A cleaning, separating and precipitating pool for a waste plastic recycling system and a precipitating method

Abstract

The present application belongs to the technical field of waste plastic precipitation, and discloses a cleaning separation and precipitation tank for a waste plastic recycling system and a precipitation method; the method comprises the following steps: S3: based on feature data sets and real-time basic data sets, the precipitation effect in the next time period is predicted to obtain a predicted precipitation effect; S4: the predicted precipitation effect is compared, and a precipitation adjustment measure report is calculated based on the real-time basic data sets; S5: based on the precipitation adjustment measure report, a real-time control adjustment device is controlled, and the separation of waste plastics is adjusted in real time based on plastic density data; S6: actual turbidity data is obtained, error calculation is performed on the actual turbidity data and predicted turbidity data, and the prediction accuracy is adjusted in real time based on the turbidity error value; in general, the present application has the significant advantages of strong precipitation effect and energy consumption optimization balance ability, large operation cost saving effect, and good continuous feedback updating system effect.

Description

Technical Field

[0001] This invention relates to the field of waste plastic sedimentation technology, and more specifically, to a cleaning, separation, and sedimentation tank and sedimentation method for a waste plastic recycling and treatment system. Background Technology

[0002] A sedimentation tank is a device that uses gravity to settle solid particles or droplets suspended in a liquid. It is mainly used in solid-liquid separation and extraction processes, serving to filter, dehydrate, and fix particles. With the rapid development of my country's economy in recent years, plastic products have been widely used in people's daily lives due to their excellent performance characteristics. Most companies that process waste plastics need to pre-clean the surface of the waste plastics after recycling them to facilitate subsequent processing. As an important piece of equipment in the cleaning and separation process, the sedimentation tank plays a very important role in the processing of waste plastics. However, traditional cleaning and separation sedimentation tanks often suffer from problems such as low cleaning efficiency due to static treatment, poor sedimentation effect and high energy consumption due to fixed intervention measures. These issues directly affect the economic benefits of waste plastic recycling companies. Therefore, how to effectively improve the cleaning and separation efficiency and sedimentation effect of waste plastics and reduce cleaning and separation energy consumption has become a major problem that waste plastic recycling companies urgently need to solve.

[0003] In view of this, the present invention proposes a cleaning, separation, and sedimentation tank and sedimentation method for a waste plastic recycling and processing system to solve the above problems. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution, wherein the method includes: S1: Based on the sensor network, collect relevant data of the sedimentation tank in real time to obtain a real-time basic dataset; this step specifically includes collecting relevant data of sedimentation tank operation in real time to provide an accurate and comprehensive data foundation for the system, providing data support for steps S2 and S3, thereby improving the overall accuracy of system prediction calculation. The real-time basic dataset includes influent data, agitation data, chemical dosing data, turbidity data, sediment height data, and plastic density data. S2: Based on the real-time basic dataset, calculate and derive the feature dataset; this step specifically includes quantifying the real-time basic dataset to ensure a true and accurate reflection of the physical state during the sedimentation process, providing a preprocessing function for the prediction calculation in step S3, and upgrading the real-time basic dataset to feature data, thereby achieving the purpose of further quantifying the physical state during the sedimentation process, thus ensuring the accuracy of the prediction calculation.

