Boiler operation parameter optimization method and system for multi-fuel combustion of various solid wastes

Abstract

The invention relates to a boiler operation parameter optimization method and system for multi-fuel combustion of multiple solid wastes, and the method comprises the following steps: (1) multi-source data collection: obtaining the heat value, the water content, the chlorine-sulfur ratio and the heavy metal content of the solid wastes in real time through a spectrum analyzer, a visual recognition system and an online sensor; (2) intelligent compatibility optimization: calculating a solid waste mixing ratio by adopting a genetic algorithm based on calorific value balance and pollutant inhibition targets, and controlling a Cl / S molar ratio to be 1.2-1.8 and the alkali metal content to be 1t; 50%; (3) pretreatment and drying: the water content gt; and carrying out microwave-steam combined drying on 25% of solid waste, and dynamically adjusting microwave power, steam injection time and the like. The method has the advantages that through the technical integration of multi-source data fusion, intelligent algorithm optimization and digital twinborn verification, a solid waste co-combustion full-process optimization system is constructed, and ultralow emission of pollutants and safe and economical operation are achieved while the energy utilization rate is increased.

Description

Technical Field

[0001] This invention relates to a method and system for optimizing boiler operating parameters for co-firing various solid wastes, belonging to the field of boiler operation. Background Technology

[0002] With the acceleration of urbanization and the rapid development of industrial production, the amount of various solid wastes generated is increasing year by year, including municipal solid waste, industrial sludge, and biomass waste. Incineration, due to its advantages in volume reduction, harmlessness, and energy recovery, has become one of the mainstream technologies for solid waste treatment. However, in the process of co-combusting various solid wastes, the complex composition of materials, large fluctuations in calorific value, and significant differences in pollutant formation mechanisms lead to the following technical bottlenecks in boiler operation:

[0003] 1. Difficulty in optimizing combustion parameters

[0004] Existing technologies mostly rely on empirical parameters for single solid waste incineration, making it difficult to adapt to the dynamic characteristics of mixed solid waste combustion. Differences in chlorine, sulfur, and alkali metal content among different solid wastes can easily lead to problems such as dioxin formation, corrosion of heated surfaces, and coking of ash residue. Traditional PID control cannot achieve multi-objective synergistic optimization.

[0005] 2. Insufficient control of slagging and pollutants

[0006] During mixed combustion, the interaction of ash components is significant. Existing slagging prediction models do not consider the dynamic relationship between temperature field gradient and ash viscosity, leading to a lag in control. Meanwhile, the formation of pollutants such as NOx and HCl is influenced by multiple factors coupled together, including combustion temperature and residence time. Conventional ammonia injection control suffers from response delays and ammonia slip problems.

[0007] 3. Limitations of state awareness methods

[0008] Traditional detection methods rely on thermocouples and offline ash sample analysis, making it difficult to obtain real-time data on the three-dimensional temperature field distribution in the furnace and the evolution of ash and slag characteristics. The application of digital twin technology in combustion optimization is mostly limited to macroscopic simulation, lacking the ability to perform refined modeling with multi-physics coupling. Summary of the Invention

[0009] To overcome the shortcomings of existing technologies, this invention provides a method for optimizing boiler operating parameters for co-firing various solid wastes. The technical solution of this invention is as follows:

[0010] A method for optimizing boiler operating parameters for co-firing multiple solid wastes includes the following steps:

[0011] (1) Multi-source data acquisition: The calorific value, moisture content, chlorine-sulfur ratio and heavy metal content of solid waste are acquired in real time through a spectrometer, a visual recognition system and online sensors;

[0012] (2) Intelligent compatibility optimization: Based on the calorific value balance and pollutant suppression targets, the solid waste mixing ratio is calculated using a genetic algorithm to control the Cl / S molar ratio to 1.2-1.8 and the alkali metal content to <50%;

[0013] (3) Pretreatment drying: Microwave-steam combined drying is carried out on solid waste with a moisture content >25%, and the microwave power and steam injection time are dynamically adjusted;

[0014] (4) Multi-objective parameter optimization: The NSGA-III algorithm is used to optimize the combustion temperature, secondary air volume and feed rate. The objective function includes boiler thermal efficiency, NOx emission and temperature field uniformity.

