Intelligent processing method and device for reclamation mud chemical additive

Abstract

The present application relates to the technical field of slurry treatment, in particular to an intelligent processing method and device for reclamation slurry chemical additives. The method comprises the following steps: S1. slurry input and pollution detection; S2. slurry pretreatment; S3. selection and preparation of peroxo organic acid; S4. material conveying; S5. oxidative degradation; S6. UV treatment; S7. detection and evaluation; S8. intelligent control system. The present application selects peroxo organic acid to remove reclamation slurry chemical additives, optimizes the use conditions and process parameters of peroxo organic acid, realizes efficient and rapid removal of chemical additives in reclamation slurry, avoids secondary pollution, and protects the marine ecological environment. In combination with a neural network system, multiple parameters in the processing process are modeled, predicted and feedback controlled, so that adaptive adjustment and dynamic optimization of the processing technology are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mud treatment, in particular to an intelligent treatment method and device for reclamation mud chemical additives. BACKGROUND

[0002] Reclamation projects often involve a large amount of filling work, in which dredged mud, waste mud and other materials can be used as filling materials. Mud often contains a large amount of chemical additives (such as dispersants, flocculants, and curing agents), which are used in the project to improve the fluidity, stability and curing performance of the mud, but their residues can cause potential harm to the marine ecological environment. At present, for the removal of chemical additives in reclamation mud, the existing technology mainly uses physical separation, chemical precipitation and biological degradation methods. The physical separation method usually involves the use of centrifuges, filters or settling tanks and other equipment to separate the solid particles in the mud from the liquid by physical force. Chemical precipitation technology relies on adding specific chemical reagents to the mud to promote the formation of insoluble precipitates of harmful chemical additives, thereby achieving removal. The biological degradation process uses the metabolic action of microorganisms to convert chemical additives into harmless or low-toxicity substances.

[0003] In recent years, peroxo organic acids (such as peroxyacetic acid and peroxyformic acid) have been gradually introduced into the field of pollutant degradation due to their strong oxidizing properties and environmental friendliness, and have been widely used in water treatment and soil remediation to degrade organic pollutants and emerging pollutants. In water treatment, peroxo organic acids can efficiently degrade organic pollutants such as dyes, pesticides, and pharmaceutical residues; in soil remediation, they also show good treatment effects on petroleum hydrocarbons and polycyclic aromatic hydrocarbons. Compared with traditional oxidizing agents, peroxo organic acids have higher oxidation potentials, can quickly degrade a variety of organic pollutants, and their decomposition products are mainly water and carbon dioxide, which do not cause secondary pollution. However, there is little research on the application of peroxo organic acids in the removal of chemical additives from reclamation mud in the existing technology. Therefore, the present application provides an efficient, environmentally friendly and economical method for treating waste mud, which can remove chemical additives and ensure the environmental safety of the mud during reclamation.

[0004] The existing research on sludge treatment technology has the following problems:

[0005] (1) By physical separation methods, such as using centrifuges or filtration equipment, a portion of suspended solid substances in water can be effectively removed. However, this method has limited effect on the removal of chemical additives dissolved in water. In addition, although biological degradation is an environmentally friendly treatment method that uses the action of microorganisms to decompose pollutants, this method has a relatively long treatment period and low treatment efficiency, so it is difficult to meet the demand for rapid treatment of reclamation mud in reclamation projects.

[0006] (2) Chemical oxidation has become one of the current research hotspots. In this field, strong oxidants such as hydrogen peroxide and potassium permanganate are widely used to degrade organic pollutants in water. Although these chemical oxidants show certain degradation capabilities under laboratory conditions, their oxidation capacity is often limited and their selectivity is poor in practical applications. In addition, these oxidants are not ideal for treating certain specific chemical auxiliaries, which limits their application in practical engineering.

[0007] (3) During the activation process of oxidants such as H2O2, PMS / PDS and PI, the pH of the solution, coexisting organic matter (NOM) and inorganic anions are easily affected, especially the toxic by-products that are easily generated during the reaction.

[0008] (4) PAA has been widely used as a disinfectant, bleaching agent, oxidant, and polymerization catalyst in food processing, aquaculture, medical, and textile industries. PAA can also effectively decompose sludge in complex processes, dissolving and destroying bacteria and extracellular polymers (EPS) in sludge flocs. In the field of water treatment, PAA can achieve a removal rate of over 95% for organic pollutants; in soil remediation, its degradation efficiency for petroleum hydrocarbon pollutants exceeds 90%. However, there is limited research on the application of peroxy organic acids in removing chemical additives from land reclamation mud.

[0009] (5) The mud composition is complex and highly variable, and the construction environment is highly variable. Traditional fixed parameter treatment methods are prone to unstable degradation efficiency, resulting in problems such as excessive addition and insufficient degradation.

[0010] (6) In land reclamation projects, the mud is usually treated off-site, that is, the mud is treated in a place far away from the reclamation area, and then the treated mud is transported to the reclamation area for land reclamation construction. However, this treatment method has a potential problem, that is, secondary pollution may occur during the transportation of the mud. This is because the mud may leak or drip during transportation, thereby polluting the environment along the transportation route. Summary of the Invention

[0011] This invention provides an intelligent treatment method and apparatus for chemical additives in land reclamation mud, aiming to solve the problems existing in current sludge treatment technologies.

[0012] This invention provides an intelligent treatment method for chemical additives in land reclamation mud, comprising the following steps:

[0013] S1. Mud Input and Pollution Detection: Extract mud samples to test their physicochemical properties and analyze the types and concentrations of chemical additives in the mud;

[0014] S2. Mud pretreatment: The mud is subjected to preliminary physical and chemical treatment, and the treated mud is transported to a storage tank for temporary storage;

[0015] S3. Selection and preparation of peroxy organic acid: according to the detection results of the types and concentration contents of chemical additives in the mud and the treatment target, the type of peroxy organic acid is selected, the concentration range of the peroxy organic acid is determined, and the peroxy organic acid solution of the corresponding concentration is prepared in a preparation box;

[0016] S4. Material delivery: the mud subjected to preliminary treatment is delivered to the reactor; the flow and pressure of the mud are monitored in real time during the delivery process, and the mud addition amount is controlled; the prepared peroxy organic acid solution is delivered to the reactor, the flow and pressure of the peroxy organic acid solution are monitored in real time during the delivery process, and the peroxy organic acid addition amount is controlled;

[0017] S5. Oxidation reaction degradation: the mud and the peroxy organic acid in the reactor are fully reacted under the control of light, stirring and temperature;

[0018] S6. UV treatment: after the oxidation reaction degradation is completed, the mud is subjected to disinfection treatment by the UV treatment system;

[0019] S7. Detection and evaluation: the mud subjected to UV treatment is transported to the storage tank for temporary storage, the concentration of pollutants in the mud sample is detected by using an ultraviolet spectrophotometer; the detection result is compared with the preset standard value to determine whether it meets the discharge standard;

[0020] S8. Intelligent control system: a convolutional neural network model is constructed, the convolutional neural network model is trained through the detection parameters, the optimal treatment parameters and the data of the treatment effect, and a pollutant degradation process prediction model is obtained; the detection parameters in S1 to S7 are input into the pollutant degradation process prediction model, and the corresponding treatment parameters in S1 to S7 are output.

