Intelligent sludge discharge and sludge dewatering compound control model system

Abstract

The invention relates to the technical field of sludge treatment, and discloses an intelligent sludge discharge and sludge dewatering compound control model system, which comprises a sludge production prediction module, a sludge dewatering control module, a sludge dewatering control module, a sludge dewatering control module, a sludge dewatering control module and a sludge dewatering control module, and is characterized in that the sludge production prediction module is used for predicting the sludge production and sludge quality of a sedimentation tank based on the sludge production and sludge quality of the water plant sedimentation tank; the feed-forward control module is used for determining a target of the water content of sludge in the sedimentation tank; the concentration target setting and dosing optimization module is used for dynamically optimizing the dosing amount of a flocculating agent in the concentration tank; and the dehydration control module is used for adjusting the adding amount of the flocculating agent in the dehydrator and the operation parameters of the dehydrator. According to the invention, the feed-forward control module determines the objective function of the sludge water content of the sedimentation tank according to the sludge concentration water content target and constructs the sludge discharge model, so that the sludge discharge opportunity and discharge capacity can be accurately set according to the sludge water content and the sludge yield, invalid or excessive sludge discharge is avoided, clear liquid discharge is reduced, and water consumption and energy consumption are effectively reduced; and energy-saving and efficient operation is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of sludge treatment, and specifically to an intelligent sludge discharge and sludge dewatering composite control model system. Background Art

[0002] The sludge treatment system of a water plant includes several technological links such as sedimentation tank sludge discharge, sludge thickening, and sludge dewatering. Before the final external transportation and disposal, the moisture content of the dewatered sludge is not only affected by the sludge dewatering link, but also by problems such as too low suspended solids in the sedimentation tank sludge discharge during the upstream sludge production stage caused by manual process parameter control, and poor sludge dewatering performance caused by inaccurate dosing in sludge thickening, ultimately resulting in too high moisture content and too large volume of the dewatered sludge, increasing the cost and environmental protection pressure for the sludge disposal of water service operation enterprises. Therefore, the industry needs to seek an intelligent composite control model for sludge that is whole-chain, whole-process, interlinked, restricted, and goal-oriented from the sludge production in the sedimentation tank of the water plant - sludge thickening - sludge dewatering to achieve sludge reduction and energy conservation and consumption reduction in the sludge treatment process. Summary of the Invention

[0003] Aiming at the deficiencies of the prior art, the present invention provides an intelligent sludge discharge and sludge dewatering composite control model system, which solves the problems in the prior art that there is a lack of coordinated control in each treatment link, and the sludge discharge in the sedimentation tank, concentration dosing, and dewatering operation mostly rely on manual experience setting, easily leading to low suspended solids in sludge discharge, inaccurate dosing, poor dewatering efficiency, and further causing high moisture content of sludge, rising disposal costs, and increased energy consumption.

[0004] To achieve the above objectives, the present invention is realized through the following technical solutions: An intelligent sludge discharge and sludge dewatering composite control model system, comprising:

[0005] A sludge production prediction module, used to predict the sludge production and sludge quality in the sedimentation tank based on the sludge production and sludge quality in the sedimentation tank of the water plant, and the sludge quality includes the moisture content of the sludge in the sedimentation tank;

[0006] A feedforward control module, used to determine the target moisture content of the sludge in the sedimentation tank according to the moisture content of the sludge in the sedimentation tank and the moisture content of the dewatered sludge, and optimize the sludge discharge cycle and sludge discharge duration in the sedimentation tank in combination with the sludge production in the sedimentation tank;

[0007] A thickening target setting and dosing optimization module, used to deduce the target moisture content of the sludge in the thickening tank based on the target moisture content of the sludge in the sedimentation tank, and dynamically optimize the dosing amount of the flocculant in the thickening tank in combination with the temperature, moisture content, and sludge retention time in the thickening tank;

[0008] The dehydration control module is used to establish a functional relationship between the flocculant dosage in the dehydrator and the dehydrated sludge moisture content, as well as a mapping relationship between the dehydrator operating parameters and the dehydrated sludge moisture content based on the target sludge moisture content in the thickening tank, thereby adjusting the flocculant dosage in the dehydrator and the dehydrator operating parameters.

[0009] Preferably, the prediction is performed based on a sludge settling prediction model, which includes:

[0010] F(DS) i =f(flow,quality,dossage);

[0011] in:

[0012] F(DS) i is the amount of dry mud in the i-th partition of the sedimentation tank;

[0013] flow is the raw water flow;

[0014] quality is the quality of raw water;

[0015] Dossage is the dosage;

[0016] i means that the sedimentation tank is divided into n areas, and each area is modeled and predicted separately, and i = 1, 2, ... n.

