CN117704416A - Automatic boiler combustion adjusting method based on AI - Google Patents
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Abstract
The invention relates to the technical field of boiler combustion regulation, in particular to an automatic boiler combustion regulation method based on AI, which comprises the following steps: collecting boiler operation data in real time, including temperature, pressure, fuel consumption and emission data; analyzing the acquired data by utilizing an algorithm of the AI unit to identify the optimal state of the operation of the boiler; based on the analysis result, the AI unit automatically generates a combustion adjustment decision; automatically adjusting fuel supply and air flow parameters to realize combustion optimization; according to the external environment change, the combustion strategy is automatically adjusted, so that the problems of combustion efficiency and emission caused by the environment change are solved; the AI unit predicts potential faults and maintenance requirements according to the performance data and the historical maintenance records of the boiler components, and plans the maintenance activities in advance.
Description
Technical Field
The invention relates to the technical field of boiler combustion regulation, in particular to an automatic boiler combustion regulation method based on AI.
Background
In conventional boiler combustion regulation methods, the control of combustion is mainly dependent on manual operation or on a fixed program based automated system. These methods often do not flexibly adapt to changing operating conditions such as fluctuations in fuel quality, changes in ambient temperature, or equipment aging. In addition, the conventional method has limitations in real-time data analysis and processing, resulting in low combustion efficiency, energy waste and excessive emission of environmental pollutants.
Under the background of increasingly strict environmental regulations and continuously rising energy costs, the improvement of combustion efficiency and the reduction of emissions become important problems of boiler operation management. Meanwhile, in order to reduce unexpected shutdown and maintenance costs, improving the service life and reliability of equipment, it becomes particularly critical to perform effective predictive maintenance on the boiler.
While some automated solutions have been proposed and applied in the prior art, they often lack sufficient flexibility and adaptability to take full advantage of real-time data and advanced analytical techniques to optimize the combustion process. Furthermore, these systems are also relatively limited in their ability to predict maintenance and failure prediction.
Therefore, there is a need to develop a new method for adjusting boiler combustion, which can integrate advanced artificial intelligence technology, realize accurate control of boiler combustion process, optimize combustion efficiency, reduce environmental pollution, and effectively predict equipment maintenance requirements. The method provides a more efficient, environment-friendly and economical solution for the operation of the boiler, and meets the strict requirements of the modern industry on energy efficiency and environment protection.
Disclosure of Invention
Based on the above objects, the present invention provides an AI-based automatic combustion adjustment method for a boiler.
An AI-based automatic combustion adjustment method for a boiler, comprising the steps of:
s1: collecting boiler operation data in real time, including temperature, pressure, fuel consumption and emission data;
s2: analyzing the data, namely analyzing the acquired data by utilizing an algorithm of an AI unit, and identifying the optimal state of the operation of the boiler;
s3: an adjustment decision, based on the analysis result, the AI unit automatically generates a combustion adjustment decision;
s4: executing adjustment, automatically adjusting fuel supply and air flow parameters, and realizing combustion optimization;
s5: the environment is adapted and adjusted, the combustion strategy is automatically adjusted according to the external environment change, and the problems of combustion efficiency and emission caused by the environment change are solved;
s6: predictive maintenance, wherein the AI unit predicts potential faults and maintenance requirements according to performance data and historical maintenance records of the boiler components, and plans maintenance activities in advance so as to avoid unexpected shutdown and improve maintenance efficiency;
s7: feedback and iteration: and feedback learning is carried out according to the regulated operation data, and the regulation strategy is continuously optimized.
Further, the data collection in S1 specifically includes:
a variety of sensors are installed at key positions of the boiler for measuring and recording the following parameters:
temperature sensor: measuring and recording the temperature of the inside and the outside of the boiler;
a pressure sensor: monitoring the pressure inside the boiler;
flow sensor: recording the flow rate of the fuel to measure the fuel consumption;
emission sensor: detecting and recording the gas components and concentrations of the boiler emissions, including CO2 and NOx;
the system comprises a central processing unit, a data acquisition unit, a communication interface, a data transmission unit and a data transmission unit, wherein the data acquisition unit is connected with each sensor and is responsible for collecting data provided by each sensor, the data collected by the data acquisition unit is transmitted to the central processing unit by the communication interface, the communication interface supports various communication protocols, and the collected data is subjected to preliminary processing before data transmission, including filtering, standardization and abnormal value detection, so that the availability and quality of the data are improved.