[0005] S3: Based on the feature dataset and real-time basic dataset, predict the sedimentation effect in the next time period to obtain the predicted sedimentation effect. This step specifically includes using the feature dataset and real-time basic dataset, with turbidity data and sediment height data as core indicators, to predict the sedimentation effect in the next time period. This enables the system to predict future sedimentation effects based on current data, providing a data basis for adjusting sedimentation measures in step S4. It also has the ability to provide early warning by combining step S4 to reflect the deterioration of sedimentation effect, thereby effectively improving the system's capability diversity. The sedimentation effect includes predicted sediment height data and predicted turbidity data. S4: Based on the comparison of the predicted sedimentation effect and the real-time basic dataset, a sedimentation adjustment measure report is calculated. This step specifically includes comparing the predicted sedimentation effect with the target value, enabling the system to reversely adjust sedimentation measures based on the prediction results. This greatly enhances the system's ability to further optimize energy consumption while ensuring sedimentation effect. It also provides the basis for the adjustment purpose and specific adjustment data for the real-time control adjustment in step S5. Furthermore, it provides a balance between sedimentation effect and sedimentation cost for the prediction in step S3 and the execution in step S5, thereby effectively improving the economic efficiency of sedimentation operations. S5: Based on the sedimentation adjustment measures report, control and adjust the equipment in real time, and adjust the separation of waste plastics in real time according to the plastic density data. This step specifically includes controlling and adjusting the influent data, stirring data and reagent dosage data in real time by executing the sedimentation adjustment measures report, and starting separation enhancement measures according to the different densities of waste plastics. This enables the system to simultaneously complete the optimization of sedimentation effect and the classification of waste plastics, thereby further improving the optimization of system energy consumption and achieving the goal of reducing enterprise processing costs. S6: Obtain actual turbidity data and calculate the error between it and the predicted turbidity data. Based on the turbidity error value, adjust the prediction accuracy in real time. This step specifically includes verifying the accuracy of the predicted sedimentation effect, enabling the system to dynamically update the model and adjust the sedimentation adjustment measures report, thereby achieving the goal of long-term performance optimization of the system, greatly improving the sustainability of system use, the accuracy of adjustment measures, and the economy of the system. Further, step S1 includes: The influent flow rate in the designated sedimentation tank is collected using a water flow meter to obtain influent data. The stirring intensity value in the specified sedimentation tank is collected using a power meter to obtain stirring data; The dosage of chemicals in a designated sedimentation tank is collected using a chemical flow meter to obtain chemical dosage data. Turbidity data is obtained by collecting the turbidity value of the water at the outlet of a designated sedimentation tank using a turbidity sensor. The sediment height data is obtained by collecting the bottom sediment height value in a designated sedimentation tank using an ultrasonic level sensor. Plastic density data is obtained by collecting the density values ​​of plastic in a designated sedimentation tank using a hydrometer. Further, step S2 includes: S2.1, calculate the settling rate based on the sediment height data in the real-time basic dataset. The specific formula for calculating the settling rate is as follows: ; Obtaining settling rate data ,in, At the current time point Sediment height data, For the previous time point Sediment height data, The sampling time interval; S2.2, calculate the turbidity change of the turbidity data in the real-time basic dataset. The specific formula for calculating the turbidity change is as follows: ; Obtain turbidity change data ,in, At the current time point Turbidity data, For the previous time point Turbidity data; S2.3, Pack the sedimentation rate data and turbidity change data to obtain the feature dataset; Further, step S3 includes: S3.1, Based on the sedimentation rate data of the feature dataset in step S2 and the sediment height data of the real-time basic dataset in step S1, the sediment height data is predicted. The specific calculation formula for the sediment height data prediction is as follows: ; Obtain predicted sediment height data ,in, For sediment height data, For predicting time; S3.2, Based on the turbidity change data of the feature dataset in step S2 and the turbidity data of the real-time basic dataset in step S1, the turbidity data is predicted. The specific calculation formula for turbidity data prediction is as follows: ; Obtain predicted turbidity data ,in, For turbidity data, is the base of the natural logarithm. The turbidity attenuation constant; Further, step S4 includes: S4.1, using the influent data, agitation data, and reagent dosage data from the real-time basic dataset, the total energy consumption of the sedimentation tank is calculated. The specific formula for calculating the total energy consumption is as follows: ; Obtain total energy consumption data ,in, For water intake data, For mixing data, For drug dosage data, Water energy consumption coefficient The energy consumption coefficient for stirring is... This is the energy consumption coefficient of the pharmaceutical agent; S4.2 When the predicted sediment height data is greater than or equal to the maximum allowable sediment height data, the stirring data is adjusted based on the predicted sediment height data in the predicted sedimentation effect. The specific calculation formula for adjusting the stirring data is as follows: ; Obtain stirring adjustment data ,in, This is the stirring gain coefficient. The maximum allowable sediment height; S4.3 When the predicted turbidity data is greater than or equal to the standard turbidity data, the influent data and reagent dosage data are adjusted based on the predicted turbidity data in the sedimentation effect prediction. The specific calculation formulas for adjusting the influent data and reagent dosage data are as follows: ; The influent adjustment data were obtained respectively. And drug delivery adjustment data ,in, This is the inlet water gain coefficient. Drug delivery gain coefficient, Standard turbidity data; S4.4 Based on the stirring adjustment data, water inlet adjustment data, and reagent dosing adjustment data in steps S4.2 and S4.3, the second water inlet data, the second stirring data, and the second reagent dosing data are calculated respectively, and then returned to step S4.1 and substituted into the total energy consumption calculation formula. S4.5, package the second influent data, the second mixing data, and the second reagent dosing data to obtain a sedimentation adjustment measures report; Further, step S5 includes: S5.1, Read the sedimentation adjustment measures report; When there is influent adjustment data in the sedimentation adjustment measures report, the influent data of the influent pump valve is adjusted to the second influent data through the programmable controller; When the sedimentation adjustment report contains stirring adjustment data, the stirring data of the stirrer motor is adjusted to the second stirring data through the programmable controller; When there is reagent dosing adjustment data in the sedimentation adjustment measures report, the reagent dosing data of the reagent metering pump is adjusted to the second reagent dosing data through the programmable controller; S5.2, activate the flotation adjustment equipment; When the plastic density data shows low-density plastics with a density of less than 1000 kg per cubic meter, air bubbles are injected into the sedimentation tank through the flotation adjustment equipment, and the waste plastics floating on the surface of the sedimentation tank are removed through the skimming equipment. High-density plastics with a density greater than 1000 kg per cubic meter will settle to the bottom of the sedimentation tank due to their own gravity. Further, step S6 includes: S6.1, by obtaining the predicted time The actual turbidity data is obtained from the turbidity data at the time of prediction, and the error is calculated between the actual turbidity data and the predicted turbidity data. The specific formula for calculating the turbidity error is as follows: ; Obtain turbidity error value ,in, This is actual turbidity data; S6.2, When the turbidity error value is greater than or equal to the standard error threshold, the turbidity attenuation constant is updated. The specific calculation formula for the update is as follows: ; The second turbidity attenuation constant was obtained. ,in, The learning rate; S6.3, output the second turbidity attenuation constant into the turbidity data prediction calculation formula in step S3.2; Furthermore, step S3.2 also includes the specific calculation formula for the turbidity attenuation constant: ; in, This refers to the current time point; Furthermore, step S4.4 also includes the specific calculation formula set for the sedimentation adjustment measures report: ; Second water intake data were obtained respectively Second mixing data Second agent delivery data ; Furthermore, a washing, separation, and sedimentation tank for a waste plastic recycling and processing system includes a real-time data acquisition module, a feature calculation and processing module, a sedimentation effect prediction module, a control decision module, an execution control module, and a feedback update module, wherein: The real-time data acquisition module is used to acquire relevant data of the sedimentation tank in real time based on the sensor network to obtain a real-time basic dataset; The feature calculation and processing module is used to calculate and derive the feature dataset based on the real-time basic dataset; The sedimentation effect prediction module is used to predict the sedimentation effect in the next time period based on the feature dataset and the real-time basic dataset, and obtain the predicted sedimentation effect. The control decision module is used to compare the predicted sedimentation effect and calculate a sedimentation adjustment measure report based on the real-time basic dataset. The execution control module is used to control the adjustment equipment in real time based on the sedimentation adjustment measure report, and to control and adjust the separation of waste plastic in real time based on the plastic density data. The feedback update module is used to acquire actual turbidity data, calculate the error between the actual turbidity data and the predicted turbidity data, and adjust the prediction accuracy in real time based on the turbidity error value.

