Leachate concentrated water softening control method based on double closed-loop predictive control

Abstract

The embodiment of the invention discloses a leachate concentrated water softening control method based on double closed-loop predictive control, and relates to the technical field of automatic control of environmental engineering. The method comprises the following steps: acquiring multi-dimensional data acquired in real time; a dynamic mass balance equation of a magnesium removal / calcium removal reaction is constructed, the dosage of NaOH / Na2CO3 is optimized in a rolling mode on the basis of a model prediction control algorithm, and accurate control over the pH value and the hardness removal rate is achieved; a membrane pollution state space equation is constructed, a membrane type switching and cleaning strategy is optimized in combination with a dynamic programming algorithm, and Pareto optimal control of flux maintenance and cost minimization is achieved. The industrial problems of low control precision, high operation cost and poor system robustness in high-hardness wastewater softening treatment are solved, and the method has remarkable technical advancement and engineering application value.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic control in environmental engineering, and particularly to a leachate concentrate softening control method based on double closed-loop predictive control. Background Art

[0002] In the softening treatment of leachate concentrate, the following technical bottlenecks exist in traditional control methods:

[0003] 1. Chemical softening link: It relies on fixed pH value setting and empirical dosing, and no real-time coupling relationship between the dynamic change of Mg 2+ / Ca 2+ concentration and the dosing amount of chemicals is established, resulting in a chemical waste rate of 20%-30%, and when the pH fluctuation exceeds ±0.5, the hardness removal rate drops by more than 15%.

[0004] 2. Ultrafiltration membrane control link: The cleaning strategy based on the pressure difference threshold lags behind the membrane fouling process, the membrane flux decay rate reaches 5% per day, and the switching of multiple membrane types depends on manual experience, and no intelligent decision-making mechanism targeting the operating cost is formed.

[0005] 3. System coordination: There is a lack of linkage control between the chemical reaction section and the membrane separation section, and problems such as membrane blockage caused by the mismatch between the particle size distribution of the solids after the reaction and the membrane pore size often occur, affecting the continuous operation time of the system.

[0006] In the prior art, the patent application CN119087802A discloses a wastewater treatment control strategy based on a fuzzy neural network. The core technology is to optimize the BP neural network using the PSO algorithm, construct a wastewater treatment prediction model, and achieve high-precision prediction of the effluent COD and suspended solids (SS) through data normalization, model training, and performance evaluation, and improve the prediction accuracy through intelligent algorithms.

[0007] This patent application has the following disadvantages: It does not model specific chemical reactions (such as Mg 2+ / Ca 2+ precipitation kinetics) in the softening process of leachate concentrate, and has strong universality but weak industry pertinence.

[0008] The patent application CN118963154A discloses a wastewater treatment control optimization method and system based on monitoring data. The core technology is to establish a process benchmark and perform abnormal node compensation by real-time monitoring of the wastewater treatment process node data, realize dynamic feedback control, and propose an "abnormal node detection-downstream compensation" mechanism.

[0009] The patent application for invention has the following disadvantages: insufficient model depth, reliance on empirical benchmark values, failure to establish a chemical reaction kinetics or membrane fouling mathematical model, and difficulty in coping with drastic fluctuations in water quality. Moreover, the coupling relationship between the chemical softening section and the membrane separation section (such as the influence of reaction product particle size on membrane pore blockage) is not associated, making it impossible to achieve cross-section collaborative optimization. Summary of the Invention

[0010] In view of this, embodiments of the present invention provide a leachate concentrate softening control method based on dual closed-loop predictive control to solve the industry problems of "low control accuracy - high operating cost - poor system robustness" in the softening treatment of high-hardness wastewater.

