AI integrated low-temperature magnetization circulating pyrolysis system

Abstract

The AI integrated low-temperature magnetization circulating pyrolysis system comprises a feeding model, a low-temperature magnetization pyrolysis model, a flue gas treatment model and an AI display module, the feeding model is used for selecting a treatment mode according to feeding components and determining operation parameters of the low-temperature magnetization pyrolysis model based on the selected treatment mode; the low-temperature magnetization pyrolysis model is used for starting a cyclic pyrolysis program after receiving the feeding information, and dynamically regulating and controlling the cyclic pyrolysis process; the flue gas treatment model is used for regulating and controlling the flue gas treatment process according to the operation data of the feeding model and the low-temperature magnetization pyrolysis model; and the AI display module is used for performing real-time monitoring display and real-time calculation analysis on the operation data of each model, and feeding back the operation data to the corresponding model to perform operation data adjustment if the operation data exceeds a preset range. Through deep integration and AI intelligent control, the purposes of greatly reducing the energy consumption of the system and remarkably improving the solid waste treatment efficiency are achieved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of low-temperature magnetization circulating pyrolysis, and particularly relates to an AI integrated low-temperature magnetization circulating pyrolysis system. BACKGROUND

[0002] The current domestic solid waste magnetization pyrolysis technology equipment has the following significant limitations: System fragmentation: the equipment function is single, or only organic solid waste is treated, or only incinerator flue gas is treated, and a complete integrated treatment system cannot be built.

[0003] Single treatment: unable to adapt to the comprehensive treatment needs of solid waste with complex components.

[0004] Low running efficiency: the intermittent running mode of one-time filling of the furnace and overall treatment is generally adopted, resulting in low treatment efficiency and high energy consumption.

[0005] Therefore, the AI integrated low-temperature magnetization circulating pyrolysis system is designed. SUMMARY

[0006] According to the embodiment of the application, an AI integrated low-temperature magnetization circulating pyrolysis system is provided, which comprises a feeding model, a low-temperature magnetization pyrolysis model, a flue gas treatment model, an AI display module and an alarm module. The feeding model is used to select a treatment mode according to the feeding component, and to determine the running parameters of the low-temperature magnetization pyrolysis model based on the selected treatment mode. The low-temperature magnetization pyrolysis model is used to start the circulating pyrolysis program after receiving the feeding information, and to dynamically regulate and control the circulating pyrolysis process. The flue gas treatment model is used to regulate and control the flue gas treatment process according to the running data of the feeding model and the low-temperature magnetization pyrolysis model. The AI display module is used to monitor and display the running data of the feeding model, the low-temperature magnetization pyrolysis model and the flue gas treatment model in real time, and to calculate and analyze the running data in real time. If the running data exceeds the preset range, the running data is adjusted by the corresponding model through the alarm module.

[0007] Further, the establishment process of the feeding model comprises the following steps: The running parameters of the low-temperature magnetization pyrolysis model corresponding to various treatment modes are preset. The feeding component, the feeding weight g1, the feeding rate v1 and the cumulative feeding amount Σg (1+2+3+…n) are obtained after the system starts running. According to the preset running parameters, a nonlinear correlation model between the medium magnetization air intake Q2 and the feeding rate v1 under various treatment modes is established, and the functional relationship is Q2=1.1~1.5 v1.

[0008] Further, the feed ingredients include: domestic waste A, low-calorific-value industrial waste B, oil-containing waste C, and dried sludge D.

[0009] Further, the process for establishing the low-temperature magnetization pyrolysis model includes the following steps: Obtain low, medium, and high level magnetized air intake Q (1, 2, 3), feed weight g1, and feed rate v1; Obtain magnetized air machine case opening amount K1, intake fan frequency H1, electric valve opening degree K2, furnace bottom temperature T D , pyrolysis temperature T R , circulating temperature T X , flue gas temperature T Q , induced fan frequency H2, flue gas emission data y, and energy consumption value W1; Through learning from historical low-temperature magnetization circulating pyrolysis data, a nonlinear correlation model between low, medium, and high level magnetized air intake Q (1, 2, 3) and pyrolysis temperature T R , flue gas emission data y, and energy consumption value W1 under different operating conditions is established. Function relationship: f1(g1, v1, T R , y, W1) = Q (1, 2, 3); Wherein: the value range of TR is 300-500℃; the value of y conforms to the national standard GB 16297-1996; After obtaining the value of Q (1, 2, 3) according to the function, the corresponding values of K1, K2, H1, and H2 are determined.

