Method for enhancing and modifying geopolymer curing performance of high-bentonite-content slurry

Abstract

The invention relates to the technical field of waste slurry treatment, in particular to a geopolymer curing performance enhancing and modifying method for high-bentonite-content slurry. Comprising a method for adding a curing and enhancing modification auxiliary agent, and the method for adding the curing and enhancing modification auxiliary agent comprises the following steps: doping the curing and enhancing modification auxiliary agent into high-bentonite-content slurry, uniformly stirring, and standing for curing; wherein the curing enhanced modification additive comprises the following raw material components in percentage by mass: 70-80% of calcium chloride, 10-20% of sodium chloride, 10-15% of nano silicon dioxide, 5-10% of a dispersing agent and 0.5-3% of an EDTA (Ethylene Diamine Tetraacetic Acid)-polymer derivative. The additive is added, so that the negative influence of bentonite in the waste slurry on the reaction of the geopolymer is effectively eliminated, the reaction conditions of the geopolymer in a complex slurry environment are optimized, and the utilization rate and the success rate of the slurry are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of waste mud treatment, and particularly to a method for enhancing the geopolymer curing performance of mud with a high bentonite content. Background Art

[0002] As a new type of cementitious material, geopolymers have the advantages of low carbon and environmental protection, attracting more and more in-depth research by scholars, and gradually becoming a substitute for high-energy-consuming cement. Moreover, its outstanding acid resistance, alkali resistance, high strength and other properties have solved many problems that cement cannot handle. At present, geopolymers have been widely used in the resource utilization of waste mud. By curing waste mud to produce functional products, waste mud can not only be disposed of at the construction site, but also be transported to the factory for processing into building materials, thus playing an important role in environmental protection and cost savings. However, the clay mineral compositions and contents of muds from different sources vary. These minerals may originate from the mud itself or may be due to the addition of additives during the construction process. For example, bentonite is generally added in slurry shield tunneling and drilling mud to improve the fluidity of the mud for convenient construction. However, it is precisely these clay minerals with different degrees of adsorption characteristics that have varying degrees of negative impacts on the depolymerization and polycondensation reactions during the geopolymer curing of mud, and thus have a negative effect on the curing of geopolymers in mud. Among them, bentonite (mainly composed of montmorillonite) is the most common clay mineral in nature and at the construction site, and also has the strongest adsorption characteristics.

[0003] The following problems exist in the traditional treatment of mud with a high bentonite content: (1) Traditional research focuses on enhancing the adsorption of bentonite, rather than suppressing its interference with the geopolymer reaction. Therefore, there is currently no specific solution to the poor effect of geopolymer curing of mud with a high clay mineral content of bentonite.

[0004] (2) There is a lack of a special curing enhancement modifier formula and standardized operation process for high-bentonite mud.

[0005] (3) The current mud curing treatment methods are simple means such as mechanical stirring and pressure filtration, and the process lacks real-time monitoring means, resulting in uncontrollable reaction conditions and large differences in curing effects. Summary of the Invention

[0006] The present invention provides a method for enhancing the geopolymer curing performance of mud with a high bentonite content, aiming to solve the existing drawbacks in the treatment of waste mud cured by geopolymers.

[0007] The present invention provides a method for enhancing the geopolymer curing performance of a slurry with a high bentonite content, including a method for adding a curing enhancement modifier. The method for adding the curing enhancement modifier includes: incorporating a curing enhancement modifier into the slurry with a high bentonite content, stirring evenly, and then standing for curing. The curing enhancement modifier includes raw material components composed of the following mass percentages: Calcium chloride 70 - 80%, sodium chloride 10 - 20%, nano-silica 10 - 15%, dispersant 5 - 10%, EDTA-polymer derivative 0.5 - 3%.

[0008] As a further improvement of the present invention, incorporating a curing enhancement modifier into the slurry with a high bentonite content, stirring evenly, and then standing for curing specifically includes: Incorporating the curing enhancement modifier at 1.7 - 6.7% of the dry mass of bentonite in the slurry, performing high-speed shear stirring, and adding a curing agent for curing treatment after standing; the curing agent includes raw material components in the following mass ratio: slag:cement:sodium silicate = 42:38:20.

[0009] As a further improvement of the present invention, the dispersant is a polycarboxylate-based dispersant, and the EDTA-polymer derivative includes PAA-EDTA and PEG-EDTA.

[0010] As a further improvement of the present invention, the preparation method of the curing enhancement modifier includes: a1. Dissolving calcium chloride and sodium chloride in deionized water according to the composition ratio to prepare a mother liquor; a2. Adding nano-silica to the mother liquor and performing ultrasonic dispersion to obtain a uniformly dispersed solution; a3. Adding an EDTA-polymer derivative to the solution obtained in a2 and stirring and mixing to obtain a uniform solution; a4. Adding a dispersant to the solution obtained in a3 and stirring and mixing until a homogeneous suspension is formed.