[0011] A leachate concentrate softening control method based on dual closed-loop predictive control includes:

[0012] Step S101: Obtain multi-dimensional data collected in real time;

[0013] Step S102: Construct a dynamic mass balance equation for the magnesium / calcium removal reaction, and based on the model predictive control algorithm, roll-optimize the dosage of NaOH / Na2CO3 to achieve precise control of the pH value and hardness removal rate;

[0014] Step S103: Construct a membrane fouling state space equation, and combine the dynamic programming algorithm to optimize the membrane type switching and cleaning strategy to achieve the Pareto optimal control of flux maintenance and cost minimization.

[0015] Further, in the step S102, the constructed dynamic mass balance equation for the magnesium / calcium removal reaction is:

[0016] Magnesium removal reaction:

[0017] ;

[0018] Wherein, : The change rate of magnesium ion concentration in the reaction tank over time, k1: Reaction rate constant, [OH - ]: Hydroxide ion concentration in the reaction tank, V: Volume of the reaction tank, Q: Inlet water flow rate, : Mass flow rate of magnesium ions brought in by the inlet water, : Mass flow rate of magnesium ions carried out by the outlet water;

[0019] Calcium removal reaction:

[0020] ;

[0021] Wherein, k2: Reaction rate constant, : Carbonate ion concentration in the reaction tank, and the meanings of other terms are the same as those in the magnesium removal reaction.

[0022] Further, the step S102 includes:

[0023] Step S1021: According to the real-time water quality data, through the dynamic mass balance equation of magnesium removal / calcium removal reaction, optimize the dosage of NaOH / Na2CO3 in real time, control the pH value and the removal rate of calcium and magnesium hardness, and dynamically correct the reaction kinetic parameters to correct the water quality fluctuation;

[0024] Step S1022: In each control period, perform quadratic programming to generate the optimal chemical dosage sequence within a preset future time period.

[0025] Further, in the step S1021, the formula for dynamically correcting the reaction kinetic parameters is:

[0026] ;

[0027] Wherein, : The reaction rate constant at time t, : The reaction rate constant at the previous moment, : The adaptive learning rate, : The measured water production hardness at the previous moment, : The predicted water production hardness of the model at the previous moment, y target : The target water production hardness;

[0028] And / or, in the step S1022, the formula for quadratic programming is:

[0029] ;

[0030] Wherein, : The predicted water production hardness at time t + k, : The target water production hardness at time t + k, Q: The hardness error weight matrix, : The chemical dosage at time t + k calculated at time t, R: The control input weight matrix, N: The control time domain.

[0031] Further, in the step S103, the constructed membrane fouling state space equation is:

[0032] <s ;

[0033] Wherein, : The membrane pore blockage rate, : The membrane pore blockage rate The rate of change with time, : The blockage rate constant, : The cleaning recovery constant, u clean : The cleaning control variable, h: The thickness of the filter cake layer on the membrane surface, : Variation rate of the thickness h of the filter cake layer on the membrane surface with time, : Filter cake growth coefficient, : Backwashing stripping coefficient, J: Membrane flux, C solid : Concentration of the mixed liquor solids.

[0034] Further, the step S103 includes:

[0035] Step S1031: Estimate the membrane pore blockage rate and the thickness of the filter cake layer on the membrane surface through real-time data, and update the membrane fouling level;

[0036] Step S1032: Establish a cost-efficiency matrix, and use the Q-learning algorithm to select the optimal membrane type;

[0037] Step S1033: When the membrane pore blockage rate is greater than or equal to the first preset threshold or the thickness of the filter cake layer on the membrane surface is greater than or equal to the second preset threshold, trigger chemical cleaning, and the cleaning liquid formula is automatically switched according to the membrane type.

[0038] Further, in the step S1032, the membrane type switching condition is:

[0039] ;

[0040] Wherein, m: Membrane type variable, A: BUFF membrane, B: Ceramic membrane, C: Hollow fiber membrane, : Membrane module cost, : Energy consumption cost, : Cleaning cost.

[0041] Further, the method further includes:

[0042] Step S104: Use the dynamic programming algorithm to solve the multi-objective optimization problem including the hardness removal rate, membrane flux, and operating cost, and realize the coordinated control of the chemical section and the membrane section.