[0006] The technical effects and advantages of the cleaning, separation, and sedimentation tank and sedimentation method used in the waste plastic recycling and treatment system of this invention are as follows: This invention utilizes a sensor network to collect real-time data from a sedimentation tank, obtaining a real-time basic dataset. Based on this dataset, a feature dataset is calculated. Using both the feature dataset and the real-time basic dataset, the sedimentation effect for the next time period is predicted, yielding a predicted sedimentation effect. The predicted sedimentation effect is compared with the real-time basic dataset, and a sedimentation adjustment report is calculated. Based on this report, the system controls the regulating equipment in real-time and adjusts the separation of waste plastics based on plastic density data. Actual turbidity data is acquired, and error calculation is performed between this data and the predicted turbidity data. The prediction accuracy is adjusted in real-time based on the turbidity error value. This allows the system to accurately measure both turbidity data and sediment height. To ensure the accuracy of prediction calculations under dynamic changes, this invention also optimizes the system's prediction calculations through step S6, enabling the system to have long-term optimization prediction capabilities, greatly improving the system's sustainable use and achieving good economic benefits. Simultaneously, the sedimentation adjustment measures report obtained through reverse calculation of the prediction results can effectively and accurately provide a data basis for the energy consumption optimization and adjustment of the sedimentation tank, allowing the system to directly and effectively transform the prediction calculation data into physical intervention operations, greatly improving the system's practicality. Overall, this invention has significant advantages such as strong sedimentation effect and energy consumption optimization balancing ability, significant cost savings, and good continuous feedback and system update effects. Attached Figure Description

[0007] Figure 1 This is a schematic diagram of a sedimentation method used in a waste plastic recycling system according to the present invention. Detailed Implementation

[0008] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0009] Example 1 Please see Figure 1 As shown in this embodiment, a cleaning, separation, and sedimentation tank and sedimentation method for a waste plastic recycling system include: S1: Based on the sensor network, collect relevant data of the sedimentation tank in real time to obtain a real-time basic dataset; this step specifically includes collecting relevant data of sedimentation tank operation in real time to provide an accurate and comprehensive data foundation for the system, providing data support for steps S2 and S3, thereby improving the overall accuracy of system prediction calculation. The real-time basic dataset includes influent data, agitation data, chemical dosing data, turbidity data, sediment height data, and plastic density data. S2: Based on the real-time basic dataset, calculate and derive the feature dataset; this step specifically includes quantifying the real-time basic dataset to ensure a true and accurate reflection of the physical state during the sedimentation process, providing a preprocessing function for the prediction calculation in step S3, and upgrading the real-time basic dataset to feature data, thereby achieving the purpose of further quantifying the physical state during the sedimentation process, thus ensuring the accuracy of the prediction calculation.

