Fuel-air ratio self-optimizing control method based on real-time monitoring of fly ash residual carbon

Abstract

The application discloses a fuel air ratio self-optimization control method based on fly ash residual carbon real-time monitoring, comprising S1: real-time monitoring step; S2: data processing and efficiency evaluation step; S3: primary air and secondary air ratio adjustment step; S4: fuel supply amount coordinated adjustment step; S5: iterative optimization step; S6: safety protection step; S7: long-term learning optimization step. The application can realize real-time sensing of the effectiveness of the combustion state, and automatically adjust the ratio relationship of the primary air, the secondary air and the fuel amount, so that the combustion process is dynamically optimized in the direction of the minimum fly ash residual carbon, that is, the highest combustion efficiency. This not only significantly improves the thermal efficiency of the boiler and reduces the solid incomplete combustion heat loss, but also helps to reduce the pollutants such as nitrogen oxides caused by improper air distribution, realizes the double benefits of energy saving and environmental protection, and meanwhile, the method reduces the manual intervention of the operation personnel, improves the unit automation level and the operation stability.

Description

Technical Field

[0001] This invention relates to the field of combustion control technology, specifically to a fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon. Background Technology

[0002] In the operation of coal-fired boilers, the ratio of fuel to air (especially primary and secondary air) is a key factor affecting combustion efficiency and pollutant emissions.

[0003] Traditional control methods mainly rely on the experience of operators or fixed thermal parameter settings, which cannot respond in real time to dynamic operating conditions such as changes in coal quality and load fluctuations. This often leads to incomplete combustion, resulting in high residual carbon content in fly ash and reduced boiler efficiency.

[0004] While existing technologies exist for monitoring the carbon content of fly ash, most are limited to offline testing or simple online display, failing to form an effective closed-loop feedback with the combustion control system and thus unable to achieve real-time, automatic, and precise optimization of the air-coal ratio.

[0005] Therefore, this application proposes a fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon. Summary of the Invention

[0006] To address this issue, the present invention provides a fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon, in order to solve the problem that although there are existing methods for monitoring the carbon content of fly ash, most of them are limited to offline testing or simple online display, and fail to form an effective closed-loop feedback with the combustion control system, thus failing to achieve real-time, automatic, and accurate optimization of the air-coal ratio.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] The fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon includes the following steps:

[0009] S1: Real-time monitoring step, through an online monitoring device installed in the boiler tail flue, continuously measuring the residual carbon content in the fly ash emitted during the current operation of the boiler;

[0010] S2: Data processing and efficiency evaluation step, receiving the residual carbon content signal in fly ash obtained from the real-time monitoring step, performing filtering and standardization processing, and calculating the residual carbon content evaluation value characterizing the current combustion efficiency;

[0011] S3: Primary air and secondary air ratio adjustment step: compare the residual carbon content assessment value with the preset target value range, and generate and output adjustment instructions for the ratio of primary air volume and secondary air volume based on the comparison result and preset control logic.

[0012] S4: Fuel supply coordination adjustment step. Based on the air volume adjustment command output by the primary air and secondary air ratio adjustment step, and the main steam pressure or load demand signal of the boiler, the pre-set coordination control model calculates and outputs a correction command for the current fuel supply to maintain the balance between total air volume and fuel volume.

[0013] S5: Iterative optimization step: After executing the air volume adjustment command and fuel quantity correction command, after a predetermined delay time, new fly ash residual carbon content data is obtained again, and steps S2 to S4 are repeated. Through continuous iteration, the fly ash residual carbon content dynamically approaches and stabilizes within the target value range.

[0014] Preferably, in the S1 real-time monitoring step, the online monitoring device employs laser-induced breakdown spectroscopy or microwave measurement technology; the continuous measurement specifically involves collecting fly ash samples at a frequency of not less than once per minute and analyzing their residual carbon content.

[0015] Preferably, the S2 data processing and efficiency evaluation step specifically includes:

[0016] S2.1: Perform a moving average filter on the raw residual carbon content data of continuous measurements to eliminate instantaneous interference;

[0017] S2.2: Perform joint correction between the filtered data and the current load signal of the boiler to obtain the converted value of fly ash residual carbon content under standard load;

[0018] S2.3: Output the converted value as the residual carbon content assessment value to the subsequent control steps.

