Composite flocculant suitable for fine-grain tin tailings and control system and method of composite flocculant

Abstract

The invention discloses a composite flocculant suitable for fine-grain tin tailings and a control system and method thereof, and belongs to the technical field of mineral processing and industrial wastewater treatment. The composite flocculant system is composed of anionic polyacrylamide and a poly-aluminum ferric silicate synergist prepared from tailings as a raw material according to a dynamic proportion, and the formula of the composite flocculant system is intelligently adjusted according to the concentration, granularity and pH value of tailing slurry detected in real time. The invention further provides a matched control system, working condition data are obtained through the online sensing module, the optimal formula and the optimal adding amount are calculated through the decision center with a built-in expert rule base and an optimization model, the flexible preparation unit is driven to precisely synthesize the flocculating agent and execute adding, and meanwhile closed-loop optimization is achieved according to sedimentation effect feedback. According to the method, through unique'main flocculant-self-produced synergist 'binary dynamic synergy, the problems of low settling efficiency, poor return water quality and high medicament cost of the fine tin tailings are solved, and self-adaptive control of the flocculation process and internal recycling of tailings resources are realized.

Description

Technical Field

[0001] This invention relates to the field of mineral processing and industrial wastewater treatment technology, and more specifically, to a composite flocculant for treating fine-particle tin beneficiation tailings slurry, as well as an intelligent, customized preparation and precise dosing system and method for the flocculant. Background Technology

[0002] In the tin ore beneficiation process, in order to effectively recover the finely embedded cassiterite, the raw ore must be ground to a relatively fine particle size. This results in the tailings slurry containing a large number of fine particles that are difficult to settle. These fine particles have a large specific surface area and complex surface charges, and they exhibit a stable colloidal dispersion in the tailings slurry, settling extremely slowly by gravity.

[0003] The efficient concentration and clarification of tailings slurry is a core element in achieving closed-loop recycling of production water in mineral processing plants, reducing fresh water intake, minimizing tailings transport load, and improving the safety and environmental protection of tailings ponds. Currently, the mineral processing industry both domestically and internationally widely employs the addition of polymeric flocculants to enhance the flocculation and sedimentation of tailings particles. However, existing technologies face the following prominent bottlenecks in practical large-scale applications: 1. The contradiction between fixed-formulation flocculants and dynamic tailings characteristics: Fluctuations in the properties of raw ore in mines lead to continuous changes in parameters such as tailings slurry concentration, particle size, and pH, while fixed-formulation flocculants are difficult to adapt to these changes. In practice, inaccurate dosing often occurs due to finer particle size or pH shift, resulting in overflow "turbidity" or waste of reagents, poor sedimentation effect, and poor water quality stability.

[0004] 2. The contradiction between extensive control and precise regulation: At present, the addition of flocculants mostly relies on manual experience or simple flow ratio control. A few automatic systems only make PID adjustments based on the lagging overflow turbidity, which is slow to respond and cannot be pre-adjusted. It is a "post-event correction" that restricts the continuous optimization of return water quality and recovery rate.

[0005] 3. The contradiction between the pressure of reagent costs and the need for cost reduction: Flocculants rely on external purchases, accounting for a high proportion of costs. Environmental protection. Stricter standards have further increased the dosage or unit price of reagents, making it a real challenge for enterprises to reduce the processing cost per ton of ore.

[0006] 4. The contradiction between the potential of tailings resources and the current technology's view of them as waste: The elements such as silicon, aluminum, and iron contained in tailings have not been utilized. Existing technologies only treat them as objects of treatment, failing to achieve the resource cycle of "treating waste with waste" and failing to reduce costs and burdens from the source.

