Monitoring and controlling method and system for use of energy-saving dual-fuel heat accumulating type burner

Abstract

The invention discloses an energy-saving dual-fuel heat accumulating type burner usage monitoring control method and system, and relates to the technical field of control and regulation, the energy-saving dual-fuel heat accumulating type burner usage monitoring control system comprises an MEMS multi-parameter sensing array, a tail gas sensor array and a distributed optical fiber temperature sensing module which are deployed in parallel; millisecond-level fuel flow and air flow adjustment is achieved through inner ring flame tensor feedback control, and second-level fuel ratio adjustment is achieved through inner ring tail gas component closed-loop self-adaptive adjustment; and the heat storage regeneration cycle, ash removal triggering and low-speed fuel and air scheduling are achieved through outer ring model predictive control scheduling. The invention provides a multi-layer closed-loop self-adaptive monitoring control method based on flame tensor, tail gas components and a temperature field, and model prediction control is introduced to carry out overall scheduling on a heat storage-combustion coupling process, so that dynamic self-adaption of a fuel ratio, online prediction of heat storage body scaling and multi-time scale coupling optimization are realized.

Description

Technical Field

[0001] The present invention relates to the field of control and regulation technology, and in particular to a method and system for monitoring and controlling the use of an energy-saving dual-fuel regenerative burner. Background Art

[0002] In high-temperature combustion equipment such as industrial kilns and boilers, dual-fuel regenerative burners are widely used to balance energy diversification and combustion stability. Such systems usually rely on regenerative bodies to recover waste heat from exhaust gases to improve thermal efficiency. However, existing technologies mostly use simple fixed-ratio switching or timed regeneration, lacking multi-dimensional real-time monitoring and refined control of the combustion process. At the same time, they are unable to predict the blockage risk caused by scaling of the regenerative body, resulting in large fluctuations in energy consumption, unstable emissions, and increased maintenance costs. Summary of the Invention

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

[0004] Therefore, the present invention addresses the problems of slow real-time response, low adjustment accuracy, and inability to warn of fouling and cleaning in existing dual-fuel regenerative burners in terms of fuel switching, waste heat recovery, and safety maintenance. A multi-layer closed-loop adaptive monitoring and control method based on flame tensor, exhaust gas composition, and temperature field is proposed, and model predictive control is introduced to coordinate the thermal storage-combustion coupling process, thereby achieving dynamic adaptation of the fuel ratio, online prediction of thermal storage body fouling, and multi-time-scale coupling optimization.

[0005] To solve the above technical problems, the present invention provides the following technical solution: a method for monitoring and controlling the use of an energy-saving dual-fuel regenerative burner, comprising:

[0006] Parallel deployment of MEMS multi-parameter sensor arrays, exhaust gas sensor arrays, and distributed fiber optic temperature sensing modules;

[0007] The inner ring flame tensor feedback control is used to achieve millisecond-level fuel flow and air flow regulation, and the inner ring exhaust gas composition closed-loop adaptive regulation is used to achieve second-level fuel ratio adjustment;

[0008] The outer loop model predictive control scheduling is used to realize the thermal storage regeneration cycle, ash cleaning triggering and slow fuel and air scheduling.

[0009] As a preferred embodiment of the energy-saving dual-fuel regenerative burner monitoring and control method of the present invention, the MEMS multi-parameter sensor array is composed of four equally spaced sensor units, including: performing self-tests and completing online calibration on each unit when the system is started, and synchronously triggering the acquisition of temperature, radiation intensity, and ion conductivity signals according to a preset rhythm during combustion;

[0010] After filtering and normalizing the original signal, a flame tensor is constructed and the eigenvector is extracted. The eigenvector is input into the flame tensor feedback algorithm to calculate the adjustment of fuel and air flow and send it to the actuator;

[0011] Baseline drift compensation and sensor health detection are performed periodically during operation, and flame tensor data is automatically archived at the end of the combustion cycle for control strategy optimization.