[0021] As a further improvement of the present application, in the step S1, the process of detecting the physical and chemical properties of the mud sample includes: detecting the pH value, conductivity value or salinity, viscosity and specific gravity of the mud sample by a pH meter, conductivity meter, viscometer and specific gravity meter respectively; the process of analyzing the types and concentration contents of chemical additives in the mud includes: extracting the mud sample to obtain supernatant by centrifugation, filtering, identifying and quantifying the chemical additive pollutants therein by liquid chromatography-mass spectrometry, separating by a chromatographic column, collecting full scan mass spectrum signals and fragment spectrum information in positive and negative electrospray ion modes respectively, identifying the types by comparing the molecular formula, accurate molecular ion peak and characteristic fragments of known compounds in a chemical additive candidate database, and combining the retention time; comparing the peak area reflecting the concentration with the standard curve established by the corresponding different substance standard, and quantifying;

[0022] The detection parameters are input into a convolutional neural network model, a convolutional neural network structure is adopted, and the optimal processing parameter combination is predicted according to the input parameters; the input parameters include but are not limited to: mud pH value , mud conductivity , chemical additive concentration , mud viscosity , specific gravity , flow rate , UV-Vis spectral absorption feature vector , the above parameters form a multi-dimensional input vector , after feature extraction by a convolutional layer, input into a fully connected regression layer, output a set of optimal process parameters Y for controlling the processing flow, including but not limited to: PAA addition amount , stirring speed , reaction time , temperature setting , UV irradiation intensity or time ; a nonlinear mapping model is constructed:

[0023] ,

[0024] wherein is a convolutional neural network model, indicates model weight parameters.

[0025] As a further improvement of the present application, in S2, the preliminary physical treatment process of the mud includes: sequentially passing the mud through a multi-stage filtration system composed of a 10mm coarse grid, a 5mm medium-fine grid and a 1mm steel wire filter screen, the 10mm coarse grid first intercepts construction waste, plastic bags and large debris, the 5mm medium-fine grid intercepts large sand particles, shell fragments and small plant stems and leaves, and the 1mm steel wire filter screen removes coarse mud and sand;

[0026] After the physical treatment is completed, the chemical treatment process of the mud includes: first, adjusting the pH value of the mud to 6.5~8.5 using a pH regulator; second, adjusting the salinity of the mud to 1‰ ~ 10‰ according to the salinity detection result, and re-measuring the salinity until it meets the standard, reducing the salinity of the mud by calculating the water addition amount by the following formula, and diluting it in proportion by adding water;

[0027] ,

[0028] Increasing the salinity of the mud by calculating the electrolyte addition amount according to the target salinity, and slowly adding electrolyte powder under stirring until it is fully dissolved; the required electrolyte mass:

[0029] ,

[0030] wherein: Target salinity, g / L or ‰; Initial salinity, g / L or ‰; V is the volume of the slurry, m 3 p is the purity of the electrolyte, mass fraction 0~1.

[0031] As a further improvement of the present application, in S3, if the types of chemical additives in the slurry are nitrogen-containing, sulfur-containing chemical additives and partially refractory organic matter, the peroxygen organic acid is peroxyacetic acid (PAA); if the types of chemical additives in the slurry are phosphorus-containing, chlorine-containing chemical additives and partially complex compounds, the peroxygen organic acid is peroxyformic acid (PFA);

[0032] According to the molecular weight, active oxygen ratio and target reactant degradation efficiency of PAA and PFA, the following concentration conversion model is established: assuming that the target is to degrade a certain type of chemical additive in the unit volume of slurry to below the preset concentration threshold, the required PAA concentration is then under similar conditions, the required PFA concentration is:

[0033] ,

[0034] Among them: , Molecular weight of PAA and PFA respectively; , Active oxygen ratio; , Reaction rate constant with target pollutants.

[0035] As a further improvement of the present application, in S4, the target ratio of slurry and peroxygen organic acid is set in advance before material delivery, the ratio of peroxygen organic acid addition amount to slurry volume is 0.1%~1%, the slurry flow Q1 is obtained during material delivery, the peroxyacetic acid flow Q2 to be added is calculated according to the set target ratio, the actual flow Q3 of the current peroxygen organic acid is obtained through the flow meter, and it is compared with the target flow Q2 in real time; if there is a deviation between the two, the output flow of peroxygen organic acid is dynamically adjusted, so that the actual flow Q3 is gradually adjusted to the target flow Q2; during the delivery process, the liquid level change in the reactor is continuously monitored, and when the liquid level reaches the set upper limit, the delivery of slurry and peroxygen organic acid is suspended.

[0036] As a further improvement of the present application, in the S5, the light irradiation control mode uses sunlight to irradiate the reaction solution; the stirring control mode uses a variable frequency motor to drive double-layer paddles to stir the reaction solution; the temperature is controlled at 25-40℃; the reactor is provided with a pH sensor and an ORP sensor to obtain the pH value and ORP value of the reaction solution, if the pH value of the reaction solution is too acidic or too alkaline, the addition amount of the peroxygen organic acid is reduced or increased, if the ORP value of the reaction solution decreases significantly, the addition amount of the peroxygen organic acid is increased or the reaction time is prolonged, if the ORP value tends to be stable, the reaction is completed, the addition of the peroxygen organic acid is stopped and the UV treatment process is switched.

[0037] As a further improvement of the present application, in the S6, the slurry is transported from the reactor to the UV treatment system, and during the transportation process, the flow rate and flow of the slurry are controlled to ensure uniform flow of the slurry in the reaction cavity, so as to ensure that the slurry stays in the reaction cavity of the UV treatment system for a specified time;

[0038] According to the prediction results of the convolutional neural network model on the slurry pollutant concentration and ultraviolet transmittance parameters, the system automatically calculates the required ultraviolet dose, and adjusts the proportional relationship between the ultraviolet light intensity and the slurry flow rate accordingly; the ultraviolet dose satisfies the following relationship:

[0039] ,

[0040] Wherein: I is the ultraviolet light intensity (mW / cm 2 ); t is the irradiation time (s); V is the treatment volume; P is the total power of the UV lamp (mJ); Q is the instantaneous flow of the slurry (m 3 / s).

[0041] As a further improvement of the present application, in the S7, the judgment result of whether it meets the emission standard includes:

[0042] Unqualified case: if the pollutant concentration exceeds the standard value, it is determined to be unqualified, the slurry is re-transported to the reactor for further treatment, and the degradation is further carried out by adjusting the parameters of the addition amount of the peroxygen organic acid, the reaction time or the light intensity;

[0043] Qualified case: if the pollutant concentration is lower than the standard value, it is determined to be qualified, and according to the different degradation effects, it is divided into the following two cases: 1) best degradation effect: the pollutant concentration is significantly lower than the standard value, the slurry is directly used for reclamation or other resource utilization approaches; 2) suboptimal degradation effect: the pollutant concentration is lower than the standard value but does not reach the best level, the slurry is used for reclamation;

[0044] The detection results, evaluation conclusions and process adjustment records are archived to generate a treatment effect report and input into the pollutant degradation process prediction model.

[0045] As a further improvement of the present application, in the S8, the construction process of the pollutant degradation process prediction model further comprises:

[0046] The mud sample data is obtained through experiments, including reaction conditions and degradation effects under different concentrations of chemical additives; a convolutional neural network is used to establish a nonlinear mapping relationship between mud parameters, treatment parameters and degradation efficiency; the network uses supervised learning, and the training sample takes "mud characteristic parameters + treatment settings" as input and "degradation effect" as output, and the output variable is degradation efficiency , and the efficiency and input-output parameters form a nonlinear multivariate regression relationship; the supervised learning model is constructed as follows:

[0047] ,

[0048] Where: X is the physical and chemical characteristics of the mud; Y is the process setting parameter; is a high-dimensional nonlinear function modeled by a convolutional neural network; is an error term;

[0049] In the model training process, the mean square error is used as the loss function:

[0050] ,

[0051] Where, i represents the training sample number; the model weight is updated by the back propagation algorithm to minimize the difference between the predicted value and the true degradation efficiency .

[0052] The present application also provides an intelligent treatment device for reclamation mud chemical additives, which is used to execute the intelligent treatment method for reclamation mud chemical additives, comprising a mud filtration unit, a chemical adjustment unit, a storage unit for storing pH adjuster or salinity adjuster, a mud storage unit, a reactor, a dispensing unit for storing peroxo organic acid, a UV treatment unit, and a mud detection unit. The feed inlet of the mud filtration unit, the chemical adjustment unit, the mud storage unit, and the reactor are connected in sequence. The storage unit is connected to the chemical adjustment unit. The dispensing unit is connected to the feed inlet of the reactor. The discharge outlet of the reactor, the UV treatment unit, and the mud detection unit are connected in sequence. The mud to be treated is input from the water inlet of the mud filtration unit and transported into the reactor. The peroxo organic acid is transported into the reactor through the dispensing unit. The treated mud is output from the discharge outlet of the reactor and discharged from the water outlet of the mud detection unit.

