A method for treating large-area mud slurry using a mud filter press

Abstract

This invention provides a method for treating large-area mud slurry using a mud filter press, belonging to the field of large-area mud slurry construction technology. The method includes: determining the optimal deployment location of the mud filter press based on the site layout; preparing a curing agent, optimizing the curing agent mix ratio, and stirring and adjusting it according to project needs to ensure quality and performance meet requirements; conveying the mud slurry to the filter press, where solid-liquid separation is achieved through high-pressure mechanical pressing; further filtering the pressed mud slurry using a filtration system to separate solid residue filter cake and filtrate; compacting the filter cake to reduce volume and improve stability; collecting the treated filter cake for backfilling, brick making, or other resource utilization according to project needs; and treating the filtrate through sedimentation, filtration, and adsorption to remove suspended solids, dissolved substances, and harmful substances to meet discharge or recycling standards. Finally, establishing a mud treatment efficiency evaluation model to optimize filter press operating parameters and improve treatment efficiency and effectiveness.

Description

Technical Field

[0001] This invention belongs to the field of large-area mud construction technology, specifically, it relates to a method for treating large-area mud construction using a mud filter press. Background Technology

[0002] Mud slurry is a highly fluid material commonly found in civil engineering construction, primarily composed of water, soil particles, and cementing materials. It is widely used in foundation pit support, ground treatment, tunnel construction, and other engineering projects, serving as an indispensable construction material. However, the generation and treatment of large quantities of mud slurry has become a significant challenge.

[0003] First, the generation and accumulation of large-scale mud slurry occupies a significant amount of on-site space, affecting normal construction operations. Construction sites often temporarily store mud slurry by stockpiling it nearby or using temporary sedimentation tanks, but this approach not only requires a large amount of space but also easily causes secondary pollution and environmental problems.

[0004] Secondly, mud contains a large amount of suspended solids, heavy metals, organic matter and other pollutants, and direct discharge will cause serious pollution to the surrounding water bodies, soil and atmosphere. Traditional mud treatment mostly uses simple mechanical sedimentation or chemical flocculation sedimentation, but the removal efficiency is limited, and the treated mud and effluent still cannot meet the discharge standards.

[0005] Furthermore, if untreated mud is used directly for backfilling, it will cause problems such as reduced foundation bearing capacity and increased settlement. Direct incineration or landfilling also have disadvantages such as high cost and high risk of secondary pollution.

[0006] Therefore, there is an urgent need to develop an efficient, environmentally friendly, and economical mud treatment technology to solve many problems in large-scale mud construction. Filtration technology, as an effective mud separation method, has received widespread attention. However, existing mud filtration processes still suffer from low processing efficiency. Summary of the Invention

[0007] In view of this, the present invention provides a method for treating large-area mud slurry using a mud filter press, which can solve the technical problem of low processing efficiency in existing mud filter press processes.

[0008] This invention is implemented as follows:

[0009] The first aspect of the present invention provides a method for treating large-area mud using a mud filter press, comprising the following steps:

[0010] S10. Based on the site layout, determine the optimal deployment location of the mud filter press to maximize the utilization of the mud pit space;

[0011] S20. Prepare the curing agent, adjust and optimize the curing agent mixing ratio, and stir and adjust according to the project needs to ensure that the quality and performance meet the requirements.

[0012] S30. The mud is transported to a mud filter press, where it is subjected to high-pressure mechanical pressing to achieve the initial separation of solid particles and liquid.

[0013] S40. The pressed mud is further filtered using a filtration system to separate the solid residue filter cake and filtrate.

[0014] S50. Compact the filter cake to reduce its volume and improve its stability, in preparation for subsequent processing.

[0015] S60. Collect the processed filter cake and use it for backfilling, brick making, or other resource utilization according to project requirements;

[0016] S70. The filtrate is treated by sedimentation, filtration and adsorption to remove suspended solids, dissolved solids and harmful substances to meet the discharge or recycling standards.

[0017] S80. Establish a mud treatment efficiency evaluation model, including treatment speed, solid-liquid separation effect, and filter cake moisture content index.

[0018] S90. Based on the evaluation model results, optimize the operating parameters of the slurry filter press, including pressure, filtration time, feed rate, filter plate spacing, vibration frequency, and filter cloth tension, in order to improve processing efficiency and effectiveness.

[0019] The determination of the optimal deployment location of the slurry filter press is achieved using a multi-objective optimization function, which is expressed as follows:

[0020]

[0021] In the formula, n is the number of mud generation points; m is the number of filter press deployment locations; x ij The value is 1 if the slurry is transported from point i to the filter press at position j, and 0 otherwise; j The value is 1 if a filter press is deployed at position j, and 0 otherwise; ij The unit transportation cost for transporting mud from point i to location j; f j Fixed costs for installing the filter press at location j; q j Cost of operating a filter press at location j; e j The cost of handling environmental impacts at location j.

[0022] This multi-objective optimization function is a mixed-integer programming problem that considers multiple factors, including the selection of filter press locations, slurry distribution, transportation costs, installation costs, operating costs, environmental costs, processing capacity, transportation distance, site area, environmental impact, and time. This problem can be solved using optimization methods such as branch and bound, cutting plane method, or heuristic algorithms. The solution to this model will provide the optimal deployment location of the slurry filter press and the optimal slurry distribution scheme.

[0023] The multi-objective optimization function includes a mud treatment capacity constraint, specifically expressed as follows:

[0024]

[0025] In the formula, v i Let P be the volume of mud produced at point i; j The processing capacity of the filter press at position j.

[0026] The multi-objective optimization function includes the constraint that each mud generation point must be assigned to a filter press, as specifically expressed below:

[0027]

[0028] The multi-objective optimization function includes a transportation distance constraint, specifically expressed as follows:

[0029]

[0030] In the formula, d ij D is the distance from point i to position j; max This represents the maximum permissible total transport distance.

[0031] The multi-objective optimization function includes site area constraints, specifically expressed as follows:

[0032]

[0033] In the formula, A j The location of the filter press is the floor area; A max This represents the maximum available total area.

[0034] The multi-objective optimization function includes environmental impact constraints, specifically represented as follows:

[0035]

[0036] In the formula, N j ,S j W j For location j, let w1, w2, and w3 be the noise, dust, and wastewater impact factors; E represents the environmental impact weights. maxTo the maximum permissible environmental impact.

[0037] The multi-objective optimization function includes time constraints, specifically expressed as follows:

[0038]

[0039] In the formula, t ij p is the time it takes to transport mud from point i to location j; j T represents the processing time of the filter press at position j. max This represents the maximum allowed total time.

[0040] The multi-objective optimization function includes decision variable constraints, specifically represented as follows:

[0041]

[0042] The step of establishing the mud treatment efficiency evaluation model specifically includes:

[0043] Based on the goals and requirements of mud treatment, establish a mud treatment efficiency evaluation index system that includes treatment speed, solid-liquid separation effect, and filter cake moisture content.

[0044] In the actual mud treatment process, the measured data of each evaluation index are systematically collected to form a sample dataset, and the dataset is statistically analyzed.

[0045] A regression analysis method was used to establish a predictive model between mud treatment efficiency indicators and key process parameters as a mud treatment efficiency evaluation model.

[0046] Sensitivity analysis was performed on the established evaluation model to quantify the impact of each process parameter on the processing efficiency index.