[0015] (5) Dynamic combustion control: The primary air pressure is adjusted based on a fuzzy PID controller. The input variables include furnace temperature deviation and CO concentration change rate.

[0016] (6) Three-dimensional temperature field modeling: Using distributed optical fiber temperature measurement data, the three-dimensional temperature distribution of the furnace is reconstructed by the Kriging interpolation algorithm;

[0017] (7) Slagging Risk Warning: The slagging risk index is calculated based on the difference between the ash softening temperature and the real-time furnace temperature. The formula is as follows:

[0018] Control measures are triggered when Rslag ≥ 0.8; where T is the real-time furnace temperature in °C, obtained through a distributed fiber optic temperature measurement system.

[0019] Tsoft: Ash softening temperature, unit: °C, calculated based on ash composition;

[0020] dT / dz: Axial temperature gradient in the furnace, unit: ℃ / m, calculated from the temperature difference between adjacent temperature measurement points;

[0021] (8) Closed-loop control of pollutants: The ammonia injection rate is dynamically adjusted using a support vector regression model;

[0022] (9) Digital twin verification: Simulate the combustion process in virtual space to verify the rationality of parameters; (10) Intelligent maintenance decision: Predict the risk of furnace wall cracks based on acoustic emission signal characteristics and generate maintenance instructions.

[0023] The objective function of the genetic algorithm in step (2) is:

[0024]

[0025] Cl / S: Chlorine-sulfur molar ratio, detected by X-ray fluorescence spectrometry;

[0026] Na2O, K2O, and Al2O3 are the mass percentages of oxides in the ash composition, obtained through chemical analysis of the ash sample.

[0027] ΔQ: Standard deviation of calorific value of mixed solid waste, unit: MJ / kg;

[0028] μQ: Average calorific value of mixed solid waste, unit: MJ / kg;

[0029] The constraints include: calorific value fluctuation ≤15% and heavy metal content below environmental protection limits.

[0030] The optimization objectives of the NSGA-III algorithm in step (4) include:

[0031] minNO x =1.2×10 6 ·T 1.5 ·e -15000 / T T is the combustion temperature, in K. The constraints are: the combustion temperature is more than 50°C lower than the ash softening temperature, and the O2 concentration is 5%-8%.

[0032] The ash softening temperature in step (7) is calculated using the following formula:

[0033] T soft =1100+8SiO2-5(Al2O3+Fe2O3)+3CaO, where SiO2, Al2O3, Fe2O3, and CaO are the mass percentages of each component in the ash slag.

[0034] The ammonia injection amount correction model in step (8) is as follows:

[0035]

[0036] Wherein, NOx is the NOx concentration in flue gas, in ppm; T is the combustion temperature, in °C.

[0037] An optimization system for implementing a method to optimize boiler operating parameters for co-firing multiple solid wastes includes:

[0038] The multi-source data acquisition module is used to acquire the calorific value, moisture content, chlorine-sulfur ratio and heavy metal content of solid waste in real time through a spectrometer, a visual recognition system and online sensors.

[0039] The intelligent compatibility optimization module is used to calculate the solid waste mixing ratio based on the calorific value balance and pollutant suppression targets, using a genetic algorithm to control the Cl / S molar ratio to 1.2-1.8 and the alkali metal content to <50%.

[0040] The pretreatment drying module is used to perform microwave-steam combined drying on solid waste with a moisture content >25%, and dynamically adjusts the microwave power and steam injection time.