[0011] As a further improvement of the present invention, the method for enhancing the geopolymer curing performance of a slurry with a high bentonite content further includes constructing an intelligent real-time detection and control system. The construction process includes: b1. Setting a bentonite characteristic response sensor on the pumping path of the slurry mixing tank to detect the bentonite content in the slurry; b2. Setting multi-modal element sensors at the input and output ports of the slurry mixing tank to detect the concentration of specified ions; b3. Setting a curing agent feeding point and an additive feeding point. The curing agent feeding point and the additive feeding point are respectively connected to the slurry mixing tank. The curing agent feeding point is used to store the curing agent, and the additive feeding point is used to store the curing enhancement modifier; b4. Obtain the parameter information of the bentonite characteristic response sensor and the multi-modal element sensor through the feedback control software, conduct real-time prediction of the bentonite content and ion concentration, make decisions on the optimal dosage of the curing agent and the curing enhancement and modification additives, and control the material feeding at the curing agent feeding point and the additive feeding point.

[0012] As a further improvement of the present invention, the bentonite characteristic response sensor includes a CEC rapid determination unit. The CEC rapid determination unit is internally provided with an ammonium ion saturation column and a conductivity detection cavity. The ammonium ion saturation column is a microfluidic exchange column filled with a displacement liquid, and the ammonium ion replacement of the interlayer cations of montmorillonite is completed when the slurry flows through at a fixed low rate; the conductivity detection cavity monitors the change of the conductivity of the displacement liquid in real time through a multi-electrode array, and calculates the CEC value in combination with the Langmuir adsorption model.

[0013] As a further improvement of the present invention, the bentonite characteristic response sensor includes a NIR auxiliary identification unit for auxiliary detection of the bentonite content. The NIR auxiliary identification unit is equipped with a near-infrared spectrometer in the characteristic wavelength range of 510-530 cm -1 interval and 1070-1090 cm -1 interval, and a quantitative relationship between the spectral absorbance and the bentonite content is established through a regression algorithm.

[0014] As a further improvement of the present invention, the process of establishing a quantitative relationship between the spectral absorbance and the bentonite content through a regression algorithm includes: c1. Prepare standard samples with known bentonite content, covering the detection range, and use a near-infrared spectrometer to measure the absorbance of each standard sample in the characteristic peak wavelength range; c2. According to the characteristic peak wavelength range tested for each sample, calculate the correlation coefficient between the wavelength corresponding to each sample and the standard characteristic peak wavelength of bentonite, retain the wavelengths with a correlation coefficient > 0.9, and automatically select and optimize the optimal wavelength combination through an intelligent algorithm; c3. Use partial least squares regression to establish a regression model, decompose the spectral data and the content data simultaneously, extract the common latent variables, and construct a linear equation with the latent variables: predicted content = a × absorbance1 + b × absorbance2 + constant, where a and b are the contribution weights of the two characteristic wavelengths of 520 cm -1 and 1080 cm -1 respectively, and absorbance1 and absorbance2 correspond to 520 cm -1 and 1080 cm -1The sum or average value of absorbance in two characteristic wavelength ranges; when the predicted content is in the low-content area (<25%) of the detection range, the predicted value = the standard PLSR result; when the predicted content is in the high-content area (>25%) of the detection range, the predicted value = the standard PLSR result + correction coefficient × ln(absorbance); the coefficients a and b are obtained by fitting the standard sample set using partial least squares method, and the correction coefficient is determined by non-linear fitting of high-concentration samples and dynamically adjusted according to environmental parameters.

[0015] As a further improvement of the present invention, the multi-modal element sensor includes ISE unit: adopting an all-solid-state Ca 2+ / Na + dual-channel ion-selective electrode, using a perovskite / graphene composite film as the electrode membrane material, the Ca 2+ ion-selective electrode and the Na + ion-selective electrode are both coated with electrode membranes to form a Ca 2+ sensitive membrane and a Na + sensitive membrane respectively, and are separated by a microchannel into two flow channels flowing through the Ca 2+ sensitive membrane and the Na + sensitive membrane respectively for synchronous detection of dual ions; Fluorescence unit: integrating the excitation light source of a dual-wavelength excitation fiber optic sensor, an Al fluorescence probe based on Lumogallion organic matter, and an Si fluorescence probe based on ammonium molybdate complex, and the excitation light source wavelengths for irradiating the Al fluorescence probe and the Si fluorescence probe are 350 - 380 nm and 500 - 510 nm respectively.

[0016] As a further improvement of the present invention, the feedback control software includes a concentration prediction model of BP neural network and a multi-modal dynamic compensation algorithm; Concentration prediction model of BP neural network: Based on a large database, obtain the parameters of the sensor or manually input parameters, perform real-time prediction of bentonite content and ion concentration, and make a decision on the optimal amount of material input. The parameters include ISE, fluorescence intensity, pH, CEC, and flow rate; Multi-modal dynamic compensation algorithm: Through sensor drift compensation and ion interference compensation, the sensor drift compensation includes ISE potential compensation and fluorescence signal attenuation correction to correct the decision of the concentration prediction model.

