Intelligent multi-effect impurity removal system for waste plastic oil refining material

Abstract

This invention discloses an intelligent multi-effect impurity removal system for waste plastic pyrolysis materials, comprising a material pretreatment unit, a feed chlorine content detection unit, an intelligent dechlorination unit, an online hydrogen chloride detection unit, and an AI control unit. The feed chlorine content detection unit is located downstream of the material pretreatment unit and is used to acquire the feed chlorine content information of the waste plastic pyrolysis materials. The intelligent dechlorination unit is used for thermal dechlorination treatment of the waste plastic pyrolysis materials and includes a heating component and a material conveying component. The online hydrogen chloride detection unit is connected to the gas outlet of the intelligent dechlorination unit and is used to acquire the hydrogen chloride concentration information generated during the thermal dechlorination process. The AI ​​control unit adjusts the dechlorination conditions adaptively according to the fluctuations in the chlorine content of the waste plastic raw materials and the actual state of the dechlorination process, reducing equipment corrosion and catalyst deactivation caused by insufficient dechlorination, and avoiding premature cracking, coking, and increased energy consumption caused by overheating.

Description

Technical Field

[0001] This invention belongs to the field of impurity removal technology, specifically relating to an intelligent multi-effect impurity removal system for waste plastic pyrolysis materials. Background Technology

[0002] Pyrolysis of waste plastics is an important method for the resource utilization of waste plastics. Due to the complex sources of waste plastics, the raw materials often contain polyvinyl chloride (PVC), chlorine-containing additives, chlorine-containing packaging materials, and other chlorine-containing impurities. These chlorine-containing components release hydrogen chloride gas during preheating or pyrolysis. Hydrogen chloride corrodes the pyrolysis reactor, pipelines, condensation equipment, and tail gas treatment equipment, and may enter subsequent catalytic treatment units, leading to catalyst poisoning, decreased activity, and shortened lifespan, thus affecting the continuous and stable operation of the waste plastics refining unit.

[0003] Existing technologies typically employ manual sorting, water washing separation, or fixed-condition pre-dechlorination to remove chlorine-containing impurities from waste plastics. Manual sorting relies on operator experience and struggles to accurately identify chlorine-containing plastics that are similar in color, broken in shape, or heavily contaminated on the surface, resulting in low processing efficiency and stability. Water washing separation primarily removes surface contaminants and some soluble impurities, but it is difficult to remove chlorine-containing components that are internal to the plastic or mixed within the plastic matrix, and it also incurs wastewater treatment costs. Fixed-condition pre-dechlorination relies on preset temperatures and residence times, making it impossible to adjust dechlorination conditions in real time according to fluctuations in the chlorine content of different batches of waste plastics.

[0004] Because the chlorine content, particle size, moisture content, and component ratio of waste plastic raw materials are constantly changing, it is difficult to balance dechlorination efficiency and material protection under fixed dechlorination conditions. When the dechlorination temperature or residence time is too low, the removal of chlorine-containing components is insufficient, which can still cause corrosion of subsequent equipment and catalyst deactivation; when the dechlorination temperature or residence time is too high, it can easily lead to premature pyrolysis, coking, oil and gas loss, and increased energy consumption of waste plastics.

[0005] Therefore, in order to address the aforementioned technical issues, it is necessary to provide an intelligent multi-effect impurity removal system for waste plastic pyrolysis materials. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent multi-effect impurity removal system for waste plastic pyrolysis materials, which can solve the technical problems mentioned in the background art.

[0007] To achieve the above objectives, a specific embodiment of the present invention provides the following technical solution: A smart multi-effect impurity removal system for waste plastic pyrolysis materials includes: a material pretreatment unit; a feed chlorine content detection unit, located downstream of the material pretreatment unit, for acquiring feed chlorine content information of the waste plastic pyrolysis materials; a smart dechlorination treatment unit, located downstream of the feed chlorine content detection unit, for performing thermal dechlorination treatment on the waste plastic pyrolysis materials, the smart dechlorination treatment unit including a heating component and a material conveying component; an online hydrogen chloride detection unit, connected to the gas outlet of the smart dechlorination treatment unit, for acquiring hydrogen chloride concentration information generated during the thermal dechlorination process; and an AI control unit, connected to the feed chlorine content detection unit, the online hydrogen chloride detection unit, the heating component, and the material conveying component, respectively; wherein, the AI ​​control unit generates temperature control commands for the heating component and conveying control commands for the material conveying component based on the feed chlorine content information and hydrogen chloride concentration information, so as to dynamically adjust the dechlorination temperature and residence time of the waste plastic pyrolysis materials in the smart dechlorination treatment unit.