[0053] The present application has the following advantages:

[0054] (1) The peroxo organic acid (such as peroxyacetic acid, peroxyformic acid) is selected by the application to remove the reclamation mud chemical additive due to its strong oxidizing property and environmental friendliness. For example, peroxyacetic acid (PAA) is a new emerging oxidant, has O-O bond, strong molecular polarity, easy to break, and strong oxidizing property.

[0055] (2) After activation, special organic free radicals such as hydroxyl radical, acetoxy radical (CH3C(O)O·), peroxyacetyl radical (CH3C(O)OO·) and peroxyalkyl radical (·OOCH3) can be generated, which has good anti-interference property and no toxic by-products, and solves the problems of insufficient oxidation capacity, poor selectivity and possible harmful by-products in the prior art.

[0056] (3) By optimizing the use conditions and process parameters of the peroxo organic acid, the chemical additive in the reclamation mud can be efficiently and quickly removed, and secondary pollution can be avoided, and the marine ecological environment can be protected.

[0057] (4) Compared with other traditional disinfectants, the peroxo organic acid is easier to implement than ozone and more economical than ultraviolet rays.

[0058] (5) The application combines a neural network system to model, predict and feedback control multiple parameters in the processing process, so as to realize adaptive adjustment and dynamic optimization of the processing technology.

[0059] (6) The application provides an in-situ treatment system, which reduces the secondary pollution problem that may be caused by the traditional ex-situ treatment method, and reduces the transportation time and cost. The mud is directly treated in the reclamation area, which avoids the environmental and economic burden that may be caused by the transported mud. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 is a process flow chart of the intelligent treatment method for the reclamation mud chemical additive of the application;

[0061] Figure 2 is a structural schematic diagram of the intelligent treatment device for the reclamation mud chemical additive of the application. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical scheme and advantages of the application more clear, the application is further described in detail below with reference to the drawings and examples.

[0063] An intelligent treatment method for reclamation mud chemical additives of the application includes the following steps:

[0064] S1. Mud input and pollution detection: draw mud samples to detect their physical and chemical properties, and analyze the types and concentration contents of chemical additives in the mud.

[0065] Before the mud enters the treatment process, it needs to be detected and analyzed for pollutants to ensure the effectiveness of the subsequent treatment process. First, the physical and chemical properties of the mud sample are detected, and the basic parameters such as pH value, conductivity value or salinity, mud viscosity, and specific gravity of the mud are detected by pH meter, conductivity meter, viscometer, and specific gravity meter, etc. Second, the types and contents of chemical additives in the mud are analyzed. The mud sample is centrifuged to obtain the supernatant, filtered, and the chemical additive pollutants are identified and quantified by liquid chromatography-mass spectrometry. After separation by a reversed-phase C18 chromatographic column, the full-scan mass spectrum signal and fragment spectrum information are collected under positive and negative electrospray ion modes, respectively. By comparing the molecular formula, accurate molecular ion peak, and characteristic fragments of known compounds in the chemical additive candidate database, combined with the retention time, the type is identified. The peak area reflecting the concentration is compared with the standard curve established by the corresponding different substance standard to quantify.

[0066] Automatic monitoring and control: input the detection data into the neural network model, the neural network adopts a convolutional neural network structure, and the optimal processing parameter combination is predicted according to the input data. The input parameters include but are not limited to: mud pH value , mud conductivity , chemical additive concentration , mud viscosity , specific gravity , flow rate , UV-Vis spectral absorption feature vector . The above parameters form a multi-dimensional input vector , which is input to the fully connected regression layer after feature extraction by the convolutional layer, and outputs a set of optimal process parameters Y for controlling the treatment process. Including but not limited to: PAA addition amount , stirring speed , reaction time , temperature setting , UV irradiation intensity or time . A nonlinear mapping model is constructed:

[0067]

[0068] wherein is a convolutional neural network model, represents the model weight parameters.

[0069] The model is trained based on the principle of supervised learning, using mean square error (MSE) as the loss function, and continuously adjusting the weight values in the network structure through the back propagation algorithm to continuously improve the prediction accuracy between input and output. The optimal parameters predicted by the system will be transmitted to the execution unit through the control module to automatically adjust the operating state of the dosing system, transfer pump, stirring equipment and UV irradiation system, realizing online optimization control of the degradation process. At the same time, the system feeds back the actual treatment results to the neural network model to form a closed-loop learning mechanism, continuously improving the process adaptability and stability of the treatment effect.

[0070] S2. Mud pretreatment: The mud is subjected to preliminary physical and chemical treatment, and the treated mud is transported to a storage tank for temporary storage.

[0071] Before the mud enters the core treatment link, it needs to be subjected to preliminary physical and chemical treatment to remove interfering substances and optimize the properties of the mud, ensuring efficient operation of the subsequent treatment process.

[0072] Physical treatment stage: First, the mud is passed through a multi-stage filtration system composed of a 10mm coarse grid (first intercepts construction waste, plastic bags, large debris, etc.), a 5mm medium-fine grid (intercepts large sand particles, shell fragments, small plant stems and leaves, etc.), and a 1mm steel wire mesh (removes coarse mud and sand, improves the uniformity of the suspended mud). The filtered mud has significantly improved flowability, reducing the risk of wear and blockage of subsequent treatment equipment. Solid waste generated during the filtration process needs to be collected and properly disposed of to avoid secondary pollution.

[0073] Chemical adjustment stage: After physical treatment, the mud is adjusted in terms of chemical properties. First, a pH adjuster (such as sodium hydroxide, lime milk or sulfuric acid) is used to adjust the pH of the mud to a range suitable for subsequent oxidation reactions (usually neutral or weakly alkaline, pH 6.5~8.5). Optimizing the pH value not only helps to improve the reaction efficiency of the oxidizing agent, but also reduces the generation of harmful gases. Second, based on the salinity detection results of the mud, the salinity is adjusted to the required value (generally controlled at 1‰~10‰), and then re-measured to ensure that the ionic strength of the mud does not inhibit the subsequent oxidation reaction. The amount of water added to reduce the salinity of the mud is calculated by the following formula, and the water is added in proportion to dilute it;

[0074]

[0075] Increasing the salinity of the mud is done by adding electrolytes, adding electrolyte powder in proportion to the target salinity, and slowly adding the electrolyte powder under stirring to fully dissolve it. The required electrolyte mass:

[0076]

[0077] wherein: is the target salinity, g / L or ‰; is the initial salinity, g / L or ‰; V is the mud volume, m 3 ; p is the purity of electrolyte, mass fraction 0~1.

[0078] After the treatment is completed, the mud is transported through a high-pressure pump and a transportation pipeline to a dedicated 10 m 3 storage tank for temporary storage. The storage tank must have anti-corrosion, anti-leakage and stirring functions to prevent mud from settling.

[0079] Automatic monitoring and control: The pH and salinity changes of the mud are monitored in real time through a pH sensor and a conductivity meter. The detection data are input into a neural network model, and the system transmits the data to a control module to automatically adjust the dosing, transportation, stirring and UV irradiation subsystems.

[0080] S3. Selection and preparation of peroxy organic acids: According to the detection results of the types and concentrations of chemical additives in the mud and the treatment target, a suitable peroxy organic acid is selected, and the concentration range of the peroxy organic acid is determined. The corresponding concentration of the peroxy organic acid solution is prepared in a preparation tank.

[0081] According to the detection results of the types and concentrations of chemical additives in the mud and the treatment target, a suitable peroxy organic acid is selected, and its concentration range (usually 0.1%-5%) is determined. A solution of a certain concentration is prepared in a preparation tank. The preparation tank is made of corrosion-resistant materials (such as stainless steel or polytetrafluoroethylene) and is equipped with stirring devices, temperature control and safety protection facilities.