[0017] The beneficial effects of the present invention are as follows: (1) By adding additives, the negative impact of bentonite in waste mud on the geopolymer reaction is effectively eliminated, the reaction conditions of the geopolymer in a complex mud environment are optimized, and the utilization rate and success rate of the mud are improved. (2) An ISE-fluorescence dual sensing device is adopted to detect the mud curing environment by measuring the ion concentration. (3) Through a feedback control software, based on a big data model, material feeding decisions are made according to the input data, realizing real-time and high-efficiency improvement of curing and synchronous curing. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the method for enhancing the geopolymer curing performance of high-bentonite-content mud of the present invention; Figure 2 It is a working principle diagram of the bentonite characteristic response sensor of the present invention; Figure 3 It is a working principle diagram of the multi-modal element sensor of the present invention; Figure 4 It is a comparative diagram of the unconfined compressive strengths of the cured bodies at 3, 7, 14, and 28 days of the shield mud and drilling mud in Example 1 of the present invention under standard curing conditions; Figure 5 It is a comparative diagram of the unconfined compressive strengths of the cured bodies at 3, 7, 14, and 28 days of the shield mud and drilling mud in Example 2 of the present invention under standard curing conditions; Figure 6 It is a comparative diagram of the unconfined compressive strengths of the cured bodies at 3, 7, 14, and 28 days of the shield mud and drilling mud in Example 3 of the present invention under standard curing conditions. DETAILED DESCRIPTION OF THE INVENTION

[0019] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0020] The present invention is a method for improving the curing strength of waste mud based on the ion selective adsorption and exchange mechanism of bentonite for Ca 2+ and Na + ions and the principle of geopolymer reaction, including a curing modification additive formula and an intelligent real-time detection-control system, which can be applied to the resource utilization of waste mud, especially for waste mud with a high bentonite content. It is a method for improving the geopolymer-based cured waste mud worthy of popularization and application.

[0021] Specifically, the method for enhancing the geopolymer curing performance of high-bentonite-content mud includes a method for adding a curing enhancement modification additive, and the method for adding the curing enhancement modification additive includes: adding a curing enhancement modification additive to the high-bentonite-content mud, stirring evenly and then standing for curing; Among them, the solidification enhancement and modification aid includes raw material components composed of the following mass percentages: Calcium chloride 70 - 80%, sodium chloride 10 - 20%, nano-silica 10 - 15%, dispersant 5 - 10%, EDTA-polymer derivative 0.5 - 3%.

[0022] Calcium chloride is used to provide Ca 2+ source, selectively adsorb ions in the slurry, and optimize the ion balance; sodium chloride is used to provide Na + source, optimize the ion balance; nano-silica is used to promote gel cross-linking; the dispersant is a polycarboxylate-based dispersant, which is used to prevent the agglomeration of the aid; the EDTA-polymer derivative includes PAA-EDTA and PEG-EDTA, which act as high-molecular-weight to block the adsorption sites of bentonite and form a dense structural layer at the same time.

[0023] The preparation method of the solidification enhancement and modification aid includes: a1. Dissolve calcium chloride and sodium chloride in deionized water according to the ratio to prepare a mother liquor with a concentration of 3 - 5 mol / L; a2. Add nano-silica to the mother liquor, and perform ultrasonic dispersion for 30 min, with an ultrasonic frequency of 20 - 40 kHz and an ultrasonic power of 300 W; a3. Add the EDTA-polymer derivative (either PAA-EDTA or PEG-EDTA is acceptable) to the solution obtained in a2, stir and mix, with a stirring speed of 300 rpm and a stirring time of 1 min; a4. Finally, add the polycarboxylate-based dispersant and stir and mix until a homogeneous suspension is obtained, with a stirring speed of 500 rpm and a stirring time of 2 h.

[0024] Dosage and usage of the solidification enhancement and modification aid: Add the solidification enhancement and modification aid at 1.7 - 6.7% of the dry mass of bentonite in the waste slurry, perform high-speed shear stirring at 1200 rpm, after 15 min, let it stand for 10 minutes, and then add the solidifying agent for solidification treatment.

[0025] The components of the solidifying agent include slag, cement, and water glass. The specific mass ratios of each raw material component are as follows: S95 slag: PO42.5 cement: water glass (modulus 1.2) = 42:38:20.

[0026] External additives, that is, additives used to improve the effect of the geopolymer solidifying agent, select the additives with better effects according to the clay mineral adsorption mechanism, the geopolymer reaction mechanism, and the experimental results, and the experimental results show that: CaCl2 > NaCl.

[0027] Clay minerals in the waste mud first cause granulation during the solidification process, which is usually visible in the early stage (several hours). These clay mineral aggregates will adsorb Si, Al, Na, Ca and the hydration gel products of the solidifying agent, affecting the microscopic environment of solidification. Due to its high cation exchange capacity and surface area, the adsorbed Ca 2+ tends to form CaCO3. In geopolmer solidification, the three-dimensional network polymerization gel and hydration gel of silicon and aluminum are the main sources of the strength of the solidified body. However, the adsorption of clay minerals reduces the required silicon-aluminum tetrahedrons and Na + / Ca 2+ , resulting in a decrease in the strength structure. At the same time, even if the hydration gel and three-dimensional network structure are formed, their distribution may be uneven, weakening the connection of the strength structure. Therefore, mud with a high clay mineral content usually exhibits lower solidification strength.