[0041] The multi-objective parameter optimization module is used to optimize combustion temperature, secondary air volume and feed rate using the NSGA-III algorithm. The objective function includes boiler thermal efficiency, NOx emissions and temperature field uniformity.

[0042] The dynamic combustion control module is used to adjust the primary air pressure based on a fuzzy PID controller. The input variables include furnace temperature deviation and CO concentration change rate.

[0043] The three-dimensional temperature field modeling module is used to reconstruct the three-dimensional temperature distribution of the furnace using distributed fiber optic temperature measurement data and the Kriging interpolation algorithm.

[0044] The slagging risk early warning module is used to calculate the slagging risk index based on the difference between the ash softening temperature and the real-time furnace temperature.

[0045] The pollutant closed-loop control module is used to dynamically correct the ammonia injection amount using a support vector regression model;

[0046] The digital twin verification module is used to simulate the combustion process in virtual space and verify the rationality of the parameters;

[0047] The intelligent maintenance decision-making module is used to predict the risk of furnace wall cracks based on acoustic emission signal characteristics and generate maintenance instructions.

[0048] The advantages of this invention are: by integrating multi-source data fusion, intelligent algorithm optimization and digital twin verification technologies, a whole-process optimization system for solid waste co-incineration is constructed, which improves energy utilization while achieving ultra-low emissions of pollutants and safe and economical operation. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the main structure of the system of the present invention. Detailed Implementation

[0050] The present invention will be further described below with reference to specific embodiments, and the advantages and features of the present invention will become clearer as a result. However, these embodiments are merely exemplary and do not constitute any limitation on the scope of the present invention. Those skilled in the art should understand that modifications or substitutions can be made to the details and form of the technical solutions of the present invention without departing from the spirit and scope of the present invention, but all such modifications and substitutions fall within the protection scope of the present invention.

[0051] See Figure 1 This invention relates to a method for optimizing boiler operating parameters for co-firing multiple solid wastes, comprising the following steps:

[0052] (1) Multi-source data acquisition: The calorific value, moisture content, chlorine-sulfur ratio and heavy metal content of solid waste are acquired in real time through a spectrometer, a visual recognition system and online sensors;

[0053] (2) Intelligent compatibility optimization: Based on the calorific value balance and pollutant suppression targets, the solid waste mixing ratio is calculated using a genetic algorithm to control the Cl / S molar ratio to 1.2-1.8 and the alkali metal content to <50%;

[0054] (3) Pretreatment drying: Microwave-steam combined drying is carried out on solid waste with a moisture content >25%, and the microwave power and steam injection time are dynamically adjusted;

[0055] (4) Multi-objective parameter optimization: The NSGA-III algorithm is used to optimize the combustion temperature, secondary air volume and feed rate. The objective function includes boiler thermal efficiency, NOx emission and temperature field uniformity.

[0056] (5) Dynamic combustion control: The primary air pressure is adjusted based on a fuzzy PID controller. The input variables include furnace temperature deviation and CO concentration change rate.

[0057] (6) Three-dimensional temperature field modeling: Using distributed optical fiber temperature measurement data, the three-dimensional temperature distribution of the furnace is reconstructed by the Kriging interpolation algorithm;

[0058] (7) Slagging Risk Warning: The slagging risk index is calculated based on the difference between the ash softening temperature and the real-time furnace temperature. The formula is as follows:

[0059] Control measures are triggered when Rslag ≥ 0.8; where T is the real-time furnace temperature in °C, obtained through a distributed fiber optic temperature measurement system.

[0060] Tsoft: Ash softening temperature, unit: °C, calculated based on ash composition;

[0061] dT / dz: Axial temperature gradient in the furnace, unit: ℃ / m, calculated from the temperature difference between adjacent temperature measurement points;

[0062] (8) Closed-loop control of pollutants: The ammonia injection rate is dynamically adjusted using a support vector regression model;

[0063] (9) Digital twin verification: Simulate the combustion process in virtual space to verify the rationality of parameters; (10) Intelligent maintenance decision: Predict the risk of furnace wall cracks based on acoustic emission signal characteristics and generate maintenance instructions.

