Multi-stage dynamic quantitative control system for wastewater treatment based on result feedback

Abstract

The present application relates to the wastewater treatment technology field in water treatment technology, especially to a kind of wastewater treatment multistage dynamic quantification control system based on result feedback, including storage module, identification module, quantification module and execution module;Storage module stores the causal chain in reaction progress, identification module identifies the matching state of current operating parameter and causal chain, and enables quantification module to determine risk index, execution module is based on the risk index determined and carries out control optimization execution corresponding measure, by building pollution transmission causal chain model, from pollution root to derived phenomenon tracking intervention has been realized, the pollution migration problem implied in wastewater treatment is converted into the identification item of quantification operation, through time sequence logic verification and multidimensional matching degree fusion mechanism, the limitation of traditional single-point identification control is broken through.In the continuity of process is guaranteed, the precision and resource utilization efficiency of membrane pollution prevention and control are improved, and the closed-loop control paradigm of traceability, prediction and dynamic quantification grading interception is formed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of wastewater treatment technology in water treatment technology, and particularly relates to a multi-stage dynamic quantitative control system for wastewater treatment based on result feedback. BACKGROUND

[0002] In recent years, with the acceleration of industrialization, the amount of high-salt and high-pollution wastewater has increased significantly. Especially in the new energy material, electronic semiconductor, petroleum chemical industry and other industries, the discharge of such wastewater not only causes serious pollution to the environment, but also wastes the potential recyclable resources in the wastewater. For this type of wastewater, the existing technology generally adopts the method of first oxidizing degradation and then treating wastewater through multi-stage filtration and adsorption mechanism.

[0003] For example, in the prior art, Chinese patent publication No. CN105668862A discloses a recovery method of wastewater containing sodium hydroxide, which recovers sodium hydroxide by adopting the method of membrane technology after precipitation and filtration. The membrane technology is to use a microfiltration membrane to remove impurities and then use a reverse osmosis membrane to concentrate, so as to obtain a recovered sodium hydroxide solution. The precipitation and filtration is as follows: the wastewater containing sodium hydroxide is sent into an adjusting tank, and after precipitation, it is filtered. The adjusting tank is a sealed adjusting tank, and the adjusting tank is combined with multiple tanks, each group has 3 to 5 tanks. The adjusting tank contains an air purification device, and the wastewater containing sodium hydroxide is fed into the adjusting tank in batches, each batch has 100 to 200 tons of wastewater per tank. The precipitation and filtration adopts double-layer filter material, and the precipitation is by static precipitation for 1 to 2 days.

[0004] In order to ensure the quality of water treatment, it is also important to control the progress in the recovery process. For example, in the prior art, Chinese patent publication No. CN119512015A discloses an intelligent wastewater treatment and zero-emission control system, which combines dynamic Bayesian network and deep reinforcement learning algorithm to realize intelligent control and resource utilization of the wastewater treatment process. The system realizes real-time monitoring of wastewater characteristics and dynamic optimization of operation parameters.

[0005] However, the prior art has the following problems:

[0006] ‌For the abnormality in the treatment process, it is only found at a single point, and there is a lack of correlation analysis of abnormal points, which further leads to the inability to timely discover the influencing factors of single-point problems and the inability to timely take effective measures to delay the wastewater treatment process. SUMMARY

[0007] The purpose of the present application is to provide a multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, so as to solve the problem that in the prior art, for the abnormality in the treatment process, it is only found at a single point, and there is a lack of correlation analysis of abnormal points, which further leads to the inability to timely discover the influencing factors of single-point problems and the inability to timely take effective measures to delay the wastewater treatment process.

[0008] To this end, the application provides a multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, which is applied to the following wastewater treatment process: adjusting the pH of wastewater and oxidizing and degrading organic matter through Fenton reaction, adding NaOH and Na2CO3 to the effluent after oxidation, generating CaCO3 and Mg(OH)2 precipitates, and intercepting the precipitates through a tubular ultrafiltration membrane, and then adsorbing the waste liquid after interception through an activated carbon and a resin exchange tower, based on the above wastewater treatment process, the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback comprises a storage module, an identification module, a quantitative module and an execution module;

[0009] The storage module stores the following causal chains:

[0010] The first causal chain comprises the following identification items in order: abnormal influent pH, excessive oxidation agent residue and tubular ultrafiltration membrane flux attenuation; the second causal chain comprises the following identification items in order: abnormal calcium ion saturation, excessive tubular ultrafiltration membrane fouling and decreased resin exchange tower adsorption capacity.

