Method for effectively improving boiler efficiency

Abstract

The invention discloses a method for effectively improving boiler efficiency, which relates to the field of industrial automation and intelligent control, and comprises the following steps: collecting real-time operation data, historical operation data and historical maintenance data of a boiler, and preprocessing the data; establishing a combustion control parameter prediction model based on an LSTM learning model, and training the model by using historical data of the boiler; inputting the preprocessed real-time data into the trained prediction model, and outputting a prediction result of the combustion control parameters; adjusting the fuel supply quantity and the air proportion in real time according to a model prediction result; analyzing historical maintenance data and real-time data by using a machine learning algorithm, predicting potential faults and maintenance requirements, and performing preventive maintenance on the boiler according to a prediction result; the maintenance process and result are recorded in detail after maintenance, and the equipment is adjusted and optimized based on the maintenance record. According to the invention, cooperative improvement of boiler operation efficiency, environmental protection and economic benefits is realized.

Description

Technical Field

[0001] The invention relates to the field of industrial automation and intelligent control, and in particular to a method for effectively improving boiler efficiency. Background Art

[0002] Boiler efficiency improvement faces the traditional path of innovation in materials and combustion technology. Today's digital transformation has spawned the integration of industrial Internet of Things, big data and AI, especially the application of LSTM in time series analysis, which has revolutionized boiler parameter optimization and health monitoring. However, the limitations of existing technologies are prominent, including the model's over-reliance on real-time data, ignoring the value of historical and maintenance data, weakening the predictive power of abnormal events; and the maintenance strategy of regular inspection or post-maintenance, which wastes resources and frequently causes hidden dangers. Summary of the invention

[0003] In view of the above existing problems, the present invention is proposed.

[0004] Therefore, the present invention provides a method for effectively improving boiler efficiency, solving the problems of limitation of relying on real-time data, inefficiency of maintenance strategy, and insufficiency of system optimization.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In a first aspect, an embodiment of the present invention provides a method for effectively improving boiler efficiency, which includes collecting real-time data, historical operation data, and historical maintenance data of boiler operation, and performing preprocessing;

[0007] A combustion control parameter prediction model is established based on the LSTM learning model, and the combustion control parameter prediction model is trained using the boiler's historical data;

[0008] The pre-processed real-time data is input into the trained combustion control parameter prediction model, and the prediction results of the combustion control parameters are output;

[0009] Adjust fuel supply and air ratio in real time according to model prediction results to optimize combustion efficiency;

[0010] Use machine learning algorithms to analyze historical maintenance data and pre-processed real-time boiler data to predict potential failures and maintenance needs;

[0011] The boiler is maintained based on the fault prediction results, the maintenance process and results are recorded after maintenance, and the equipment is adjusted and optimized.

[0012] As a preferred solution of the method for effectively improving boiler efficiency of the present invention, the following specific steps are taken: real-time collection of boiler operating parameters such as fuel flow, combustion chamber temperature, oxygen concentration, and flue gas temperature; and extraction of boiler operating data and maintenance data from a historical database:

[0013] Define the real-time data vector as R t =[R1(t), R2(t), R3(t), R4(t)];

[0014] Among them, R t is the real-time operation data vector, t represents time, R1(t) represents combustion flow, R2(t) represents combustion chamber temperature, R3(t) represents oxygen concentration, and R4(t) represents flue gas temperature;

[0015] Define historical operation data as H r ;

[0016] Define historical maintenance data as H m ;

[0017] The steps for preprocessing the collected data are as follows:

[0018] First, the real-time data R t 、Historical operation data r and historical maintenance data H m The specific steps for standardization are as follows:

[0019] Real-time data t The expression for the standardized processing is:

[0020]

[0021] Among them, R * t represents the real-time data after standardization, μ(R j ) represents the mean of data point j, σ(R j ) represents the standard deviation of data point j, m represents the total number of data points in the data set, and α represents a positive adjustment parameter;

[0022] Historical operation data r The expression for the standardized processing is:

[0023]