[0019] Preferably, in the S3 primary air to secondary air ratio adjustment step, the preset control logic is fuzzy control logic or control logic based on expert experience rules; when the residual carbon content assessment value is higher than the upper limit of the target value range, the control logic prioritizes increasing the ratio of secondary air volume to primary air volume; when the assessment value is lower than the lower limit of the target value range, the control logic prioritizes decreasing the ratio of secondary air volume to primary air volume.

[0020] Preferably, step S3 further includes a sub-step for limiting the rate of change of air volume, which sets a limit on the rate of change of the generated primary and secondary air adjustment commands.

[0021] Preferably, in the S4 fuel supply coordination adjustment step, the preset coordination control model is a feedforward-feedback composite control model; wherein, the feedforward part calculates the initial correction value of the fuel quantity according to the amplitude of the air volume adjustment command and the preset air-fuel ratio relationship, and the feedback part adjusts the initial correction value according to the main steam pressure deviation, and finally synthesizes the correction command of the fuel supply quantity.

[0022] Preferably, it also includes S6: a safety protection step, which monitors the boiler furnace negative pressure and flue gas oxygen content in real time; if the furnace negative pressure or flue gas oxygen content exceeds the safe operating range during the self-optimization process, the adjustment command output of steps S3 and S4 is immediately suspended, and the air volume and fuel volume are controlled at the state before the suspension until the operating parameters are restored to the safe range.

[0023] Preferably, in the S5 iterative optimization step, the predetermined delay time is determined by the total time required from the issuance of the control command to the time when fly ash moves to the monitoring point in the flue and is sampled and analyzed, and then to the time when the combustion system generates a stable response to the adjustment. This time is obtained through on-site test calibration.

[0024] Preferably, it also includes S7: a long-term learning optimization step, which periodically records the residual carbon content assessment value, the corresponding air-coal ratio instruction, and the boiler load, and uses historical data to train or update the control logic parameters in step S3 and the collaborative control model parameters in step S4.

[0025] The present invention has the following advantages: The present invention can sense the effectiveness of the combustion state in real time and automatically adjust the ratio of primary air, secondary air and fuel, so that the combustion process is always dynamically optimized in the direction of minimizing fly ash and residual carbon, that is, maximizing combustion efficiency. This not only significantly improves the thermal efficiency of the boiler and reduces the heat loss from incomplete combustion of solids, but also helps to reduce pollutants such as nitrogen oxides caused by improper air distribution, achieving the dual benefits of energy saving and environmental protection. At the same time, this method reduces the manual intervention of operators and improves the automation level and operational stability of the unit. Attached Figure Description

[0026] To more intuitively illustrate the prior art and this application, exemplary drawings are provided below. It should be understood that the specific shapes and structures shown in the drawings should not generally be regarded as limiting conditions for implementing this application; for example, based on the technical concept disclosed in this application and the exemplary drawings, those skilled in the art are able to easily make conventional adjustments or further optimizations to the addition / reduction / classification, specific shapes, positional relationships, connection methods, size ratios, etc. of certain units (components).

[0027] Figure 1 A flowchart of a fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon provided in an embodiment of this application. Detailed Implementation

[0028] The following specific embodiments illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that these embodiments are merely for further explanation of the present invention and should not be construed as limiting the scope of protection of the present invention. Technical engineers in the field can make some non-essential improvements and adjustments to the present invention based on the above-described content. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Please see Figure 1 A fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon includes the following steps:

[0030] S1: Real-time monitoring step, through an online monitoring device installed in the boiler tail flue, continuously measuring the residual carbon content in the fly ash emitted during the current operation of the boiler;

[0031] S2: Data processing and efficiency evaluation step, receiving the residual carbon content signal in fly ash obtained from the real-time monitoring step, performing filtering and standardization processing, and calculating the residual carbon content evaluation value characterizing the current combustion efficiency;

[0032] S3: Primary air and secondary air ratio adjustment step: compare the residual carbon content assessment value with the preset target value range, and generate and output adjustment instructions for the ratio of primary air volume and secondary air volume based on the comparison result and preset control logic.

[0033] S4: Fuel supply coordination adjustment step. Based on the air volume adjustment command output by the primary air and secondary air ratio adjustment step, and the main steam pressure or load demand signal of the boiler, the pre-set coordination control model calculates and outputs a correction command for the current fuel supply to maintain the balance between total air volume and fuel volume.

