Edge computing real-time regulation method and system for plastic pyrolysis staged reaction

Abstract

The present application relates to pyrolysis furnace regulation and control technical field, especially to a kind of plastic pyrolysis fractional reaction's edge computing real-time regulation and control method and system. Including: the state feature reflecting partition reaction ability is constructed;Based on reaction state feature, the operating parameters of low-temperature dechlorination partition are adjusted, the dynamic change trend of chlorine element removal rate is obtained, when the dechlorination efficiency is lower than threshold value, the operating parameters suitable for current working condition are obtained;According to the operating parameters after adjustment, the material flow and heat distribution of main pyrolysis partition are synergistically regulated, the reaction efficiency distribution is constructed, whether the temperature gradient between each partition meets the requirement of smooth transition is judged;When satisfied, the current partition synergistic state is maintained and control parameter is updated, the stable configuration of pyrolysis furnace overall operation is obtained.The present application solves the problems of plastic pyrolysis process, such as multi-partition synergy difficulty, response lag and unstable pollutant control, realizes real-time optimization, stable operation and efficient low-emission control of pyrolysis process.

Description

Technical Field

[0001] This invention relates to the field of pyrolysis furnace control technology, and in particular to a real-time control method and system for edge calculation of plastic pyrolysis staged reaction. Background Technology

[0002] In the field of plastic solid waste resource utilization, pyrolysis technology is widely used for volume reduction and energy recovery because it can decompose plastics into combustible gases, liquid oils, and solid residues under anaerobic or oxygen-deficient conditions. However, with the increasing complexity of plastic composition, especially the large-scale emergence of chlorinated plastics, composite plastics, and mixed organic waste, traditional single-condition or extensively controlled pyrolysis processes are no longer able to simultaneously meet the requirements of reaction efficiency, energy consumption control, and pollutant emission.

[0003] Existing plastic pyrolysis furnaces typically employ a multi-zone structure, progressively decomposing plastics through staged reaction processes such as preheating and softening, low-temperature dechlorination, main pyrolysis, and high-temperature cracking. However, in actual operation, significant differences exist between the zones in terms of temperature levels, material throughput, and reaction mechanisms. Furthermore, these zones are highly coupled; any deviation of operating parameters from the optimal range in any zone can lead to problems such as unbalanced heat distribution, material accumulation, incomplete reaction, or fluctuations in pollutant emissions. Especially when low-temperature dechlorination efficiency is insufficient or material load varies significantly, relying on fixed parameters or manual experience for adjustments often fails to respond promptly to changes in operating conditions, thus affecting the stability and continuity of the entire pyrolysis process.

[0004] Based on this, the present invention is proposed. By deploying computing nodes in each zone of the pyrolysis furnace, multi-source operating data is processed and analyzed locally, enabling dynamic perception, rapid decision-making, and closed-loop optimization of the plastic pyrolysis staged reaction process, thereby improving the overall stability, energy efficiency, and environmental performance of the pyrolysis system. Summary of the Invention

[0005] This invention provides a real-time edge computing control method and system for plastic pyrolysis and classification reactions, which is used to achieve dynamic matching and continuous optimization of materials, heat and reaction efficiency during the plastic pyrolysis and classification reaction process, significantly improving the operational stability, energy efficiency and pollutant control capabilities of the pyrolysis furnace.

[0006] In a first aspect, the present invention provides a method for real-time control of edge calculation in a plastic pyrolysis staged reaction, characterized in that the method includes:

[0007] Step S1: By deploying computing nodes in each section of the pyrolysis furnace, real-time data from each section is acquired and fused to obtain the reaction state characteristics of each section;

[0008] Step S2: Based on the reaction state characteristics, fine-tune the parameters of the low-temperature dechlorination zone to determine the dynamic trend of chlorine removal rate; if the dynamic trend is lower than the preset threshold, adjust the material input rate of the relevant zone and integrate the heat flow coordination parameters between adjacent zones to obtain the adjusted operating parameters.

[0009] Step S3: Based on the adjusted operating parameters, regulate the material flow rate and heat distribution of the main pyrolysis zone to obtain the reaction efficiency distribution; based on the reaction efficiency distribution and combined with the deep processing data, determine whether the gradient of each zone meets the requirements for a smooth transition.

[0010] Step S4: If the smooth transition requirement is met, maintain the current collaborative state and update the control parameters; based on the updated control parameters, iteratively adjust the overall operation of the pyrolysis furnace to obtain a stable operating configuration state.

[0011] As a preferred embodiment of the present invention, step S1 involves obtaining the reaction state characteristics of each partition, including:

[0012] Edge computing nodes are deployed for the preheating and softening zone, the low-temperature dechlorination zone, the main pyrolysis zone, and the high-temperature pyrolysis zone, respectively. Real-time temperature and raw material composition data are obtained from each zone through independent data acquisition technology. Multi-source information from each zone is integrated using data fusion processing to generate characteristic data reflecting the reaction state. The operating status of each zone is analyzed through the characteristic data to determine the dynamic distribution of the reaction state characteristics. The reaction state characteristics of each zone are continuously monitored and updated.

[0013] As a preferred embodiment of the present invention, step S2, determining the dynamic trend of the key reaction rate, includes:

[0014] Data on chlorine removal was extracted from the low-temperature dechlorination zone. Based on this data, the reaction efficiency of the low-temperature dechlorination zone was analyzed. The temperature of the low-temperature dechlorination zone was precisely adjusted using regional stratification control technology. The reaction conditions of the low-temperature dechlorination zone were optimized by adjusting the material flow rate. The dynamic trend of the chlorine removal rate was calculated based on the adjusted temperature and flow rate data.

[0015] As a preferred embodiment of the present invention, step S2, obtaining the adjusted operating parameters, includes:

[0016] By comparing the dynamic trend of chlorine removal rate with a preset threshold, it is determined whether adjustment is needed. If it is lower than the preset threshold, the material stratification transfer rate of the preheating and softening zone is adjusted using dynamic flow allocation technology. The adjusted preheating input rate is calculated by integrating inter-regional heat flow coordination parameters. The operating parameters of the preheating and softening zone are updated based on the adjusted preheating input rate. The rationality of the material transfer rate adjustment is verified by monitoring the effect of the adjustment. The adjusted preheating input rate is then transmitted to the relevant zones for coordinated control.

[0017] As a preferred embodiment of the present invention, step S3, obtaining reaction efficiency distribution data, includes:

[0018] Based on the adjusted preheating input rate, material flow is regulated for the main pyrolysis zone; heat distribution in the main pyrolysis zone is optimized using heat carrier distribution regulation technology; reaction rate optimization technology is used to perform matching calculations on the ratio of input material flow to heat carrier; decomposition efficiency distribution data of the main pyrolysis zone is generated through the matching calculations; the decomposition efficiency distribution data is used to characterize the reaction effect of the main pyrolysis zone.

[0019] As a preferred embodiment of the present invention, step S3, determining whether the gradient of each partition meets the requirements for a smooth transition, includes:

[0020] Obtain the decomposition efficiency distribution data of the main pyrolysis zone; combine the deep pyrolysis processing data of the high-temperature pyrolysis zone to analyze the temperature management effect of the overall pyrolysis process; calculate the temperature gradient distribution between each zone based on the temperature management effect; determine whether the smooth transition requirement is met based on the temperature gradient distribution; if the smooth transition requirement is not met, generate an adjustment command and transmit it to the relevant zone; if the smooth transition requirement is met, record the current temperature gradient data.