[0008] In one or more embodiments of the present invention, the feed chlorine content detection mechanism includes a near-infrared spectroscopy detection unit, which is used to collect near-infrared spectral data of waste plastic pyrolysis materials and obtain feed chlorine content information based on the chlorine-containing characteristic information in the near-infrared spectral data.

[0009] In one or more embodiments of the present invention, the intelligent dechlorination treatment mechanism includes multiple dechlorination treatment sections arranged sequentially along the material conveying direction. Each dechlorination treatment section is equipped with a corresponding temperature detection unit and a heating execution unit. The AI ​​control mechanism controls the heating temperature of each dechlorination treatment section to form a segmented dechlorination temperature field.

[0010] In one or more embodiments of the present invention, the material conveying assembly includes a screw conveyor assembly, and the AI ​​control mechanism changes the residence time of waste plastic pyrolysis material in the intelligent dechlorination treatment unit by adjusting the rotation speed of the screw conveyor assembly.

[0011] In one or more embodiments of the present invention, the AI ​​control mechanism includes a data acquisition unit, a model inference unit, and an execution control unit; the data acquisition unit is used to acquire information on the chlorine content of the feed, the hydrogen chloride concentration, the temperature information of the intelligent dechlorination treatment mechanism, and the operating information of the material conveying component; the model inference unit is used to calculate the dechlorination control strategy based on the artificial intelligence control model; the execution control unit is used to output control commands to the heating component and the material conveying component according to the dechlorination control strategy.

[0012] In one or more embodiments of the present invention, the artificial intelligence control model is one or more combinations of a neural network model, a fuzzy neural network model, a time-series prediction model, and a reinforcement learning model.

[0013] In one or more embodiments of the present invention, the AI ​​control mechanism adopts a control method combining feedforward control and feedback control; wherein, the feedforward control generates an initial temperature control command and an initial conveying control command based on the chlorine content information of the feed; and the feedback control modifies the initial temperature control command and / or the initial conveying control command based on the hydrogen chloride concentration information.

[0014] In one or more embodiments of the present invention, when the hydrogen chloride concentration information is higher than a preset concentration range, the AI ​​control mechanism increases the heating temperature of the heating component and / or decreases the conveying speed of the material conveying component; when the hydrogen chloride concentration information is lower than a preset concentration range and the feed chlorine content information is lower than a preset chlorine content threshold, the AI ​​control mechanism decreases the heating temperature of the heating component and / or increases the conveying speed of the material conveying component.

[0015] In one or more embodiments of the present invention, the AI ​​control mechanism further includes a self-learning optimization unit, which is used to update the control parameters of the AI ​​control mechanism according to the dechlorination operation data; the dechlorination operation data includes one or more of the following: feed chlorine content information, hydrogen chloride concentration information, dechlorination temperature, material residence time, chlorine content of the material after dechlorination, and unit processing energy consumption.

[0016] In one or more embodiments of the present invention, a safety constraint mechanism is further included, which is connected to the AI ​​control mechanism and is used to limit the temperature control commands and conveying control commands output by the AI ​​control mechanism, so that the dechlorination temperature of the intelligent dechlorination treatment mechanism does not exceed the preset temperature upper limit and the conveying speed of the material conveying component is within the preset speed range.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: it effectively solves the problem of insufficient dechlorination and difficulty in balancing over-treatment in the existing waste plastic pyrolysis impurity removal system. While improving the removal effect of chlorine-containing impurities, it reduces the corrosion risk of hydrogen chloride to pyrolysis reactors, conveying pipelines, condensation equipment and tail gas treatment equipment, and reduces the problems of catalyst poisoning, activity reduction and life shortening caused by chlorine-containing components entering the subsequent catalytic treatment unit, thereby improving the continuous and stable operation capability and economy of the waste plastic pyrolysis system. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a process flow diagram of an intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to an embodiment of the present invention.

[0020] Figure 2 This is a diagram of AI feedforward-feedback composite control logic in one embodiment of the present invention; Figure 3 This is a segmented structural diagram of the intelligent dechlorination treatment mechanism in one embodiment of the present invention; Figure 4 This is a flowchart of the operation of the AI ​​self-learning optimization unit in one embodiment of the present invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the technical solutions in this disclosure, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of this disclosure.