[0010] S3: Based on the feature dataset and real-time basic dataset, predict the sedimentation effect in the next time period to obtain the predicted sedimentation effect. This step specifically includes using the feature dataset and real-time basic dataset, with turbidity data and sediment height data as core indicators, to predict the sedimentation effect in the next time period. This enables the system to predict future sedimentation effects based on current data, providing a data basis for adjusting sedimentation measures in step S4. It also has the ability to provide early warning by combining step S4 to reflect the deterioration of sedimentation effect, thereby effectively improving the system's capability diversity. The sedimentation effect includes predicted sediment height data and predicted turbidity data. S4: Based on the comparison of the predicted sedimentation effect and the real-time basic dataset, a sedimentation adjustment measure report is calculated. This step specifically includes comparing the predicted sedimentation effect with the target value, enabling the system to reversely adjust sedimentation measures based on the prediction results. This greatly enhances the system's ability to further optimize energy consumption while ensuring sedimentation effect. It also provides the basis for the adjustment purpose and specific adjustment data for the real-time control adjustment in step S5. Furthermore, it provides a balance between sedimentation effect and sedimentation cost for the prediction in step S3 and the execution in step S5, thereby effectively improving the economic efficiency of sedimentation operations. S5: Based on the sedimentation adjustment measures report, control and adjust the equipment in real time, and adjust the separation of waste plastics in real time according to the plastic density data. This step specifically includes controlling and adjusting the influent data, stirring data and reagent dosage data in real time by executing the sedimentation adjustment measures report, and starting separation enhancement measures according to the different densities of waste plastics. This enables the system to simultaneously complete the optimization of sedimentation effect and the classification of waste plastics, thereby further improving the optimization of system energy consumption and achieving the goal of reducing enterprise processing costs. S6: Obtain actual turbidity data and calculate the error between it and the predicted turbidity data. Based on the turbidity error value, adjust the prediction accuracy in real time. This step specifically includes verifying the accuracy of the predicted sedimentation effect, enabling the system to dynamically update the model and adjust the sedimentation adjustment measures report, thereby achieving the goal of long-term performance optimization of the system, greatly improving the sustainability of system use, the accuracy of adjustment measures, and the economy of the system. The core of this invention lies in providing a precise and comprehensive data foundation for the system based on a sensor network, and performing quantitative calculations on the real-time basic dataset to provide data support for subsequent predictive calculations of the system. This enables the system to predict the sedimentation effect in the future time period and provides data basis for the system to control and adjust sedimentation measures based on the sedimentation effect. Simultaneously, in conjunction with the execution layer, the system data is transformed into physical world manipulation, and the system performance is continuously optimized based on the actual sedimentation effect. Specifically, by collecting the real-time basic dataset in step S1 and performing quantitative calculations on the real-time basic dataset in step S2, the system can truly and accurately reflect the physical state of waste plastics during the sedimentation process through data, and lay the data foundation for predictive calculations in step S3. Step S3 provides the system with a prediction of the sedimentation effect in the next time period, enabling step S4 to generate relevant instructions for sedimentation adjustment measures in the sedimentation tank and providing data support for the execution of step S5. Finally, step S6 enables the system to have the ability to perform long-term optimization predictive calculations. Overall, steps S1 to S6 enable the system to effectively balance sedimentation effect and sedimentation cost, which includes energy consumption cost and maintenance cost. By predicting sedimentation effects based on real-time basic datasets and feature datasets, the system can maintain the accuracy of prediction results under dynamic changes in turbidity and sediment height data. Combined with the prediction accuracy adjustment mechanism in step S6, the system's adaptability and responsiveness to dynamic changes are effectively improved. By processing the predicted sedimentation effects, the system can effectively unify calculation data and execution measures, greatly improving the system's ability to convert calculation data into physical world operations. This effectively reduces the time lag between traditional decision support and manual execution, thereby effectively improving the system's practicality. This method has the ability to accurately predict sedimentation effects in the next time period, implement corresponding sedimentation measures adjustments based on the predicted sedimentation effects, and optimize the system over the long term. Step S1 includes: The influent flow rate in the designated sedimentation tank is collected using a water flow meter to obtain influent data. The stirring intensity value in the specified sedimentation tank is collected using a power meter to obtain stirring data; The dosage of chemicals in a designated sedimentation tank is collected using a chemical flow meter to obtain chemical dosage data. Turbidity data is obtained by collecting the turbidity value of the water at the outlet of a designated sedimentation tank using a turbidity sensor. The sediment height data is obtained by collecting the bottom sediment height value in a designated sedimentation tank using an ultrasonic level sensor. Plastic density data is obtained by collecting the density values ​​of plastic in a designated sedimentation tank using a hydrometer. The core of this implementation method lies in the fact that by arranging a variety of sensors to form a sensor network, the system can collect influent data, agitation data, chemical dosing data, turbidity data, sediment height data, and plastic density data in a designated sedimentation tank in real time. This achieves the purpose of providing multi-source data for the system and ensuring the real-time transmission of data, providing a data foundation for subsequent system calculations. By collecting multiple types of data, the system can also effectively improve the comprehensiveness of the data required for calculations, thereby further improving