[0017] Preferably, when generating the sludge discharge cycle and sludge discharge duration control instructions, the feedforward control module determines the moisture content target function of the sludge water discharged from the sedimentation tank according to the sludge concentration moisture content target, predicts the sludge production based on the water quality, water volume and dosage data of the water plant inlet water, and couples the moisture content target of the sludge water discharged from the sedimentation tank to construct a sludge discharge model.

[0018] Preferably, the mud discharge model includes:

[0019] D(ss) i =f(F(DS) i ,t1);

[0020] in:

[0021] D(ss) i is the sludge discharge of the i-th partition of the sedimentation tank at the sludge solid concentration ss;

[0022] F(DS) i is the amount of dry mud in the i-th partition of the sedimentation tank;

[0023] t1 is the sedimentation time;

[0024] t2=V / v;

[0025] in:

[0026] t2 is the duration for which the sludge discharge valve or the sludge suction machine is opened during each sludge discharge;

[0027] V is the volume of the sludge water;

[0028] v is the sludge discharge speed.

[0029] Preferably, the dosage of the flocculant in the dynamic optimization thickening tank is determined based on the thickened sludge solid concentration model, and the establishment of the thickened sludge solid concentration model includes:

[0030] Establish a coagulant dosage model based on the data of the water treatment plant, where the data of the water treatment plant includes the influent sludge solid concentration Ci, the thickened sludge solid concentration Co, the residence time Ts, the effluent water quality Tut, the temperature T, and the dosage of the flocculant PAM n 。

[0031] Preferably, the thickened sludge solid concentration model includes:

[0032]

[0033] Wherein:

[0034] PAM n is the dosage of the flocculant in the thickening tank;

[0035] f1(Co,Ci) is the function of the influent and effluent sludge solid concentration coefficients in the thickening tank;

[0036] f2(Ts,Tut)*T is the function of the supernatant water quality coefficients in the thickening tank.

[0037] Preferably, the adjustment of the dosage of the flocculant in the dewatering machine and the operating parameters of the dewatering machine are carried out based on the operating parameters of the dewatering machine and the dewatered sludge moisture content model, and the establishment of the operating parameters of the dewatering machine and the dewatered sludge moisture content model includes:

[0038] According to the rotational speed r of the centrifuge dewatering machine, the torque N, the differential speed R of the screw propeller, the influent sludge quantity Qn, and the dosage of the flocculant PAM in the dewatering machine t and the target Ct of the solid concentration of the dewatered sludge, establish the model of the operating parameters of the dewatering machine and the moisture content of the dewatered sludge.

[0039] Preferably, the dewatering machine parameter and the dewatered sludge moisture content model include:

[0040] F(r,N,R) Ct = f3(Co,Ct,PAM t );

[0041] PAM t = f4(C1,C2,Qn);

[0042] Wherein:

[0043] F(r,N,R) Ct Control parameters of a dehydrator targeted at

[0044] Ct is the target solid concentration of dewatered sludge.

[0045] Co is the solid concentration of concentrated sludge.

[0046] PAM t is the dosage of flocculant for the sludge dehydrator.

[0047] C1 is the solid concentration of the influent sludge to the dehydrator.

[0048] C2 is the solid concentration of the effluent sludge from the dehydrator.

[0049] Qn is the influent sludge volume.

[0050] The present invention provides an intelligent sludge discharge and sludge dehydration composite control model system, which has the following beneficial effects:

[0051] 1. Through the feedforward control module, the present invention determines the moisture content target function of the sludge discharge water in the sedimentation tank according to the target moisture content of the concentrated sludge, and constructs a sludge discharge model, so that the present invention can accurately set the sludge discharge timing and sludge discharge volume according to the sludge moisture content and sludge production volume in the sedimentation tank, thereby avoiding ineffective or excessive sludge discharge, reducing the discharge volume of excess clear liquid, effectively reducing the water resource consumption and energy load in the system operation, and achieving the purpose of energy conservation, consumption reduction and operation efficiency optimization.

[0052] 2. Through the sludge production prediction module, the present invention establishes a sludge discharge volume modeling function, so that the present invention can dynamically predict the dry sludge volume in different areas of the sedimentation tank and accurately set the sludge discharge strategy accordingly, realizing the accurate matching of the sludge discharge volume and the actual sludge load. This model-based regulation method is significantly superior to the traditional manual setting or program control method, and can ensure that the sludge layer at the bottom of the sedimentation tank is within the stable control range, avoiding sludge layer floating, short circuit or discharge of untreated sludge, thus ensuring the stability and compliance of the effluent water quality of the sedimentation tank.