[0022] The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials in one embodiment of the present invention is mainly used for material impurity removal and dechlorination pretreatment at the front end of waste plastic pyrolysis refining. The equipment identifies the chlorine content of the feed material at the front end and, combined with online feedback of the hydrogen chloride release concentration during dechlorination, uses AI control to dynamically adjust the dechlorination temperature and material residence time. This ensures that the waste plastic material undergoes sufficient but not excessive dechlorination treatment before entering subsequent pyrolysis refining or catalytic treatment units, thereby reducing problems such as hydrogen chloride corrosion, catalyst poisoning, premature cracking, coking, and increased energy consumption.

[0023] like Figures 1-4As shown, the intelligent multi-effect impurity removal system for waste plastic pyrolysis materials includes a material pretreatment unit, a feed chlorine content detection unit, an intelligent dechlorination unit, an online hydrogen chloride detection unit, and an AI control unit connected in sequence. Waste plastic raw materials first enter the material pretreatment unit, where they undergo crushing, screening, magnetic separation, and homogenization to form waste plastic pyrolysis materials suitable for continuous conveying and thermal dechlorination. Subsequently, the material passes through the feed chlorine content detection unit, which performs online detection of the chlorine content in the material and transmits the results to the AI ​​control unit. The AI ​​control unit pre-determines the required dechlorination intensity for the current batch of material based on the feed chlorine content information and generates initial dechlorination control parameters. The material then enters the intelligent dechlorination unit for thermal dechlorination under a controlled temperature field and controlled residence time. The chlorine-containing tail gas generated during dechlorination is discharged from the gas outlet, and the hydrogen chloride concentration is monitored in real time by the online hydrogen chloride detection unit. The AI ​​control unit corrects the aforementioned initial dechlorination control parameters based on the hydrogen chloride concentration information.

[0024] Specifically, the material pretreatment unit may include a crusher, a screening machine, a buffer silo, a quantitative feeder, and a conveying device. The crusher is used to break large pieces of waste plastic into flakes, blocks, or granules. Preferably, the particle size of the crushed material is controlled within the range of 10mm to 30mm. This particle size range is beneficial for the near-infrared spectroscopy detection device to stably identify the surface and local composition of the material, and also for uniform heat transfer during the subsequent thermal dechlorination process. The screening machine is used to remove excessively large or small particles. Excessively large particles can be returned to the crusher for re-crushing, while excessively small powdery particles can be collected via a bypass to avoid dust generation, coking, or conveyor blockage during the dechlorination process. The buffer silo is used to stabilize fluctuations in the incoming material, and the quantitative feeder is used to ensure a relatively stable flow rate of material entering the chlorine content detection device.

[0025] Preferably, the material pretreatment mechanism can also be equipped with a magnetic separation unit and an air separation unit. The magnetic separation unit is used to remove magnetic metal impurities such as iron nails, iron sheets, and metal wires mixed in with waste plastics, preventing them from scratching the screw conveyor assembly or affecting heating uniformity after entering the intelligent dechlorination treatment mechanism. The air separation unit is used to separate lightweight films and foam plastics from heavy impurities, making the composition of materials entering the dechlorination treatment mechanism more stable. Through the above pretreatment, not only can the accuracy of subsequent chlorine content detection be improved, but the risk of blockage, local overheating, and mechanical wear in the dechlorination treatment mechanism can also be reduced.

[0026] The feed chlorine content detection unit is located downstream of the material pretreatment unit. This unit includes a near-infrared spectroscopy detection unit. This unit may include a near-infrared light source, a spectral acquisition probe, a material detection window, a background correction module, and a spectral analysis module. When waste plastic pyrolysis material passes through the detection area, the near-infrared light source illuminates the material surface, the spectral acquisition probe acquires the reflectance or diffuse reflectance spectrum, and the spectral analysis module estimates the feed chlorine content based on the chlorine-containing characteristic information in the spectral data.

[0027] In practical applications, the surface color, contamination level, moisture content, and particle size differences of waste plastics can all affect spectral detection results. Therefore, near-infrared spectroscopy detection units can be further configured with spectral preprocessing algorithms, such as smoothing filtering, baseline correction, standard normal variable transformation, multivariate scattering correction, and principal component dimensionality reduction. By preprocessing the raw spectral data, the influence of material surface conditions and ambient light interference on the detection results can be reduced, improving the stability of the chlorine content information in the feed. The chlorine content information in the feed can be expressed as an estimated chlorine content value or as a graded result such as high chlorine, medium chlorine, or low chlorine. For continuous production scenarios, the AI ​​control mechanism can average or weight the detection results according to a fixed time window to avoid frequent fluctuations in control parameters caused by a single abnormal detection point.

