High-efficiency combustion method of plastic-waste spinning composite fuel based on multi-stage adaptive cutter

Abstract

The present application relates to solid waste resource utilization and efficient energy conversion technical field, and disclose a plastic-waste spinning composite fuel efficient combustion method based on multi-stage adaptive cutter, the method first carries out raw material component and calorific value analysis, establishes dynamic formula model;Adopt three-stage adaptive crushing system combined with laser particle size feedback, process the material into uniform flaky particles, synchronously integrate magnetic separation, air separation technology to remove metal / sand impurities;Through waste heat drying control moisture content, adopt intelligent multi-point feeding system in the combustion stage, according to the temperature field / oxygen concentration field in the furnace dynamic adjustment cloth distribution, cooperate with variable ladder furnace bed and guide plate design, optimize fuel residence time, real-time monitoring system linkage MPC algorithm closed loop control air ratio, maintain combustion efficiency and temperature fluctuation, finally realize flue gas waste heat recovery for drying process, after cloth bag dust removal+SCR denitration, the emission standard is up to standard, so as to promote the solid waste resource utilization and cement production low carbonization collaborative development.

Description

Technical Field

[0001] This invention relates to the field of solid waste resource utilization and efficient energy conversion technology, specifically to a method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools. Background Technology

[0002] In the field of solid alternative fuel technology, plastic and waste textile composite fuels show great application potential due to their high calorific value and wide availability. However, their complex material properties pose serious challenges to traditional crushing, screening, and combustion technologies.

[0003] First, in the raw material pretreatment stage, traditional crushing and screening technologies are ill-suited to the complex characteristics of this composite fuel. The plastic components are diverse in material composition, some exhibiting high toughness and low melting points, while the waste textile components suffer from severe fiber entanglement. This leads to issues such as blade adhesion and wear, cutting edge melting, and rotor blockage when using conventional crushing equipment, resulting in low crushing efficiency and increased energy consumption. Furthermore, existing screening and impurity removal technologies cannot effectively separate hard impurities such as metals and sand mixed in with flexible materials, directly affecting the purity and quality of the subsequent fuel. More critically, traditional technologies struggle to stably process this heterogeneous composite fuel into fuel products with uniform morphology and particle size; the significant differences in particle size directly lead to instability and low efficiency in the subsequent combustion process.

[0004] Secondly, in the combustion stage, traditional technologies suffer from systematic design and control deficiencies. On the one hand, the location and method of the feeding point lack reasonable design for the characteristics of composite fuels, resulting in uneven spatial distribution of fuel within the decomposition furnace, which fails to fully mix with oxygen, leading to problems such as incomplete combustion and large fluctuations in heat release. On the other hand, the system lacks effective means for real-time monitoring and closed-loop control of key parameters in the combustion process (such as temperature and oxygen concentration), and cannot adaptively adjust process parameters according to dynamic changes in fuel composition and actual combustion conditions, making it difficult to break through the bottleneck in combustion efficiency.

[0005] Finally, the structural design of traditional decomposition furnaces and hearths is mainly based on the combustion characteristics of homogeneous fuels such as coal, failing to fully consider the significant differences between plastic and waste textile composite fuels in terms of combustion speed and heat release patterns. The inherent hearth structure cannot ensure that the fuel achieves full and uniform tumbling and combustion during its movement within the furnace, resulting in problems such as low heat utilization, easy generation of localized high temperatures, and slagging.

[0006] Therefore, there is an urgent need for an innovative solution that covers the entire process from pretreatment, feeding, monitoring and control to furnace structure, in order to overcome the inherent defects of existing technologies and achieve efficient, stable and clean combustion of plastic-waste textile composite fuel. Summary of the Invention

[0007] (a) Technical problems to be solved

[0008] To address the shortcomings of existing technologies, this invention provides a high-efficiency combustion method for plastic-waste textile composite fuel based on multi-stage adaptive blades. It has the advantages of efficient pretreatment, stable and controllable combustion, and strong system adaptability, and solves the problems of pretreatment clogging and wear, uneven combustion, low efficiency, and mismatch of furnace structure caused by the complex characteristics of composite fuels in traditional technologies.

[0009] (II) Technical Solution

[0010] To achieve the above objectives, the present invention provides the following technical solution: a method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools, comprising the following steps:

[0011] Step 1: Fuel Characteristic Analysis: Conduct rapid composition and characteristic analysis on plastic and waste textile raw materials from complex sources to clarify their material type and mixing ratio;

[0012] Step 2, Dynamic formulation control: The ratio of plastic and waste textile raw materials used for fuel is dynamically controlled within a set range, and a composition-calorific value correlation model is established based on experimental data to predict the calorific value of the fuel.