[0043] Further, in the step S104, the objective function constructed including the hardness removal rate, membrane flux maintenance, and chemical agent cost is:

[0044] , ;

[0045] Wherein, F: Objective function, : Hardness removal rate weight coefficient, : Total hardness of the real-time produced water, y target : Target total hardness of the produced water, : Membrane flux weight coefficient, : Cumulative value of the membrane flux within the period T, : Chemical agent cost weight coefficient, c i: Unit price of the i-th reagent, m i : Dosage of the i-th reagent, n: number of reagent types;

[0046] And / or, in the step S104, when the turbidity of the membrane tank is greater than the third preset threshold, automatically extend the sedimentation time of the reaction tank;

[0047] And / or, in the step S104, when the membrane flux is continuously lower than 80% of the design value for a certain period of time, reversely correct the dynamic mass balance equation of the magnesium / calcium removal reaction and increase the dosage of Na2CO3.

[0048] Furthermore, the method further includes:

[0049] Step S105: Dynamically adjust the control parameters based on the particle swarm optimization algorithm to adapt to water quality fluctuations and equipment aging.

[0050] The present invention has the following beneficial effects:

[0051] Through the deep integration of theoretical modeling, intelligent algorithms and engineering practice, the present invention solves the industry problems of "low control accuracy - high operation cost - poor system robustness" in the softening treatment of high-hardness wastewater, and has significant technological advancement and engineering application value. Description of the Drawings

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0053] Figure 1 It is a flow chart of the leachate concentrate softening control method based on double closed-loop predictive control of the present invention;

[0054] Figure 2 It is a double closed-loop predictive control architecture diagram of the present invention, which shows the coupling relationship between the inner-loop chemical reaction control and the outer-loop membrane system decision-making;

[0055] Figure 3 It is a flow chart of the multi-objective optimization algorithm of the present invention, which presents the complete logic from data acquisition to control output. Detailed Embodiments

[0056] The following will describe the embodiments of the present invention in detail with reference to the drawings.

[0057] It should be clear that the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0058] An embodiment of the present invention provides a leachate concentrate softening control method based on double closed-loop predictive control, as Figures 1-3 shown, including:

[0059] Step S101 (real-time multi-dimensional data acquisition): Obtain real-time multi-dimensional data;

[0060] This step corresponds to Figure 2 the "data acquisition layer" in Figure 3 and the "data layer" in

[0061] Specifically, when implemented, at least part of the multi-dimensional data is collected by core sensors. The core sensors may include a pH meter and an online magnesium ion detector set in the magnesium removal reaction tank, a calcium ion online detector and a conductivity meter set in the calcium removal reaction tank, and a membrane pressure difference sensor and a produced water turbidity meter set in the membrane system. The collection frequency can be flexibly set according to needs, for example, 100 ms / time.

[0061] Step S102 (inner loop model predictive control): Construct a dynamic mass balance equation for the magnesium removal / calcium removal reaction, and based on the model predictive control algorithm, roll and optimize the dosing amounts of NaOH / Na2CO3 to achieve precise control of the pH value and the hardness removal rate;

[0062] This step corresponds to Figure 2 the inner loop (chemical reaction control) in 2+ / Ca 2+ : Through the dynamic equilibrium equation of Mg 2+ / Ca 2+ concentration, combined with the MPC (Model Predictive Control) algorithm, roll and optimize the dosing amount of the reagent to control the pH value at 11.0 - 11.5, ensuring that the calcium and magnesium removal rate ≥ 90%;

[0063] Input parameters: real-time ion concentration, pH value, influent flow rate;

[0064] Output control: frequency of the NaOH / Na2CO3 metering pump, rotational speed of the stirrer.