[0047] The established mud treatment efficiency evaluation model is integrated into the operation and management platform of the mud treatment system. The model is called in real time to predict the treatment efficiency index, and the model parameters are updated regularly based on actual operation data.

[0048] The mud treatment efficiency evaluation model is specifically represented as follows:

[0049] y = f(x; θ) + ∈;

[0050] in, For the efficiency index vector of the sample dataset, Let v be the feature vector of the sample dataset, θ be the model parameters, and ∈ be the random error term; v represents the processing speed, η represents the solid-liquid separation effect, and w represents the filter cake moisture content; specifically, the processing speed v represents the amount of mud processed per unit time, the solid-liquid separation effect η represents the degree of separation between solid matter and liquid, and the filter cake moisture content w represents the residual moisture content in the filter cake.

[0051] The sample dataset is represented as follows:

[0052] Where N represents the number of sample datasets.

[0053] Furthermore, step S10 specifically includes: spatial analysis and identification of mud generation points, multi-objective optimization of the mud filter press deployment location, and scheme visualization and evaluation. First, through on-site investigation of the construction site, spatial analysis algorithms are used to cluster the mud generation points and identify the main mud generation areas. Then, a multi-objective optimization model is established, including objective functions such as minimizing total transportation costs, minimizing total processing time, and minimizing environmental impact. Under constraints such as mud processing capacity, site area, and environmental impact threshold, a mixed integer programming algorithm is used to solve the optimization problem and determine the optimal deployment location of the mud filter press. Finally, the optimization results are visualized on a site plan to evaluate the economics, environmental impact, and operational feasibility of the scheme, and iterative optimization is performed as necessary. Through these steps, the optimal deployment location of the mud filter press can be determined, maximizing the use of existing resources, reducing overall operating costs, and meeting environmental impact constraints.

[0054] Furthermore, step S20 includes the following specific steps: curing agent performance testing, mix proportion optimization, on-site verification, and quality control. First, the available curing agents are subjected to physicochemical property testing, including indicators such as gelation time, compressive strength, and pH value, and compared with process requirements. Then, single-factor experiments or orthogonal experiments are used to systematically explore the effects of different curing agent dosages, water-cement ratios, and admixture dosages on the curing effect, determining the optimal curing agent mix proportion. Next, the optimized curing agent mix proportion is tested and verified on-site, detecting indicators such as strength, permeability, and stability after curing to ensure compliance with engineering requirements. Finally, a quality control system for curing agent preparation is established, including incoming raw material inspection, monitoring of the preparation process, and testing of finished product performance, ensuring that the quality and performance of the curing agent consistently meet standard requirements. Through these steps, a high-quality curing agent meeting engineering needs can be prepared, laying the foundation for subsequent slurry solidification treatment.

[0055] Furthermore, step S30 specifically includes: slurry pretreatment, high-pressure mechanical pressing, pressing parameter optimization, and performance monitoring. First, the slurry collected on-site is transported to the filter press inlet for necessary pretreatment, such as adjusting the pH value and adding flocculants, to improve the slurry's filterability. Then, high-pressure mechanical pressing is used to perform preliminary dehydration and separation of the slurry. By adjusting parameters such as pressure and pressing time, the pressing process is optimized to achieve the highest possible solid-liquid separation effect. Simultaneously, indicators such as pressure, flow rate, and discharge concentration are monitored in real time during the pressing process, and parameters are adjusted promptly to ensure stable and controllable pressing results. Through these steps, high-pressure mechanical force can be used to perform preliminary solid-liquid separation of the slurry, preparing it for subsequent deep filtration.

[0056] Furthermore, step S40 specifically includes: filtration system selection, filtration process optimization, filtration performance monitoring, and filter cake / filtrate treatment. First, based on the sludge characteristics, separation objectives, and processing capacity requirements, suitable filtration equipment is selected, such as belt filter presses, centrifuges, and vacuum filters. Then, for different filtration equipment, filtration parameters are optimized, such as filter media type, filtration pressure, and filtration time, and the optimal filtration process parameters are determined through experiments or simulation prediction methods. Next, indicators such as pressure difference, discharge concentration, and filter cake moisture content are monitored in real time during the filtration process, and filtration parameters are adjusted promptly to ensure stable filtration results. Finally, the separated solid filter cake and liquid filtrate are further processed. The filter cake can be compacted and dried, while the filtrate requires further purification treatment, such as sedimentation, flocculation, and adsorption. Through these steps, filtration technology can further separate solid particles and clean filtrate to meet the requirements for subsequent utilization or discharge.

[0057] Furthermore, step S50 includes the following specific steps: filter cake moisture content determination, compaction equipment selection, compaction parameter optimization, compaction process monitoring, and compaction effect evaluation. First, the moisture content of the filter cake is measured promptly using a drying method or other rapid measurement method. Then, based on the filter cake characteristics and compaction targets, suitable compaction equipment, such as a hydraulic press or vibrating table, is selected. Next, optimal compaction pressure, time, vibration frequency, and other parameters are determined through experiments or empirical formulas to obtain the target moisture content and volume reduction rate. Simultaneously, pressure, moisture content, and other indicators are monitored in real time during the compaction process, and parameters are adjusted promptly to ensure stable compaction results. Finally, the compacted filter cake is tested and evaluated for moisture content, strength, and other indicators to ensure it meets the requirements for subsequent treatment or utilization. Through these steps, mechanical compaction can further dehydrate and reduce the volume of the filter cake, preparing it for subsequent resource utilization or landfill disposal.

[0058] Furthermore, step S60 includes the following specific steps: filter cake property analysis, resource utilization plan formulation, resource utilization implementation, and utilization effect evaluation. First, the compacted filter cake undergoes physicochemical property analysis, including indicators such as moisture content, strength, and heavy metal content, providing a basis for subsequent utilization plan formulation. Then, based on the properties of the filter cake and engineering requirements, specific resource utilization plans are formulated, such as backfilling and brick making, and relevant process parameters are optimized for different utilization methods. Next, according to the formulated utilization plan, the treated filter cake is transported to a designated location for actual utilization. For example, in backfilling, layered laying and compaction are adopted; in brick making, the filter cake is mixed with other raw materials, shaped, and fired. Finally, the actual effect of resource utilization is evaluated, including indicators such as utilization volume and environmental impact, and the plan is optimized and adjusted as necessary. Through these steps, the resource value of the treated filter cake can be fully utilized, waste recycling can be achieved, and the environmental burden can be reduced.

[0059] Furthermore, step S70 specifically includes: filtrate property detection, treatment process selection, treatment process parameter optimization, treatment effect monitoring, and post-treatment water quality evaluation. First, the physicochemical properties of the separated filtrate are tested, including pH value, suspended solids content, and heavy metal concentration, providing a basis for subsequent treatment plan formulation. Then, based on the filtrate properties and discharge / reuse requirements, a suitable advanced treatment process is selected, such as flocculation sedimentation, adsorption, or membrane separation. Next, for the selected treatment process, key parameters such as flocculant dosage, adsorbent type, and membrane flux are optimized through experiments or simulations to obtain the best treatment effect. Simultaneously, key indicators during the treatment process, such as effluent pH value, turbidity, and heavy metal concentration, are monitored in real time, and process parameters are adjusted promptly to ensure stable and compliant treatment results. Finally, the treated water sample undergoes comprehensive testing to confirm that all indicators meet discharge or reuse standards, and the treatment process is further optimized if necessary. Through these steps, appropriate advanced treatment technologies can be used to remove suspended solids, dissolved substances, and harmful substances from the filtrate, meeting water discharge or reuse standards.

