Biomass boiler combustion efficiency optimization method based on historical data

Abstract

The application discloses a biomass boiler combustion efficiency optimization method based on historical data, relates to the field of industrial process control and optimization, and comprises the following steps: collecting historical operation data of a biomass boiler; segmenting the historical operation data according to time sequences, dividing out a high-efficiency operation section and a low-efficiency operation section of the boiler, and taking operation parameters of the high-efficiency operation section as optimization benchmark data; establishing a correlation model between boiler combustion efficiency and operation parameters, and calculating weight coefficients of each parameter through a least square method; performing weighted calculation on the weight coefficients and real-time operation parameters to obtain a boiler combustion efficiency score, and triggering optimization control when the efficiency score is lower than a preset threshold; and dynamically adjusting a fuel supply rate and oxygen content according to the optimization benchmark data. The application collects and deeply analyzes historical operation data of the biomass boiler, gradually makes the operation state of the boiler close to the high-efficiency section, and thus improves the combustion efficiency and reduces energy waste and pollution emission.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of industrial process control and optimization, and in particular to a biomass boiler combustion efficiency optimization method based on historical data. BACKGROUND

[0002] With the increasing global energy shortage problem, the development and utilization of clean renewable energy has attracted widespread attention. Biomass energy, as a renewable energy, not only can reduce the dependence on fossil fuels, but also has good environmental benefits, so it has been rapidly popularized worldwide. Biomass boiler, as an important equipment for utilizing biomass energy, has been widely used in the fields of heating, power generation and industrial production. The traditional operation of biomass boiler relies on preset parameters or manual experience adjustment, which is difficult to fully adapt to complex fuel characteristics and variable operating conditions, which often leads to low boiler operation efficiency, energy waste and increased pollutant emissions. Therefore, one of the current research focuses is to mine the operation rules of biomass boiler through data-driven methods and then optimize the combustion efficiency.

[0003] In the prior art, researchers have tried biomass boiler optimization methods based on online monitoring and control. For example, some studies use Internet of Things technology to collect real-time boiler operation data and achieve partial optimization of the combustion process through intelligent control algorithms. However, these methods usually have the following shortcomings: first, real-time control strategies are based only on current operating condition data, lacking comprehensive mining of historical operation data, making it difficult to establish an effective operation parameter optimization model; second, in existing methods, the optimization of combustion efficiency often uses the adjustment of a single indicator (such as oxygen content or fuel supply rate), ignoring the synergistic effect between parameters, resulting in limited optimization effect; third, when the boiler operating conditions fluctuate greatly, the existing methods respond slowly and may be difficult to adapt to complex operating condition changes in real time. These shortcomings lead to the fact that the existing technology still has room for improvement in terms of fuel utilization rate improvement and pollutant emission control. SUMMARY

[0004] In view of the problems existing in the prior art, the present application provides a biomass boiler combustion efficiency optimization method based on historical data.

[0005] Therefore, the present application provides a biomass boiler combustion efficiency optimization method based on historical data, which can solve the problems mentioned in the background art.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] In a first aspect, the present application provides a biomass boiler combustion efficiency optimization method based on historical data, which includes,

[0008] Collecting biomass boiler historical operation data; the historical operation data includes boiler temperature, oxygen content, steam pressure and fuel supply rate;

[0009] Segmenting the historical operation data according to time sequence, dividing the high-efficiency operation section and the low-efficiency operation section of the boiler, and taking the operation parameters of the high-efficiency operation section as optimization benchmark data;

[0010] Based on the optimization benchmark data, an association model between the boiler combustion efficiency and the operation parameters is established, and the weight coefficients of each parameter are calculated by the least square method;

[0011] The weight coefficients are weighted with real-time operation parameters to obtain a boiler combustion efficiency score, and optimization control is triggered when the efficiency score is lower than a preset threshold;

[0012] According to the optimization benchmark data, the fuel supply rate and the oxygen content are dynamically adjusted.

[0013] As a preferred scheme of the biomass boiler combustion efficiency optimization method based on historical data, wherein: obtaining the optimization benchmark data includes:

[0014] The historical operation data is divided according to the basic time unit T;

[0015] The thermal efficiency value η is calculated in each time unit;

[0016] The operation interval with the thermal efficiency value greater than the preset threshold and the duration exceeding the preset value is divided into a high-efficiency operation section, and the comprehensive score S of the high-efficiency operation section is calculated;

[0017] The operation data with the comprehensive score in the front row in the high-efficiency operation section is extracted, and the optimization benchmark value X is calculated.