[0007] Existing patents primarily utilize static formulations for composite flocculants, and their automatic control systems are mostly single-parameter, lag-feedback mechanisms, lacking sufficient intelligence. Furthermore, none of these systems systematically integrate the four stages of online tailings characteristic sensing, dynamic flocculant optimization, precise dosing control, and tailings resource utilization. Therefore, there is an urgent need in this field for an integrated technology capable of real-time tailings characteristic sensing, intelligent dynamic preparation of suitable flocculants, precise feedforward-feedback control, and synergistic utilization of tailings resources to reduce overall costs, in order to overcome the common challenge of efficient, stable, and economical dehydration of fine-grained tin tailings. This invention is based on this need. Summary of the Invention

[0008] The purpose of this invention is to overcome the technical defects of existing technologies in the dehydration of fine-grained tin tailings, such as poor adaptability, extensive control, high cost, and lack of resource recycling, and to provide an integrated technical solution that can achieve dynamic optimization of flocculant formulation, precise control of addition, and resource utilization of tailings.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a composite flocculant suitable for fine-grained tin tailings, wherein the composite flocculant is a dynamic compound system composed of anionic polyacrylamide and polyaluminum ferric silicate synergist; the polyaluminum ferric silicate synergist is self-made from mine tailings using a wet process; the formulation composition range, based on dry basis effective ingredient weight parts, is: 70-90 parts of anionic polyacrylamide and 10-30 parts of polyaluminum ferric silicate synergist.

[0010] Furthermore, the molecular weight of the anionic polyacrylamide ranges from 8 million to 12 million.

[0011] Furthermore, the polymeric aluminum iron silicate synergist is a liquid product with a chain or network polymeric structure.

[0012] Furthermore, the preparation method of the polymerized aluminum ferric silicate synergist is as follows: (1) The tailings of the mining area are ground and then acid leached to obtain a leachate rich in aluminum and iron ions. The leachate is then purified. (2) Add silicon-containing compounds to the purified leachate and carry out a polymerization reaction under controlled pH and temperature conditions. After aging, a polymerized aluminum ferrosilicon liquid product is obtained.

[0013] The present invention also provides a control system for preparing and adding the above-mentioned composite flocculant, comprising: (1) Online sensing module, used for online real-time detection of solid mass concentration, specific fine particle content and pH value of tailings slurry; (2) Intelligent decision-making and control module, connected to the sensing module, has an expert knowledge base and dynamic optimization algorithm model built in it, which is used to determine the optimal ratio of anionic polyacrylamide and polymeric aluminum ferric silicate synergist, as well as the amount of tailings added per ton of dry tailings in real time based on the sensing data. (3) Flexible preparation module, connected to the intelligent decision and control module, is used to receive formula instructions and automatically complete the precise metering and mixing of anionic polyacrylamide solution and polymeric aluminum ferric silicate synergist working solution, and output composite flocculant working solution; (4) Precision dosing and effect feedback module, used to add the composite flocculant working liquid to the designated position of the tailings process according to the decision amount, monitor the overflow water quality and underflow concentration of the thickener, and feed the monitoring data back to the intelligent decision and control module.

[0014] Furthermore, the expert knowledge base includes expert rules relating tailings slurry particle size characteristics, concentration, and flocculant component ratio; the dynamic optimization algorithm model takes unit treatment cost and effluent quality compliance as optimization objectives.

[0015] Furthermore, the flexible preparation module includes an on-site preparation unit for polymeric aluminum ferric silicate synergist, which converts mine tailings into liquid polymeric aluminum ferric silicate product through acid leaching, purification, and polymerization processes.

[0016] The present invention also provides a method for processing fine-grained tin tailings slurry using the above-described control system, comprising the following steps: S1. The solid concentration, key fine particle content and pH value of the tailings slurry to be treated are obtained in real time through the sensing module; S2. Based on the data obtained in step S1, the intelligent decision-making and control module calls expert rules and runs an optimization model to determine the optimal ratio of anionic polyacrylamide and polymeric aluminum ferrosilicon synergist and the amount of tailings added per ton of dry tailings under the current working conditions. S3. The flexible preparation module prepares the composite flocculant working solution in real time according to the formula determined in step S2; S4. The precise addition and effect feedback module, based on the feed flow rate and the determined ton-of-ore addition amount, precisely adds the working fluid prepared in step S3 into the tailings slurry and mixes it. S5. Monitor the overflow water quality and bottom flow concentration after sedimentation and separation, and feed this information back to the intelligent decision-making and control module to adaptively fine-tune and optimize subsequent decision parameters.

