090] When the unit switches to 75% load operation, the system calls the multi-dimensional cost model to start the particle swarm optimization algorithm. The objective function is set to minimize the unit power comprehensive cost C T , and the constraint conditions include the emission limit value of NO x ≤ 50 mg / Nm 3 and the upper limit of each coal inventory. The algorithm completes multiple iterations in ten seconds, and outputs the optimal coal blending scheme as "Mongolian coal 63% + Indonesian coal 37%". The prediction shows that the comprehensive cost is reduced to 0.213 yuan / kWh, the NO x concentration is 42 mg / Nm 3 , which is in the safe interval, and the wear risk level is evaluated as medium risk. The scheme automatically generates an instruction document through the visual decision platform, including the target ratio, cost composition and risk warning parameters.

[0091] The following is an embodiment of the device of the present application, which can be used to execute the method embodiment of the present application. For details not disclosed in the device embodiment, please refer to the method embodiment of the present application.

[0092] In another embodiment of the present application, an automatic coal blending control system based on real coal price calculation is also provided, which runs the steps of the automatic coal blending control method based on real coal price calculation described above, and the system comprises:

[0093] A database generation module is configured to combine real-time coal prices with different boiler operation characteristics to establish a dynamic coal quality cost database about boiler efficiency, coal consumption rate, environmental protection cost, and heating surface wear cost;

[0094] A model establishment module is configured to establish a coal blending strategy optimization model with the minimum power supply comprehensive cost as the target, with the blending ratio of coal as the decision variable, and to determine the model constraint condition;

[0095] A model solution module is configured to input real-time boiler operation characteristics into the coal blending strategy optimization model, and to obtain a coal blending scheme with the minimum power supply comprehensive cost based on the model constraint condition in the dynamic coal quality cost database;

[0096] A coal blending control module is configured to convert the coal blending scheme into the running time of the coal conveying belt to realize automatic coal blending control.

[0097] In another embodiment of the present application, a terminal device is also provided, which comprises a processor and a memory, the memory is configured to store a computer program, the computer program comprises program instructions, and the processor is configured to execute the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions to implement a corresponding method flow or a corresponding function; the processor described in the embodiment of the present application can implement the operation of the automatic coal blending control method based on real coal price calculation.

[0098] In still another embodiment, the present application provides a storage medium, specifically a computer readable storage medium (Memory), which is a memory device in the terminal equipment, for storing programs and data. It should be understood that the computer readable storage medium herein can include the built-in storage medium in the terminal equipment, and of course can also include the expansion storage medium supported by the terminal equipment. The computer readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions adapted to be loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory, or a non-volatile memory such as at least one disk memory. The one or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to implement the corresponding steps of the automatic coal blending control method based on real coal price calculation in the above embodiment.

[0099] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0100] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing apparatus to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing apparatus generate a means for implementing the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocks Figure 1 The means for implementing the functions specified in one or more flows and / or blocks.

[0101] These computer program instructions can also be stored in a computer readable storage medium that can direct the computer or other programmable data processing apparatus to work in a specific manner, so that the instructions stored in the computer readable storage medium produce a product including an instruction means, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 The functions specified in one or more flows and / or blocksFigure 1 the function specified in the one or more blocks.

[0102] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide a process for implementing the flow Figure 1 the flow or flows and / or blocks Figure 1 the steps of the function specified in the one or more blocks.

[0103] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the present application, but not to limit it, although the above embodiments of the present application have been described in detail, those skilled in the art should understand: the specific embodiments of the present application can be modified or replaced by the same, without departing from the spirit and scope of the present application, any modification or equivalent replacement, which should be covered within the scope of protection of the claims of the present application.

Claims

1. An automatic coal blending control method based on real coal price calculation, characterized by, The specific steps are as follows: The real-time coal price is combined with the boiler operation characteristics under different conditions to establish a dynamic coal quality cost database about boiler efficiency, coal consumption rate, environmental protection cost and heating surface wear cost; The blending ratio of coal is taken as the decision variable, and a coal blending strategy optimization model is established to minimize the comprehensive power supply cost, and the model constraint conditions are determined; The real-time boiler operation characteristics are input into the coal blending strategy optimization model, and the coal blending strategy optimization model is optimized in the dynamic coal quality cost database based on the model constraint conditions to obtain the coal blending scheme that minimizes the comprehensive power supply cost; The coal blending scheme is converted into the running time of the coal conveying belt to realize automatic coal blending control.

2. The automatic coal blending control method based on real coal price calculation according to claim 1, characterized in that, The dynamic coal quality cost database is established according to the combustion adjustment experiment matrix, and specifically: The boiler operation characteristics include load conditions, air distribution modes, coal blending schemes and operation parameters. Different load conditions, air distribution modes, coal blending schemes and operation parameters are set to conduct combustion experiments, and the boiler efficiency, environmental protection cost and heating surface wear cost data under corresponding conditions are obtained; The above data are combined with the real-time coal price to constitute the dynamic coal quality cost database. The dynamic coal quality cost database includes a basic layer, a calculation layer and an economic layer. The basic layer is used to record the original experimental data, including: condition number, load, air distribution mode, coal blending ratio, real-time operation parameter; the calculation layer is used to store the combustion experiment results, including: actual boiler thermal efficiency, coal consumption rate, environmental protection cost matrix and wear cost coefficient; the economic layer is used to integrate the real-time coal price data input from outside.