[0012] As a preferred embodiment of the energy-saving dual-fuel regenerative burner monitoring and control method of the present invention, the flame tensor feedback control includes forming a three-dimensional tensor from the flame temperature signal, radiation intensity signal, and ion conductivity signal collected by each sensor module, and reducing the dimension to a combustion state feature vector using a principal component analysis algorithm;

[0013] The combustion state characteristic vector calculates the incremental control amount of the fuel flow and the air flow through a preset mapping function, and outputs control instructions to the oil circuit proportional valve and the air circuit proportional valve in a 10 millisecond period.

[0014] As a preferred embodiment of the energy-saving dual-fuel regenerative burner use monitoring and control method of the present invention, the exhaust gas sensor array includes a non-dispersive infrared sensor installed on the regenerative body outlet pipe for measuring oxygen concentration, and a MEMS gas sensor installed on the regenerative body return air pipe for measuring carbon monoxide concentration and nitrogen oxide concentration;

[0015] The sensor sends the signal to the data acquisition module with a sampling period of 1 second.

[0016] As a preferred embodiment of the energy-saving dual-fuel regenerative burner monitoring and control method of the present invention, the tail gas composition closed-loop adaptive adjustment includes defining the tail gas three-component concentration error vector:

[0017] For each component exist Fuzzy sets are classified by Gaussian function:

[0018] Put all membership degrees into column vectors in component and subset order:

[0019] Set three sets of weight matrices respectively ,Will Map out the proportional, integral, and differential increments of the fuel and air channels:

[0020]

[0021] Each correspond In the initial gain vector Based on this, each feed gain is updated, where Reference or or :

[0022] Perform multi-input and multi-output PID operation on the error vector with time-varying gain:

[0023]

[0024] in, is the error vector of the three components concentration of exhaust gas, is the oxygen concentration error at time t, is the carbon monoxide concentration error at time t, is the nitrogen oxide concentration error at time t, is the gain correction value of the corresponding PID link in the fuel channel, is the gain correction value of the corresponding PID link in the air channel, t is the time variable, For the The real-time concentration error of various exhaust gas components, ; For the Online measurement of the concentration of various exhaust gas components, For the The target concentration of the exhaust gas components is set. ; For the The error in The membership degree of fuzzy subsets; For the Ingredient No. The center and standard deviation of the Gaussian membership function; is the combined column vector of all membership degrees, is the number of subsets; is the proportional, integral, and differential gain mapping weight matrix; 、 、 are the proportional, integral and differential gain increment vectors obtained after mapping; 、 、 are the initial proportional, integral, and differential gain vectors respectively; 、 、 are the time-varying proportional, integral, and differential gain vectors, respectively, including the fuel and air parameters; is the control quantity output vector of the oil circuit proportional valve and the air circuit proportional valve, corresponding to the fuel and air flow rates respectively. It is an element-by-element multiplication operation, used for component-wise product of vectors or matrices and vectors.

[0025] As a preferred embodiment of the energy-saving dual-fuel regenerative burner monitoring and control method described in the present invention, the distributed optical fiber temperature sensing module includes time-distance mapping calibration of quartz optical fibers pre-buried along four channels of the regenerative body. Within each measurement cycle (0.2 seconds), the controller sequentially triggers laser pulses and obtains the time delay and intensity of the echo signal through the optical time domain reflectometry measurement terminal. The signal processing unit maps the echo pulse propagation time to spatial position and divides the echo signal into 0.5-meter segments. The reflection intensity method is used to analyze the intensity ratio of the Stokes and anti-Stokes components in each Raman scattering signal segment, and the temperature of each segment is calculated based on a pre-established temperature-intensity calibration curve.

[0026] The four-way segmented temperature profiles are interpolated, spliced ​​and filtered with moving average to generate the latest temperature field data and push it to the host computer control module. During operation, the attenuation mutation detection and recalibration sub-processes are run regularly to ensure the temperature measurement accuracy.