[0053] 3. Through the concentrated target setting and dosing optimization module, the present invention dynamically optimizes the dosage of flocculant in the thickening tank, and the dehydration control module adjusts the dosage of flocculant in the dehydrator, which not only significantly improves the use efficiency of the flocculant, reduces the operation cost, but also makes the flocculation effect more stable and the separation performance better, providing guarantee for the compliance of the supernatant water quality and the control of the moisture content of the sludge cake, and achieving the comprehensive purpose of medicine saving, stable effect and consumption reduction.

[0054] 4. By adjusting the operating parameters of the dehydrator according to the dehydrator operating parameters and the moisture content model of the dewatered sludge, the present invention can achieve dynamic adjustment of the key operating parameters of the dewatering equipment, making the operating state highly match the influent sludge load and sludge characteristics. By adjusting the rotational speed and differential speed in real time, the separation speed of the mud and water and the conveying rhythm of the mud cake can be flexibly controlled. The torque is used to reflect the change of the equipment load and assist in judging the change trend of the dewatering resistance. Combined with the real-time data of the influent sludge volume, precise control is carried out, significantly improving the dewatering efficiency and the stability of the sludge discharge. At the same time, the operation abnormality or the fluctuation of the dewatering effect caused by the sludge quality fluctuation is reduced, achieving the dual goals of equipment energy efficiency optimization and adaptive operation. Description of the Drawings

[0055] Figure 1 It is a schematic diagram of the system architecture of the present invention. Detailed Embodiments

[0056] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the specification of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0057] For a better understanding of the present invention, the above content will be described in detail below in conjunction with specific embodiments.

[0058] Please refer to the attached Figure 1 , the embodiment of the present invention provides an intelligent sludge discharge and sludge dewatering composite control model system, including:

[0059] A sludge production prediction module, which is used to predict the sludge production and sludge quality of the sedimentation tank based on the sludge production and sludge quality of the sedimentation tank in the water treatment plant, and the sludge quality includes the moisture content of the sludge in the sedimentation tank;

[0060] In this embodiment, the sludge production prediction module is the front-end link of the system and is the key basis for realizing the full-link sludge linkage control. Through in-depth mining and modeling of the operation data, this module predicts in advance the change trend of the sludge production and sludge quality of the sedimentation tank under different operating conditions, providing accurate source input parameters for subsequent feedforward control, thickening adjustment, and dewatering scheduling.

[0061] Generally, due to the differences in water quality, water volume, and dosing agents in the sedimentation tanks of sewage treatment plants, strong temporal and spatial non-uniformities will occur, directly affecting the sludge production and sludge properties. In order to realize the scientific management and automatic control of the sludge discharge process, it is necessary to establish a quantifiable prediction model for the sludge production behavior of the sedimentation tank. For this reason, the present invention proposes a data-driven sludge sedimentation prediction model, which refines the modeling of the sludge production state in the sedimentation tank through regional division and parameter association.

[0062] Specifically, the sludge production prediction module is used to predict the sludge production volume and quality of the sedimentation tank based on the sludge production volume and quality of the water plant sedimentation tank. Among them, the sludge quality mainly refers to the moisture content of the sludge in the sedimentation tank. Specifically, the prediction model takes the dry sludge volume of the sedimentation tank partition as the target variable, and uses multi-parameter regression or fitting methods to establish a function relationship model between the raw water volume, water quality, chemical dosage, and dry sludge volume.

[0063] In a possible implementation manner, in order to better reflect the sedimentation difference of the sludge inside the sedimentation tank, the entire sedimentation tank is divided into several independent partitions, and the sludge production volume of each partition is predicted separately to ensure that the model has higher spatial resolution and adaptability.

[0064] As an option, the prediction model includes:

[0065] F(DS) i = f(flow,quality,dossage);

[0066] Where:

[0067] F(DS) i Is the dry sludge volume of the i-th partition of the sedimentation tank;

[0068] flow is the raw water flow;

[0069] quality is the raw water quality;

[0070] dossage is the chemical dosage;

[0071] i indicates that the sedimentation tank is divided into n regions, and each region is modeled and predicted separately, and i = 1, 2,... n, that is, it represents the section number of the sedimentation tank division. For example, the sedimentation tank is divided into 3 to 6 sections according to the sludge load gradient or flow field characteristics, and each section is independently modeled to adapt to different working conditions.

[0072] In some embodiments, to further improve the generalization ability and robustness of the model, the modeling process can optimize the parameter weights in combination with the operating conditions, and introduce a time series window to construct a delay correlation compensation mechanism. For example, a sliding time window is used to weight the change trend to enhance the response ability to sudden water quality fluctuations.

[0073] In another implementation manner, to adapt to the different structural characteristics of different types of sedimentation tanks (such as horizontal flow type, vertical flow type, inclined plate type, etc.), the system can adaptively adjust the number of section divisions and the section modeling strategy according to the sedimentation efficiency distribution to enhance the model versatility.