[0082] Peroxyacetic acid (PAA): suitable for degrading nitrogen-containing, sulfur-containing chemical additives and some refractory organic matter. Prepared with hydrogen peroxide (H2O2, concentration usually 30%-50%) and glacial acetic acid (CH3COOH). Mix hydrogen peroxide and glacial acetic acid in a certain proportion (usually 1:1 to 1:2, volume ratio), add a small amount of sulfuric acid (H2SO4, about 1% volume ratio) as a catalyst, stir at room temperature for 2-4 hours to generate peroxyacetic acid solution. After the reaction is completed, the concentration of peroxyacetic acid in the solution is detected, and it is diluted to the target concentration as needed.

[0083] Percarbonate (PFA): has good oxidation effect on phosphorus-containing, chlorine-containing chemical additives and some complexes. Usually prepared with hydrogen peroxide (H2O2, concentration usually 30%-50%) and formic acid (HCOOH). Mix hydrogen peroxide and formic acid in a certain proportion (usually 1:1 to 1:2, volume ratio), add a small amount of sulfuric acid (H2SO4, about 1% volume ratio) as a catalyst, stir at room temperature for 2-4 hours to generate peroxyformic acid solution. After the reaction is completed, the concentration of peroxyformic acid in the solution is detected, and it is diluted to the target concentration as needed.

[0084] Both peracetic acid (PAA) and performic acid (PFA) are strong oxidants, providing active oxygen, and theoretically have oxidative degradation effects on multiple types of chemical additives under sufficient dosage and reaction conditions. PAA mainly acts through hydroxyl and carbon-centered radicals, and is more efficient in degrading nitrogen-containing and sulfur-containing additives, because these groups are easily broken by the peroxide bond of peracetic acid and attacked by free radicals. Therefore, for nitrogen-containing, sulfur-containing chemical additives and some difficult-to-degrade organic matter, peroxo organic acid is preferably peracetic acid (PAA). PFA mainly acts through hydroxyl and HCOOOH radicals, and has more advantages in degrading phosphorus-containing and chlorine-containing additives, because its oxidation potential is higher than that of PAA, and it can better break C-P and C-Cl bonds. Therefore, for phosphorus-containing, chlorine-containing chemical additives and some complexes, peroxo organic acid is preferably performic acid (PFA).

[0085] According to the molecular weights, active oxygen ratios, and degradation efficiencies of target reactants of PAA and PFA, the following empirical concentration conversion model is established: assuming that the target is to degrade a certain type of chemical additive (based on the same chemical additive) in a unit volume (1 m 3 ) of slurry to below a pre-set concentration threshold, the required PAA concentration is , then under similar conditions, the required PFA concentration is:

[0086]

[0087] wherein: , are the molecular weights of PAA and PFA, respectively; , are the active oxygen ratios of PAA and PFA, respectively; , are the reaction rate constants of PAA and PFA for the corresponding target pollutants. For example, in actual measurement, for the SDS and PAM additive systems commonly contained in reclamation slurry, if the addition concentration of PAA is 0.30% (mass / volume fraction), the recommended concentration of PFA is about 0.22%~0.25% to achieve similar degradation efficiency.

[0088] Automatic monitoring and control: the system is equipped with an automatic dosing module, which automatically selects a more suitable oxidant type and addition concentration range according to the pollutant type and concentration predicted by the front-end neural network, automatically completes the selection and quantitative preparation of peroxo organic acid after receiving the control instruction, and monitors and feedback controls in real time through the concentration sensor and flow meter to ensure stable reagent concentration and safe reaction. During the treatment process, if the concentration or composition of the slurry pollutants changes, the system can automatically adjust the formula to improve the treatment adaptability and dynamic response capability.

[0089] S4. Material delivery: Deliver the preliminarily treated slurry to the reactor 5; monitor the flow rate and pressure of the slurry in real time during the delivery process, and control the slurry addition amount; deliver the prepared peroxo organic acid solution to the reactor 5, monitor the flow rate and pressure of the peroxo organic acid solution in real time during the delivery process, and control the peroxo organic acid addition amount.

[0090] Slurry delivery: Pump the preliminarily treated slurry to the reactor 5 through a centrifugal pump and a corrosion-resistant pipeline system. During the delivery process, sensors are equipped to monitor the flow rate and pressure of the slurry in real time, ensuring its stability and continuity. Monitoring the slurry pressure can timely detect the risk of blockage caused by high viscosity or solid particle deposition of the slurry, thereby avoiding pipeline rupture or pump body damage caused by abnormally high local pressure.

[0091] Peroxo organic acid delivery: Deliver the prepared peroxo organic acid solution to the reactor 5 through a metering pump, equipped with sensors to monitor the flow rate and pressure of the slurry in real time. The pipeline and pump body need to be made of corrosion-resistant materials (such as stainless steel or polytetrafluoroethylene) to prevent corrosion by peroxo organic acid. During the delivery process, the addition amount of peroxo organic acid is monitored and accurately controlled in real time by a flow meter.

[0092] Automatic monitoring and control: According to the predicted optimal dosage, through the automatic control system, the linkage control of slurry and peroxo organic acid delivery is realized to ensure that both enter the reactor 5 synchronously according to the preset ratio, avoiding the influence on the treatment effect caused by the ratio imbalance; the ratio of peroxo organic acid addition amount to slurry volume is about 0.1%~1% (v / v), and the common range of the mixing ratio of peroxoformic acid and peroxoacetic acid is PAA : PFA = 3:1 ~ 1:3 (volume ratio). The PLC-based automatic control system is adopted to collect the instantaneous flow signals of the slurry and the medicament in real time by a flow meter, and the running frequency of the two frequency conversion pumps is adjusted by a proportional control algorithm to accurately control the feeding ratio of the two fluids. After the system is started, the PLC automatically reads the slurry flow rate (Q1), and calculates the peroxoacetic acid flow rate (Q2) to be added according to the set target ratio. Then, the control system obtains the current actual flow rate (Q3) of the medicament by a flow meter, and compares it with the target value in real time. If there is a deviation, the output frequency of the medicament pump is dynamically adjusted to make Q3 gradually approach Q2, realizing closed-loop control to ensure the accuracy and stability of the treatment ratio. During the operation, the system continuously monitors the liquid level change in the reaction tank. When the liquid level reaches the set upper limit (such as 95%), the controller will automatically suspend the operation of the two pumps to prevent overflow accidents.

[0093] S5. Oxidative reaction degradation: The slurry and peroxo organic acid in the reactor 5 are fully reacted under the control of light, stirring, and temperature.

[0094] Oxidative degradation is the core step of the slurry treatment process. Through the synergistic effect of light, stirring, and temperature control, the peroxo organic acid and the pollutants in the slurry are ensured to fully react, realizing efficient degradation of pollutants.

[0095] The role of light conditions: In the reactor 5, sunlight is used to irradiate the reaction solution to excite peroxo organic acid to generate highly active free radicals (such as hydroxyl radical OH· and carbon-centered radical R-C·). These free radicals have extremely strong oxidizing ability and can rapidly decompose organic pollutants in the slurry, converting them into low-toxic or non-toxic small molecular substances (such as carbon dioxide, water, and oxygen).

[0096] Continuous stirring of the stirrer: To ensure that the peroxo organic acid in the reaction solution fully contacts the pollutants, the reactor 5 is equipped with a high-efficiency stirrer composed of a variable frequency motor (adjustable speed, 100~600 rpm) and a three-blade inclined paddle plus a turbine type double paddle (the upper inclined blade propeller stirs the liquid to form an axial flow, and the lower turbine paddle strongly shears to disperse particles). The stirrer uniformly mixes the solution through continuous stirring, avoiding local high or low concentrations, thereby improving the reaction efficiency. The stirring speed needs to be adjusted according to the viscosity of the solution and the volume of the reactor 5 to ensure the mixing effect while avoiding excessive shearing that leads to decomposition of peroxo organic acid.

[0097] Adjustment of the temperature control system: The reactor 5 is equipped with a temperature control system to accurately adjust the temperature of the reaction solution, maintaining the reaction temperature within the optimal range (usually 25~40℃). High temperature may lead to decomposition of peroxo organic acid or increase in side reactions, while low temperature will reduce the reaction rate. The temperature control system is linked with the temperature sensor to monitor and automatically adjust the temperature in real time, ensuring the stability of the reaction conditions.