[0018] As a preferred scheme of the biomass boiler combustion efficiency optimization method based on historical data, wherein: the calculation of the thermal efficiency value η is as follows:

[0019]

[0020] Wherein, D is the steam production, h2 is the superheated steam enthalpy value, h1 is the feedwater enthalpy value, B is the fuel consumption, Q net The low calorific value of fuel, k1 is the furnace temperature correction coefficient, and k2 is the oxygen content correction coefficient;

[0021] Wherein, the calculation formula of the furnace temperature correction coefficient k1 is:

[0022] k1=1-0.0015|T r -T s |

[0023] wherein T r is the actual furnace temperature, T s is the standard furnace temperature;

[0024] The calculation formula of the oxygen content correction coefficient k2 is:

[0025]

[0026] wherein O2 is the measured oxygen volume fraction in the flue gas.

[0027] As a preferred scheme of the biomass boiler combustion efficiency optimization method based on historical data, the calculation of the comprehensive score S of the high-efficiency operation section is as follows:

[0028]

[0029] wherein w1, w2, and w3 are weight coefficients, P is the actual steam pressure, P s is the standard steam pressure;

[0030] The calculation of the optimization reference parameter value X is as follows:

[0031]

[0032] wherein x i is the parameter measurement value, S i is the comprehensive score at the corresponding time, and n is the number of data points.

[0033] As a preferred scheme of the biomass boiler combustion efficiency optimization method based on historical data, the correlation model is a polynomial correlation model of the thermal efficiency value η and the normalized parameter, as shown in the following formula:

[0034]

[0035] wherein i=1, 2, 3, and 4 respectively correspond to the furnace temperature, the oxygen content, the steam pressure, and the fuel supply rate, a i , b i are undetermined coefficients, c is an interaction term coefficient, and X norm,i is a normalization processing function.

[0036] The normalization processing function is expressed by the following formula:

[0037]

[0038] wherein X i is the actual parameter value, X max is the normalized parameter value, and X minrespectively, are the maximum and minimum values of the parameter history, and α i is a dynamic correction coefficient of the parameter.

[0039] As a preferred scheme of the biomass boiler combustion efficiency optimization method based on historical data, the weight coefficient is calculated as follows:

[0040]

[0041] The weight coefficient w j is calculated as follows:

[0042]

[0043] Where λ is a smoothing factor (0.1), μ is a time decay coefficient (0.001), t j is the interval of the data point from the current time, and η j is the actual efficiency value, is the average efficiency value.

[0044] As a preferred scheme of the biomass boiler combustion efficiency optimization method based on historical data, the boiler combustion efficiency score includes:

[0045] The normalized parameters are weighted and superimposed with the corresponding weight coefficients to obtain an initial efficiency score;

[0046] The initial efficiency score is corrected for time accumulation effect. When the parameter deviates from the optimization reference value, the score penalty is gradually increased by an exponential function.

[0047] The initial efficiency score after time accumulation effect correction is corrected for parameter coupling. The boiler combustion efficiency score is obtained.

[0048] The parameter coupling correction is to increase the additional score adjustment according to the consistency of the deviation direction when multiple parameters deviate from the reference value.

[0049] As a preferred scheme of the biomass boiler combustion efficiency optimization method based on historical data, the preset threshold includes a dynamic warning threshold and a forced intervention threshold.

[0050] When the boiler combustion efficiency score is below the dynamic warning threshold and the duration exceeds 15 minutes, an optimization suggestion is issued. When the boiler combustion efficiency score is below the forced intervention threshold or below the dynamic warning threshold for more than 30 minutes, the optimization control process is triggered.

[0051] The dynamic warning threshold and the forced intervention threshold are calculated as follows:

[0052]

[0053] wherein, μ S (t) and σ S (t) are the moving average and standard deviation of the scores in the last 7 days, respectively, p is the threshold period adjustment coefficient, θ1(t) is the dynamic early warning threshold, θ2(t) is the forced intervention threshold, and T is the characteristic period, i.e., 24 hours.