[0010] Further, the process for establishing the flue gas treatment model includes the following steps: Obtain feed ingredients, feed weight g1, and feed rate v1; Obtain medium and high level magnetized air intake Q2, Q3, pyrolysis temperature T R , and flue gas temperature T Q ; Obtain heat exchanger inlet temperature T A , heat exchanger outlet temperature T C , flue gas online detection data dioxin y1, sulfur dioxide y2, nitrogen oxides y3, and particulate matter y4; Establish a function relationship model between high level magnetized air intake Q3 and pyrolysis temperature TR, flue gas online detection data y1, y2, y3, and y4, flue gas temperature T Q , and heat exchanger outlet temperature T C ; Function relationship: f2(TR, y1, y2, y3, y4, TQ) = Q3; Wherein: the value range of TR is 300-500℃; the value of y conforms to the national standard GB 16297-1996; The functional relationship between the flue gas temperature TQ and the outlet temperature Tc of the heat exchanger is Tc=KTQ; wherein: K is a constant, which is determined according to the design parameters of the heat exchanger; Tc <100℃.

[0011] Further, the AI display module monitors the display, real-time calculation and analysis data respectively: The total amount of solid waste treatment: Σg=g1+g2+g3+…gn; The energy consumption value: W1=W 点火 +W 热交换 +W 烟气 +W 风机 ; The running state data: K1, K2, T D , T R , T X , T C ; The flue gas emission data: y1, y2, y3, y4.

[0012] Further, it further comprises a fire-fighting module for acquiring the running temperature of the system and the flame detection data, triggering the alarm module to perform fire-fighting alarm.

[0013] According to the AI integrated low-temperature magnetization circulating pyrolysis system provided by the embodiment of the application, the following beneficial effects are achieved: 1. Deep integration: seamlessly integrating the links of feeding, pyrolysis reaction, flue gas purification treatment and energy recycling and utilization, forming a closed loop and continuously operating whole.

[0014] 2. Circulating pyrolysis: adopting a circulating and continuous pyrolysis mode, significantly improving the treatment efficiency and breaking through the bottleneck of the intermittent mode.

[0015] 3. AI intelligent driving: using artificial intelligence (AI) technology to monitor and intelligently optimize the system operation parameters (such as temperature, feeding rate, gas flow, etc.) in real time. Through AI control, the whole process is intelligently operated, and different solid waste characteristics and working conditions are accurately matched.

[0016] Through deep integration and AI intelligent control, the system ultimately achieves the goal of significantly reducing system energy consumption and significantly improving solid waste treatment efficiency, and meets the efficient, energy-saving and environmentally friendly treatment needs of complex solid waste with multiple components.

[0017] It is to be understood that both the foregoing general description and the following detailed description are exemplary and intended to provide further explanation of the subject technology claimed. BRIEF DESCRIPTION OF DRAWINGS

[0018] Figure 1 A structure diagram of an AI integrated low-temperature magnetization circulating pyrolysis system according to an embodiment of the present application. DETAILED DESCRIPTION

[0019] The preferred embodiments of the present application will be described in detail below with reference to the accompanying drawings, and the present application will be further described.

[0020] Firstly, an AI integrated low-temperature magnetization circulating pyrolysis system according to an embodiment of the present application will be described. Figure 1 The AI integrated low-temperature magnetization circulating pyrolysis system according to the embodiment of the present application is used for low-temperature magnetization circulating pyrolysis of solid waste, and has a wide range of application scenarios.

[0021] As shown in the figure, the AI integrated low-temperature magnetization circulating pyrolysis system according to the embodiment of the present application has a feed model, a low-temperature magnetization pyrolysis model, a flue gas treatment model, an AI display module, and an alarm module. Figure 1

[0022] Specifically, as shown in the figure, the feed model is used to select a treatment mode according to the feed composition, and to determine the operating parameters of the low-temperature magnetization pyrolysis model based on the selected treatment mode. The establishment process of the feed model includes the following steps: Figure 1 The operating parameters of the low-temperature magnetization pyrolysis model corresponding to various treatment modes are preset, as shown in the following table:

[0023] After the system is started and runs, the feed composition, the feed weight g1, the feed rate v1, and the cumulative feed amount Σg (1+2+3+…n) are obtained. The feed composition includes household garbage A, low-calorific-value industrial waste B, oil-containing waste C, and dried sludge D.

