Intelligent river sludge flocculation treatment method and system

Abstract

This invention discloses an intelligent method and system for treating river silt flocculation, belonging to the field of river silt treatment technology. The method includes: after measuring and analyzing parameters such as silt moisture content in a pre-testing laboratory, the control system automatically sets the initial flocculant dosage ratio, the speed gradient of the stirring facility in the pretreatment unit, and the stirring time of the stirring facility. The silt and flocculant are sent into the pretreatment unit for treatment, and the silt settling ratio curve is obtained. The control system constructs response surface model experimental point parameters based on the results. Using these parameters, the silt and flocculant are batch-controlled to be treated in the treatment unit to obtain experimental results. Multiple response surface models are output to obtain the optimal working parameters. After the reaction is completed under the optimal working parameters, the flocculated supernatant and concentrated silt are automatically discharged. The method has a high degree of intelligence and good implementation effect.

Description

Technical Field

[0001] This invention belongs to the field of river silt treatment technology, and more specifically, relates to an intelligent river silt flocculation treatment method and system. Background Technology

[0002] River silt, as a major endogenous source of pollution, is one of the causes of black and odorous water bodies. Silt treatment is a crucial measure for the remediation of black and odorous water bodies, and the key to silt treatment lies in sludge-water separation and sludge solidification. Silt-water separation aims to remove as much water as possible from the silt, obtaining more free water and less concentrated silt. Higher silt moisture content results in a larger silt volume. By reducing the silt moisture content, free water can be separated while significantly reducing the silt volume, thereby reducing the cost of subsequent silt solidification. For example, reducing the silt moisture content from 98% to 96% results in half the volume of free water and half the volume of concentrated silt, reducing the silt volume by half. Flocculation and concentration of silt is an effective means of reducing silt moisture content. Currently, the main method is to add flocculants to the silt to change its properties, and through stirring, promote flocculation and sedimentation. The supernatant obtained is the separated free water, and the silt that settles at the bottom is the concentrated silt.

[0003] The flocculation and sedimentation of silt are influenced by many factors, such as the type of flocculant, the dosage of flocculant, the stirring method, the reaction time, and the sedimentation time. Even the composition and moisture content of the silt itself can affect the flocculation and sedimentation effect, and this effect is non-linear. For example, excessive or insufficient flocculant dosage and stirring time are not conducive to silt flocculation. Generally, the composition of silt in a river channel does not vary much, but the moisture content of silt brought ashore varies greatly. The density of silt near the riverbank is higher than that in the center of the river, but its moisture content is lower. Different silt moisture contents require different types of flocculants, dosages of flocculants, stirring methods, reaction times, and sedimentation times to achieve the best treatment effect. At the same time, different combinations of these internal working conditions will affect the treatment effect.

[0004] Patent application CN 116282834 A discloses a method for treating sludge flocculation and sedimentation by testing the zeta potential of the sludge slurry. Based on the specific gravity d, the system automatically calculates, controls, and adjusts the dosage of aluminum hydroxide and cationic polyacrylamide solution, and then obtains the optimal dosage based on the results. Patent CN 115108617B discloses a coagulation dosing method and system, which constructs a fuzzy neural network control system using raw water flow rate, raw water turbidity, dosage, floc fractal dimension, and actual post-settling water turbidity. The system autonomously learns and adjusts the dosage, and then obtains the optimal dosage based on the results.

[0005] All of the above patent applications use automatic control systems to explore the optimal dosage of flocculating chemicals. However, such methods only explore the effect of the single factor of dosage on the flocculation results and cannot effectively explore the effects of multiple factors such as the moisture content of raw materials, stirring method, reaction time, and sedimentation time on the sludge results.

[0006] In practical engineering implementation, the desired effect of sludge separation is to reduce the sludge moisture content as much as possible by adjusting various operating parameters. Since multiple operating parameters have a non-linear relationship with the desired value, single-factor studies are often unfavorable. In the past, the optimal parameters were explored by experimentalists conducting orthogonal experiments in the laboratory. However, due to the large number of influencing factors and the need for multiple steps such as sampling, testing, and analysis, the orthogonal experiment method is time-consuming and labor-intensive, and cannot adapt to changes in sludge moisture content in a timely manner.

[0007] To meet engineering requirements, a simple, quick, and scientific method is needed to explore the optimal operating parameters. Summary of the Invention

[0008] In view of the above-mentioned defects or improvement needs of existing technologies, this invention proposes an intelligent river silt flocculation treatment method and system, which can autonomously adapt to river silt with different water contents. It achieves intelligent operation by automatically controlling the dosage of flocculation chemicals and the output of water, which significantly reduces the time and cost of manual operation.