[0011] The identification module matches the current operating parameters with the identification items in the causal chains, and in response to the number of matched items between the operating parameters and the identification items of any causal chain being not less than two and the appearance order of the identification items being consistent with the order of the corresponding causal chain, the quantitative module is enabled to work; the quantitative module is respectively provided with the weight coefficients of the first causal chain and the second causal chain, and respectively determines the matching degrees of the current operating parameters with the first causal chain and the second causal chain, and generates a membrane pollution risk index based on the matching degrees and the weight coefficients; the execution module responds to the low, medium or high three levels of the risk index to respectively perform tubular ultrafiltration membrane backflushing, adding a scale inhibitor to the precipitation link, or switching the tubular ultrafiltration membrane.

[0012] As a preferred technical scheme of the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the storage module is respectively provided with the numerical value ranges of the corresponding parameters for each identification item of the first causal chain and the second causal chain, and the time interval ranges of adjacent identification items.

[0013] For the first causal chain, the specific numerical value ranges of the corresponding parameters include: the influent pH value range, the oxidation-reduction potential range after the end of Fenton reaction, and the range of the change rate of the transmembrane pressure difference of the tubular ultrafiltration membrane.

[0014] For the second causal chain, the specific numerical value ranges of the corresponding parameters include: the calcium ion saturation range, the transmembrane pressure difference range of the tubular ultrafiltration membrane and the adsorption capacity range of the resin exchange tower.

[0015] As a preferred technical scheme of the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the identification module determines that the operation parameter matches the identification item in response to the operation parameter being in the value range corresponding to the identification item, determines the time interval of the identification item based on the trigger node of the matching of two adjacent identification items, and determines that the occurrence order of the identification item is consistent with the order of the corresponding causal chain in response to the time interval being in the corresponding time interval range.

[0016] As a preferred technical scheme of the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the quantitative module determines the matching degree of the current operation parameter with the first causal chain and the second causal chain by comprehensively determining the parameter matching degree and the time sequence matching degree, and specifically includes the following processes:

[0017] For each matched identification item, a parameter matching degree is generated according to the deviation of the current operation parameter value from the median value of the corresponding value range;

[0018] For each pair of sequentially matched adjacent identification items, a time sequence matching degree of the adjacent identification items is generated according to the deviation of the actual trigger time interval from the median value of the corresponding time interval range;

[0019] The single parameter matching degree of all identification items in the same causal chain and the time sequence matching degree between adjacent identification items are weighted and fused to generate the matching degree of the operation parameter with the causal chain.

[0020] As a preferred technical scheme of the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the quantitative module sets the weight coefficient of the second causal chain to be greater than the weight coefficient of the first causal chain.

[0021] As a preferred technical scheme of the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the determination process of the matching degree of the single parameter by the quantitative module specifically includes:

[0022] Determine the difference between the current operation parameter and the median value of its corresponding value range;

[0023] According to the preset deviation matching degree mapping relationship, the matching degree of the identification item is output;

[0024] The mapping relationship satisfies that the matching degree is the maximum value when the difference is zero, and the matching degree approaches zero when the difference approaches the range boundary.

[0025] As a preferred technical scheme of the multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, the determination process of the time sequence matching degree of the adjacent identification items by the quantitative module specifically includes:

[0026] Determine the difference between the time interval of the trigger node of the matching of two adjacent identification items and the median value of the corresponding time interval range;

[0027] If the difference is within the allowable fluctuation zone, the timing matching degree is determined as the maximum value, if the difference exceeds the allowable fluctuation zone but does not reach the boundary of the timing interval range, the timing matching degree is linearly decreased, and if the difference exceeds the boundary of the timing interval range, the timing matching degree is zero.

[0028] As a preferred technical scheme of the multi-level dynamic quantitative control system for wastewater treatment based on result feedback, the quantitative module determines the timing interval range of the two matching items from the abnormality of the influent pH to the over-standard oxidation agent residue based on the Fenton reaction rate and the timing interval range of the two matching items from the abnormality of the calcium ion saturation to the over-standard pipe ultrafiltration membrane fouling based on the calcium carbonate crystal nucleus growth rate for the first causal chain.