the accuracy of the calculations. Step S2 includes: S2.1, calculate the settling rate based on the sediment height data in the real-time basic dataset. The specific formula for calculating the settling rate is as follows: ; Obtaining settling rate data ,in, At the current time point Sediment height data, For the previous time point Sediment height data, The sampling time interval; S2.2, calculate the turbidity change of the turbidity data in the real-time basic dataset. The specific formula for calculating the turbidity change is as follows: ; Obtain turbidity change data ,in, At the current time point Turbidity data, For the previous time point Turbidity data; S2.3, Pack the sedimentation rate data and turbidity change data to obtain the feature dataset; The core of this implementation method lies in the fact that by extracting feature data from the real-time basic dataset collected in step S1, the dynamic data in the real-time basic dataset can be effectively quantified and the changing trend of the dynamic data can be shown. Specifically, this implementation method obtains sedimentation rate data and turbidity change data related to sedimentation effect by preprocessing the dynamic data in the real-time basic dataset, which provides a data basis for the prediction calculation in step S3. By converting the dynamic data in the real-time basic dataset into a modelable physical quantity, the accuracy of the prediction calculation can be further improved. Step S3 includes: S3.1, Based on the sedimentation rate data of the feature dataset in step S2 and the sediment height data of the real-time basic dataset in step S1, the sediment height data is predicted. The specific calculation formula for the sediment height data prediction is as follows: ; Obtain predicted sediment height data ,in, For sediment height data, For predicting time; S3.2, Based on the turbidity change data of the feature dataset in step S2 and the turbidity data of the real-time basic dataset in step S1, the turbidity data is predicted. The specific calculation formula for turbidity data prediction is as follows: ; Obtain predicted turbidity data ,in, For turbidity data, is the base of the natural logarithm. The turbidity attenuation constant; The core of this implementation method lies in combining the feature dataset obtained in step S2 with the sediment height data and turbidity data in the real-time basic dataset, enabling the system to predict future effects based on the current state and output the predicted sedimentation effect to step S4 for adjusting the sedimentation measures in step S4. Specifically, this implementation method uses a physical-empirical model to perform predictive calculations on the feature dataset and the real-time basic dataset, enabling the system to predict the future state of the sedimentation tank based on the current real-time data. This greatly improves the system's predictability of sedimentation tank operation risks and its guidance for subsequent sedimentation tank operations, thereby helping staff to significantly reduce unnecessary losses during enterprise operation and improve enterprise economic efficiency. Step S4 includes: S4.1, using the influent data, agitation data, and reagent dosage data from the real-time basic dataset, the total energy consumption of the sedimentation tank is calculated. The specific formula for calculating the total energy consumption is as follows: ; Obtain total energy consumption data ,in, For water intake data, For mixing data, For drug dosage data, Water energy consumption coefficient The energy consumption coefficient for stirring is... This is the energy consumption coefficient of the pharmaceutical agent; It should be explained that the water energy consumption coefficient is used to constrain the influent data. When the predicted turbidity data is less than or equal to the standard turbidity data, the water energy consumption coefficient is reduced, thereby reducing the influent volume and achieving energy saving. The stirring energy consumption coefficient is used to constrain the stirring data. When the predicted sediment height data is less than or equal to the maximum allowable sediment height data, the stirring energy consumption coefficient is reduced, thereby reducing the stirring intensity and achieving energy saving. The chemical energy consumption coefficient is used to constrain the chemical dosage data. When the predicted turbidity data is less than or equal to the standard turbidity data, the chemical energy consumption coefficient is reduced, thereby reducing the amount of chemical dosage and achieving cost saving. S4.2 When the predicted sediment height data is greater than or equal to the maximum allowable sediment height data, the stirring data is adjusted based on the predicted sediment height data in the predicted sedimentation effect. The specific calculation formula for adjusting the stirring data is as follows: ; Obtain stirring adjustment data ,in, This is the stirring gain coefficient. The maximum allowable sediment height; It should be explained that the stirring adjustment gain coefficient is used to correct for deviations in stirring data and from the predicted sediment height data. It is negatively correlated; S4.3 When the predicted turbidity data is greater than or equal to the standard turbidity data, the influent data and reagent dosage data are adjusted based on the predicted turbidity data in the sedimentation effect prediction. The specific calculation formulas for adjusting the influent data and reagent dosage data are as follows: ; The influent adjustment data were obtained respectively. And drug delivery adjustment data ,in, This is the inlet water gain coefficient. Drug delivery gain coefficient, Standard turbidity data; It should be explained that the influent gain coefficient is used to correct for deviations in the influent data and the predicted turbidity data. The correlation is negative; the pesticide dosing gain coefficient is used to correct the pesticide dosing data and to offset the deviation from the predicted turbidity data. It is positively correlated; S4.4 Based on the stirring adjustment data, water inlet adjustment data, and reagent dosing adjustment data in steps S4.2 and S4.3, the second water inlet data, the second stirring data, and the second reagent dosing data are calculated respectively, and then returned to step S4.1 and substituted into the total energy consumption calculation formula. S4.5, package the second influent data, the second mixing data, and the second reagent dosing data to obtain a sedimentation adjustment measures report; The core of this implementation method lies in the fact that by performing reverse calculations on the predicted sedimentation results obtained in step S3, the optimal adjustment amounts for the influent data, stirring data, and reagent dosage data are obtained respectively. This enables the system to minimize energy consumption while ensuring the sedimentation effect of waste plastics. Specifically, this implementation method