[0074] Specifically, during operation, the system continuously collects the raw water volume, water quality, and chemical dosage, and docks with the SCADA or data platform through a data interface to continuously optimize the online modeling or offline regression model parameters. The output result of the sludge production prediction model will be used as an important input parameter for the feedforward control module to set the initial time window and target moisture content for sludge discharge in the sedimentation tank, forming the logical starting point for the intelligent sludge discharge control of the system.

[0075] In summary, through the construction of a distribution prediction function and the comprehensive modeling method combining water quality, water volume, and chemical factors, the sludge production prediction module not only realizes the accurate quantification of the sludge production characteristics of the sedimentation tank but also provides scientific data support for the control objectives of subsequent modules.

[0076] The feedforward control module is used to determine the target moisture content of the sludge in the sedimentation tank based on the moisture content of the sludge in the sedimentation tank and the moisture content of the dewatered sludge, and optimize the sludge discharge cycle and duration of the sedimentation tank in combination with the sludge production volume of the sedimentation tank.

[0077] In this embodiment, the feedforward control module is in a core position connecting the front and the back and is an important bridge connecting the sludge production prediction module and the downstream concentration and dewatering control module. The introduction of this module enables the system to prospectively optimize the sludge discharge strategy of the upstream sedimentation tank in advance before the specific moisture content control requirements are put forward in the dewatering process, effectively improving the coherence and response efficiency of the overall control.

[0078] Generally, due to the lag and uncontrollability of the sludge discharge operation in the sedimentation tank, it will have a direct impact on the inlet concentration and flow rate of the downstream thickener. Traditional control systems often set the sludge discharge cycle based on on-site experience and lack a data-driven scientific prediction mechanism, which easily leads to problems such as non-compliance with the moisture content standard and large fluctuations in the dosage of flocculants.

[0079] Specifically, the feedforward control module is used to determine the target moisture content of the sludge in the sedimentation tank based on the moisture content of the sludge in the sedimentation tank and the target moisture content of the dewatered sludge, and further optimize the cycle and duration of sludge discharge in the sedimentation tank in combination with the sludge production volume predicted by the sludge production prediction module. The role of the feedforward control module is not only reflected in parameter prediction and calculation but also includes active intervention according to downstream targets to achieve top-down moisture content regulation.

[0080] Specifically, the module first calculates a difference threshold interval by receiving the target moisture content of the dewatered sludge set by the dewatering control module and combining the moisture content of the sludge collected in the current sedimentation tank, and uses this as the benchmark for adjusting the upstream moisture content target. On this basis, the target moisture content of the thickened sludge is determined according to the operating parameters of the thickener and the sludge retention time, and further trace back to the moisture content setting of the sedimentation tank that affects the source.

[0081] In a possible implementation, the module takes the influent water quality of the water plant (including parameters such as COD, SS, NH3-N, etc.), influent flow rate, and chemical dosage as input variables to predict the sludge production trend at present or in the short term. The prediction result will act together with the sludge moisture content target of the sedimentation tank as input to drive the parameter estimation of the sludge discharge model.

[0082] To more effectively control the sludge discharge rhythm of the sedimentation tank, a sludge discharge model is integrated in the feedforward control module. The sludge discharge model includes:

[0083] The sludge discharge model includes:

[0084] D(ss) i = f(F(DS) i , t1);

[0085] Where:

[0086] D(ss) i is the sludge discharge amount of the i-th partition of the sedimentation tank at the sludge solid concentration ss;

[0087] F(DS) i is the dry sludge amount of the i-th partition of the sedimentation tank;

[0088] t1 is the sedimentation time;

[0089] t2 = V / v;

[0090] Where:

[0091] t2 is the duration when the sludge discharge valve or sludge suction machine is opened each time for sludge discharge;

[0092] V is the volume of mud and water;

[0093] v is the sludge discharge speed.

[0094] In some embodiments, the feedforward control module calculates the sludge discharge cycle and sludge discharge time in real time according to the above model, and continuously corrects the model parameters according to the actual operation feedback. This adjustment process deals with the error between the sedimentation tank data and the real-time data by introducing a variable weight function to avoid the model deviation caused by a single data source.

[0095] As an option, the model can be dynamically fitted in the deployment by combining the moisture content of the sedimentation tank measured by an online sensor. By adding an adjustment coefficient and introducing an adaptive mechanism based on error feedback, the calculation of the sludge discharge amount is made more real-time and flexible.