[0028] In addition to the near-infrared spectroscopy detection unit, the feed chlorine content detection mechanism can also be equipped with one or more of the following: X-ray fluorescence detection unit, Raman spectroscopy detection unit, laser-induced breakdown spectroscopy detection unit, or machine vision recognition unit. The near-infrared spectroscopy detection unit is suitable for rapidly identifying plastic types and chlorine-containing characteristics; the X-ray fluorescence detection unit is suitable for identifying inorganic chlorides or chlorine-containing additives; and the machine vision recognition unit is suitable for assisting in the judgment of PVC materials based on their color, shape, transparency, and surface texture. Multi-source detection data can be fused within the AI ​​control mechanism to obtain more reliable feed chlorine content information, further improving the identification accuracy and anti-interference capability under complex waste plastic feed conditions.

[0029] The intelligent dechlorination unit is located downstream of the feed chlorine content detection unit and is used for thermal dechlorination of waste plastic pyrolysis materials. This intelligent dechlorination unit can be a horizontal cylindrical structure, with a material conveying assembly inside the cylinder and a heating assembly located on the outside of the cylinder or within the cylinder jacket. The material conveying assembly is preferably a screw conveyor, driven by a variable frequency motor. The AI ​​control mechanism adjusts the frequency of the variable frequency motor to change the rotational speed of the screw conveyor, thereby changing the residence time of the waste plastic pyrolysis materials within the intelligent dechlorination unit. The screw conveyor also agitates and stirs the material while conveying it, promoting uniform heating and improving dechlorination efficiency.

[0030] The heating components can be electric heating jackets, heat-conducting oil jackets, hot air jackets, infrared heating units, or electromagnetic heating units. Preferably, the intelligent dechlorination processing mechanism is divided into multiple dechlorination processing sections along the material conveying direction, such as a preheating zone, a main dechlorination zone, and a stable release zone. Each dechlorination processing section is equipped with a temperature detection unit and a heating execution unit. The temperature detection unit can be a thermocouple, a resistance temperature detector (RTD), or an infrared temperature measuring device, and the heating execution unit can be an independent heating module. The AI ​​control mechanism can control the heating temperature of each dechlorination processing section separately, creating a segmented dechlorination temperature field within the intelligent dechlorination processing mechanism.

[0031] Typically, an intelligent dechlorination treatment unit comprises three continuously arranged dechlorination treatment sections. The first dechlorination treatment section is a preheating zone, mainly used to remove free water and low-boiling-point volatile components from the material, and to gradually increase the material temperature to avoid localized thermal shock caused by direct entry into the high-temperature zone. The second dechlorination treatment section is the main dechlorination zone, mainly used to promote the release of hydrogen chloride gas from chlorine-containing components. The third dechlorination treatment section is a homogenization or stabilization zone, mainly used to further release residual hydrogen chloride and stabilize the material state. Through segmented heating, the equipment can avoid premature material decomposition caused by using a single high-temperature condition, while ensuring sufficient release time for chlorine-containing components.

[0032] In terms of specific parameters, the dechlorination temperature of the intelligent dechlorination treatment unit can be controlled within the range of 200℃ to 320℃. For materials with low chlorine content, the AI ​​control unit can select a lower dechlorination temperature and a faster conveying speed to reduce energy consumption and minimize material thermal damage; for materials with high chlorine content, the AI ​​control unit can increase the temperature of the main dechlorination zone or reduce the speed of the screw conveyor assembly to extend the material residence time and improve dechlorination sufficiency. It should be noted that the above temperature range is a preferred embodiment, and adjustments can be made based on the actual material thermal stability, dechlorination reaction characteristics, and subsequent process requirements, depending on the plastic composition and equipment scale.

[0033] To reduce oxidation reactions and safety risks, the intelligent dechlorination treatment unit can also be equipped with an inert gas protection unit. This inert gas protection unit is connected to the intelligent dechlorination treatment unit and is used to introduce nitrogen, argon, or low-oxygen circulating gas into the dechlorination chamber to maintain a low-oxygen environment. Preferably, the intelligent dechlorination treatment unit also includes an oxygen content detection unit and a pressure detection unit. When the oxygen content exceeds a preset value or the chamber pressure is abnormal, the AI ​​control mechanism or safety restraint mechanism can simultaneously reduce the heating power, stop feeding, increase the inert gas flow rate, or activate the pressure relief protection device. This feature reduces the risk of oxidation, combustion, or deflagration of waste plastics during heating, improving the safety of continuous equipment operation.