[0021] As a preferred embodiment of the present invention, in step S4, if the smooth transition requirement is met, the current cooperative state is maintained and the control parameters are updated, including:

[0022] By determining whether the temperature gradient of each zone meets the requirements for a smooth transition, it is determined whether to maintain the current state of coordinated heat flow between regions. If the smooth transition requirements are met, chlorine removal verification information is extracted from the pollutant monitoring feedback data. Based on the chlorine removal verification information, the pollutant emission control effect is analyzed. Based on the control effect, the updated pollutant emission control parameters are determined. The updated control parameters are stored in the system database.

[0023] As a preferred embodiment of the present invention, step S4, obtaining a stable operating configuration state, includes:

[0024] By deploying edge nodes, updated pollutant emission control parameters are transmitted to each section of the pyrolysis furnace. Based on the updated control parameters, the operating configuration of each section is adjusted. Real-time data from all sections are integrated to analyze the stability of the overall operating status. Through the stability analysis, a continuous and stable operating configuration status is generated. Based on the operating configuration status, the overall operating effect of the pyrolysis furnace is continuously monitored. The operating configuration status is used as the basis data for the next iteration adjustment.

[0025] Secondly, the present invention also provides a real-time control system for edge calculation of plastic pyrolysis grading reactions, used to implement the above-mentioned method, the system comprising:

[0026] The state feature acquisition unit is used to acquire real-time data from each partition of the pyrolysis furnace by deploying computing nodes in each partition and performing fusion processing to obtain the reaction state features of each partition.

[0027] The dechlorination trend analysis unit is used to fine-tune the parameters of the low-temperature dechlorination zone according to the reaction state characteristics, and determine the dynamic trend of chlorine removal rate.

[0028] The operating parameter adjustment unit is used to adjust the material input rate of the relevant partition when the dynamic change trend is lower than a preset threshold, and to integrate the heat flow coordination parameters between adjacent partitions to obtain the adjusted operating parameters.

[0029] The gradient transition judgment unit is used to regulate the material flow and heat distribution of the main pyrolysis zone according to the adjusted operating parameters to obtain the reaction efficiency distribution; and to judge whether the gradient of each zone meets the requirements for a smooth transition by combining the reaction efficiency distribution with the deep processing data.

[0030] The stable configuration acquisition unit is used to maintain the current collaborative state and update the control parameters when the smooth transition requirements are met; and to iteratively adjust the overall operation of the pyrolysis furnace according to the updated control parameters to obtain a stable operating configuration state.

[0031] Thirdly, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0032] The beneficial effects of this invention are as follows:

[0033] This invention constructs state characteristics reflecting the reaction capacity of different zones, and uses the chlorine removal rate of the low-temperature dechlorination zone as a key constraint. When a decrease in dechlorination efficiency is detected, it can promptly link the upstream preheating and softening zone, adjusting the preheating input rate through dynamic flow allocation and heat flow co-calculation to match the material pretreatment intensity with the downstream reaction demand, effectively preventing chlorine accumulation in the system. Furthermore, based on the adjusted preheating input rate and the real-time temperature state of the main pyrolysis zone, it determines the target material input amount of the main pyrolysis zone through a material flux calculation model, and combines a heat carrier distribution control and reaction rate optimization model to match the material flow and heat supply ratio. The resulting decomposition efficiency distribution not only reflects the reaction effect of the main pyrolysis zone but also achieves continuous connection between staged reactions; it also... By fusing the decomposition efficiency distribution with the deep pyrolysis data of the high-temperature pyrolysis zones, the overall temperature management effect of the pyrolysis process is obtained. Based on this, the temperature gradient distribution between each zone is calculated, and by comparing it with the smooth transition threshold, the continuity and stability of heat transfer between zones are determined. When the gradient is abnormal, the heat flow or material configuration is adjusted in a timely manner to avoid thermal shock and reaction imbalance. After the smooth transition conditions are met, the dechlorination effect is verified and the pollutant emission control parameters are updated in conjunction with pollutant monitoring feedback. The overall stability is analyzed by integrating the whole furnace operation data to generate a continuous and stable operating configuration state. Through the mutual cooperation of the above technical solutions, the dynamic matching and continuous optimization of materials, heat and reaction efficiency in the plastic pyrolysis staged reaction process are realized, which significantly improves the pyrolysis furnace operation stability, energy efficiency level and pollutant control capability. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart of the real-time control method for edge calculation of the plastic pyrolysis staged reaction in the embodiment;

[0036] Figure 2 This is a schematic diagram of the iterative optimization process of chlorine removal rate in the examples;

[0037] Figure 3 This is a schematic diagram illustrating the changes in energy consumption after iterative optimization of the chlorine removal rate in the examples;

[0038] Figure 4 This is a structural diagram of the edge calculation real-time control system for the pyrolysis staged reaction of plastics in the embodiment. Detailed Implementation

[0039] This invention provides a method and system for real-time control of edge calculation in the pyrolysis and classification reaction of plastics. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0040] For ease of understanding, the specific process of the embodiments of the present invention will be described below, such as... Figure 1 As shown in the figure, an embodiment of the present invention provides a real-time control method for edge calculation of a plastic pyrolysis grading reaction, comprising:

[0041] Step S1: By deploying computing nodes in each zone of the pyrolysis furnace, real-time data from each zone is acquired and fused to obtain the reaction state characteristics of each zone; specifically including:

[0042] Edge computing nodes are deployed for the preheating and softening zone, the low-temperature dechlorination zone, the main pyrolysis zone, and the high-temperature pyrolysis zone, respectively. Real-time temperature and raw material composition data are obtained from each zone through independent data acquisition technology. Multi-source information from each zone is integrated using data fusion processing to generate characteristic data reflecting the reaction state. The operating status of each zone is analyzed through the characteristic data to determine the dynamic distribution of the reaction state characteristics. The reaction state characteristics of each zone are continuously monitored and updated.

[0043] Specifically, in this embodiment, for application scenarios where the operating conditions of each reaction stage in the pyrolysis staged reaction of plastics differ significantly, the reaction state changes rapidly, and the requirements for real-time control and response are high, this invention is proposed to achieve real-time coordinated control of the subsequent staged reaction in the pyrolysis furnace.

[0044] During the operation of the pyrolysis furnace, the plastic raw material undergoes multiple reaction stages, including preheating and softening, low-temperature dechlorination, main pyrolysis, and high-temperature pyrolysis. Each stage has significant differences in temperature range, reaction mechanism, and key control objectives. To avoid the problems of large data delays, untimely local state responses, and mutual interference between different zones caused by traditional centralized data acquisition and processing methods under complex operating conditions, this embodiment first deploys independent edge computing nodes in the preheating and softening zone, low-temperature dechlorination zone, main pyrolysis zone, and high-temperature pyrolysis zone of the pyrolysis furnace. This enables each zone to have local data acquisition, preprocessing, and preliminary analysis capabilities. Each edge computing node establishes a data connection with the central control module through wired or wireless communication, thereby ensuring data real-time performance while reducing the system load and communication delay caused by the centralized transmission of large-scale raw data.

