Sludge treatment device and control method
By introducing multi-layer sensors and neural network models into the sludge treatment device, and combining them with particle swarm optimization algorithms, the sludge dewatering process can be accurately simulated and monitored in real time, improving dewatering efficiency and model prediction accuracy, and reducing filter cloth wear.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-11-26
- Publication Date
- 2026-03-10
AI Technical Summary
The existing sludge treatment process lacks accurate simulation and real-time monitoring, resulting in low matching degree of operating parameters, lag in parameter adjustment, unstable dewatering efficiency, large fluctuations in filter cake moisture content, and a lack of intelligent control and sensing detection structures.
Multi-layer sensors and sensor networks are used to monitor sludge characteristics. A dewatering mixing model is constructed by combining a BP neural network model and a particle swarm optimization algorithm to achieve real-time data acquisition and closed-loop control, thereby optimizing dewatering parameters.
It enables precise simulation and real-time monitoring of the sludge dewatering process, improving model prediction accuracy and dewatering efficiency while reducing filter cloth wear.
Smart Images
Figure CN121627286A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of sludge treatment, in particular to a sludge treatment device and control method. BACKGROUND
[0002] Sludge treatment needs to be dewatered by filter pressing, and the solid-liquid separation is realized by filter cloth extrusion, but the prior art has the following defects: first, the control mode depends on manual experience or simple feedback adjustment, the dewatering process is not accurately simulated, resulting in low matching degree of operation parameters; second, when the sludge characteristics fluctuate, the parameter adjustment lags behind, the dewatering efficiency is unstable, and the moisture content of filter cake fluctuates greatly; third, the device lacks a sensing detection structure suitable for intelligent control, and cannot provide comprehensive data support for accurate control. SUMMARY
[0003] The purpose of the present application is to provide a sludge treatment device and control method, which realizes accurate simulation and real-time monitoring of the dewatering process, improves model prediction accuracy, dewatering efficiency and reduces filter cloth consumption.
[0004] To achieve the above purpose, the present application provides a sludge treatment device, which comprises a rack, a first filter cloth unwinding mechanism, a first filter cloth winding mechanism, a conveyor belt, a sludge laying mechanism, a filter pressing dewatering mechanism, a second filter cloth unwinding mechanism and a second filter cloth winding mechanism, the sludge laying mechanism comprises a first support frame, a sludge feeding pipe is arranged on the first support frame, and a moisture content sensor, a concentration sensor and a specific resistance sensor are arranged in the sludge feeding pipe; The first filter cloth unwinding mechanism and the second filter cloth unwinding mechanism each comprise a filter cloth and a filter cloth driving structure, the filter cloth is composed of a filter layer, a sensing layer and a supporting layer, the sensing layer is a flexible polyimide substrate, a thin film pressure sensor is embedded on the sensing layer, and the filter cloth driving mechanism comprises a second support frame, a filter cloth speed sensor is arranged on the second support frame; The filter pressing dewatering mechanism comprises a filter pressing roller shaft, and a tension sensor is arranged at the end of the filter pressing roller shaft; The second filter cloth winding mechanism comprises a third support frame, a partition plate parallel to the conveyor belt is arranged on the third support frame, and an infrared moisture content meter is arranged at the bottom of the partition plate.
[0005] Preferably, the first filter cloth unwinding mechanism and the first filter cloth winding mechanism are arranged at the two ends of the rack respectively, the conveyor belt is arranged on the rack, the sludge laying mechanism is arranged on one side of the top of the rack, the filter pressing dewatering mechanism is arranged on the rack on one side of the conveying direction of the conveyor belt, the second filter cloth unwinding mechanism is arranged between the sludge laying mechanism and the filter pressing dewatering mechanism, and the second filter cloth winding mechanism is arranged on the rack on one side of the conveying direction of the conveyor belt. The second air cylinder is fixedly installed on the first support frame on both sides of the sludge feeding pipe, the piston rods of the two second air cylinders are connected with the limiting frame, the limiting frame is not provided with a top plate and a bottom plate, and the control box is arranged on one side of the first support frame. The filter pressing roller shafts are connected with the moving frame through bearings at both ends, the moving frame is connected with the fourth support frame in a sliding mode, the top of the moving frame is connected with the piston rod of the third air cylinder, the third air cylinder is fixed on the top of the fourth support frame, and the fifth driving motor is installed on one side of the moving frame.