[0098] Automatic monitoring and control: Install pH and ORP sensors in reactor 5, transmit analog signals to neural network system, signals are recorded in real time after digitalization, realize real-time monitoring of reaction process. The change of pH value can be used to judge whether the reaction system is too acidic or too alkaline, and the amount of reagent is adjusted in time to prevent excessive reaction or drug efficacy decline; ORP value reflects the oxidation ability of the system, when ORP maintains in the set range (usually 650~800 mV), it means that the system has good oxidation performance, if ORP decreases significantly and pollutant concentration does not meet the standard, reagent dosage should be increased or reaction time should be prolonged in time. In addition, through sampling port, the trend of pollutant concentration is detected. Regular monitoring of pollutant concentration is used to evaluate the progress of the reaction, when the concentration decreases significantly and ORP value gradually approaches stability, it means that the reaction is basically completed, and it can be considered to stop dosing or switch to the next processing stage. Through data recording and trend analysis, reagent dosage rate, stirring intensity and reaction time and other parameters can be further optimized to improve processing efficiency, reduce resource waste and ensure long-term stable operation of the system.

[0099] S6. UV treatment: After the completion of oxidation reaction degradation, the slurry is treated by UV treatment system for disinfection.

[0100] After the completion of oxidation reaction degradation, the slurry is further treated by UV treatment system for disinfection to completely kill pathogenic microorganisms and degrade residual organic pollutants.

[0101] Composition of UV treatment system: The UV treatment system mainly consists of UV lamp tube 71, quartz sleeve 72, UV treatment cavity 73, control system and monitoring equipment, etc. The UV light emitted by the UV lamp tube 71 has a wavelength of 254 nm, with high sterilization and degradation capacity. The quartz sleeve 72 is used to protect the UV lamp tube 71 from direct contact with the slurry, while ensuring the effective penetration of ultraviolet light. The UV treatment cavity 73 is designed as a closed structure to prevent ultraviolet light leakage and improve treatment efficiency.

[0102] Slurry transportation and treatment: The slurry is transported from the reactor 5 to the UV treatment system through corrosion-resistant pipeline. During transportation, the flow rate and flow of the slurry need to be controlled to ensure uniform flow in the reaction cavity, so as to ensure sufficient residence time (usually 10~30 seconds) of the slurry in the UV reaction cavity.

[0103] According to the prediction results of the convolutional neural network model on the parameters such as slurry pollutant concentration, ultraviolet transmittance (UVT), etc., the system automatically calculates the required ultraviolet dose (UV Dose), and adjusts the proportional relationship between ultraviolet intensity and slurry flow rate accordingly. The ultraviolet dose satisfies the following relationship:

[0104]

[0105] Where: I is the UV light intensity (mW / cm2); t is the irradiation time (s); V is the treatment volume; P is the total UV lamp power (mJ); Q is the instantaneous slurry flow rate (m / s). 2 3

[0106] Automatic monitoring and control: The PLC control system receives neural network prediction data, adjusts the UV light intensity and slurry flow rate in real time, and ensures stable treatment effect. During system operation, the UV sensor detects the ultraviolet absorption value (UVT) in real time. According to the sensor feedback, the PLC control system dynamically adjusts the working power (or the number of lamp tubes) of the UV lamp to keep the UV dose in the reactor 5 within the target range. At the same time, the system is also equipped with a flow meter and a variable frequency pump to automatically adjust the slurry flow rate. When the organic load of the incoming slurry increases or the light transmittance decreases, the system will automatically increase the lamp tube output intensity, extend the irradiation time, or reduce the slurry flow rate; when the organic load of the incoming slurry decreases or the light transmittance increases, the system will automatically weaken the lamp tube output intensity, shorten the irradiation time, or increase the slurry flow rate.

[0107] S7. Detection and evaluation: The UV-treated slurry is transported to a storage tank for temporary storage, and the concentration of pollutants in the slurry sample is detected using an ultraviolet spectrophotometer; the detection results are compared with the preset standard value to determine whether they meet the discharge standard.

[0108] Slurry storage and sampling: The treated slurry is transported to a dedicated storage tank for temporary storage. The storage tank needs to have corrosion resistance, leakage prevention and stirring functions to prevent slurry sedimentation or property changes. Regularly extract slurry samples through the sampling port on the storage tank to ensure representative sampling.

[0109] Pollutant detection: The concentration of pollutants in the slurry sample is detected using an ultraviolet spectrophotometer (UV-Vis). The ultraviolet spectrophotometer can quickly and accurately determine the characteristic absorption peak of organic pollutants and calculate their concentration through the standard curve method.

[0110] Evaluation and judgment: Compare the detection results with the preset standard value:

[0111] Unqualified condition: If the concentration of pollutants exceeds the standard value, it is judged as unqualified. At this time, the slurry needs to be transported to the reactor 5 for re-treatment by adjusting the dosage of peroxo organic acid, reaction time or light intensity, etc. to further improve the degradation effect.

[0112] ​​Qualified situation: if the pollutant concentration is lower than the standard value, it is determined to be qualified. According to the different degradation effects, it is divided into the following two cases: optimal degradation effect: the pollutant concentration is significantly lower than the standard value, and the slurry can be directly used for reclamation or other resource utilization approaches. Suboptimal degradation effect: the pollutant concentration is close to the standard value but does not reach the optimal level, and the slurry can still be used for reclamation.

[0113] Neural network feedback: the detection results, evaluation conclusions and process adjustment records are archived, and a treatment effect report is generated. The convolutional neural network model optimization module collects and analyzes the pollutant data and operating parameters before and after treatment, and establishes a prediction model of the pollutant degradation process. The model can automatically identify the nonlinear relationship between different slurry characteristics and process parameters, predict the treatment effect in real time, and adjust key parameters such as reagent addition, ultraviolet irradiation intensity and flow rate based on the prediction results. With the continuous accumulation of operating data, the system model will be continuously updated and optimized to improve the prediction accuracy and process adaptability. In addition, the pollutant detection results (output) after each treatment are compared with the model prediction parameters (input), and the neural network weights are adjusted through the back propagation mechanism to improve the prediction accuracy of subsequent processes.

[0114] S8. Intelligent control system: a convolutional neural network model is constructed, the convolutional neural network model is trained through the detection parameters, optimal treatment parameters and treatment effect data, and a pollutant degradation process prediction model is obtained; the detection parameters in S1 to S7 are input into the pollutant degradation process prediction model, and the corresponding treatment parameters in S1 to S7 are output.

[0115] In the present application, a PLC (programmable logic controller) system and a convolutional neural network (CNN) model cooperatively construct an intelligent automatic control system for precisely regulating the process of adding chemical additives in peracetic acid in-situ treatment of slurry. The PLC system is responsible for real-time control of field devices, including data acquisition and execution logic of slurry conveying pump, peracetic acid dosing pump, stirrer, UV irradiation system, temperature control module, and flow, pressure, pH, ORP, etc. sensors. The system ensures the stability and safety of device operation, and realizes standardized automatic control of the treatment process.

[0116] The convolutional neural network (CNN) model is integrated in the edge computing unit, mainly used for processing a large amount of real-time data collected by sensors (such as UV-Vis spectrum, image features, historical operation parameters, etc.), automatically identifying the nonlinear relationship between slurry pollutant characteristics and treatment effect. By comparing and learning with sample data in the historical database, CNN can dynamically predict the treatment effect under the current working condition and give suggestions on optimal dosing ratio, stirring rate, reaction time, etc.

[0117] The information interaction between the PLC system and the CNN is realized: the optimized control parameters output by the CNN are input as the set values of the PLC, and the PLC automatically adjusts the operation strategy of the actuator accordingly; at the same time, the PLC transmits the real-time running state and detection data in the field to the CNN model for continuous learning and parameter correction.