[0024] Among them, H * r represents the historical operation data after standardization, H r,j represents the jth element in the historical operation data Hr, μ(H r ) indicates H r The average value of H r The standard deviation of j and w k is the weight factor, n is the number of elements in the historical operation data;

[0025] Historical maintenance data m The expression for the standardized processing is:

[0026]

[0027] Among them, H * m represents the historical maintenance data after standardization, H m,i represents the i-th data point in the historical maintenance dataset, μ(H m ) represents the mean of the historical maintenance data set, σ(H m ) represents the standard deviation of the historical maintenance data set, α represents the adjustment parameter, N represents the total number of data points in the historical maintenance data set, and w i represents the weight of the i-th data point.

[0028] As a preferred solution of the method for effectively improving boiler efficiency of the present invention, the historical operation data and the historical maintenance data are fused based on similarity, and the specific steps are as follows:

[0029] The similarity between real-time data and historical operation data is expressed as:

[0030]

[0031] Among them, S r (t) represents the similarity between real-time data and historical operation data, δ d represents the distance attenuation coefficient, d i represents the distance metric function on the i-th dimension, n represents the number of dimensions of the data point, and w i represents the weight of the i-th dimension;

[0032] The similarity between real-time data and historical maintenance data is expressed as:

[0033]

[0034] Among them, S m (t) represents the similarity between real-time data and historical maintenance data, δ d,i represents the adjustment parameter on the i-th dimension;

[0035] Finally, the real-time data, historical operation data and historical maintenance data are fused based on similarity, and the expression is as follows:

[0036]

[0037] Among them, F(t) represents the data after the fusion of real-time data, historical operation data and historical maintenance data, and W r(t) represents the dynamic weight of real-time data at time t, W m (t) represents the dynamic weight of the historical maintenance data at time t, Sr′(t): this is the similarity score after nonlinear transformation between the real-time data and the historical operation data at time t, Sm′(t): this is the similarity score after nonlinear transformation between the real-time data and the historical maintenance data at time t.

[0038] As a preferred solution of the method for effectively improving boiler efficiency described in the present invention, a combustion control parameter prediction model is established based on the LSTM learning model, and the combustion control parameter prediction model is trained through the historical data of the boiler, and its expression is:

[0039]

[0040] Among them, Model LSTM represents the LSTM-based deep learning model, α i (t) represents the attention weight, h i (t) represents the hidden state vector, and t represents time.

[0041] As a preferred solution of the method for effectively improving boiler efficiency of the present invention, the pre-processed real-time data is input into the combustion control parameter prediction model after training, and the prediction result of the combustion control parameter is output, and its expression is:

[0042]

[0043] Among them, Y^(t) represents the prediction result, β(t) represents the smoothing factor that changes with time, Y^(t-1) represents the prediction result of the previous moment, which serves as the basis for the prediction at the current moment, f represents a nonlinear function, (1-β(t)) represents a coefficient used to adjust the impact of the model's output at the current time step on the final prediction, Z(t) represents the additional information introduced to assist the prediction, and g represents the combined function.

[0044] As a preferred solution of the method for effectively improving boiler efficiency of the present invention, the fuel supply and air ratio are adjusted in real time according to the model prediction results to optimize the combustion efficiency;

[0045] When the fuel supply and air ratio are lower than the predicted result, increase the fuel supply and air ratio; if higher than the predicted result, reduce the fuel supply and air ratio to ensure that all fuel molecules can react with sufficient oxygen;

[0046] As a preferred solution of the method for effectively improving boiler efficiency of the present invention, wherein: a machine learning algorithm is used to analyze historical maintenance data and pre-processed real-time boiler data to predict potential failures and maintenance needs;

[0047] First, an LSTM model is built based on the fused data F(t) to predict and output potential failures and maintenance requirements. Its expression is:

[0048]

[0049] Where S^(t) represents the prediction of potential failures and maintenance requirements at time t, LSTM represents the prediction model, and α l (t) represents the attention weight of the lth hidden unit, h l (t) represents the hidden state of the lth hidden unit, L represents the number of hidden layers of LSTM, ΔZ k (t) represents the change in the kth additional information at time t, β k represents the smoothing factor of the kth additional information at time t, K represents the number of types of additional information;