[0064] Based on the above steps, the following advantages are achieved:

[0065] 1. End-to-end collaborative optimization

[0066] By integrating multi-source data and linking intelligent algorithms, the entire chain of solid waste detection, compatibility optimization and combustion control is coordinated, overcoming the shortcomings of isolated operation of each link in traditional technologies, and significantly improving energy utilization efficiency and pollutant control level.

[0067] 2. Dynamic adaptive control

[0068] Based on a composite control strategy of fuzzy PID and support vector regression, and combined with real-time monitoring data, the operating parameters are dynamically adjusted to effectively adapt to the fluctuations in solid waste composition and ensure stable and controllable combustion status.

[0069] 3. Precise risk warning

[0070] By innovatively integrating ash and slag characteristic analysis with three-dimensional temperature field modeling technology, a multi-dimensional slagging risk early warning mechanism is constructed to identify abnormal operating conditions in advance and trigger prevention and control measures, thereby significantly improving operational safety.

[0071] 4. Intelligent decision support

[0072] The combination of digital twin verification and acoustic emission monitoring technology enables visualized diagnosis and predictive maintenance of equipment status, reduces human judgment errors, and extends the service life of key components.

[0073] 5. Upgraded environmental performance

[0074] By modeling the pollutant formation mechanism and implementing closed-loop control, the emission of harmful substances such as dioxins and NOx can be significantly suppressed, meeting stringent environmental protection standards, while reducing the consumption of auxiliary reagents.

[0075] 6. Energy efficiency and cost optimization

[0076] The combined effect of microwave-steam drying and multi-objective parameter optimization reduces pretreatment energy consumption while improving overall combustion efficiency, achieving a dual breakthrough in economic and environmental benefits.

[0077] 7. Enhanced system robustness

[0078] The three-dimensional temperature field reconstruction and redundant control design effectively address local anomalies under complex combustion conditions, ensuring reliable system operation under extreme conditions.

[0079] The objective function of the genetic algorithm in step (2) is:

[0080]

[0081] Cl / S: Chlorine-sulfur molar ratio, detected by X-ray fluorescence spectrometry;

[0082] Na2O, K2O, and Al2O3 are the mass percentages of oxides in the ash composition, obtained through chemical analysis of the ash sample.

[0083] ΔQ: Standard deviation of calorific value of mixed solid waste, unit: MJ / kg;

[0084] μQ: Average calorific value of mixed solid waste, unit: MJ / kg;

[0085] The constraints include: calorific value fluctuation ≤15% and heavy metal content below environmental protection limits.

[0086] The advantages of the genetic algorithm objective function in step (2) are as follows:

[0087] 1. Synergistic inhibition of pollutants

[0088] By precisely controlling the Cl / S molar ratio (1.2-1.8), dioxin formation and high-temperature chlorine corrosion are effectively suppressed, while SO2 emissions exceeding standards due to excessive sulfur content are avoided. Compared to traditional empirical ratios, this significantly reduces the processing load on the flue gas purification system.

[0089] 2. Optimization of ash and slag characteristics

[0090] Limiting the alkali metal content reduces ash viscosity and coking tendency, extends the boiler heating surface cleaning cycle, and reduces the risk of boiler shutdown due to ash melting.

[0091] 3. Improved combustion stability

[0092] Using the standard deviation of calorific value ΔQ as the optimization target, the calorific value fluctuation of mixed solid waste is compressed to within ±15%, ensuring stable combustion conditions and avoiding drastic fluctuations in furnace temperature or flameout accidents caused by sudden changes in calorific value.

[0093] 4. Multi-constraint intelligent optimization

[0094] Genetic algorithms overcome the limitations of manual trial and error by enabling global search capabilities and quickly identify the optimal matching scheme while meeting multiple constraints such as environmental limits for heavy metals and calorific value balance.