[0029] As a preferred technical scheme of the multi-level dynamic quantitative control system for wastewater treatment based on result feedback, the quantitative module generates the matching degree process of the operating parameters and the causal chain by weighting and fusing the single parameter matching degrees of all identified items in the same causal chain and the timing matching degrees between adjacent identified items, and the first initial weight is assigned to the first parameter matching degree, the second initial weight is assigned to the first timing matching degree, and the subsequent items are sequentially decreased based on the first initial weight and the second initial weight.

[0030] Wherein, the sum of the weights assigned to each parameter matching degree and the sum of the weights assigned to each timing matching degree are both one.

[0031] As a preferred technical scheme of the multi-level dynamic quantitative control system for wastewater treatment based on result feedback, the identification module records the operating parameters matched with the identified items.

[0032] The beneficial effects of the present application are:

[0033] The multi-level dynamic quantitative control system for wastewater treatment based on result feedback of the present application realizes the whole process tracking and advanced intervention from the pollution source to the derived phenomenon by constructing a pollution transmission causal chain model. The core lies in converting the pollution migration law implied in the wastewater treatment process into a quantifiable sequence of identified items, breaking through the limitations of traditional single-point control through timing logic verification and multi-dimensional matching degree fusion mechanism. The system not only guarantees the continuity of the process, but also significantly improves the accuracy of membrane pollution prevention and control and the resource utilization efficiency, forms a closed-loop control paradigm of traceability, prediction, dynamic quantification and hierarchical interception.

[0034] Further, the present application associates the originally discrete process parameters into an ordered pollution transmission chain through the causal chain preset by the storage module. The identification module not only verifies the over-standard single parameter, but also requires the identified items to appear in the preset order and meet the timing rules, ensuring that the alarm signal truly reflects the pollution migration process. This mechanism triggers the prevention and control action before the pollution affects the downstream key equipment, cutting off the pollution chain from the source.

[0035] Further, the quantitative module of the present application introduces dual evaluation indexes of parameter matching degree and timing matching degree, and generates a comprehensive risk index through weighted fusion. The parameter matching degree reflects the closeness of the current parameters to the pollution threshold value; the timing matching degree verifies the rationality of the cause and effect; the fusion of the two avoids the misjudgment risk of the traditional single threshold control, and makes the risk index more objectively represent the joint threat strength of multi-level pollution.

[0036] Further, the execution module of the present application optimizes the hierarchical response and cross-unit cooperation, and the execution module dynamically starts differentiated prevention and control strategies according to the risk index of low, medium and high levels: for the low risk level, the membrane backflush is started for early pollution to avoid drug abuse; for the medium risk level, the antisludging agent is injected into the precipitation link to inhibit the spread of the scaling trend; for the high risk level, the standby membrane group is switched in parallel with the adjustment of the subsequent unit to block the pollution transmission across the process. The hierarchical mechanism accurately delivers the prevention and control resources to the weakest link, while ensuring the treatment efficiency and minimizing energy and material consumption.

[0037] Further, the present application assigns different weights to different causal chains, and strengthens the sensitivity to the pollution source through the principle of maximizing the weight of the first identification item. Ensure that the identification control is tilted towards the core contradiction. BRIEF DESCRIPTION OF DRAWINGS

[0038] Figure 1 The structural block diagram of the wastewater treatment multi-level dynamic quantitative control system based on result feedback in the embodiment of the present application is shown in the figure;

[0039] Figure 2 The flowchart of the wastewater treatment process in the embodiment of the present application is shown in the figure;

[0040] Figure 3 The working flowchart of the wastewater treatment multi-level dynamic quantitative control system based on result feedback in the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0041] The technical solutions of the present application will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0042] The embodiments of the present application will be described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference signs represent the same or similar elements or elements with the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as a limitation of the present application.

[0043] As Figure 2As shown, for better understanding of the present application, the embodiment first describes a wastewater treatment process for executing the control system of the present application, which specifically comprises: S1, adjusting the pH of the inlet liquid to 2-4, adding H2O2 and Fe 2+ to perform Fenton reaction and oxidize and degrade organic matter; S2, adding NaOH and Na2CO3 to the effluent after oxidation to generate CaCO3 and Mg(OH)2 precipitates, which are concentrated and intercepted by a tubular ultrafiltration membrane; S3, adsorbing part of the organic matter by activated carbon; S4, adjusting the pH to 6-8, and adsorbing residual Ca 2+ , Mg 2+ and heavy metals by a first resin exchange tower; adjusting the pH to 5-6, and adsorbing fluorine and silicon to the standard concentration by a second resin exchange tower; S5, concentrating and intercepting Na2SO4-rich liquid by fractional salt, and the permeate is NaCl-rich liquid; S6, obtaining NaCl crystals and anhydrous Na2SO4 by evaporation crystallization.