[0027] As a preferred embodiment of the energy-saving dual-fuel regenerative burner monitoring and control method of the present invention, the outer loop model predictive control scheduling includes, to describe the temperature of the two sections of the regenerative body, the air-fuel ratio and the regeneration effect, defining:

[0028]

[0029] in, is an instantaneous regeneration trigger signal, then:

[0030] Get the current working point Compute the Jacobian:

[0031]

[0032] Get the discrete increment model:

[0033]

[0034] in, ; The predicted step Increment state and control stack as vector

[0035]

[0036] use

[0037]

[0038] Establish

[0039]

[0040] Constructing weighted quadratic forms

[0041] Inequality constraints separate controlled variables and states:

[0042]

[0043] Call the quadratic programming solver to minimize and satisfy , and get the optimal increment ; Only take the first increment:

[0044]

[0045] renew

[0046]

[0047] And repeat the operation periodically;

[0048] in, For the moment The state vector of For the moment The temperature of the first heat storage body, For the moment The second stage heat storage temperature, For the moment Air richness factor, For the moment The control vector of For the moment Fuel flow, For the moment Air flow, For the moment Instantaneous regeneration trigger signal, binary (0 or 1), is the discrete sampling period (control period length), is the heat capacity of the first and second heat storage bodies, is the specific heat capacity of air, is the combustion product temperature, is the thermal coupling time constant of the two thermal storage bodies, is the ambient temperature, is the air rich coefficient response time constant, is the operating point state and control vector used in linearization, is the state deviation, To control the deviation, is the state Jacobian matrix, To control the Jacobian matrix, To predict the number of time domain steps, is the predicted state transition matrix, is the predictive control transfer matrix, is the predicted incremental state stack vector, is the predicted incremental control stack vector, Q is the state weight matrix, is the control weight matrix, Hessian matrix, is the coefficient vector of the QP linear term, is the objective function, is the control constraint matrix, is the upper limit vector of the control constraint, is the state constraint matrix, is the state constraint vector, are the upper and lower limit vectors of the control quantity, is the upper and lower limit vector of the state quantity, is the optimal incremental control sequence after solution, To take the first segment of the sequence as the current increment, is the updated control vector, is the identity matrix and k is the time index.

[0049] As a preferred solution for the energy-saving dual-fuel regenerative burner usage monitoring and control system described in the present invention, it includes: an industrial computer, a data acquisition module, a signal isolation device, a high-speed proportional valve driver, an air path proportional valve actuator, a laser pulse source and an optical time domain reflection measurement terminal, a MEMS sensor module and an exhaust gas sensor module. The industrial computer has a built-in quadratic programming solver and communicates with each module through a field bus. The high-speed proportional valve driver receives the control instructions output by the industrial computer and drives the oil path proportional valve and the air path proportional valve respectively to implement adjustments.

[0050] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, steps of a method for monitoring and controlling the use of an energy-saving dual-fuel regenerative burner are implemented.

[0051] A computer-readable storage medium stores a computer program thereon, wherein when the computer program is executed by a processor, the computer program implements the steps of a method for monitoring and controlling the use of an energy-saving dual-fuel regenerative burner.

[0052] The beneficial effects of the present invention are as follows: by integrating flame multi-parameter feedback, exhaust gas online detection and distributed temperature sensing, the present invention can carry out inner-loop rapid response, exhaust gas closed-loop regulation and outer-loop predictive scheduling at the millisecond, second and minute levels respectively, to achieve refined control of fuel and air flow and prevent heat storage body blockage. At the same time, model predictive control is used to optimize the regeneration cycle and cleaning strategy, thereby improving the overall system thermal efficiency, reducing fuel consumption and pollution emissions, and reducing the frequency of manual maintenance, ensuring long-term stable and reliable operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A schematic flow chart of a method for monitoring and controlling the use of an energy-saving dual-fuel regenerative burner provided in accordance with one embodiment of the present invention. DETAILED DESCRIPTION

[0055] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0056] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0057] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0058] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0059] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0060] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0061] Example 1, reference Figure 1 , which is the first embodiment of the present invention, provides an energy-saving dual-fuel regenerative burner use monitoring and control method, comprising:

[0062] S1: Parallel deployment of MEMS multi-parameter sensing array, exhaust gas sensor array and distributed fiber optic temperature sensing module.