[0050] As a preferred solution of the method for effectively improving boiler efficiency of the present invention, wherein: judging whether preventive maintenance is needed according to the fault prediction result, when maintenance measures are needed, the measures include inspecting and replacing damaged parts, cleaning and lubricating the system to reduce wear, updating software and firmware to fix known vulnerabilities, and performing regular diagnostic tests to confirm the health status of the system;

[0051] After maintenance is completed, detailed records are kept of each maintenance work performed. Based on the maintenance records, common failure modes in the equipment are identified, the root causes of each failure mode are understood, and key areas for optimization are determined based on the severity, frequency, and detectability of the failure mode. Consumable parts and components with high failure rates are upgraded, and replacement parts with higher quality and better suited to the working environment are used.

[0052] In a second aspect, an embodiment of the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the method for effectively improving boiler efficiency as described in the first aspect of the present invention is implemented.

[0053] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the method for effectively improving boiler efficiency as described in the first aspect of the present invention is implemented.

[0054] The beneficial effects of the present invention are: by integrating data collection and preprocessing, LSTM model prediction, real-time parameter adjustment and fault prediction maintenance, the operation of the boiler is optimized in all aspects. First, the efficient integration and preprocessing of multi-source data lays the foundation for accurate analysis and eliminates noise interference. Secondly, LSTM model training predicts combustion parameters, greatly improves combustion efficiency, saves energy and reduces emissions, reduces unplanned downtime, and ensures economic and stable operation. Furthermore, the fuel-to-air ratio is regulated in real time to maximize combustion efficiency, reduce costs, enhance environmental protection, and promote sustainable production. Finally, intelligently predict faults, implement preventive maintenance, significantly reduce maintenance costs, improve equipment reliability and production continuity, and enhance the company's market competitiveness. The invention achieves the coordinated improvement of boiler operating efficiency, environmental protection and economic benefits, marking a major breakthrough in industrial intelligent control technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use 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 ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0056] Figure 1 This is a flow chart of the method for effectively improving boiler efficiency in Example 1.

[0057] Figure 2 This is the decision diagram of the combustion parameter prediction model and the fault prediction model in Example 1. DETAILED DESCRIPTION

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0061] Example 1, reference Figure 1 and Figure 2, which is the first embodiment of the present invention, provides a method for effectively improving boiler efficiency, comprising the following steps:

[0062] S1. Collect boiler operating parameters such as fuel flow, combustion chamber temperature, oxygen concentration, and flue gas temperature in real time; extract boiler operating data and maintenance data from the historical database. The specific steps are as follows:

[0063] Define the real-time data vector as R t =[R1(t), R2(t), R3(t), R4(t)];

[0064] Among them, R t is the real-time operation data vector, t represents time, R1(t) represents combustion flow, R2(t) represents combustion chamber temperature, R3(t) represents oxygen concentration, and R4(t) represents flue gas temperature;

[0065] Define historical operation data as H r ;

[0066] Define historical maintenance data as H m ;

[0067] The steps for preprocessing the collected data are as follows:

[0068] First, the real-time data R t 、Historical operation data r and historical maintenance data H m The specific steps for standardization are as follows:

[0069] Real-time data t The expression for the standardized processing is:

[0070]

[0071] Among them, R * t represents the real-time data after standardization, μ(R j ) represents the mean of data point j, σ(R j ) represents the standard deviation of data point j, m represents the total number of data points in the data set, and α represents a positive adjustment parameter;

[0072] Historical operation data r The expression for the standardized processing is:

[0073]

[0074] Among them, H * r represents the historical operation data after standardization, H r,j represents the jth element in the historical operation data Hr, μ(Hr ) indicates H r The average value of H r The standard deviation of j and w k is the weight factor, n is the number of elements in the historical operation data;

[0075] Historical maintenance data m The expression for the standardized processing is:

[0076]

[0077] Among them, H * m represents the historical maintenance data after standardization, H m,i represents the i-th data point in the historical maintenance dataset, μ(H m ) represents the mean of the historical maintenance data set, σ(H m ) represents the standard deviation of the historical maintenance data set, α represents the adjustment parameter, N represents the total number of data points in the historical maintenance data set, and w i represents the weight of the i-th data point.