[0017] Furthermore, the total amount of dry tailings added determined in step S2 ranges from 18 grams to 28 grams, and the total amount is calculated based on the total effective dry-basis components of anionic polyacrylamide and polyaluminum ferric silicate synergist.

[0018] The control system for preparing and adding composite flocculants provided by this invention achieves fully automated, precise, and adaptive control through a closed-loop architecture of "sensing-decision-execution-optimization," and has the following innovative features: 1. Rule-based decision-making logic with built-in expert knowledge base The expert knowledge base is a digital collection of experience formed by summarizing and verifying long-term production data, systematic small-scale tests, and industrial test results from the target mining area. Its core rules link key characteristic parameters of tailings slurry with flocculant formulations.

[0019] 2. Dynamic Algorithm Model The dynamic optimization algorithm model is the "computational core" of intelligent decision-making. It transcends simple rule matching and achieves optimal economic benefits under multiple constraints. The model is constructed as follows: a. Objective function: Minimize the cost of reagents per ton of dry tailings treatment, C. total : C total =P PAM ·X PAM +P PSAF ·X PSAF Among them, P PAM P PSAF These are the unit prices for PAM and self-made PSAF, respectively. This unit price data is maintained and updated by the system; X PAM X PSAF The decision variables are the dosages of the two components.

[0020] b. Core constraints: (1) Water quality constraints: predicted overflow water suspended solids concentration (SS) pred It must be lower than the set standard SS max SS pred It is calculated in real time by a multivariate nonlinear regression model trained on historical data, whose input variables include real-time sensing of tailings slurry particle size, concentration, pH, and the reagent ratio and total dosage for decision-making. (2) Proportioning constraint: The ratio of PAM to PSAF dosage X PAM / X PSAF It must be within the safe and effective range defined based on expert rules; (3) Total amount constraint: Total amount added X PAM +X PSAF It is limited to the range of empirically effective drugs to prevent waste or shortage of drugs.

[0021] c. Solution and Decision: The model employs a hybrid solution strategy combining linear programming and genetic algorithms. First, a feasible initial solution is quickly generated based on expert rules. Then, under the condition of satisfying all constraints, the solution minimizes C. total The model iteratively optimizes the target, ultimately outputting the optimal mix ratio and total dosage under the current operating conditions. It runs automatically once after each update of online sensing data, achieving dynamic optimization.

[0022] This invention pioneers a dynamic binary system of "PAM main agent + tailings source PSAF synergist". This system is not a simple mixture of two agents, but rather an intelligent decision-making system that adjusts the ratio in real time based on online sensing data of tailings characteristics.

[0023] 3. It has realized a circular utilization model that shifts from "reliance on external purchases" to "waste-to-waste treatment". Another significant feature of this invention is the construction of an internal recycling chain for tailings resources. Through a supporting on-site preparation module, some tailings that are difficult to settle are transformed into highly efficient PSAF flocculant enhancers through wet processes such as acid leaching, purification, and polymerization, and then reused in this process. This not only significantly reduces reliance on purchased reagents and overall costs, but also achieves resource-based reduction of solid waste at the source, resulting in both economic and environmental benefits.

[0024] Through synergistic innovation at the three levels mentioned above, this invention has formed a highly targeted, adaptive, low-cost, and resource-recycling overall solution for the efficient treatment of fine-grained tin tailings, effectively overcoming common industry problems such as low settling efficiency, poor return water quality, unstable operation, and high reagent costs in traditional processes. Attached Figure Description

[0025] Figure 1 This is a flowchart of the intelligent preparation and dosing system of the present invention; Figure 2 This is a flowchart of the synergist preparation process. Detailed Implementation

[0026] The embodiments of the present invention will be described in detail below with reference to the examples, but the scope of protection of the present invention is not limited to the following examples.