[0034] S5: Iterative optimization step: After executing the air volume adjustment command and fuel quantity correction command, after a predetermined delay time, new fly ash residual carbon content data is obtained again, and steps S2 to S4 are repeated. Through continuous iteration, the fly ash residual carbon content dynamically approaches and stabilizes within the target value range.

[0035] In its implementation, this invention transforms the "fly ash residual carbon content," a key indicator of combustion economy, from a traditional post-analysis parameter into a core feedback variable that can be acquired in real time and directly drive the control system's actions. This constructs a complete adaptive optimization closed loop of "monitoring-evaluation-adjustment-verification." Real-time monitoring in step S1 forms the foundation of this loop, enabling online sensing of combustion product quality and providing the possibility for subsequent dynamic control. Step S2 converts the original monitoring signal into a reliable and usable efficiency assessment value, a prerequisite for precise control. Step S3 proactively and intelligently adjusts the distribution strategy of primary and secondary air based on the real-time feedback of combustion efficiency (i.e., the residual carbon assessment value), a direct means of optimizing combustion organization and reducing mechanical incomplete combustion losses. Step S4 embodies the system's global coordination, ensuring that while optimizing the air ratio, it maintains a basic balance between total air volume and fuel quantity, preventing significant fluctuations in combustion conditions due to air volume adjustments and guaranteeing boiler operation safety. The iterative optimization step S5 endows this method with dynamic adaptability and continuous optimization capabilities. Through the coordinated efforts of the above steps, combustion control is transformed from a mode that relies on fixed parameters and human experience to a "closed-loop" self-optimization mode based on real-time feedback of combustion effects. This allows the system to automatically track and adapt to changes in coal type, load, and equipment status, thereby continuously controlling the residual carbon content of fly ash and the corresponding heat loss to the lowest possible level, thus improving boiler efficiency.

[0036] In the S1 real-time monitoring step, the online monitoring device uses laser-induced breakdown spectroscopy or microwave measurement technology; the continuous measurement specifically involves collecting fly ash samples at a frequency of not less than once per minute and analyzing their residual carbon content.

[0037] By employing laser-induced breakdown spectroscopy (LIBS) or microwave measurement technology, rapid, accurate, and non-contact analysis of carbon content in fly ash samples can be achieved, ensuring the reliability of the raw data and the advancement of the technology. Limiting the measurement frequency to no less than once per minute ensures the timeliness of the feedback signal, enabling the control system to perceive changes in the combustion state in near real-time, providing a data foundation for rapid response and fine adjustment. This significantly shortens the "sensing-response" delay of the control system, making adjustments to fluctuations in operating conditions more timely and effective, avoiding overshoot or undershoot caused by information lag, thereby improving the convergence speed and stability of the entire self-optimization process.

[0038] The S2 data processing and efficiency evaluation steps specifically include:

[0039] S2.1: The raw carbon content data measured continuously is subjected to moving average filtering to eliminate instantaneous interference. Moving average filtering can effectively smooth the "noise" data caused by instantaneous fluctuations in sampling or analysis, extract an effective signal that can truly reflect the combustion trend, and improve the reliability of the evaluation value.

[0040] S2.2: The filtered data is combined with the current load signal of the boiler to obtain the converted value of fly ash residual carbon content under standard load. The load-based calibration eliminates the natural influence of boiler load changes on fly ash residual carbon content (for example, residual carbon is usually higher at low load). The monitoring values ​​under different loads are corrected to the same standard scale for comparison and judgment, making the "residual carbon content assessment value" a purely reflective indicator of air distribution quality that is decoupled from the load.

[0041] S2.3: Output the converted value as the residual carbon content assessment value to the subsequent control steps.

[0042] When implemented, this scheme allows the preset target value range to be set as a relatively fixed high-efficiency range. The control system optimizes against this target under any load, which simplifies the control logic and greatly improves the applicability and accuracy of the optimization strategy across the entire operating range.

[0043] In the S3 primary and secondary air ratio adjustment step, the preset control logic is fuzzy control logic or control logic based on expert experience rules; when the residual carbon content assessment value is higher than the upper limit of the target value range, the control logic prioritizes increasing the ratio of secondary air volume to primary air volume; when the assessment value is lower than the lower limit of the target value range, the ratio of secondary air volume to primary air volume is prioritized decreasing.