[0054] In a second aspect, the embodiments of the present application provide a biomass boiler combustion efficiency optimization system based on historical data, which comprises:

[0055] a collection module for collecting historical operation data of the biomass boiler; the historical operation data comprises boiler temperature, oxygen content, steam pressure and fuel supply rate;

[0056] a segmented judgment module for segmenting the historical operation data according to time sequence, dividing out a high-efficiency operation section and a low-efficiency operation section of the boiler, and taking the operation parameters of the high-efficiency operation section as optimization benchmark data;

[0057] a model construction module for establishing a correlation model between boiler combustion efficiency and operation parameters based on the optimization benchmark data, and calculating the weight coefficients of each parameter by least square method;

[0058] a trigger adjustment module for performing weighted calculation on the weight coefficients and real-time operation parameters to obtain a boiler combustion efficiency score, triggering optimization control when the efficiency score is lower than a preset threshold, and dynamically adjusting the fuel supply rate and oxygen content according to the optimization benchmark data.

[0059] The present application has the beneficial effect that by comprehensively collecting and deeply analyzing the historical operation data of the biomass boiler, combining the dynamic correction model of thermal efficiency, accurately identifying the high-efficiency operation section and extracting the optimization benchmark parameters, a correlation model between combustion efficiency and operation parameters is established. The model is optimized by polynomial fitting and weighted least square method, comprehensively considers the time decay and coupling effect between parameters, greatly improves the accuracy and stability of efficiency prediction. In actual operation, the scheme realizes continuous monitoring and intelligent adjustment of the boiler combustion efficiency through dynamic normalization and efficiency score evaluation of real-time operation data, triggers optimization control when the efficiency score is lower than the threshold, and finally adjusts the fuel supply rate and oxygen content dynamically, so that the boiler operation state gradually approaches the high-efficiency interval, thereby improving the combustion efficiency and reducing energy waste and pollution emission. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings. Among them:

[0061] Figure 1 A flow chart of a biomass boiler combustion efficiency optimization method based on historical data. DETAILED DESCRIPTION

[0062] In order to make the above objectives, features and advantages of the present application more apparent, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without any creative effort should fall within the scope of protection of the present application.

[0063] In the following description, many specific details are set forth in order to provide a thorough understanding of the present application. However, the present application can be practiced in other manners different from those described herein, and those skilled in the art can make similar generalizations without departing from the scope of the present application. Therefore, the present application is not limited to the specific embodiments disclosed below.

[0064] Secondly, the "one embodiment" or "embodiment" referred to herein means that the specific features, structures or characteristics can be included in at least one implementation of the present application. The "in one embodiment" appearing in different places in the specification does not mean the same embodiment, nor is it an independent or alternative embodiment that excludes other embodiments.

[0065] The present application is described in detail in conjunction with the schematic diagram. In the detailed description of the embodiments of the present application, the cross-sectional view of the device structure will be partially enlarged without the general proportion for the convenience of description, and the schematic diagram is only an example, which should not limit the scope of protection of the present application herein. In addition, the three-dimensional spatial dimensions of length, width and depth should be included in actual manufacture.

[0066] Meanwhile, in the description of the present application, it should be noted that the terms "up, down, in and out" and the like indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first, second or third" are only for the purpose of description, and cannot be understood as indicating or implying relative importance.

[0067] Unless otherwise defined, the terms "mounting, connecting, associating" in the present application should be interpreted broadly, for example: it can be fixed connection, detachable connection or integral connection; it can also be mechanical connection, electrical connection or direct connection, it can also be indirectly connected through intermediate medium, or it can be the internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances.

[0068] Embodiment 1

[0069] Reference Figure 1 For the first embodiment of the present application, the embodiment provides a biomass boiler combustion efficiency optimization method based on historical data, comprising:

[0070] S1: collecting historical operation data of the biomass boiler, wherein the historical operation data includes boiler temperature, oxygen content, steam pressure and fuel supply rate;

[0071] The historical operation data of the biomass boiler is collected, and the historical operation data includes the following parameters: the boiler furnace temperature is collected by 8 temperature measuring points of K-type thermocouple arranged around the furnace, and the temperature measuring points are uniformly distributed along the circumference of the furnace and are 50mm away from the furnace wall; the oxygen content in the boiler exhaust gas is collected by a zirconia oxygen sensor at the outlet of the flue, and the sampling frequency of the zirconia oxygen sensor is 2 times per second; the steam pressure is collected by a pressure sensor arranged at the outlet of the superheater, and the measurement range of the pressure sensor is 0-2.5MPa; the fuel supply rate is calculated by a speed sensor of a chain conveyor, and the feeding amount of the chain conveyor is linearly related to the speed; the collection time span of the historical operation data is 90 consecutive days, and the sampling interval is 1 minute.