[0118] Data acquisition and modeling: initially, sample data of mud are obtained through experiments, including reaction conditions and degradation effects under different concentrations of chemical additives; a neural network is used to establish a nonlinear mapping relationship between mud parameters, treatment parameters and degradation efficiency; the network uses supervised learning, and the training sample takes “mud characteristic parameters + treatment settings” as input and “degradation effect” as output. The output variable is degradation efficiency (η) ), which constitutes a nonlinear multivariate regression relationship with input-output parameters. The supervised learning model is constructed as follows:

[0119]

[0120] Where: X is the physical and chemical characteristics of the mud; Y is the process setting parameter; is a high-dimensional nonlinear function modeled by a convolutional neural network (CNN); is an error term.

[0121] In the model training process, mean square error (MSE) is used as the loss function:

[0122]

[0123] Where, i represents the training sample number; the model weight is constantly updated through the back propagation algorithm to minimize the difference between the predicted value and the true degradation efficiency .

[0124] Online prediction and control: ① Sensing and monitoring module: before the mud on the construction site enters the treatment system, the sensor obtains the current mud parameters, such as pH, ORP, conductivity, temperature, flow rate, type and concentration of chemical additives, etc. ② Data preprocessing module: filter, standardize and extract features from the collected data. ③ Neural network prediction module: input the data into the neural network model, and use the convolutional neural network structure to predict the optimal treatment parameters (including PAA addition amount, stirring speed, reaction time, temperature setting, etc.); the system transmits the prediction results to the control module to automatically adjust the dosing, transmission, stirring and UV irradiation subsystems to ensure the best degradation efficiency. ④ Process control module: automatically adjust the running state of the treatment device according to the output results of the neural network, such as controlling the dosing amount of the metering pump, stirring intensity, UV irradiation power, etc.

[0125] Dynamic learning and optimization: ① Learning update module: compare the pollutant detection results (output) after each processing with the model prediction parameters (input), adjust the neural network weight through the back propagation mechanism, and improve the subsequent prediction accuracy. The system periodically collects the pollutant concentration of the treated mud; the actual detection results are used to correct the prediction error, automatically optimize the neural network structure and weight; as the number of processing batches increases, the system gradually forms an "experience-based self-learning model" to adapt to changes in different working conditions and mud components. ② Data recording and trend analysis module: used for long-term operation data storage, trend analysis and abnormal alarm.

[0126] The application provides an intelligent processing device for reclamation mud chemical additives, which is used for executing an intelligent processing method for reclamation mud chemical additives. The device comprises a mud filtering unit 1, a chemical adjusting unit 2, a medicine storage unit 3 for storing pH adjusters or salinity adjusters, a mud storage unit 4, a reactor 5, a medicine dispensing unit 6 for storing peroxo organic acids, a UV processing unit 7, and a mud detection unit 8. The feed inlet of the mud filtering unit 1, the chemical adjusting unit 2, and the mud storage unit 4 are sequentially connected. The medicine storage unit 3 is connected with the chemical adjusting unit 2. The medicine dispensing unit 6 is connected with the feed inlet of the reactor 5. The discharge outlet of the reactor 5, the UV processing unit 7, and the mud detection unit 8 are sequentially connected. The untreated mud is input from the water inlet 13 of the mud filtering unit 1 and delivered into the reactor 5. The peroxo organic acids are delivered into the reactor 5 through the medicine dispensing unit 6. The treated mud is output from the discharge outlet of the reactor 5 and delivered to the water outlet 82 of the mud detection unit 8.

[0127] Mud delivery: The mud after preliminary treatment is pumped to the reactor 5 through the centrifugal pump and the corrosion-resistant pipeline, and sequentially subjected to physical filtration in the mud filtering unit 1 and chemical treatment in the chemical adjusting unit 2. The medicine storage unit 3 provides the pH adjusters or salinity adjusters for adjusting the pH value and salinity of the mud in the chemical adjusting unit 2 according to the needs of the chemical treatment process. During the delivery process, sensors are arranged to monitor the flow and pressure of the mud in real time, so as to ensure the stability and continuity of the mud.

[0128] Perox organic acid delivery: The peroxo organic acids are prepared in the medicine dispensing unit 6, and the prepared peroxo organic acid solution is delivered into the reactor 5 through the metering pump. Sensors are arranged to monitor the flow and pressure of the mud in real time. The pipeline and pump body need to be made of corrosion-resistant materials (such as stainless steel or polytetrafluoroethylene) to prevent corrosion of the peroxo organic acids. During the delivery process, the flow meter is used to monitor and accurately control the addition amount of the peroxo organic acids in real time.

[0129] Transportation of treated sludge: After the sludge in the reactor 5 is treated by the peroxide organic acid, it is pumped to the UV treatment unit 7 for further disinfection treatment to completely kill pathogenic microorganisms therein and degrade residual organic pollutants. The disinfected sludge is pumped to the sludge detection unit 8 for temporary storage, and sampling detection is performed on the sludge. When the detection is qualified, the sludge is discharged from the water outlet 82 of the sludge detection unit 8, so that the discharged sludge meets the discharge standard.

[0130] Before the sludge enters the core treatment link, it needs to be subjected to preliminary physical and chemical treatment to remove interfering substances and optimize the properties of the sludge, so as to ensure the efficient operation of the subsequent treatment process. The physical treatment stage is performed by the filtration unit, and the chemical adjustment stage is performed by the chemical adjustment unit 2.

[0131] The sludge filtration unit 1 includes a filter tank 11 and a multi-stage filtration assembly 12, and the multi-stage filtration assembly 12 includes a coarse grid, a medium grid, and a steel wire screen, which are sequentially installed in the filter tank 11 along the liquid flow direction. The sludge passes through the multi-stage filtration assembly 12 composed of a 10mm coarse grid (first intercepts construction waste, plastic bags, and large debris), a 5mm medium grid (intercepts large sand particles, shell fragments, and small plant stems and leaves), and a 1mm steel wire screen (removes coarse mud and sand and improves the uniformity of the suspended slurry). The filtered sludge has significantly improved flowability, while reducing the risk of wear and blockage of subsequent treatment equipment. The solid waste generated during the filtration process needs to be collected and properly disposed of to avoid secondary pollution.

[0132] The chemical adjustment unit 2 includes a chemical adjustment tank 21, a pH sensor, a conductivity meter, and a chemical adjustment agitator 22, and the pH adjustment agent storage tank 31 and the salinity adjustment agent storage tank 32 are connected to the chemical adjustment tank 21, respectively, and the pH sensor, the conductivity meter, and the chemical adjustment agitator 22 are installed in the chemical adjustment tank 21, respectively.

[0133] The chemical adjustment tank 21 is also provided with an adjustment tank sampling port 23 for regular sampling and detecting the concentration change of the chemically treated sludge.

[0134] The sludge storage unit 4 includes a sludge storage tank 41 and a sludge storage agitator 42, and the sludge storage tank 41 is respectively connected to the chemical adjustment unit 2 and the reactor 5, and the sludge storage agitator 42 is installed in the sludge storage tank 41. After the treatment is completed, the sludge is transported to a dedicated 10m 3 The sludge storage tank 41 temporarily stores the sludge. The sludge storage tank 41 needs to have corrosion resistance, leakage prevention, and stirring functions to prevent the sludge from settling.

[0135] The reactor 5 comprises a transparent plate 51, a temperature control system 52, a high-efficiency stirrer 53, a pH sensor, and an ORP sensor. The transparent plate 51 forms the chamber of the reactor 5. The temperature control system 52, the high-efficiency stirrer 53, the pH sensor, and the ORP sensor are installed in the chamber of the reactor 5.

[0136] The reactor 5 is made of transparent plates 51 to allow sunlight to irradiate the reaction solution and generate high-activity free radicals through the excitation of peroxy organic acids. The reactor is equipped with a temperature control system 52 to accurately adjust the temperature of the reaction solution and maintain the reaction temperature within the optimal range (usually 25-40℃). The pH sensor and the ORP sensor are installed in the reactor to monitor the pH value and the ORP value of the reaction process in real time.