[0006] Preferably, the first filter cloth unwinding mechanism and the first filter cloth winding mechanism are the same in structure, the first filter cloth unwinding mechanism comprises a fifth support frame, a first cloth roll is rotatably arranged on the fifth support frame, the first cloth roll is connected with the first driving motor, a lifting platform is arranged above the first cloth roll, the lifting platform is hingedly connected with the first air cylinder, and the lifting platform and the first air cylinder are both hingedly connected to the rack. The lifting platform comprises a feeding roller shaft, the feeding roller shaft is connected with the second driving motor, a first guide roller is arranged on the feeding roller shaft in the reverse direction of the conveying belt conveying direction, and guide plates are arranged between the first guide roller and the feeding roller shaft and on the feeding roller shaft along the conveying belt conveying direction.
[0007] Preferably, the second filter cloth unwinding mechanism and the second filter cloth winding mechanism are the same in structure, the second filter cloth unwinding mechanism comprises a second support frame and a third support frame, the second support frame is provided with a third driving motor, the third driving motor is connected with the second guide roller, and the second guide roller is connected with the second support frame through bearings at both ends. The third support frame is arranged on the rack on the side of the second support frame along the conveying belt conveying direction, a second cloth roll is arranged on the top of the third support frame, and the second cloth roll is connected with the fourth driving motor. The top of the third support frame of the second filter cloth unwinding mechanism and the bottom of the third support frame of the second filter cloth winding mechanism are both provided with a third guide roller.
[0008] The application provides a control method of a sludge treatment device, and specifically comprises the following steps: S1, constructing a sludge dewatering mixed model; S2, real-time data acquisition and simulation prediction; S3, optimal parameter calculation and real-time adjustment; S4, model iteration optimization and closed-loop control.
[0009] Preferably, the specific steps of constructing the sludge dewatering mixed model in S1 are as follows: S1.1, based on the Karman filter equation, combining the gravity dewatering and pressing dewatering two-stage characteristics of the belt filter press, a dewatering rate model is constructed, the dewatering rate model comprises a gravity dewatering model and a pressing dewatering rate model, and the specific steps are as follows: ; ; in, This refers to the dehydration rate during the gravity dehydration stage. The gravity dehydration coefficient is... This represents the average thickness of the sludge within the filter press channel. It is the acceleration due to gravity. The dynamic viscosity of the filtrate. The initial specific resistance of the sludge. This represents the initial concentration of the sludge. This refers to the dehydration rate during the pressing and dehydration stage. This is the pressing and dehydration coefficient. The pressure applied to the press roller assembly, For real-time specific resistance of sludge. Real-time sludge concentration; S1.2. A three-layer BP neural network model is adopted, and the neural network is trained using historical operating data to obtain the specific drag data correction coefficient. and A data correction model is constructed to correct the sludge specific resistance and dewatering rate calculated by the dewatering rate model, which are the input parameters of the dewatering rate model. The specific calculation formula is shown below: ; ; in, To predict the specific resistance of sludge, The sludge specific resistance calculated for the dewatering rate model. The specific resistance of the sludge is the output of the BP neural network model; To predict the dehydration rate, The dehydration rate is calculated using a dehydration rate model. The dehydration rate is the output of the BP neural network model; S1.3. The weights of the dehydration rate model and the data correction model are dynamically allocated using the entropy weight method, and the final hybrid model is obtained by fusion. The calculation formula is shown below: ; in, The weights for the dehydration rate model, Adjust the model weights to fit the data. , For the dehydration rate model, To correct the model based on the data.
[0010] Preferably, the specific steps of S2 are as follows: S2.1 Collect real-time data, including initial moisture content. initial sludge concentration Initial specific resistance of sludge Current pressing pressure Filter cloth speed Ambient temperature Filtrate dynamic viscosity ; S2.1. After preprocessing the real-time data, input it into the hybrid model to simulate and predict the dewatering effect parameters, including the predicted filter cake moisture content. and predicted solid recovery The predicted solid-phase recovery rate is calculated using the following formula: ; in, This refers to the sludge feed flow rate. The concentration of solids in the filtrate. This is the flow rate of the filtrate discharged.