[0078] In general, through the comprehensive analysis of real-time data and historical data, a more accurate prediction model can be established to achieve dynamic optimization of combustion parameters and early identification of potential faults, thereby improving boiler operating efficiency, reducing maintenance costs and extending equipment life.

[0079] S2. Data fusion of historical operation data and historical maintenance data based on similarity is extremely important in intelligent boiler management and predictive maintenance systems. Its core goal is to integrate and utilize historical information by quantifying the similarity between historical data and current operating status, thereby improving the accuracy and reliability of the prediction model. The specific steps are as follows:

[0080] The similarity between real-time data and historical operation data is expressed as:

[0081]

[0082] Among them, S r (t) represents the similarity between real-time data and historical operation data, δ d represents the distance attenuation coefficient, d i represents the distance metric function on the i-th dimension, n represents the number of dimensions of the data point, and w i represents the weight of the i-th dimension;

[0083] The similarity between real-time data and historical maintenance data is expressed as:

[0084]

[0085] Among them, S m (t) represents the similarity between real-time data and historical maintenance data, δ d,i represents the adjustment parameter on the i-th dimension;

[0086] Finally, the real-time data, historical operation data and historical maintenance data are fused based on similarity, and the expression is as follows:

[0087]

[0088] Among them, F(t) represents the data after the fusion of real-time data, historical operation data and historical maintenance data, and W r (t) represents the dynamic weight of real-time data at time t, W m (t) represents the dynamic weight of the historical maintenance data at time t, Sr′(t): this is the similarity score after nonlinear transformation between the real-time data and the historical operation data at time t, Sm′(t): this is the similarity score after nonlinear transformation between the real-time data and the historical maintenance data at time t.

[0089] Furthermore, similarity-based data fusion is a key step in realizing intelligent boiler management and predictive maintenance. It not only improves the accuracy and robustness of the prediction model, but also provides strong support for the formulation of preventive maintenance strategies, thereby significantly improving the safety, economy and efficiency of boiler operation.

[0090] S3. A combustion control parameter prediction model is established based on the LSTM learning model. The combustion control parameter prediction model is trained through the historical data of the boiler. This operation can accurately predict the prediction ability of the model, and the LSTM model can be adaptively adjusted through training. The expression is:

[0091]

[0092] Among them, Model LSTM represents the LSTM-based deep learning model, α i (t) represents the attention weight, h i (t) represents the hidden state vector, and t represents time.

[0093] Furthermore, establishing a combustion control parameter prediction model based on the LSTM learning model and training it with historical data can improve the accuracy of the prediction.

[0094] S4. Input the processed real-time data into the combustion control parameter prediction model after training. The real-time data is used to enable the boiler system to dynamically adjust the combustion parameters according to the latest operating status. The model calculates and outputs the prediction results of the combustion control parameters based on the input real-time data. The expression is:

[0095]

[0096] Among them, Y^(t) represents the prediction result, β(t) represents the smoothing factor that changes with time, Y^(t-1) represents the prediction result of the previous moment, which serves as the basis for the prediction at the current moment, and f represents a nonlinear function.

[0097] (1-β(t)) represents a coefficient used to adjust the impact of the model's output at the current time step on the final prediction, Z(t) represents the additional information introduced to assist prediction, and g represents the combination function.

[0098] Furthermore, the model's predictive capabilities help maintain the boiler in optimal operation and avoid reduced efficiency and equipment damage due to improper combustion parameters, thereby enhancing the stability and safety of the entire system and significantly improving the operating efficiency of the boiler. This also means higher energy conversion rates, less energy waste, and less environmental impact. For industrial production, this is the key to improving competitiveness and sustainability.