[0013] Step 3, Multi-stage pretreatment: Based on the fuel characteristic data obtained in Step 1, set the crushing target parameters. The fuel enters a three-stage adaptive crushing system consisting of coarse crushing, medium crushing and fine crushing. The system provides real-time feedback on the material particle size through intelligent monitoring units set between stages and dynamically adjusts the speed and gap of each stage crusher.

[0014] Step 4, Intelligent Screening and Impurity Removal: The crushed fuel enters the intelligent screening and impurity removal stage, which uses a combination of multi-stage screening, magnetic separation and air separation technology to remove metal and sand inert impurities.

[0015] Step 5, Drying and Moisture Control: The fuel that has been screened and impurity removed enters the drying process. By controlling the drying temperature and time, the moisture content of the fuel is stably reduced to below the preset threshold.

[0016] Step Six: Intelligent Feeding and Distribution Control: The dried fuel enters the decomposition furnace through the intelligent feeding system. The system dynamically adjusts the feeding point position and feeding speed based on the real-time feedback of the furnace temperature field and oxygen concentration field from the temperature sensor and oxygen detector, so that the fuel is evenly distributed on the specially designed adaptive furnace bed.

[0017] Step 7, Adaptive Hearth Structure Control: Fuel is burned on an adaptive hearth with an intelligent combustion control system. The hearth step height, tilt angle and internal flow guiding structure are automatically adjusted according to fuel characteristics and combustion state. The intelligent combustion control system actively controls the fuel movement path and residence time, and simultaneously optimizes the airflow distribution.

[0018] Step 8: Real-time combustion monitoring and dynamic control: During the fuel combustion process, temperature, oxygen concentration and flame morphology parameters are collected in real time through a sensor network deployed in the furnace. The data is transmitted to the intelligent combustion control system, which performs closed-loop dynamic control of feeding, air supply and furnace bed parameters based on a preset optimization algorithm.

[0019] Step 9, Heat Utilization and Emission Optimization: Finally, the heat energy released by complete combustion is utilized efficiently to directly serve the pre-decomposition process of cement raw materials. At the same time, the generated flue gas is purified, and emission indicators are monitored and optimized.

[0020] Preferably, the fuel characteristic analysis process in step one is as follows:

[0021] S1.1 First, use near-infrared spectroscopy or X-ray fluorescence analyzer to quickly test the incoming plastic and waste textile raw materials to identify the material type and impurity content, and control the analysis time to 5-10 minutes per batch;

[0022] S1.2, The calorific value of each component is then determined by thermogravimetric analysis and calorimetry. A component-calorific value correlation model was established. The model is based on multiple linear regression, and its calculation formula is as follows:

[0023] =a×plastic ratio +b×waste textile ratio +c×impurity ratio;

[0024] In the formula, a, b, and c are the coefficients for the plastic ratio, waste textile ratio, and impurity ratio, respectively, which are obtained through experimental calibration.

[0025] Preferably, the dynamic formulation control process in step two is as follows:

[0026] S2.1 Based on the prediction results of the composition-calorific value correlation model in S1.2, the mixing ratio of plastic and waste textiles is dynamically adjusted to the range of 4:6 to 6:4 through the automatic batching system, and the calorific value prediction error is controlled within ±2%.

[0027] S2.2 Output a fuel characteristic report including calorific value, moisture content and impurity content, and generate a unique identifier for each batch of fuel.

[0028] Preferably, the multi-stage preprocessing process in step three is as follows:

[0029] S3.1, Coarse crushing stage: A twin-shaft shear crusher is used, with a set speed of 80-100 rpm and a crushing gap of 10-15 mm. It can process large materials to a particle size of <45 mm. When coarse crushing high-toughness plastics, hydraulic overload protection is added.

[0030] S3.2, Medium crushing stage: Use a roller crusher with a set speed of 100-150 rpm and a gap of 5-10 mm to further crush the material to a particle size of 10-20 mm. The roller surface is designed with a toothed structure.

[0031] S3.3 Fine crushing stage: A hammer crusher is used, with a set speed of 150-200 rpm and a gap of 2-5 mm. The final product is flaky fuel with a particle size of 5-15 mm. A rotating scraper is installed inside the crushing chamber to automatically clean up fiber accumulation.

[0032] Laser particle size analyzers are installed between the three crushers to monitor the particle size of the material in real time. The data is fed back to the PLC control center of the crushing system for dynamic adjustment of the crusher parameters.