[0044] On the basis of the above wastewater treatment process, please refer to Figure 1 , the embodiment further provides a multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, which comprises a storage module, an identification module, a quantitative module and an execution module.

[0045] The storage module stores the following causal chains:

[0046] The first causal chain comprises the following identification items in order: abnormal inlet water pH, excessive oxidation agent residue and tubular ultrafiltration membrane flux attenuation; the second causal chain comprises the following identification items in order: abnormal calcium ion saturation, excessive tubular ultrafiltration membrane fouling and decreased resin exchange tower adsorption capacity.

[0047] The identification module matches the current operating parameter with the identification items in the causal chain, and in response to the number of matching operating parameters with the identification items of any causal chain being not less than two and the order of the identification items being consistent with the order of the corresponding causal chain, the quantification module is enabled to work, and the identification module records the operating parameters matched with the identification items and feeds back to the user end; the quantification module is respectively provided with weight coefficients of the first causal chain and the second causal chain (the weight coefficient of the second causal chain is greater than that of the first causal chain, and in the embodiment, the weight coefficient of the first causal chain is 0.24, and the weight coefficient of the second causal chain is 0.76), the quantification module respectively determines the matching degrees of the current operating parameter with the first causal chain and the second causal chain, and generates a risk index of membrane pollution based on the matching degrees and the weight coefficients; the execution module responds to the three levels of low, medium or high of the risk index to correspondingly perform the reverse flushing of the tubular ultrafiltration membrane, add a scale inhibitor to the sedimentation link, or switch the tubular ultrafiltration membrane. In the above embodiment, by constructing a pollution transmission causal chain model, the whole process tracking and advanced intervention from the pollution source to the derived phenomenon are realized. The core lies in converting the pollution migration law implied in the wastewater treatment process into a quantifiable operation identification item sequence, breaking through the limitation of traditional single-point control through the time sequence logic verification and multi-dimensional matching degree fusion mechanism. The system significantly improves the accuracy of membrane pollution prevention and control and the resource utilization efficiency while ensuring the continuity of the process, forms a closed-loop control paradigm of traceability prediction, dynamic quantification and hierarchical interception.

[0048] On the basis of the above embodiment, the storage module is respectively provided with a numerical range of the corresponding parameter for each identification item of the first causal chain and the second causal chain, and a time interval range of adjacent identification items;

[0049] For the first causal chain, the specific numerical range of the corresponding parameter includes: the influent pH value range, the oxidation-reduction potential range after the Fenton reaction is completed, and the range of the change rate of the transmembrane pressure difference of the tubular ultrafiltration membrane.

[0050] For the second causal chain, the specific numerical range of the corresponding parameter includes: the calcium ion saturation range, the transmembrane pressure difference range of the tubular ultrafiltration membrane, and the adsorption amount range of the resin exchange tower. In detail, for the first causal chain, the cause is that in the Fenton reaction, too low pH leads to the formation of inert complex of divalent Fe ion, inhibiting the generation of hydroxyl radical, and the undecomposed or ozone penetrates to the tubular ultrafiltration membrane, attacks the surface bonding of the tubular ultrafiltration membrane, causes the exposure of hydrophobic groups, and after the exposure of the hydrophobic groups, accelerates the adsorption of organic matter, forming a dense filter cake layer. For the second causal chain, the cause is that Ca 2+ CaCO3 microcrystals are generated by homogeneous nucleation, and after the deposition of the CaCO3 microcrystals on the membrane surface, part of the CaCO3 microcrystals are deposited on the membrane surface, and the rest of the CaCO3 microcrystals are deposited on the membrane surface. 2+Penetration to the resin tower competition chelation sites, reduce the resin adsorption capacity. Exemplarily, the numerical range setting in this embodiment is shown in the following table, which can be adjusted in combination with the actual reaction rate in implementation,