[0011] Preferably, the specific steps of S3 are as follows: S3.1 Setting the Optimization Objective Function: With maximizing dewatering efficiency and minimizing filter cloth loss as the dual objectives, the optimization model is established as follows: ; The constraints are: , , , ; in, To optimize the objective function value, This is the weighting coefficient for dehydration efficiency. For the minimum dehydration rate, For the maximum dehydration rate, This is the filter cloth loss weighting coefficient. Minimum pressing pressure, To the maximum pressing pressure, For minimum filter cloth speed, Maximum filter cloth speed; S3.2. The optimal parameters are solved using the particle swarm optimization algorithm to obtain the optimal pressing pressure. Optimal filter cloth speed The formula for calculating the optimal pressing pressure is as follows: ; in, The target moisture content after sludge dewatering. The predicted moisture content of the sludge after dewatering. This refers to the minimum permissible moisture content of the sludge after dewatering. S3.3 The PLC controller in the control box issues control commands to adjust the extension and retraction of the third cylinder and the speed of the first and third drive motors.
[0012] Preferably, the specific steps of S4 are as follows: S4.1, Collect the actual moisture content of the dewatered sludge. and actual solid recovery rate ; S4.2 Calculate the deviation between the predicted value and the actual value. The calculation formula is as follows: ; ; in, This represents the deviation value of the moisture content. This represents the deviation value of the solid phase recovery rate; S4.3, when or At the same time, update the weights and thresholds of the BP neural network model and adjust the specific resistance data correction coefficient. and Iterative optimization of the hybrid model; S4.4 Repeat S2-S4 to form a closed-loop control of data acquisition, simulation, adjustment and optimization, and continuously optimize the dehydration process.
[0013] Therefore, the present invention employs the above-mentioned sludge treatment device and control method to achieve accurate simulation and real-time monitoring of the dewatering process, thereby improving the model prediction accuracy, dewatering efficiency, and reducing filter cloth wear.
[0014] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description
[0015] Fig. 1 This is a schematic diagram of the structure of a sludge treatment device according to the present invention; Fig. 2 This is a schematic diagram of the structure of the filter press dewatering mechanism of a sludge treatment device according to the present invention; Figure Labels 1. Frame; 2. Fifth support frame; 3. First fabric roll; 4. Lifting platform; 5. First cylinder; 6. Second drive motor; 7. Second support frame; 8. Third drive motor; 9. Third support frame; 10. Second fabric roll; 11. Third guide roller; 12. Filter press roller shaft; 13. Fourth support frame; 14. Third cylinder; 15. Moving frame; 16. Fifth drive motor; 17. Control box; 18. First support frame; 19. Limit frame; 20. Second cylinder. Detailed Implementation
[0016] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.
[0017] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.
[0018] Example 1 like Figs. 1-2 As shown, the present invention provides a sludge treatment device, including a frame 1, a first filter cloth unwinding mechanism, a first filter cloth winding mechanism, a conveyor belt, a sludge laying mechanism, a filter press dewatering mechanism, a second filter cloth unwinding mechanism, and a second filter cloth winding mechanism. The frame 1 has a first filter cloth unwinding mechanism and a first filter cloth winding mechanism at both ends, respectively. A conveyor belt is mounted on the frame 1. A sludge laying mechanism is mounted on one side of the top of the frame 1. A filter press dewatering mechanism is mounted on the side of the frame 1 along the conveyor belt's transport direction. The sludge laying mechanism and the filter press dewatering mechanism... A second filter cloth unwinding mechanism is provided, and a second filter cloth winding mechanism is provided on the frame 1 on one side of the filter press dewatering mechanism along the conveyor belt transport direction; the conveyor belt is connected to the conveyor belt drive motor through existing technology to realize the transport of sludge, the sludge laying mechanism can evenly lay the sludge on the bottom filter cloth, the filter press dewatering mechanism can squeeze and dewater the sludge, and the first filter cloth unwinding mechanism, the first filter cloth winding mechanism, the second filter cloth unwinding mechanism, and the second filter cloth winding mechanism can realize the unwinding and winding of the filter cloth, which facilitates the cleaning and drying of the filter cloth after the dewatering treatment is completed.