[0033] Preferably, the intelligent screening and impurity removal process in step four is as follows:

[0034] S4.1 Intelligent screening: The first-stage vibrating screen removes large impurities; the second-stage rotary screen separates qualified fuel; and the third-stage airflow screen separates fine powder and light impurities.

[0035] S4.2 Impurity Removal: A permanent magnet drum separator with a magnetic field strength ≥0.5Tesla is used to remove metallic impurities with a removal rate >95%; an air classifier is used to separate heavy impurities in sand and gravel based on density differences.

[0036] Preferably, the drying and moisture control in step five includes:

[0037] (1) Drying process parameters: A rotary dryer is used to control the drying temperature at 100-150℃ and the drying time is 10-20 minutes. The hot air source is the waste heat of the decomposition furnace to realize energy recycling.

[0038] (2) Real-time moisture control: Use an online near-infrared moisture meter to monitor the fuel moisture content in real time and control the target moisture content to be stable at <4%. When the moisture exceeds the standard, increase the drying temperature or extend the residence time; when the moisture is too low, decrease the temperature.

[0039] Preferably, the intelligent feeding and distribution control in step six:

[0040] (1) Intelligent feeding: A multi-point pneumatic feeding system is adopted, with feeding points located at the top and side of the decomposition furnace, and the feeding speed is adjustable from 0.5 to 2 tons / hour;

[0041] (2) Distribution control: Real-time data from thermocouples and oxygen sensors inside the furnace are used to dynamically adjust the location and speed of the feeding point. When the local temperature inside the furnace is below 850℃, the amount of material fed in that area is increased; when the oxygen concentration is below 2%, the amount of material fed is reduced and the air supply is increased.

[0042] Preferably, the adaptive hearth structure control in step seven includes:

[0043] (1) Adjustment of furnace bed parameters: The step height is adjusted in the range of 50-200mm by hydraulic drive, and the tilt angle is adjusted in the range of 10-30° by electric actuator. The control signal is based on fuel characteristics and combustion state.

[0044] (2) Flow guiding and separation design: High temperature resistant flow guide plates are installed on the stepped surface. The angle of the flow guide plates is adjustable to guide the flow of hot air and control the deviation of hot air flow velocity in each area of ​​the furnace bed within ±5%. Baffles are set between the steps.

[0045] Preferably, the real-time combustion monitoring and dynamic control process in step eight is as follows:

[0046] S5.1. Thermocouples, oxygen sensors, flame imagers and pressure sensors are deployed in the combustion zone, furnace outlet and reflux zone to form a complete sensor network. All sensors collect data synchronously at a frequency of 1 to 2 times / second.

[0047] S5.2 Intelligent Dynamic Control Execution: A model predictive control algorithm is set in the intelligent control system. Based on the real-time data of S5.1, the algorithm dynamically adjusts the feeding speed, air volume and furnace bed motion parameters with the control objectives of combustion efficiency >95%, temperature fluctuation <±10℃ and oxygen concentration stable at 2%~4% to achieve closed-loop optimization.

[0048] S5.3 When the flame imager detects an abnormal fluctuation in the flame flicker frequency > 5 Hz, the system will automatically trigger an emergency control strategy to reduce the amount of material fed and increase the oxygen concentration in the air supply.

[0049] Preferably, in step nine, the heat utilization and emission optimization are as follows: the combustion heat is directly used for cement raw meal pre-decomposition, the decomposition furnace temperature is controlled at 850-900℃, the raw meal decomposition rate is >90%, and the waste heat of the flue gas is recovered through a heat exchanger for use in the drying process. The discharged flue gas is treated by a bag filter and an SCR denitrification system, and the final emission indicators are controlled at: dust <10mg / Nm³, NO x <200mg / Nm³, SO2<50mg / Nm³, and data are transmitted in real time to the environmental protection platform through a continuous emission monitoring system.

[0050] Compared with existing technologies, this invention provides a method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools, which has the following beneficial effects:

[0051] 1. This invention solves the problems of tool wear, clogging and over-grinding caused by high-toughness plastics and entangled fibers by constructing a multi-stage adaptive crushing + intelligent screening and impurity removal + precision drying synergistic pretreatment system. This achieves the beneficial effect of greatly improving the uniformity of fuel particle size, the proportion of flakes and the purity, thus laying a solid physical foundation for subsequent efficient and stable combustion.

[0052] 2. This invention solves the problems of uneven furnace distribution, large combustion fluctuations and low efficiency caused by complex and variable fuel characteristics by integrating dynamic formula regulation, intelligent feeding distribution, adaptive hearth and model predictive control (MPC) into a whole-process intelligent combustion control system. This achieves the beneficial effects of stable combustion efficiency of over 95%, temperature fluctuation controlled within ±10℃, and real-time suppression of incomplete combustion.