[0095] 5. Environmental compliance guarantee

[0096] By using algorithms to rigidly constrain the content of heavy metals, the accumulation of toxic elements such as Hg and Pb in fly ash can be prevented from the source, thereby reducing subsequent disposal costs.

[0097] The optimization objectives of the NSGA-III algorithm in step (4) include:

[0098] minNO x =1.2×10 6 ·T 1.5 ·e -15000 / T T represents the combustion temperature in K, and the constraints are: the combustion temperature is more than 50°C lower than the ash softening temperature, and the O2 concentration is 5%-8%. The advantages of the NSGA-III algorithm optimization in step (4) are as follows:

[0099] 1. Multi-objective collaborative optimization

[0100] Breaking through the limitations of traditional single-objective optimization, this approach simultaneously improves thermal efficiency, reduces NOx emissions, and enhances temperature field uniformity. It provides the optimal parameter combination for multi-dimensional performance balance through Pareto front solution sets.

[0101] 2. Proactive prevention and control of slagging risk

[0102] The rigid constraint ensures that the combustion temperature is 50°C or more below the ash softening temperature, thereby inhibiting ash melting and adhesion from a thermodynamic source and avoiding the decrease in heat transfer efficiency and the risk of tube rupture caused by coking on the heated surface.

[0103] 3. Precise control of combustion atmosphere

[0104] By strictly controlling the O2 concentration within the range of 5%-8%, we can ensure complete combustion of fuel while preventing excessive air from increasing heat loss in the flue gas, thus achieving efficient and low-consumption operation.

[0105] 4. Temperature field uniformity optimization

[0106] With the standard deviation of the temperature field ΔT as the optimization target, the local high temperature zone (suppressing NOx generation) and low temperature zone (reducing unburned carbon loss) are effectively eliminated, so that the temperature difference of the furnace cross section is ≤30℃, and the combustion stability is significantly improved.

[0107] 5. Global optimization capability of the algorithm

[0108] The reference point mechanism of NSGA-III enhances the search efficiency in high-dimensional target space. Compared with the traditional NSGA-II algorithm, it improves the speed of obtaining high-quality solution sets by 40%, and is particularly good at handling the complex trade-off between thermal efficiency and pollutant emissions.

[0109] 6. Adaptability to dynamic operating conditions

[0110] The algorithm's built-in constraint handling mechanism can automatically adapt to fluctuations in solid waste composition. When the calorific value of the feed changes by ±10%, it can still quickly converge to an optimal solution that meets the safety constraints, ensuring the robustness of the system.

[0111] The ash softening temperature in step (7) is calculated using the following formula:

[0112] T soft =1100+8SiO2-5(Al2O3+Fe2O3)+3CaO, where SiO2,

[0113] Al2O3, Fe2O3, and CaO represent the mass percentages of each component in the ash residue.

[0114] The advantages of calculating the ash softening temperature in step (7) are as follows:

[0115] 1. Real-time dynamic prediction

[0116] The softening temperature can be directly calculated from the ash composition, eliminating the need for offline laboratory testing and enabling second-level assessment of slagging risk, thus significantly improving the timeliness of prevention and control.

[0117] 2. Precise ingredient correlation

[0118] The formula quantifies the effects of different oxides (such as SiO2 increasing melting and Al2O3 decreasing melting) on ​​the characteristics of ash slag, accurately reflects the melting behavior of mixed ash slag, and guides the safe setting of combustion temperature.

[0119] 3. Wide applicability

[0120] It is compatible with various types of solid waste ash and residue, such as municipal solid waste and industrial sludge, breaking through the dependence of traditional methods on specific ash samples and lowering the technical implementation threshold.

[0121] The ammonia injection amount correction model in step (8) is as follows:

[0122]

[0123] Wherein, NOx is the NOx concentration in flue gas, in ppm; T is the combustion temperature, in °C.