[0072] S2: segmenting the historical operation data according to time sequence, dividing out the high-efficiency operation section and the low-efficiency operation section of the boiler, and taking the operation parameters of the high-efficiency operation section as the optimization reference data;

[0073] The historical operation data is segmented according to time sequence: first, the 90 consecutive days of data are divided into 30 minute basic time units T, and the thermal efficiency value η is calculated in each time unit, and the calculation formula of the thermal efficiency value η is:

[0074]

[0075] Wherein, D is the steam production (t / h), h2 is the enthalpy value of superheated steam (kJ / kg), h1 is the enthalpy value of feed water (kJ / kg), B is the fuel consumption (t / h), Q net is the low heat value of fuel (kJ / kg), k1 is the furnace temperature correction coefficient, and k2 is the oxygen content correction coefficient.

[0076] The calculation formula of the furnace temperature correction coefficient k1 is:

[0077] k1 = 1-0.0015|T r -T s |

[0078] Wherein, T r is the actual furnace temperature (℃), T s is the standard furnace temperature 850℃.

[0079] The calculation formula of the oxygen content correction coefficient k2 is:

[0080]

[0081] Wherein, O2 is the measured oxygen volume fraction in the flue gas (%).

[0082] When the thermal efficiency value η is greater than 85% and the duration exceeds 120 minutes, the operation interval is divided into high efficiency operation section, and the comprehensive score S of the high efficiency operation section is calculated:

[0083]

[0084] Wherein, w1, w2, w3 are weight coefficients, P is the actual steam pressure, P s is the standard steam pressure 1.8MPa.

[0085] Extract the operation data in the high efficiency operation section where the comprehensive score S is located in the front 30% interval, and calculate the optimization reference parameter value X:

[0086]

[0087] Wherein, x i is the parameter measurement value, S i is the comprehensive score at the corresponding time, and n is the number of data points; The optimization reference data includes the parameter range of furnace temperature 850±20℃, oxygen content 6±0.5%, steam pressure 1.8±0.1MPa, fuel supply rate 2.5±0.2t / h, etc.

[0088] More preferably, the combustion efficiency of the biomass boiler has an optimal matching range with the furnace temperature, and too high or too low will result in efficiency decline, when the temperature is too high (> 870℃), it will aggravate the heat loss of the furnace wall, and cause the precipitation of alkali metals in the biomass fuel, and form coking on the heating surface, when the temperature is too low (< 830℃), it will cause incomplete combustion, increase the loss of combustible material, and easily form carbon monoxide, therefore, the furnace temperature correction coefficient is used for calculation and correction, and when the temperature deviates from the standard value of 850℃, the efficiency decreases by 0.15% per 1℃ deviation. Since the excess air coefficient and the boiler efficiency have a parabolic relationship, there is an optimal value, too high oxygen content will increase the heat loss of exhaust gas, and too low oxygen content will cause incomplete combustion, therefore, the oxygen content correction coefficient is used to effectively correct the deviation of the excess air coefficient from the optimal value.

[0089] The product relationship of k1 and k2 reflects the coupling effect of temperature and oxygen content, when both parameters are in the optimal range, the total correction coefficient is close to 1, and the deviation of any parameter from the optimal value will cause the efficiency calculation value to decrease. After using the correction coefficient, the error between the efficiency calculation value and the calculation result of the actual heat balance method is reduced by about 40%, effectively avoiding the one-sidedness of simply relying on the input-output ratio to calculate the efficiency.

[0090] S3: based on the optimization reference data, a correlation model between the boiler combustion efficiency and the operation parameters is established, and the weight coefficients of each parameter are calculated by the least square method;

[0091] Firstly, the furnace temperature, oxygen content, steam pressure and fuel supply rate are normalized, the interval mapping method is used to map the parameter value to the [0, 1] interval, a dynamic correction term based on the time period is introduced to the normalized parameter, the dynamic correction term includes a 24-hour periodic sinusoidal fluctuation and a parameter deviation exponential decay, and the specific process is as follows:

[0092]

[0093] Wherein, X i is the actual parameter value, X max and X min are the maximum and minimum values of the parameter history respectively, α i is the parameter dynamic correction coefficient, and X norm,i is the normalization processing function.