[0053] 3. This invention addresses the problems of low heat utilization and high pollutant emissions in traditional technologies by directly applying the heat energy from complete combustion to the decomposition of cement raw materials and coupling it with a high-efficiency flue gas purification system. This achieves a raw material decomposition rate >90%, a flue gas waste heat recovery efficiency >80%, and ensures that dust and NO emissions are minimized. x The SO2 emission targets are far superior to the environmental protection standards, achieving a balance between economic and environmental benefits. Attached Figure Description

[0054] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. 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.

[0056] Please see Figure 1 A method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools includes the following steps:

[0057] Step 1: Fuel Characteristic Analysis: Conduct rapid composition and characteristic analysis on plastic and waste textile raw materials from complex sources to clarify their material types and mixing ratios, providing data support for subsequent fuel formulation design;

[0058] Step 2, Dynamic formulation control: The ratio of plastic and waste textile raw materials used for fuel is dynamically controlled within a set range, and a composition-calorific value correlation model is established based on experimental data to predict the calorific value of the fuel, providing core data support for the setting of process parameters for the entire process.

[0059] Step 3, Multi-stage pretreatment: Based on the fuel characteristic data obtained in Step 1, the crushing target parameters are set. The fuel enters a three-stage adaptive crushing system consisting of coarse crushing, medium crushing and fine crushing. The system provides real-time feedback on the particle size of the material through intelligent monitoring units set between stages, and dynamically adjusts the speed and gap of each stage crusher. This step aims to effectively process high-toughness plastics and easily entangled fibers into flaky fuel with uniform particle size and regular shape, laying the physical foundation for subsequent efficient combustion.

[0060] Step 4, Intelligent Screening and Impurity Removal: The crushed fuel enters the intelligent screening and impurity removal stage, which uses a combination of multi-stage screening, magnetic separation and air separation technology to remove metal and sand inert impurities. This step effectively improves the purity of the fuel and avoids wear, blockage and interference with combustion reaction caused by impurities in the subsequent furnace bed, ensuring the stability of fuel quality.

[0061] Step 5, Drying and Moisture Control: After screening and impurity removal, the fuel enters the drying process. By precisely controlling the drying temperature and time, the moisture content of the fuel is stably reduced to below the preset threshold. This operation not only improves the net calorific value of the fuel, but also eliminates the combustion instability caused by moisture fluctuations. It is an important prerequisite for achieving stable combustion and efficient energy release.

[0062] Step Six: Intelligent Feeding and Distribution Control: The dried fuel enters the decomposition furnace through the intelligent feeding system. The system dynamically adjusts the feeding point position and feeding speed based on the real-time feedback of the furnace temperature field and oxygen concentration field from the temperature sensor and oxygen detector, so that the fuel falls evenly on the specially designed adaptive furnace bed, ensuring that the fuel is spatially rationally distributed in the initial stage, creating the best conditions for full mixing with the combustion air.

[0063] Step 7, Adaptive Furnace Structure Control: Fuel is burned on an adaptive furnace bed equipped with an intelligent combustion control system. The furnace bed step height, tilt angle, and internal flow guiding structure are automatically fine-tuned according to fuel characteristics and combustion state. The intelligent combustion control system actively controls the fuel movement path and residence time, and simultaneously optimizes airflow distribution, thereby effectively avoiding accumulation or perforation caused by fiber entanglement and particle separation, ensuring uniform and smooth movement and combustion of fuel throughout the process.

[0064] Step 8: Real-time combustion monitoring and dynamic control: During the fuel combustion process, temperature, oxygen concentration and flame morphology parameters are collected in real time through a sensor network deployed in key locations inside the furnace. The data is transmitted to the intelligent combustion control system. Based on a preset optimization algorithm, the system performs closed-loop dynamic control of feeding, air supply and furnace bed parameters, thereby eliminating combustion fluctuations in a timely manner and ensuring that the entire combustion process is always in an efficient and stable ideal state.

[0065] Step Nine, Heat Utilization and Emission Optimization: Finally, the heat energy released by complete combustion is utilized efficiently to directly serve the pre-decomposition process of cement raw materials. At the same time, the generated flue gas is purified, and emission indicators are monitored and optimized. This step not only maximizes the recovery of heat energy and minimizes coal consumption, but also ensures that the entire combustion process is clean and environmentally friendly, thereby achieving a balance between energy and environmental benefits.

