An energy flow and loss data online monitoring system and monitoring method

Abstract

The application provides an energy flow direction and loss data online monitoring system and a monitoring method, and belongs to the technical field of energy measurement. Unit operation data are collected in real time through a data collection module, and the collected operation data are filtered through a preprocessing module. An operation calculation module analyzes the thermal efficiency of the unit based on the filtered data. The units are sorted according to the thermal efficiency from large to small, the unit with the lowest thermal efficiency is selected, and multiple heating surfaces with a temperature difference between the flue gas inlet temperature and the working medium outlet temperature not less than the minimum temperature difference are analyzed. The pressure loss of the multiple heating surfaces is calculated. An energy-loss model of the unit is constructed, and the online monitoring of the energy flow direction and loss data is realized. The method preferably overcomes the difficulty caused by loss fluctuation on energy flow direction and efficiency calculation, has good universality and robustness, and is suitable for operation condition evaluation of a coal-fired thermal power generating unit and adjustment and optimization of combustion on the basis.

Description

Technical Field

[0001] This invention belongs to the field of energy measurement technology, specifically relating to an online monitoring system and method for energy flow and loss data. Background Technology

[0002] Power plants have played a vital role in my country's rapid economic and social development, and the generating unit is a crucial energy conversion device among the three main pieces of equipment in a power plant. Its function is to convert the chemical energy of fuel into thermal energy, and then use this thermal energy to heat water in the boiler, creating superheated steam with sufficient quantity and quality (temperature and pressure) for the turbine. However, energy losses occur during unit operation. Traditional loss analysis, due to numerous shortcomings, cannot fully reflect the role of the economizer in the unit's energy utilization, and is insufficient to provide useful references for parameter matching between coal and the unit, thus failing to meet our current needs.

[0003] my country's overall coal consumption level for large-scale thermal power units has reached the world's advanced level. Conducting in-depth, comprehensive, and refined energy-saving diagnosis and optimization research is of great significance for the deep-level energy conservation and consumption reduction of coal-fired power units. Energy-saving diagnosis of thermal power units guides energy-saving work by analyzing the deviation between actual energy consumption and baseline energy consumption. Therefore, accurately obtaining baseline values ​​is a crucial aspect of deep energy conservation in large-scale thermal power units. Baseline values ​​refer to the parameter values ​​corresponding to the unit's optimal operating conditions under the current operating boundary. Scientifically determining the baseline values ​​of operating parameters is the core and challenging issue in unit energy consumption diagnosis and optimization research, especially under the current variable boundary conditions of load, coal quality, and environment faced by my country's thermal power units.

[0004] As a key piece of equipment in the operation of coal-fired power plants, the generating unit is also a major focus for energy conservation and consumption reduction. Adjusting and optimizing combustion is an effective means to improve boiler efficiency and reduce coal consumption. Combustion efficiency is a crucial indicator for measuring the energy flow and loss data of the generating unit. Its results are significant for evaluating the overall combustion status of the boiler, modeling the boiler as a whole and its individual components, and optimizing boiler combustion based on boiler efficiency monitoring.

[0005] In the prior art, Chinese patent application number 201010128915.2 proposes a "real-time correction method for the calorific value of coal-fired boiler fuel." This technology calculates the work capacity coefficient of coal quality in real time based on the total fuel quantity, unit load, and unit power generation coal consumption rate. The obtained coefficient is converted into a fuel calorific value correction coefficient to correct the calorific value of the coal entering the furnace. This technology can correct the calorific value of fuel in real time to a certain extent, so as to adjust the coal feed rate and overcome the impact of coal quality fluctuations. However, this method requires the boiler to be under stable operating conditions and cannot be used for correcting the calorific value of coal under varying boiler operating conditions, thus having certain limitations. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention proposes an online monitoring method for energy flow direction and loss data, comprising the following steps:

[0007] S1. The unit's operating data is collected in real time through the data acquisition module, and the sampled operating data is filtered through the preprocessing module.

[0008] S2. The calculation module analyzes the unit's thermal efficiency based on the filtered data.

[0009] S3. Sort the units according to thermal efficiency from high to low, select the unit with the lowest thermal efficiency, and analyze the heating surface where the difference between flue gas inlet temperature and working fluid outlet temperature is not less than the minimum temperature difference.

[0010] S4. Calculate the pressure loss of multiple heated surfaces obtained in step S3;

[0011] S5. Construct a unit energy-loss model.

[0012] Further, step S2 includes the following steps:

[0013] S21. Calculate the heat absorption of the working fluid. :

[0014] (1);

[0015] Among them, D bi C is the working fluid flow rate. pi For isobaric specific heat capacity, For temperature rise;

[0016] S22. Construct the unit's heat balance equation:

[0017] (2);

[0018] In the formula: Q is the sum of boiler heat losses; a Heat from flue gas recovered by the air preheater; The calorific value of the fuel;

[0019] S23, Computer group thermal efficiency :

[0020] (3).