[0019] The sludge laying mechanism includes a first support frame 18, on which a sludge feed pipe is mounted. A moisture content sensor, a concentration sensor, and a specific resistance sensor are installed inside the sludge feed pipe. The first support frame 18 provides support for the sludge feed pipe, and the moisture content sensor, concentration sensor, and specific resistance sensor can detect the sludge's moisture content, concentration, and specific resistance. Second cylinders 20 are fixedly installed on the first support frame 18 on both sides of the sludge feed pipe. The limiting frame 19 has no top or bottom plate to facilitate sludge feeding and laying. The piston rods of both second cylinders 20 are connected to the limiting frame 19, allowing the second cylinders 20 to raise and lower the limiting frame 19, thus controlling the sludge laying thickness. A control box 17 is located on one side of the first support frame 18, containing a PLC controller for controlling the sludge treatment process.
[0020] Both the first and second filter cloth unwinding mechanisms include a filter cloth and a filter cloth driving structure. The filter cloth consists of a filter layer, a sensing layer, and a support layer. The sensing layer is a flexible polyimide substrate, and a thin-film pressure sensor is embedded in the sensing layer. The thin-film pressure sensor can detect the pressure on the filter cloth. The filter cloth driving mechanism includes a second support frame 7, on which a filter cloth speed sensor is installed. The speed sensor can detect the unwinding speed of the filter cloth.
[0021] The first filter cloth unwinding mechanism and the first filter cloth winding mechanism have the same structure. The first filter cloth unwinding mechanism includes a fifth support frame 2, on which a first cloth roll 3 is rotatably mounted, providing support for the first cloth roll 3. The first cloth roll 3 is connected to a first drive motor, which can drive the first cloth roll 3 to rotate, thus realizing the unwinding and winding of the filter cloth. A lifting platform 4 is provided above the first cloth roll 3, and the lifting platform 4 is hinged to a first cylinder 5. Both the lifting platform 4 and the first cylinder 5 are hinged to the frame 1. The first cylinder 5 can drive the lifting platform 4 to rotate, facilitating the installation and removal of the first cloth roll 3.
[0022] The lifting platform 4 includes a feeding roller shaft, which is connected to a second drive motor 6. The second drive motor 6 can drive the feeding roller shaft to rotate, thereby moving the filter cloth and realizing the conveying of the filter cloth. A first guide roller is provided on the feeding roller shaft in the opposite direction of the conveyor belt. Guide plates are provided between the first guide roller and the feeding roller shaft, as well as along the conveyor belt direction of the feeding roller shaft. The first guide roller and the guide plates can support the filter cloth and prevent it from stacking and twisting during the conveying process.
[0023] The second filter cloth unwinding mechanism has the same structure as the second filter cloth winding mechanism. The second filter cloth unwinding mechanism includes a second support frame 7 and a third support frame 9. A third drive motor 8 is installed on the second support frame 7. The third drive motor 8 is connected to the second guide roller. Both ends of the second guide roller are connected to the second support frame 7 through bearings. The second support frame 7 provides support for the third drive motor 8 and the second guide roller. The third drive motor 8 can drive the second guide roller to rotate.
[0024] The second filter cloth winding mechanism includes a third support frame 9, on which a partition parallel to the conveyor belt is provided. An infrared moisture meter is provided at the bottom of the partition, and the partition provides support for the infrared moisture meter. The infrared moisture meter is perpendicular to the conveyor belt and can detect the moisture content of the sludge after filter pressing and dewatering.
[0025] The third support frame 9 is located on the frame 1 on one side of the second support frame 7 along the conveyor belt transport direction. The top of the third support frame 9 is provided with a second fabric roll 10, which is connected to the fourth drive motor. The third support frame 9 provides support for the second fabric roll 10 and the fourth drive motor. The fourth drive motor can drive the second fabric roll 10 to rotate, thereby realizing the winding and unwinding of the filter cloth.
[0026] The top of the third support frame 9 of the second filter cloth unwinding mechanism and the bottom of the third support frame 9 of the second filter cloth winding mechanism are both equipped with third guide rollers 11. The third guide rollers 11 can adjust the tension of the filter cloth to ensure that the filter cloth is wound and unwound smoothly.