[0063] The MEMS multi-parameter sensor array consists of four equally spaced sensor units. When the system starts, each unit performs a self-test and completes online calibration. During combustion, the temperature, radiation intensity, and ion conductivity signals are collected synchronously according to a preset rhythm.

[0064] After filtering and normalizing the original signal, a flame tensor is constructed and the eigenvector is extracted. The eigenvector is input into the flame tensor feedback algorithm to calculate the adjustment of fuel and air flow and send it to the actuator;

[0065] Baseline drift compensation and sensor health detection are performed periodically during operation, and flame tensor data is automatically archived at the end of the combustion cycle for control strategy optimization.

[0066] The exhaust gas sensor array includes a non-dispersive infrared sensor installed on the thermal storage outlet pipe to measure the oxygen concentration, and a MEMS gas sensor installed on the thermal storage return air pipe to measure the carbon monoxide concentration and nitrogen oxide concentration;

[0067] The sensor sends the signal to the data acquisition module with a sampling period of 1 second.

[0068] The distributed fiber optic temperature sensing module includes time-distance mapping calibration of quartz optical fibers embedded in four channels along the thermal storage body. During each measurement cycle (0.2 seconds), a controller sequentially triggers laser pulses and acquires the time delay and intensity of the echo signal through the optical time domain reflectometry terminal. The signal processing unit maps the echo pulse propagation time to spatial position and divides the echo into 0.5-meter segments. The reflection intensity method is used to analyze the intensity ratio of the Stokes and anti-Stokes components in each Raman scattering signal segment, and the temperature of each segment is calculated using a pre-established temperature-intensity calibration curve.

[0069] The four-way segmented temperature profiles are interpolated, spliced ​​and filtered with moving average to generate the latest temperature field data and push it to the host computer control module. During operation, the attenuation mutation detection and recalibration sub-processes are run regularly to ensure the temperature measurement accuracy.

[0070] S2: Millisecond-level fuel flow and air flow regulation is achieved through inner-ring flame tensor feedback control, and second-level fuel ratio adjustment is achieved through inner-ring exhaust gas composition closed-loop adaptive control.

[0071] The flame tensor feedback control includes forming a three-dimensional tensor from the flame temperature signal, radiation intensity signal and ion conductivity signal collected by each sensor module, and reducing the dimension to a combustion state feature vector through a principal component analysis algorithm;

[0072] The combustion state characteristic vector calculates the incremental control amount of the fuel flow and the air flow through a preset mapping function, and outputs control instructions to the oil circuit proportional valve and the air circuit proportional valve in a 10 millisecond period.

[0073] The closed-loop adaptive adjustment of exhaust gas components includes defining the concentration error vectors of the three exhaust gas components:

[0074] For each component exist Fuzzy sets are classified by Gaussian function:

[0075] Put all membership degrees into column vectors in component and subset order:

[0076] Set three sets of weight matrices respectively ,Will Map out the proportional, integral, and differential increments of the fuel and air channels:

[0077]

[0078] Each correspond In the initial gain vector Based on this, each feed gain is updated, where Reference or or :

[0079] Perform multi-input and multi-output PID operation on the error vector with time-varying gain:

[0080]

[0081] in, is the error vector of the three components concentration of exhaust gas, is the oxygen concentration error at time t, is the carbon monoxide concentration error at time t, is the nitrogen oxide concentration error at time t, is the gain correction value of the corresponding PID link in the fuel channel, is the gain correction value of the corresponding PID link in the air channel, t is the time variable, For the The real-time concentration error of various exhaust gas components, ; For the Online measurement of the concentration of various exhaust gas components, For the The target concentration of the exhaust gas components is set. ; For the The error in The membership degree of fuzzy subsets; For the Ingredient No. The center and standard deviation of the Gaussian membership function; is the combined column vector of all membership degrees, is the number of subsets; is the proportional, integral, and differential gain mapping weight matrix; 、 、 are the proportional, integral and differential gain increment vectors obtained after mapping respectively; 、 、 are the initial proportional, integral, and differential gain vectors respectively; 、 、 are the time-varying proportional, integral, and differential gain vectors, respectively, including the fuel and air parameters; is the control quantity output vector of the oil circuit proportional valve and the air circuit proportional valve, corresponding to the fuel and air flow rates respectively. It is an element-by-element multiplication operation, used for component-wise product of vectors or matrices and vectors.