[0060] Furthermore, step S80 includes the following specific steps: indicator system construction, data collection and analysis, model establishment and verification, sensitivity analysis, and model application and updating. First, based on the goals and requirements of mud treatment, an indicator system for evaluating mud treatment efficiency is established, including treatment speed, solid-liquid separation effect, and filter cake moisture content. Then, during actual mud treatment, measured data for each evaluation indicator are systematically collected, and statistical analysis is performed to understand the relationships between the indicators. Next, regression analysis, grey relational analysis, and other modeling methods are used to establish an evaluation model for mud treatment efficiency. The model is verified and calibrated using measured data to ensure the accuracy and reliability of the model's predictions. Afterward, sensitivity analysis is performed on the evaluation model to identify the key factors that have the greatest impact on treatment efficiency, providing a basis for subsequent process optimization. Finally, the established evaluation model is applied to the actual mud treatment process, and the model parameters are updated regularly to ensure that the model continuously reflects the actual situation. Through these steps, a systematic mud treatment efficiency evaluation system can be established, providing a scientific basis for optimizing mud treatment.

[0061] Furthermore, step S90 includes the following specific steps: key parameter identification, single-factor experiments, orthogonal experimental optimization, on-site verification and adjustment, and automatic control system development. First, based on the aforementioned efficiency evaluation model, key parameters affecting the processing efficiency of the slurry filter press are identified, such as pressure, filtration time, feed rate, filter plate spacing, vibration frequency, and filter cloth tension. Then, for each key parameter, a single-factor experimental method is used to investigate its influence on processing efficiency and determine the optimal value range for each parameter. Next, an orthogonal experimental design method is used to systematically study the combined optimization of multiple key parameters, identifying the optimal combination of process parameters that comprehensively considers all parameters. Subsequently, the optimized combination of process parameters is applied to the actual slurry treatment process. Through on-site monitoring and effect evaluation, the parameters are further optimized and adjusted to ensure that the treatment effect meets the requirements. Finally, based on the optimized parameters, an automatic control system for the slurry filter press is developed to achieve real-time monitoring and automatic adjustment of key parameters, improving the stability and reliability of the treatment process. Through these steps, the processing efficiency and quality of the slurry filter press can be further improved.

[0062] Optionally, the single-factor test step in step S90 includes: conducting tests using different combinations of parameters such as pressure, filtration time, feed rate, filter plate spacing, vibration frequency, and filter cloth tension to test the impact of each parameter on the processing efficiency and determine the optimal value range of each parameter.

[0063] Optionally, the orthogonal experimental optimization step in step S90 includes: using orthogonal experimental design to systematically explore the combination optimization of multiple key parameters such as pressure, filtration time, feed rate, filter plate spacing, vibration frequency, and filter cloth tension, and to find the optimal combination of process parameters that comprehensively considers each parameter.

[0064] Optionally, the on-site verification and adjustment step in step S90 includes: applying the optimized combination of process parameters to the actual slurry treatment process, evaluating the treatment effect by on-site monitoring of indicators such as discharge concentration, pressure difference between inlet and outlet of the filter press, and moisture content of the filter cake, and adjusting the parameters appropriately according to the monitoring results to ensure that the treatment effect meets the requirements.

[0065] Optionally, the automatic control system development step in step S90 includes: developing an automatic control system for the slurry filter press based on the optimized process parameters, which can monitor key indicators such as pressure, flow rate, and moisture content in real time, and automatically adjust parameters such as pressure, feed rate, and vibration frequency according to a preset control algorithm to ensure the stability and reliability of the slurry treatment process.

[0066] Compared with existing technologies, the beneficial effects of the mud filter press method for treating large-area mud construction provided by this invention are:

[0067] First, this method determines the optimal layout scheme of the mud filter press by spatial analysis of the mud generation point and multi-objective optimization of the mud filter press deployment location, which maximizes the use of existing mud pool locations and shortens the mud transportation distance, thereby significantly reducing the overall operating cost.

[0068] Secondly, this method employs high-quality curing agent formulation and performance control measures to ensure the stability and reliability of slurry solidification treatment, laying a solid foundation for subsequent resource utilization. Simultaneously, parameter optimization and performance monitoring during the pressure filtration process improve the initial separation effect, creating favorable conditions for subsequent deep filtration treatment.

[0069] Furthermore, this method provides targeted treatment and utilization for both the filter cake and the filtrate, achieving effective separation of solid particles and clean water, and significantly reducing environmental impact through resource utilization. Compared to traditional simple sedimentation or landfill treatment, this method substantially reduces the risk of secondary pollution.

[0070] Finally, this method establishes a comprehensive evaluation system for mud treatment efficiency and uses optimization algorithms to determine the optimal combination of key process parameters, achieving intelligent control of the mud treatment process and continuously improving treatment efficiency and quality. This method based on mathematical modeling and algorithm optimization significantly improves the reliability and stability of mud treatment.

[0071] In summary, the method of this invention fully integrates engineering practice and scientific theory, systematically optimizes the entire process of mud pressure filtration, and solves the technical problem of low processing efficiency that still exists in existing mud pressure filtration processes. Attached Figure Description

[0072] Figure 1 A flowchart of the method provided by the present invention;

[0073] Figure 2 This is the construction process flow chart in Example 2;

[0074] Figure 3 This is a plan view of the filter press in Example 2. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0076] like Figure 1 The diagram shown is a flowchart of a method for treating large-area mud slurry using a mud filter press, provided by the present invention. This method includes the following steps:

[0077] S10. Based on the site layout, determine the optimal deployment location of the mud filter press to maximize the utilization of the mud pit space;

[0078] S20. Prepare the curing agent, adjust and optimize the curing agent mixing ratio, and stir and adjust according to the project needs to ensure that the quality and performance meet the requirements.

[0079] S30. The mud is transported to a mud filter press, where it is subjected to high-pressure mechanical pressing to achieve the initial separation of solid particles and liquid.

[0080] S40. The pressed mud is further filtered using a filtration system to separate the solid residue filter cake and filtrate.

[0081] S50. Compact the filter cake to reduce its volume and improve its stability, in preparation for subsequent processing.

[0082] S60. Collect the processed filter cake and use it for backfilling, brick making, or other resource utilization according to project requirements;

[0083] S70. The filtrate is treated by sedimentation, filtration and adsorption to remove suspended solids, dissolved solids and harmful substances to meet the discharge or recycling standards.

[0084] S80. Establish a mud treatment efficiency evaluation model, including treatment speed, solid-liquid separation effect, and filter cake moisture content index.

[0085] S90. Based on the evaluation model results, optimize the operating parameters of the slurry filter press, including pressure, filtration time, feed rate, filter plate spacing, vibration frequency, and filter cloth tension, in order to improve processing efficiency and effectiveness.

[0086] The specific implementation methods of the above steps are described in detail below:

[0087] Step S10: Determine the optimal deployment location of the slurry filter press based on the site layout.

[0088] 1. Mud Generation Point Location Analysis: First, a field survey is conducted at the construction site to determine the precise location and volume of mud generation points. Based on the distribution of mud generation points and the volume of mud generated, spatial analysis algorithms are used to perform cluster analysis on the mud generation points to identify the main mud generation areas.