[0051] Matching table of identification item numerical range

[0052] Identified item Numerical type Numerical range Influent pH abnormality pH value 1.7-2.0 Oxidizing agent residue overproof Oxidation-reduction potential value 430 mV-480 mV Tube ultrafiltration membrane flux attenuation Tube ultrafiltration membrane transmembrane pressure difference change rate relative to previous time +4% / min~+10% / min Calcium ion saturation abnormality Calcium ion saturation 0.78~0.92 Tube ultrafiltration membrane fouling overproof Tube ultrafiltration membrane transmembrane pressure difference 65 kPa~78 kPa Resin exchange tower adsorption capacity decline Heavy metal interception rate attenuation percentage relative to previous time 15%

[0053] The time interval range of adjacent identification items in this embodiment is as follows, and of course, it can also be self-calibrated through limited times of test statistics and corresponding process standards according to the corresponding phenomena in implementation,

[0054] Matching table of time interval range of adjacent identification items

[0055] Adjacent identified item Numerical range / min Influent pH abnormality~oxidizing agent residue overproof 2~10 Oxidizing agent residue overproof~tube ultrafiltration membrane flux attenuation 5~15 Calcium ion saturation abnormality~tube ultrafiltration membrane fouling overproof 10~35 Tube ultrafiltration membrane fouling overproof~resin exchange tower adsorption capacity decline 15~45

[0056] In the above table, the time interval range of the abnormal water inlet pH to the excessive oxidation agent residue is determined based on the Fenton reaction rate, and the time interval range of the two matching items of the abnormal calcium ion saturation to the excessive fouling of the tubular ultrafiltration membrane is determined based on the calcium carbonate crystal nucleus growth rate; the remaining adjacent identification items are determined based on the historical data of the wastewater treatment process.

[0057] Based on the above parameter range and time interval range configuration, the identification module determines that the operating parameter matches the identification item in response to the operating parameter being within the numerical range corresponding to the identification item, and determines the time interval of the identification item based on the trigger node of the matching of two adjacent identification items, and determines that the occurrence order of the identification item is consistent with the order of the corresponding causal chain in response to the time interval being within the corresponding time interval range.

[0058] Specifically, the quantification module determines the matching degree of the current operating parameter with the first causal chain and the second causal chain by comprehensively determining the parameter matching degree and the time matching degree, and specifically includes the following processes:

[0059] 1) For each matched identification item, generate a parameter matching degree according to the deviation of the current operating parameter value from the median value of the corresponding numerical range, including: determining the difference between the current operating parameter and the median value of its corresponding numerical range;

[0060] According to the preset deviation-matching degree mapping relationship, output the matching degree of the identification item;

[0061] Wherein the mapping relationship satisfies: when the difference value is zero, the matching degree is the maximum value 1, and when the difference value approaches the range boundary, the matching degree approaches zero.

[0062] 2) For each pair of sequentially matched adjacent identification items, generate a time matching degree of adjacent identification items according to the deviation of the actual trigger time interval from the median value of the corresponding time interval range;

[0063] determining the difference between the time interval of the trigger node of the two adjacent identified items matching and the middle value of the corresponding time interval range;

[0064] If the difference is within the allowable fluctuation zone (in this embodiment, 50% of the original time interval range centered on the middle value of the above time interval range), the time matching degree is determined as the maximum value 1, if the difference exceeds the allowable fluctuation zone but does not reach the boundary of the time interval range, the time matching degree decreases linearly, and if the difference exceeds the boundary of the time interval range, the time matching degree is zero.

[0065] 3) The single-item parameter matching degree of all identified items in the same causal chain and the time matching degree between adjacent identified items are weighted and fused to generate the matching degree of the running parameter and the causal chain.

[0066] In the process of generating the matching degree of the running parameter and the causal chain by the weighting and fusion of the single-item parameter matching degree of all identified items in the same causal chain and the time matching degree between adjacent identified items in 3), the first initial weight (in this embodiment, 0.62) is assigned to the first item parameter matching degree, and the second initial weight (in this embodiment, 0.38) is assigned to the first item time matching degree. The weight of the subsequent item parameter matching degree decreases based on the first initial weight, and the weight of the subsequent item time matching degree decreases based on the second initial weight, and the sum of the weights assigned to each item parameter matching degree and the sum of the weights assigned to each item time matching degree are both one.