[0009] To achieve the above objectives, according to one aspect of the present invention, an intelligent method for treating river silt flocculation is provided, comprising:

[0010] Obtain the silt moisture content ω, density ρ, dynamic viscosity μ, suspended solids concentration S, and flow rate Q of the river silt;

[0011] The initial flocculant dosage ratio α, the speed gradient G0 of the pretreatment unit stirring facility, and the stirring time T0 of the stirring facility are set according to the sludge moisture content ω, density ρ, dynamic viscosity μ, suspended solids concentration S, and flow rate Q. The set stirring power is obtained from the speed gradient G0 of the pretreatment unit stirring facility.

[0012] In the pretreatment unit, the mixing equipment is turned on according to the set mixing power to mix the sludge and flocculant evenly. The mixing equipment stops when the set mixing time T0 is reached.

[0013] The liquid level and transparency data parameters of the sludge mixture during the sludge flocculation process were measured and analyzed. The liquid level and transparency data parameters of the sludge mixture during the sludge flocculation process were analyzed, the response model factors were constructed, and the test point parameters of the response surface were output.

[0014] Repeat the above steps to obtain the response surface test point parameters based on the response surface test point parameters. After data analysis based on the sludge mixture level and transparency data parameters during the reaction process, output the response curve model.

[0015] Optimal operating parameters are generated based on the response curve model;

[0016] The sludge and flocculant are transported to the reaction unit according to the optimal operating parameters, and the reaction is carried out according to the set parameters. The reaction is stopped after the reaction is completed.

[0017] The transparency of the sludge mixture after the reaction is completed is measured. If the expected reaction effect is achieved after the transparency analysis of the sludge mixture after the reaction is completed, the supernatant is discharged by controlling the gate opening and closing of the reaction unit, thus completing the sludge flocculation and concentration.

[0018] In some alternative implementations, the silt moisture content ω, density ρ, dynamic viscosity μ, suspended solids concentration S, and flow rate Q of the river silt are obtained by online electromagnetic flowmeters, online suspended solids analyzers, and online solution density analyzers.

[0019] In some optional implementations, the initial flocculant dosage ratio α0 is set to 1‰, and the pretreatment unit velocity gradient is set to G0 = 800s. -1 The mixing time of the mixing equipment is T0 = 30 min.

[0020] In some optional implementations, when the average relative deviation of the detection parameters during the sludge flocculation process is ≤0.1% for several consecutive times, the parameter measurement is stopped and analysis is performed.

[0021] In some optional implementations, the analysis of data parameters on the liquid level and transparency of the sludge mixture during the sludge flocculation process, the construction of response model factors, and the output of response surface test point parameters include:

[0022] Trend analysis was performed on the liquid level and transparency data of the sludge mixture during the sludge flocculation process to generate curves of transparency and sedimentation ratio changing with time, and to obtain the maximum transparency h0 and sedimentation ratio Y0 values ​​and sedimentation time t2 under the initial data parameters.

[0023] Based on the sedimentation ratio, response model factors were constructed, including sludge moisture content ω, flocculant dosage ratio α, treatment unit velocity gradient G, stirring time t1 and settling time t2. The sludge moisture content was used as the grouping, and the model was divided into 1 group.

[0024] Four influencing factors were set for each group. Among them, the sedimentation time was obtained by direct analysis of the liquid level and transparency data of the sludge mixture during the continuous measurement of sludge flocculation process.

[0025] To reduce the number of experimental points, response surface methodology was designed using three influencing factors: flocculant dosage ratio, treatment unit velocity gradient, and stirring time. The design included 8 extreme points, 6 surface points, and 4 center points, totaling 18 experimental points. The extreme range for the flocculant dosage ratio was [0.1‰, 5‰], and the extreme range for the treatment unit velocity gradient was [100s]. -1 2000s -1 The extreme range of stirring time is [3min, 30min]. The working parameter values ​​at the four center points are as follows: flocculant addition ratio, treatment unit velocity gradient, and stirring time values ​​are (0.2 / Y0*α0, 0.2 / Y0*G0, 0.2 / Y0*T0), (0.5 / Y0*α0, 0.5 / Y0*G0, 0.5 / Y0*T0), (0.7 / Y0*α0, 0.7 / Y0*G0, 0.7 / Y0*T0), and (0.9 / Y0*α0, 0.9 / Y0*G0, 0.9 / Y0*T0).