calculates the total energy consumption value using the influent data, stirring data, and reagent dosage data in the real-time basic dataset. Then, based on the different contents in the predicted sedimentation results, the optimal adjustment amounts for the influent data, stirring data, and reagent dosage data are calculated in reverse to obtain a sedimentation adjustment measure report. Finally, the optimal adjustment amount is fed back to step S4.1 to calculate the optimal total energy consumption value. This gives the system the ability to calculate the optimal adjustment amount in reverse based on the prediction results, thereby effectively improving the system's ability from prediction calculation to execution. Step S5 includes: S5.1, Read the sedimentation adjustment measures report; When there is influent adjustment data in the sedimentation adjustment measures report, the influent data of the influent pump valve is adjusted to the second influent data through the programmable controller; When the sedimentation adjustment report contains stirring adjustment data, the stirring data of the stirrer motor is adjusted to the second stirring data through the programmable controller; When there is reagent dosing adjustment data in the sedimentation adjustment measures report, the reagent dosing data of the reagent metering pump is adjusted to the second reagent dosing data through the programmable controller; S5.2, activate the flotation adjustment equipment; When the plastic density data shows low-density plastics with a density of less than 1000 kg per cubic meter, air bubbles are injected into the sedimentation tank through the flotation adjustment equipment, and the waste plastics floating on the surface of the sedimentation tank are removed through the skimming equipment. High-density plastics with a density greater than 1000 kg per cubic meter will settle to the bottom of the sedimentation tank due to their own gravity. The core of this implementation step is that, based on the sedimentation adjustment measures report in step S4, the programmable controller is used for execution, and the low-density plastic is separated by flotation through the flotation adjustment equipment. This allows the system to simultaneously complete the flotation and sedimentation of waste plastics, effectively reducing the operating energy consumption of the sedimentation tank and thus greatly improving the system's economy. Step S6 includes: S6.1, by obtaining the predicted time The actual turbidity data is obtained from the turbidity data at the time of prediction, and the error is calculated between the actual turbidity data and the predicted turbidity data. The specific formula for calculating the turbidity error is as follows: ; Obtain turbidity error value ,in, This is actual turbidity data; S6.2, When the turbidity error value is greater than or equal to the standard error threshold, the turbidity attenuation constant is updated. The specific calculation formula for the update is as follows: ; The second turbidity attenuation constant was obtained. ,in, The learning rate; S6.3, output the second turbidity attenuation constant into the turbidity data prediction calculation formula in step S3.2; The core of this implementation method lies in the real-time adjustment and optimization of the system's prediction calculation accuracy by verifying the error of the predicted turbidity data. This enables the system to have the ability to be optimized and updated over a long period of time, thereby greatly improving the sustainability of the system's use. Specifically, this implementation method obtains the turbidity error value by subtracting the actual turbidity data from the predicted turbidity data. The turbidity error value is then compared with a threshold. When the turbidity error value does not meet the comparison conditions, the predicted turbidity data is calibrated by updating the turbidity attenuation constant, thereby further improving the accuracy of the system's prediction calculation. This implementation step enables the system to complete a closed-loop feedback optimization. Step S3.2 also includes the specific calculation formula for the turbidity attenuation constant: ; in, This refers to the current time point; Step S4.4 also includes the specific calculation formula set for the sedimentation adjustment measures report: ; Second water intake data were obtained respectively Second mixing data Second agent delivery data ; Based on the above, this embodiment also provides a cleaning, separation, and sedimentation tank for a waste plastic recycling and treatment system. The cleaning, separation, and sedimentation tank for a waste plastic recycling and treatment system includes a real-time data acquisition module, a feature calculation and processing module, a sedimentation effect prediction module, a control decision module, an execution control module, and a feedback update module. In this embodiment, the beneficial effects are achieved by using a sensor network to collect relevant data from the sedimentation tank in real time, obtaining a real-time basic dataset; based on the real-time basic dataset, a feature dataset is calculated and derived; based on the feature dataset and the real-time basic dataset, the sedimentation effect in the next time period is predicted, obtaining a predicted sedimentation effect; based on the predicted sedimentation effect, a sedimentation adjustment measure report is calculated according to the real-time basic dataset; based on the sedimentation adjustment measure report, the regulating equipment is controlled in real time, and the separation of waste plastics is controlled and adjusted in real time according to the plastic density data; actual turbidity data is acquired, and error calculation is performed between the actual turbidity data and the predicted turbidity data; based on the turbidity error value, the prediction accuracy is adjusted in real time; enabling the system to accurately measure turbidity data and sediment. The invention ensures the accuracy of prediction calculations under dynamic changes in high-precision data. Furthermore, through step S6, the invention optimizes the system's prediction calculations through feedback, enabling the system to achieve long-term optimization and prediction capabilities. This significantly enhances the system's sustainable use and achieves good economic efficiency. Simultaneously, the sedimentation adjustment measures report obtained through reverse calculation of the prediction results can effectively and accurately provide a data basis for the energy consumption optimization and adjustment of sedimentation tanks. This allows the system to directly and effectively transform prediction calculation data into physical intervention operations, greatly improving the system's practicality. Overall, the invention has significant advantages such as strong sedimentation effect and energy consumption optimization balance, significant cost savings, and good continuous feedback and system update effects.