[0096] In another implementation, the module can also automatically adjust the structural form and calculation method of the parameters required in the sludge discharge model according to different types of sedimentation tanks (such as vertical flow type, inclined plate type, etc.). For example, for a high-density sedimentation tank, the sludge discharge speed can be set in the form of a variable function to better fit its transient sludge discharge fluctuation characteristics.​​

[0097] The sludge discharge cycle and duration parameters output by this module will ultimately be sent to the on-site PLC or sludge discharge execution unit in the form of instructions, forming a precise sludge discharge control strategy driven by data and achieving an effective matching of the upstream regulation to the downstream target.

[0098] In summary, the feedforward control module realizes the quantitative modeling and intelligent control of the sludge discharge behavior by constructing a control strategy with the dewatering moisture content as the target, the sludge production as the constraint, and the sludge discharge moisture content of the sedimentation tank as the adjustment target, which is an important manifestation of the intelligence of this system.

[0099] The concentration target setting and dosing optimization module is used to deduce the target moisture content of the sludge in the thickening tank based on the target moisture content of the sludge in the sedimentation tank, and dynamically optimize the dosing amount of the flocculant in the thickening tank in combination with the temperature, sludge moisture content, and sludge retention time in the thickening tank;

[0100] In this embodiment, the concentration target setting and dosing optimization module undertakes the transitional function of connecting the sludge discharge regulation of the sedimentation tank and the operation of the dewatering system. Its main role is to deduce the target moisture content of the sludge applicable to the thickening link based on the target moisture content of the sludge set by the upstream sedimentation tank, and dynamically adjust the dosing amount of the flocculant to match the change trend of the sludge properties and ensure the stable operation of the thickening process.

[0101] Generally, due to the influence of the upstream working conditions, water quality fluctuations, and sludge discharge strategies on the moisture content of the sludge discharge water from the sedimentation tank, the solid concentration of the influent sludge in the thickening tank fluctuates significantly. If the dosing strategy of the flocculant is not dynamically regulated, it is very easy to cause excessive or insufficient flocculant, which will in turn affect the sludge sedimentation efficiency, the quality of the supernatant water, and even the feeding quality of the downstream dewatering system. Therefore, designing a module that can automatically adjust the dosing amount according to real-time data feedback is one of the key links to achieve full-process intelligent control.

[0102] Specifically, the concentration target setting and dosing optimization module is used to deduce the target moisture content of the sludge in the thickening tank based on the target moisture content of the sludge in the sedimentation tank. This target can be established through a theoretical mapping function, considering the solid load transfer efficiency within the system and the treatment capacity of the thickening tank. Generally, the following mapping strategy can be adopted: According to the set target range of the moisture content of the sludge discharge water from the sedimentation tank (such as 0.6% - 1.2%), combined with the influent flow rate and the actual removal rate of the thickening tank, the target moisture content of the thickened sludge suitable for the current working conditions (such as 6% - 8%) is deduced as a reference for subsequent dosing adjustment.

[0103] In a possible implementation, to achieve dynamic optimization control of the flocculant dosage, the module constructs a data-driven model for the solid concentration of thickened sludge. This model comprehensively considers various influencing factors, including but not limited to the influent sludge solid concentration, the current solid concentration of thickened sludge, the sludge retention time, the temperature of the thickening tank, the effluent water quality, and the existing dosage, to establish a quantitative model for the optimal dosage of coagulant.

[0104] Specifically, the establishment of the coagulant dosage model includes the following data elements:

[0105] Influent sludge solid concentration Ci (unit: kg / m 3 ): Reflects the basic concentration level of the sludge entering the thickening tank;

[0106] Solid concentration of thickened sludge Co (unit: kg / m 3 ): Represents the concentration level of the sludge discharged from the current thickening tank;

[0107] Retention time Ts (unit: h): Affects the flocculation reaction time and the efficiency of mud-water separation;

[0108] Effluent water quality Tut (including indicators such as suspended solids SS and turbidity): Affects whether the quality of the supernatant discharge meets the standards;

[0109] Temperature inside the tank T (unit: °C): Affects the floc formation rate and reaction equilibrium;

[0110] Flocculant dosage PAM n (unit: g / m 3 ): System execution data.

[0111] That is, the model for the solid concentration of thickened sludge can be expressed as the following structure:

[0112]

[0113] Where:

[0114] PAM n Is the flocculant dosage in the thickening tank;

[0115] f1(Co, Ci) is the coefficient function of the influent and effluent solid concentrations in the thickening tank;

[0116] f2(Ts, Tut) * T is the coefficient function of the supernatant water quality in the thickening tank.

[0117] As an option, the model for the solid concentration of thickened sludge can also be constructed by methods such as nonlinear regression, multivariable least squares fitting, neural networks, etc., or a multi-factor linear weight function can be used for approximation in specific implementations to enable quick integration into the control system for online calculation.