[0026] In some alternative implementation schemes, after obtaining the optimal operating parameters, the river silt and flocculant are directly discharged into the reaction unit, where the reaction unit operates according to the optimal operating parameters.

[0027] In some optional implementations, after reacting according to the optimal operating parameters, the detection system measures the transparency of the mixture and calculates the sedimentation ratio Ys. The result is compared with the optimal result of the response surface model. If the average relative deviation is less than 5%, the reaction reaches the expected value and ends. Otherwise, the steps of establishing the experimental points of the response surface are repeated. Y0 in the four center point values ​​is replaced with Ys, while the other experimental points remain unchanged. The four center point experiments are repeated. After obtaining the experimental data, the response surface model is improved to obtain the corrected optimal operating parameters.

[0028] In some alternative implementations, after the transparency of the mixture reaches the expected value, the gate of the reaction unit is controlled to open at the level of the transparency depth, so that the supernatant will be discharged smoothly from the reaction unit.

[0029] According to another aspect of the present invention, an intelligent river silt flocculation treatment system is provided, comprising: a pre-testing chamber, a rapid analytical instrument, a control system, a pretreatment unit, a mixing device, a testing system, and a reaction unit;

[0030] The rapid analytical instruments in the pre-testing chamber analyze the parameters of sludge moisture content ω, density ρ, dynamic viscosity μ, suspended solids concentration S, and flow rate Q, and transmit the data parameters to the control system. The control system automatically sets the initial flocculant addition ratio α, the speed gradient G0 of the pretreatment unit stirring facility, and the stirring time T0 of the stirring facility according to the data parameters.

[0031] The control system transports the sludge and flocculant to the pretreatment unit;

[0032] In the pretreatment unit, the mixing equipment is turned on according to the set mixing power to mix the sludge and flocculant evenly. The mixing equipment stops when the set mixing time is reached.

[0033] The detection system quickly measures and analyzes the liquid level and transparency of the sludge mixture during the sludge flocculation process, and transmits the data values ​​to the control system.

[0034] The control system analyzes the detection parameters during the sludge flocculation process, constructs response model factors, and outputs the test point parameters of the response surface.

[0035] The control system repeats the above steps to obtain the test point parameters of the response surface based on the test point parameters. The detection system transmits the reaction process data values ​​to the control system, and the control system outputs the response curve model after performing data analysis.

[0036] The control system generates optimal operating parameters based on the response curve model results;

[0037] The control system delivers sludge and flocculant to the reaction unit according to the optimal operating parameters, and the reaction proceeds according to the set parameters. The reaction is stopped after completion.

[0038] The detection system automatically measures the transparency of the sludge mixture after the reaction is completed and transmits the data value to the control system. After analysis, if the expected reaction effect is achieved, the control system will discharge the supernatant by controlling the gate switch of the reaction unit, thus completing the sludge flocculation and concentration.

[0039] In some alternative implementations, the indoor testing instruments include online electromagnetic flowmeters, online suspended solids analyzers, and online solution density analyzers;

[0040] The detection instruments located within the processing unit include an online level gauge and an online solution transparency analyzer, with a data acquisition density of no less than 5 times / second.

[0041] In summary, compared with the prior art, the above-described technical solutions conceived by this invention can achieve the following beneficial effects:

[0042] (1) This invention is the first to apply the response surface model method to the optimal working parameters of river silt flocculation. Using the silt settling ratio as the evaluation index, under the condition that the evaluation index is affected by multiple factors such as silt moisture content, flocculant addition ratio, treatment unit speed gradient, stirring time, and sedimentation time, the boundary conditions of the response surface model are defined and the selection of test points is optimized, so as to achieve rapid and efficient exploration to obtain the optimal working parameters.

[0043] (2) The entire process can be automated. The control system can intelligently establish the response surface model and the parameters of the response surface test point. Since different response curve models can be automatically obtained and automatic conditions can be applied under different silt moisture content conditions, it can effectively adapt to various working conditions. This is very beneficial to the frequent changes in silt moisture content during river dredging, which causes changes in working parameters, reduces manual operation time and cost, and improves overall efficiency. Attached Figure Description

[0044] Figure 1 This is a flowchart of an intelligent river silt flocculation treatment method provided in an embodiment of the present invention;

[0045] Figure 2 This is a response surface model influence factor hierarchy diagram provided in an embodiment of the present invention;

[0046] Figure 3 This is a component and working diagram of one embodiment provided by the present invention;

[0047] Figure 4 This is a graph showing the change in flocculation effect under initial flocculation conditions, provided by an embodiment of the present invention.