[0089] 2. Optimization of Sludge Filter Press Location: A multi-objective optimization model is established, with objective functions including minimizing total transportation costs, minimizing total processing time, and minimizing environmental impact. Constraints include sludge processing capacity, site area, and environmental impact threshold. A mixed-integer programming algorithm is used to solve this optimization problem to obtain the optimal deployment location for the sludge filter press.

[0090] 3. Visualization and Evaluation of the Solution: The optimization results will be visualized on the site plan, intuitively reflecting the deployment location of the slurry filter press and the slurry transportation path. Simultaneously, the economic efficiency, environmental impact, and operational feasibility of the solution will be evaluated, and iterative optimization will be conducted as necessary.

[0091] The purpose of this step is to determine the optimal deployment location of the sludge filter press to maximize the use of existing sludge pond locations, shorten sludge transportation distances, reduce overall costs, and meet environmental impact constraints. Through spatial analysis, multi-objective optimization, and scheme evaluation, an optimal deployment scheme that comprehensively considers various factors is obtained.

[0092] Step S20: Prepare and optimize the curing agent mixing ratio

[0093] 1. Curing agent performance testing: Conduct physicochemical property tests on available curing agents, including indicators such as gelation time, compressive strength, and pH value, and compare and analyze them with process requirements.

[0094] 2. Mix Proportion Optimization: Using single-factor or orthogonal experimental methods, the effects of different curing agent dosages, water-cement ratios, and admixture dosages on the curing effect were systematically explored to determine the optimal curing agent mix proportion. The optimized curing agent mix proportion was obtained through data fitting and analysis.

[0095] 3. On-site verification: The optimized curing agent mix ratio will be tested on-site to verify its properties, including strength, permeability, and stability after curing, to ensure it meets project requirements. The mix ratio may be further optimized if necessary.

[0096] 4. Quality Control: Establish a quality control system for curing agent formulation, including incoming raw material inspection, monitoring of the formulation process, and testing of finished product performance. Ensure that the quality and performance of the curing agent consistently meet standard requirements.

[0097] The purpose of this step is to prepare a high-quality curing agent that meets engineering requirements, laying the foundation for subsequent slurry solidification treatment. Through systematic performance testing, mix design optimization, and on-site verification, the stability and reliability of the curing agent's performance are ensured. Simultaneously, quality control measures are established to guarantee the quality of the curing agent during preparation and use.

[0098] Step S30: The slurry is conveyed to a filter press for preliminary separation.

[0099] 1. Slurry Pretreatment: The slurry collected on-site is transported to the feed inlet of the filter press. Necessary pretreatment is performed on the slurry, such as adjusting the pH value and adding flocculants, to improve its filterability.

[0100] 2. High-pressure mechanical pressing: This method uses high-pressure mechanical pressing to initially dehydrate and separate the mud. During the pressing process, the mud is strongly compressed, and solid particles and liquid are initially separated.

[0101] 3. Optimization of pressing parameters: By adjusting parameters such as pressure and pressing time, the pressing process is optimized to achieve the highest possible solid-liquid separation effect. Optimal pressing parameters can be determined using experimental methods or model prediction.

[0102] 4. Performance monitoring: Real-time monitoring of indicators such as pressure, flow rate, and output concentration during the pressing process, and timely adjustment of parameters to ensure stable and controllable pressing effect.

[0103] The purpose of this step is to use high-pressure mechanical force to perform preliminary solid-liquid separation of the slurry, preparing it for subsequent deep filtration. By optimizing pressing parameters and monitoring performance in real time, the effectiveness of the preliminary separation is maximized.

[0104] Step S40: Further separate solids and liquids using a filtration system.

[0105] 1. Filtration system selection: Select appropriate filtration equipment, such as belt filter press, centrifuge, vacuum filter, etc., based on the characteristics of the sludge, separation objectives, and processing capacity requirements.

[0106] 2. Filtration Process Optimization: Optimize filtration parameters such as filter media type, filtration pressure, and filtration time for different filtration equipment. Determine the optimal filtration process parameters through experimentation or simulation prediction.

[0107] 3. Filtration performance monitoring: Real-time monitoring of indicators such as pressure difference, discharge concentration, and filter cake moisture content during the filtration process, and timely adjustment of filtration parameters to ensure stable filtration effect.

[0108] 4. Filter cake and filtrate treatment: The separated solid filter cake and liquid filtrate are then subjected to further processing. The filter cake can be compacted and dried, while the filtrate requires further purification treatment, such as sedimentation, flocculation, and adsorption.

[0109] The purpose of this step is to further separate solid particles and a clean filtrate using filtration technology. By appropriately selecting filtration equipment and optimizing filtration parameters, the solid-liquid separation effect is maximized. Simultaneously, the separated two-phase materials undergo targeted post-treatment to meet the requirements for subsequent utilization or discharge.

[0110] Step S50: Compact the filter cake.

[0111] 1. Filter cake moisture content determination: Use the drying method or other rapid determination methods to measure the moisture content of the filter cake in a timely manner.

[0112] 2. Compaction equipment selection: Select appropriate compaction equipment, such as hydraulic press, vibrating table, etc., according to the characteristics of filter cake and compaction target.

[0113] 3. Compaction parameter optimization: Determine the optimal compaction pressure, time, vibration frequency, and other parameters through experiments or empirical formulas to obtain the target moisture content and volume reduction rate.

[0114] 4. Compaction process monitoring: Real-time monitoring of indicators such as pressure and moisture content during the compaction process, timely adjustment of parameters, and ensuring stable compaction results.

[0115] 5. Compaction effect evaluation: The moisture content, strength and other indicators of the compacted filter cake are tested and evaluated to ensure that they meet the requirements for subsequent treatment or utilization.

[0116] The purpose of this step is to further dehydrate and reduce the volume of the filter cake through mechanical compaction, preparing it for subsequent resource utilization or landfill disposal. By optimizing compaction process parameters and monitoring the process, stable and reliable compaction results are ensured.

[0117] Step S60: Utilize the compacted filter cake for resource recovery.

[0118] 1. Filter cake property analysis: The physicochemical properties of the compacted filter cake are analyzed, including indicators such as moisture content, strength, and heavy metal content, to provide a basis for the formulation of subsequent utilization plans.

[0119] 2. Resource Utilization Plan Development: Based on the properties and characteristics of the filter cake and engineering requirements, develop specific resource utilization plans, such as backfilling and brick making. Optimize relevant process parameters for different utilization methods.

[0120] 3. Implementation of resource utilization: According to the established utilization plan, the processed filter cake is transported to the designated location for actual utilization. For example, when backfilling, measures such as layering and compaction are adopted; when making bricks, the filter cake is mixed with other raw materials, shaped, and fired.

[0121] 4. Evaluation of the effectiveness of resource utilization: Evaluate the actual effectiveness of resource utilization, including indicators such as utilization amount and environmental impact, and optimize and adjust the plan if necessary.

[0122] The purpose of this step is to fully utilize the resource value of the processed filter cake, maximize the recycling of waste, and reduce the environmental impact. Through systematic property analysis and the development of targeted utilization plans, the environmental friendliness and economic efficiency of filter cake resource utilization are ensured.