[0045] With the support of the aforementioned edge computing nodes, a sensor array matching the reaction characteristics of each partition is deployed within each partition using a partitioned independent data acquisition technology. This array is used to collect real-time temperature data and plastic raw material composition data within each partition. The raw material composition data includes at least the chlorine content information closely related to the pyrolysis and classification reaction. The collected temperature and raw material composition data are first stored and managed locally on the corresponding partition's edge computing node, thus avoiding data cross-interference between different partitions and ensuring the independence and traceability of data in both time and space dimensions. This provides a reliable data foundation for subsequent partitioned collaborative analysis. After acquiring multi-source real-time data from each partition, data fusion processing is further performed on the multi-source data in the edge computing node or central control module to form reaction state characteristic data that comprehensively reflects the state of the plastic pyrolysis and classification reaction. The preferred method for this data fusion processing is... The Kalman filter algorithm is employed to establish a state-space model of the reaction states in each zone. Temperature and raw material composition data are recursively estimated. In the specific processing, the system state at the current moment is first predicted based on the reaction state characteristics of the previous moment. Then, the prediction results are corrected by combining real-time temperature and composition measurements. This effectively suppresses measurement noise and random fluctuations while improving the accuracy of the estimation of the true reaction state. Based on the fused data results, feature values ​​characterizing the reaction states of each zone are further calculated. These feature values ​​include, but are not limited to, the average temperature of the zone, the temperature gradient within the zone, the rate of change of raw material composition, and key reaction efficiency indicators related to the pyrolysis reaction of plastics. These feature values ​​are combined according to a predetermined data structure to form a reaction state feature dataset corresponding to each zone, reflecting the actual reaction level of the plastic raw materials at different reaction stages.

[0046] After obtaining the aforementioned reaction state characteristic data, further analysis is conducted to determine the dynamic distribution of the reaction state characteristics of each zone over time. Based on this, operational status indicators for each zone are calculated, such as reaction efficiency indicators characterizing the degree of reaction sufficiency and stability indicators measuring operational stability. The dynamic distribution results of the reaction state characteristics are then generated based on these operational status indicators. These dynamic distribution results can intuitively reflect the state changes of each zone during the plastic pyrolysis and classification reaction process and can be used to identify potential risk conditions such as sudden temperature changes and abnormal compositional variations. After completing the dynamic analysis of the reaction state characteristics, these characteristics are used as the basis for subsequent real-time control to support zone coordination. The system calculates and makes decisions regarding control parameters. Simultaneously, during continuous operation of the pyrolysis furnace, a fixed data update cycle is set, such as re-collecting temperature and raw material composition data for each zone at minute intervals, and updating the reaction state characteristics in real time based on the newly collected data. This ensures that the reaction state characteristics always accurately reflect the actual operating state of the plastic pyrolysis staged reaction. Through this technical solution, independent acquisition, multi-source fusion, dynamic analysis, and continuous updating of real-time data for each zone based on an edge computing architecture are achieved in the plastic pyrolysis staged reaction scenario. This provides a reliable, precise, and real-time foundation of reaction state characteristics for subsequent fine-tuning of key zone parameters, zone collaborative control, and iterative optimization of the overall operating state.

[0047] Step S2: Based on the reaction state characteristics, fine-tune the parameters of the low-temperature dechlorination zone to determine the dynamic trend of chlorine removal rate; if the dynamic trend is lower than the preset threshold, adjust the material input rate of the relevant zone and integrate the heat flow coordination parameters between adjacent zones to obtain the adjusted operating parameters.

[0048] In step S2, determining the dynamic trend of the chlorine removal rate includes:

[0049] Data on chlorine removal was extracted from the low-temperature dechlorination zone. Based on this data, the reaction efficiency of the low-temperature dechlorination zone was analyzed. The temperature of the low-temperature dechlorination zone was precisely adjusted using regional stratification control technology. The reaction conditions of the low-temperature dechlorination zone were optimized by adjusting the material flow rate. The dynamic trend of the chlorine removal rate was calculated based on the adjusted temperature and flow rate data.

[0050] Specifically, in this embodiment, since the low-temperature dechlorination zone has a key impact on the overall pyrolysis efficiency and pollutant control during the plastic pyrolysis grading reaction, after obtaining the reaction state characteristics of each zone, it is necessary to fine-tune the parameters of the above-mentioned low-temperature dechlorination zone to achieve real-time control of the key reaction process and provide reliable criteria for subsequent zone coordinated adjustment.

[0051] Specifically, after the plastic raw material enters the low-temperature dechlorination zone, the edge computing nodes deployed in this zone first analyze the real-time collected raw material composition data to extract the chlorine concentration value and the chlorine removal amount per unit time, which are directly related to the dechlorination reaction. This chlorine removal data originates from continuous monitoring results of the sensor array within the zone and is processed locally at the edge computing nodes, thus avoiding the impact of data transmission delays on the timeliness of reaction control. Based on the relationship between the current chlorine concentration value and the initial raw material input concentration, the initial chlorine removal ratio of the low-temperature dechlorination zone is calculated. After obtaining the chlorine removal data, the reaction efficiency of the low-temperature dechlorination zone is further quantitatively analyzed based on the correspondence between the removal amount and the raw material input amount, forming a reaction efficiency index reflecting the sufficiency of the dechlorination reaction in this zone. This reaction efficiency index serves as an important basis for subsequent temperature and material control, used to determine whether the current operating conditions deviate from the expected target range. The integrated regional stratified control technology precisely adjusts the temperature parameters of the low-temperature dechlorination zone. Specifically, the low-temperature dechlorination zone is divided into at least three layers along the material flow direction, with temperature sensors and independently controllable heating elements installed in each layer. Independent temperature adjustment of each layer is achieved through a feedback control loop. When the reaction efficiency analysis results indicate that the dechlorination efficiency is lower than the preset target value, the required temperature adjustment is determined based on the efficiency deviation, and the target temperature value of each layer is determined through calculation methods such as linear interpolation. For example, when the dechlorination efficiency is lower than the preset target value, the heating input in the middle layer is appropriately increased, while the temperature of the upper and lower layers is kept in a relatively stable range. This creates a more uniform temperature gradient distribution within the zone that is conducive to the dechlorination reaction. For plastic raw materials with high chlorine content or large compositional fluctuations, the above regional stratified control technology can be extended to a multi-layer structure. By monitoring the heat conduction between layers, it is ensured that the adjusted temperature deviation is limited to a predetermined range, thereby further improving the stability and uniformity of the dechlorination reaction.

[0052] After precisely adjusting the temperature of the low-temperature dechlorination zone, the reaction conditions were further optimized by adjusting the material flow rate in that zone. Specifically, based on the adjusted temperature distribution, the flow rate of the material in the low-temperature dechlorination zone was appropriately reduced, thereby extending the residence time of the plastic raw material in that zone and allowing the chlorine removal reaction to proceed under more complete conditions. The aforementioned material flow rate adjustment parameters and temperature adjustment parameters form a corresponding relationship, together constituting the current operating status adjustment result of that zone. After the temperature and material flow rate adjustments were completed, the dynamic trend of the chlorine removal rate was further calculated and evaluated based on the adjusted operating parameters. Specifically, chlorine removal data collected at multiple consecutive time points were constructed into a time series dataset, combined with the corresponding temperature data and material flow rate data. The data is used to calculate the chlorine removal rate. During time series processing, the moving average or exponential smoothing method is preferred to smooth the removal rate data to reduce the interference of short-term fluctuations on the judgment results and extract trend indicators reflecting the overall direction of change. Based on this, the trend indicators are fitted using the least squares method to quantify the slope value of the chlorine removal rate over time, thus forming a trend parameter that reflects the dynamic evolution characteristics of the dechlorination reaction, i.e., the dynamic trend of the chlorine removal rate. Through the above technical solution, real-time perception, fine adjustment, and effect judgment of key reaction processes in low-temperature dechlorination zones are realized in the plastic pyrolysis staged reaction scenario based on edge computing and multi-parameter collaborative analysis, thus laying a solid foundation for the stable operation of the entire real-time control method.