[0048] Figure 5 This is a response surface model diagram of one embodiment provided by the present invention;

[0049] The components include: 1. Control system; 2. Sludge suction equipment; 3. Flocculant delivery pump; 4. Pre-test chamber; 5. Rapid analytical instrument; 6. Mixing equipment; 7. Open curbstone; 8. Pressure sensor; 9. Level gauge; 10. Transparency meter; and 11. Reaction unit gate. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0051] Response surface methodology (RSM) is a statistical method that utilizes appropriate experimental design methods and experimental data to fit the functional relationship between factors and response values ​​using a multiple quadratic regression equation. Analyzing the regression equation helps identify optimal process parameters and solve multivariate problems. RSM can reduce the number of sludge flocculation experiments and quickly analyze significant relationships. Furthermore, by systematically controlling various operating parameters, automated experiments can be achieved to obtain optimal working parameters, thereby effectively reducing costs associated with reagents, energy consumption, manpower, and time.

[0052] The technical solution adopted in this invention is:

[0053] Step 1: The river silt is transported from the river or silt dredger to the pre-processing laboratory of the treatment facility by the suction equipment to quickly measure and analyze the silt moisture content ω, density ρ, dynamic viscosity μ, suspended solids concentration S, and flow rate Q. The data parameters are then transmitted to the control system, which automatically sets the initial flocculant dosage ratio α, the speed gradient G0 of the pretreatment unit mixing facility, and the mixing time T0 of the mixing facility based on the data parameters.

[0054] Step 2: The control system transports the sludge and flocculant to the pretreatment unit;

[0055] Step 3: In the pretreatment unit, the mixing equipment is turned on according to the set mixing power to mix the sludge and flocculant evenly. The mixing equipment stops when the set mixing time is reached.

[0056] Step 4: The detection system quickly measures and analyzes the liquid level and transparency data parameters of the sludge mixture during the sludge flocculation process, and transmits the data values ​​to the control system.

[0057] Step 5: The control system analyzes the detection parameters during the sludge flocculation process, constructs response model factors, and outputs the test point parameters of the response surface.

[0058] Step 6: The control system repeats steps 3 to 5 according to the test point parameters. The detection system transmits the data values ​​of the sludge mixture level and transparency during the reaction process to the control system. The control system outputs a response curve model after data analysis.

[0059] Step 7: The control system generates optimal operating parameters based on the response curve model results;

[0060] Step 8: The control system delivers the sludge and flocculant to the reaction unit according to the optimal operating parameters, and the reaction proceeds according to the set parameters. The reaction is stopped after completion.

[0061] Step 9: The detection system automatically measures the transparency of the sludge mixture after the reaction is completed and transmits the data value to the control system. After analysis, the control system proceeds to the next step if the expected reaction effect is achieved.

[0062] Step 10: The control system discharges the supernatant by controlling the gate switch of the reaction unit, thus completing the sludge flocculation and concentration.

[0063] In the above scheme, the detection instruments in the detection room in step 1 include an online electromagnetic flowmeter, an online suspended solids analyzer, and an online solution density analyzer. The detector converts the electrical signals generated during the detection process into specific parameter data and transmits them to the control system processing terminal. During the sludge transportation process, the parameters of the sludge in the river channel can be quickly measured and analyzed, and the data acquisition density is not less than 1 time / s.

[0064] The control system includes hardware and software facilities such as a data analysis model, a response surface model, a computer, and a PLC. Based on data parameters, the control system automatically sets the initial flocculant dosage ratio α0 = 1‰, calculated using the formula α = flocculant concentration * flocculant flow rate / (sludge density * sludge flow rate). The control system also automatically sets the power of the mixing equipment within the pretreatment unit based on the sludge flow rate, ensuring a velocity gradient G0 = 800 s for the pretreatment unit. -1 The power of the mixing facility = velocity gradient * velocity gradient * dynamic viscosity * volume of reacted sludge. The control system automatically sets the mixing time of the mixing facility to T0 = 30 min.

[0065] In the above scheme, step 2 precisely controls the amount of flocculant added by controlling the power output of the dosing pump according to the initial data parameters. The flocculant is transported to the pretreatment unit through the dosing pump and pipeline. The control system precisely controls the sludge flow rate by controlling the opening and closing degree of the solenoid valve of the sludge inlet pipe.