[0124] The advantages of the dynamic correction of ammonia injection amount in step (8) are as follows:

[0125] 1. Improved denitrification efficiency

[0126] Based on the coordinated correction of real-time NOx concentration and combustion temperature, the ammonia injection quantity is precisely matched to avoid excessive or insufficient ammonia injection, achieving a win-win situation of efficient denitrification and resource conservation.

[0127] 2. Rapid response to fluctuations

[0128] The dynamic model can respond in real time to fluctuations in NOx generation caused by changes in fuel calorific value or load adjustments, maintaining stable emissions compliance.

[0129] 3. Environmental and economic benefits

[0130] By optimizing the ammonia injection strategy under high-temperature conditions using temperature correlation terms, the corrosion of the air preheater by ammonia escape can be reduced, extending equipment life and lowering operation and maintenance costs.

[0131] The softening temperature result in step (7) is used to define the safety boundary for combustion temperature optimization, while step (8) is used to refine the control of pollutants based on the actual combustion state. The two form a "prediction-control" closed loop to jointly ensure the efficient and clean operation of the boiler.

[0132] This invention also relates to an optimization system for implementing a method for optimizing boiler operating parameters for co-firing multiple solid wastes, comprising:

[0133] The multi-source data acquisition module 1 is used to acquire the calorific value, moisture content, chlorine-sulfur ratio and heavy metal content of solid waste in real time through a spectrometer, a visual recognition system and online sensors.

[0134] The intelligent compatibility optimization module 2 is used to calculate the solid waste mixing ratio based on the calorific value balance and pollutant suppression targets, and control the Cl / S molar ratio to 1.2-1.8 and the alkali metal content to <50%.

[0135] The pretreatment drying module 3 is used to perform microwave-steam combined drying on solid waste with a moisture content >25%, and dynamically adjusts the microwave power and steam injection time.

[0136] The multi-objective parameter optimization module 4 is used to optimize combustion temperature, secondary air volume and feed rate using the NSGA-III algorithm. The objective function includes boiler thermal efficiency, NOx emissions and temperature field uniformity.

[0137] Dynamic combustion control module 5 is used to adjust the primary air pressure based on a fuzzy PID controller. The input variables include furnace temperature deviation and CO concentration change rate.

[0138] The 3D temperature field modeling module 6 is used to reconstruct the 3D temperature distribution of the furnace using distributed fiber optic temperature measurement data and the Kriging interpolation algorithm.

[0139] The slagging risk early warning module 7 is used to calculate the slagging risk index based on the difference between the ash softening temperature and the real-time furnace temperature.

[0140] Pollutant closed-loop control module 8 is used to dynamically correct the ammonia injection amount using a support vector regression model;

[0141] Digital twin verification module 9 is used to simulate the combustion process in virtual space and verify the rationality of parameters;

[0142] The intelligent maintenance decision module 10 is used to predict the risk of furnace wall cracks based on the characteristics of acoustic emission signals and generate maintenance instructions.

[0143] The specific technical advantages of this optimization system are as follows:

[0144] 1. Full-process closed-loop control system: The system integrates 10 functional modules such as data acquisition, compatibility optimization, and combustion regulation, and constructs a closed-loop control chain of "perception-decision-execution-verification" to realize the full life cycle management of solid waste co-incineration and overcome the efficiency loss caused by the fragmentation of each link in traditional technology.

[0145] 2. Multi-dimensional intelligent optimization capability: Through the synergy of genetic algorithms and NSGA-III, material compatibility, combustion parameters and pollution control targets are optimized simultaneously, solving industry problems of conflicting constraints and significantly improving the overall performance of the system.

[0146] 3. Dynamic response and precise control: By integrating fuzzy PID control and support vector regression, an adaptive adjustment mechanism for combustion state is established, which tracks changes in operating conditions in real time and responds quickly to ensure that operating parameters are always in the optimal range.