[0028] Therefore, this method uses Na + / Ca 2+ salt as an admixture to pretreat the waste mud to eliminate / cover the influence of the adsorbability of bentonite on geopolmer solidification: The Na + / Ca 2+ added through the admixture will be adsorbed by bentonite first, the adsorption of bentonite is offset, and bentonite tends to adsorb Na + / Ca 2+ ions, which depends on the large specific surface area structure of bentonite itself and has a large affinity for Na + / Ca 2+ ions. These ions can effectively fill the interlayer space of clay minerals and maintain the stability of their layered structure. Therefore, when the geopolmer solidifying agent is added later, the probability that the ions dissolved from the raw materials and the hydration products generated by the reaction are adsorbed by bentonite will be greatly reduced. In addition, Na + / Ca 2+ itself is the ion required to participate in the geopolmer reaction to generate hydration gel and three-dimensional network structure. The addition of the admixture improves the ion concentration in the mud and also promotes the geopolmer reaction. The reason for choosing chloride salt is that the geopolmer reaction is sensitive to the concentration of other ions, including but not limited to the influence of the concentration of other anions on the pH value and the competition with cations such as aluminum and silicon, thus affecting the polymerization reaction of geopolymers, etc. Although Cl - ions may also participate in the geopolmer reaction, the reaction-generated chlorine-containing and poorly soluble Friedel and Kuzel salts can provide strength for the solidified mud and further improve the porosity of the mud solidified body.

[0029] Such as Figure 1As shown in the figure, the method for enhancing and modifying the geopolymer curing performance of the high-bentonite-content mud further includes constructing an intelligent real-time detection and control system. The construction process includes: b1. Set a bentonite characteristic response sensor on the pumping path of the mud mixing tank to detect the bentonite content in the mud; b2. Set multi-modal element sensors at the input and output ports of the mud mixing tank to detect the concentration of specified ions; b3. Set a curing agent feeding point and an auxiliary agent feeding point. The curing agent feeding point and the auxiliary agent feeding point are respectively connected to the mud mixing tank. The curing agent feeding point is used to store the curing agent, and the auxiliary agent feeding point is used to store the curing enhancement and modification auxiliary agent; b4. Obtain the parameter information of the bentonite characteristic response sensor and the multi-modal element sensor through the feedback control software, perform real-time prediction of the bentonite content and ion concentration, make decisions on the optimal dosage of the curing agent and the curing enhancement and modification auxiliary agent, and control the material feeding of the curing agent feeding point and the auxiliary agent feeding point.

[0030] The intelligent real-time detection and control system is regulated based on the functions of the auxiliary agents, aiming to achieve rapid, large-scale, time-saving and labor-saving treatment of the mud. The intelligent real-time detection and control system consists of the recognition and detection module hardware and the feedback control software. The hardware includes a bentonite characteristic response sensor group and a multi-modal ion sensor. The former is used for detecting the bentonite content, and the latter is used for real-time detection of the ion concentration. The feedback control software analyzes the existing input data based on the large model and regulates the dosage of the auxiliary agent / curing agent.

[0031] As Figure 2 shown, the bentonite characteristic response sensor is used to identify and detect the bentonite content in the mud during the mud pumping process, providing data reference for subsequent regulation of the auxiliary agent / curing agent. The bentonite characteristic response sensor includes a CEC rapid determination unit and a NIR auxiliary identification unit.

[0032] The CEC rapid determination unit is internally provided with an ammonium ion saturation column and a conductivity detection cavity. The ammonium ion saturation column is filled with a microfluidic exchange column (diameter 5 mm, length 10 cm) of NH4Cl solution (1 mol / L). When the mud passes through at a fixed low rate of flow, the ammonium ion replacement of the interlayer cations of montmorillonite is completed; the conductivity detection cavity real-time monitors the change of the conductivity of the replacement liquid through a multi-electrode array (spacing 2 mm), and calculates the CEC value in combination with the Langmuir adsorption model, and can complete the preliminary detection of the bentonite content within a few minutes.

[0033] The NIR auxiliary identification unit is equipped with 510~530 cm -1 interval and 1070~1090 cm -1A near-infrared spectrometer for the interval characteristic wavelength range, respectively aiming at the Si-O-Al bending vibration peak and Si-O-Si stretching vibration peak of montmorillonite, establishes the quantitative relationship between the spectral absorbance and the bentonite content through a regression algorithm, and is used for the auxiliary detection of the bentonite content.

[0034] Among them, the calculation process of establishing the quantitative relationship between the spectral absorbance and the bentonite content through the regression algorithm includes: data acquisition → spectral preprocessing → characteristic wavelength screening → modeling → prediction.

[0035] c1. Prepare standard samples with known bentonite contents, covering the detection range (0-100%), and then use a near-infrared spectrometer to measure the absorbance of each sample in the characteristic peak wavelength range.

[0036] c2. According to the characteristic peak wavelength range tested for each sample, calculate the correlation coefficient between the wavelength corresponding to each sample and the standard characteristic peak wavelength of bentonite, retain the wavelengths with a correlation coefficient > 0.9, and automatically select and optimize the optimal wavelength combination through an intelligent algorithm (such as a genetic algorithm).