[0094] Wherein, the calculation of the parameter dynamic correction coefficient is as follows:

[0095]

[0096] Among them, β is the fluctuation amplitude coefficient (value is 0.1), t is the current time, T is the characteristic period (24 hours), γ is the attenuation coefficient (value is 0.05), X opt Optimize the baseline value.

[0097] Secondly, a polynomial prediction model containing linear terms, quadratic terms, and cross terms is constructed. The linear terms in the polynomial prediction model reflect the direct impact of a single parameter on efficiency, the quadratic terms reflect the nonlinear characteristics of parameter changes, and the cross terms characterize the coupling effect between parameters. That is, a polynomial correlation model between boiler combustion efficiency η and normalized parameters is established, as shown in the following formula:

[0098]

[0099] Among them, i=1, 2, 3, 4 correspond to furnace temperature, oxygen content, steam pressure and fuel supply rate respectively, a i 、b i is the unknown coefficient, and c is the interaction coefficient.

[0100] Then, a weighted least squares objective function is constructed based on historical data. The weight coefficient in the weighted least squares objective function is determined by the data timeliness and efficiency deviation. The data timeliness is described by an exponential decay function, and the efficiency deviation is quantified by a Gaussian kernel function. The weighted least squares objective function is shown in the following formula:

[0101]

[0102] Among them, the weight coefficient w j The calculation formula is:

[0103]

[0104] Among them, λ is the smoothing factor (value is 0.1), μ is the time attenuation coefficient (value is 0.001), t j is the interval between the data point and the current time, η j is the actual efficiency value, is the average efficiency value.

[0105] Finally, the normal equations are solved by matrix decomposition to obtain the coefficients of the model. The positive and negative signs of the coefficients represent the promoting or inhibiting effect of the parameters on efficiency, and the absolute values ​​of the coefficients reflect the degree of influence of the parameters. The normal equations are shown below:

[0106] A=(X T WX) -1 X T WY

[0107] Wherein, X is a parameter matrix, W is a weight diagonal matrix, Y is an efficiency vector; the optimal coefficient matrix A contains the weight coefficients of each parameter term in the model.

[0108] S4: The weight coefficients are weighted with real-time operating parameters to obtain a boiler combustion efficiency score, and optimization control is triggered when the efficiency score is lower than a preset threshold;

[0109] First, collect the operating data of the boiler in the last 30 minutes, including the furnace temperature, oxygen content, steam pressure and fuel supply rate; the collected operating data is processed by data smoothing in units of 1 minute, and the exponential moving average method is used to eliminate the interference caused by parameter fluctuations, and the time constant of the smoothing processing is 5 minutes; the smoothed operating parameters are dynamically normalized, and the adaptive interval mapping method is used in the dynamic normalization process, referring to the parameter distribution characteristics of the previous 24 hours.

[0110] The normalized parameters are weighted and superimposed with the corresponding weight coefficients to obtain an initial efficiency score:

[0111]

[0112] Wherein, w i is the weight coefficient of each parameter, k ij is the parameter interaction weight coefficient, t is the current time point,

[0113] is the normalized value of the i-th parameter at time t, is the normalized value of the j-th parameter at time t.

[0114] The initial efficiency score is introduced to the time cumulative effect correction, when the parameters continuously deviate from the optimization reference value, the score penalty is gradually increased by the exponential function, and the parameter coupling correction term is introduced, when multiple parameters deviate from the reference value at the same time, according to the consistency of the deviation direction, additional score adjustment is increased, and finally the corrected boiler combustion efficiency score is obtained, as shown in the following formula:

[0115]

[0116] Wherein, λ is the cumulative effect coefficient (0.1), τ is the cumulative time window (30 minutes), X opt is the optimization reference value, η1 is the coupling correction coefficient (0.05), D i is the normalized deviation of parameter i, sgn() is the sign function, s is the integral variable, indicating the time period from t-τ to t, X n (s) is the normalized parameter value at time s.

[0117] The preset threshold of the efficiency score is determined by an adaptive mechanism: based on the efficiency score data of the last 7 days, the mean and standard deviation of the score are calculated, and the mean minus 1.5 times the standard deviation is taken as the dynamic early warning threshold, and the mean minus 2 times the standard deviation is taken as the forced intervention threshold; when the efficiency score is lower than the dynamic early warning threshold and the duration exceeds 15 minutes, the system issues an optimization suggestion; when the efficiency score is lower than the forced intervention threshold or continues to be lower than the dynamic early warning threshold for more than 30 minutes, the optimization control process is triggered.