[0082] S3: Use outer loop model predictive control scheduling to realize heat storage regeneration cycle and ash cleaning triggering and slow fuel and air scheduling.

[0083] The outer loop model predictive control scheduling includes the following definitions to describe the temperature of the two sections of the thermal storage body, the air-fuel ratio, and the regeneration effect:

[0084]

[0085] in, is an instantaneous regeneration trigger signal, then:

[0086] Get the current working point Compute the Jacobian:

[0087]

[0088] Get the discrete increment model:

[0089]

[0090] in, ; The predicted step Increment state and control stack as vector

[0091]

[0092] use

[0093]

[0094] Establish

[0095]

[0096] Constructing weighted quadratic forms

[0097] Inequality constraints separate controlled variables and states:

[0098]

[0099] Call the quadratic programming solver to minimize and satisfy , and get the optimal increment ; Only take the first increment:

[0100]

[0101] renew

[0102]

[0103] And repeat the operation periodically;

[0104] in, For the moment The state vector of For the moment The temperature of the first heat storage body, For the moment The second stage heat storage temperature, For the moment Air richness factor, For the moment The control vector of For the moment Fuel flow, For the moment Air flow, For the moment Instantaneous regeneration trigger signal, binary (0 or 1), is the discrete sampling period (control period length), is the heat capacity of the first and second heat storage bodies, is the specific heat capacity of air, is the combustion product temperature, is the thermal coupling time constant of the two thermal storage bodies, is the ambient temperature, is the air rich coefficient response time constant, is the operating point state and control vector used in linearization, is the state deviation, To control the deviation, is the state Jacobian matrix, To control the Jacobian matrix, To predict the number of time domain steps, is the predicted state transition matrix, is the predictive control transfer matrix, is the predicted incremental state stack vector, is the predicted incremental control stack vector, Q is the state weight matrix, is the control weight matrix, Hessian matrix, is the coefficient vector of the QP linear term, is the objective function, is the control constraint matrix, is the upper limit vector of the control constraint, is the state constraint matrix, is the state constraint vector, are the upper and lower limit vectors of the control quantity, is the upper and lower limit vector of the state quantity, is the optimal incremental control sequence after solution, To take the first segment of the sequence as the current increment, is the updated control vector, is the identity matrix and k is the time index.

[0105] It should be noted that the cleaning trigger refers to the signal automatically generated in the outer loop model predictive control to start the reverse blowing or spray cleaning action of the heat storage body when it is predicted that the thickness of the ash layer inside the heat storage body or the blockage risk reaches the set threshold based on the distributed temperature profile and scaling prediction model.

[0106] The term "slow fuel" does not refer to a new type of fuel, but rather a term proposed to distinguish the control rhythm. Specifically, it refers to the low-frequency, long-cycle optimization scheduling of the total fuel volume (oil and gas dual fuel) in the outer-loop model predictive control (MPC), which complements the high-frequency flame tensor feedback in the inner loop and the exhaust closed-loop adaptive adjustment in the middle loop. In other words, "slow" emphasizes the longer scheduling cycle used to plan the fuel supply curve for the next stage, rather than a certain fuel composition itself.

[0107] Example 2 is the second embodiment of the present invention, which provides an energy-saving dual-fuel regenerative burner use monitoring and control system. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0108] The system includes: an industrial computer, a data acquisition module, a signal isolation device, a high-speed proportional valve driver, an air path proportional valve actuator, a laser pulse source and an optical time domain reflectometry measurement terminal, a MEMS sensor module and an exhaust gas sensor module. The industrial computer has a built-in quadratic programming solver and communicates with each module via a field bus. The high-speed proportional valve driver receives control instructions output by the industrial computer and drives the oil path proportional valve and the air path proportional valve respectively to implement adjustments.