[0066] In the above scheme, the pretreatment unit in step 3 is a square or cylindrical container reaction zone, which is used by the control system to obtain the optimal working parameters in the initial state of the reaction. After the optimal working parameters are obtained, it is transformed into a treatment unit. The stirring equipment is an underwater thruster or a stirring paddle, which is determined according to the size and volume of the container. The power is set according to the control system settings.

[0067] In the above scheme, the detection instruments located in the processing unit in step 4 include an online level gauge and an online solution transparency analyzer. The detection instruments convert the electrical signals generated during the detection process into specific parameter data and transmit them to the control system processing terminal. The data acquisition density is not less than 5 times / s.

[0068] In the above scheme, in step 4, when the average relative deviation of the detection parameters during the sludge flocculation process is ≤0.1% for 5 consecutive times, the detection system stops parameter measurement and transmits the data to the control system for analysis.

[0069] In the above scheme, step 5 involves the control system performing trend analysis on the reaction process data to generate curves showing the changes in transparency and sedimentation ratio over time, thus obtaining the initial data parameters α0 = 1‰ and G0 = 800s. -1The parameters for the response surface methodology are: maximum transparency h0 and settling ratio Y0 at T0 = 30 min, and settling time t2. Based on the settling ratio, response model factors are constructed, typically including five influencing factors: sludge moisture content ω, flocculant dosage ratio α, treatment unit velocity gradient G, mixing time t1, and settling time t2. Sludge moisture content is an external influencing factor, and the control system does not regulate it. The sludge moisture content is used as a group. The other four influencing factors, along with flocculant concentration and flow rate, treatment unit mixing power, mixing time, and settling time, are set according to the control system parameters at the response surface methodology test points. The parameter priority order is: flocculant concentration and flow rate > treatment unit mixing power > mixing time > settling time.

[0070] In the above scheme, there are a total of 5 response model factors in step 5, which are grouped by the sludge moisture content into 1 group. Each group has 4 influencing factors. Among them, the sedimentation time can be directly analyzed based on the continuously measured sludge mixture level and transparency detection parameters to obtain the optimal parameter. To reduce the number of experimental points, 3 influencing factors, namely flocculant dosage ratio, treatment unit velocity gradient, and stirring time, are used. The response surface experimental points are designed according to the sequential central composite surface design method. There are 8 extreme points, 6 surface points, and 4 center points, for a total of 18 experimental points. The extreme range of flocculant dosage ratio is [0.1‰, 5‰], and the extreme range of treatment unit velocity gradient is [100s]. -1 2000s -1 The extreme range of stirring time is [3 min, 30 min]. The working parameter values ​​of the four center points, flocculant addition ratio, treatment unit velocity gradient, and stirring time are (0.2 / Y0*α0, 0.2 / Y0*G0, 0.2 / Y0*T0), (0.5 / Y0*α0, 0.5 / Y0*G0, 0.5 / Y0*T0), (0.7 / Y0*α0, 0.7 / Y0*G0, 0.7 / Y0*T0), and (0.9 / Y0*α0, 0.9 / Y0*G0, 0.9 / Y0*T0).

[0071] In the above scheme, after obtaining the optimal operating parameters in step 7, the control system directly discharges the river silt and flocculant into the reaction unit, where it operates according to the optimal operating parameters.

[0072] In the above scheme, after the reaction processing unit reacts according to the optimal operating parameters in step 9, the detection system measures the transparency of the mixture and calculates the sedimentation ratio Ys. The result is compared with the optimal result of the response surface model. When the average relative deviation is less than 5%, the reaction reaches the expected value and ends. Otherwise, step 5 is repeated to establish response surface experimental points. Y0 in the four center point values ​​is replaced with Ys, and the other experimental points remain unchanged. The four center point experiments are repeated. After obtaining the experimental data, the response surface model is improved to obtain the corrected optimal operating parameters.

[0073] In the above scheme, after the detection system determines that the transparency of the mixture reaches the expected value in step 10, the control system controls the gate switch of the reaction unit to open at the transparency depth, so that the supernatant will be discharged smoothly from the reaction unit.