[0099] S5. Adjust the fuel supply and air ratio in real time according to the model prediction results to optimize the combustion efficiency; if the current actual fuel supply is lower than the ideal value predicted by the model, the control system will automatically increase the fuel supply to meet higher combustion needs; if the current actual fuel supply is higher than the ideal value predicted by the model, the control system will automatically reduce the fuel supply. This dynamic adjustment ensures sufficient fuel supply while avoiding waste and incomplete combustion caused by excessive supply; in coordination with the adjustment of the fuel supply, the air ratio will also be adjusted accordingly according to the prediction results. When the actual air ratio is lower than the predicted value, the system will increase the air supply to ensure that enough oxygen is in full contact with the fuel molecules to promote complete combustion. If the air ratio is too high, the system will appropriately reduce the air supply to prevent heat loss caused by excessive oxidation.

[0100] Furthermore, the ultimate goal of this dynamic adjustment is to achieve complete combustion, that is, to ensure that all fuel molecules can react with sufficient oxygen to produce carbon dioxide and water, while minimizing the emission of unburned carbon particles and other pollutants. Complete combustion not only improves energy conversion efficiency and reduces fuel consumption, but also reduces negative impacts on the environment.

[0101] S6. Use machine learning algorithms to analyze historical maintenance data and pre-processed real-time boiler data to predict potential failures and maintenance needs;

[0102] First, an LSTM model is built based on the fused data F(t) to predict and output potential failures and maintenance requirements. Its expression is:

[0103]

[0104] Where S^(t) represents the prediction of potential failures and maintenance requirements at time t, LSTM represents the prediction model, and α l (t) represents the attention weight of the lth hidden unit, h l (t) represents the hidden state of the lth hidden unit, L represents the number of hidden layers of LSTM, ΔZ k (t) represents the change in the kth additional information at time t, β k represents the smoothing factor of the kth additional information at time t, K represents the number of types of additional information;

[0105] Furthermore, this can improve maintenance efficiency. By predicting possible failures in advance, maintenance work can be arranged more reasonably and unplanned downtime can be avoided, thereby improving production efficiency. It can also reduce maintenance costs, reduce unnecessary maintenance work, avoid large-scale repairs caused by failures, and effectively reduce overall maintenance costs.

[0106] S7. Determine whether preventive maintenance is needed based on the fault prediction results. When maintenance measures are needed, the measures include inspecting and replacing damaged parts to avoid failures; cleaning and lubricating the system to reduce wear and extend equipment life; updating software and firmware to fix known vulnerabilities, performing regular diagnostic tests, and improving system stability and security; confirming the health status of the system and promptly identifying potential problems;

[0107] Furthermore, regular cleaning and lubrication of the system can reduce wear between components and extend the service life of the equipment. In addition, by replacing vulnerable parts and parts with high failure rates, the service life of the equipment can be further extended. Updating software and firmware can fix known security vulnerabilities, improve system stability and security, and reduce safety hazards.

[0108] After maintenance is completed, a detailed record of each maintenance work performed is kept. The maintenance date is recorded to facilitate tracking of maintenance cycles and frequencies, maintenance activities performed, and parts replaced. Based on the maintenance records, common failure modes in the equipment are identified, and the root causes of each failure mode are understood. Based on the severity, frequency, and detectability of the failure mode, key areas for optimization are determined, and those issues that have the greatest impact on equipment operation are prioritized. Consumable parts and parts with high failure rates are upgraded, and replacement parts with higher quality and better suited to the working environment are used.

[0109] Furthermore, by recording the content of each maintenance in detail, common failure modes in equipment can be identified and the root cause of each failure mode can be understood. This helps to optimize maintenance strategies, solve key problems in a targeted manner, and reduce the number of maintenance times. Analysis based on maintenance records can provide management with data-driven decision support to help them make smarter decisions on equipment upgrades and spare parts procurement. By improving the energy efficiency of equipment and reducing maintenance frequency, energy and resource consumption can be reduced, and sustainable development can be promoted. In general, by implementing preventive maintenance strategies based on fault prediction, not only can the reliability and safety of equipment be significantly improved, but also maintenance costs can be reduced, equipment life can be extended, and production efficiency can be improved, bringing multiple benefits to industrial production.