Further, the algorithm of the AI unit in S2 specifically includes a neural network model for processing and analyzing data collected from the boiler, and the neural network model specifically includes:
feature extraction: extracting key parameters of boiler operation, including temperature fluctuation, pressure change, fuel consumption rate and emission level;
and (3) state identification: training a model by using the processed data, identifying the current boiler operation state, and distinguishing normal operation, low-efficiency operation and potential fault states;
optimization suggestion generation: based on the state recognition results of the neural network model, optimization suggestions for boiler combustion efficiency and emission control are generated, wherein the optimization suggestions comprise adjusting combustion parameters and changing fuel supply strategies.
Further, the neural network model is specifically as follows:
input layer: input data x: including temperature, pressure, fuel consumption, emissions data, input data vector xεR n Where n is the number of input features, specifically designed for boiler data;
convolution layer: for processing spatial data features, the convolution operation is defined as:
C i ＝ReLU(W c *h i-1 +b c ) Wherein W is c Is the weight of the convolution layer, b c Is an offset term, which represents a convolution operation;
and (3) a circulating layer: for processing time series data, including historical temperature, pressure changes, the cyclic operation is defined as: r is R i ＝tanh(W r ·[R i-1 ,h i ]+b r ) Wherein W is r Is the weight of the cyclic layer, b r Is a bias term, [ R ] i-1 ,h i ]Representing a combination of a previous state of the loop layer and a current input;
full tie layer: as described above, for generating final outputs from features extracted from the convolutional and cyclic layers: y=softmax (W f ·R m +b f ) Wherein W is f Is the weight of the full connection layer, b f Is a bias term, R m Is the output of the last loop layer;
above, W c ,W r ,W f : weight matrix corresponding to convolution layer and circulation layer respectivelyA ring layer and a full connection layer for converting input data into a characteristic representation, b c ,b r ,b f : bias terms, corresponding to convolution layer, loop layer and full-connection layer, respectively, for increasing flexibility of the model, reLU (modified linear unit): an activation function for convolving the layer to increase the nonlinearity, tanh (hyperbolic tangent function): another activation function, used in a cyclic network, softmax: an activation function for the multi-classification problem for the output layer to convert the result into a probability distribution.
Further, the adjusting decision in S3 specifically includes:
decision generation unit: comprising an AI module for generating a combustion regulation decision based on the analysis of the boiler operating data, the AI module analyzing the data using a neural network model and outputting recommended regulation parameters,
decision logic: the decision of the AI module is based on the following criteria:
combustion efficiency: the AI module calculates the current combustion efficiency by analyzing temperature, pressure and fuel consumption data and recommends an adjustment strategy to achieve the optimal efficiency;
emission control: by analyzing the emission data, focusing on the concentration of harmful gases, the AI module recommends an adjustment strategy to reduce emissions, conforming to environmental standards;
parameter optimization advice: the AI module will generate specific combustion parameter adjustment recommendations including fuel supply, air flow, combustion temperature to optimize the combustion process;
automatic adjusting function: the system also comprises a function of automatically executing the adjustment strategy recommended by the AI module, so as to realize automatic adjustment, wherein the adjustment is continuous or based on a specific time interval.
Further, the combustion efficiency is calculated by the following formula:where η is combustion efficiency, and:
actual heat output Q Actual practice is that of ＝m Fuel and its production process X LHV x combustion efficiency, where m Fuel and its production process LHV is the low heating value for the mass flow of fuel;
theoretical heat output Q Theory of ＝m Material X HHV, wherein HHV is the high-order heating value;
the emission control is calculated by the following formula: e= Σ (C i ×R i ) Wherein E is emission control, C i Representing the concentration of a particular emission species, R i The emission ratio representing the emissions depends on the fuel type and combustion conditions.
Further, the performing adjustment of S4 includes a controller for adjusting the fuel supply amount and the air flow rate, the controller receiving the optimization advice from the AI module and automatically adjusting the corresponding control valve and regulator according to the advice;
fuel supply regulation: adjusting the fuel flow by controlling the opening of the fuel supply valve to achieve the optimal supply recommended by the AI module, the adjustment strategy including increasing or decreasing the fuel supply to increase combustion efficiency or reduce emissions;
air flow adjustment: the air flow is adjusted by adjusting the fan speed or the opening of the air inlet valve to meet the optimization proposal of the AI module, and the ratio of air to fuel is adjusted according to the optimal combustion conditions to ensure complete combustion and minimum emissions.