[0066] Specifically, the fuel characteristic analysis process in step one is as follows:

[0067] S1.1 First, use a near-infrared spectroscopy (NIR) or X-ray fluorescence (XRF) analyzer to quickly test the incoming plastic (such as PE, PP, PET) and waste textile (such as cotton, polyester) raw materials to identify the material type and impurity content (such as metal, sand and gravel), and control the analysis time to 5-10 minutes per batch.

[0068] S1.2, The calorific value of each component is then determined by thermogravimetric analysis (TGA) and calorimetry. The unit is kcal / kg. A composition-calorific value correlation model is established. The model is based on multiple linear regression, and its calculation formula is as follows:

[0069] =a×plastic ratio +b×waste textile ratio +c×impurity ratio;

[0070] In the formula, a, b, and c are the coefficients of the plastic ratio, waste textile ratio, and impurity ratio, respectively, which are obtained by experimental calibration (for example, the contribution coefficient of plastic to the calorific value, a, is 8000-10000 kcal / kg, and that of waste textile is 4000-6000 kcal / kg).

[0071] Specifically, the dynamic formulation control process in step two:

[0072] S2.1 Based on the prediction results of the composition-calorific value correlation model in S1.2, the mixing ratio of plastic and waste textiles is dynamically adjusted to the range of 4:6 to 6:4 through the automatic batching system, and the calorific value prediction error is controlled within ±2%.

[0073] S2.2 Outputs a fuel characteristic report including calorific value, moisture content, and impurity content, providing data support for setting subsequent crushing, drying, and combustion process parameters. Each batch of fuel is assigned a unique identifier for full-process traceability.

[0074] Specifically, the multi-stage preprocessing process in step three:

[0075] S3.1, Coarse crushing stage: A twin-shaft shear crusher is used, with a set speed of 80-100 rpm and a crushing gap of 10-15 mm. It processes large materials to a particle size of <45 mm. When coarse crushing high-toughness plastics (such as PE), a hydraulic overload protection is added to prevent the blades from jamming.

[0076] S3.2, Medium crushing stage: Use a roller crusher with a set speed of 100-150 rpm and a gap of 5-10 mm to further crush the material to a particle size of 10-20 mm. The roller surface is designed with a toothed structure to enhance the pulling and shearing of fibrous materials.

[0077] S3.3 Fine crushing stage: A hammer crusher is used, with a set speed of 150-200 rpm and a gap of 2-5 mm. The final product is flaky fuel with a particle size of 5-15 mm. A rotating scraper is installed inside the crushing chamber to automatically clean up fiber accumulation, thereby avoiding the problem of waste textile entanglement.

[0078] Laser particle size analyzers (measuring range 0.1-100 mm) are installed between the three crushers to monitor the particle size of the material in real time. The data is fed back to the PLC control center of the crushing system for dynamic adjustment of the crusher parameters: when the particle size exceeds the standard (e.g., >15 mm), the system will automatically increase the speed of the fine crusher or reduce the gap; when the particle size is too small (e.g., <5 mm), the system will automatically reduce the speed to avoid over-crushing, so that the target particle size uniformity reaches more than 2.4% (calculated as the ratio of the standard deviation of particle size distribution to the average particle size). The proportion of fuel flakes after crushing needs to exceed 98%, and the fiber entanglement rate should be reduced to <2%.

[0079] Specifically, the intelligent screening and impurity removal process in step four is as follows:

[0080] S4.1 Intelligent screening: The first-stage vibrating screen (aperture 10-20mm) removes large impurities; the second-stage rotary screen (aperture 5-10mm) separates qualified fuel; the third-stage airflow screen (wind speed 5-10m / s) separates fine powder and light impurities.

[0081] S4.2 Impurity Removal: A permanent magnet drum separator with a magnetic field strength ≥0.5Tesla is used to remove metallic impurities with a removal rate >95%; an air classifier (wind speed 8~12m / s) is used to separate heavy impurities in sand and gravel based on density differences, controlling the sand and gravel removal rate >95%; the total content of fuel impurities (metal, sand, gravel, etc.) after screening is controlled at <1%, and the purity is ≥98%.

[0082] Specifically, step five, drying and moisture control, includes:

[0083] (1) Drying process parameters: A rotary dryer is used to control the drying temperature at 100-150℃ (this temperature can avoid the melting point of plastics, such as PE melting point of 120-130℃), the drying time is 10-20 minutes, and the hot air source is the waste heat of the decomposition furnace to realize energy recycling;

[0084] (2) Real-time moisture control: Use an online near-infrared moisture meter to monitor the fuel moisture content in real time and control the target moisture content to be stable at <4%. When the moisture exceeds the standard, increase the drying temperature or extend the residence time; when the moisture is too low, reduce the temperature to prevent calorific value loss.