[0034] The online hydrogen chloride detection unit is connected to the gas outlet of the intelligent dechlorination treatment unit to obtain real-time information on the concentration of hydrogen chloride generated during the thermal dechlorination process. The online hydrogen chloride detection unit can employ an electrochemical HCl sensor, an infrared absorption HCl detector, an ultraviolet differential absorption detector, or other suitable online analyzers for hydrogen chloride concentration detection. To avoid the influence of high temperature, dust, tar, or water vapor on detection accuracy, a sampling pipeline, filter, condenser / demister, and heating device can be installed between the gas outlet and the online hydrogen chloride detection unit. The filter intercepts dust and fine carbonaceous particles, the condenser / demister removes some condensate and tar droplets, and the heating device prevents hydrogen chloride and water vapor in the pipeline from condensing into acid and corroding the sampling pipeline.

[0035] The online hydrogen chloride detection system can be installed upstream of the exhaust gas absorption and treatment unit, or detection points can be set both upstream and downstream of the unit. The upstream detection point reflects the release intensity of chlorine-containing components within the intelligent dechlorination unit and serves as the primary basis for feedback control by the AI ​​control mechanism. The downstream detection point monitors the absorption effect of the exhaust gas absorption and treatment unit; when the downstream hydrogen chloride concentration abnormally increases, it can indicate absorbent failure, insufficient alkalinity of the spray solution, or packing blockage. The exhaust gas absorption and treatment unit can employ an alkaline spray tower, an alkaline adsorption bed, a dry deacidification tower, or a combination thereof to neutralize and absorb the hydrogen chloride gas released during the dechlorination process, thereby reducing the corrosiveness of the exhaust gas emissions.

[0036] The AI ​​control mechanism is connected to the feed chlorine content detection unit, the online hydrogen chloride detection unit, the heating component, and the material conveying component. The AI ​​control mechanism can be housed in an industrial control cabinet or implemented using a combination of edge computing units, industrial computers, PLCs, and AI inference modules. The AI ​​control mechanism includes a data acquisition unit, a model inference unit, an execution control unit, and a self-learning optimization unit. The data acquisition unit acquires information such as feed chlorine content, hydrogen chloride concentration, actual temperature of each dechlorination treatment section, screw conveyor rotation speed, material flow rate, inert gas flow rate, oxygen content, pressure, and energy consumption. The model inference unit calculates the dechlorination control strategy based on the artificial intelligence control model. The execution control unit outputs control commands to the heating component and the material conveying component. The self-learning optimization unit updates control parameters or model parameters based on historical operating data.

[0037] Artificial intelligence control models can be neural network models, fuzzy neural network models, time-series predictive models, reinforcement learning models, or combinations thereof. For example, the model inference unit can use a time-series predictive model to predict the trend of hydrogen chloride concentration changes in the exhaust gas over a future period, and then use fuzzy control rules or reinforcement learning strategies to output the corresponding temperature and conveying speed adjustments. For scenarios with large fluctuations in the amount of waste plastic material fed into the system, the model can use the chlorine content of the feed material, material flow rate, current temperature, current screw speed, and historical HCl concentration change rate as inputs, and the target temperature of each heating zone and the target speed of the screw conveyor assembly as outputs. Compared with traditional fixed-condition or single PID control, this AI control method can learn the nonlinear relationship between different material compositions and dechlorination behavior, thereby improving the adaptability of dechlorination control.

[0038] In actual operation, when waste plastic pyrolysis material enters the feed chlorine content detection unit, the near-infrared spectroscopy detection unit first obtains the chlorine content information of the material. The AI ​​control unit performs feedforward control based on this chlorine content information. If the material is detected to be high-chlorine, the AI ​​control unit pre-increases the temperature setpoint of the main dechlorination zone and reduces the speed of the screw conveyor assembly, allowing the material to have a longer residence time in the intelligent dechlorination treatment unit. If the material is detected to be low-chlorine, the AI ​​control unit lowers the temperature setpoint or increases the conveying speed, thereby avoiding unnecessary overheating of the material. This feedforward control can respond in advance before the material enters the dechlorination treatment unit, reducing under-chlorination or over-treatment caused by control lag.