[0123] Step S70: Deep treatment of the filtrate

[0124] 1. Filtrate property testing: The physicochemical properties of the separated filtrate are tested, including pH value, suspended solids content, heavy metal concentration, etc., to provide a basis for the formulation of subsequent treatment plans.

[0125] 2. Process selection: Select appropriate advanced treatment processes based on the properties of the filtrate and discharge / reuse requirements, such as flocculation sedimentation, adsorption, membrane separation, etc.

[0126] 3. Process parameter optimization: For the selected process, optimize key parameters such as flocculant dosage, adsorbent type, and membrane flux through experiments or simulations to achieve the best treatment effect.

[0127] 4. Treatment effect monitoring: Real-time monitoring of key indicators during the treatment process, such as effluent pH, turbidity, and heavy metal concentration, and timely adjustment of process parameters to ensure stable and compliant treatment results.

[0128] 5. Post-treatment water quality evaluation: Conduct comprehensive testing on the treated water samples to confirm that all indicators meet the discharge or reuse standards. Further optimize the treatment process if necessary.

[0129] The purpose of this step is to remove suspended solids, dissolved substances, and harmful materials from the filtrate using appropriate advanced treatment technologies, ensuring that the treated water meets discharge or reuse standards. Through optimization of process parameters and real-time monitoring of treatment effects, the process is ensured to be stable and efficient, minimizing environmental impact.

[0130] Step S80: Establish a mud treatment efficiency evaluation model

[0131] 1. Construction of indicator system: Based on the goals and requirements of mud treatment, establish a mud treatment efficiency evaluation indicator system including treatment speed, solid-liquid separation effect, filter cake moisture content, etc.

[0132] 2. Data collection and analysis: In the actual mud treatment process, the measured data of each evaluation index are systematically collected, and the data are statistically analyzed to understand the relationship between the indexes.

[0133] 3. Model Establishment and Validation: Regression analysis and grey relational analysis were used to establish an evaluation model for mud treatment efficiency. The model was validated and calibrated using measured data to ensure the accuracy and reliability of its predictions.

[0134] 4. Sensitivity Analysis: Conduct sensitivity analysis on the evaluation model to identify the key factors that have the greatest impact on processing efficiency, providing a basis for subsequent process optimization.

[0135] 5. Model Application and Updates: The established evaluation model will be applied to the actual mud treatment process, and the model parameters will be updated regularly to ensure that the model continuously reflects the actual situation.

[0136] The purpose of this step is to establish a systematic evaluation system for mud treatment efficiency, quantifying the key performance indicators of the treatment process. Through data analysis and modeling, key factors affecting treatment efficiency are identified, providing a scientific basis for subsequent process optimization. Simultaneously, this evaluation model can be continuously applied in actual operation, providing decision support for optimizing mud treatment.

[0137] Step S90: Optimize the operating parameters of the mud filter press

[0138] 1. Key parameter identification: Based on the aforementioned efficiency evaluation model, key parameters affecting the processing efficiency of the slurry filter press are identified, such as pressure, filtration time, feed rate, filter plate spacing, vibration frequency, and filter cloth tension.

[0139] 2. Single-factor experiments: For each key parameter, single-factor experiments are conducted to investigate its impact on treatment efficiency. The optimal range of values ​​for each parameter is determined by analyzing the experimental data.

[0140] 3. Orthogonal experimental optimization: The orthogonal experimental design method is used to systematically study the combination optimization of multiple key parameters and find the optimal combination of process parameters that comprehensively considers all parameters.

[0141] 4. On-site verification and adjustment: The optimized combination of process parameters is applied to the actual mud treatment process. Through on-site monitoring and effect evaluation, the parameters are further optimized and adjusted to ensure that the treatment effect meets the requirements.

[0142] 5. Development of Automatic Control System: Based on the optimized parameters, develop an automatic control system for the mud filter press to achieve real-time monitoring and automatic adjustment of key parameters, thereby improving the stability and reliability of the processing.

[0143] 6. Establishment of Optimization Parameter Model: Based on the results of the aforementioned single-factor and orthogonal experiments, an optimization model is established between the key parameters of the slurry filter press and its processing efficiency. This model can be a regression model, a neural network model, or other data-driven model. The model can describe the degree of influence and interaction of each parameter on the processing indicators.

[0144] 7. Model Simulation and Optimization: Using the established optimization parameter model, numerical optimization algorithms, such as genetic algorithms and particle swarm optimization, are employed to find the optimal combination of process parameters that achieves the best processing efficiency. Model simulation is then used to analyze the impact of different parameter combinations on the processing effect.

[0145] 8. On-site verification and feedback: The optimized combination of process parameters is applied to the actual mud treatment process, and the treatment effect is tested and evaluated on-site. Based on the actual results, the parameters of the optimized model are adjusted appropriately to ensure that the model prediction results match the actual situation.

[0146] 9. Automatic Optimization Control: The optimized process parameters are integrated into the automatic control system of the slurry filter press, enabling real-time monitoring and automatic optimization adjustment of key parameters. The control system can automatically adjust parameters based on real-time monitored processing indicators, continuously optimizing slurry treatment efficiency.

[0147] The purpose of this step is to establish an optimized parameter model, use numerical optimization algorithms to find the optimal combination of process parameters, and integrate it into the automatic control system to achieve intelligent optimization of the operating parameters of the slurry filter press, thereby continuously improving the efficiency and quality of slurry treatment. This requires continuously refining the optimization model based on actual operating data and enhancing the adaptive capability of the control system.

[0148] In summary, this method of using a mud filter press for large-area mud treatment fully considers various factors in the mud treatment process. Through systematic optimization design and intelligent parameter control, it maximizes mud treatment efficiency while minimizing environmental impact. This method embodies the combination of engineering practice and scientific theory, providing reliable technical support for large-scale mud treatment.

[0149] Specifically, for steps S80 and S90, step S80 involves establishing a mud treatment efficiency evaluation model:

[0150] The purpose of this step is to establish a systematic evaluation system for mud treatment efficiency, and to quantitatively describe the key performance indicators of the treatment process. The specific steps are as follows:

[0151] 1. Construction of the indicator system

[0152] Based on the goals and requirements of mud treatment, a mud treatment efficiency evaluation index system was established, including treatment rate (v), solid-liquid separation effect (η), and filter cake moisture content (w). Here, treatment rate (v) represents the amount of mud processed per unit time, solid-liquid separation effect (η) represents the degree of separation between solids and liquids, and filter cake moisture content (w) represents the residual moisture content in the filter cake. These indicators comprehensively reflect the key performance characteristics of the mud treatment process.

[0153] 2. Data Collection and Analysis

[0154] In actual mud treatment processes, measured data of various evaluation indicators are systematically collected to form a sample dataset. in Let i be the feature vector of the i-th sample. These are the corresponding efficiency index values. Statistical analysis is performed on the dataset to understand the correlations and underlying patterns among the various indicators.

[0155] 3. Model Building and Validation

[0156] A regression analysis method was used to establish a predictive model between mud treatment efficiency and key process parameters. The model is assumed to be in the following form:

[0157] y = f(x; θ) + ∈;

[0158] in For efficiency index vectors, Let θ be the feature vector, θ be the model parameters, and ∈ be the random error term. Linear regression, neural networks, and other methods can be used to fit the model parameters θ, and cross-validation and other methods can be used to evaluate the model's predictive performance to ensure that the model's predictions match the actual situation.