[0137] The high-efficiency stirrer 53 comprises a variable frequency motor and double-layer paddles. The upper layer of the double-layer paddles is a three-blade inclined paddle structure, and the lower layer of the double-layer paddles is a turbine paddle structure. The variable frequency motor connects and drives the double-layer paddles. The high-efficiency stirrer 53 is composed of a variable frequency motor and a three-blade inclined paddle plus turbine double-layer paddle. The upper inclined paddle of the three-blade inclined paddle plus turbine double-layer paddle propels the paddle to stir the liquid, forming an axial flow, and the lower turbine paddle provides strong shear and disperses particles.

[0138] The reactor 5 is equipped with a reactor sampling port 54 for regular sampling and detection of the concentration trend of pollutants. Regular monitoring of the concentration of pollutants is used to evaluate the progress of the reaction. When the concentration decreases significantly and the ORP value gradually approaches stability, it indicates that the reaction is basically complete, and it is considered to stop dosing or switch to the next treatment stage.

[0139] The dosing unit 6 comprises raw material storage tanks 61, a dosing box 62, and a dosing storage tank 63. The raw material storage tanks 61 are connected to the inlet end of the dosing box 62. The dosing box 62 is equipped with a dosing stirrer 64. The outlet end of the dosing box 62 is connected to the dosing storage tank 63, which is connected to the inlet of the reactor 5.

[0140] The UV treatment unit 7 comprises a UV lamp 71, a quartz sleeve 72, a sealed UV treatment cavity 73, a UV stirrer 74, and a UV sensor. The UV lamp 71, the UV stirrer 74, and the UV sensor are connected inside the UV treatment cavity 73. The quartz sleeve 72 is connected to the outside of the UV lamp 71. The UV treatment cavity 73 is connected to the reactor 5 and the mud detection unit 8.

[0141] After the oxidation reaction is completed, the mud is further treated by the UV treatment unit 7 to completely kill pathogenic microorganisms and degrade residual organic pollutants.

[0142] Composition of the UV treatment system: The UV light emitted by the UV lamp tube 71 has a wavelength of 254 nm, with high sterilization and degradation capacity. The quartz sleeve 72 is used to protect the UV lamp tube 71 from direct contact with the mud, while ensuring the effective penetration of ultraviolet light. The UV treatment cavity 73 is designed as a closed structure to prevent ultraviolet light leakage and improve treatment efficiency. The UV agitator 74 continuously stirs to prevent the mud from settling.

[0143] Transportation and treatment of the mud: The mud is transported from the reactor to the UV treatment unit 7 through corrosion-resistant pipes. During transportation, the flow rate and flow of the mud need to be controlled to ensure uniform flow in the reaction cavity, so that it has enough residence time in the UV treatment cavity 73.

[0144] The mud detection unit 8 includes a detection storage container 81, a mud detection agitator 83 connected in the detection storage container 81, and a mud outlet 82 provided on the detection storage container 81. Storage and sampling of the mud: The treated mud is transported to a dedicated storage tank for temporary storage. The storage tank needs to have corrosion resistance, leakage prevention and stirring function to prevent the mud from settling or changing its properties. Regularly take mud samples through the sampling port on the storage tank to ensure that the sampling is representative.

[0145] The treatment method and device of the reclamation mud chemical additive of the present application have the following advantages:

[0146] (1) High degradation efficiency: Peroxy organic acid has strong oxidizing property, which can quickly degrade the chemical additives in the reclamation mud, especially the high molecular polymer additives, with degradation efficiency of more than 90%. It improves the environmental friendliness of the mud and reduces the impact of harmful substances on the ecological environment.

[0147] (2) Environmentally friendly: Compared with the traditional chemical precipitation method, the peroxo organic acid used in this method has lower environmental toxicity, does not introduce new pollutants, and its decomposition products are harmless substances such as water and carbon dioxide, which will not cause secondary pollution and can further reduce environmental risk.

[0148] (3) Improve resource utilization rate: The treated mud can be directly used for reclamation, which not only reduces the discharge and landfill of waste mud, but also effectively replaces part of the reclamation materials, reduces the dependence on natural resources such as sea sand in reclamation projects, and realizes resource utilization.

[0149] (4) Strong applicability: This method can be applied to the treatment of different types of waste mud, whether it is mud generated during offshore drilling, port construction or marine engineering construction, it can be effectively treated, and it is suitable for different soil requirements in reclamation projects.

[0150] (5) Simple operation, reduce cost: simple treatment process, the whole process can be completed in situ on the reclamation site, reducing the long-distance transportation demand of slurry, reducing the treatment and disposal cost, improving the engineering efficiency.

[0151] (6) Process optimization: through online monitoring equipment, the degradation process can be mastered in real time, ensuring that the treatment effect reaches the expected target. At the same time, through real-time feedback to adjust the pH value, salinity, temperature and the dosage of peroxy organic acid and other key parameters, the degradation effect of peroxy organic acid on chemical additives can be further improved, the whole treatment process can be optimized, and the efficiency and economy of slurry treatment can be ensured.

[0152] The above is a further detailed description of the present application in combination with specific preferred embodiments, and the specific implementation of the present application cannot be limited to these descriptions. For ordinary skilled persons in the technical field to which the present application belongs, without departing from the concept of the present application, a number of simple deductions or substitutions can be made, which should be considered as falling within the scope of protection of the present application.

Claims

1. A smart treatment method for chemical additives in land reclamation mud, characterized in that, Includes the following steps: S1. Mud Input and Pollution Detection: Extract mud samples to test their physicochemical properties and analyze the types and concentrations of chemical additives in the mud; S2. Mud Pretreatment: The mud undergoes preliminary physical and chemical treatment, and the treated mud is then transported to a storage tank for temporary storage. S3. Selection and preparation of peroxy organic acids: Based on the detection results of the types and concentrations of chemical additives in the mud and the treatment objectives, select the types of peroxy organic acids, determine the range of peroxy organic acid concentrations to be used, and prepare peroxy organic acid solutions of the corresponding concentrations in the preparation tank. S4. Material conveying: The pre-treated slurry is conveyed to the reactor; the flow rate and pressure of the slurry are monitored in real time during the conveying process, and the amount of slurry added is controlled; the prepared peroxy organic acid solution is conveyed to the reactor, and the flow rate and pressure of the peroxy organic acid solution are monitored in real time during the conveying process, and the amount of peroxy organic acid added is controlled. S5. Oxidative degradation: In the reactor, the mud and peroxy organic acids react fully under the control of light, stirring, and temperature. S6. UV treatment: After the oxidative degradation is completed, the mud is disinfected by a UV treatment system; S7. Detection and evaluation: The UV-treated sludge is temporarily stored in a storage tank, and the concentration of pollutants in the sludge sample is detected using a UV spectrophotometer; the detection results are compared with the preset standard values ​​to determine whether they meet the emission standards. S8. Intelligent Control System: Construct a convolutional neural network model, train the convolutional neural network model with data on detection parameters, optimal processing parameters, and processing effects to obtain a pollutant degradation process prediction model; input the detection parameters in S1 to S7 into the pollutant degradation process prediction model, and output the corresponding processing parameters in S1 to S7.

2. The intelligent treatment method for chemical additives in reclamation mud according to claim 1, characterized in that, In step S1, the physicochemical property detection process of the mud sample includes: detecting the pH value, conductivity value, or salinity, viscosity, and specific gravity of the mud sample using a pH meter, conductivity meter, viscometer, and hydrometer, respectively; the process of analyzing the types and concentrations of chemical additives in the mud includes: extracting the mud sample, centrifuging to obtain the supernatant, filtering, identifying and quantifying the chemical additive contaminants using liquid chromatography-mass spectrometry, separating them by chromatographic column, acquiring full-scan mass spectrometry signals and fragment spectrum information in positive and negative electrospray ionization modes, identifying the type by comparing the molecular formula, precise molecular ion peak, and characteristic fragments of known compounds in the chemical additive candidate database, combined with retention time; and comparing the peak area with standard curves established for different substances to reflect the concentration, and quantifying the concentration. The detection parameters are input into a convolutional neural network model. Using a convolutional neural network structure, the optimal combination of processing parameters is predicted based on the input parameters. Input parameters include, but are not limited to, mud pH value. mud conductivity Chemical additive concentration Mud viscosity ,proportion Flow rate UV-Vis spectral absorption eigenvectors The above parameters constitute a multidimensional input vector. After features are extracted by the convolutional layer, they are input into the fully connected regression layer, which outputs a set of optimal process parameters Y for controlling the processing flow, including but not limited to: PAA addition amount. Stirring speed Reaction time Temperature setting UV irradiation intensity or duration Construct a nonlinear mapping model: ， in It is a convolutional neural network model. This represents the model weight parameters.