[0027] The filter press dewatering mechanism includes a filter press roller shaft 12, with a tension sensor at one end. The tension sensor detects the tension of the filter cloth during the filter press dewatering process. Both ends of the filter press roller shaft 12 are connected to a movable frame via bearings. The movable frame is slidably connected to a fourth support frame 13. The top of the movable frame is connected to the piston rod of a third cylinder 14, which is fixed to the top of the fourth support frame 13. A fifth drive motor 15 is mounted on one side of the movable frame. The third cylinder 14 can drive the movable frame to slide on the fourth support frame 13, thereby adjusting the position of the filter press roller shaft 12 and changing the pressure of the filter press roller shaft 12 on the sludge and filter cloth. The fifth drive motor 15 is connected to the movable frame 16, driving the filter press roller shaft 12 to rotate, thus achieving filter press dewatering of the sludge. The first drive motor, the second drive motor 6, the third drive motor 8, the fourth drive motor and the fifth drive motor 15 move the frame; 16 have the same rotation speed to ensure that the conveying speed of the upper and lower filter cloths is the same, and to avoid the filter cloths stacking and affecting sludge dewatering.
[0028] This invention provides a control method for a sludge treatment device, specifically including the following steps: S1. Construct a sludge dewatering mixing model; The specific steps for constructing a sludge dewatering hybrid model are as follows: S1.1 Based on the Karman filtration equation and considering the two-stage characteristics of gravity dewatering and pressing dewatering in a belt filter press, a dewatering rate model is constructed. The dewatering rate model includes a gravity dewatering model and a pressing dewatering rate model, as shown below: ; ; in, This refers to the dehydration rate during the gravity dehydration stage. The gravity dehydration coefficient is... This represents the average thickness of the sludge within the filter press channel. It is the acceleration due to gravity. The dynamic viscosity of the filtrate. The initial specific resistance of the sludge. This represents the initial concentration of the sludge. This refers to the dehydration rate during the pressing and dehydration stage. This is the pressing and dehydration coefficient. The pressure applied to the press roller assembly, For real-time specific resistance of sludge. Real-time sludge concentration; S1.2. A three-layer BP neural network model is adopted, and the neural network is trained using historical operating data to obtain the specific drag data correction coefficient. and A data correction model is constructed to correct the sludge specific resistance and dewatering rate calculated by the dewatering rate model, which are the input parameters of the dewatering rate model. The specific calculation formula is shown below: ; ; in, To predict the specific resistance of sludge, The sludge specific resistance calculated for the dewatering rate model. The specific resistance of the sludge is the output of the BP neural network model; To predict the dehydration rate, The dehydration rate is calculated using a dehydration rate model. The dehydration rate is the output of the BP neural network model; S1.3. The weights of the dehydration rate model and the data correction model are dynamically allocated using the entropy weight method, and the final hybrid model is obtained by fusion. The calculation formula is shown below: ; in, The weights for the dehydration rate model, Adjust the model weights to fit the data. , For the dehydration rate model, To correct the model based on the data.
[0029] S2. Real-time data acquisition and simulation prediction; The specific steps are as follows: S2.1 Collect real-time data, including initial moisture content. initial sludge concentration Initial specific resistance of sludge Current pressing pressure Filter cloth speed Ambient temperature Filtrate dynamic viscosity ; S2.1. After preprocessing the real-time data, input it into the hybrid model to simulate and predict the dewatering effect parameters, including the predicted filter cake moisture content. and predicted solid recovery The predicted solid-phase recovery rate is calculated using the following formula: ; in, This refers to the sludge feed flow rate. The concentration of solids in the filtrate. This is the flow rate of the filtrate discharged.
[0030] S3. Optimal parameter calculation and real-time adjustment; The specific steps are as follows: S3.1 Setting the Optimization Objective Function: With maximizing dewatering efficiency and minimizing filter cloth loss as the dual objectives, the optimization model is established as follows: ; The constraints are: , , , ; in, To optimize the objective function value, This is the weighting coefficient for dehydration efficiency. For the minimum dehydration rate, For the maximum dehydration rate, This is the filter cloth loss weighting coefficient. Minimum pressing pressure, To the maximum pressing pressure, For minimum filter cloth speed, Maximum filter cloth speed; S3.2. The optimal parameters are solved using the particle swarm optimization algorithm to obtain the optimal pressing pressure. Optimal filter cloth speed The formula for calculating the optimal pressing pressure is as follows: ; in, The target moisture content after sludge dewatering. The predicted moisture content of the sludge after dewatering. This refers to the minimum permissible moisture content of the sludge after dewatering. S3.3 The PLC controller in the control box issues control commands to adjust the extension and retraction of the third cylinder and the speed of the first and third drive motors.