[0074] The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0075] This invention provides a method and system for intelligent flocculation treatment of river silt. Figure 1 This is a flowchart of an intelligent river silt flocculation treatment method. Figure 2 In response to the influence factor hierarchy diagram of the surface model, Figures 3-5 The example data diagram includes the following steps:

[0076] Step 1: River silt is transported from the river or a silt suction dredger to the pre-processing testing chamber 4 of the treatment facility by suction equipment 2. Rapid analysis instruments 5 quickly extract the silt, which has a moisture content ω of 96% and a density ρ of 1.025 g / cm³. 3 The dynamic viscosity μ is 864×10 -6 Pa·s and suspended solids concentration S were 40.23 kg / m³. 3 The flow rate Q is 0.02m³. 3 The system transmits data parameters to control system 1, which, based on the precondition of a flocculant density of 100 g / L, controls the flocculant flow rate to 0.205 L / s, ensuring an initial flocculant dosage ratio α0 of 1‰. Control system 1 also controls the flocculant flow rate to 0.205 L / s based on a single-stage treatment volume of 10 m³ / s. 3 Under the preconditioning conditions, the stirring power of the pretreatment unit is controlled at 2.16kW to achieve a facility velocity gradient G0 of 500s. -1 The control system 1 controls the initial mixing time T0 of the mixing facility to be 30 minutes;

[0077] Step 2: Control system 1 controls the flocculant flow rate to 0.205 L / s by adjusting the power of flocculant delivery pump 3 and delivers it to the pretreatment unit. Control system 1 simultaneously controls suction equipment 2 to transport sludge to the pretreatment unit. The size of the pretreatment unit is L×B×H=3m×3m×4m.

[0078] Step 3: In the pretreatment unit, after the level gauge 9 detects that the liquid level has reached 3.3m, the control system 1 stops the suction equipment 2, the flocculant delivery pump 3 and the corresponding valves, and then turns on the control stirring equipment 7 and controls the power to 2.16kW. The sludge and flocculant are stirred evenly in the pretreatment unit. When the stirring time reaches 30min, the control system 1 stops the stirring equipment 6 from running.

[0079] Step 4: In the pretreatment unit, the level gauge 9 and the transparency meter 10 quickly measure and analyze the data parameters of the sludge mixture level H and transparency h during the sludge flocculation process. The data acquisition density is 5 times / s, and the data values ​​are transmitted to the control system 1.

[0080] Step 5: Control system 1 analyzes the detection parameters during the sludge flocculation process. Settling ratio = H / h. The trend analysis chart is attached. Figure 4 Construct response model factors, with the following hierarchical structure of influencing factors. Figure 2 As shown, parameters such as flocculant dosage ratio, velocity gradient, and stirring time are constructed by grouping sludge by moisture content, and the parameters of the test points on the response surface are output.

[0081] Table 1 Parameters of Experimental Points on Response Surface

[0082]

[0083] Step 6: The control system repeats steps 3-5 based on the test point parameters. The detection system transmits the reaction process data values ​​to the control system, which then analyzes the data and outputs a response curve model. One of the model diagrams is shown below. Figure 5 As shown;

[0084] Step 7: Based on the response curve model results, the control system achieves a maximum settling ratio of 82% under a sludge moisture content of 96%. The optimal operating parameters are: flocculant dosage ratio of 1.85‰ and velocity gradient of 1120s. -1 Stirring time: 10 min; sedimentation time: 5 min.

[0085] Step 8: The control system delivers the sludge and flocculant to the reaction unit according to the optimal operating parameters, and the reaction is carried out according to the parameters set in Step 7. The reaction is stopped after 10 minutes.

[0086] Step 9: The transparency meter 10 automatically measures the transparency of the sludge mixture after the reaction is completed and transmits the data value to the control system. The next step is carried out after the transparency reaches 2.7 μm.

[0087] Step 10: The control system opens the gate 11 of the control reaction unit to a height of 2.7m to discharge the supernatant. After the supernatant is discharged, the control system opens the valve of the concentrated sludge discharge pipe and uses the pressure sensor 8 to determine whether the concentrated sludge in the treatment unit has been completely discharged.

[0088] Step 11: After the sludge has completed flocculation and the supernatant and concentrated sludge have been completely discharged, the next batch of reaction begins. When the sludge moisture content changes by more than 5%, steps 1-10 are repeated to establish another set of response surface models. After multiple runs, the system can establish optimal reaction operating parameters based on sludge with different moisture contents and make real-time adjustments.