[0021] Furthermore, in step S3, the minimum temperature difference ΔT between the working fluid at the outlet of each heating surface and the corresponding steam is set. min The steam inlet temperature T of the heated surface I is determined using the narrow-point temperature difference method in heat balance calculations. iand working fluid outlet temperature T o Temperature difference ΔT I , will ΔT I and minimum temperature difference ΔT min By comparison, ΔT is obtained. I Greater than or equal to the temperature difference ΔT min n heated surfaces.

[0022] Furthermore, in step S4, based on the pressure loss-energy relationship, the total pressure loss of the heated surface is expressed as:

[0023] (4);

[0024] In the formula, D represents the total pressure loss of the heated surface. i For the steam energy of each heated surface, k i The energy resistance coefficient of the heated surface. This represents the density of the vapor.

[0025] Furthermore, in step S5, under the condition that the unit structure is constant and the pressure loss has been determined, the steam pressure P0 and the steam valve opening Gv meet the unit's required energy load N:

[0026] (5);

[0027] For a given energy load N e The energy flow model for the generating unit is as follows:

[0028] (6);

[0029] (7);

[0030] In the formula, N represents the given energy load and the rated energy load of the unit; X represents the loss constraint parameter.

[0031] This invention also proposes an online monitoring system for energy flow and loss data, which is used to realize an online monitoring method for energy flow and loss data, including: a data acquisition module, a preprocessing module, a data storage module, a running calculation module, and a model building module;

[0032] The data acquisition module is used to collect unit operation data in real time;

[0033] The preprocessing module is used to filter the real-time acquired unit operation data;

[0034] The filtered data is stored in the data repository.

[0035] The operation calculation module is used to analyze the thermal efficiency of the unit based on the filtered data.

[0036] The model building module is used to calculate the pressure loss of multiple heated surfaces and to build a unit energy-loss model.

[0037] Furthermore, a database of loss cause analysis and guidance suggestions is established based on historical data. When loss occurs, the cause of the loss is identified from the database, and the operators are guided to perform operations to eliminate the loss.

[0038] Furthermore, an intelligent control platform is built to realize online monitoring of energy flow and loss data. The intelligent control platform includes a controller and a server. The intelligent control platform conducts bidirectional data transmission with multiple unit DCS systems, and transmits real-time operating data to the server of the intelligent control platform through an interface protocol.

[0039] Compared with the prior art, the present invention has the following beneficial technical effects:

[0040] The system collects real-time unit operation data through a data acquisition module, and filters the sampled operation data through a preprocessing module. The operation calculation module analyzes the unit's thermal efficiency based on the filtered data. The units are sorted from highest to lowest thermal efficiency, and the unit with the lowest thermal efficiency is selected. Multiple heating surfaces with a temperature difference between the flue gas inlet temperature and the working fluid outlet temperature not less than the minimum temperature difference are analyzed. The pressure loss of multiple heating surfaces is calculated. An energy-loss model of the unit is constructed, realizing online monitoring of energy flow and loss data. This effectively overcomes the difficulties caused by loss fluctuations in energy flow and efficiency calculation, and has good versatility and robustness. It is suitable for evaluating the operating status of coal-fired power generating units and adjusting and optimizing combustion based on this evaluation. Attached Figure Description

[0041] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a schematic diagram of the online monitoring method for energy flow and loss data of the present invention;

[0043] Figure 2 This is a schematic diagram of the structure of the online monitoring system for energy flow and loss data of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0045] In the accompanying drawings of specific embodiments of the present invention, in order to better and more clearly describe the working principle of each component in the system and show the connection relationship of each part in the device, only the relative positional relationship between each component is clearly distinguished. It does not constitute a limitation on the signal transmission direction, connection sequence, or size, dimension, and shape of each part within the component or structure.

[0046] like Figure 1 The diagram shown is a flowchart illustrating the online monitoring method for energy flow and loss data according to this application.

[0047] S1: The data acquisition module collects unit operation data in real time, and the preprocessing module filters the sampled operation data.

[0048] Specifically, the preprocessing module performs bad pixel processing and data smoothing on the real-time unit operation data collected by the data acquisition module for subsequent analysis of the unit's thermal efficiency. This data preprocessing module includes a bad pixel processing unit and a data smoothing processing unit.

[0049] The defective pixel processing unit uses a polynomial sliding fit method to identify and remove defective pixels. The data smoothing processing unit uses a data weighted filtering method.