3. The intelligent treatment method for chemical additives in reclamation mud according to claim 1, characterized in that, In S2, the preliminary physical treatment process of the mud includes: passing the mud through a multi-stage filtration system consisting of a 10mm coarse screen, a 5mm medium-fine screen, and a 1mm wire mesh. The 10mm coarse screen first intercepts construction waste, plastic bags, and large debris. The 5mm medium-fine screen intercepts large sand particles, shell fragments, and small plant stems and leaves. The 1mm wire mesh removes coarse mud and sand. After physical treatment, the chemical treatment of the mud includes: First, using a pH adjuster to adjust the pH value of the mud to 6.5~8.5; Second, based on the salinity test results of the mud, adjust its salinity to 1‰~10‰, and then remeasure its salinity until it is confirmed to meet the standard. The mud salinity is reduced by calculating the amount of water to be added using the following formula, and diluting it with water proportionally. ， To increase mud salinity, calculate the electrolyte addition amount based on the target salinity, and then slowly add the electrolyte powder while stirring, ensuring it dissolves completely. Required electrolyte mass: ， in: Target salinity, in g / L or ‰; Initial salinity, g / L or ‰; V is the mud volume, m. 3 p represents the purity of the electrolyte, with a mass fraction of 0-1%.

4. The intelligent treatment method for chemical additives in reclamation mud according to claim 1, characterized in that, In S3, if the chemical additives in the slurry are nitrogen-containing, sulfur-containing, and some recalcitrant organic matter, then peracetic acid (PAA) is used as the peroxy organic acid; if the chemical additives in the slurry are phosphorus-containing, chlorine-containing, and some complexes, then performic acid (PFA) is used as the peroxy organic acid. Based on the molecular weight, reactive oxygen species ratio, and degradation efficiency of the target reactants for PAA and PFA, the following concentration conversion model is established: Assuming the goal is to degrade a certain type of chemical additive in a unit volume of sludge to below a preset concentration threshold, the required PAA concentration is: Under similar conditions, the required PFA concentration is... for: ， in: , These are the molecular weights of PAA and PFA, respectively. , The proportion of reactive oxygen species; , is the reaction rate constant with the target pollutant.

5. The intelligent treatment method for chemical additives in reclamation mud according to claim 1, characterized in that, In S4, the target ratio of mud to peroxy organic acid is preset before material transportation. The ratio of the amount of peroxy organic acid added to the volume of mud is 0.1%~1%. The mud flow rate Q1 is obtained during material transportation. The flow rate of peracetic acid to be added Q2 is calculated according to the set target ratio. The actual flow rate of peroxy organic acid Q3 is obtained through the flow meter and compared with the target flow rate Q2 in real time. If there is a deviation between the two, the output flow rate of the peroxy organic acid is dynamically adjusted so that the actual flow rate Q3 is gradually adjusted to the target flow rate Q2. During the transportation process, the liquid level change in the reactor is continuously monitored. When the liquid level reaches the set upper limit, the transportation of mud and peroxy organic acid is suspended.

6. The intelligent treatment method for chemical additives in reclamation mud according to claim 1, characterized in that, In step S5, the illumination is controlled by sunlight irradiating the reaction solution; the stirring is controlled by a variable frequency motor driving a double-layer impeller to stir the reaction solution; the temperature is controlled at 25~40℃; the reactor is equipped with a pH sensor and an ORP sensor to obtain the pH and ORP values ​​of the reaction solution. If the pH of the reaction solution is too acidic or too alkaline, the amount of peroxy organic acid added is reduced or increased. If the ORP value of the reaction solution decreases significantly, the amount of peroxy organic acid added is increased or the reaction time is extended. If the ORP value tends to stabilize, the reaction ends, the addition of aerobic organic acid is stopped, and the process is switched to UV treatment.

7. The intelligent treatment method for chemical additives in reclamation mud according to claim 1, characterized in that, In step S6, the slurry is transported from the reactor to the UV treatment system. During the transport process, the flow rate and volume of the slurry are controlled to ensure that the slurry flows evenly in the reaction chamber and that the slurry stays in the reaction chamber of the UV treatment system for a specified time. Based on the prediction results of mud pollutant concentration and ultraviolet transmittance parameters by the convolutional neural network model, the system automatically calculates the required ultraviolet dose and adjusts the ratio between ultraviolet light intensity and mud flow rate accordingly; the ultraviolet dose satisfies the following relationship: ， Where: I is the ultraviolet light intensity (mW / cm²) 2 ); t is the irradiation time (s); V is the treatment volume; P is the total power of the UV lamp (mJ); Q is the instantaneous flow rate of the mud (m³). 3 / s).

8. The intelligent treatment method for chemical additives in reclamation mud according to claim 1, characterized in that, In S7, the determination result of whether the emission standards are met includes: Non-compliance: If the pollutant concentration exceeds the standard value, it is deemed non-compliant, and the sludge is transported back to the reactor for further treatment. The degradation is further carried out by adjusting the parameters of the dosage of peroxy organic acid, reaction time, or light intensity. Acceptance criteria: If the pollutant concentration is lower than the standard value, it is considered acceptable. Based on the different degradation effects, it is divided into the following two situations: 1) Best degradation effect: The pollutant concentration is significantly lower than the standard value, and the mud is directly used for land reclamation or other resource utilization; 2) Second best degradation effect: The pollutant concentration is lower than the standard value but has not reached the best level, and the mud is used for land reclamation. Each test result, assessment conclusion, and process adjustment is recorded and archived, a treatment effect report is generated, and the data is input into the pollutant degradation process prediction model.

9. The intelligent treatment method for chemical additives in reclamation mud according to claim 1, characterized in that, In step S8, the process of constructing the pollutant degradation process prediction model further includes: Experimental data on mud samples were obtained, including the reaction conditions and degradation effects under different concentrations of chemical additives. A nonlinear mapping relationship between mud parameters, treatment parameters, and degradation efficiency was established using a convolutional neural network. The network employed supervised learning, with training samples using "mud feature parameters + treatment settings" as input and "degradation effect" as output, with degradation efficiency as the output variable. This efficiency has a non-linear multivariate regression relationship with the input-output parameters; the supervised learning model is constructed as follows: ， Where: X represents the physicochemical characteristics of the mud; Y represents the process setting parameters; For high-dimensional nonlinear functions modeled using convolutional neural networks; This is the error term; During model training, mean squared error is used as the loss function: ， Where i represents the training sample number; model weights Update using the backpropagation algorithm to minimize the predicted value. Compared with actual degradation efficiency The differences.

10. An intelligent processing device for chemical additives in land reclamation mud, characterized in that, The intelligent treatment method for the chemical additives used in reclamation mud as described in claim 1 includes a mud filtration unit, a chemical adjustment unit, a storage unit for storing pH or salinity adjusters, a mud storage unit, a reactor, a dosing unit for storing peroxy organic acids, a UV treatment unit, and a mud detection unit. The mud filtration unit, chemical adjustment unit, mud storage unit, and reactor inlet are connected in sequence. The storage unit is connected to the chemical adjustment unit. The dosing unit is connected to the reactor inlet. The reactor outlet, UV treatment unit, and mud detection unit are connected in sequence. The mud to be treated is input from the mud filtration unit inlet and transported into the reactor. The peroxy organic acids are transported into the reactor through the dosing unit. The treated mud is output from the reactor outlet and transported to the mud detection unit outlet for discharge.

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