[0089] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0090] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for intelligent flocculation treatment of river silt, characterized in that, include: Obtain the silt moisture content ω, density ρ, dynamic viscosity μ, suspended solids concentration S, and flow rate Q of the river silt; The initial flocculant dosage ratio α0, the speed gradient G0 of the pretreatment unit stirring facility, and the stirring time T0 of the stirring facility are set according to the sludge moisture content ω, density ρ, dynamic viscosity μ, suspended solids concentration S, and flow rate Q. The set stirring power is obtained from the speed gradient G0 of the pretreatment unit stirring facility. In the pretreatment unit, the mixing equipment is turned on according to the set mixing power to mix the sludge and flocculant evenly. The mixing equipment stops when the set mixing time T0 is reached. The liquid level and transparency data parameters of the sludge mixture during the sludge flocculation process were measured and analyzed. The liquid level and transparency data parameters of the sludge mixture during the sludge flocculation process were analyzed, the response model factors were constructed, and the test point parameters of the response surface were output. Repeat the above steps to obtain the response surface test point parameters based on the response surface test point parameters. After data analysis based on the sludge mixture level and transparency data parameters during the reaction process, output the response curve model. Optimal operating parameters are generated based on the response curve model; The sludge and flocculant are transported to the reaction unit according to the optimal operating parameters, and the reaction is carried out according to the set parameters. The reaction is stopped after the reaction is completed. The transparency of the sludge mixture after the reaction is completed is measured. If the expected reaction effect is achieved after the transparency analysis of the sludge mixture after the reaction is completed, the supernatant is discharged by controlling the gate opening and closing of the reaction unit to complete the sludge flocculation and concentration. The analysis of data parameters on the liquid level and transparency of the sludge mixture during the sludge flocculation process is used to construct response model factors and output response surface test point parameters, including: Trend analysis was performed on the liquid level and transparency data of the sludge mixture during the sludge flocculation process to generate curves of transparency and sedimentation ratio changing with time, and to obtain the maximum transparency h0 and sedimentation ratio Y0 values ​​and sedimentation time t2 under the initial data parameters. Based on the sedimentation ratio, response model factors were constructed, including sludge moisture content ω, flocculant dosage ratio α, treatment unit velocity gradient G, stirring time t1 and settling time t2. The sludge moisture content was used as the grouping, and the model was divided into 1 group. Four influencing factors were set for each group. Among them, the sedimentation time was obtained by direct analysis of the liquid level and transparency data of the sludge mixture during the continuous measurement of sludge flocculation process. To reduce the number of experimental points, response surface methodology was designed using three influencing factors: flocculant dosage ratio, treatment unit velocity gradient, and stirring time. The design included 8 extreme points, 6 surface points, and 4 center points, totaling 18 experimental points. The extreme range for flocculant dosage ratio was [0.1‰, 5‰], and the extreme range for treatment unit velocity gradient was [100 s]. -1 , 2000 s -1 The extreme range of stirring time is [3min, 30min]. The working parameter values ​​at the four center points are as follows: flocculant addition ratio, treatment unit velocity gradient, and stirring time values ​​are (0.2 / Y0*α0, 0.2 / Y0*G0, 0.2 / Y0*T0), (0.5 / Y0*α0, 0.5 / Y0*G0, 0.5 / Y0*T0), (0.7 / Y0*α0, 0.7 / Y0*G0, 0.7 / Y0*T0), and (0.9 / Y0*α0, 0.9 / Y0*G0, 0.9 / Y0*T0).

2. The method according to claim 1, characterized in that, The silt water content ω, density ρ, dynamic viscosity μ, suspended solids concentration S, and flow rate Q of the river silt were obtained using an online electromagnetic flowmeter, an online suspended solids analyzer, and an online solution density analyzer.

3. The method according to claim 1, characterized in that, The initial flocculant dosage ratio was set at α0 = 1‰, and the velocity gradient of the pretreatment unit was set at G0 = 800s. -1 The mixing time of the mixing equipment is T0=30min.

4. The method according to claim 1, characterized in that, If the average relative deviation of the detection parameters during the sludge flocculation process is ≤0.1% for several consecutive times, then the parameter measurement should be stopped and analysis should be performed.

5. The method according to claim 4, characterized in that, After obtaining the optimal operating parameters, the river silt and flocculant are directly discharged into the reaction unit, where the reaction unit operates according to the optimal operating parameters.