[0027] like Figure 1 and Figure 2 As shown, this invention achieves its goals through system construction and initialization, online sensing of tailings slurry characteristics, intelligent dynamic decision-making regarding flocculant formulation and dosage, flexible and real-time preparation of composite flocculants, precise addition and mixing of flocculants, effect monitoring and closed-loop optimization, and preparation of tailings source enhancement components, wherein: (1) System Construction and Initialization: Construct an intelligent preparation and dosing system, which includes an online sensing module, an intelligent decision-making and control module, a flexible preparation module, and a precise dosing and effect feedback module. In the initialization stage, based on the tailings characteristics and historical data of the target concentrator, an expert knowledge base and optimization algorithm model should be pre-set in the decision-making module, and control parameters should be set with the goals of achieving water quality standards and cost optimization.

[0028] (2) Online sensing of tailings slurry characteristics: The tailings slurry entering the thickening system is continuously and in real time detected through the online sensing module to obtain at least its solid mass concentration, percentage content of key fine particles and pH value.

[0029] (3) Intelligent dynamic decision-making on flocculant formulation and dosage: The decision module receives real-time sensing data, calls expert rules for preliminary judgment, and runs a dynamic optimization algorithm model to calculate the optimal ratio of each component in the composite flocculant under the current working conditions, as well as the dosage per ton of dry tailings. This dosage, after model optimization, can be controlled within the range of 18 to 28 grams, with the specific value changing dynamically with the working conditions.

[0030] (4) Flexible and immediate preparation of composite flocculant: Based on the determined formula, the flexible preparation module delivers each component to the mixing and maturation tank in proportion through a precision metering device. Under stirring conditions, the components are fully mixed and matured for a predetermined time to prepare a homogeneous and stable composite flocculant working solution.

[0031] (5) Precise addition and mixing of flocculant: The precise addition module calculates and controls the output flow of the metering pump in real time based on the real-time solid flow rate of the slurry and the addition amount per ton of dry ore issued by the decision module. The prepared working solution is precisely injected into the tailings slurry flow through the optimized addition point to ensure rapid and uniform mixing.

[0032] (6) Effect monitoring and closed-loop optimization: The turbidity and underflow concentration of the thickener overflow water are monitored in real time through the effect feedback module. The monitoring data is fed back to the decision module, compared with the actual control target, and the optimization model parameters or expert rules are automatically fine-tuned or calibrated to adaptively optimize the subsequent decision output, thereby realizing the complete intelligent closed-loop control of "perception-decision-execution-optimization".

[0033] (7) Preparation of tailings source enhancement components (can be operated in parallel or intermittently): After grinding the tailings to a density of over 90% -200 mesh, acid leaching is performed. The specific process parameters are as follows: using dilute sulfuric acid with a mass concentration of 8% to 12%, reacting at 60℃ to 80℃ and a liquid-to-solid ratio of 3:1 to 5:1. After purification, the leachate is polymerized with sodium silicate at a Si / (Al+Fe) molar ratio of 0.8 to 1.2, under conditions of controlled pH of 3.0 to 4.0. After aging, a polymerized aluminum ferrosilicon liquid product is obtained and stored as an enhancement component for later use. Example 1

[0034] This embodiment was implemented in the tailings thickening section of a large tin mine to verify the adaptive control capability and comprehensive benefits of the intelligent system of the present invention under real and complex working conditions.

[0035] (1) Implementation Background: The tin tailings slurry produced by this plant has a fine particle size, with -37μm particles accounting for about 45%-50%, and the solids concentration fluctuating between 8%-13%. The original dewatering process used a fixed addition of a single type of anionic PAM flocculant, with the dosage manually adjusted. Historical operating data showed that due to the fluctuating properties of the tailings, the original system was unstable, with a pre-plant water return rate of about 75%-77%, and the suspended solids concentration in the overflow water was high, typically above 70 mg / L, and fluctuated greatly. The quality of the returned water affected subsequent production.

[0036] (2) System deployment: The complete intelligent preparation and dosing system of the present invention is deployed in this section, including online concentration meter and particle size analyzer, industrial control computer equipped with pre-trained expert knowledge base, multi-component automatic batching and maturation device, and frequency conversion metering pump and water quality monitoring instrument.