[0110] This embodiment also provides a computer device, which is suitable for the method of effectively improving boiler efficiency, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method of effectively improving boiler efficiency as proposed in the above embodiment.

[0111] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0112] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for effectively improving the efficiency of the boiler as proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0113] In summary, the present invention realizes the monitoring and understanding of user behavior through the analysis of user file operation behavior, can accurately capture the user's operation habits and behavior patterns, provide basic data for the subsequent generation of encryption strategies, and also help the system to detect and prevent abnormal behaviors; predict the user's future operations according to the user's behavior habits, so as to formulate encryption strategies more intelligently; more accurately protect user privacy, reduce the risk of data leakage, and improve the data security and confidentiality of the system; the present invention better adapts to the user's operation habits and needs, improves the intelligence level of the encryption algorithm and user experience, and enhances the flexibility and adaptability of the system.

[0114] Example 2, referring to Table 1, is the second example of the present invention. To further verify the advancement of the present invention, experimental simulation data of a method for effectively improving boiler efficiency is provided.

[0115] In order to verify the effectiveness of the present invention, an industrial coal-fired boiler was selected as the test object. The rated power of the boiler is 20MW, the design fuel is bituminous coal, and the design fuel consumption rate is 12 tons per hour. During the test, the boiler was operated continuously for one week (168 hours) and was divided into two stages: the first stage was the conventional operation stage under the existing technology, which lasted for 48 hours; the second stage was the operation stage using the method of the present invention, which lasted for 120 hours.

[0116] Phase I (conventional operation under existing technology)

[0117] Data collection: The sensors installed on the boiler collect operating parameters such as fuel flow, combustion chamber temperature, oxygen concentration, flue gas temperature, etc. in real time.

[0118] Maintenance records: Extract boiler operation data and maintenance records for the past year from the historical database.

[0119] Maintenance strategy: According to the conventional maintenance strategy, regular inspection and maintenance are carried out without data-based failure prediction.

[0120] The second stage (operation of the method of the present invention)

[0121] Data preprocessing: According to the content of the present invention, the real-time data is standardized, and the historical operation data and the historical maintenance data are fused based on similarity.

[0122] Model training: Use historical data to train the LSTM learning model and establish a combustion control parameter prediction model.

[0123] Real-time prediction: The preprocessed real-time data is input into the trained model to output the prediction results of the combustion control parameters.

[0124] Parameter adjustment: Real-time adjustment of fuel supply and air ratio based on prediction results.

[0125] Failure prediction: Use machine learning algorithms to analyze historical maintenance data and real-time data to predict potential failures and maintenance needs.

[0126] Preventive maintenance: Take preventive maintenance measures based on the predicted results and record the maintenance process in detail.

[0127] The details are shown in Table 1:

[0128] Table 1 Comparison of boiler efficiency between this technology and existing technology

[0129]

[0130] Tabular data analysis,

[0131] Conventional operation stage under the existing technology: Fuel flow rate: The average fuel flow rate is 12.5 tons / hour. Combustion chamber temperature: The average combustion chamber temperature is 1400°C. Oxygen concentration: The average oxygen concentration is 15.5%. Flue gas temperature: The average flue gas temperature is 250°C. Boiler efficiency: The average boiler efficiency is 85.0%.

[0132] Operation phase using the method of the present invention: Fuel flow rate: The average fuel flow rate is reduced to 12.0 tons / hour, indicating that the fuel consumption is reduced. Combustion chamber temperature: The average combustion chamber temperature is increased to 1420°C, indicating that the combustion efficiency is improved. Oxygen concentration: The average oxygen concentration is reduced to 14.5%, indicating that the air ratio is optimized and the consumption of excess air is reduced. Flue gas temperature: The average flue gas temperature is reduced to 240°C, indicating that the heat recovery efficiency is improved. Boiler efficiency: The average boiler efficiency is increased to 88.5%, indicating that the method of the present invention significantly improves the overall efficiency of the boiler.