[0053] Further, in step S2, the adjusted operating parameters are obtained, including:

[0054] By comparing the dynamic trend of chlorine removal rate with a preset threshold, it is determined whether adjustment is needed. If it is lower than the preset threshold, the material stratification transfer rate of the preheating and softening zone is adjusted using dynamic flow allocation technology. The adjusted preheating input rate is calculated by integrating inter-regional heat flow coordination parameters. The operating parameters of the preheating and softening zone are updated based on the adjusted preheating input rate. The rationality of the material transfer rate adjustment is verified by monitoring the effect of the adjustment. The adjusted preheating input rate is then transmitted to the relevant zones for coordinated control.

[0055] Specifically, in this embodiment, after calculating the dynamic change trend of chlorine removal rate in the low-temperature dechlorination zone, it is determined whether the upstream zone's operating status needs to be adjusted in conjunction with the above dynamic change trend, so as to obtain the adjusted operating parameters for zone coordinated control, so as to ensure the continuity, stability and efficiency of the plastic pyrolysis classification reaction process at the overall level.

[0056] The dynamic trend of the chlorine removal rate is calculated by the edge computing node of the low-temperature dechlorination zone based on real-time collected temperature and material composition data, and is quantitatively characterized as the chlorine removal ratio or the rate of change of removal amount per unit time. The dynamic trend is compared with a preset threshold, which is used to characterize the minimum rate of change required for the low-temperature dechlorination reaction to maintain stable and efficient operation under the current plastic pyrolysis process conditions. When the comparison result shows that the dynamic trend of the chlorine removal rate is lower than the preset threshold, it is determined that the reaction state of the current low-temperature dechlorination zone is insufficient to meet the stable operation requirements of the subsequent main pyrolysis and high-temperature pyrolysis zones, thereby triggering the coordinated adjustment of the operating parameters of the upstream preheating and softening zone to avoid the accumulation of chlorine in the system and the risk of pollutant emissions.

[0057] When the adjustment conditions are met, the material stratification and transfer rate in the preheating and softening zone is first adjusted using dynamic flow allocation technology. This dynamic flow allocation technology is based on real-time information obtained from independent data collection of the zones. It manages the plastic raw materials entering the preheating and softening zone in stratified manner according to differences in material composition and sets material transfer rates for different levels. In specific implementation, it is preferable to adjust the transfer rates of easily softened, low-chlorine content, and high-chlorine content materials in the preheating and softening zone differently based on the dechlorination reaction status feedback from the low-temperature dechlorination zone. For example, the transfer rate of easily softened materials in the upper layer is appropriately reduced, while the transfer rate of high-chlorine content materials in the lower layer is maintained or finely adjusted. This makes the materials entering the low-temperature dechlorination zone more uniform in physical state and composition distribution, which is conducive to the full progress of the subsequent dechlorination reaction. In this way, the dynamic flow allocation results directly correspond to the reaction requirements of the low-temperature dechlorination zone, realizing upstream material pretreatment optimization based on reaction status feedback.

[0058] Based on the adjustment of the material stratification and transfer rate in the preheating and softening zone, the preheating input rate of the preheating and softening zone is recalculated by further integrating inter-regional heat flow coordination parameters. These inter-regional heat flow coordination parameters are derived from the heat transfer relationship between adjacent zones, specifically including the heat transfer coefficient between the preheating and softening zone and the low-temperature dechlorination zone, the real-time temperature data of the two zones, and the temperature gradient difference between the zones. Specifically, real-time temperature data of each zone is first extracted, and the heat flow exchange between adjacent zones is quantified using the heat balance equation. Then, multiple heat flow coordination parameters are fused using a weighted average method to obtain a coordinated heat flow value that reflects the continuity of the overall pyrolysis process. This coordinated heat flow value is further substituted into the preheating input rate adjustment model to calculate the adjusted preheating input rate that matches the current zone's heat load and material handling capacity, thereby ensuring the material transfer rate... Even with changes in the rate, the preheating and softening zone can still maintain a reasonable temperature level and heat supply efficiency. The aforementioned rate adjustment model is a parameter calculation model built in the edge computing node, constrained by the low-temperature dechlorination reaction requirements and centered on the heat and material synergy between the zones. It uses the dynamic change trend of the chlorine removal rate in the low-temperature dechlorination zone as the adjustment trigger input, and incorporates the real-time temperature data, heat transfer coefficient, and material stratification transfer status between the preheating and softening zone and the low-temperature dechlorination zone into the heat balance calculation relationship. The original input rate of the preheating and softening zone is weighted and corrected, thereby outputting an adjusted preheating input rate that matches the current dechlorination efficiency, heat flow synergy status, and material pretreatment requirements. This ensures that the upstream preheating intensity can effectively support the downstream dechlorination reaction and maintain the stable operation of the pyrolysis process without disrupting the overall flux continuity.

[0059] After obtaining the adjusted preheating input rate, it is directly applied to the operation control of the preheating softening zone, updating the zone's operating parameters. This updating includes not only adjusting the material input rate but also matching the temperature setpoint, heating power, and necessary auxiliary process parameters, such as oxygen or inert gas supply. By updating the calculated preheating input rate one-to-one with the zone control parameters, the operating state of the preheating softening zone is synchronized with the reaction requirements of the low-temperature dechlorination zone, thus achieving coordinated matching between zones at the system level. After completing the operating parameter update, through... Real-time monitoring of the adjustment effect verifies the rationality of the material transfer rate and preheating input rate adjustments. Specifically, within a preset time window after parameter adjustment, chlorine concentration change data of the low-temperature dechlorination zone is continuously collected, and the residual chlorine concentration before and after adjustment is compared and analyzed. When the monitoring results show that the chlorine concentration decrease reaches or exceeds the expected target, the adjustment of the material transfer rate and preheating input rate is deemed effective, thus confirming the rationality and sustainability of the adjusted operating parameters. Conversely, the relevant parameters can be readjusted based on the monitoring results to avoid energy waste caused by ineffective or excessive adjustments.

[0060] After confirming the rationality of the adjusted preheating input rate, the adjusted preheating input rate and related operating parameters are transmitted to downstream zones such as the main pyrolysis zone via edge computing nodes. This is used to guide the coordinated control of material flow ratio and heat distribution. Through the transmission and sharing of these parameters between zones, the main pyrolysis zone can adjust its operating strategy in a timely manner based on the upstream pretreatment status. This forms a closed-loop control mechanism in the overall plastic pyrolysis staged reaction process, with the low-temperature dechlorination reaction status as the core feedback signal and the coordinated response of upstream preheating and downstream main pyrolysis as the control means. The above technical solution not only realizes the effective acquisition and application of the adjusted operating parameters, but also ensures that the plastic pyrolysis staged reaction process can maintain stable, efficient and low-emission operation under complex working conditions.

[0061] Step S3: Based on the adjusted operating parameters, regulate the material flow rate and heat distribution of the main pyrolysis zone to obtain the reaction efficiency distribution; based on the reaction efficiency distribution and combined with the deep processing data, determine whether the gradient of each zone meets the requirements for a smooth transition.

[0062] In step S3, the reaction efficiency distribution data is obtained, including:

[0063] Based on the adjusted preheating input rate, material flow is regulated for the main pyrolysis zone; heat distribution in the main pyrolysis zone is optimized using heat carrier distribution regulation technology; reaction rate optimization technology is used to perform matching calculations on the ratio of input material flow to heat carrier; decomposition efficiency distribution data of the main pyrolysis zone is generated through the matching calculations; the decomposition efficiency distribution data is used to characterize the reaction effect of the main pyrolysis zone.