[0037] (3) Dynamic operation process: The system is put into continuous operation. During operation, when the online sensing module detects that the content of key fine particles in the feed tailings slurry increases from 22% to 28%, the intelligent decision-making module starts optimization calculation according to expert rules. After the decision is output, the preparation module automatically performs formula adjustment. The dosing module accurately adds the feed according to the optimized unit consumption based on the real-time feed rate.

[0038] (4) Implementation Results: Compared with the traditional manual control period, the system control of this invention has achieved the following stable results: ① Operational Indicators: It has achieved a saving effect of reducing the flocculant consumption per ton of dry tailings by about 15%; the pre-plant water return rate is stable in the range of 78%-80%; the overflow water concentration is stably controlled within the target value of less than 50 mg / L, and the fluctuation range is significantly reduced. ② Economic Benefits: Considering the reduction in reagent consumption and the unit price advantage brought by the use of some self-made synergists, the cost of reagents for treating tons of dry tailings has been significantly reduced. Example 2

[0039] (1) Preparation of the synergistic component (PSAF): ① Raw material pretreatment: Take the zinc-tin tailings from the above-mentioned beneficiation plant, crush and grind them to a fineness of -200 mesh (over 90%). ② Acid leaching purification: Use dilute sulfuric acid with a mass concentration of 8%-12% and leach at 60℃-80℃ with a liquid-solid ratio of 3:1-5:1 for about 2 hours. After solid-liquid separation, a product rich in Al is obtained. 3+ Fe 3+ ③ Polymerization: The leachate is purified and the pH is adjusted to 3.0-4.0. Under stirring, sodium silicate solution is added at a Si / (Al+Fe) molar ratio of 0.8-1.2 to carry out the polymerization reaction. After aging, a stable polymerized aluminum iron silicate liquid product is obtained.

[0040] (2) Application verification: The self-made PSAF was introduced into the composite flocculant system of the present invention as a "characteristic adaptation and synergistic component". Parallel comparative tests conducted in the concentrator showed that, when the same effluent standard and underflow concentration were achieved, the PAM+PSAF system of the present invention could save about 20% of the PAM dosage compared with PAM alone, and the flocculation and sedimentation speed was faster and the water quality was better.

[0041] (3) Benefit Analysis: The preparation and application process constitutes a key link in the resource recycling of this invention. By "treating waste with waste," it directly reduces dependence on purchased reagents and overall costs. This reflects the outstanding feature of this invention, which combines economic and environmental benefits in "treating waste with waste."

[0042] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any minor modifications, equivalent substitutions, and improvements made to the above embodiments based on the technical essence of the present invention should be included within the protection scope of the present invention.

Claims

1. A composite flocculant suitable for fine-grained tin tailings, characterized in that, The composite flocculant is a dynamic compound system consisting of anionic polyacrylamide and polyaluminum ferric silicate synergist; the polyaluminum ferric silicate synergist is self-made from mine tailings through a wet process; based on the dry basis of effective ingredients by weight, its formula composition range is: 70-90 parts of anionic polyacrylamide and 10-30 parts of polyaluminum ferric silicate synergist.

2. The composite flocculant according to claim 1, characterized in that, The molecular weight of the anionic polyacrylamide ranges from 8 million to 12 million.

3. The composite flocculant according to claim 1, characterized in that, The polymeric aluminum ferric silicate synergist is a liquid product with a chain or network polymeric structure.

4. A control system for preparing and adding the composite flocculant according to any one of claims 1 to 3, characterized in that, include: (1) Online sensing module, used for online real-time detection of solid mass concentration, specific fine particle content and pH value of tailings slurry; (2) Intelligent decision-making and control module, connected to the sensing module, has an expert knowledge base and dynamic optimization algorithm model built in it, which is used to determine the optimal ratio of anionic polyacrylamide and polymeric aluminum ferric silicate synergist, as well as the amount of tailings added per ton of dry tailings in real time based on the sensing data. (3) Flexible preparation module, connected to the intelligent decision and control module, is used to receive formula instructions and automatically complete the precise metering and mixing of anionic polyacrylamide solution and polymeric aluminum ferric silicate synergist working solution, and output composite flocculant working solution; (4) Precision dosing and effect feedback module, used to add the composite flocculant working liquid to the designated position of the tailings process according to the decision amount, monitor the overflow water quality and underflow concentration of the thickener, and feed the monitoring data back to the intelligent decision and control module.