[0011] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0012] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention 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 embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0013] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a system claim may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0014] Finally, it should be noted that the above 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 preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A sedimentation method for a waste plastic recycling system, characterized in that, The method includes: S1: Based on a sensor network, collect relevant data from the sedimentation tank in real time to obtain a real-time basic dataset; S2: Based on the real-time basic dataset, calculate and derive the feature dataset; S3: Based on the feature dataset and the real-time basic dataset, predict the sedimentation effect in the next time period to obtain the predicted sedimentation effect; S4: Based on the comparison of predicted sedimentation effects, and using real-time basic datasets, calculate a sedimentation adjustment measures report; S5: Based on the sedimentation adjustment measures report, control and adjust the equipment in real time, and control and adjust the separation of waste plastics in real time according to the plastic density data; S6: Obtain the actual turbidity data and calculate the error between it and the predicted turbidity data. Adjust the prediction accuracy in real time based on the turbidity error value.

2. The sedimentation method for a waste plastic recycling system according to claim 1, characterized in that, S1 includes: The real-time basic dataset includes influent data, agitation data, chemical dosing data, turbidity data, sediment height data, and plastic density data; The influent flow rate in the designated sedimentation tank is collected using a water flow meter to obtain influent data. The stirring intensity value in the specified sedimentation tank is collected using a power meter to obtain stirring data; The dosage of chemicals in a designated sedimentation tank is collected using a chemical flow meter to obtain chemical dosage data. Turbidity data is obtained by collecting the turbidity value of the water at the outlet of a designated sedimentation tank using a turbidity sensor. The sediment height data is obtained by collecting the bottom sediment height value in a designated sedimentation tank using an ultrasonic level sensor. The density of plastic in a designated sedimentation tank is collected using a hydrometer to obtain plastic density data.