[0064] Specifically, after adjusting the parameters of the preheating softening zone and the low-temperature dechlorination zone and obtaining the adjusted preheating input rate, the reaction efficiency of the main pyrolysis zone is calculated to achieve continuous, stable, and efficient operation of the plastic pyrolysis process under staged reaction conditions. Specifically, the edge computing node first receives the adjusted preheating input rate output from the preheating softening zone. This preheating input rate originates from the dynamic adjustment result of the material stratification transport rate within the preheating softening zone and is correlated with the real-time temperature data of this zone and the main pyrolysis zone. The preheating input rate and real-time temperature data are then input together into the material throughput calculation module built into the edge computing node. In this model, the target material input amount of the main pyrolysis zone under the current operating conditions is calculated. This target material input amount reflects the material supply level required for the main pyrolysis zone to achieve complete pyrolysis under given temperature and reaction time conditions in the plastic pyrolysis staged reaction scenario. Based on the above target material input amount, the material conveying device of the main pyrolysis zone is controlled in a closed loop, and the material flow rate is adjusted in real time to ensure that the plastic material entering the main pyrolysis zone is uniform in spatial and temporal distribution, thereby avoiding material accumulation, local overheating, or incomplete reaction caused by local feeding being too fast or too slow. During the above process, the edge computing node continuously monitors the main pyrolysis zone. The changes in material flow rate were monitored, and the data were compared with the target material input to verify whether the material flow control effect met the expected reaction stability requirements. The aforementioned material throughput calculation model was obtained through a combination of offline training under historical operating conditions and online correction during operation. The training data came from actual operating records of the pyrolysis furnace at different operating stages. The offline training stage used historical stable operating conditions as the sample basis, using the adjusted preheating input rate data and real-time temperature data of the main pyrolysis zone under the corresponding operating conditions as model input features. The main pyrolysis zone, verified under this operating condition to not cause material accumulation and maintain stable decomposition efficiency, was also included. The material input quantity is used as the target output to fit and train the model parameters, thereby establishing a mapping relationship between the material supply capacity and the reaction carrying capacity. After the model is put into operation, the edge computing nodes further use feedback data such as temperature stability, decomposition efficiency distribution, and whether the operation is abnormal as correction samples obtained during real-time operation. The model output is compared with the actual operating effect, and the model parameters are fine-tuned online when deviations occur. This allows the material throughput calculation model to continuously adapt to different raw material characteristics, heat flow coordination state, and load change conditions, ensuring that the target material input quantity output always remains consistent with the actual operating capacity of the plastic pyrolysis and classification reaction.

[0065] While controlling the material flow, the heat distribution in the main pyrolysis zone is optimized using heat carrier distribution control technology. Specifically, edge computing nodes, based on independent data acquisition technology for each zone, acquire real-time temperature data and plastic raw material composition data at different locations within the main pyrolysis zone. This multi-source data is then fused to form a basic dataset reflecting the current thermal state and material characteristics of the main pyrolysis zone. Based on this, and combined with inter-regional heat flow coordination parameters, the heat carrier distribution requirements of the main pyrolysis zone under the current material load and reaction stage are calculated. These heat carrier distribution requirements characterize the injection ratio, injection path, and injection rate of the heat carrier at different locations within the zone, ensuring that the heat is matched to the material flow path. This method achieves uniform heating of plastic materials within the main pyrolysis zone. Based on the calculation results, the injection point and injection rate of the heat carrier are dynamically adjusted to create a heat distribution structure within the main pyrolysis zone that matches the pyrolysis reaction requirements of the plastic. By comparing and analyzing the temperature data before and after adjustment, it is verified whether the heat distribution achieves a smooth transition of the temperature gradient. When handling special conditions such as high-chlorinated plastic raw materials, the heat carrier distribution is set to a gradual change mode along the material flow direction. That is, a relatively high proportion of heat carrier is provided in the zone entrance area, and then the heat carrier supply is gradually reduced to more accurately match the heating requirements of the plastic material at different pyrolysis stages, thereby reducing energy consumption and improving the pollutant removal effect.

[0066] After coordinating the control of material flow and heat distribution, the ratio of input material flow to heat carrier within the main pyrolysis zone is matched and calculated using reaction rate optimization technology. Specific edge computing nodes extract the reaction state characteristics of the main pyrolysis zone from the aforementioned data fusion processing. These reaction state characteristics include at least parameters such as the regional average temperature, temperature gradient, material residence time, and plastic composition change rate. Based on this, a ratio matching model centered on the Arrhenius reaction rate equation is constructed. The aforementioned reaction state characteristics are used as model input parameters, and real-time material flow data and heat carrier distribution data are also input into the model for calculation to obtain the optimal ratio of material flow to heat carrier that maximizes the plastic decomposition rate under the current operating conditions. Through this ratio matching calculation, the optimal ratio can be calculated based on changes in low-temperature dechlorination effect, raw material composition fluctuations, or zone temperature variations. Under conditions such as temperature changes, the reaction conditions of the main pyrolysis zone are dynamically adjusted to ensure that the plastic pyrolysis reaction is always maintained in the high-efficiency range. Based on the above matching calculation results, the edge computing nodes further simulate the reaction rate at different spatial locations within the main pyrolysis zone, generating decomposition efficiency distribution data for the main pyrolysis zone. This decomposition efficiency distribution is used to characterize the change in pyrolysis conversion efficiency of the plastic material from the inlet to the outlet of the main pyrolysis zone, thus forming an efficiency distribution map reflecting the uniformity and sufficiency of the reaction within the zone. Through the above technical solution, the above decomposition efficiency distribution is transmitted to the corresponding control module as the basis for subsequent high-temperature pyrolysis zone deep processing control, thereby realizing continuous coordinated control of each zone in terms of material, heat, and reaction efficiency during the plastic pyrolysis staged reaction process, ensuring real-time optimization and stable operation of the overall pyrolysis process.

[0067] Further, in step S3, determining whether the gradient of each partition meets the smooth transition requirement includes:

[0068] Obtain the decomposition efficiency distribution data of the main pyrolysis zone; combine the deep pyrolysis processing data of the high-temperature pyrolysis zone to analyze the temperature management effect of the overall pyrolysis process; calculate the temperature gradient distribution between each zone based on the temperature management effect; determine whether the smooth transition requirement is met based on the temperature gradient distribution; if the smooth transition requirement is not met, generate an adjustment command and transmit it to the relevant zone; if the smooth transition requirement is met, record the current temperature gradient data.

[0069] Specifically, after obtaining the above-mentioned decomposition efficiency distribution, the multi-partition data fusion and gradient analysis mechanism is used to determine whether the smooth transition requirements between each partition are met, so as to ensure the continuity and stability of the plastic pyrolysis classification reaction in terms of space and heat distribution.