[0118] Therefore, the dynamic early warning threshold and the forced intervention threshold are calculated as follows:

[0119]

[0120] Wherein, μ S (t) and σ S (t) are the moving average and standard deviation of the score of the last 7 days, and ρ is the threshold period adjustment coefficient (0.05).

[0121] The optimization control is triggered when the following conditions are met:

[0122]

[0123]

[0124] Wherein, I[] is an indicator function, T1 is the early warning judgment time window (30 minutes), and T2 is the forced intervention judgment time window (5 minutes).

[0125] S5: dynamically adjusting the fuel supply rate and oxygen content according to the optimization reference data, so that the boiler operating parameters gradually approach the parameter interval of the high-efficiency operation section.

[0126] Further, the embodiment also provides a biomass boiler combustion efficiency optimization system based on historical data, comprising:

[0127] A collection module is configured to collect historical operation data of the biomass boiler; the historical operation data includes boiler temperature, oxygen content, steam pressure and fuel supply rate;

[0128] A segmented judgment module is configured to segment the historical operation data according to time sequence, divide the high-efficiency operation section and the low-efficiency operation section of the boiler, and take the operating parameters of the high-efficiency operation section as optimization reference data;

[0129] A model construction module is configured to establish a correlation model between the boiler combustion efficiency and the operating parameters based on the optimization reference data, and calculate the weight coefficients of each parameter by the least square method;

[0130] The trigger adjustment module is configured to weight the weight coefficient with real-time operation parameters to obtain a boiler combustion efficiency score, and trigger optimization control when the efficiency score is lower than a preset threshold, and dynamically adjust a fuel supply rate and oxygen content according to the optimization reference data.

[0131] The embodiment also provides a computer device suitable for the biomass boiler combustion efficiency optimization method based on historical data, including a memory and a processor; the memory is configured to store computer executable instructions, and the processor is configured to execute the computer executable instructions to implement the biomass boiler combustion efficiency optimization method based on historical data proposed in the above embodiment.

[0132] The computer device can be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured 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 operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, trackball or touchpad arranged on the shell of the computer device, or an external keyboard, touchpad or mouse, etc.

[0133] The embodiment also provides a storage medium having a computer program stored thereon, and the program is executed by a processor to implement the biomass boiler combustion efficiency optimization method based on historical data proposed in the above embodiment.

[0134] The storage medium proposed in the embodiment belongs to the same inventive concept as the data storage method proposed in the above embodiment, and the technical details not described in detail in the embodiment can be referred to the above embodiment, and the embodiment has the same beneficial effects as the above embodiment.

[0135] Embodiment 2

[0136] For the second embodiment of the present application, the embodiment provides a biomass boiler combustion efficiency optimization method based on historical data. In order to verify the beneficial effects of the present application, economic benefit calculation and simulation experiments are used for scientific demonstration.

[0137] This study selected a power company's biomass power plant in Jiangxi Province as the test site. The plant primarily uses agricultural straw and forestry waste as fuel, and the boiler has a rated evaporation capacity of 20 tons per hour. The test team first conducted a comprehensive inspection of the boiler's operating system and instrument calibration to ensure the accuracy and reliability of data collection.

[0138] To accurately capture the operating parameters of the biomass boiler, the team deployed a high-precision sensor array in the boiler system. The furnace temperature is monitored using eight K-type thermocouples, which are evenly distributed along the circumference of the furnace. Each temperature measurement point maintains a standard distance of 50 mm from the furnace wall to ensure spatial uniformity of temperature measurement. Oxygen content monitoring is performed by a high-sensitivity zirconium oxide oxygen sensor with a sampling frequency of 2 times per second, which can capture changes in oxygen concentration at the flue outlet in real time. The steam pressure sensor is installed at the superheater outlet, with a measurement range of 0-2.5 MPa, meeting the pressure monitoring needs under various operating conditions.

[0139] The data collection process was carried out strictly according to a pre-defined protocol. 90 consecutive days of operating data were systematically recorded, with sampling occurring every minute, resulting in a dataset containing over 120,000 data points. This dataset not only recorded operating parameters but also provided a solid foundation for subsequent in-depth analysis.