[0159] 4. Sensitivity Analysis

[0160] Sensitivity analysis was performed on the established evaluation model to quantify the impact of each process parameter on the processing efficiency index. Grey relational analysis can be used to calculate the correlation degree γ between each parameter and the efficiency index.

[0161]

[0162] Where, Δ(x) j ,y k )=|x j -y k | represents the absolute difference between the j-th parameter and the k-th indicator, and ρ is the resolution coefficient. The larger the correlation value, the more significant the influence of the parameter on the indicator.

[0163] 5. Model Application and Updates

[0164] The established mud treatment efficiency assessment model is integrated into the operation and management platform of the mud treatment system, allowing for real-time prediction of treatment efficiency indicators. Simultaneously, the model parameters θ are updated periodically based on actual operational data to ensure the model continuously and accurately reflects actual operating conditions.

[0165] The following is step S90, which involves optimizing the operating parameters of the mud filter press:

[0166] The purpose of this step is to establish an optimized parameter model, use numerical optimization algorithms to find the optimal combination of process parameters, and integrate it into the automatic control system to achieve intelligent optimization of the operating parameters of the slurry filter press, thereby continuously improving the efficiency and quality of slurry treatment. The specific steps are as follows:

[0167] 1. Key parameter identification

[0168] Based on the aforementioned efficiency evaluation model, key parameters affecting the processing efficiency of the slurry filter press are identified, including pressure P, filtration time t, feed rate Q, filter plate spacing d, vibration frequency f, and filter cloth tension σ.

[0169] 2. Single-factor experiment

[0170] For each key parameter, a single-factor experimental method was used to investigate its influence on treatment efficiency. For example, a single-factor experiment was conducted on pressure P, where different pressure values ​​P = {P1, P2, ..., P...} were set. m The effects of the parameters on the processing speed v, solid-liquid separation efficiency η, and filter cake moisture content w were tested, yielding the functional relationships v = g1(P), η = g2(P), and w = g3(P). The optimal range of values ​​for each parameter was determined by analyzing the experimental data.

[0171] 3. Orthogonal Experiment Optimization

[0172] An orthogonal experimental design method was used to systematically study the combined optimization of multiple key parameters. The design of L... N (k m An orthogonal experimental matrix of type N is used, where N is the number of trials, k is the number of parameter levels, and m is the number of parameters. Based on the results of single-factor experiments, the range of values ​​for each parameter is determined, and then orthogonal experiments are conducted to test the response values ​​of each index. Analysis of variance is used to analyze the experimental results and find the optimal combination of process parameters that comprehensively considers all parameters.

[0173] 4. Optimize the parameter model establishment

[0174] Based on the aforementioned single-factor and orthogonal experimental results, an optimization model was established between the key parameters of the slurry filter press and its treatment efficiency. This model can describe the degree of influence and interaction of each parameter on the treatment indicators. The assumed model form is as follows:

[0175] y = f(x; θ) + ∈

[0176] in, For efficiency index vectors, Let θ be the parameter vector, θ be the model parameters, and ∈ be the random error term. The model parameters θ can be fitted using methods such as multiple linear regression and neural networks.

[0177] 5. Model Simulation and Optimization

[0178] Using the established optimization parameter model, numerical optimization algorithms such as genetic algorithm and particle swarm optimization are employed to find the optimal combination of process parameters x that achieves the best processing efficiency. * By using model simulation, the impact of different parameter combinations on the processing effect is analyzed, and the global optimal solution is found.

[0179] 6. Optional, on-site verification and feedback

[0180] The optimized process parameter combination x * The model is applied to actual mud treatment processes to conduct on-site tests and evaluations of its treatment effects. Based on the actual results, the parameters θ of the optimization model are appropriately adjusted to ensure that the model's predictions match the actual situation.

[0181] 7. Optional, automatic optimization control

[0182] The optimized process parameters are integrated into the automatic control system of the slurry filter press, enabling real-time monitoring and automatic optimization of key parameters. The control system can automatically adjust parameter x based on the real-time monitored processing index y, continuously optimizing the processing effect and improving the efficiency and quality of slurry treatment.

[0183] In summary, by establishing an optimized parameter model, employing numerical optimization algorithms to find the optimal process parameters, and integrating this model into the automatic control system, intelligent optimization of the operating parameters of the mud filter press was achieved, continuously improving the efficiency and quality of mud treatment. This method fully utilizes mathematical modeling, algorithm optimization, and automatic control technologies, providing reliable technical support for large-scale mud treatment.

[0184] Specifically, the principle of this invention is to achieve refined control over the entire mud treatment process by adopting systematic optimization design and intelligent parameter control methods.

[0185] First, through spatial analysis of the sludge generation points and multi-objective optimization of the filter press deployment locations, the optimal layout scheme for the sludge filter press was determined. This optimized design, based on actual engineering needs and operating conditions, maximizes the use of existing sludge pond locations and shortens sludge transportation distances, thereby significantly reducing overall operating costs. Simultaneously, environmental impact factors were considered, making the sludge treatment solution more environmentally friendly and sustainable.

[0186] Secondly, in the mud pretreatment and solidification stages, this invention employs high-quality solidifying agent formulation and performance control measures. Through systematic mix ratio optimization and on-site verification, the stable and reliable performance of the solidifying agent is ensured, laying the foundation for subsequent resource utilization. This emphasis on process quality control significantly improves the reliability of mud solidification treatment.

[0187] Furthermore, during the pressure filtration process, this invention maximizes the initial separation effect through parameter optimization and real-time performance monitoring. This method, based on process modeling and feedback control, makes pressure filtration more stable and efficient, creating favorable conditions for subsequent deep filtration.

[0188] Finally, this invention employs targeted treatment and utilization measures for both the filter cake and the filtrate. Through compaction and drying, the volume and moisture content of the filter cake are effectively controlled; while the filtrate undergoes deep purification through sedimentation, flocculation, and adsorption, meeting the standards for discharge or reuse. This solid-liquid separation and separate treatment method significantly improves the efficiency of resource utilization and minimizes environmental impact.

[0189] In summary, the core of the method of this invention lies in the systematic optimization of the entire mud treatment process, making full use of mathematical modeling, algorithm optimization and automatic control and other technical means to achieve refined management and control of mud treatment.

[0190] To better understand and implement this invention, the following is an example of a specific application scenario: A municipal rail transit project is a large-scale underground railway project involving extensive excavation and foundation treatment. These projects generated a large amount of mud waste, posing a significant challenge to construction management. To properly handle this mud, the project adopted the mud filter press method for treating large-area mud construction proposed in this invention.

[0191] 1. Identification and distribution analysis of mud generation points

[0192] According to the construction plan and site survey, there are eight main mud generation points in the project, located in the excavation area and the foundation treatment site. Each generation point generates approximately 100-300 m³ of mud. 3 The volume of mud produced varies by day, with a total mud production of approximately 1500 m³. 3 / d. To optimize the deployment of the slurry filter press, spatial analysis and cluster identification were first performed on these slurry generation points.

[0193] Using GIS spatial analysis software, the location information of eight mud generation points was entered into the system. The K-means clustering algorithm was then used to divide these points into three main cluster regions, located on the west, central, and east sides of the project. The mud generation points within each cluster region were relatively concentrated, providing a basis for subsequent optimization of the filter press deployment.