[0031] S4. Model Iterative Optimization and Closed-Loop Control; The specific steps are as follows: S4.1, Collect the actual moisture content of the dewatered sludge. and actual solid recovery rate ; S4.2 Calculate the deviation between the predicted value and the actual value. The calculation formula is as follows: ; ; in, This represents the deviation value of the moisture content. This represents the deviation value of the solid phase recovery rate; S4.3, when or At the same time, update the weights and thresholds of the BP neural network model and adjust the specific resistance data correction coefficient. and Iterative optimization of the hybrid model; S4.4 Repeat S2-S4 to form a closed-loop control of data acquisition, simulation, adjustment and optimization, and continuously optimize the dehydration process.
[0032] Therefore, the present invention employs the above-mentioned sludge treatment device and control method to achieve accurate simulation and real-time monitoring of the dewatering process, thereby improving the model prediction accuracy, dewatering efficiency, and reducing filter cloth wear.
[0033] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. 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 still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.
Claims
1. A sludge treatment apparatus characterised in that: The machine frame, the first filter cloth unwinding mechanism, the first filter cloth winding mechanism, the conveying belt, the sludge laying mechanism, the filter pressing dewatering mechanism, the second filter cloth unwinding mechanism and the second filter cloth winding mechanism are arranged on the machine frame. The first filter cloth unwinding mechanism and the second filter cloth unwinding mechanism each comprise a filter cloth and a filter cloth driving structure. The filter cloth comprises a filter layer, a sensing layer and a supporting layer. The sensing layer is a flexible polyimide substrate.
2. A sludge treatment apparatus according to claim 1, characterised in that: The sensing layer is embedded with a thin film pressure sensor. The filter cloth driving structure comprises a second supporting frame. The second supporting frame is provided with a filter cloth speed sensor.
3. A sludge treatment apparatus as claimed in claim 2, characterised in that: The filter pressing dewatering mechanism comprises a filter pressing roller shaft. The filter pressing roller shaft is provided with a tension sensor at the end.
4. A sludge treatment apparatus as claimed in claim 3, characterised in that: The second filter cloth winding mechanism comprises a third supporting frame. The third supporting frame is provided with a partition plate parallel to the conveying belt. The partition plate is provided with an infrared water content meter at the bottom. The machine frame is provided with the first filter cloth unwinding mechanism and the first filter cloth winding mechanism at both ends. The machine frame is provided with the conveying belt. The machine frame is provided with the sludge laying mechanism at one side of the top. The sludge laying mechanism is provided with the filter pressing dewatering mechanism on the machine frame at one side along the conveying direction of the conveying belt. The sludge laying mechanism and the filter pressing dewatering mechanism are provided with the second filter cloth unwinding mechanism. The filter pressing roller shaft is connected with the moving frame through bearings at both ends. The moving frame is connected with the fourth supporting frame in a sliding mode. The moving frame is connected with the piston rod of the third cylinder at the top. The third cylinder is fixed on the fourth supporting frame at the top. The moving frame is provided with the fifth driving motor at one side. The first filter cloth unwinding mechanism and the first filter cloth winding mechanism are the same in structure. The first filter cloth unwinding mechanism comprises a fifth supporting frame. The fifth supporting frame is provided with a first cloth roll in rotation. The first cloth roll is connected with the first driving motor. The first cloth roll is provided with a lifting platform above. The lifting platform and the first cylinder are hinged on the machine frame. The lifting platform comprises a feeding roller shaft. The feeding roller shaft is connected with the second driving motor. The feeding roller shaft is provided with a first guide roller in the reverse direction of the conveying belt. The first guide roller and the feeding roller shaft are provided with guide plates in the conveying direction of the conveying belt. The second filter cloth unwinding mechanism and the second filter cloth winding mechanism are the same in structure. The second filter cloth unwinding mechanism comprises a second supporting frame and a third supporting frame. The second supporting frame is provided with the third driving motor. The third driving motor is connected with the second guide roller. The second guide roller is connected with the second supporting frame through bearings at both ends. The third supporting frame is located on the machine frame at one side along the conveying direction of the conveying belt. The third supporting frame is provided with a second cloth roll at the top. The second cloth roll is connected with the fourth driving motor. The third supporting frame of the second filter cloth unwinding mechanism and the third supporting frame of the second filter cloth winding mechanism are each provided with a third guide roller.