[0044] The aforementioned intelligent control method can mimic the experience of skilled operators, transforming vague language judgments such as "high residual carbon" and "low residual carbon" into specific airflow adjustment actions. The clearly stated strategy of "prioritizing increasing the proportion of secondary air when residual carbon is high" directly addresses one of the key causes of residual carbon generation in fly ash: oxygen deficiency during the combustion stage. By strengthening the later mixing and oxygen supply, it promotes the combustion of residual carbon, thereby enabling the control system to stably execute optimal experience, avoiding the arbitrariness and inconsistency of manual adjustments, and ensuring the reliable reproduction of optimization effects.

[0045] The S3 step also includes a sub-step for limiting the rate of change of air volume, which sets a limit on the rate of change of the generated primary and secondary air adjustment commands to ensure that the air volume adjustment process is smooth and avoids impacting the combustion stability of the boiler.

[0046] Combustion systems have significant thermal inertia; rapid changes in airflow can drastically alter the aerodynamic and temperature fields within the furnace, potentially leading to combustion instability, flame flickering, or even flameout. By limiting the rate of change of adjustment commands, the airflow regulation process is forced to become smooth and gradual. This provides a stable "buffer period" for combustion conditions while the combustion system seeks its optimization goals, ensuring a smooth transition of the combustion state to a new, more optimal equilibrium point. This avoids operational safety issues caused by overly aggressive optimization operations, making this self-optimizing method highly practical and safe while pursuing economic efficiency.

[0047] In the S4 fuel supply coordination adjustment step, the preset coordination control model is a feedforward-feedback composite control model. The feedforward part calculates a preliminary correction value for the fuel quantity based on the magnitude of the airflow adjustment command and a preset air-fuel ratio. The feedback part adjusts this preliminary correction value based on the main steam pressure deviation, ultimately synthesizing the corrected fuel supply command. The feedforward part directly and quickly calculates the corresponding change in fuel quantity based on the airflow adjustment magnitude, aiming to maintain a basic match in the air-fuel ratio as soon as possible—a "predictive" compensation with a rapid response. The feedback part fine-tunes the fuel quantity using the main steam pressure, a core controlled parameter, to eliminate the inaccuracies of the feedforward model and the effects of other disturbances—a "corrective" compensation that ensures control accuracy.

[0048] The feedforward-feedback composite control model enables the fuel quantity and air volume to achieve coordinated changes in a "fast and slow combination, coarse and fine adjustment" manner. It not only responds quickly to the demand for air volume adjustment, but also firmly stabilizes the main steam pressure, the core parameter representing the boiler's energy balance. Thus, while optimizing combustion details, it ensures the stability of the entire unit's power output, achieving a unity of local optimization and global stability.

[0049] It also includes S6: safety protection steps, which monitor the boiler furnace negative pressure and flue gas oxygen content in real time; if the furnace negative pressure or flue gas oxygen content exceeds the safe operating range during the self-optimization process, the adjustment command output of steps S3 and S4 will be immediately suspended, and the air volume and fuel volume will be controlled at the state before the suspension until the operating parameters are restored to the safe range.

[0050] Step S6 establishes a real-time online security monitoring and intervention mechanism. When the self-optimization adjustment touches a security boundary, the system can immediately pause the optimization process and "freeze" the current state, thus setting a security red line for the automatic optimization process and ensuring the security of this method during implementation.

[0051] In the S5 iterative optimization step, the predetermined delay time is determined as follows: the total time required from the issuance of the control command to the time when fly ash moves to the monitoring point in the flue and is sampled and analyzed, and then to the time when the combustion system generates a stable response to the adjustment. This time is obtained through on-site test calibration.

[0052] There is a significant physical delay in the entire process, from the issuance of the adjustment command to the movement of fly ash to the monitoring point, and then to the combustion system generating a stable response to the new operating conditions. Ignoring this delay and performing the next adjustment before the system has fully responded can lead to misjudgments and oscillating optimization. This total delay time was obtained through field testing and calibrated to match the actual time delay of the physical process, thus improving the efficiency and reliability of the optimization algorithm.

[0053] It also includes S7: a long-term learning optimization step, which periodically records the residual carbon content assessment value, the corresponding air-coal ratio command, and the boiler load. Historical data is used to train or update the control logic parameters in step S3 and the collaborative control model parameters in step S4. The characteristics of the boiler, the range of coal types, and even the state of the burner will slowly change over time, and fixed control logic and model parameters may gradually deviate from the optimal range. The long-term learning optimization step, by periodically recording historical data of "operating condition-command-result" and using this data to train and update control parameters, essentially enables the system to learn from long-term operating experience, allowing the optimization effect to be maintained over the long term.