[0050] S2: The calculation module analyzes the unit's thermal efficiency based on the filtered data.

[0051] S21: Calculate the heat absorbed by the working fluid.

[0052] The heat absorbed by the working fluid is the heat absorbed Q of the unit output. b It can be decomposed into the sum of the heat absorbed by each heated surface. :

[0053] (1);

[0054] D bi C is the working fluid flow rate. pi For isobaric specific heat capacity, For temperature rise.

[0055] S22: Construct the unit's thermal balance equation.

[0056] To meet the heat absorption Q of the working fluidb The requirement is that the boiler's standard coal consumption is B. s The heat balance equation for the unit is then:

[0057] (2);

[0058] In the formula: Q is the sum of boiler heat losses; a Heat from flue gas recovered by the air preheater; This refers to the calorific value of the fuel.

[0059] S23: Thermal efficiency of computer group.

[0060] Since the heat recovered by the air preheater does not change the boiler's thermal efficiency, the unit's thermal efficiency can be expressed as:

[0061] (3).

[0062] S3: Sort the units according to their thermal efficiency from highest to lowest, select the unit with the lowest thermal efficiency, and analyze the heating surface where the temperature difference between the flue gas inlet temperature and the working fluid outlet temperature is not less than the minimum temperature difference.

[0063] Set the minimum temperature difference ΔT between the working fluid at the outlet of each heating surface and the corresponding steam. min The steam inlet temperature T of the heated surface I is determined using the narrow-point temperature difference method in heat balance calculations. i and working fluid outlet temperature T o Temperature difference ΔT I , will ΔT I and minimum temperature difference ΔT min By comparison, ΔT is obtained. I Greater than or equal to the temperature difference ΔT min n heated surfaces.

[0064] S4: Calculate the pressure loss of multiple heated surfaces obtained in step S3.

[0065] Based on the pressure loss-energy relationship, pressure loss can be expressed as:

[0066] (4);

[0067] In the formula, D represents the total pressure loss of the heated surface. i For the steam energy of each heated surface, k i The energy resistance coefficient of the heated surface. This represents the density of the vapor.

[0068] S5. Construct a unit energy-loss model.

[0069] Given a fixed unit structure and known pressure loss, the steam pressure P0 and steam valve opening Gv satisfy the unit's required energy load N.

[0070] (5);

[0071] For a given energy load N e The energy flow model of the unit can be represented as:

[0072] (6);

[0073] (7);

[0074] In the formula, N represents the given energy load and the rated energy load of the unit; X represents the loss constraint parameter.

[0075] The energy flow and loss data online monitoring system proposed in this invention collects unit operation data in real time based on the data acquisition module, and filters the real-time collected unit operation data through the preprocessing module. The filtered data is stored in the data storage repository, which has the advantages of easy data acquisition and no need to add additional hardware.

[0076] The calculation module analyzes the unit's thermal efficiency based on filtered data, while the model building module calculates the pressure loss of multiple heat exchange surfaces and constructs an energy-loss model for the unit. This improves the accuracy of identifying the heat absorption of each heat exchange surface under different loads. It effectively overcomes the difficulties caused by loss fluctuations in energy flow and efficiency calculation, and has good versatility and robustness. It is suitable for evaluating the operating status of coal-fired power generating units and adjusting and optimizing combustion based on this evaluation.

[0077] like Figure 2 The diagram shown is a schematic of the structure of the online monitoring system for energy flow and loss data of this application. The online monitoring system for energy flow and loss data includes: a data acquisition module, a preprocessing module, a data storage module, a calculation module, and a model building module.

[0078] The data acquisition module is used to collect unit operation data in real time.

[0079] The preprocessing module is used to filter the real-time acquired unit operation data. Specifically, the preprocessing module performs bad pixel processing and data smoothing on the real-time acquired unit operation data from the data acquisition module for subsequent analysis of the unit's thermal efficiency. This data preprocessing module includes a bad pixel processing unit and a data smoothing unit.

[0080] The defective pixel processing unit uses a polynomial sliding fit method to identify and remove defective pixels.

[0081] In a preferred embodiment, if the real-time data is defective, the unit design value can be used to interpolate under different loads as a default value to replace the real-time data for calculation, ensuring that the online monitoring method can perform calculations normally.

[0082] The data smoothing processing unit is implemented through a data weighted filtering method.

[0083] The filtered data is stored in the data repository.

[0084] The calculation module is used to analyze the unit's thermal efficiency based on the filtered data.

[0085] The model building module is used to calculate the pressure loss of multiple heated surfaces and build a unit energy-loss model. It analyzes the causes of losses calculated by the unit energy-loss model and provides corresponding operational guidance and suggestions.