[0037] c3. Establish a regression model using partial least squares regression (PLSR). Decompose the spectral data (X) and the content data (Y) simultaneously, extract the common principal components (latent variables), and construct a linear equation with the latent variables: predicted content = a × absorbance1 + b × absorbance2 + constant, where a and b are the contribution weights of the two characteristic wavelengths of 520 cm -1 and 1080 cm -1 respectively, and absorbance1 and absorbance2 correspond to the sum or average value of the absorbances in the two characteristic wavelength ranges of 520 cm -1 and 1080 cm -1 respectively. Aiming at the problem of absorbance saturation of high-content bentonite samples in the mud, a non-linear compensation term is added, that is: in the low-content area (<25%) of the detection range, the predicted value = the standard PLSR result; in the high-content area (>25%) of the detection range, the predicted value = the standard PLSR result + correction coefficient × ln(absorbance). The coefficients a and b are obtained by fitting the standard sample set through partial least squares method; the correction coefficient is determined by non-linear fitting of high-concentration samples and is dynamically adjusted according to environmental parameters.

[0038] Such as Figure 1As shown, the modal element sensor is placed at the input and output ports of the mud mixing tank to detect the concentration of specified ions. Combining and optimizing traditional fluorescence sensing and ion-selective electrode (ISE) technologies, the modal element sensor adopts an ISE-fluorescence dual-mode probe structure to achieve synchronous, rapid, and efficient detection of multiple elements (here sodium, calcium, silicon, and aluminum). The free sodium and calcium elements mostly exist in the form of ions in the mud, so the ISE technology is selected for detection; the free silicon and aluminum elements mostly exist in the form of monomers combined with oxygen atoms in the mud, so the fluorescence sensing technology is selected for detection. By detecting calcium, sodium, silicon, and aluminum, the content of these elements is used to reflect the effect of additive regulation and whether the geopolymer reaction environment has reached the optimal conditions.

[0039] As Figure 3 shown, the modal element sensor includes: ISE unit: Adopting an all-solid-state Ca 2+ / Na + dual-channel ion-selective electrode, the electrode membrane material is a perovskite / graphene composite membrane (thickness 50μm), and dual-ion synchronous detection is achieved through microchannel separation.

[0040] Fluorescence unit: Integrating the laser light source of a dual-wavelength excitation fiber optic sensor, embedding an Al fluorescence probe (based on Lumogallion organic matter) and a complex fluorescence probe for Si response (based on ammonium molybdate-(NH4)6Mo7O 24 ), and the excitation light source wavelengths are 350~380nm and 500~510nm respectively.

[0041] Working principle of the ISE unit: The mud is injected from the inlet, divided into two paths through the microchannel, and flows through the Ca 2+ and Na + sensitive membrane (i.e., the electrode membrane) regions respectively, selectively corresponding to Na + and Ca 2+ respectively, and generating a membrane potential, and the signal is output through the conductive layer.

[0042] Ca 2+ sensitive membrane: A composite membrane of europium-doped calcium perovskite (KCaF3) and graphene, responsive to Ca 3+ ; Na⁺ sensitive membrane: A composite membrane of aluminum-doped NASICON-type perovskite (Na3Zr 2+ Al 3+ Si2PO 1.8 Al 0.2 Si2PO 12 ), responsive to Na + ).

[0043] The present invention combines fluorescence sensor and ion selective electrode (ISE) technologies for real-time monitoring and detection of ion concentrations in mud, aiming to efficiently and accurately measure the concentrations of sodium (Na + ), calcium (Ca 2+ ), silicon (Si) and aluminum (Al 3+ ) ions in the mud, thereby optimizing the geopolmer curing process and achieving large-scale and standardized solidification treatment of waste mud. The working principle of this method is described as follows: ① For Na + and Ca 2+ ions, dedicated ion selective electrodes are used, and the concentration is measured based on the principle of selective permeability of the membrane and potential difference. The electrode is immersed in the sample, and the ions interact with the membrane surface, generating a potential difference that has a linear relationship with the ion concentration in the sample. The ion concentration can be obtained through calculation. These two ions exhibit good conductivity and strong ion exchange ability in aqueous solution, so they are suitable for precise measurement using ion selective electrodes.

[0044] ② For the detection of silicon and aluminum ions, silicon usually exists in a polymeric state, forming silicate, and its concentration in water as a monomer is relatively low, making it difficult to directly measure through traditional ion selective electrodes. The measurement of aluminum ions is affected by its complex behavior under acidic conditions in water and is not suitable for using standard ion selective electrodes either. Therefore, fluorescence sensor technology is adopted. After the fluorescent probe binds to Si and Al ions in the sample, a fluorescent signal with a specific wavelength is generated. By irradiating the sample with an excitation light source, the fluorescent probe is excited, and the intensity of the fluorescent signal is proportional to the ion concentration. The detector measures the fluorescence intensity in real time and converts it into the corresponding ion concentration through a standard curve.

[0045] ③ Finally, combining the real-time data from the ion selective electrode and the fluorescence sensor, comprehensive analysis and feedback are carried out through an intelligent control system. The system dynamically adjusts the ion concentration in the mud according to the set optimal reaction conditions to ensure the best effect of the geopolmer curing process.