3. The sedimentation method for a waste plastic recycling system according to claim 1, characterized in that, S2 includes: S2.1, calculate the settling rate based on the sediment height data in the real-time basic dataset. The specific formula for calculating the settling rate is as follows: ； Obtaining settling rate data ,in, At the current time point Sediment height data, For the previous time point Sediment height data, The sampling time interval; S2.2, calculate the turbidity change of the turbidity data in the real-time basic dataset. The specific formula for calculating the turbidity change is as follows: ； Obtain turbidity change data ,in, At the current time point Turbidity data, For the previous time point Turbidity data; S2.3, Pack the sedimentation rate data and turbidity change data to obtain the feature dataset.

4. The sedimentation method for a waste plastic recycling system according to claim 3, characterized in that, S3 includes: S3.1, Based on the sedimentation rate data of the feature dataset in step S2 and the sediment height data of the real-time basic dataset in step S1, the sediment height data is predicted. The specific calculation formula for the sediment height data prediction is as follows: ； Obtain predicted sediment height data ,in, For sediment height data, For predicting time; S3.2, Based on the turbidity change data of the feature dataset in step S2 and the turbidity data of the real-time basic dataset in step S1, the turbidity data is predicted. The specific calculation formula for turbidity data prediction is as follows: ； Obtain predicted turbidity data ,in, For turbidity data, is the base of the natural logarithm. is the turbidity attenuation constant.

5. The sedimentation method for a waste plastic recycling system according to claim 4, characterized in that, S4 includes: S4.1, using the influent data, agitation data, and reagent dosage data from the real-time basic dataset, the total energy consumption of the sedimentation tank is calculated. The specific formula for calculating the total energy consumption is as follows: ； Obtain total energy consumption data ,in, For water intake data, For mixing data, For drug dosage data, Water energy consumption coefficient The energy consumption coefficient for stirring is... This is the energy consumption coefficient of the pharmaceutical agent; S4.2 When the predicted sediment height data is greater than or equal to the maximum allowable sediment height data, the stirring data is adjusted based on the predicted sediment height data in the predicted sedimentation effect. The specific calculation formula for adjusting the stirring data is as follows: ； Obtain stirring adjustment data ,in, This is the stirring gain coefficient. The maximum allowable sediment height; S4.3 When the predicted turbidity data is greater than or equal to the standard turbidity data, the influent data and reagent dosage data are adjusted based on the predicted turbidity data in the sedimentation effect prediction. The specific calculation formulas for adjusting the influent data and reagent dosage data are as follows: ； The influent adjustment data were obtained respectively. And drug delivery adjustment data ,in, This is the inlet water gain coefficient. Drug delivery gain coefficient, Standard turbidity data; S4.4 Based on the stirring adjustment data, water inlet adjustment data, and reagent dosing adjustment data in steps S4.2 and S4.3, the second water inlet data, the second stirring data, and the second reagent dosing data are calculated respectively, and then returned to step S4.1 and substituted into the total energy consumption calculation formula. S4.5, package the second influent data, the second mixing data, and the second reagent dosage data to obtain the sedimentation adjustment measures report.

6. The sedimentation method for a waste plastic recycling system according to claim 5, characterized in that, S5 includes: S5.1, Read the sedimentation adjustment measures report; When there is influent adjustment data in the sedimentation adjustment measures report, the influent data of the influent pump valve is adjusted to the second influent data through the programmable controller; When the sedimentation adjustment report contains stirring adjustment data, the stirring data of the stirrer motor is adjusted to the second stirring data through the programmable controller; When there is reagent dosing adjustment data in the sedimentation adjustment measures report, the reagent dosing data of the reagent metering pump is adjusted to the second reagent dosing data through the programmable controller; S5.2, activate the flotation adjustment equipment; When the plastic density data shows low-density plastics with a density of less than 1000 kg per cubic meter, air bubbles are injected into the sedimentation tank through the flotation adjustment equipment, and the waste plastics floating on the surface of the sedimentation tank are removed through the skimming equipment. High-density plastics with a density greater than 1000 kg per cubic meter will settle to the bottom of the sedimentation tank due to their own gravity.

7. The sedimentation method for a waste plastic recycling system according to claim 6, characterized in that, S6 includes: S6.1, by obtaining the predicted time The actual turbidity data is obtained from the turbidity data at the time of prediction, and the error is calculated between the actual turbidity data and the predicted turbidity data. The specific formula for calculating the turbidity error is as follows: ； Obtain turbidity error value ,in, This is actual turbidity data; S6.2, When the turbidity error value is greater than or equal to the standard error threshold, the turbidity attenuation constant is updated. The specific calculation formula for the update is as follows: ； The second turbidity attenuation constant was obtained. ,in, The learning rate; S6.3, output the second turbidity attenuation constant into the turbidity data prediction calculation formula in step S3.

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8. The sedimentation method for a waste plastic recycling system according to claim 3, characterized in that, Step S3.2 also includes the specific calculation formula for the turbidity attenuation constant: ； in, This refers to the current time.

9. The sedimentation method for a waste plastic recycling system according to claim 5, characterized in that, Step S4.4 also includes the specific calculation formula set for the sedimentation adjustment measures report: ； Second water intake data were obtained respectively Second mixing data Second agent delivery data .

10. A washing, separation, and sedimentation tank for a waste plastic recycling system, applied to the sedimentation method for a waste plastic recycling system according to any one of claims 1-9, characterized in that, It includes a real-time data acquisition module, a feature calculation and processing module, a sedimentation effect prediction module, a control decision module, an execution control module, and a feedback update module.

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