[0039] After the material is heated and releases hydrogen chloride in the intelligent dechlorination unit, the online hydrogen chloride detection unit monitors the concentration of hydrogen chloride in the exhaust gas in real time and sends the detection results to the AI ​​control unit. The AI ​​control unit compares the real-time hydrogen chloride concentration information with the preset concentration range, the target dechlorination state, or the model prediction value. If the hydrogen chloride concentration is higher than the preset concentration range, it indicates that the current material releases hydrogen chloride at a high intensity, or the dechlorination process is not yet fully completed. The AI ​​control unit can increase the heating temperature of the heating components, especially the temperature of the main dechlorination zone or the stabilization zone; it can also reduce the conveying speed of the material conveying components to extend the material residence time. These two actions can be performed individually or in combination. Through this adjustment method, the equipment can enhance the dechlorination effect and reduce the risk of residual chlorine-containing components entering subsequent refining units.

[0040] If the hydrogen chloride concentration is below the preset range and the chlorine content of the feed is also below the preset chlorine content threshold, it indicates that the current chlorine load on the material is low or the dechlorination reaction is nearing completion. In this case, the AI ​​control mechanism can reduce the heating temperature of the heating components or increase the conveying speed of the material conveying components to reduce the material's residence time in the high-temperature environment. This prevents premature pyrolysis, softening, sticking, or coking of waste plastic materials and reduces unit processing energy consumption, achieving a dynamic balance between sufficient dechlorination and preventing excessive heat treatment.

[0041] Preferably, the AI ​​control mechanism can also incorporate the operating status of subsequent catalytic treatment units into the control logic. For example, the subsequent catalytic treatment units can provide the AI ​​control mechanism with information such as catalyst bed pressure drop, reaction temperature, product distribution, catalyst activity index, regeneration frequency, or catalyst usage time. When the AI ​​control mechanism determines that the catalyst activity is decreasing at an accelerated rate, and this decreasing trend is related to insufficient dechlorination at the front end, it can automatically tighten the dechlorination control target, such as lowering the allowable effluent chlorine content target value, or increasing the response sensitivity to abnormal fluctuations in hydrogen chloride concentration.

[0042] The self-learning optimization unit can record dechlorination operation data after each production batch or each set time window. This data includes one or more of the following: feed chlorine content, hydrogen chloride concentration, dechlorination temperature, material residence time, post-dechlorination chlorine content, unit processing energy consumption, material flow rate, and subsequent catalyst activity changes. The self-learning optimization unit can update the control parameters of the AI ​​control mechanism based on this data. For example, if a certain type of waste plastic achieves a high dechlorination rate and low energy consumption under a specific temperature and residence time combination, this parameter combination can be recorded as the preferred initial control strategy for that type of material. If a control strategy reduces chlorine content but leads to a significant increase in energy consumption or increased coking tendency, the weight of that strategy can be reduced. Through long-term operation, the equipment can gradually adapt to the compositional characteristics of waste plastics from different sources, improving control accuracy and operational economy.

[0043] The AI ​​control mechanism can also be configured with reward functions or evaluation metrics. These metrics can simultaneously consider chlorine removal rate, peak HCl content in the tail gas, unit processing energy consumption, coking degree of the discharged material, and catalyst activity decay rate. When the chlorine removal rate is high, energy consumption is low, and catalyst activity decay is minimal, the system assigns a high evaluation to the current control strategy; conversely, when insufficient dechlorination or overheating leads to coking and increased energy consumption, the system assigns a low evaluation. In this way, the AI ​​control mechanism can continuously optimize temperature and conveying control commands, gradually bringing the equipment control strategy closer to an optimal balance between dechlorination efficiency, energy consumption, and material protection.

[0044] To ensure that the control commands output by the AI ​​control mechanism do not lead to dangerous or unreasonable operating conditions, this embodiment also includes a safety constraint mechanism. This safety constraint mechanism is connected to the AI ​​control mechanism and is used to limit the temperature control commands and conveying control commands output by the AI ​​control mechanism. Specifically, the safety constraint mechanism can set upper and lower limits for dechlorination temperature, screw conveyor rotation speed, oxygen content, chamber pressure, and temperature rise rate. When the temperature control command output by the AI ​​control mechanism exceeds the preset upper temperature limit, the safety constraint mechanism limits the command to ensure that the dechlorination temperature of the intelligent dechlorination treatment unit does not exceed the safe range. When the conveying control command output by the AI ​​control mechanism causes the screw conveyor rotation speed to fall below the preset lower limit, the safety constraint mechanism restricts the command to prevent material from softening, sticking, coking, or clogging due to prolonged residence.