[0046] As Figure 1 shown, the feedback control software mainly consists of a concentration prediction model of an improved BP neural network and a multi-modal dynamic compensation algorithm. The working principle of the concentration prediction model of the improved BP neural network is based on a large database, and various parameters (ISE, fluorescence intensity, pH, CEC, flow rate, etc.) input by sensors or manually are used to predict the bentonite content and ion concentration in real time, and make decisions on the optimal dosage of material input. The multi-modal dynamic compensation algorithm mainly includes sensor drift compensation (ISE potential compensation, fluorescence signal attenuation correction) and ion interference compensation, which correct the decisions of the concentration prediction model.

[0047] Among them, the large database is the data summary obtained through the tests of a large number of experimental groups, including the bentonite content (characterization methods include X-ray diffraction (XRD), X-ray fluorescence (XRF), and NIR near-infrared spectroscopy), calcium, sodium, silicon, and aluminum ion concentrations, pH value, cation exchange capacity (CEC), dosage of curing agent, unconfined compressive strength (UCS), water content of slurry, etc.

[0048] Making a decision on the optimal amount of material input is to input the data obtained from real-time monitoring to the trained model. The model analyzes the reaction environment based on the existing training data and finally gives feedback control. An influence weight is set for the data detected in the model.

[0049] Sensor drift compensation: According to the law of sensor drift, mathematical models (such as linear regression, curve fitting, etc.) are used to estimate and correct the drift.

[0050] Ion interference compensation: Through a large number of actual detections, the law of mutual interference is obtained to form a linear regression model, and then it is corrected. At the same time, the microchannel separation and the structure of the specific membrane in the ISE unit are also anti-interference measures.

[0051] The concentration prediction model of the improved BP neural network is a model trained by combining deep learning optimization techniques and domain knowledge embedding on the basis of the traditional BP framework. The improvement principles include: 1) Network structure optimization: A double hidden layer design is adopted. The first hidden layer captures global features, and the second layer extracts high-order non-linear relationships, suppressing noise by gradually reducing the dimension. 2) Upgrade of the regularization strategy: With a probability of p = 0.3, the entire neuron connection path is randomly discarded to force the network to learn redundant features. 3) Dynamic training mechanism: New data is collected every 30 minutes, and the distribution difference between the new and old data is calculated. If the threshold θ = 0.1, parameter fine-tuning is triggered, that is, the first two layers are frozen, and only the last two layers are updated.

[0052] The calculation process of the multi-modal dynamic compensation algorithm is: synchronous extraction of multi-modal features → cross-interference modeling → online weight assignment → dynamic compensation correction → confidence feedback update.

[0053] The following examples are listed to illustrate the application of this method.

[0054] Example 1:

[0055] Solidification experiments were respectively carried out using the waste slurry of slurry shield in engineering (the dry matter content of bentonite is 13%) and drilling mud (the dry matter content of bentonite is 25%). The dosage of the solidifying agent is 20% of the dry mass of the slurry. Before solidification, a gradient dosage of 1.7% - 6.7% of the solidification-enhancing and modifying additive was added for pretreatment, and the CP group is the group without adding the solidification-enhancing and modifying additive. The solidification-enhancing and modifying additive used includes raw material components composed of the following mass percentages: calcium chloride 70%, sodium chloride 14.5%, nano-silica 10%, dispersant 5%, EDTA-polymer derivative 0.5%.

[0056] Under standard curing conditions, the unconfined compressive strengths of the solidified bodies at 3, 7, 14, and 28 days were tested, and the results are as Figure 4 shown, Figure 4 The left figure in the middle corresponds to the shield slurry, and the right figure corresponds to the drilling mud, Figure 4 and the dosages of the additive in the middle are taken as 1.7%, 3.4%, 5%, and 6.7% respectively.

[0057] For the shield slurry group: The effect is optimal when the dosage of the modifier is 3.4% of the dry mass of bentonite, and the 28-day solidification strength is increased to 315.1% of the original (164.5 kPa → 518.3 kPa). For the drilling mud group: The effect is optimal when the dosage of the modifier is 3.4% of the dry mass of bentonite, and the 28-day solidification strength is increased to 183.1% of the original (160.1 kPa → 293.2 kPa).

[0058] Example 2:

[0059] Solidification experiments were respectively carried out using the waste slurry of slurry shield in engineering (the dry matter content of bentonite is 13%) and drilling mud (the dry matter content of bentonite is 25%). The dosage of the solidifying agent is 20% of the dry mass of the slurry. Before solidification, a gradient dosage of 1.7% - 6.7% of the solidification-enhancing and modifying additive was added for pretreatment, and the CP group is the group without adding the solidification-enhancing and modifying additive. The solidification-enhancing and modifying additive used includes raw material components composed of the following mass percentages: calcium chloride 74.5%, sodium chloride 10%, nano-silica 10%, dispersant 5%, EDTA-polymer derivative 0.5%.

[0060] Under standard curing conditions, the unconfined compressive strengths of the solidified bodies at 3, 7, 14, and 28 days were tested, and the results are as Figure 5 shown, Figure 5 The left figure in the middle corresponds to the shield slurry, and the right figure corresponds to the drilling mud, Figure 5 and the dosages of the additive in the middle are taken as 1.7%, 3.4%, 5%, and 6.7% respectively.