[0070] The aforementioned decomposition efficiency distribution is used to characterize the changes in pyrolysis conversion rate of plastic materials at different spatial locations within the main pyrolysis zone, thereby forming spatial distribution data reflecting the uniformity and sufficiency of the reaction within the main pyrolysis zone. This decomposition efficiency distribution data is stored in a local or central database as the basic input data for subsequent multi-zone collaborative analysis and temperature management assessment. Based on this, deep pyrolysis processing data for the high-temperature pyrolysis zone is obtained. This deep pyrolysis processing data includes at least parameters such as the real-time pyrolysis temperature, residual component concentration, and pyrolysis gas production rate of the high-temperature pyrolysis zone. The decomposition efficiency distribution data of the main pyrolysis zone is then integrated with the deep pyrolysis data of the high-temperature pyrolysis zone via edge computing nodes. The data is fused and processed by introducing a weighted average method based on the partitioned heat flow coordination parameters to perform unified modeling of multi-source data. The weighting coefficients corresponding to different partitions are set according to the role of each partition in the plastic pyrolysis staged reaction process and the continuity of heat flow. For example, in a typical plastic pyrolysis scenario, the data of the main pyrolysis partition is given a higher weight, and the data of the high-temperature pyrolysis partition is given a relatively lower weight to reflect the process characteristics of the plastic pyrolysis reaction progressing step by step from the main pyrolysis to the deep pyrolysis. Through the above data fusion process, temperature management effect data reflecting the overall pyrolysis process operation status is obtained. The above temperature management effect data also includes the average temperature value of each partition.

[0071] After obtaining temperature management effectiveness data, the average temperature stability and temperature fluctuation range of the overall pyrolysis process are calculated based on this data. Statistical analysis of the temperature changes of each zone over time is performed to determine whether the heat input and consumption during the current plastic pyrolysis and grading reaction are in balance. When the calculated temperature fluctuation range is within a preset range, it indicates that the current heat management strategy can effectively suppress heat loss and improve energy utilization. The average temperature values ​​of the preheating softening zone, low-temperature dechlorination zone, main pyrolysis zone, and high-temperature pyrolysis zone are extracted from the temperature management effectiveness data. Based on the spatial arrangement of each zone within the pyrolysis furnace, the temperature difference between adjacent zones is calculated, thus obtaining the temperature gradient between each zone. This temperature gradient is obtained by comparing adjacent zones... The average temperature difference between zones is divided by the corresponding zone length to calculate the spatial transition of heat in the plastic pyrolysis staged reaction process. Based on the above calculation results, temperature gradient distribution data is generated to form a continuous temperature gradient distribution sequence covering the entire pyrolysis process. After obtaining the temperature gradient distribution sequence, the edge computing nodes compare the above temperature gradient distribution sequence with the preset smooth transition threshold. When the temperature gradient between all adjacent zones does not exceed the above smooth transition threshold, it is determined that the smooth transition requirement between zones is met, indicating that the plastic material can achieve continuous and uniform heating and reaction in the staged pyrolysis process. In this case, the current temperature gradient distribution data is stored in the database as a reference benchmark for subsequent operation condition optimization and parameter iteration adjustment.

[0072] Conversely, when the temperature gradient between at least one adjacent zone exceeds the aforementioned smooth transition threshold, it is determined that there is a risk of local heat transition instability in the current pyrolysis process, and a corresponding adjustment command is automatically generated. The adjustment command is generated based on the zone location and gradient deviation degree corresponding to the threshold gradient, and is used to guide the relevant zone to make targeted adjustments to the heat carrier input, material flow rate, or zone operating parameters. The adjustment command is transmitted to the control device of the corresponding zone in real time through the edge computing node, and the temperature gradient change after adjustment is continuously monitored during the execution process until the temperature gradient between each zone falls back to the preset threshold range, thereby restoring the smooth transition state between each zone in the plastic pyrolysis staged reaction process. The above technical solution realizes real-time control and stable operation of the plastic pyrolysis staged reaction under complex working conditions through a collaborative judgment mechanism of decomposition efficiency distribution, deep pyrolysis data, and temperature gradient analysis.

[0073] Step S4: If the smooth transition requirement is met, maintain the current collaborative state and update the control parameters; based on the updated control parameters, iteratively adjust the overall operation of the pyrolysis furnace to obtain a stable operating configuration state.

[0074] In step S4, if the smooth transition requirement is met, the current cooperative state is maintained and the control parameters are updated, including:

[0075] By determining whether the temperature gradient of each zone meets the requirements for a smooth transition, it is determined whether to maintain the current state of coordinated heat flow between regions. If the smooth transition requirements are met, chlorine removal verification information is extracted from the pollutant monitoring feedback data. Based on the chlorine removal verification information, the pollutant emission control effect is analyzed. Based on the control effect, the updated pollutant emission control parameters are determined. The updated control parameters are stored in the system database.

[0076] Specifically, after completing the temperature gradient calculation for each zone and confirming that the gradient distribution meets the requirements for a smooth transition, the system further enters the stage of maintaining the coordinated state and updating control parameters, so as to achieve adaptive optimization of pollutant emission control parameters while ensuring the continuous and stable pyrolysis process.

[0077] Specifically, edge computing nodes continuously collect real-time temperature data from the preheating and softening zone, the low-temperature dechlorination zone, the main pyrolysis zone, and the high-temperature cracking zone. Based on the spatial relative positions of each zone within the pyrolysis furnace, the temperature data of adjacent zones are processed accordingly to calculate the temperature difference between adjacent zones, thereby forming a temperature gradient distribution sequence that reflects the heat transfer characteristics of the entire plastic pyrolysis staged reaction process. This temperature gradient distribution sequence is used to characterize the continuous change of heat flow between each zone, and can intuitively reflect whether there is a sudden change in heat or a risk of local overheating under the current operating conditions. The temperature gradient distribution sequence is compared and analyzed with a preset smooth transition threshold. When it is determined that the temperature gradient between all adjacent zones is within the threshold range, it is considered that the heat transition between each zone is in a smooth state, thereby determining to maintain the current heat flow coordination state between regions without additional intervention in the heat flow distribution strategy. This judgment mechanism can avoid unnecessary frequent adjustments during plastic pyrolysis, reduce energy loss, and maintain the continuous stability of reaction conditions.

[0078] Based on the confirmation of maintaining the current heat flow coordination state, chlorine removal verification information related to the low-temperature dechlorination zone is extracted from the pollutant monitoring feedback data. This chlorine removal verification information is collected in real time by sensors distributed in the low-temperature dechlorination zone and its downstream locations. It includes at least the chlorine removal rate per unit time and the concentration data of residual chlorine in the pyrolysis product stream. The above data is processed and correlated by edge computing nodes. The removal rate data is compared with the preset dechlorination efficiency standard. At the same time, the residual concentration data is statistically analyzed. By calculating its mean and variance, the uniformity and stability of pollutant emissions in the time and spatial dimensions are evaluated. Based on the above analysis results, a control effect score is generated to characterize the current pollutant emission control effect, thereby realizing a quantitative evaluation of the dechlorination effect.

[0079] When the control effect score is higher than the preset score, it is determined that the pollutant emission control effect under the current plastic pyrolysis staged reaction condition is good, indicating that under the existing stable temperature gradient and heat flow synergy, the reaction parameters of the low-temperature dechlorination zone can effectively support the overall emission control target. Conversely, when the control effect score is lower than the preset score, by analyzing the removal rate fluctuation and residual concentration distribution, zones or operating stages with emission risks can be identified. By fusing the chlorine removal verification information with the temperature data of multiple zones, a refined assessment of the pollutant emission status can be achieved while meeting the stability of the pyrolysis reaction.

[0080] After completing the pollutant emission control effect analysis, updated pollutant emission control parameters are determined based on the control effect. These control parameters include at least the temperature threshold, material flow setpoint, and adjustment parameters related to heat flow synergy, all related to the dechlorination reaction. Subsequently, the updated pollutant emission control parameters are stored in the system database to form parameter records corresponding to the current plastic pyrolysis and classification reaction conditions. Through centralized storage and management of control parameters, the historical optimal parameter combinations can be quickly recalled during subsequent overall operation adjustments or condition switching, and iterative optimization can be performed in combination with real-time data. This provides continuous data support and decision-making basis for the long-term stable operation and low-emission control of the plastic pyrolysis and classification reaction.