[0045] Furthermore, the safety restraint mechanism can be configured with multi-level alarm and interlock control functions. When the hydrogen chloride concentration, oxygen content, cavity pressure, heating zone temperature runaway, or screw conveyor motor load abnormally increases, the equipment can execute different protective actions according to the level of abnormality. For example, for minor deviations, the system only issues an alarm and the AI ​​control mechanism automatically corrects the control parameters; for moderate deviations, the system can reduce the feed rate, reduce the heating power, or increase the inert gas flow rate; for severe deviations, the system can stop feeding, stop heating, and initiate emergency exhaust and tail gas absorption treatment processes. Through the safety restraint mechanism, over-adjustment of the AI ​​control strategy under complex operating conditions can be avoided, improving the reliability of the equipment in industrial applications.

[0046] To facilitate operator monitoring and maintenance, a human-machine interface (HMI) and a data storage system can be set up. The HMI displays the current feed chlorine content, exhaust hydrogen chloride concentration, temperature of each dechlorination zone, screw conveyor rotation speed, material residence time, unit processing energy consumption, AI-recommended control strategies, alarm information, and equipment operating status. Operators can use the HMI to set target discharge chlorine content, target HCl concentration range, upper temperature limit, speed range, and operating mode. The data storage system stores historical monitoring data, control commands, alarm records, model update records, and operational reports, providing a data foundation for subsequent process optimization, equipment maintenance, and quality traceability.

[0047] During equipment startup, operators can initially select either automatic or semi-automatic mode. In automatic mode, the AI ​​control system automatically adjusts the temperature and conveyor speed based on the chlorine content and hydrogen chloride concentration of the feed material. In semi-automatic mode, the AI ​​control system provides recommended control parameters, which are then confirmed and executed by the operator. For newly commissioned equipment or new types of waste plastic materials, semi-automatic mode is beneficial for accumulating initial data and reducing control risks before the model is fully adapted. As operational data accumulates, the equipment can be gradually switched to automatic mode to achieve continuous and intelligent impurity removal and dechlorination.

[0048] The operation process of this embodiment can be summarized as follows: After entering the material pretreatment unit, waste plastic raw materials are crushed, screened, and homogenized to form waste plastic refining materials with relatively stable particle size. The feed chlorine content detection unit performs online detection on the material to obtain feed chlorine content information. The AI ​​control unit generates initial temperature control commands and initial conveying control commands based on the feed chlorine content information. The material enters the intelligent dechlorination treatment unit, where hydrogen chloride is released under a segmented dechlorination temperature field and adjustable residence time. The hydrogen chloride online detection unit monitors the hydrogen chloride concentration in the tail gas in real time. The AI ​​control unit dynamically corrects the heating temperature and conveying speed based on the hydrogen chloride concentration information. The dechlorination operation data is recorded and used for subsequent self-learning optimization. The safety constraint unit limits and interlocks the control commands output by the AI ​​control unit.

[0049] Through the above-described structure and control method, the present invention has at least the following effects.

[0050] The equipment can adjust its operation based on changes in the chlorine content of the feed material, solving the problem that traditional fixed-condition pre-dechlorination cannot adapt to fluctuations in chlorine content of different batches of waste plastics. For high-chlorine materials, the equipment can enhance dechlorination treatment in advance; for low-chlorine materials, the equipment can reduce the dechlorination intensity to avoid ineffective energy consumption.

[0051] The equipment can provide feedback correction based on the hydrogen chloride concentration in the exhaust gas, ensuring that the control process not only relies on feed detection results but also reflects the actual release status during dechlorination. This compensates for potential errors from relying solely on feed detection, improving the stability and reliability of dechlorination control.

[0052] The equipment uses an AI control mechanism to simultaneously adjust the heating and material conveying components, achieving coordinated control of dechlorination temperature and residence time. Compared to adjusting only the temperature or only the conveying speed, this dual-variable coordinated adjustment can maintain better dechlorination performance over a wider range of material fluctuations and reduce premature cracking and coking caused by overheating.

[0053] A segmented dechlorination temperature field allows waste plastic materials to undergo a gradual heating, primary dechlorination, and stable release process, reducing the risk of localized overheating and improving the uniformity of the thermal dechlorination process. This helps reduce oil and gas loss, inhibit coking, and improve the quality of subsequent refining materials.

[0054] The self-learning optimization unit can continuously update control parameters based on long-term operating data, enabling the equipment to gradually adapt to waste plastic raw materials from different sources, with different compositions, and different chlorine content levels. The longer the equipment operates, the more stable the control strategy developed for a specific raw material system becomes, thereby improving production efficiency.