Further, the environment adaptation of S5 specifically includes:
an environment monitoring unit: comprises a plurality of sensors for monitoring external environment parameters in real time, wherein the external environment parameters comprise air temperature, humidity, air pressure and wind speed,
data integration and analysis: the environmental data is transmitted to an AI unit together with the boiler operation data for analysis, and potential combustion efficiency and emission problems caused by environmental changes are identified;
the AI unit generates suggestions for adjusting the combustion strategy based on the environmental data and the boiler performance data to adapt to environmental changes, the adjustments including changing fuel supply amounts, adjusting a mixing ratio of air and fuel, modifying combustion temperatures to maintain optimal combustion efficiency and minimum emission levels, and automatically controlling the adjustments of corresponding control parameters based on the suggestions of the AI unit.
Further, the predictive maintenance includes:
data collection and integration: a database integrating real-time performance monitoring and historical maintenance records of the boiler components, collecting data including temperature, pressure, combustion efficiency, component wear degree, historical maintenance and replacement records, and an AI unit analyzing the performance data of the components by using a machine learning algorithm, identifying trends and modes in the data, and predicting potential faults and performance degradation;
predictive maintenance decision: based on the analysis results, the AI unit predicts the maintenance requirements and optimal maintenance time windows for each critical component, generating a maintenance schedule including suggested maintenance activities, schedules, and required resources.
Further, the machine learning algorithm is based on an LSTM (long-short-term memory network) model, which is specifically as follows:
convolution layer: a convolution layer is introduced before the conventional LSTM for extracting spatial features in the time series data, the convolution operation being expressed as: c (C) t ＝ReLU(W c *x t +b c ) Wherein W is c And b c The weight and bias parameters of the convolution layer respectively, which represent the convolution operation;
convolving the LSTM unit: combining convolution operation with LSTM unit, the method can process space and time characteristics simultaneously, and the expression of convolution LSTM is as follows:
an input door: i.e t ＝σ(W ii *C t +W hi *h t-1 +b ii +b hi )
Forgetting the door: f (f) t ＝σ(W if *C t +W hf *h t-1 +b if +b hf )
Cell state:
final cell state:
output door: o (o) t ＝σ(W io *C t +W ho *h t-1 +b io +b ho )
Final hidden state: h is a t ＝o t *tanh(C t )
Abnormality detection: anomaly detection is performed using the convolved LSTM processed time series data to predict maintenance requirements.
The invention has the beneficial effects that:
the present invention enables accurate adjustment of fuel supply and air flow by using an advanced AI analysis and control system, achieving optimal combustion conditions. The AI unit may determine optimal combustion parameters based on real-time data analysis, thereby significantly improving combustion efficiency. This reduces not only fuel consumption but also overall energy costs. In addition, by continuously optimizing the combustion process, the system can adapt to various operating conditions and maintain long-term efficient operation.
According to the invention, through fine control of the combustion process, the emission of harmful gases such as NOx, SOx, CO2 and the like is effectively reduced, and the AI unit can adjust the combustion strategy according to environmental changes and the actual running condition of the boiler, so that the emission is ensured to always meet the environmental protection standard. This is particularly important in meeting increasingly stringent environmental regulations, helping to mitigate environmental pollution while protecting businesses from fines or operational restrictions that may be incurred by violating emissions regulations.
The invention can predict potential faults and maintenance requirements of boiler components by utilizing the improved convolution LSTM network in combination with the anomaly detection technology. The predictive maintenance method enables maintenance work to be planned and implemented in advance without disturbing normal operation, and the risk of unexpected shutdown and maintenance cost are greatly reduced. Meanwhile, through early recognition and treatment of faults, the service life of equipment is prolonged, and the reliability and safety of integral operation are improved.
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In order to more clearly illustrate the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only of the invention and that other drawings can be obtained from them without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method according to an embodiment of the invention.
Detailed Description
The present invention will be further described in detail with reference to specific embodiments in order to make the objects, technical solutions and advantages of the present invention more apparent.
It is to be noted that unless otherwise defined, technical or scientific terms used herein should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The terms "first," "second," and the like, as used herein, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
As shown in fig. 1, the AI-based automatic combustion adjustment method for a boiler includes the steps of:
s1: collecting boiler operation data in real time, including temperature, pressure, fuel consumption and emission data;
s2: analyzing the data, namely analyzing the acquired data by utilizing an algorithm of an AI unit, and identifying the optimal state of the operation of the boiler;
s3: an adjustment decision, based on the analysis result, the AI unit automatically generates a combustion adjustment decision;
s4: executing adjustment, automatically adjusting fuel supply and air flow parameters, and realizing combustion optimization;
s5: the environment is adapted and adjusted, the combustion strategy is automatically adjusted according to the external environment change, and the problems of combustion efficiency and emission caused by the environment change are solved;
s6: predictive maintenance, wherein the AI unit predicts potential faults and maintenance requirements according to performance data and historical maintenance records of the boiler components, and plans maintenance activities in advance so as to avoid unexpected shutdown and improve maintenance efficiency;
s7: feedback and iteration: and feedback learning is carried out according to the regulated operation data, and the regulation strategy is continuously optimized.