[0067] After determining the matching degree, the quantification module adds the matching degrees of the two causal chains after weighting based on the weight coefficients of the causal chains (in this embodiment, the weight coefficient of the first causal chain is 0.7, and the weight coefficient of the second causal chain is 0.3) to determine the risk index. It should be understood that the weight coefficients of the matching degrees of the first causal chain and the second causal chain, as well as the first initial weight and the second initial weight, can be adjusted based on the number of occurrences of the identified items in the actual working condition. The higher the number of occurrences of the identified items, the higher the initial weight and the weight coefficient corresponding to it. The user can continuously calibrate and optimize according to the actual scene to make the execution decision conditions of the execution module match the actual scene well, so that the actions corresponding to the three degrees of range are more targeted and matched.

[0068] After the above-mentioned quantification module works, the execution module responds to the low (0-0.2), medium (0.2-0.5) or high (0.5-1) three degree ranges of the risk index respectively to execute the reverse flushing of the tubular ultrafiltration membrane, add the scale inhibitor to the precipitation link, or switch the tubular ultrafiltration membrane. In detail, when the risk index is low, the system detects the early signs of the pollution chain, but has not formed substantial harm. At this time, the purpose of executing the membrane reverse flushing is to timely remove the loose pollution deposition layer on the membrane surface through physical reverse flushing. This intervention can effectively interrupt the initial accumulation stage of the pollution chain, and avoid the development of the pollution from the reversible adsorption state to the irreversible fouling. The reverse flushing operation is based on the principle of minimizing intervention, prevents the continuous enrichment of micro-pollutants on the membrane surface while maintaining the continuous operation of the system. Its core advantage is to block the pollution process at the lowest energy cost, and keep the membrane flux stable. The medium risk index reflects that the pollution chain has entered the development stage, and the system identifies clear risk signals such as fouling tendency or accumulation of oxidation by-products. At this stage, the scale inhibitor is added to the precipitation link, mainly for chemical stabilization treatment of the supersaturated scale-forming ions in the solution. The scale inhibitor changes the crystal growth kinetics, interferes with the regular arrangement of microcrystals such as calcium carbonate, and makes it form loose and easy-to-peel amorphous structure. This measure intervenes in the pollution transmission process from the material transformation level, not only relieves the fouling pressure of the subsequent membrane unit, but also avoids the damage to the membrane by direct chemical cleaning. The essence is to establish a chemical barrier in the middle of the pollution chain through precise chemical intervention. The high risk index marks that the pollution chain has been completely activated, and the system detects a serious pollution situation of multi-stage linkage. At this time, switching the standby membrane group is the key measure to prevent system failure, and its core value is to realize process fault tolerance. By quickly isolating the contaminated membrane element, the diffusion path of the pollution to the subsequent resin adsorption and nanofiltration units is blocked. This response not only ensures the continuity of the treatment capacity, but also wins the chemical cleaning time window for the contaminated membrane group through physical isolation.

[0069] Obviously, the above embodiments of the present application are only examples for clearly illustrating the present application, and are not intended to limit the embodiments of the present application. Based on the above description, other different forms of changes or variations can be made by those skilled in the art. Here, all the embodiments are not required to be exhausted. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the claims of the present application.

Claims

1. A multi-stage dynamic quantitative control system for wastewater treatment based on result feedback, applied to the following wastewater treatment process: adjusting the pH of wastewater and oxidatively degrading organic matter through Fenton reaction, adding NaOH and Na2CO3 to the effluent after oxidation, generating CaCO3 and Mg(OH)2 precipitates, and performing interception on the precipitates through a tubular ultrafiltration membrane, and the waste liquid after interception is respectively adsorbed through an activated carbon and a resin exchange tower, characterized in that, The wastewater treatment multi-stage dynamic quantitative control system based on result feedback comprises: a storage module, which stores the following causal chains: a first causal chain, which comprises the following identification items in sequence: abnormal influent pH, excessive oxidation agent residue, and attenuation of tubular ultrafiltration membrane flux; a second causal chain, which comprises the following identification items in sequence: abnormal calcium ion saturation, excessive tubular ultrafiltration membrane fouling, and decreased resin exchange tower adsorption capacity; an identification module, which matches the current operating parameters with the identification items in the causal chains, and enables an energy quantization module to work in response to the number of matched identification items being not less than two and the order of the identification items being consistent with the order of the corresponding causal chain; a quantization module, which is respectively provided with weight coefficients of the first causal chain and the second causal chain, respectively determines the matching degrees of the current operating parameters and the first causal chain and the second causal chain, and generates a membrane fouling risk index based on the matching degrees and the weight coefficients; an execution module, which respectively performs tubular ultrafiltration membrane backflushing, adds a scale inhibitor to a sedimentation link, or switches the tubular ultrafiltration membrane in response to the risk index being in a low, medium, or high range.