[0024] According to the preset operating parameters, a nonlinear correlation model between the medium magnetization air intake Q2 and the feed rate v1 under various treatment modes is established, and the functional relationship is Q2=1.1~1.5 v1.

[0025] It should be noted that the feed rate v1 in the model can be obtained by setting a certain calculation method through hardware programming according to the real-time data of the feed composition (A\B\C\D) and the feed weight g1.

[0026] The feed rate is adjusted according to the feedback of the flue gas emission data and the energy consumption data according to the optimal scheme after the feed composition data of the solid waste are obtained to determine the low-temperature magnetization pyrolysis operating parameters.

[0027] Specifically, as shown in the figure, the low-temperature magnetization pyrolysis model is used to start the circulating pyrolysis program after receiving the feed information, and to dynamically control the circulating pyrolysis process. The establishment process of the low-temperature magnetization pyrolysis model includes the following steps: Figure 1 ​​​Obtain low, medium and high magnetized air intake Q (1, 2, 3), feed weight g1 and feed rate v1; Obtain magnetized air tank opening K1, intake fan frequency H1, electric valve opening K2, furnace bottom temperature T D , pyrolysis temperature T R , circulating temperature T X , flue gas temperature T Q , induced fan frequency H2, flue gas emission data y and energy consumption value W1; Through learning historical low-temperature magnetization circulating pyrolysis data, a nonlinear correlation model between low, medium and high magnetized air intake Q (1, 2, 3) and pyrolysis temperature T R , flue gas emission data y and energy consumption value W1 under different operating conditions is established; an AI power method is applied to establish a low-temperature magnetization circulating pyrolysis model to ensure that the solid energy can be completely resolved and the pyrolysis temperature is controlled at 300-500℃ during pyrolysis, and the generation of dioxin is inhibited.

[0028] Functional relationship: f1(g1, v1, T R , y, W1) = Q(1, 2, 3); Wherein: the value range of T R is 300-500℃; the value of y conforms to the national standard GB 16297-1996; After obtaining the value of Q (1, 2, 3) according to the function, the values of K1, K2, H1 and H2 are determined.

[0029] It should be noted that f1 is a correlation function of the above variables. Due to the complexity of the working conditions, the specific expression cannot be obtained, but the correlation function can be approximated by a series of methods, such as training an AI model that can rival the effect of f1 using data-driven methods.

[0030] Specifically, as shown in Figure 1 , the flue gas treatment model is used to regulate the flue gas treatment process according to the operation data of the feed model and the low-temperature magnetization pyrolysis model. The establishment process of the flue gas treatment model includes the following steps: Obtain the feed composition, feed weight g1 and feed rate v1; Obtain medium and high magnetized air intake Q2, Q3, pyrolysis temperature T R , flue gas temperature T Q ; Obtain heat exchanger inlet temperature T A (i.e. low-temperature magnetization circulating pyrolysis flue gas temperature T Q ), heat exchanger outlet temperature T C , flue gas online detection data dioxin y1, sulfur dioxide y2, nitrogen oxides y3 and particulate matter y4; A function relationship model between the high-position magnetized air intake amount Q3 and the pyrolysis temperature TR and the online detection data y1, y2, y3, y4 of the flue gas, and the function relationship between the flue gas temperature TQ and the heat exchanger outlet temperature Tc is established. Q The function relationship is f2(TR, y1, y2, y3, y4, TQ) = Q3. The value range of TR is 300-500℃, and the value of y conforms to the national standard GB 16297-1996. The function relationship between the flue gas temperature TQ and the heat exchanger outlet temperature Tc is Tc = KTQ, wherein K is a constant determined according to the design parameters of the heat exchanger, and Tc < 100℃.

[0031] It should be noted that Q3 is also affected by the feed composition A\B\C\D of the solid waste, the feed amount g1, and the feed rate v1. The model obtains the optimal medium and high-position magnetized air intake amount in the flue gas treatment process through continuous learning and analysis during operation.

[0032] Specifically, as shown in Figure 1 , the AI display module is used for real-time monitoring and display of the operation data of the feed model, the low-temperature magnetized pyrolysis model, and the flue gas treatment model, real-time calculation and analysis. If the operation data exceeds the preset range, the alarm module is used to feed back to the corresponding model for operation data adjustment.

[0033] Further, in the present embodiment, the data monitored and displayed in real time by the AI display module are as follows: The total amount of solid waste treatment: Σg = g1+g2+g3+…gn; The energy consumption value: W1 = W 点火 + W 热交换 + W 烟气 + W 风机 ; The operation state data: K1, K2, T D , T R , T X , T C ; The flue gas emission data: y1, y2, y3, y4.