[0147] 4. Three-dimensional perception and risk prevention and control: Based on distributed optical fiber temperature measurement and Kriging interpolation algorithm, three-dimensional monitoring of furnace temperature field is realized; the slagging early warning module combines ash and slag characteristics and temperature gradient analysis to build a multi-level safety protection system.

[0148] 5. Intelligent maintenance decision support: The linkage between acoustic emission signal analysis and digital twin verification enables early fault diagnosis and predictive maintenance of equipment status, reducing the risk of unplanned downtime.

[0149] 6. Deep synergy in environmental protection efficiency: The closed-loop control of pollutants and combustion optimization form a linkage mechanism to suppress NOx generation at the source and optimize end-of-pipe treatment efficiency, thereby achieving the goal of clean emissions.

[0150] The overall technical benefits of this system:

[0151] 1. Significantly improved operating efficiency: Through multi-objective collaborative optimization and real-time dynamic control, the system significantly improves boiler thermal efficiency while reducing fuel consumption and auxiliary energy usage, thereby maximizing energy utilization.

[0152] 2. Enhanced safety and reliability: The organic combination of three-dimensional temperature field monitoring and slagging risk early warning effectively prevents major safety hazards such as coking on the heating surface and cracks in the furnace wall, ensuring long-term stable operation of the equipment.

[0153] 3. Breakthrough in environmental compliance: A multi-level pollution prevention and control system, from compatibility optimization to precise control of ammonia injection volume, helps enterprises achieve green and sustainable development.

[0154] 4. Optimized operation and maintenance costs: The intelligent maintenance decision module reduces the frequency of manual inspections, and the predictive maintenance strategy extends the service life of key components, significantly reducing the operation and maintenance costs throughout the entire equipment lifecycle.

[0155] 5. Process adaptability expansion: The modular design supports flexible configuration and can be quickly adapted to different types of solid waste, treatment scale and environmental protection standards, providing a highly compatible solution for technology promotion.

[0156] This system reconstructs the intelligent control paradigm of solid waste co-firing boilers through algorithm empowerment, data-driven approach and hardware collaboration, filling the technological gap in the field of multi-solid waste co-processing, providing reliable technical support for the resource utilization of urban solid waste, and has significant industry demonstration value.

[0157] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. A method for optimizing boiler operating parameters for co-firing multiple solid wastes, characterized in that, Includes the following steps: (1) Multi-source data acquisition: The calorific value, moisture content, chlorine-sulfur ratio and heavy metal content of solid waste are acquired in real time through a spectrometer, a visual recognition system and online sensors; (2) Intelligent compatibility optimization: Based on the calorific value balance and pollutant suppression targets, the solid waste mixing ratio is calculated using a genetic algorithm to control the Cl / S molar ratio to 1.2-1.8 and the alkali metal content to <50%; (3) Pretreatment drying: Microwave-steam combined drying is carried out on solid waste with a moisture content >25%, and the microwave power and steam injection time are dynamically adjusted; (4) Multi-objective parameter optimization: The NSGA-III algorithm is used to optimize the combustion temperature, secondary air volume and feed rate. The objective function includes boiler thermal efficiency, NOx emission and temperature field uniformity. (5) Dynamic combustion control: The primary air pressure is adjusted based on a fuzzy PID controller. The input variables include furnace temperature deviation and CO concentration change rate. (6) Three-dimensional temperature field modeling: Using distributed optical fiber temperature measurement data, the three-dimensional temperature distribution of the furnace is reconstructed by the Kriging interpolation algorithm; (7) Slagging Risk Warning: The slagging risk index is calculated based on the difference between the ash softening temperature and the real-time furnace temperature. The formula is as follows: Control measures are triggered when Rslag ≥ 0.8; where T is the real-time furnace temperature in °C, obtained through a distributed fiber optic temperature measurement system. Tsoft: Ash softening temperature, unit: °C, calculated based on ash composition; dT / dz: Axial temperature gradient in the furnace, unit: ℃ / m, calculated from the temperature difference between adjacent temperature measurement points; (8) Closed-loop control of pollutants: The ammonia injection rate is dynamically adjusted using a support vector regression model; (9) Digital twin verification: Simulate the combustion process in virtual space to verify the rationality of parameters; (10) Intelligent maintenance decision-making: Based on the characteristics of acoustic emission signals, predict the risk of furnace wall cracks and generate maintenance instructions.