2. The result feedback based multi-level dynamic quantification control system for wastewater treatment of claim 1, wherein, The storage module is respectively provided with a numerical range of a corresponding parameter for each identification item in the first causal chain and the second causal chain, and a time interval range of adjacent identification items; wherein, for the first causal chain, the specific numerical range of the corresponding parameter comprises: an influent pH value range, an oxidation-reduction potential range after the Fenton reaction is completed, and a range of the change rate of the transmembrane pressure difference of the tubular ultrafiltration membrane; for the second causal chain, the specific numerical range of the corresponding parameter comprises: a calcium ion saturation range, a transmembrane pressure difference range of the tubular ultrafiltration membrane, and an adsorption capacity range of the resin exchange tower.

3. The result feedback based multi-level dynamic quantification control system for wastewater treatment of claim 2, wherein, The identification module determines that the operating parameters match the identification items in response to the operating parameters being in the numerical range corresponding to the identification items, and determines the time interval of the identification items based on the trigger nodes of the matching of two adjacent identification items, and determines that the order of the identification items is consistent with the order of the corresponding causal chain in response to the time interval being in the corresponding time interval range.

4. The result feedback based multi-level dynamic quantification control system for wastewater treatment of claim 3, wherein, The quantization module determines the matching degrees of the current operating parameters and the first causal chain and the second causal chain by comprehensively determining the parameter matching degree and the time sequence matching degree, and specifically comprises the following processes: for each matched identification item, a parameter matching degree is generated according to the deviation degree of the current operating parameter value from the median value of the corresponding numerical range; for each pair of sequentially matched adjacent identification items, a time sequence matching degree of the adjacent identification items is generated according to the deviation degree of the actual trigger time interval from the median value of the corresponding time interval range; the single parameter matching degree of all identification items in the same causal chain and the time sequence matching degree between adjacent identification items are weighted and fused to generate the matching degree of the operating parameters and the causal chain.

5. The result feedback based multi-level dynamic quantification control system for wastewater treatment of claim 1, wherein, The weight coefficient of the second causal chain set by the quantization module is greater than the weight coefficient of the first causal chain.

6. The result feedback based multi-level dynamic quantification control system for wastewater treatment of claim 4, wherein, The determination process of the matching degree of the single parameter by the quantization module specifically comprises: determining the difference between the current operating parameter and the median value of the corresponding numerical range; outputting the matching degree of the identification item according to a preset deviation matching degree mapping relationship. The mapping relationship satisfies: the matching degree is the maximum value when the difference is zero, and the matching degree approaches zero when the difference approaches the range boundary.

7. The result feedback based multi-level dynamic quantification control system for wastewater treatment of claim 4, wherein, The determination process of the time sequence matching degree of the adjacent identified items by the quantification module specifically includes: determining the difference between the time interval of the trigger node of the matching of the two adjacent identified items and the corresponding time interval range median value; if the difference is within the allowable fluctuation zone, the time sequence matching degree is determined as the maximum value, if the difference exceeds the allowable fluctuation zone but does not reach the time interval range boundary, the time sequence matching degree is linearly decreased, and if the difference exceeds the time interval range boundary, the time sequence matching degree is zero.

8. The result feedback based multi-level dynamic quantification control system for wastewater treatment of claim 2, wherein, The quantification module determines the time interval range of the two matching items of the first causal chain based on the Fenton reaction rate, that the abnormal inflow pH leads to excessive oxidation agent residue, and based on the calcium carbonate crystal nucleus growth rate, that the abnormal calcium ion saturation leads to excessive fouling of the tubular ultrafiltration membrane.

9. The result feedback based multi-level dynamic quantification control system for wastewater treatment of claim 4, wherein, The quantification module weights and fuses the single-item parameter matching degrees of all identified items in the same causal chain and the time sequence matching degrees between adjacent identified items to generate the matching degree process of the operation parameter and the causal chain, and assigns a first initial weight to the first-item parameter matching degree, a second initial weight to the first-item time sequence matching degree, and a position sequence decreasing weight to the subsequent items based on the first initial weight and the second initial weight. The sum of the weights assigned to each parameter matching degree and the sum of the weights assigned to each time sequence matching degree are both one.

10. The result feedback based multi-level dynamic quantification control system for wastewater treatment of claim 1, wherein, The identification module records the operation parameters matched with the identified items.

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