[0081] Furthermore, in step S4, a stable operating configuration state is obtained, including:

[0082] By deploying edge nodes, updated pollutant emission control parameters are transmitted to each section of the pyrolysis furnace. Based on the updated control parameters, the operating configuration of each section is adjusted. Real-time data from all sections are integrated to analyze the stability of the overall operating status. Through the stability analysis, a continuous and stable operating configuration status is generated. Based on the operating configuration status, the overall operating effect of the pyrolysis furnace is continuously monitored. The operating configuration status is used as the basis data for the next iteration adjustment.

[0083] Specifically, after updating the pollutant emission control parameters, a distributed edge node collaboration and iterative stability analysis mechanism is used to obtain an operating configuration state that can support the long-term stable operation of the plastic pyrolysis staged reaction, thereby achieving continuous optimization of the overall operation of the pyrolysis furnace.

[0084] Specifically, edge node deployment technology is first used to distribute the updated pollutant emission control parameters to various functional zones of the pyrolysis furnace. These edge nodes are deployed in the preheating and softening zone, the low-temperature dechlorination zone, the main pyrolysis zone, and the high-temperature pyrolysis zone, respectively, forming local closed-loop control units with the temperature sensors, component detection devices, and actuators in their respective zones. After generating the updated pollutant emission control parameters, the central control system sends these parameters as data packets to each edge node via wired or wireless communication networks. After receiving and decoding the parameters, each edge node directly maps them to the control objects in the corresponding zone, enabling rapid parameter activation. This distributed transmission method effectively shortens the transmission path of the control parameters, making parameter distribution and execution seamless. The overall response time of the process is controlled within a preset range to meet the real-time requirements of the plastic pyrolysis and grading reaction. After the parameters are issued, each zone adjusts its own operating configuration according to the received pollutant emission control parameters. Specifically, each zone controller adjusts the temperature setpoint, material conveying rate, and working status of related actuators within the zone according to the aforementioned control parameters to match the zone's operating conditions with the current dechlorination efficiency target and pyrolysis reaction requirements. For example, in the low-temperature dechlorination zone, the zone temperature setpoint can be increased and the material flow rate adjusted synchronously according to the new control parameters to enhance the chlorine removal effect. In the main pyrolysis zone and the high-temperature pyrolysis zone, the heat carrier supply or material flow rate is matched and adjusted according to the corresponding parameters to form a coordinated and consistent operating state at the zone level.

[0085] After the operational configuration adjustments for each partition are completed, real-time operational data from all partitions are integrated to analyze the stability of the overall operational status of the pyrolysis furnace. Specific edge nodes continuously collect real-time temperature and raw material composition data for their respective partitions, and integrate them into a unified state vector using data fusion methods to characterize the overall operational status of the pyrolysis furnace under the current plastic pyrolysis staged reaction conditions. Subsequently, statistical analysis is performed on the key parameters in the aforementioned state vector. The variance of each parameter over time is calculated to assess the degree of fluctuation in the overall operational status. When the calculated variance is lower than a preset threshold, it indicates that the operational status of each partition changes little, and the overall operation is in a stable range; conversely, it indicates that the system exhibits a fluctuating trend and requires further parameter fine-tuning. Based on the above stability analysis results, the corresponding operational configuration status is generated. When the stability analysis results indicate that the system is in a stable operating state, the generated operating configuration includes fixed values ​​for the temperature gradient of each zone, material flow rate, and related control parameters, which serve as the stable operating benchmark under the current conditions. When the stability analysis results show slight fluctuations, some parameters are slightly adjusted based on the existing configuration, and the stability analysis is repeatedly performed through iterative calculations until the variance of the overall state vector falls below a preset threshold, thereby obtaining a continuous and stable operating configuration. Through this iterative generation process, the system can gradually converge to the stable operating range without causing drastic fluctuations, thus improving the robustness of the plastic pyrolysis classification reaction process.

[0086] After obtaining the above-mentioned operating configuration status, the overall operating effect of the pyrolysis furnace is continuously monitored based on this configuration status. Specifically, the real-time monitored chlorine removal rate is compared with the preset target value to determine whether the current operating configuration can meet the pollutant control requirements. When the actual removal rate is higher than the target threshold, the current operating configuration status is confirmed to be effective. Simultaneously, the above-mentioned operating configuration status is stored as the basis data for the next iteration adjustment, enabling subsequent adjustments to perform target-based optimization based on the existing stable state. For example, if a predetermined dechlorination efficiency has been achieved in the previous iteration, the target value can be gradually increased in the next iteration to achieve continuous improvement in pollutant control effect and energy efficiency level. This technical solution, through a combination of edge computing node collaboration, stability analysis, and iterative optimization, achieves long-term stable maintenance and continuous optimization of the pyrolysis furnace operating configuration status during the plastic pyrolysis staged reaction process. Figure 2 and Figure 3 As shown, Figure 2 and Figure 3 As shown, Figure 2A line chart is used to present the changes in target and actual chlorine removal rates over multiple iterations. The horizontal axis represents the iteration rounds, from round 1 to round 5, and the vertical axis represents the chlorine removal rate (in %). The dark gray solid line with circular markers represents the actual chlorine removal rate, and the light gray dashed line with square markers represents the target chlorine removal rate. The chart also indicates the initial actual value of 4% and the first-round target value of 4.5%, clearly demonstrating the target setting and actual achievement of the iterative optimization. Figure 3 As shown, the change in energy consumption after iterative optimization has resulted in improved energy efficiency.

[0087] This invention also provides a real-time edge calculation control system for plastic pyrolysis and grading reactions, used to implement the above-mentioned method, such as... Figure 4 As shown, the system includes:

[0088] The state feature acquisition unit is used to acquire real-time data from each partition of the pyrolysis furnace by deploying computing nodes in each partition and performing fusion processing to obtain the reaction state features of each partition.

[0089] The dechlorination trend analysis unit is used to fine-tune the parameters of the low-temperature dechlorination zone according to the reaction state characteristics, and determine the dynamic trend of chlorine removal rate.

[0090] The operating parameter adjustment unit is used to adjust the material input rate of the relevant partition when the dynamic change trend is lower than a preset threshold, and to integrate the heat flow coordination parameters between adjacent partitions to obtain the adjusted operating parameters.

[0091] The gradient transition judgment unit is used to regulate the material flow and heat distribution of the main pyrolysis zone according to the adjusted operating parameters to obtain the reaction efficiency distribution; and to judge whether the gradient of each zone meets the requirements for a smooth transition by combining the reaction efficiency distribution with the deep processing data.

[0092] The stable configuration acquisition unit is used to maintain the current collaborative state and update the control parameters when the smooth transition requirements are met; and to iteratively adjust the overall operation of the pyrolysis furnace according to the updated control parameters to obtain a stable operating configuration state.