[0109] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0110] Example 3

[0111] The third embodiment of the present invention is different from the first two embodiments in that:

[0112] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media capable of storing program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0113] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0114] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0115] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0116] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0117] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for monitoring and controlling the use of an energy-saving dual-fuel regenerative burner, characterized in that: include, Parallel deployment of MEMS multi-parameter sensor arrays, exhaust gas sensor arrays, and distributed fiber optic temperature sensing modules; The inner ring flame tensor feedback control is used to achieve millisecond-level fuel flow and air flow regulation, and the inner ring exhaust gas composition closed-loop adaptive regulation is used to achieve second-level fuel ratio adjustment; The outer loop model predictive control scheduling is used to realize the thermal storage regeneration cycle, ash cleaning triggering and slow fuel and air scheduling.

2. The energy-saving dual-fuel regenerative burner usage monitoring and control method according to claim 1, characterized in that: The MEMS multi-parameter sensor array consists of four equally spaced sensor units. When the system starts, each unit performs a self-test and completes online calibration. During combustion, the temperature, radiation intensity, and ion conductivity signals are collected synchronously according to a preset rhythm. After filtering and normalizing the original signal, a flame tensor is constructed and the eigenvector is extracted. The eigenvector is input into the flame tensor feedback algorithm to calculate the adjustment of fuel and air flow and send it to the actuator; Baseline drift compensation and sensor health detection are performed periodically during operation, and flame tensor data is automatically archived at the end of the combustion cycle for control strategy optimization.

3. The energy-saving dual-fuel regenerative burner usage monitoring and control method according to claim 2, characterized in that: The flame tensor feedback control includes forming a three-dimensional tensor from the flame temperature signal, radiation intensity signal and ion conductivity signal collected by each sensor module, and reducing the dimension to a combustion state feature vector through a principal component analysis algorithm; The combustion state characteristic vector calculates the incremental control amount of the fuel flow and the air flow through a preset mapping function, and outputs control instructions to the oil circuit proportional valve and the air circuit proportional valve in a 10 millisecond period.

4. The energy-saving dual-fuel regenerative burner usage monitoring and control method according to claim 3, characterized in that: The exhaust gas sensor array includes a non-dispersive infrared sensor installed on the thermal storage outlet pipe to measure the oxygen concentration, and a MEMS gas sensor installed on the thermal storage return air pipe to measure the carbon monoxide concentration and nitrogen oxide concentration; The sensor sends the signal to the data acquisition module with a sampling period of 1 second.

5. The energy-saving dual-fuel regenerative burner usage monitoring and control method according to claim 4, characterized in that: The closed-loop adaptive adjustment of exhaust gas components includes defining the concentration error vectors of the three exhaust gas components: For each component exist Fuzzy sets are classified by Gaussian function: Put all membership degrees into column vectors in component and subset order: Set three sets of weight matrices respectively ,Will Map out the proportional, integral, and differential increments of the fuel and air channels: Each correspond In the initial gain vector Based on this, each feed gain is updated, where Reference or or : Perform multi-input and multi-output PID operation on the error vector with time-varying gain: in, is the error vector of the three components concentration of exhaust gas, is the oxygen concentration error at time t, is the carbon monoxide concentration error at time t, is the nitrogen oxide concentration error at time t, is the gain correction value of the corresponding PID link in the fuel channel, is the gain correction value of the corresponding PID link in the air channel, t is the time variable, For the The real-time concentration error of various exhaust gas components, ; For the Online measurement of the concentration of various exhaust gas components, For the The target concentration of the exhaust gas components is set. ; For the The error in The membership degree of fuzzy subsets; For the Ingredient No. The center and standard deviation of the Gaussian membership function; is the combined column vector of all membership degrees, is the number of subsets; is the proportional, integral, and differential gain mapping weight matrix; 、 、 are the proportional, integral and differential gain increment vectors obtained after mapping; 、 、 are the initial proportional, integral, and differential gain vectors respectively; 、 、 are the time-varying proportional, integral, and differential gain vectors, respectively, including the fuel and air parameters; is the control quantity output vector of the oil circuit proportional valve and the air circuit proportional valve, corresponding to the fuel and air flow rates respectively. It is an element-by-element multiplication operation, used for component-wise product of vectors or matrices and vectors.