The data acquisition in S1 specifically includes:
a variety of sensors are installed at key positions of the boiler for measuring and recording the following parameters:
temperature sensor: measuring and recording the temperature of the inside and the outside of the boiler;
a pressure sensor: monitoring the pressure inside the boiler;
flow sensor: recording the flow rate of the fuel to measure the fuel consumption;
emission sensor: detecting and recording the gas components and concentrations of the boiler emissions, including CO2 and NOx;
the system comprises a central processing unit, a data acquisition unit, a communication interface, a data transmission unit and a data transmission unit, wherein the data transmission unit is connected with each sensor and is responsible for collecting data provided by each sensor, the data collected by the data acquisition unit is transmitted to the central processing unit by the communication interface, the communication interface supports various communication protocols, and the collected data is subjected to preliminary processing before data transmission, including filtering, standardization and abnormal value detection, so that the availability and quality of the data are improved.
In the central processing system, software for receiving, storing, displaying and analyzing sensor data is operated, and the software platform is provided with a user-friendly interface, can display data in real time and provides historical data inquiry and trend analysis functions.
The algorithm of the AI unit in S2 specifically includes a neural network model for processing and analyzing data collected from the boiler, the neural network model specifically including:
feature extraction: extracting key parameters of boiler operation, including temperature fluctuation, pressure change, fuel consumption rate and emission level;
and (3) state identification: training a model by using the processed data, identifying the current boiler operation state, and distinguishing normal operation, low-efficiency operation and potential fault states;
optimization suggestion generation: based on the state recognition result of the neural network model, generating optimization suggestions for boiler combustion efficiency and emission control, wherein the optimization suggestions comprise adjusting combustion parameters and changing fuel supply strategies;
the foregoing describes an advanced analysis system utilizing neural network algorithms that is specifically designed for boiler data analysis and optimization. Through accurate feature extraction and complex pattern recognition, the neural network can effectively recognize the running state of the boiler and provide real-time optimization suggestions. In addition, the continuous learning and adaptation mechanisms ensure that the system is continually advancing over time, adapting to various operating conditions and environmental changes.
The neural network model is specifically as follows:
input layer: input data x: including temperature, pressure, fuel consumption, emissions data, input data vector xεR n Where n is the number of input features, specifically designed for boiler data;
convolution layer: for processing spatial data features (if the data has spatial correlation), such as temperature and pressure distribution at different sensor locations, the convolution operation is defined as:
C i ＝ReLU(W c *h i-1 +b c ) Wherein W is c Is the weight of the convolution layer, b c Is an offset term, which represents a convolution operation;
and (3) a circulating layer: for processing time series data, including historical temperature, pressure changes, the cyclic operation is defined as: r is R i ＝tanh(W r ·[R i-1 ,h i ]+b r ) Wherein W is r Is the weight of the cyclic layer, b r Is a bias term, [ R ] i-1 ,h i ]Representing a combination of a previous state of the loop layer and a current input;
full tie layer: as above, for generating final outputs from the features extracted by the convolutional and cyclic layers: y=softmax (W f ·R m +b f ) Wherein W is f Is the weight of the full connection layer, b f Is a bias term, R m Is the output of the last loop layer;
above, W c ,W r ,W f : a weight matrix corresponding to the convolution layer, the circulation layer and the full connection layer for converting the input data into characteristic representation, b c ,b r ,b f : bias terms, corresponding to convolution layer, loop layer and full-connection layer, respectively, for increasing flexibility of the model, reLU (modified linear unit): an activation function for convolving the layer to increase the nonlinearity, tanh (hyperbolic tangent function): another activation function, used in a cyclic network, softmax: an activation function for the multi-classification problem for the output layer to convert the result into a probability distribution;
the introduction of convolution layers can better process spatial data, such as temperature distribution, from different parts of the boiler. The introduction of a cyclic layer facilitates analysis of time series data, such as pressure changes over the last few hours. And the full-connection layer fuses the characteristics and outputs a final combustion regulation suggestion.