[0118] In some embodiments, to improve the adaptability of the model under low-temperature and high-load conditions, a temperature sensitivity factor can be introduced into the model to adjust the model weights according to seasonal changes, avoiding the problem of excessive chemical dosing caused by low temperatures in winter.

[0119] In another implementation, the update frequency of the model is related to the degree of fluctuation of the sludge inlet concentration in the thickening tank. If the system detects that the change range of the inlet sludge concentration exceeds the set threshold (such as 15%), the model parameter recalculation is immediately triggered to adapt to the new operating conditions.

[0120] Specifically, the dosing amount of the flocculant output by the model is adjusted in real time to the dosing pump actuator through the thickening tank control unit to form a data closed-loop, ensuring the dosing accuracy and continuous stability of the thickening process, and providing an ideal sludge input state for subsequent dewatering control.

[0121] In summary, the thickening target setting and dosing optimization module realizes the dynamic dosing adjustment guided by the target moisture content by constructing a thickened sludge solid concentration model closely related to the operating state, significantly enhancing the coordinated control ability of the system for the whole process of sludge treatment, and is one of the key components of the present invention.

[0122] The dewatering control module is used to establish the functional relationship between the dosing amount of the flocculant in the dewatering machine and the moisture content of the dewatered sludge, as well as the mapping relationship between the operating parameters of the dewatering machine and the moisture content of the dewatered sludge based on the target moisture content of the sludge in the thickening tank, so as to adjust the dosing amount of the flocculant in the dewatering machine and the operating parameters of the dewatering machine.

[0123] In this embodiment, the dewatering control module constitutes the terminal link of the entire treatment chain, and its control effect directly affects the quality of the dewatered sludge and the economy and environmental protection of subsequent transportation and disposal. This module not only undertakes the target moisture content of the thickened sludge provided by the thickening target setting and dosing optimization module, but also undertakes the precise adjustment function of the operating state of the dewatering equipment and the dosage of the flocculant, and is the key control unit to realize the controllable end sludge discharge effect and the stable operation of the system.

[0124] Generally, the operating state of dewatering equipment (such as a centrifuge) highly depends on the properties of the inlet sludge and the fluctuation of its solid concentration, and the dosing of the flocculant plays a decisive role in the dewatering performance of the sludge. In traditional operating methods, fixed operating parameters are often set based on experience, which is difficult to adapt to the dynamic changes of the inlet load and sludge quality, resulting in a high moisture content of the dewatered sludge or resource waste. Therefore, it is necessary to construct a module that can dynamically adjust the dewatering control strategy to optimize the operating parameters and dosing amount based on the model-driven method.

[0125] Specifically, the dehydration control module is used to establish the functional relationship between the flocculant dosage in the dehydrator and the moisture content of the dehydrated sludge, as well as the mapping relationship between the operating parameters of the dehydrator and the moisture content of the dehydrated sludge based on the target moisture content of the sludge in the thickening tank. Through this control strategy, the comprehensive and closed-loop regulation of the operation process of the dehydrator is realized.

[0126] Specifically, the system first sets the control target for the moisture content of the dehydrated sludge according to the target moisture content of the thickening tank and in combination with the operating target of the current dehydration section. On this basis, the following two mapping relationships are established respectively:

[0127] The function relationship of the flocculant dosage, which is used to describe the influence of the flocculant dosage on the moisture content of the dehydrated sludge;

[0128] The operating parameter mapping model, which is used to quantify the correlation between the operating state parameters of the dehydration equipment and the moisture content.

[0129] Including:

[0130] The model of the dehydrator parameters and the moisture content of the dehydrated sludge includes:

[0131] F(r,N,R) Ct = f3(Co,Ct,PAM t );

[0132] PAM t = f4(C1,C2,Qn);

[0133] Where:

[0134] F(r,N,R) Ct Is the control parameter of the dehydrator with the target;

[0135] Ct is the target solid concentration of the dehydrated sludge;

[0136] Co is the solid concentration of the thickened sludge;

[0137] PAM t Is the flocculant dosage of the sludge dehydrator;

[0138] C1 is the solid concentration of the sludge entering the dehydrator;

[0139] C2 is the solid concentration of the sludge leaving the dehydrator;

[0140] Qn is the sludge inlet flow rate.

[0141] In some embodiments, to enhance the adaptability and deployability of the model, the functional relationships in the above model can be trained by means of polynomial fitting, multivariate regression analysis or BP neural network, etc., and continuously iteratively updated according to the actual operating conditions of the equipment to improve the prediction accuracy.