[0140] During the data processing phase, the research team first preprocessed the raw data. Using 30-minute intervals as the basic time unit, they calculated the thermal efficiency for each time period. This thermal efficiency calculation took into account several key parameters, including steam production, steam enthalpy, feedwater enthalpy, and fuel consumption. Correction factors for furnace temperature and oxygen content were also introduced, making the efficiency assessment more accurate and comprehensive.

[0141] Through a detailed analysis of 90 days of data, we identified several high-efficiency operating periods. These periods all share a common characteristic: thermal efficiency consistently exceeding 85% for at least 120 minutes. We then comprehensively scored these high-efficiency operating periods, ultimately extracting the top 30% of the operating data as the optimization benchmark. The following records were obtained:

[0142] Table 1 Biomass boiler operating efficiency test data

[0143]

[0144] In-depth analysis of the test data reveals that the operating efficiency of biomass boilers is influenced by the complex coupling of multiple parameters, and that there is an optimal operating parameter range. The tabular data clearly demonstrates that when the furnace temperature is between 840-862°C, the oxygen content is between 5.8-6.3%, the steam pressure is between 1.75-1.85 MPa, and the fuel supply rate is between 2.3-2.7 t / h, the boiler thermal efficiency remains high at 85.7%-88.1%.

[0145] Compared with the traditional single-parameter control method, the multi-parameter dynamic optimization strategy proposed in this invention significantly improves the operating efficiency of the biomass boiler. By introducing temperature correction coefficients and oxygen content correction coefficients, not only can the single parameter be accurately adjusted, but more importantly, the coupling effect between parameters can be captured. For example, when the furnace temperature deviates slightly from the standard value of 850℃, a deviation of 1℃ will result in a 0.15% decrease in efficiency; at the same time, the fluctuation of oxygen content will also cause nonlinear changes in thermal efficiency.

[0146] The innovation of this research lies in the construction of a polynomial prediction model containing linear terms, quadratic terms and cross terms. This model not only reflects the direct impact of a single parameter on efficiency, but also embodies the complex interaction between parameters. By using the weighted least squares method to determine the model coefficients, researchers can quantify the specific contribution of each parameter to the boiler efficiency.

[0147] Compared with the traditional method of relying on empirical adjustment, the adaptive optimization control mechanism of this invention is more advanced. It can dynamically adjust the early warning threshold and intervention threshold according to the operating data of the last 7 days, achieving real-time and accurate monitoring of the boiler operating state. When the efficiency score is continuously below the dynamic early warning threshold for 15 minutes, the system will actively issue optimization recommendations; if it continues to be below the forced intervention threshold or is in a suboptimal state for a long time, it will directly trigger the parameter adjustment process.

[0148] It should be noted that the above examples are only used to illustrate the technical solutions of the present invention and are not limiting. 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 can be modified or replaced equivalently without departing from the spirit and scope of the present invention, and all should be covered within the scope of the claims of the present invention.