The neural network with the structure is more suitable for processing complex data in automatic combustion regulation of the boiler, and can provide more accurate regulation decision.
The adjustment decision in S3 specifically includes:
decision generation unit: comprising an AI module for generating a combustion regulation decision based on the analysis of the boiler operating data, the AI module analyzing the data using a neural network model and outputting recommended regulation parameters,
decision logic: the decision of the AI module is based on the following criteria:
combustion efficiency: the AI module calculates the current combustion efficiency by analyzing temperature, pressure and fuel consumption data and recommends an adjustment strategy to achieve the optimal efficiency;
emission control: by analyzing the emission data, focusing on the concentration of harmful gases, the AI module recommends an adjustment strategy to reduce emissions, conforming to environmental standards;
parameter optimization advice: the AI module will generate specific combustion parameter adjustment recommendations including fuel supply, air flow, combustion temperature to optimize the combustion process;
automatic adjusting function: the system also comprises a function of automatically executing the adjustment strategy recommended by the AI module, so as to realize automatic adjustment, wherein the adjustment is continuous or based on a specific time interval.
The combustion efficiency is calculated by the following formula:where η is combustion efficiency, and:
actual heat output Q Actual practice is that of ＝m Fuel and its production process X LHV x combustion efficiency, where m Fuel and its production process LHV is the low heating value for the mass flow of fuel;
theoretical heat output Q Theory of ＝m Material X HHV, wherein HHV is the high-order heating value;
emission control is calculated by the following formula: e= Σ (C i ×R i ) Wherein E is emission control, C i Represents the concentration of a particular emission species (e.g., NOx, SOx, CO2, etc.), R i The emission ratio representing emissions, depending on the fuel type and combustion conditions;
AI module application.
Optimization target: the goal of the AI module is to maximize combustion efficiency eta while minimizing emissions level E,
algorithm application: the AI module may employ optimization algorithms (e.g., genetic algorithms, particle swarm optimization, etc.) to adjust combustion parameters to achieve these goals.
Parameter adjustment: including fuel supply, air flow, combustion temperature, etc., these parameters will be adjusted to optimize η, E.
The performing adjustments of S4 include a controller for adjusting the fuel supply and air flow, the controller receiving optimization recommendations from the AI module and automatically adjusting the corresponding control valves and regulators according to the recommendations;
fuel supply regulation: adjusting the fuel flow by controlling the opening of the fuel supply valve to achieve the optimal supply recommended by the AI module, the adjustment strategy including increasing or decreasing the fuel supply to increase combustion efficiency or reduce emissions;
air flow adjustment: adjusting the air flow rate by adjusting the fan speed or the opening of the air inlet valve to be in accordance with the optimization suggestion of the AI module, and adjusting the proportion of air and fuel according to the optimal combustion condition so as to ensure complete combustion and minimum emission;
the system monitors the adjusted combustion parameters (e.g., temperature, pressure, emissions data) in real time and feeds these data back to the AI module, which evaluates the adjustment based on the feedback data and further optimizes its adjustment recommendations.
During the adjustment process, the safety operation parameters of the boiler, such as the upper limit of pressure and temperature, are continuously monitored, and any adjustment beyond the safety range triggers a safety protection mechanism to avoid potential safety risks.
The foregoing describes an integrated boiler combustion parameter automatic adjustment system. The system is capable of adjusting fuel supply and air flow in real time to improve combustion efficiency and reduce emissions based on analysis and optimization recommendations of the AI module. By means of a real-time feedback and safety monitoring mechanism, the system ensures the effectiveness and safety of the adjustment process.
The environment adaptation adjustment of S5 specifically includes:
an environment monitoring unit: comprises a plurality of sensors for monitoring external environment parameters in real time, wherein the external environment parameters comprise air temperature, humidity, air pressure and wind speed,
data integration and analysis: the environmental data is transmitted to the AI unit for analysis along with boiler operating data (e.g., temperature, pressure, fuel consumption, emissions data), identifying potential combustion efficiency and emissions problems caused by environmental changes;
the AI unit generates suggestions for adjusting the combustion strategy based on the environmental data and the boiler performance data to adapt to environmental changes, the adjustments including changing fuel supply amounts, adjusting a mixing ratio of air and fuel, modifying combustion temperatures to maintain optimal combustion efficiency and minimum emission levels, and automatically controlling the adjustments of corresponding control parameters based on the suggestions of the AI unit.