[0061] Shield mud group: The optimal effect is achieved when the admixture content of the modifier is 3.4% of the dry mass of bentonite, and the 28-day curing strength is increased to 346.5% of the original (164.5 kPa → 570 kPa). Drilling mud group: The optimal effect is achieved when the admixture content of the modifier is 5.0% of the dry mass of bentonite, and the 28-day curing strength is increased to 284.3% of the original (160.1 kPa → 455.1 kPa).

[0062] Example 3:

[0063] Solidification experiments were carried out using the waste mud from slurry shield in the project (the dry matter content of bentonite is 13%) and drilling mud (the dry matter content of bentonite is 25%) respectively. The admixture content of the solidifying agent is 20% of the dry mass of the mud, and a gradient admixture of 1.7% - 6.7% of the solidification-enhancing modifier is added for pretreatment before solidification. The CP group is the group without adding the solidification-enhancing modifier. The solidification-enhancing modifier used includes raw material components composed of the following mass percentages: 80% calcium chloride, 10% sodium chloride, 15% nano-silica, 7% dispersant, and 3% EDTA-polymer derivative.

[0064] Under standard curing conditions, the unconfined compressive strength of the solidified body at 3, 7, 14, and 28 days was tested, and the results are as Figure 6 shown,[[]]END]] Figure 6 The left figure in the middle corresponds to shield mud, and the right figure corresponds to drilling mud. Figure 6 The admixture content of the modifier in the middle is taken as 1.7%, 3.4%, 5%, and 6.7% respectively.

[0065] Shield mud group: The optimal effect is achieved when the admixture content of the modifier is 3.4% of the dry mass of bentonite, and the 28-day curing strength is increased to 201.0% of the original (164.5 kPa → 330.6 kPa). Drilling mud group: The optimal effect is achieved when the admixture content of the modifier is 3.4% of the dry mass of bentonite, and the 28-day curing strength is increased to 117.6% of the original (160.1 kPa → 188.2 kPa).

[0066] In the above examples, the dispersant and EDTA-polymer derivative should be in small and appropriate amounts, otherwise it will affect the function of the solidifying agent.

[0067] By using this method, the negative impact of bentonite in the geopolymer reaction can be effectively eliminated, the strength of the solidified mud can be improved, the resource utilization efficiency of solid waste can be enhanced, and it can be increased to 250% - 320% at most within the optimal admixture range. Although there may be significant differences in the solidification effects of different muds, the strength improvement is indeed remarkable, and it is also very effective in eliminating the effect of bentonite in the geopolymer reaction.

[0068] On the basis of using additives to solidify and modify waste mud, combined with an intelligent real-time detection and control system, real-time and efficient control of solidification is achieved, effectively solving the problem of inconsistent solidification effects of multi-source mud. The specific comparison with traditional methods is shown in the following table:

[0069] The above content is a further detailed description of the present invention in combination with specific preferred embodiments. It cannot be determined that the specific implementation of the present invention is only limited to these descriptions. For those of ordinary skill in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions or substitutions can be made, which should all be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for enhancing the geopolymerization curing performance of a slurry with a high bentonite content, characterized in that, Including a method for adding a curing and strengthening modifier, the method for adding the curing and strengthening modifier includes: adding the curing and strengthening modifier to the slurry with a high bentonite content, stirring evenly, and then standing for curing; Wherein the curing and strengthening modifier includes raw material components composed of the following mass percentages: Calcium chloride 70 - 80%, sodium chloride 10 - 20%, nano-silica 10 - 15%, dispersant 5 - 10%, EDTA-polymer derivative 0.5 - 3%.

2. The geopolymer curing performance enhancement and modification method of the high-bentonite-content mud according to claim 1, wherein Adding the curing and strengthening modifier to the slurry with a high bentonite content, stirring evenly, and then standing for curing, specifically includes: Adding the curing and strengthening modifier at 1.7 - 6.7% of the dry mass of bentonite in the slurry, performing high-speed shear stirring, and adding a curing agent for curing treatment after standing; the curing agent includes raw material components in the following mass ratio: slag:cement:sodium silicate = 42:38:

20.

3. The method for enhancing the geopolymer curing performance of the mud with high bentonite content according to claim 1, characterized in that, The dispersant is a polycarboxylate-based dispersant, and the EDTA-polymer derivative includes PAA-EDTA and PEG-EDTA.

4. The method for enhancing the geopolymer curing performance of the mud with high bentonite content according to claim 1, characterized in that, The preparation method of the curing and strengthening modifier includes: a1. Dissolving calcium chloride and sodium chloride in deionized water according to the composition ratio to prepare a mother liquor; a2. Adding nano-silica to the mother liquor and performing ultrasonic dispersion to obtain a uniformly dispersed solution; a3. Adding the EDTA-polymer derivative to the solution obtained in a2, and stirring and mixing to obtain a uniform solution; a4. Adding the dispersant to the solution obtained in a3, and stirring and mixing until a homogeneous suspension is formed.