[0054] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon, characterized in that, Includes the following steps: S1: Real-time monitoring step, through an online monitoring device installed in the boiler tail flue, continuously measuring the residual carbon content in the fly ash emitted during the current operation of the boiler; S2: Data processing and efficiency evaluation step, receiving the residual carbon content signal in fly ash obtained from the real-time monitoring step, performing filtering and standardization processing, and calculating the residual carbon content evaluation value characterizing the current combustion efficiency; S3: Primary air and secondary air ratio adjustment step: compare the residual carbon content assessment value with the preset target value range, and generate and output adjustment instructions for the ratio of primary air volume and secondary air volume based on the comparison result and preset control logic. S4: Fuel supply coordination adjustment step. Based on the air volume adjustment command output by the primary air and secondary air ratio adjustment step, and the main steam pressure or load demand signal of the boiler, the pre-set coordination control model calculates and outputs a correction command for the current fuel supply to maintain the balance between total air volume and fuel volume. S5: Iterative optimization step: After executing the air volume adjustment command and fuel quantity correction command, after a predetermined delay time, new fly ash residual carbon content data is obtained again, and steps S2 to S4 are repeated. Through continuous iteration, the fly ash residual carbon content dynamically approaches and stabilizes within the target value range.

2. The fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon as described in claim 1, characterized in that, In the S1 real-time monitoring step, the online monitoring device uses laser-induced breakdown spectroscopy or microwave measurement technology; the continuous measurement specifically involves collecting fly ash samples at a frequency of not less than once per minute and analyzing their residual carbon content.

3. The fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon as described in claim 2, characterized in that, The S2 data processing and efficiency evaluation steps specifically include: S2.1: Perform a moving average filter on the raw residual carbon content data of continuous measurements to eliminate instantaneous interference; S2.2: Perform joint correction between the filtered data and the current load signal of the boiler to obtain the converted value of fly ash residual carbon content under standard load; S2.3: Output the converted value as the residual carbon content assessment value to the subsequent control steps.

4. The fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon as described in claim 1, characterized in that, In the S3 primary and secondary air ratio adjustment step, the preset control logic is fuzzy control logic or control logic based on expert experience rules; when the residual carbon content assessment value is higher than the upper limit of the target value range, the control logic prioritizes increasing the ratio of secondary air volume to primary air volume; when the assessment value is lower than the lower limit of the target value range, the ratio of secondary air volume to primary air volume is prioritized decreasing.

5. The fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon as described in claim 4, characterized in that, The S3 step also includes a sub-step for limiting the rate of change of air volume, which sets a limit on the rate of change of the generated primary and secondary air adjustment commands.

6. The fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon as described in claim 1, characterized in that, In the S4 fuel supply coordination adjustment step, the preset coordination control model is a feedforward-feedback composite control model; wherein, the feedforward part calculates the initial correction value of the fuel quantity according to the amplitude of the air volume adjustment command and the preset air-fuel ratio relationship, and the feedback part adjusts the initial correction value according to the main steam pressure deviation, and finally synthesizes the correction command of the fuel supply quantity.

7. The fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon as described in claim 6, characterized in that, It also includes S6: safety protection steps, which monitor the boiler furnace negative pressure and flue gas oxygen content in real time; if the furnace negative pressure or flue gas oxygen content exceeds the safe operating range during the self-optimization process, the adjustment command output of steps S3 and S4 will be immediately suspended, and the air volume and fuel volume will be controlled at the state before the suspension until the operating parameters are restored to the safe range.

8. The fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon as described in claim 1, characterized in that, In the S5 iterative optimization step, the predetermined delay time is determined as follows: the total time required from the issuance of the control command to the time when fly ash moves to the monitoring point in the flue and is sampled and analyzed, and then to the time when the combustion system generates a stable response to the adjustment. This time is obtained through on-site test calibration.

9. The fuel-air ratio self-optimization control method based on real-time monitoring of fly ash residual carbon as described in claim 1, characterized in that, It also includes S7: a long-term learning optimization step, which periodically records the residual carbon content assessment value, the corresponding air-coal ratio instruction, and the boiler load, and uses historical data to train or update the control logic parameters in step S3 and the collaborative control model parameters in step S4.

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