2. The method for optimizing boiler operating parameters for co-firing multiple solid wastes according to claim 1, characterized in that, The objective function of the genetic algorithm in step (2) is: Cl / S: Chlorine-sulfur molar ratio, detected by X-ray fluorescence spectrometry; Na2O, K2O, and Al2O3 are the mass percentages of oxides in the ash composition, obtained through chemical analysis of the ash sample. ΔQ: Standard deviation of calorific value of mixed solid waste, unit: MJ / kg; μQ: Average calorific value of mixed solid waste, unit: MJ / kg; The constraints include: calorific value fluctuation ≤15% and heavy metal content below environmental protection limits.

3. The method for optimizing boiler operating parameters for co-firing multiple solid wastes according to claim 1, characterized in that, The optimization objective of the NSGA-III algorithm in step (4) includes: minNo x =1.2×10 6 ·T 1.5 ·e -15000 / T T is the combustion temperature, in K. The constraints are: the combustion temperature is more than 50°C lower than the ash softening temperature, and the O2 concentration is 5%-8%.

4. The method for optimizing boiler operating parameters for co-firing multiple solid wastes according to claim 1, characterized in that, The ash softening temperature in step (7) is calculated using the following formula: T soft =1100+8SiO2-5(Al2O3+Fe2O3)+3CaO, where SiO2, Al2O3, Fe2O3, and CaO are the mass percentages of each component in the ash slag.

5. The method for optimizing boiler operating parameters for co-firing multiple solid wastes according to claim 1, characterized in that, The ammonia injection amount correction model in step (8) is as follows: Wherein, NOx is the NOx concentration in flue gas, in ppm; T is the combustion temperature, in °C.

6. An optimization system for implementing the boiler operating parameter optimization method for co-firing of multiple solid wastes as described in claims 1-5, characterized in that, include: The multi-source data acquisition module is used to acquire the calorific value, moisture content, chlorine-sulfur ratio and heavy metal content of solid waste in real time through a spectrometer, a visual recognition system and online sensors. The intelligent compatibility optimization module is used to calculate the solid waste mixing ratio based on the calorific value balance and pollutant suppression targets, using a genetic algorithm to control the Cl / S molar ratio to 1.2-1.8 and the alkali metal content to <50%. The pretreatment drying module is used to perform microwave-steam combined drying on solid waste with a moisture content >25%, and dynamically adjusts the microwave power and steam injection time. The multi-objective parameter optimization module is used to optimize combustion temperature, secondary air volume and feed rate using the NSGA-III algorithm. The objective function includes boiler thermal efficiency, NOx emissions and temperature field uniformity. The dynamic combustion control module is used to adjust the primary air pressure based on a fuzzy PID controller. The input variables include furnace temperature deviation and CO concentration change rate. The three-dimensional temperature field modeling module is used to reconstruct the three-dimensional temperature distribution of the furnace using distributed fiber optic temperature measurement data and the Kriging interpolation algorithm. The slagging risk early warning module is used to calculate the slagging risk index based on the difference between the ash softening temperature and the real-time furnace temperature. The pollutant closed-loop control module is used to dynamically correct the ammonia injection amount using a support vector regression model; The digital twin verification module is used to simulate the combustion process in virtual space and verify the rationality of the parameters; The intelligent maintenance decision-making module is used to predict the risk of furnace wall cracks based on acoustic emission signal characteristics and generate maintenance instructions.

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