5. The control system according to claim 4, characterized in that, The expert knowledge base contains expert rules relating tailings slurry particle size characteristics, concentration, and flocculant component ratio; the dynamic optimization algorithm model takes unit treatment cost and effluent quality compliance as optimization objectives.

6. The control system according to claim 4, characterized in that, The flexible preparation module includes an on-site preparation unit for polymeric aluminum ferric silicate synergist, which converts mine tailings into liquid polymeric aluminum ferric silicate through acid leaching, purification, and polymerization processes.

7. A method for processing fine-grained tin tailings slurry using the control system described in any one of claims 4 to 6, characterized in that, Includes the following steps: S1. The solid concentration, key fine particle content and pH value of the tailings slurry to be treated are obtained in real time through the sensing module; S2. Based on the data obtained in step S1, the intelligent decision-making and control module calls expert rules and runs an optimization model to determine the optimal ratio of anionic polyacrylamide and polymeric aluminum ferrosilicon synergist and the amount of tailings added per ton of dry tailings under the current working conditions. S3. The flexible preparation module prepares the composite flocculant working solution in real time according to the formula determined in step S2; S4. The precise addition and effect feedback module, based on the feed flow rate and the determined ton-of-ore addition amount, precisely adds the working fluid prepared in step S3 into the tailings slurry and mixes it. S5. Monitor the overflow water quality and bottom flow concentration after sedimentation and separation, and feed this information back to the intelligent decision-making and control module to adaptively fine-tune and optimize subsequent decision parameters.

8. The method according to claim 7, characterized in that, The total amount of tailings added per ton of dry tailings determined in step S2 ranges from 18 grams to 28 grams, and the total amount is calculated based on the total effective dry-basis components of anionic polyacrylamide and polyaluminum ferric silicate synergist.

9. The composite flocculant according to claim 1 or 3, characterized in that, The preparation method of the polymerized aluminum ferric silicate synergist is as follows: (1) The tailings of the mining area are ground and then acid leached to obtain a leachate rich in aluminum and iron ions. The leachate is then purified. (2) Add silicon-containing compounds to the purified leachate and carry out a polymerization reaction under controlled pH and temperature conditions. After aging, a polymerized aluminum ferrosilicon liquid product is obtained.

10. The control system according to claim 4, characterized in that, The method for constructing the dynamic optimization algorithm model is as follows: a. Objective function: Minimize the cost of reagents per ton of dry tailings treatment, C. total : C total =P PAM ·X PAM +P PSAF ·X PSAF Among them, P PAM P PSAF These are the unit prices for PAM and self-made PSAF, respectively. This unit price data is maintained and updated by the system; X PAM X PSAF The decision variables are the dosages of the two components. b. Core constraints: (1) Water quality constraints: predicted overflow water suspended solids concentration (SS) pred Below the set standard SS max ;SS pred It is calculated in real time by a multivariate nonlinear regression model trained on historical data, whose input variables include real-time sensing of tailings slurry particle size, concentration, pH, and the reagent ratio and total dosage for decision-making. (2) Proportioning constraint: The ratio of PAM to PSAF dosage X PAM / X PSAF Within the safe and effective range defined by expert rules; (3) Total amount constraint: Total amount added X PAM +X PSAF Limit the dosage to the range that is effective based on experience, in order to prevent waste or shortage of medicine; c. Solution and Decision-Making: The model employs a hybrid solution strategy combining linear programming and genetic algorithms. First, a feasible initial solution is quickly generated based on expert rules. Then, under the condition of satisfying all constraints, the solution minimizes C. total The model iteratively optimizes the target and ultimately outputs the optimal mix ratio and total dosage under the current working conditions. The model runs automatically once after each online sensing data update to achieve dynamic optimization.

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