[0194] 2. Optimization of mud filter press deployment

[0195] Based on the spatial distribution characteristics of the mud generation points and considering the actual conditions at the engineering site, a mixed-integer programming method was used to optimize the deployment location of the mud filter press. The optimization objectives included minimizing total transportation costs, shortening total processing time, and minimizing environmental impact. Constraints included mud processing capacity, site area, and environmental impact threshold.

[0196] By solving this optimization problem, the optimal solution was determined to deploy one mud filter press each on the west, central, and east sides of the project. This not only maximizes the use of existing mud pit locations and significantly shortens the mud transportation distance, but also keeps the environmental impact within acceptable limits.

[0197] 3. Preparation and quality control of curing agent

[0198] Considering the actual characteristics of the mud in this project, an environmentally friendly cement-based curing agent was selected. Firstly, the physicochemical properties of this curing agent were systematically tested. The results showed that its setting time was 2-4 hours, and its 28-day compressive strength reached 8 MPa, meeting the project requirements.

[0199] Then, the mixing ratio of the curing agent was optimized through single-factor and orthogonal experiments. After multiple rounds of experiments, the optimal mixing ratio was determined to be 10% curing agent, 0.5 water-cement ratio, and 2% admixture. This scheme not only meets the strength requirements but is also environmentally friendly.

[0200] To ensure the stable quality of the curing agent, a strict quality control system has been established. This includes raw material inspection upon arrival, monitoring of the formulation process, and performance testing of the finished product. Furthermore, the curing agent is formulated using specialized mixing equipment to ensure the accuracy of the mixing ratio.

[0201] 4. Preliminary separation of mud

[0202] The slurry collected on-site was transported to the first filter press located on the west side of the project for preliminary separation. First, the slurry underwent pretreatment with pH adjustment and flocculant addition to improve its filterability. Then, inside the filter press, the slurry underwent mechanical pressing at a high pressure of 75 bar, resulting in initial solid-liquid separation.

[0203] By optimizing the pressure and time parameters, the optimal parameters for the pressure filtration process were determined to be: 75 bar pressure and 45 min pressing time. Under these conditions, the concentration of the pressed output reached 30%, meeting the requirements for subsequent filtration. Throughout the entire pressure filtration process, pressure, flow rate, concentration, and other indicators were monitored in real time to ensure the stability of the separation effect.

[0204] 5. Deep Filtration and Processing

[0205] The initially separated slurry was transported to the second belt filter press in the middle section for deep filtration. By optimizing parameters such as filter media type, filtration pressure, and filtration time, the optimal filtration process conditions were finally determined to be: polyester long fiber filter media, filtration pressure of 2 bar, and filtration time of 90 min.

[0206] Under these process conditions, the moisture content of the filtered cake is reduced to 25%, meeting the requirements for subsequent treatment. Simultaneously, the clarified liquid produced by filtration undergoes further deep treatment, including flocculation sedimentation and activated carbon adsorption, and all indicators meet the Class A standard of the "Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants," allowing for direct discharge.

[0207] 6. Resource utilization of filter cake

[0208] The filter cake with a moisture content of 25% obtained after pressure filtration was first compacted using a hydraulic press. After compaction at 35 bar, the moisture content of the filter cake was further reduced to 18%, and the volume was reduced to 1 / 3 of its original size.

[0209] Based on the physicochemical analysis of the filter cake, its main components are soil particles and a small amount of cement, making it perfectly suitable for use as backfill material. Therefore, the compacted filter cake was transported to the foundation treatment site on the east side of the project, laid in layers, and compacted for foundation reinforcement. This not only solved the filter cake disposal problem but also saved a significant amount of natural backfill material.

[0210] Monitoring showed that the backfilling treatment had no adverse effects on the surrounding soil and groundwater, fully meeting environmental protection requirements. Furthermore, compared to direct landfilling, this resource utilization method reduces the environmental burden and achieves waste recycling.

[0211] 7. Advanced treatment of filtrate

[0212] After preliminary separation in a filter press and deep filtration in a belt filter press, a large amount of clear filtrate was obtained. Systematic physicochemical property tests were performed on the filtrate, and the results showed that it still contained small amounts of suspended solids, dissolved substances, and heavy metals, among other contaminants.

[0213] To ensure the filtrate meets discharge standards, a third treatment unit—a membrane concentration unit—was deployed on the east side. By optimizing parameters such as membrane material, operating pressure, and residence time, the final removal rates were: suspended solids 99.5%, COD 95%, and heavy metals over 90%. All indicators of the treated filtrate met the Class A standard of the "Discharge Standard of Pollutants for Municipal Wastewater Treatment Plants," and it can be safely discharged.

[0214] Furthermore, the concentrate produced by the membrane concentration unit contains a high concentration of pollutants, making it unsuitable for direct discharge. Therefore, a deep treatment process combining chemical oxidation and activated carbon adsorption was adopted to further remove organic matter and heavy metals, ultimately meeting the requirements of the "Hazardous Waste Identification Standard" and allowing it to be safely disposed of by a qualified unit.

[0215] 8. Intelligent optimization of mud treatment efficiency

[0216] To continuously improve the efficiency and quality of mud treatment, this project established a mud treatment efficiency evaluation system covering factors such as treatment speed, solid-liquid separation effect, and filter cake moisture content. Through systematic monitoring and data analysis of various indicators during the actual treatment process, corresponding predictive models were established.

[0217] Grey relational analysis was used to quantify the impact of key parameters such as pressure, filtration time, and feed rate on processing efficiency. Based on this, a genetic algorithm was employed to optimize the combination of these process parameters, determining the parameter settings that achieve the optimal processing efficiency, including: pressure 70 bar, filtration time 50 min, and feed rate 150 m / s². 3 / h etc.

[0218] Finally, the optimized process parameters were integrated into the automatic control system of the filter press. This control system can monitor various performance indicators in real time and automatically adjust parameters such as pressure, feed rate, and vibration frequency according to preset optimization algorithms, thereby continuously optimizing the treatment effect. Through this intelligent control method, the stability and reliability of slurry treatment are greatly improved.

[0219] In this embodiment 1, the project achieved significant results in the mud treatment process:

[0220] 1) The total operating cost is reduced by more than 30% compared to the traditional solution, demonstrating good economic efficiency;

[0221] 2) The environmental impact indicators of the treated filtrate and filter cake all meet or exceed the relevant standards, and the environmental protection is effectively guaranteed;

[0222] 3) The overall processing efficiency has increased by more than 40% compared to before, meeting the needs of large-scale mud construction.

[0223] Additionally, the following is a simplified embodiment of the present invention, specifically Example 2: Figure 2 The on-site mud treatment construction process is described as follows: mud preparation → filtration and pressing → solid-liquid separation → filter cake treatment → filtrate treatment → mud cake compression → recycling.

[0224] (I) Construction Preparation

[0225] 1. Location preparation:

[0226] Using BIM simulation and 3D animation technology, the pile foundation area is divided into grids to determine the optimal deployment, ensuring maximum utilization of the mud concentration points and confirming the optimal locations of the mud pit and filter press.

[0227] 2. Preparation of admixtures:

[0228] Due to the liquid state and polluting nature of the mud, it cannot be discharged or buried on-site. Therefore, after the mud is drained by roller pressing, a sedimentation and solidification agent is added to the treated mud. After the mud is solidified, it is compressed into mud cakes and discharged.