Predictive maintenance includes:
data collection and integration: a database integrating real-time performance monitoring and historical maintenance records of the boiler components, collecting data including temperature, pressure, combustion efficiency, component wear degree, historical maintenance and replacement records, and an AI unit analyzing the performance data of the components by using a machine learning algorithm, identifying trends and modes in the data, and predicting potential faults and performance degradation;
predictive maintenance decision: based on the analysis results, the AI unit predicts the maintenance requirements and optimal maintenance time windows for each critical component, generating a maintenance schedule including suggested maintenance activities, schedules, and required resources.
Upon predicting an impending failure or performance degradation, the system will automatically issue an early warning and notify a maintenance team, including detailed information of the failure prediction, suggested countermeasures, and maintenance guidelines.
The machine learning algorithm is based on an LSTM (long-short term memory network) model, which is specifically as follows:
convolution layer: a convolution layer is introduced before the conventional LSTM for extracting spatial features in the time series data, the convolution operation being expressed as: c (C) t ＝ReLU(W c *x t +b c ) Wherein W is c And b c The weight and bias parameters of the convolution layer respectively, which represent the convolution operation;
convolving the LSTM unit: combining convolution operation with LSTM unit, the method can process space and time characteristics simultaneously, and the expression of convolution LSTM is as follows:
an input door: i.e t ＝σ(W ii *C t +W hi *h t-1 +b ii +b hi )
Forgetting the door: f (f) t ＝σ(W if *C t +W hf *h t-1 +b if +b hf )
Cell state:
final cell state:
output door: o (o) t ＝σ(W io *C t +W ho *h t-1 +b io +b ho )
Final hidden state: h is a t ＝o t *tanh(C t )
Abnormality detection: anomaly detection is performed using the convolved LSTM processed time series data to predict maintenance requirements.
When the invention is applied, the following steps are carried out:
data preprocessing: first, the performance data of the boiler assembly is processed through the convolution layer, and key spatial features are extracted.
Modeling a time sequence: the convolution LSTM network processes time series data and learns the spatio-temporal pattern of the data.
And (3) fault prediction: based on the output of the convolution LSTM and the anomaly detection algorithm, possible failures and maintenance requirements are predicted.
Model optimization: and continuously optimizing a convolution LSTM model according to actual data, and improving the accuracy and efficiency of prediction.
The improvement combines the spatial feature extraction capability of the convolutional neural network and the time sequence processing capability of the LSTM, is more suitable for processing complex performance data of the boiler component, and can provide more accurate and timely maintenance and fault prediction.
1. Convolution layer parameters:
W c the weight matrix of the convolution layer is used for extracting spatial characteristics in boiler performance data, such as temperature distribution measured by different sensors.
b c The bias term of the convolution layer is used for adjusting convolution output and ensuring the effectiveness of feature extraction.
C t Through convolution operationThe feature map is then prepared for the next time series analysis.
2. Convolving LSTM cell parameters:
W ii ,W if ,W iC ,W io the weight matrix respectively represents an input gate, a forget gate, a unit updating gate and an output gate and is used for controlling the inflow, the reservation, the updating and the output of information.
W hi ,W hf ,W hC ,W ho These weight matrices are used to process the hidden state of the previous time step, as above.
b ii ,b if ,b iC ,b io Bias terms for the individual gates are used to fine tune the threshold of the gate control.
C t The state of the unit stores important information to the current time step for capturing long-term dependencies in the time series.
h t Hidden state, representing the output of the current time step, can be used for subsequent fault prediction and decision.
3. Abnormality detection parameter:
and the abnormal threshold value is a threshold value set according to the historical maintenance data and the fault record and is used for detecting whether the current performance data is abnormal or not.
Those of ordinary skill in the art will appreciate that: the discussion of any of the embodiments above is merely exemplary and is not intended to suggest that the scope of the invention is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be implemented in any order and there are many other variations of the different aspects of the invention as described above, which are not provided in detail for the sake of brevity.
The present invention is intended to embrace all such alternatives, modifications and variances which fall within the broad scope of the appended claims. Therefore, any omission, modification, equivalent replacement, improvement, etc. of the present invention should be included in the scope of the present invention.