[0093] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0094] In summary, this invention constructs state characteristics reflecting the reaction capacity of different zones, and uses the chlorine removal rate of the low-temperature dechlorination zone as a key constraint. When a decrease in dechlorination efficiency is detected, it can promptly link the upstream preheating and softening zone, adjusting the preheating input rate through dynamic flow allocation and heat flow co-calculation to match the material pretreatment intensity with the downstream reaction demand, effectively preventing chlorine accumulation within the system. Furthermore, based on the adjusted preheating input rate and the real-time temperature state of the main pyrolysis zone, a material throughput calculation model determines the target material input amount for the main pyrolysis zone. Combined with a heat carrier distribution control and reaction rate optimization model, the material flow and heat supply ratio are matched and calculated. The resulting decomposition efficiency distribution not only reflects the reaction effect of the main pyrolysis zone but also achieves continuous connection between staged reactions. The decomposition efficiency distribution of each zone is fused with the deep pyrolysis data of the high-temperature pyrolysis zone to obtain the overall temperature management effect of the pyrolysis process. Based on this, the temperature gradient distribution between each zone is calculated, and by comparing it with the smooth transition threshold, it is determined whether the heat transfer between zones is continuous and stable. When the gradient is abnormal, the heat flow or material configuration is adjusted in time to avoid thermal shock and reaction imbalance. After the smooth transition condition is met, the dechlorination effect is verified and the pollutant emission control parameters are updated in combination with pollutant monitoring feedback. The overall stability is analyzed by integrating the whole furnace operation data to generate a continuous and stable operating configuration state. Through the cooperation of the above technical solutions, the dynamic matching and continuous optimization of materials, heat and reaction efficiency in the plastic pyrolysis staged reaction process are realized, which significantly improves the pyrolysis furnace operation stability, energy efficiency level and pollutant control capability.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time control of edge calculation in a plastic pyrolysis staged reaction, characterized in that, The method includes: Step S1: By deploying computing nodes in each section of the pyrolysis furnace, real-time data from each section is acquired and fused to obtain the reaction state characteristics of each section; Step S2: Based on the reaction state characteristics, fine-tune the parameters of the low-temperature dechlorination zone to determine the dynamic trend of chlorine removal rate; if the dynamic trend is lower than the preset threshold, adjust the material input rate of the relevant zone and integrate the heat flow coordination parameters between adjacent zones to obtain the adjusted operating parameters. Step S3: Based on the adjusted operating parameters, regulate the material flow rate and heat distribution of the main pyrolysis zone to obtain the reaction efficiency distribution; based on the reaction efficiency distribution and combined with the deep processing data, determine whether the gradient of each zone meets the requirements for a smooth transition. Step S4: If the smooth transition requirement is met, maintain the current collaborative state and update the control parameters; based on the updated control parameters, iteratively adjust the overall operation of the pyrolysis furnace to obtain a stable operating configuration state; The process involves adjusting the material input rate of relevant zones and integrating the heat flow coordination parameters between adjacent zones to obtain adjusted operating parameters. This includes: determining whether adjustment is needed by comparing the dynamic trend of chlorine removal rate with a preset threshold; if it is lower than the preset threshold, adjusting the material input rate of the preheating and softening zone using dynamic flow allocation technology; calculating the adjusted preheating input rate by integrating the heat flow coordination parameters between zones; updating the operating parameters of the preheating and softening zone based on the adjusted preheating input rate; verifying the rationality of the material input rate adjustment by monitoring the effect of the adjustment; and transmitting the adjusted preheating input rate to relevant zones for coordinated control.

2. The method as described in claim 1, characterized in that, In step S1, the reaction state characteristics of each partition are obtained, including: Edge computing nodes are deployed for the preheating and softening zone, the low-temperature dechlorination zone, the main pyrolysis zone, and the high-temperature pyrolysis zone, respectively. Real-time temperature and raw material composition data are obtained from each zone through independent data acquisition technology. Multi-source information from each zone is integrated using data fusion processing to generate characteristic data reflecting the reaction state. The operating status of each zone is analyzed through the characteristic data to determine the dynamic distribution of the reaction state characteristics. The reaction state characteristics of each zone are continuously monitored and updated.

3. The method as described in claim 1, characterized in that, In step S2, the dynamic trend of chlorine removal rate is determined, including: Data on chlorine removal was extracted from the low-temperature dechlorination zone. Based on this data, the reaction efficiency of the low-temperature dechlorination zone was analyzed. The temperature of the low-temperature dechlorination zone was precisely adjusted using regional stratification control technology. The reaction conditions of the low-temperature dechlorination zone were optimized by adjusting the material flow rate. The dynamic trend of the chlorine removal rate was calculated based on the adjusted temperature and flow rate data.

4. The method as described in claim 1, characterized in that, In step S3, reaction efficiency distribution data are obtained, including: Based on the adjusted preheating input rate, material flow rate is regulated for the main pyrolysis zone; heat distribution in the main pyrolysis zone is optimized using heat carrier distribution regulation technology; reaction efficiency optimization technology is used to perform matching calculations on the input material flow rate and heat carrier ratio; reaction efficiency distribution data of the main pyrolysis zone is generated through the matching calculations; the reaction efficiency distribution data is used to characterize the reaction effect of the main pyrolysis zone.

5. The method as described in claim 4, characterized in that, In step S3, it is determined whether the gradient of each partition meets the requirements for a smooth transition, including: Obtain the reaction efficiency distribution data of the main pyrolysis zone; combine the deep pyrolysis processing data of the high-temperature pyrolysis zone to analyze the temperature management effect of the overall pyrolysis process; calculate the temperature gradient distribution between each zone based on the temperature management effect; determine whether the smooth transition requirement is met based on the temperature gradient distribution; if the smooth transition requirement is not met, generate an adjustment command and transmit it to the relevant zone; if the smooth transition requirement is met, record the current temperature gradient data.

6. The method as described in claim 1, characterized in that, In step S4, if the smooth transition requirement is met, the current cooperative state is maintained and the control parameters are updated, including: By determining whether the temperature gradient of each zone meets the requirements for a smooth transition, it is determined whether to maintain the current state of coordinated heat flow between regions. If the smooth transition requirements are met, chlorine removal verification information is extracted from the pollutant monitoring feedback data. Based on the chlorine removal verification information, the pollutant emission control effect is analyzed. Based on the control effect, the updated pollutant emission control parameters are determined. The updated control parameters are stored in the system database.

7. The method as described in claim 6, characterized in that, In step S4, a stable running configuration state is obtained, including: By deploying edge nodes, updated pollutant emission control parameters are transmitted to each section of the pyrolysis furnace. Based on the updated control parameters, the operating configuration of each section is adjusted. Real-time data from all sections are integrated to analyze the stability of the overall operating status. Through the stability analysis, a continuous and stable operating configuration status is generated. Based on the operating configuration status, the overall operating effect of the pyrolysis furnace is continuously monitored. The operating configuration status is used as the basis data for the next iteration adjustment.

8. A real-time control system for edge calculation of a plastic pyrolysis staged reaction, used to implement the method as described in any one of claims 1-7, characterized in that, The system includes: The state feature acquisition unit is used to acquire real-time data from each partition of the pyrolysis furnace by deploying computing nodes in each partition and performing fusion processing to obtain the reaction state features of each partition. The dechlorination trend analysis unit is used to fine-tune the parameters of the low-temperature dechlorination zone according to the reaction state characteristics, and determine the dynamic trend of chlorine removal rate. The operating parameter adjustment unit is used to adjust the material input rate of the relevant partition when the dynamic change trend is lower than a preset threshold, and to integrate the heat flow coordination parameters between adjacent partitions to obtain the adjusted operating parameters. The gradient transition judgment unit is used to regulate the material flow and heat distribution of the main pyrolysis zone according to the adjusted operating parameters to obtain the reaction efficiency distribution; and to judge whether the gradient of each zone meets the requirements for a smooth transition by combining the reaction efficiency distribution with the deep processing data. The stable configuration acquisition unit is used to maintain the current collaborative state and update the control parameters when the smooth transition requirements are met; and to iteratively adjust the overall operation of the pyrolysis furnace according to the updated control parameters to obtain a stable operating configuration state.

9. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the method as described in any one of claims 1-7.

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