[0142] As an option, to cope with external disturbances such as equipment aging or environmental temperature changes, the system can incorporate a self-learning mechanism. When it detects that the deviation between the target moisture content of the dewatered sludge and the actual value exceeds the set threshold (such as ±2%), it automatically adjusts or reconstructs the key parameters in the model to maintain a stable output of the dewatering quality.

[0143] In another possible implementation, the system converts the recommended dosage of the flocculant and the combination of control parameters output by the model into specific instructions and sends them to the dosing pump control unit and the dehydrator frequency converter to achieve online closed-loop control. This control chain can be deployed through a PLC or DCS platform, with high reliability and real-time performance, and is adapted to the operation requirements of sewage treatment plants of different scales.

[0144] In summary, the dewatering control module realizes the synchronous optimization of the flocculant dosing and the operating status of the dewatering equipment by establishing a model of the dehydrator parameters and the moisture content of the dewatered sludge, which is one of the important technical supports for realizing the full-process intelligentization of sludge treatment in the present invention.

[0145] Moreover, to achieve closed-loop control and refined management of the whole process, the system can also introduce an operation feedback mechanism for multi-level actuators. Through real-time status perception and control parameter adjustment at key nodes, it ensures that the control strategy has the ability to respond promptly and adjust precisely. The feedback mechanisms at all levels cooperate to construct an adaptive closed-loop control system for the whole process from sludge discharge to thickening and then to dewatering, which is specifically described as follows:

[0146] Regarding the operation feedback of the sludge discharge facility actuator, generally, the system can collect the opening and closing status of the sludge discharge valve and the operating frequency, start and stop time of the sludge suction machine in real time as the basis for judging the effectiveness of instruction execution. In a possible implementation, when the system issues a sludge discharge instruction, the control system will compare the feedback signal to determine whether the sludge discharge valve opens and closes on time and whether the sludge suction machine starts normally to ensure the closed-loop execution of the control logic. In addition, as an option, the system also incorporates the real-time data of the solid concentration of the influent sludge in the thickening tank into the feedback link. If it is detected that the influent concentration is lower than the set threshold, the sludge discharge duration will be automatically shortened to avoid the imbalance of the mud-water ratio caused by low-load operation. Otherwise, it will be appropriately extended to improve the stability of the influent concentration.

[0147] Regarding the operation feedback of the sludge thickening facility actuator, while controlling the flocculant dosing, the system continuously obtains the real-time solid concentration data of the thickened sludge. Specifically, when the feedback data shows that the solid concentration of the discharged sludge is low, the control logic can determine that the current dosing amount is insufficient and automatically increase the dosing rate of the flocculant; when the concentration exceeds the set range, the dosing amount will be appropriately reduced to avoid waste of resources and deterioration of the supernatant water quality caused by excessive flocculation. In some embodiments, this feedback mechanism can further combine auxiliary variables such as the temperature in the tank and the residence time to construct a more complex control and adjustment path to improve the stability of the thickening effect.

[0148] In terms of the operation feedback of the actuator of the sludge dewatering facility, the system monitors the solid concentration of the sludge cake produced by the dewatering machine in real time and uses it as the key judgment basis for dewatering quality. If the system detects that the solid concentration of the dewatered sludge is lower than the target value, it can simultaneously increase the dosage of the flocculant and optimize the combination of operating parameters, such as increasing the rotation speed, increasing the differential speed of the screw propeller, or appropriately adjusting the torque, to enhance the centrifugal force and improve the solid-liquid separation efficiency. On the contrary, if the sludge cake concentration is too high and exceeds the set range, it may indicate over-dewatering or energy consumption waste, and the system will accordingly reduce the operating intensity and the dosage to balance energy efficiency and output quality. In a feasible implementation, the dewatering control module can also incorporate the influent sludge load fluctuation and dynamically correct the adjustment range of each parameter to achieve adaptive closed-loop control under multi-variable linkage.

[0149] In summary, through the operation feedback mechanism of the above three control levels, the system constructs a complete set of intelligent closed-loop control logics covering the whole process of sludge discharge - thickening - dewatering. This mechanism effectively improves the operation stability and automation level, significantly reduces the dependence on manual intervention, and realizes the intelligent perception, dynamic response, and efficient coordination of the sludge treatment process.