6. The energy-saving dual-fuel regenerative burner usage monitoring and control method according to claim 5, characterized in that: The distributed fiber optic temperature sensing module includes time-distance mapping calibration of quartz optical fibers pre-buried along four channels of the thermal storage body. During each measurement cycle, a controller sequentially triggers laser pulses and obtains the time delay and intensity of the echo signal through the optical time domain reflectometry measurement terminal. The signal processing unit maps the echo pulse propagation time to spatial position and divides the echo pulse into 0.5-meter segments. The reflection intensity method is used to analyze the intensity ratio of the Stokes and anti-Stokes components in each Raman scattering signal segment, and the temperature of each segment is calculated based on a pre-established temperature-intensity calibration curve. The four-way segmented temperature profiles are interpolated, spliced ​​and filtered with moving average to generate the latest temperature field data and push it to the host computer control module. During operation, the attenuation mutation detection and recalibration sub-processes are run regularly to ensure the temperature measurement accuracy.

7. The energy-saving dual-fuel regenerative burner usage monitoring and control method according to claim 6, characterized in that: The outer loop model predictive control scheduling includes the following definitions to describe the temperature of the two sections of the thermal storage body, the air-fuel ratio, and the regeneration effect: in, is an instantaneous regeneration trigger signal, then: Get the current working point Compute the Jacobian: Get the discrete increment model: in, ; The predicted step Increment state and control stack as vector use Establish Constructing weighted quadratic forms Inequality constraints separate controlled variables and states: Call the quadratic programming solver to minimize and satisfy , and get the optimal increment ; Only take the first increment: renew And repeat the operation periodically; in, For the moment The state vector of For the moment The temperature of the first heat storage body, For the moment The second stage heat storage temperature, For the moment Air richness factor, For the moment The control vector of For the moment Fuel flow, For the moment Air flow, For the moment Instantaneous regeneration trigger signal, binary, is the discrete sampling period, is the heat capacity of the first and second heat storage bodies, is the specific heat capacity of air, is the combustion product temperature, is the thermal coupling time constant of the two thermal storage bodies, is the ambient temperature, is the air rich coefficient response time constant, is the operating point state and control vector used in linearization, is the state deviation, To control the deviation, is the state Jacobian matrix, To control the Jacobian matrix, To predict the number of time domain steps, is the predicted state transition matrix, is the predictive control transfer matrix, is the predicted incremental state stack vector, is the predicted incremental control stack vector, Q is the state weight matrix, is the control weight matrix, Hessian matrix, is the coefficient vector of the QP linear term, is the objective function, is the control constraint matrix, is the upper limit vector of the control constraint, is the state constraint matrix, is the state constraint vector, are the upper and lower limit vectors of the control quantity, is the upper and lower limit vector of the state quantity, is the optimal incremental control sequence after solution, To take the first segment of the sequence as the current increment, is the updated control vector, is the identity matrix and k is the time index.

8. A system using the energy-saving dual-fuel regenerative burner usage monitoring and control method according to any one of claims 1 to 7, characterized in that: It includes an industrial computer, a data acquisition module, a signal isolation device, a high-speed proportional valve driver, an air path proportional valve actuator, a laser pulse source and an optical time domain reflection measurement terminal, a MEMS sensor module and an exhaust gas sensor module. The industrial computer has a built-in quadratic programming solver and communicates with each module through a field bus. The high-speed proportional valve driver receives the control instructions output by the industrial computer and drives the oil path proportional valve and the air path proportional valve respectively to implement adjustments.

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

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

Images