Claims

1. A method for optimizing the combustion efficiency of a biomass boiler based on historical data, characterized by: include: Collecting historical operating data of the biomass boiler; the historical operating data includes boiler temperature, oxygen content, steam pressure and fuel supply rate; Segmenting the historical operating data according to time series to divide the boiler into high-efficiency operating segments and low-efficiency operating segments, and using operating parameters of the high-efficiency operating segments as optimization benchmark data; Based on the optimized benchmark data, a correlation model between boiler combustion efficiency and operating parameters is established, and the weight coefficient of each parameter is calculated by the least squares method; Performing weighted calculation on the weight coefficient and the real-time operating parameters to obtain a boiler combustion efficiency score, and triggering optimization control when the efficiency score is lower than a preset threshold; dynamically adjusting the fuel supply rate and oxygen content according to the optimized benchmark data; Obtaining the optimized benchmark data includes: Dividing the historical operation data into basic time units T; Calculate the thermal efficiency value η in each time unit; Divide the operating interval in which the thermal efficiency value is greater than a preset threshold and the duration exceeds a preset value into a high-efficiency operating segment, and calculate the comprehensive score S of the high-efficiency operating segment; Extracting the operation data with the highest comprehensive score in the efficient operation segment and calculating the optimization benchmark value X; The calculation of the thermal efficiency value η is shown in the following formula: Where D is steam production, h2 is superheated steam enthalpy, h1 is feed water enthalpy, B is fuel consumption, Q net is the low calorific value of the fuel, k1 is the furnace temperature correction coefficient, and k2 is the oxygen content correction coefficient; The calculation formula of the furnace temperature correction coefficient k1 is: k1=1-0.0015|T r -T s | Among them, T r is the actual furnace temperature, T s is the standard furnace temperature; The calculation formula of the oxygen content correction coefficient k2 is: Among them, O2 is the volume fraction of oxygen in the measured flue gas; The calculation formula S of the comprehensive score of the efficient operation segment is as follows: Among them, w1, w2, w3 are weight coefficients, P is the actual steam pressure, P s is the standard steam pressure; The calculation of the optimization reference value X is shown in the following formula: Among them, x i is the measured value of each parameter, S i is the comprehensive score at the corresponding moment, and n is the number of data points; The correlation model is a polynomial correlation model between the thermal efficiency value η and the normalization parameter, as shown in the following formula: Show: Among them, i=1, 2, 3, 4 correspond to furnace temperature, oxygen content, steam pressure and fuel supply rate respectively, a i 、b i is the unknown coefficient, c is the interaction coefficient, X norm,i is the normalization function; The normalization function is expressed as follows: Among them, X i is the actual parameter value, X max and X min are the historical maximum and minimum values ​​of the parameters, α i is the parameter dynamic correction coefficient.

2. The method for optimizing the combustion efficiency of a biomass boiler based on historical data according to claim 1, characterized in that: The weight coefficient is calculated as follows: Among them, the weight coefficient w j The calculation formula is: Among them, λ is the smoothing factor, μ is the time decay coefficient, t j is the interval between the data point and the current time, η j is the actual efficiency value, is the average efficiency value, is the efficiency value predicted by the model.

3. The method for optimizing the combustion efficiency of a biomass boiler based on historical data according to claim 2, wherein: The boiler combustion efficiency score includes: The normalized parameters are weighted and superimposed with the corresponding weight coefficients to obtain the initial efficiency score; Performing a time-accumulation effect correction on the initial efficiency score; the time-accumulation effect correction is to gradually increase the score penalty through an exponential function when the parameter continues to deviate from the optimized benchmark value; Performing parameter coupling correction on the initial efficiency score after the time cumulative effect correction to obtain the boiler combustion efficiency score; The parameter coupling correction is to add additional score adjustments based on the consistency of the deviation directions when multiple parameters deviate from the baseline values ​​at the same time.

4. The method for optimizing biomass boiler combustion efficiency based on historical data according to claim 3, characterized in that: The preset thresholds include a dynamic warning threshold and a forced intervention threshold; When the boiler combustion efficiency score is lower than the dynamic warning threshold and the duration exceeds 15 minutes, an optimization suggestion is issued; when the boiler combustion efficiency score is lower than the mandatory intervention threshold or is continuously lower than the dynamic warning threshold for more than 30 minutes, an optimization control process is triggered; The calculation of the dynamic warning threshold and the mandatory intervention threshold is shown in the following formula: Among them, μ S (t) and σ S (t) are the moving average and standard deviation of the efficiency score S(t) in the last 7 days, ρ is the threshold period adjustment coefficient, θ1(t) is the dynamic warning threshold, θ2(t) is the mandatory intervention threshold, and T is the characteristic period, i.e., 24 hours.

5. A biomass boiler combustion efficiency optimization system based on historical data, based on the biomass boiler combustion efficiency optimization method based on historical data according to any one of claims 1 to 4, characterized in that: include: A collection module for collecting historical operation data of the biomass boiler; the historical operation data includes boiler temperature, oxygen content, steam pressure and fuel supply rate; a segmentation judgment module for segmenting the historical operation data according to a time series, dividing the boiler into high-efficiency operation segments and low-efficiency operation segments, and using the operating parameters of the high-efficiency operation segments as optimization benchmark data; A model building module is used to establish a correlation model between boiler combustion efficiency and operating parameters based on the optimization benchmark data, and calculate the weight coefficient of each parameter by the least square method; The trigger adjustment module is used to perform weighted calculation on the weight coefficient and the real-time operating parameters to obtain the boiler combustion efficiency score. When the efficiency score is lower than a preset threshold, the optimization control is triggered and the fuel supply rate and oxygen content are dynamically adjusted according to the optimization benchmark data.

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