[0085] Specifically, in step six, intelligent feeding and distribution control:

[0086] (1) Intelligent feeding: A multi-point pneumatic feeding system is adopted, with feeding points located at the top and side of the decomposition furnace (at least 3 points), and the feeding speed is adjustable from 0.5 to 2 tons / hour;

[0087] (2) Distribution control: Real-time data from thermocouples and oxygen sensors inside the furnace are used to dynamically adjust the location and speed of the feeding point. When the local temperature inside the furnace is below 850℃, the amount of material fed in that area is increased. When the oxygen concentration is below 2%, the amount of material fed is reduced and the air supply is increased. The above operations make the temperature deviation of the furnace section < ±10℃, thereby ensuring that the fuel and oxygen are fully mixed.

[0088] The advantages are: by constructing a multi-stage adaptive crushing + intelligent screening and impurity removal + precise drying synergistic pretreatment system, the problems of blade wear, clogging and over-grinding caused by high-toughness plastics and entangled fibers are solved, and the beneficial effects of significantly improving fuel particle size uniformity (standard deviation <2.4%), flake ratio (>98%) and purity (≥98%) are achieved, thus laying a solid physical foundation for subsequent efficient and stable combustion.

[0089] Specifically, step seven, adaptive hearth structure control, includes:

[0090] (1) Adjustment of furnace bed parameters: The step height is adjusted in the range of 50-200mm by hydraulic drive, and the tilt angle is adjusted in the range of 10-30° by electric actuator. The control signal is based on fuel characteristics (when fiber content >30%, the step height is increased to more than 150mm) and combustion status (when temperature fluctuation >±15℃, the angle is adjusted).

[0091] (2) Flow guiding and separation design: High temperature resistant guide plates (made of 310S stainless steel) are installed on the stepped surface. The angle is adjustable (15-45°) to guide the flow of hot air and control the deviation of hot air flow velocity in each area of ​​the furnace bed within ±5% (flow velocity range 1.5~2.5m / s). Baffles (height 100~300mm) are set between the steps to prevent fuel cross-layering and ensure that the fuel residence time is 10~15 minutes to achieve complete combustion.

[0092] Specifically, step eight involves real-time combustion monitoring and dynamic control:

[0093] S5.1. Thermocouples (accuracy ±1℃), oxygen sensors (accuracy ±0.1%), flame imagers (to monitor flame fluctuations) and pressure sensors are deployed in the combustion zone, furnace outlet and reflux zone to form a complete sensor network. All sensors collect data synchronously at a frequency of 1 to 2 times / second to ensure comprehensive and high refresh rate perception of the furnace status.

[0094] S5.2 Intelligent Dynamic Control Execution: A Model Predictive Control (MPC) algorithm is set in the intelligent control system. Based on the real-time data of S5.1, the algorithm dynamically adjusts the feeding speed, air volume (with the ratio of primary air to secondary air controlled between 1:1 and 1:2) and furnace bed motion parameters to achieve closed-loop optimization, with combustion efficiency >95%, temperature fluctuation <±10℃, and oxygen concentration stable between 2% and 4% as the core control objectives.

[0095] S5.3 When the flame imager detects an abnormal fluctuation in the flame flicker frequency > 5 Hz, the system will automatically trigger an emergency control strategy to reduce the feed rate and increase the oxygen concentration in the air supply to quickly suppress unstable combustion, prevent incomplete combustion, and ensure that the entire process is always in an efficient and stable ideal state.

[0096] The advantages are: by integrating dynamic formula control, intelligent feeding distribution, adaptive hearth, and model predictive control (MPC) into a whole-process intelligent combustion control system, it solves the problems of uneven furnace distribution, large combustion fluctuations, and low efficiency caused by the complex and variable characteristics of fuel. As a result, it achieves the beneficial effects of stable combustion efficiency of over 95%, temperature fluctuation control within ±10℃, and real-time suppression of incomplete combustion.

[0097] Specifically, in step nine, heat utilization and emission optimization involves: combustion heat being directly used for cement raw meal pre-decomposition, with the decomposition furnace temperature controlled at 850-900℃ and a raw meal decomposition rate >90%. Waste heat from the flue gas is recovered via a heat exchanger (recovery efficiency >80%) for use in the drying process. The discharged flue gas is treated by a bag filter (dust removal efficiency >99%) and an SCR denitrification system (denitrification efficiency >85%), with final emission indicators controlled at: dust <10mg / Nm³, NO<10mg / Nm³. xThe emissions are <200mg / Nm³ and SO2 <50mg / Nm³, and the data is transmitted in real time to the environmental protection platform through a continuous emission monitoring system (CEMS) to ensure compliance with environmental standards.