Claims (10)
1. An AI-based automatic combustion adjustment method for a boiler, comprising the steps of:
s1: collecting boiler operation data in real time, including temperature, pressure, fuel consumption and emission data;
s2: analyzing the data, namely analyzing the acquired data by utilizing an algorithm of an AI unit, and identifying the optimal state of the operation of the boiler;
s3: an adjustment decision, based on the analysis result, the AI unit automatically generates a combustion adjustment decision;
s4: executing adjustment, automatically adjusting fuel supply and air flow parameters, and realizing combustion optimization;
s5: the environment is adapted and adjusted, the combustion strategy is automatically adjusted according to the external environment change, and the problems of combustion efficiency and emission caused by the environment change are solved;
s6: predictive maintenance, wherein the AI unit predicts potential faults and maintenance requirements according to performance data and historical maintenance records of the boiler components, and plans maintenance activities in advance so as to avoid unexpected shutdown and improve maintenance efficiency;
s7: feedback and iteration: and feedback learning is carried out according to the regulated operation data, and the regulation strategy is continuously optimized.
2. The AI-based boiler automatic combustion adjustment method of claim 1, wherein the data acquisition in S1 specifically comprises:
a variety of sensors are installed at key positions of the boiler for measuring and recording the following parameters:
temperature sensor: measuring and recording the temperature of the inside and the outside of the boiler;
a pressure sensor: monitoring the pressure inside the boiler;
flow sensor: recording the flow rate of the fuel to measure the fuel consumption;
emission sensor: detecting and recording the gas components and concentrations of the boiler emissions, including CO2 and NOx;
the system comprises a central processing unit, a data acquisition unit, a communication interface, a data transmission unit and a data transmission unit, wherein the data acquisition unit is connected with each sensor and is responsible for collecting data provided by each sensor, the data collected by the data acquisition unit is transmitted to the central processing unit by the communication interface, the communication interface supports various communication protocols, and the collected data is subjected to preliminary processing before data transmission, including filtering, standardization and abnormal value detection, so that the availability and quality of the data are improved.
3. The AI-based boiler automated combustion conditioning method of claim 2, wherein the algorithm of the AI unit in S2 specifically includes a neural network model for processing and analyzing data collected from the boiler, the neural network model specifically including:
feature extraction: extracting key parameters of boiler operation, including temperature fluctuation, pressure change, fuel consumption rate and emission level;
and (3) state identification: training a model by using the processed data, identifying the current boiler operation state, and distinguishing normal operation, low-efficiency operation and potential fault states;
optimization suggestion generation: based on the state recognition results of the neural network model, optimization suggestions for boiler combustion efficiency and emission control are generated, wherein the optimization suggestions comprise adjusting combustion parameters and changing fuel supply strategies.
4. The AI-based boiler automatic combustion adjustment method of claim 3, wherein the neural network model is specifically as follows:
input layer: input data x: including temperature, pressure, fuel consumption, emissions data, input data vector xεR n Where n is the number of input features, specifically designed for boiler data;
convolution layer: for processing spatial data features, the convolution operation is defined as:
C i ＝ReLU(W c *h i-1 +b c ) Wherein W is c Is the weight of the convolution layer, b c Is an offset term, which represents a convolution operation;
and (3) a circulating layer: for processing time-series data, including historical temperature, pressure variations, cyclic operation definitionsThe method comprises the following steps: r is R i ＝tanh(W r ·[R i-1 ，h i ]+b r ) Wherein W is r Is the weight of the cyclic layer, b r Is a bias term, [ R ] i-1 ，h i ]Representing a combination of a previous state of the loop layer and a current input;
full tie layer: as described above, for generating final outputs from features extracted from the convolutional and cyclic layers: y=softmax (W f ·R m +b f ) Wherein W is f Is the weight of the full connection layer, b f Is a bias term, R m Is the output of the last loop layer;
above, W c ，W r ，W f : a weight matrix corresponding to the convolution layer, the circulation layer and the full connection layer for converting the input data into characteristic representation, b c ，b r ，b f : bias terms, corresponding to the convolution layer, the loop layer and the full connection layer, respectively, are used for increasing the flexibility of the model, and ReLU: an activation function for the convolutional layer to increase the nonlinearity, tanh: another activation function, used in a cyclic network, softmax: an activation function for the multi-classification problem for the output layer to convert the result into a probability distribution.
5. The AI-based boiler automatic combustion adjustment method of claim 4, wherein the adjustment decision in S3 specifically comprises:
decision generation unit: comprising an AI module for generating a combustion regulation decision based on the analysis of the boiler operating data, the AI module analyzing the data using a neural network model and outputting recommended regulation parameters,
decision logic: the decision of the AI module is based on the following criteria:
combustion efficiency: the AI module calculates the current combustion efficiency by analyzing temperature, pressure and fuel consumption data and recommends an adjustment strategy to achieve the optimal efficiency;
emission control: by analyzing the emission data, focusing on the concentration of harmful gases, the AI module recommends an adjustment strategy to reduce emissions, conforming to environmental standards;
parameter optimization advice: the AI module will generate specific combustion parameter adjustment recommendations including fuel supply, air flow, combustion temperature to optimize the combustion process;
automatic adjusting function: the system also comprises a function of automatically executing the adjustment strategy recommended by the AI module, so as to realize automatic adjustment, wherein the adjustment is continuous or based on a specific time interval.