5. A method of controlling a sludge treatment plant according to any one of claims 1 to 4, characterized in that: Specifically comprising the following steps: S1, construct a sludge dewatering mixed model; S2, real-time data acquisition and simulation prediction; S3, optimal parameter calculation and real-time adjustment; S4, model iteration optimization and closed-loop control.
6. A control method of a sludge treatment apparatus according to claim 5, characterized in that: The specific steps of constructing a sludge dewatering mixed model in S1 are as follows: S1.1, based on the karman filtration equation, combined with the characteristics of gravity dewatering and pressing dewatering of the belt filter press, a dewatering rate model is constructed, including a gravity dewatering model and a pressing dewatering rate model, as follows: ； ; wherein, is the dewatering rate for the gravity dewatering phase, is the gravity dewatering coefficient, is the average thickness of the sludge within the filter press channel, is the acceleration due to gravity, is the dynamic viscosity of the filtrate, is the initial specific resistance of the sludge, is the initial concentration of the sludge; is the dewatering rate for the press dewatering phase, is the press dewatering coefficient, is the pressure applied by the press roll set, is the real-time specific resistance of the sludge, is the real-time concentration of the sludge; S1.2, adopt 3-layer BP neural network model, train neural network through historical operation data, get specific resistance data correction coefficient and , build data correction model, correct the input parameters of dewatering rate model, sludge specific resistance and dewatering rate calculated by dewatering rate model, the specific calculation formula is as follows: ； ； wherein, is the predicted sludge specific resistance, is the sludge specific resistance calculated by the dewatering rate model, is the sludge specific resistance output by the BP neural network model; is the predicted dewatering rate, is the dewatering rate calculated by the dewatering rate model, is the dewatering rate output by the BP neural network model; S1.3, dynamically allocate the weights of the dewatering rate model and the data correction model by entropy weight method, and fuse to get the final mixed model, the calculation formula is as follows: ； wherein, is a weight for the dehydration rate model, is a weight for the data correction model, , is a dehydration rate model, is a data correction model.
7. A control method of a sludge treatment apparatus according to claim 6, characterized in that: The specific steps of S2 are as follows: S2.1, collecting real-time data, including initial moisture content , initial sludge concentration , initial specific resistance of sludge , current pressing pressure , filter cloth speed , ambient temperature , dynamic viscosity of filtrate ; S2.1, the real-time data is pre-processed and input into the mixed model, and the simulation prediction dewatering effect parameters include prediction filter cake moisture content and prediction solid phase recovery rate The prediction solid phase recovery rate is calculated, and the specific formula is as follows: ； wherein, is the sludge feed flow rate, is the solids concentration in the filtrate, is the filtrate discharge flow rate.
8. A control method of a sludge treatment apparatus according to claim 7, characterized by: The specific steps of S3 are as follows: S3.1, set the optimization objective function: maximize the dewatering efficiency and minimize the filter cloth loss as the double objective, establish the optimization model as follows: ； The constraints are: , , , ; wherein, is a target function value, is a dewatering efficiency weight coefficient, is a minimum dewatering rate, is a maximum dewatering rate, is a filter cloth wear weight coefficient, is a minimum press pressure, is a maximum press pressure, is a minimum filter cloth speed, is a maximum filter cloth speed; S3.2, the optimal parameters are solved by using particle swarm optimization algorithm, and the optimal squeezing pressure is obtained , the optimal filter cloth speed The calculation formula of the optimal squeezing pressure is as follows: ； wherein, is a target moisture content after dewatering of the sludge, is a predicted moisture content after dewatering of the sludge, is a permissible minimum moisture content after dewatering of the sludge; S3.3, the PLC controller in the control box issues control instructions to adjust the extension amount of the third cylinder, the rotation speed of the first drive motor and the third drive motor.
9. A control method of a sludge treatment apparatus according to claim 8, characterized in that: The specific steps of S4 are as follows: S4.1, Collecting the actual moisture content after sludge dewatering and the actual solid phase recovery rate ; S4.2, calculate the deviation between the predicted value and the actual value, the calculation formula is as follows: ； ； wherein, is a deviation value for the water content, is a deviation value for the solid phase recovery rate; S4.3、when or the weight and threshold of the BP neural network model are updated, the specific resistance data correction coefficient is adjusted and , the hybrid model is iteratively optimized; S4.4, repeat S2-S4 to form a closed-loop control of acquisition-simulation-adjustment-optimization, continuously optimize the dewatering process.