[0133] Beneficial effect analysis,

[0134] Fuel consumption reduction: Under the condition of ensuring the same output power, the fuel flow rate is reduced from 12.5 tons / hour to 12.0 tons / hour, which shows that the present invention can reduce fuel consumption by optimizing combustion parameters, thereby reducing operating costs. Combustion efficiency improvement: The combustion chamber temperature is increased from 1400℃ to 1420℃, indicating that the fuel burns more fully, improves combustion efficiency, and reduces pollutant emissions.

[0135] Oxygen concentration optimization: The oxygen concentration dropped from 15.5% to 14.5%, indicating that more efficient combustion was achieved by dynamically adjusting the air ratio, reducing the use of excess air and reducing energy consumption. Flue gas temperature reduction: The flue gas temperature dropped from 250°C to 240°C, indicating that the heat recovery efficiency was improved, further reducing energy waste. Boiler efficiency improvement: The boiler efficiency increased from 85.0% to 88.5%, significantly improving energy conversion efficiency, reducing operating costs, and enhancing economic benefits.

[0136] In summary, the present invention significantly improves the operating efficiency and economy of the boiler through accurate analysis of real-time data and dynamic adjustment of combustion parameters, while also reducing environmental pollution. Compared with the prior art, the method of the present invention has significant advantages in fuel consumption, combustion efficiency, oxygen concentration optimization, flue gas temperature reduction and boiler efficiency improvement, demonstrating its creativity and novelty.

[0137] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for effectively improving boiler efficiency, characterized in that: include: Collect real-time data, historical operation data and historical maintenance data of boiler operation and perform pre-processing; A combustion control parameter prediction model is established based on the LSTM learning model, and the combustion control parameter prediction model is trained using the boiler's historical data; The pre-processed real-time data is input into the trained combustion control parameter prediction model, and the prediction results of the combustion control parameters are output; Adjust fuel supply and air ratio in real time according to model prediction results to optimize combustion efficiency; Use machine learning algorithms to analyze historical maintenance data and pre-processed real-time boiler data to predict potential failures and maintenance needs; The boiler is maintained based on the fault prediction results, the maintenance process and results are recorded after maintenance, and the equipment is adjusted and optimized.

2. The method for effectively improving boiler efficiency according to claim 1, characterized in that: Collect boiler operating parameters such as fuel flow, combustion chamber temperature, oxygen concentration, and flue gas temperature in real time; extract boiler operating data and maintenance data from the historical database. The specific steps are as follows: Define the real-time data vector as R t =[R1(t), R2(t), R3(t), R4(t)]; Among them, R t is the real-time operation data vector, t represents time, R1(t) represents combustion flow, R2(t) represents combustion chamber temperature, R3(t) represents oxygen concentration, and R4(t) represents flue gas temperature; Define historical operation data as H r ; Define historical maintenance data as H m ; The steps for preprocessing the collected data are as follows: First, the real-time data R t 、Historical operation data r and historical maintenance data H m The specific steps for standardization are as follows: Real-time data t The expression for the standardized processing is: Among them, R * t represents the real-time data after standardization, μ(R j ) represents the mean of data point j, σ(R j ) represents the standard deviation of data point j, m represents the total number of data points in the data set, and α represents a positive adjustment parameter; Historical operation data r The expression for the standardized processing is: Among them, H * r represents the historical operation data after standardization, H r,j represents the jth element in the historical operation data Hr, μ(H r ) indicates H r The average value of H r The standard deviation of j and w k is the weight factor, n is the number of elements in the historical operation data; Historical maintenance data m The expression for the standardized processing is: Among them, H * m represents the historical maintenance data after standardization, H m,i represents the i-th data point in the historical maintenance dataset, μ(H m ) represents the mean of the historical maintenance data set, σ(H m ) represents the standard deviation of the historical maintenance data set, α represents the adjustment parameter, N represents the total number of data points in the historical maintenance data set, and w i represents the weight of the i-th data point.