[0150] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principle and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent sludge drainage and sludge dewatering composite control model system, characterized in that It includes: A sludge production prediction module for predicting the sludge production volume and quality of a sedimentation tank based on the sludge production volume and quality of the sedimentation tank in a water treatment plant, where the sludge quality includes the sludge moisture content of the sedimentation tank; A feedforward control module for determining the target sludge moisture content in the sedimentation tank according to the sludge moisture content in the sedimentation tank and the dehydrated sludge moisture content, and optimizing the sludge discharge cycle and discharge duration of the sedimentation tank in combination with the sludge production volume of the sedimentation tank; A thickening target setting and dosing optimization module for deriving the target sludge moisture content of the thickener based on the target sludge moisture content in the sedimentation tank, and dynamically optimizing the flocculant dosage in the thickener in combination with the temperature, sludge moisture content, and sludge retention time in the thickener; A dehydration control module for establishing a functional relationship between the flocculant dosage in the dehydrator and the dehydrated sludge moisture content, and a mapping relationship between the operating parameters of the dehydrator and the dehydrated sludge moisture content based on the target sludge moisture content of the thickener, so as to adjust the flocculant dosage in the dehydrator and the operating parameters of the dehydrator.

2. The intelligent sludge discharging and sludge dewatering composite control model system according to claim 1, characterized in that The prediction is carried out based on a sludge sedimentation prediction model, and the sludge sedimentation prediction model includes: F(DS) i = f(flow, quality, dosage); Wherein: F(DS) i is the dry sludge quantity in the i-th partition of the sedimentation tank; flow is the raw water flow rate; quality is the raw water quality; dossage is the chemical dosage; i indicates that the sedimentation tank is divided into n regions, and each region is modeled and predicted separately, and i = 1, 2,... n.

3. The intelligent sludge discharging and sludge dewatering composite control model system according to claim 2, characterized in that, When generating the sludge discharge cycle and discharge duration control instructions, the feedforward control module determines the target moisture content function of the sedimentation tank sludge discharge water according to the target sludge thickening moisture content, predicts the sludge production volume based on the data of the influent water quality, water volume, and chemical dosage of the water treatment plant, and couples the target moisture content of the sedimentation tank sludge discharge water to construct a sludge discharge model.

4. An intelligent sludge drainage and sludge dewatering composite control model system according to claim 3, characterized in that, The sludge discharge model includes: D(ss) i = f(F(DS) i , t1); Wherein: D(ss) i is the sludge discharge volume of the i-th partition of the sedimentation tank at the sludge solid concentration ss; F(DS) i is the dry mud quantity in the i-th partition of the sedimentation tank; t1 is the sedimentation time; t2 = V / v; Wherein: t2 is the duration when the sludge discharge valve or sludge suction machine is opened during each sludge discharge; V is the volume of muddy water; v is the sludge discharge speed.

5. An intelligent sludge discharging and sludge dewatering composite control model system according to claim 1, characterized in that, The dynamic optimization of the flocculant dosage in the thickener is determined based on a thickened sludge solid concentration model, and the establishment of the thickened sludge solid concentration model includes: Establish a coagulant dosing model based on the data of the waterworks, where the data of the waterworks includes the influent sludge solid concentration Ci, the concentrated sludge solid concentration Co, the residence time Ts, the effluent water quality Tut, the temperature T, and the flocculant dosing amount PAM n .

6. The intelligent sludge discharging and sludge dewatering composite control model system according to claim 5, characterized in that, The thickened sludge solid concentration model includes: Wherein: PAM n is the dosage of flocculant in the thickening tank; f1(Co,Ci) is the function of the solid concentration coefficient of the influent and effluent sludge in the thickener; f2(Ts,Tut)*T is the function of the supernatant water quality coefficient of the thickener.

7. An intelligent sludge discharging and sludge dewatering composite control model system according to claim 1, characterized in that, The adjustment of the flocculant dosage in the dehydrator and the operating parameters of the dehydrator is carried out based on a dehydrator parameter and dehydrated sludge moisture content model and a flocculant dosage and dehydrated sludge moisture content model, and the establishment of the dehydrator parameter and dehydrated sludge moisture content model and the flocculant dosage and dehydrated sludge moisture content model includes: Based on the rotational speed r, torque N, differential speed R of the screw propeller, influent sludge flow rate Qn and the dosage of flocculant PAM in the centrifuge t and the target solid concentration Ct of the dewatered sludge, establish the models of centrifuge parameters vs. moisture content of the dewatered sludge and the dosage of flocculant vs. moisture content of the dewatered sludge.

8. An intelligent sludge discharging and sludge dewatering composite control model system according to claim 7, characterized in that, The dehydrator parameter and dehydrated sludge moisture content model and the flocculant dosage and dehydrated sludge moisture content model include: F(r,N,R) Ct = f3(Co,Ct,PAM t ); PAM t = f4(C1, C2, Qn); Wherein: F(r, N, R) Ct Control parameters of a dehydrator targeted at Ct is the target solid concentration of the dehydrated sludge; Co is the solid concentration of the thickened sludge; PAM t is the dosage of flocculant for the sludge dewatering machine; C1 is the solid concentration of the influent sludge of the dehydrator; C2 is the solid concentration of the effluent sludge of the dehydrator; Qn is the influent sludge volume.

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