[0098] The advantages are: by directly using the heat energy from complete combustion for the decomposition of cement raw materials and coupling it with a high-efficiency flue gas purification system, the problems of low heat utilization and high pollutant emissions in traditional technologies are solved, thereby achieving a raw material decomposition rate of >90%, a flue gas waste heat recovery efficiency of >80%, and ensuring that dust and NO are effectively controlled. x The SO2 emission targets are far superior to the environmental protection standards, achieving a balance between economic and environmental benefits.

[0099] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools, characterized in that, Includes the following steps: Step 1: Fuel Characteristic Analysis: Conduct rapid composition and characteristic analysis on plastic and waste textile raw materials from complex sources to clarify their material type and mixing ratio; Step 2, Dynamic formulation control: The ratio of plastic and waste textile raw materials used for fuel is dynamically controlled within a set range, and a composition-calorific value correlation model is established based on experimental data to predict the calorific value of the fuel. Step 3, Multi-stage pretreatment: Based on the fuel characteristic data obtained in Step 1, set the crushing target parameters. The fuel enters a three-stage adaptive crushing system consisting of coarse crushing, medium crushing and fine crushing. The system provides real-time feedback on the material particle size through intelligent monitoring units set between stages and dynamically adjusts the speed and gap of each stage crusher. Step 4, Intelligent Screening and Impurity Removal: The crushed fuel enters the intelligent screening and impurity removal stage, which uses a combination of multi-stage screening, magnetic separation and air separation technology to remove metal and sand inert impurities. Step 5, Drying and Moisture Control: The fuel that has been screened and impurity removed enters the drying process. By controlling the drying temperature and time, the moisture content of the fuel is stably reduced to below the preset threshold. Step Six: Intelligent Feeding and Distribution Control: The dried fuel enters the decomposition furnace through the intelligent feeding system. The system dynamically adjusts the feeding point position and feeding speed based on the real-time feedback of the furnace temperature field and oxygen concentration field from the temperature sensor and oxygen detector, so that the fuel is evenly distributed on the specially designed adaptive furnace bed. Step 7, Adaptive Hearth Structure Control: Fuel is burned on an adaptive hearth with an intelligent combustion control system. The hearth step height, tilt angle and internal flow guiding structure are automatically adjusted according to fuel characteristics and combustion state. The intelligent combustion control system actively controls the fuel movement path and residence time, and simultaneously optimizes the airflow distribution. Step 8: Real-time combustion monitoring and dynamic control: During the fuel combustion process, temperature, oxygen concentration and flame morphology parameters are collected in real time through a sensor network deployed in the furnace. The data is transmitted to the intelligent combustion control system, which performs closed-loop dynamic control of feeding, air supply and furnace bed parameters based on a preset optimization algorithm. Step 9, Heat Utilization and Emission Optimization: Finally, the heat energy released by complete combustion is utilized efficiently to directly serve the pre-decomposition process of cement raw materials. At the same time, the generated flue gas is purified, and emission indicators are monitored and optimized.

2. The method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools according to claim 1, characterized in that: The fuel characteristic analysis process in step one is as follows: S1.1 First, use near-infrared spectroscopy or X-ray fluorescence analyzer to quickly test the incoming plastic and waste textile raw materials to identify the material type and impurity content, and control the analysis time to 5-10 minutes per batch; S1.2, The calorific value of each component is then determined by thermogravimetric analysis and calorimetry. A component-calorific value correlation model was established. The model is based on multiple linear regression, and its calculation formula is as follows: =a×plastic ratio +b×waste textile ratio +c×impurity ratio; In the formula, a, b, and c are the coefficients for the plastic ratio, waste textile ratio, and impurity ratio, respectively, which are obtained through experimental calibration.

3. The method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools according to claim 1, characterized in that: The dynamic formulation control process in step two: S2.1 Based on the prediction results of the composition-calorific value correlation model in S1.2, the mixing ratio of plastic and waste textiles is dynamically adjusted to the range of 4:6 to 6:4 through the automatic batching system, and the calorific value prediction error is controlled within ±2%. S2.2 Output a fuel characteristic report including calorific value, moisture content and impurity content, and generate a unique identifier for each batch of fuel.