3. The method for effectively improving boiler efficiency according to claim 2, characterized in that: The historical operation data and historical maintenance data are fused based on similarity. The specific steps are as follows: The similarity between real-time data and historical operation data is expressed as: Among them, S r (t) represents the similarity between real-time data and historical operation data, δ d represents the distance attenuation coefficient, d i represents the distance metric function on the i-th dimension, n represents the number of dimensions of the data point, and w i represents the weight of the i-th dimension; The similarity between real-time data and historical maintenance data is expressed as: Among them, S m (t) represents the similarity between real-time data and historical maintenance data, δ d,i represents the adjustment parameter on the i-th dimension; Finally, the real-time data, historical operation data and historical maintenance data are fused based on similarity, and the expression is as follows: Among them, F(t) represents the data after the fusion of real-time data, historical operation data and historical maintenance data, and W r (t) represents the dynamic weight of real-time data at time t, W m (t) represents the dynamic weight of the historical maintenance data at time t, Sr′(t): this is the similarity score after nonlinear transformation between the real-time data and the historical operation data at time t, Sm′(t): this is the similarity score after nonlinear transformation between the real-time data and the historical maintenance data at time t.

4. The method for effectively improving boiler efficiency according to claim 3, characterized in that: A combustion control parameter prediction model is established based on the LSTM learning model. The combustion control parameter prediction model is trained using the historical data of the boiler. The expression is: Among them, Model LSTM represents the LSTM-based deep learning model, α i (t) represents the attention weight, h i (t) represents the hidden state vector, and t represents time.

5. The method for effectively improving boiler efficiency according to claim 4, characterized in that: The preprocessed real-time data is input into the trained combustion control parameter prediction model, and the prediction results of the combustion control parameters are output. The expression is: Among them, Y^(t) represents the prediction result, β(t) represents the smoothing factor that changes with time, Y^(t-1) represents the prediction result of the previous moment, which serves as the basis for the prediction at the current moment, f represents a nonlinear function, (1-β(t)) represents a coefficient used to adjust the impact of the model's output at the current time step on the final prediction, Z(t) represents the additional information introduced to assist the prediction, and g represents the combined function.

6. The method for effectively improving boiler efficiency according to claim 5, characterized in that: Adjust fuel supply and air ratio in real time according to model prediction results to optimize combustion efficiency; When the fuel supply and air ratio are lower than the predicted results, increase the fuel supply and air ratio. If they are higher than the predicted results, reduce the fuel supply and air ratio to ensure that all fuel molecules can react with sufficient oxygen.

7. The method for effectively improving boiler efficiency according to claim 6, characterized in that: Use machine learning algorithms to analyze historical maintenance data and pre-processed real-time boiler data to predict potential failures and maintenance needs; First, an LSTM model is built based on the fused data F(t) to predict and output potential failures and maintenance requirements. Its expression is: Where S^(t) represents the prediction of potential failures and maintenance requirements at time t, LSTM represents the prediction model, and α l (t) represents the attention weight of the lth hidden unit, h l (t) represents the hidden state of the lth hidden unit, L represents the number of hidden layers of LSTM, ΔZ k (t) represents the change in the kth additional information at time t, β k represents the smoothing factor of the kth additional information at time t, and K represents the number of types of additional information.

8. The method for effectively improving boiler efficiency according to claim 7, characterized in that: Determine whether preventive maintenance is needed based on the fault prediction results. When maintenance measures are needed, they include inspecting and replacing damaged parts, cleaning and lubricating the system to reduce wear, updating software and firmware to fix known vulnerabilities, and performing regular diagnostic tests to confirm the health status of the system; After maintenance is completed, detailed records are kept of each maintenance work performed. Based on the maintenance records, common failure modes in the equipment are identified, the root causes of each failure mode are understood, and key areas for optimization are determined based on the severity, frequency, and detectability of the failure mode. Consumable parts and components with high failure rates are upgraded, and replacement parts with higher quality and better suited to the working environment are used.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for effectively improving boiler efficiency according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for effectively improving boiler efficiency according to any one of claims 1 to 7 are implemented.

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