3. The automatic coal blending control method based on real coal price calculation according to claim 2, characterized in that, The boiler efficiency includes the actual boiler thermal efficiency, which is calculated by the counterbalance method, including the exhaust gas temperature and the excess air coefficient calculation of the exhaust gas heat loss, the fly ash carbon content calculation of the solid incomplete combustion heat loss, and the CO concentration calculation of the gas incomplete combustion heat loss; The coal consumption rate is obtained by the ratio of the measured fuel consumption and the power generation; The environmental protection cost includes desulfurization cost and denitration cost, the desulfurization cost includes limestone consumption and its purchase unit price, the limestone consumption is obtained according to the measured SO2 concentration and flue gas volume, combined with the design parameters of the desulfurization system; the denitration cost includes liquid ammonia / urea consumption and its purchase unit price, the liquid ammonia / urea consumption is obtained according to the inlet NO x concentration, flue gas flow and ammonia escape rate of the SCR system; The heating surface wear cost is obtained according to the heating surface metal wear rate, material, labor and repair cost, and the heating surface metal wear rate is obtained according to the coal quality grinding index, fly ash particle size distribution and flue gas velocity field simulation.

4. The automatic coal blending control method based on real coal price calculation according to claim 1, characterized in that, The coal blending strategy optimization model is as follows: In the formula, C T represents the comprehensive coal price; n represents the total number of coal types participating in blending; p i represents the blending ratio of the i-th coal, 0 i <1, and P i represents the real-time mill price of the i-th coal; g represents the coal consumption rate under the working condition, which is fed back by the database according to the input blending ratio; C S and C N respectively represent the desulfurization cost and the denitration cost, and the desulfurization cost and the denitration cost constitute the environmental protection cost; C W represents the wear cost; Desulfurization cost C S The calculation formula is as follows: C S = Q y · [SO2] · η1· M1 / P In the formula, Q y is the flue gas flow; [SO2] is the SO2 concentration, η1 is the desulfurization system efficiency; M1 is the total cost of desulfurization limestone used; P is the power generation capacity; Desulfurization cost C N The calculation formula is as follows: C N = Q y · [NO x ]· η2· M2 / P In the formula, [NO x ] is the NO x concentration, η2 is the denitration system efficiency; M2 is the total cost of the denitration ammonia gas or urea used; Wear cost C N The calculation formula is as follows: In the formula, HGI is the wear coefficient of the mixed coal; k, a, and b are empirical parameters; v y is the flue gas velocity field; The constraint conditions are as follows: (1) The load constraint must meet the scheduling demand; (2) Environmental constraints limit NO x ≤ 50 mg / Nm 3 , SO2≤ 35 mg / Nm 3 ; (3) The amount of each coal type does not exceed the upper limit of the coal yard inventory.

5. The automatic coal blending control method based on real coal price calculation according to claim 4, characterized in that, In the step of inputting the real-time boiler operation characteristics into the coal blending strategy optimization model, the coal blending strategy optimization model is optimized in the dynamic coal quality cost database based on the model constraint conditions to obtain the coal blending scheme that minimizes the comprehensive power supply cost. The particle swarm optimization algorithm is used to optimize the coal blending scheme that minimizes the comprehensive power supply cost in the dynamic coal quality cost database.

6. The automatic coal blending control method based on real coal price calculation according to claim 5, characterized in that, The real-time boiler operation characteristics include target load, coal price list and inventory threshold, and the coal blending strategy optimization model outputs the optimal coal blending scheme.

7. An automatic coal blending control system based on real coal price calculation, characterized in that, It includes: The database generation module is used to combine the real-time coal price with the boiler operation characteristics under different conditions to establish a dynamic coal quality cost database about boiler efficiency, coal consumption rate, environmental protection cost and heating surface wear cost; The model establishment module is used to take the blending ratio of coal as the decision variable, establish a coal blending strategy optimization model to minimize the comprehensive power supply cost, and determine the model constraint conditions; A model solving module, configured to input real-time boiler operation characteristics into a coal blending strategy optimization model, and the coal blending strategy optimization model is configured to search for a coal blending scheme with minimized power supply comprehensive cost in a dynamic coal quality cost database based on model constraints; A coal blending control module, configured to convert the coal blending scheme into operation time of a coal conveying belt to realize automatic coal blending control.

8. A terminal device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the automatic coal blending control method based on real coal price calculation according to any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 8. The computer program is executed by the processor to implement the steps of the automatic coal blending control method based on real coal price calculation according to any one of claims 1-6.

10. A computer program product comprising a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the automatic coal blending control method based on real coal price calculation according to any one of claims 1-6.

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