[0086] Preferably, a database of loss cause analysis and guidance suggestions is established based on historical data. When a problem occurs, the direct cause of the energy loss is identified from the database, and the cause analysis and guidance suggestions are transmitted back to the DCS operator station via a transmission protocol to guide operators in eliminating the loss. Based on the loss situation observed in the DCS and the real-time push of cause analysis and guidance suggestions, operators adjust their operations in real time, continuously bringing the actual operating values ​​closer to the optimal design values, thereby reducing the loss and achieving the goal of energy saving and consumption reduction.

[0087] In a preferred embodiment, an intelligent control platform is built to realize online monitoring of energy flow and loss data. The intelligent control platform includes a controller and a server, and establishes bidirectional data transmission between the intelligent control platform and multiple unit DCS systems. The real-time operating data of multiple unit DCS systems is transmitted to the server of the intelligent control platform through an agreed interface protocol.

[0088] In a preferred embodiment, the intelligent control platform uses a web server, enabling operators to access the intelligent control platform anytime, anywhere using any mobile terminal such as a computer or mobile phone through a browser. Operators can remotely manage and control energy flow and loss data in real time through the browser, which greatly improves the management and operation efficiency of heating projects.

[0089] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0090] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for online monitoring of energy flow direction and loss data, characterized in that, Includes the following steps: S1. The unit's operating data is collected in real time through the data acquisition module, and the sampled operating data is filtered through the preprocessing module. S2. The calculation module analyzes the unit's thermal efficiency based on the filtered data. S21. Calculate the heat absorption of the working fluid. : （1）； Among them, D bi Let C be the working fluid flow rate of the i-th heated surface. pi Let i be the isobaric specific heat capacity of the i-th heated surface. Let be the temperature rise of the i-th heated surface; S22. Construct the unit's heat balance equation: （2）； In the formula: Q is the sum of boiler heat losses; a Heat from flue gas recovered by the air preheater; The calorific value of the fuel; S23, Computer group thermal efficiency : （3）； Among them, B s This refers to the standard coal consumption of the boiler. S3. Sort the units according to thermal efficiency from highest to lowest, select the unit with the lowest thermal efficiency, and analyze multiple heat transfer surfaces where the difference between flue gas inlet temperature and working fluid outlet temperature is not less than the minimum temperature difference. S4. Calculate the pressure loss of multiple heated surfaces obtained in step S3; According to the pressure loss-energy relationship, the total pressure loss of the heated surface is expressed as: （4）； In the formula, D represents the total pressure loss of the heated surface. i Let k be the steam energy of the i-th heated surface. i Let be the energy resistance coefficient of the i-th heated surface. Let be the steam density of the i-th heated surface; S5. Construct a unit energy-loss model; Given a fixed unit structure and known pressure loss, the steam pressure P0 and steam valve opening Gv satisfy the unit's required energy load N. （5）； For a given energy load N e The energy flow model for the generating unit is as follows: （6）； （7）； In the formula, N represents the given energy load and the rated energy load of the unit; X represents the loss constraint parameter.

2. The online monitoring method for energy flow direction and loss data according to claim 1, characterized in that, In step S3, the minimum temperature difference ΔT between the working fluid at the outlet of each heating surface and the corresponding steam is set. min The steam inlet temperature T of the heated surface I is determined using the narrow-point temperature difference method in heat balance calculations. i and working fluid outlet temperature T o Temperature difference ΔT I , will ΔT I and minimum temperature difference ΔT min By comparison, ΔT is obtained. I Greater than or equal to the temperature difference ΔT min n heated surfaces.

3. An online monitoring system for energy flow direction and loss data, used to implement the online monitoring method for energy flow direction and loss data as described in any one of claims 1-2, comprising: Data acquisition module, preprocessing module, data storage module, computational execution module, model building module; The data acquisition module is used to collect unit operation data in real time; The preprocessing module is used to filter the real-time acquired unit operation data; The filtered data is stored in the data repository. The operation calculation module is used to analyze the thermal efficiency of the unit based on the filtered data. The model building module is used to calculate the pressure loss of multiple heated surfaces and to build a unit energy-loss model.

4. The online monitoring system for energy flow and loss data according to claim 3, characterized in that, A database of loss cause analysis and guidance suggestions is established based on historical data. When loss occurs, the cause of the loss is identified from the database, and the operators are guided to perform operations to eliminate the loss.

5. The online monitoring system for energy flow and loss data according to claim 3, characterized in that, An intelligent control platform is built to realize online monitoring of energy flow and loss data. The intelligent control platform includes a controller and a server. The intelligent control platform conducts bidirectional data transmission with multiple unit DCS systems, and transmits real-time operating data to the server of the intelligent control platform through an interface protocol.

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