6. The AI-based boiler automatic combustion adjustment method of claim 5, wherein the combustion efficiency is calculated by the following formula:where η is combustion efficiency, and:
actual heat output Q Actual practice is that of ＝m Fuel and its production process X LHV x combustion efficiency, where m Fuel and its production process LHV is the low heating value for the mass flow of fuel;
theoretical heat output Q Theory of ＝m Fuel and its production process X HHV, wherein HHV is the high-order heating value;
the emission control is calculated by the following formula: e= Σ (C i ×R i ) Wherein E is emission control, C i Representing the concentration of a particular emission species, R i The emission ratio representing the emissions depends on the fuel type and combustion conditions.
7. The AI-based boiler automated combustion conditioning method of claim 6, wherein the performing conditioning of S4 includes a controller for adjusting fuel supply and air flow, the controller receiving optimization recommendations from the AI module and automatically adjusting the respective control valves and regulators based on the recommendations;
fuel supply regulation: adjusting the fuel flow by controlling the opening of the fuel supply valve to achieve the optimal supply recommended by the AI module, the adjustment strategy including increasing or decreasing the fuel supply to increase combustion efficiency or reduce emissions;
air flow adjustment: the air flow is adjusted by adjusting the fan speed or the opening of the air inlet valve to meet the optimization proposal of the AI module, and the ratio of air to fuel is adjusted according to the optimal combustion conditions to ensure complete combustion and minimum emissions.
8. The AI-based boiler automatic combustion adjustment method of claim 7, wherein the environmental adaptation of S5 specifically comprises:
an environment monitoring unit: comprises a plurality of sensors for monitoring external environment parameters in real time, wherein the external environment parameters comprise air temperature, humidity, air pressure and wind speed,
data integration and analysis: the environmental data is transmitted to an AI unit together with the boiler operation data for analysis, and potential combustion efficiency and emission problems caused by environmental changes are identified;
the AI unit generates suggestions for adjusting the combustion strategy based on the environmental data and the boiler performance data to adapt to environmental changes, the adjustments including changing fuel supply amounts, adjusting a mixing ratio of air and fuel, modifying combustion temperatures to maintain optimal combustion efficiency and minimum emission levels, and automatically controlling the adjustments of corresponding control parameters based on the suggestions of the AI unit.
9. The AI-based boiler automatic combustion adjustment method of claim 8, wherein the predictive maintenance comprises:
data collection and integration: a database integrating real-time performance monitoring and historical maintenance records of the boiler components, collecting data including temperature, pressure, combustion efficiency, component wear degree, historical maintenance and replacement records, and an AI unit analyzing the performance data of the components by using a machine learning algorithm, identifying trends and modes in the data, and predicting potential faults and performance degradation;
predictive maintenance decision: based on the analysis results, the AT unit predicts maintenance requirements and optimal maintenance time windows for each critical component, generating a maintenance plan including recommended maintenance activities, schedules, and required resources.
10. The AI-based boiler automated combustion conditioning method of claim 9, wherein the machine learning algorithm is based on an LSTM model, the LSTM model being specifically:
convolution layer: a convolution layer is introduced before the conventional LSTM for extracting spatial features in the time series data, the convolution operation being expressed as: c (C) t ＝ReLU(W c *x t +b c ) Wherein W is c And b c The weight and bias parameters of the convolution layer respectively, which represent the convolution operation;
convolving the LSTM unit: combining convolution operation with LSTM unit, the method can process space and time characteristics simultaneously, and the expression of convolution LSTM is as follows:
an input door: i.e t ＝σ(W ii *C t +W hi *h t-1 +b ii +b hi )
Forgetting the door: f (f) t ＝σ(W if *C t +W hf *h t-1 +b if +b hf )
Cell state:
final cell state:
output door: o (o) t ＝σ(W io *C t +W ho *h t-1 +b io +b ho )
Final hidden state: h is a t ＝o t *tanh(C t )
Abnormality detection: anomaly detection is performed using the convolved LSTM processed time series data to predict maintenance requirements.
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