[0229] By jointly studying the mixing ratio of the curing agent in the laboratory, we aim to maximize its effect on mud treatment and ensure the sedimentation of mud on site.

[0230] 3. Venue preparation:

[0231] A mobile slurry filter press refers to a slurry filter press placed on a flatbed truck, equipped with a conveyor, folding platform, guardrails, slurry mixing tank, high-pressure pump, etc., and moved along with the pile foundation construction area.

[0232] The mud filter press is equipped with dump trucks and loaders. The working area of ​​the filter press is 3m*17m.

[0233] 4. Mechanical Preparation: Filter presses are divided into plate and frame filter presses and chamber filter presses. They are intermittent pressure filtration devices used for solid-liquid separation of various suspensions. They rely on a pressing device to press the filter plates together, and then the suspension is pumped into the filter chamber, where solid particles and liquid materials are separated through the filter cloth.

[0234] (II) Key Processes

[0235] 1. Construction Deployment

[0236] Root Figure 3 As shown, based on the site layout, the optimal site deployment is determined to maximize the utilization of the mud pit location;

[0237] 2. Preparation of curing agent

[0238] Before proceeding with mud treatment, prepare the curing agent. Optimize the curing agent mix ratio, mixing and adjusting it according to project requirements to ensure quality and performance meet specifications.

[0239] 3. Filtering and pressing

[0240] The core of mud treatment is filtration and pressing using a mud filter. The mud passes through the filter system of the mud filter and undergoes high-pressure mechanical pressing, separating solid particles, while water and dissolved substances are discharged through the filter screen, thus achieving the purification and treatment of the mud.

[0241] 4. Solid-liquid separation

[0242] During the filtration and pressing process, solid particles in the slurry are separated to form solid residue, while the filtered liquid is called filtrate. Through proper treatment and collection, the solid residue and filtrate can be effectively separated for separate processing or disposal.

[0243] 5. Filter cake processing

[0244] After filtration and pressing, the solid particles in the slurry form a compacted solid residue called filter cake. Filter cake treatment typically includes pressing, drying, and collection to reduce volume and improve the stability of the solid residue, facilitating subsequent processing and disposal.

[0245] 6. Filtrate treatment

[0246] The filtered liquid filtrate requires further treatment to remove suspended solids, dissolved substances, and harmful materials, meeting discharge or recycling requirements. Common treatment methods include sedimentation, filtration, adsorption, and ion exchange to improve the quality and cleanliness of the filtrate.

[0247] 7. Recycling

[0248] After mud treatment, the treated mud and filtrate can be recycled. With proper treatment and adjustment, the mud and filtrate can be reused in engineering construction, achieving effective resource utilization and sustainable environmental development.

[0249] The mud pressure machine mud treatment technology achieves efficient treatment and purification of mud through mechanical pressure. It has the advantages of fast processing speed, good effect and simple operation. It is widely used in civil engineering, tunnel engineering and underground engineering and other fields, providing a reliable mud treatment solution for engineering construction.

[0250] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for treating large-area mud slurry using a mud filter press, characterized in that, Includes the following steps: S10. Based on the site layout, determine the optimal deployment location of the mud filter press to maximize the utilization of the mud pit space; S20. Prepare the curing agent, adjust and optimize the curing agent mixing ratio, and stir and adjust according to the project needs to ensure that the quality and performance meet the requirements. S30. The mud is transported to a mud filter press, where it is subjected to high-pressure mechanical pressing to achieve the initial separation of solid particles and liquid. S40. The pressed mud is further filtered using a filtration system to separate the solid residue filter cake and filtrate. S50. Compact the filter cake to reduce its volume and improve its stability, in preparation for subsequent processing. S60. Collect the processed filter cake and use it for backfilling, brick making, or other resource utilization according to project requirements; S70. The filtrate is treated by sedimentation, filtration and adsorption to remove suspended solids, dissolved solids and harmful substances to meet the discharge or recycling standards. S80. Establish a mud treatment efficiency evaluation model, including treatment speed, solid-liquid separation effect, and filter cake moisture content index. S90. Based on the evaluation model results, optimize the operating parameters of the slurry filter press, including pressure, filtration time, feed rate, filter plate spacing, vibration frequency, and filter cloth tension, in order to improve processing efficiency and effectiveness. The optimal deployment location of the sludge filter press is determined using a multi-objective optimization function, which is expressed as follows: ； In the formula, The number of mud generation points; Number of locations for the filter press deployment; If the slurry is transported from point i to the filter press at position j, the value is 1; otherwise, it is 0. The value is 1 if a filter press is deployed at position j, and 0 otherwise. The unit transportation cost for transporting mud from point i to location j; Fixed costs for installing the filter press at location j; Cost of operating a filter press at location j; Cost of handling environmental impacts at location j; The multi-objective optimization function includes a mud processing capacity constraint, specifically expressed as follows: ； In the formula, Let be the volume of mud produced at point i; The processing capacity of the filter press at position j; The multi-objective optimization function includes the constraint that each mud generation point must be assigned to a filter press, specifically as follows: ； The multi-objective optimization function includes a transportation distance constraint, specifically expressed as follows: ； In the formula, Let be the distance from point i to position j; The maximum permissible total transport distance; The multi-objective optimization function includes site area constraints, specifically expressed as follows: ； In the formula, The location of the filter press is the floor area. The maximum usable total area; The multi-objective optimization function includes environmental impact constraints, specifically expressed as follows: ； In the formula, For location j, the noise, dust, and wastewater influencing factors are: Environmental impact weighting; To the maximum permissible environmental impact; The multi-objective optimization function includes time constraints, specifically expressed as follows: ； In the formula, The time taken to transport mud from point i to location j; Let j be the processing time of the filter press at position j; This represents the maximum allowed total time.

2. The method for treating large-area mud slurry using a mud filter press according to claim 1, characterized in that, The multi-objective optimization function includes decision variable constraints, specifically represented as follows: 。 3. The method for treating large-area mud slurry using a mud filter press according to claim 2, characterized in that, The steps for establishing a mud treatment efficiency evaluation model specifically include: Based on the goals and requirements of mud treatment, establish a mud treatment efficiency evaluation index system that includes treatment speed, solid-liquid separation effect, and filter cake moisture content. In the actual mud treatment process, the measured data of each evaluation index are systematically collected to form a sample dataset, and the dataset is statistically analyzed. A regression analysis method was used to establish a predictive model between mud treatment efficiency indicators and key process parameters as a mud treatment efficiency evaluation model. Sensitivity analysis was performed on the established evaluation model to quantify the impact of each process parameter on the processing efficiency index. The established mud treatment efficiency evaluation model is integrated into the operation and management platform of the mud treatment system. The model is called in real time to predict the treatment efficiency index, and the model parameters are updated regularly based on actual operation data. The mud treatment efficiency evaluation model is specifically represented as follows: ； in, For the efficiency index vector of the sample dataset, For the feature vectors of the sample dataset, For model parameters, This is the random error term; Indicates processing speed. Indicates the solid-liquid separation effect. This indicates the moisture content of the filter cake; more specifically, the processing speed. This indicates the amount of mud processed per unit time, representing the solid-liquid separation effect. Indicates the degree of separation between solid and liquid substances; filter cake moisture content. This indicates the residual moisture content in the filter cake; The sample dataset is represented as follows: ;in, Indicates the number of sample datasets.

Images