[0034] The results are displayed on the terminal intelligent screen according to the calculation data of the internal model (total amount of solid waste treatment, energy consumption value, operation state, flue gas emission value, etc.); Further, as shown in Figure 1 ​As shown in the figure, an AI-integrated low-temperature magnetized cyclic pyrolysis system according to an embodiment of the present invention further includes: a fire suppression module, used to acquire the system's operating temperature and flame detection data, and trigger an alarm module to issue a fire alarm. The flame detection data is acquired by an infrared flame detector.

[0035] In this embodiment, the feeding model selects a suitable treatment mode based on the composition of the solid waste feed. Each treatment mode corresponds to specific low-temperature cyclic pyrolysis parameters, which are then optimized based on the feed rate. Upon receiving the feed information, the low-temperature magnetized cyclic pyrolysis model initiates the cyclic pyrolysis program. Based on feedback data such as temperature, high, medium, and low-level magnetized air intake, and flue gas emission values, the model dynamically regulates the cyclic pyrolysis process through calculations performed by the computing module.

[0036] For example, see below: 1. Cyclic pyrolysis temperature T R If the temperature exceeds 450℃, the fault alarm module will immediately sound an alarm and simultaneously send a command to the low-temperature magnetized pyrolysis model to reduce the mid-position magnetized air intake Q2 and send a command to the feeding system to increase the feeding rate V1.

[0037] Reducing the intake air volume Q2 of the mid-position magnetization chamber can be achieved by reducing the opening amount K1 of the magnetization chamber. 中 Reduce the opening degree K2 of the electric valve 中 Reduce the frequency H1 of the magnetized fan 高 The model selects the optimal method based on calculations.

[0038] 2. Furnace bottom temperature T D If the temperature is below 250°C, the ignition system needs to be restarted, the low-level magnetized air intake Q1 increased, and the feed rate V1 increased. Increasing the low-level magnetized air intake Q1 can be achieved by increasing the opening depth of the magnetization enclosure K1. 低 Increase the opening degree K2 of the electric valve 低 Increase the frequency H1 of the magnetizing fan 低 In this way.

[0039] In the embodiments of this application, the flue gas treatment model regulates the flue gas treatment process based on the operating data of the feeding model and the low-temperature magnetization pyrolysis model to ensure that the flue gas meets the emission standards.

[0040] For example, see below: If the flue gas emission value exceeds the national emission standard, the display and alarm system will sound an alarm and simultaneously issue a command to the low-temperature magnetized pyrolysis system to increase the high-level magnetized air intake Q3 and reduce the feed rate V1.

[0041] Above, refer to Figure 1 An AI-integrated low-temperature magnetized cyclic pyrolysis system according to an embodiment of the present invention is described, which has the following beneficial effects: 1. Deep integration: seamlessly integrate feedstock, pyrolysis reaction, flue gas purification treatment, and energy recovery and utilization links to form a closed loop and continuously operating whole.

[0042] 2. Circulating pyrolysis: adopt a circulating and continuous pyrolysis mode to significantly improve the processing efficiency and break through the bottleneck of batch mode.

[0043] 3. AI intelligent driving: use artificial intelligence (AI) technology to monitor and intelligently optimize system operating parameters (such as temperature, feed rate, gas flow, etc.) in real time. Through AI control, realize intelligent operation in the whole process, and accurately match different solid waste characteristics and working conditions.

[0044] Through deep integration and AI intelligent control, the system ultimately achieves the goal of significantly reducing system energy consumption and significantly improving solid waste processing efficiency, and meets the efficient, energy-saving, and environmentally friendly processing needs of complex solid waste with multiple components.

[0045] It should be noted that in this specification, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices that include a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the elements defined by the statement "include" do not exclude the presence of other identical elements in the process, method, article or device that includes the elements.

[0046] Although the content of the present application has been described in detail by the above preferred embodiments, it should be recognized that the above description should not be considered as a limitation of the present application. After reading the above content, various modifications and alternatives of the present application will be obvious to those skilled in the art. Therefore, the protection scope of the present application should be defined by the appended claims.