6. The method according to claim 5, characterized in that, After reacting according to the optimal operating parameters, the detection system measures the transparency of the mixture and calculates the sedimentation ratio Ys. The result is compared with the optimal result of the response surface model. When the average relative deviation is less than 5%, the reaction reaches the expected value and ends. Otherwise, the steps of establishing the experimental points of the response surface are repeated. Y0 in the four center point values ​​is replaced with Ys, while the other experimental points remain unchanged. The four center point experiments are repeated. After obtaining the experimental data, the response surface model is improved to obtain the corrected optimal operating parameters.

7. The method according to claim 6, characterized in that, Once the transparency of the mixture reaches the expected value, the gate of the reaction unit is opened at the desired height to match the transparency depth, allowing the supernatant to be discharged smoothly from the reaction unit.

8. An intelligent river silt flocculation treatment system, characterized in that, include: Pre-testing chamber, rapid analytical instruments, control system, pretreatment unit, stirring equipment, detection system and reaction unit; The rapid analytical instruments in the pre-testing chamber analyze the parameters of sludge moisture content ω, density ρ, dynamic viscosity μ, suspended solids concentration S, and flow rate Q, and transmit the data parameters to the control system. The control system automatically sets the initial flocculant addition ratio α0, the speed gradient G0 of the pretreatment unit stirring facility, and the stirring time T0 of the stirring facility according to the data parameters. The control system transports the sludge and flocculant to the pretreatment unit; In the pretreatment unit, the mixing equipment is turned on according to the set mixing power to mix the sludge and flocculant evenly. The mixing equipment stops when the set mixing time is reached. The detection system quickly measures and analyzes the liquid level and transparency of the sludge mixture during the sludge flocculation process, and transmits the data values ​​to the control system. The control system analyzes the detection parameters during the sludge flocculation process, constructs response model factors, and outputs the test point parameters of the response surface. The control system repeats the above steps to obtain the test point parameters of the response surface based on the test point parameters. The detection system transmits the reaction process data values ​​to the control system, and the control system outputs the response curve model after performing data analysis. The control system generates optimal operating parameters based on the response curve model results; The control system delivers sludge and flocculant to the reaction unit according to the optimal operating parameters, and the reaction proceeds according to the set parameters. The reaction is stopped after completion. The detection system automatically measures the transparency of the sludge mixture after the reaction is completed and transmits the data value to the control system. After analysis, if the expected reaction effect is achieved, the control system will discharge the supernatant by controlling the gate switch of the reaction unit, thus completing the sludge flocculation and concentration. The process involves analyzing the detection parameters during the sludge flocculation process, constructing response model factors, and outputting response surface test point parameters, including: Trend analysis was performed on the liquid level and transparency data of the sludge mixture during the sludge flocculation process to generate curves of transparency and sedimentation ratio changing with time, and to obtain the maximum transparency h0 and sedimentation ratio Y0 values ​​and sedimentation time t2 under the initial data parameters. Based on the sedimentation ratio, response model factors were constructed, including sludge moisture content ω, flocculant dosage ratio α, treatment unit velocity gradient G, stirring time t1 and settling time t2. The sludge moisture content was used as the grouping, and the model was divided into 1 group. Four influencing factors were set for each group. Among them, the sedimentation time was obtained by direct analysis of the liquid level and transparency data of the sludge mixture during the continuous measurement of sludge flocculation process. To reduce the number of experimental points, response surface methodology was designed using three influencing factors: flocculant dosage ratio, treatment unit velocity gradient, and stirring time. The design included 8 extreme points, 6 surface points, and 4 center points, totaling 18 experimental points. The extreme range for flocculant dosage ratio was [0.1‰, 5‰], and the extreme range for treatment unit velocity gradient was [100 s]. -1 , 2000 s -1 The extreme range of stirring time is [3min, 30min]. The working parameter values ​​at the four center points are as follows: flocculant addition ratio, treatment unit velocity gradient, and stirring time values ​​are (0.2 / Y0*α0, 0.2 / Y0*G0, 0.2 / Y0*T0), (0.5 / Y0*α0, 0.5 / Y0*G0, 0.5 / Y0*T0), (0.7 / Y0*α0, 0.7 / Y0*G0, 0.7 / Y0*T0), and (0.9 / Y0*α0, 0.9 / Y0*G0, 0.9 / Y0*T0).

9. The system according to claim 8, characterized in that, The testing instruments in the laboratory include online electromagnetic flowmeters, online suspended solids analyzers, and online solution density analyzers; The detection instruments located within the processing unit include an online level gauge and an online solution transparency analyzer, with a data acquisition density of no less than 5 times / second.

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