4. The method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools according to claim 1, characterized in that: The multi-level preprocessing process in step three: S3.1, Coarse crushing stage: A twin-shaft shear crusher is used, with a set speed of 80-100 rpm and a crushing gap of 10-15 mm. It can process large materials to a particle size of <45 mm. When coarse crushing high-toughness plastics, hydraulic overload protection is added. S3.2, Medium crushing stage: Use a roller crusher with a set speed of 100-150 rpm and a gap of 5-10 mm to further crush the material to a particle size of 10-20 mm. The roller surface is designed with a toothed structure. S3.3 Fine crushing stage: A hammer crusher is used, with a set speed of 150-200 rpm and a gap of 2-5 mm. The final product is flaky fuel with a particle size of 5-15 mm. A rotating scraper is installed inside the crushing chamber to automatically clean up fiber accumulation. Laser particle size analyzers are installed between the three crushers to monitor the particle size of the material in real time. The data is fed back to the PLC control center of the crushing system for dynamic adjustment of the crusher parameters.

5. The method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools according to claim 1, characterized in that: The intelligent screening and impurity removal process in step four is as follows: S4.1 Intelligent screening: The first-stage vibrating screen removes large impurities; the second-stage rotary screen separates qualified fuel; and the third-stage airflow screen separates fine powder and light impurities. S4.2 Impurity Removal: A permanent magnet drum separator with a magnetic field strength ≥0.5Tesla is used to remove metallic impurities with a removal rate >95%; an air classifier is used to separate heavy impurities in sand and gravel based on density differences.

6. The method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools according to claim 1, characterized in that: The drying and moisture control in step five includes: (1) Drying process parameters: A rotary dryer is used to control the drying temperature at 100-150℃ and the drying time is 10-20 minutes. The hot air source is the waste heat of the decomposition furnace to realize energy recycling. (2) Real-time moisture control: Use an online near-infrared moisture meter to monitor the fuel moisture content in real time and control the target moisture content to be stable at <4%. When the moisture exceeds the standard, increase the drying temperature or extend the residence time; when the moisture is too low, decrease the temperature.

7. The method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools according to claim 1, characterized in that: The intelligent feeding and distribution control in step six: (1) Intelligent feeding: A multi-point pneumatic feeding system is adopted, with feeding points located at the top and side of the decomposition furnace, and the feeding speed is adjustable from 0.5 to 2 tons / hour; (2) Distribution control: Real-time data from thermocouples and oxygen sensors inside the furnace are used to dynamically adjust the location and speed of the feeding point. When the local temperature inside the furnace is below 850℃, the amount of material fed in that area is increased; when the oxygen concentration is below 2%, the amount of material fed is reduced and the air supply is increased.

8. The method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools according to claim 1, characterized in that: The adaptive hearth structure control in step seven includes: (1) Adjustment of furnace bed parameters: The step height is adjusted in the range of 50-200mm by hydraulic drive, and the tilt angle is adjusted in the range of 10-30° by electric actuator. The control signal is based on fuel characteristics and combustion state. (2) Flow guiding and separation design: High temperature resistant flow guide plates are installed on the stepped surface. The angle of the flow guide plates is adjustable to guide the flow of hot air and control the deviation of hot air flow velocity in each area of ​​the furnace bed within ±5%. Baffles are set between the steps.

9. The method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools according to claim 1, characterized in that: The real-time combustion monitoring and dynamic control process in step eight: S5.

1. Thermocouples, oxygen sensors, flame imagers and pressure sensors are deployed in the combustion zone, furnace outlet and reflux zone to form a complete sensor network. All sensors collect data synchronously at a frequency of 1 to 2 times / second. S5.2 Intelligent Dynamic Control Execution: A model predictive control algorithm is set in the intelligent control system. Based on the real-time data of S5.1, the algorithm dynamically adjusts the feeding speed, air volume and furnace bed motion parameters with the control objectives of combustion efficiency >95%, temperature fluctuation <±10℃ and oxygen concentration stable at 2%~4% to achieve closed-loop optimization. S5.3 When the flame imager detects an abnormal fluctuation in the flame flicker frequency > 5 Hz, the system will automatically trigger an emergency control strategy to reduce the feed rate and increase the oxygen concentration in the air supply.

10. The method for efficient combustion of plastic-waste textile composite fuel based on multi-stage adaptive cutting tools according to claim 1, characterized in that: In step nine, heat utilization and emission optimization are implemented as follows: Combustion heat is directly used for cement raw meal pre-decomposition, with the decomposition furnace temperature controlled at 850-900℃ and a raw meal decomposition rate >90%. Waste heat from the flue gas is recovered via a heat exchanger for use in the drying process. The discharged flue gas is treated by a bag filter and an SCR denitrification system, with final emission indicators controlled at: dust <10mg / Nm³, NO<10mg / Nm³. x <200mg / Nm³, SO2<50mg / Nm³, and data are transmitted in real time to the environmental protection platform through a continuous emission monitoring system.

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