[0055] It will be apparent to those skilled in the art that this disclosure is not limited to the details of the exemplary embodiments described above, and that this disclosure can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of this disclosure is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within this disclosure. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0056] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. A smart multi-effect impurity removal system for waste plastic pyrolysis materials, characterized in that, include: Material pretreatment mechanism; A chlorine content detection device is located downstream of the material pretreatment device to obtain chlorine content information of the waste plastic pyrolysis material. An intelligent dechlorination treatment unit is located downstream of the feed chlorine content detection unit and is used to perform thermal dechlorination treatment on waste plastic pyrolysis materials. The intelligent dechlorination treatment unit includes a heating component and a material conveying component. The hydrogen chloride online detection unit is connected to the gas outlet of the intelligent dechlorination treatment unit and is used to obtain the concentration information of hydrogen chloride generated during the thermal dechlorination process; The AI ​​control mechanism is connected to the feed chlorine content detection mechanism, the online hydrogen chloride detection mechanism, the heating component, and the material conveying component, respectively. The AI ​​control mechanism generates temperature control commands for the heating component and conveying control commands for the material conveying component based on the chlorine content and hydrogen chloride concentration information of the feed material, so as to dynamically adjust the dechlorination temperature and residence time of the waste plastic pyrolysis material in the intelligent dechlorination treatment unit.

2. The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to claim 1, characterized in that, The feed chlorine content detection mechanism includes a near-infrared spectroscopy detection unit, which is used to collect near-infrared spectral data of waste plastic pyrolysis materials and obtain feed chlorine content information based on the chlorine-containing characteristic information in the near-infrared spectral data.

3. The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to claim 1, characterized in that, The intelligent dechlorination treatment mechanism includes multiple dechlorination treatment sections arranged sequentially along the material conveying direction. Each dechlorination treatment section is equipped with a corresponding temperature detection unit and a heating execution unit. The AI ​​control mechanism controls the heating temperature of each dechlorination treatment section to form a segmented dechlorination temperature field.

4. The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to claim 1, characterized in that, The material conveying assembly includes a screw conveyor assembly, and the AI ​​control mechanism changes the residence time of waste plastic pyrolysis materials in the intelligent dechlorination treatment unit by adjusting the rotation speed of the screw conveyor assembly.

5. The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to claim 1, characterized in that, The AI ​​control mechanism includes a data acquisition unit, a model inference unit, and an execution control unit; The data acquisition unit is used to acquire information on the chlorine content of the feed, the hydrogen chloride concentration, the temperature of the intelligent dechlorination treatment mechanism, and the operating information of the material conveying components; The model inference unit is used to calculate the dechlorination control strategy based on the artificial intelligence control model; The execution control unit is used to output control commands to the heating component and the material conveying component according to the dechlorination control strategy.

6. The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to claim 5, characterized in that, The artificial intelligence control model is one or more combinations of neural network models, fuzzy neural network models, time series prediction models, and reinforcement learning models.

7. The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to claim 1, characterized in that, The AI ​​control mechanism adopts a control method that combines feedforward control and feedback control. The feedforward control generates initial temperature control commands and initial conveying control commands based on the chlorine content information of the feed material. The feedback control modifies the initial temperature control command and / or the initial delivery control command based on the hydrogen chloride concentration information.

8. The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to claim 7, characterized in that, When the hydrogen chloride concentration information is higher than the preset concentration range, the AI ​​control mechanism increases the heating temperature of the heating component and / or decreases the conveying speed of the material conveying component; when the hydrogen chloride concentration information is lower than the preset concentration range and the feed chlorine content information is lower than the preset chlorine content threshold, the AI ​​control mechanism decreases the heating temperature of the heating component and / or increases the conveying speed of the material conveying component.

9. The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to claim 1, characterized in that, The AI ​​control mechanism also includes a self-learning optimization unit, which is used to update the control parameters of the AI ​​control mechanism based on the dechlorination operation data; The dechlorination operation data includes one or more of the following: feed chlorine content, hydrogen chloride concentration, dechlorination temperature, material residence time, chlorine content of the material after dechlorination, and unit processing energy consumption.

10. The intelligent multi-effect impurity removal system for waste plastic pyrolysis materials according to claim 1, characterized in that, It also includes a safety constraint mechanism, which is connected to the AI ​​control mechanism to limit the temperature control commands and conveying control commands output by the AI ​​control mechanism, so that the dechlorination temperature of the intelligent dechlorination treatment mechanism does not exceed the preset temperature upper limit, and the conveying speed of the material conveying component is within the preset speed range.

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