5. The geopolymer curing performance enhancement modification method of the high-bentonite-content mud according to claim 1, characterized in that It also includes constructing an intelligent real-time detection and regulation system, and the construction process includes: b1. Setting a bentonite characteristic response sensor on the pumping path of the slurry mixing tank for detecting the bentonite content in the slurry; b2. Setting multi-modal element sensors at the input and output ports of the slurry mixing tank respectively for detecting the concentration of specified ions; b3. Setting a curing agent feeding point and an additive feeding point, the curing agent feeding point and the additive feeding point are respectively connected to the slurry mixing tank, the curing agent feeding point is used to store the curing agent, and the additive feeding point is used to store the curing and strengthening modifier; b4. Obtaining the parameter information of the bentonite characteristic response sensor and the multi-modal element sensor through feedback regulation software, performing real-time prediction of the bentonite content and ion concentration, making a decision on the optimal amount of material input of the curing agent and the curing and strengthening modifier, and controlling the material input of the curing agent feeding point and the additive feeding point.

6. The geopolymer curing performance enhancement modification method of the high-bentonite-content mud according to claim 5, characterized in that, The bentonite characteristic response sensor includes a CEC rapid determination unit, the CEC rapid determination unit is internally provided with an ammonium ion saturation column and a conductivity detection cavity, the ammonium ion saturation column is a microfluidic exchange column filled with a displacement solution, and when the slurry flows through at a fixed low rate, the ammonium ion replacement of the interlayer cations of montmorillonite is completed; the conductivity detection cavity monitors the change of the conductivity of the displacement solution in real time through a multi-electrode array, and calculates the CEC value in combination with the Langmuir adsorption model.

7. The method for enhancing the geopolymer curing performance of the high-bentonite-content mud according to claim 5, characterized in that, The bentonite characteristic response sensor includes an NIR auxiliary identification unit for auxiliary detection of bentonite content. The NIR auxiliary identification unit is equipped with a near-infrared spectrometer in the characteristic wavelength range of 510-530 cm -1 interval and 1070-1090 cm -1 interval, and a quantitative relationship between spectral absorbance and bentonite content is established through a regression algorithm.

8. The geopolymer curing performance enhancement and modification method for the high-bentonite-content mud according to claim 7, wherein, The process of establishing the quantitative relationship between the spectral absorbance and the bentonite content through the regression algorithm includes: c1. Preparing standard samples with known bentonite content to cover the detection range, and measuring the absorbance of each standard sample in the characteristic peak wavelength range using a near-infrared spectrometer; c2. Calculate the correlation coefficient between the wavelength corresponding to each sample and the standard characteristic peak wavelength of bentonite according to the characteristic peak wavelength range measured for each sample, retain the wavelengths with a correlation coefficient > 0.9, and automatically select and optimize the optimal wavelength combination through an intelligent algorithm; c3. Use partial least squares regression to establish a regression model, decompose spectral data and content data simultaneously, extract common latent variables, and construct a linear equation with the latent variables: predicted content = a × absorbance 1 + b × absorbance 2 + constant, where a and b are the contribution weights of the two characteristic wavelengths at 520 cm -1 and 1080 cm -1 respectively, and absorbance 1 and absorbance 2 correspond to the sum or average value of absorbances in the two characteristic wavelength ranges at 520 cm -1 and 1080 cm -1 respectively; when the predicted content is in the low content area (<25%) of the detection range, the predicted value = standard PLSR result; when the predicted content is in the high content area (>25%) of the detection range, the predicted value = standard PLSR result + correction coefficient × ln(absorbance); coefficients a and b are obtained by fitting the standard sample set using partial least squares method, and the correction coefficient is determined by non-linear fitting of high-concentration samples and dynamically adjusted with environmental parameters.

9. The method for enhancing the geopolymer curing performance of the mud with high bentonite content according to claim 5, characterized in that, The multimodal element sensor includes ISE unit: Adopting all-solid-state Ca 2+ / Na + dual-channel ion-selective electrode, using perovskite / graphene composite film as the electrode membrane material, the Ca 2+ ion-selective electrode and the Na + ion-selective electrode are both coated with electrode membranes to form the Ca 2+ sensitive membrane and the Na + sensitive membrane respectively, and are separated by a microchannel into two flow channels flowing through the Ca 2+ sensitive membrane and the Na + sensitive membrane respectively for synchronous dual-ion detection; a fluorescence unit: integrating the excitation light source of a dual-wavelength excitation optical fiber sensor, an Al fluorescence probe based on Lumogallion organic matter, and an Si fluorescence probe based on ammonium molybdate complex. The excitation light source wavelengths for irradiating the Al fluorescence probe and the Si fluorescence probe are 350 - 380 nm and 500 - 510 nm respectively.

10. The geopolymer curing performance enhancement and modification method of the high-bentonite-content mud according to claim 5, characterized in that, The feedback regulation software includes a concentration prediction model of a BP neural network and a multimodal dynamic compensation algorithm; Concentration prediction model of a BP neural network: Based on a large database, obtain the parameters of the sensor or manually input parameters to perform real-time prediction of bentonite content and ion concentration, and make a decision on the optimal amount of material to be put in. The parameters include ISE, fluorescence intensity, pH, CEC, and flow rate; Multimodal dynamic compensation algorithm: Through sensor drift compensation and ion interference compensation, the sensor drift compensation includes ISE potential compensation and fluorescence signal attenuation correction to correct the decision of the concentration prediction model.

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