Claims

1. An AI integrated low temperature magnetized cycle pyrolysis system, characterized in that, It comprises a feed model, a low-temperature magnetization pyrolysis model, a flue gas treatment model, an AI display module, and an alarm module. The feed model is used to select a treatment mode according to the feed composition and determine the operating parameters of the low-temperature magnetization pyrolysis model based on the selected treatment mode. The low-temperature magnetization pyrolysis model is used to start a cyclic pyrolysis program after receiving feed information and dynamically regulate the cyclic pyrolysis process. The flue gas treatment model is used to regulate the flue gas treatment process according to the operating data of the feed model and the low-temperature magnetization pyrolysis model. The AI display module is used to monitor and display the operating data of the feed model, the low-temperature magnetization pyrolysis model, and the flue gas treatment model in real time, perform real-time calculation and analysis, and if the operating data exceeds the preset range, feedback the operating data to the corresponding model for adjustment through the alarm module.

2. The Al-integrated low-temperature magnetized cycle pyrolysis system of claim 1, wherein, The establishment process of the feed model comprises the following steps: Preset operating parameters of the low-temperature magnetization pyrolysis model corresponding to various treatment modes; After the system starts running, the feed composition, feed weight g1, feed rate v1, and cumulative feed amount Σg (1+2+3+…n) are obtained; According to the preset operating parameters, a nonlinear correlation model between the medium magnetization air intake Q2 and the feed rate v1 under various treatment modes is established, and the functional relationship is Q2=1.1~1.5 v1.

3. The Al-integrated low-temperature magnetized cycle pyrolysis system of claim 2, wherein, The feed composition includes household garbage A, low-calorific value industrial waste B, oil-containing waste C, and dried sludge D.

4. The Al-integrated low-temperature magnetized cycle pyrolysis system of claim 2, wherein, The establishment process of the low-temperature magnetization pyrolysis model comprises the following steps: Obtain low, medium, and high magnetization air intake Q (1, 2, 3), feed weight g1, and feed rate v1; Obtaining the opening amount K1 of the magnetized air machine case, the frequency H1 of the air inlet fan, the opening degree K2 of the electric valve, the furnace bottom temperature T D , the pyrolysis temperature T R , the circulating temperature T X , the flue gas temperature T Q , the induced fan frequency H2, the flue gas emission data y, and the energy consumption value W1; Through learning the historical low-temperature magnetization cycle pyrolysis data, a nonlinear correlation model between the low, medium and high position magnetization air intake Q (1, 2, 3) and the pyrolysis temperature T, flue gas emission data y and energy consumption value W1 under different operating conditions is established. R , Function relationship: f1(g1, v1, T R , y, W1) = Q(1, 2, 3); Wherein: T R The value range of 300~500℃; the value of y conforms to GB 16297-1996 national standard; After obtaining the values of Q (1, 2, 3) according to the function, determine the corresponding K1, K2, H1, and H2 values.

5. The Al-integrated low-temperature magnetized cycle pyrolysis system of claim 4, wherein, The establishment process of the flue gas treatment model comprises the following steps: Obtain the feed composition, feed weight g1, and feed rate v1; Obtaining middle-high bit magnetization air intake amount Q2, Q3, pyrolysis temperature T R , flue gas temperature T Q ; Acquiring heat exchanger inlet temperature T A , heat exchanger outlet temperature T C , flue gas online detection data dioxin y1, sulfur dioxide y2, nitrogen oxide y3, particulate matter y4; Establish a function relationship model between high-position magnetization air intake volume Q3 and pyrolysis temperature T R and the function relationship model between flue gas online detection data y1, y2, y3, y4, flue gas temperature T Q and heat exchanger outlet temperature T c ​ The functional relationship is f2 (TR, y1, y2, y3, y4, TQ) = Q3; Wherein: the value range of TR is 300~500℃; the value of y conforms to the national standard GB 16297-1996; The functional relationship between flue gas temperature TQ and heat exchanger outlet temperature Tc is Tc=KTQ; wherein: K is a constant determined according to the design parameters of the heat exchanger; Tc <100℃.

6. The Al-integrated low-temperature magnetized cycle pyrolysis system of claim 5, wherein, The data monitored and displayed by the AI display module in real time are: Total solid waste treatment amount: Σg = g1+g2+g3+…gn; Energy consumption value: W1 = W 点火 + W 热交换 + W 烟气 + W 风机 ; Operating state data: K1, K2, T D , T R , T X , T C ; Flue gas emission data: y1, y2, y3, y4.

7. The Al-integrated low-temperature magnetized cycle pyrolysis system of claim 1, wherein, It also comprises a fire-fighting module for obtaining system operating temperature and flame detection data to trigger the alarm module for fire-fighting alarm.

Images