[0065] As an optional embodiment, in step S102, the constructed dynamic mass balance equation for the magnesium removal / calcium removal reaction is:

[0066] Magnesium removal reaction (main reaction for Mg 2+ removal):

[0067] ;

[0068] Among them, is the change rate of the concentration of magnesium ions (Mg 2+ ) in the reaction pool with respect to time (unit: );

[0069] is the reaction rate constant, which describes the rate of the reaction of Mg 2+ with OH - to form Mg(OH)2 precipitate;

[0070] is the concentration of hydroxide ions in the reaction pool (unit: mol / L), which is controlled by the dosage of NaOH;

[0071] V = 0.54m 3 is the volume of the reaction pool, and Q = 0.5m³ / h is the influent flow rate;

[0072] is the mass flow rate of magnesium ions brought in by the influent (unit: mol / s);

[0073] is the mass flow rate of magnesium ions taken out by the effluent (unit: mol / s).

[0074] Calcium removal reaction (Ca 2+ removal main reaction):

[0075] ;

[0076] Among them, is the reaction rate constant;

[0077] is the concentration of carbonate ions in the reaction pool, which is jointly determined by the hydrolysis of HCO3 - after the addition of NaOH and the direct addition of Na2CO3;

[0078] The meanings of other items are the same as those in the magnesium removal reaction.

[0079] This step corresponds to Figure 2 the "chemical reaction kinetics model" in

[0080] As another alternative embodiment, the step S102 includes:

[0081] Step S1021 (prediction model update): According to the real-time water quality data (Mg 2+ / Ca 2+Concentration), through the chemical reaction kinetics model, that is, the dynamic mass balance equation of the magnesium / calcium removal reaction, the dosing amounts of NaOH / Na2CO3 are optimized in real time, the pH value and the removal rates of calcium and magnesium hardness are controlled, the lag problem of the traditional control method is solved, and the reaction kinetics parameters (i.e., the previous reaction rate constants k1 / k2) are dynamically corrected to correct the water quality fluctuations (such as a ±10% change in the total hardness of the raw water);

[0082] This step corresponds to Figure 3 "Chemical reaction model update" in

[0083] Preferably, in the step S1021, the formula for dynamically correcting the reaction kinetics parameters is:

[0084] ;

[0085] Wherein, is the reaction rate constant at time t (i = 1 for the magnesium removal reaction, i = 2 for the calcium removal reaction);

[0086] is the reaction rate constant at the previous moment;

[0087] is the adaptive learning rate, controlling the parameter correction amplitude;

[0088] is the measured water production hardness at the previous moment (such as calcium / magnesium hardness);

[0089] is the predicted water production hardness by the model at the previous moment;

[0090] y target is the target water production hardness (such as calcium hardness ≤ 300 mg / L).

[0091] Step S1022 (rolling optimization): In each control cycle (such as 1 min), quadratic programming is solved to generate the optimal chemical dosing sequence within a preset future time period (which can be flexibly set according to requirements, 30 min in this embodiment).

[0092] This step corresponds to Figure 3 "Rolling optimization: quadratic programming solution" in

[0093] Preferably, in the step S1022, the formula for quadratic programming solution is:

[0094] ;

[0095] Wherein, is the predicted water production hardness at time t + k at time t;

[0096] is the target water production hardness at time t + k (e.g., total hardness ≤ 1800 mg / L);

[0097] Q is the hardness error weight matrix, which gives priority to ensuring water quality compliance;

[0098] is the chemical agent dosage at time t + k calculated at time t (NaOH / Na2CO3);

[0099] R is the control input weight matrix, which avoids large fluctuations in chemical agent dosage.

[0100] N is the control time domain, which refers to the number of time steps of future control actions that need to be determined at the current moment. For example, N = 60, that is, each optimization generates 60 future control cycles (each cycle is 1 minute, a total of 60 minutes).

[0101] The constraint conditions can be flexibly set according to actual production conditions (such as the size of the reaction kettle and the cost) and production requirements. In the embodiments of the present invention, the constraint conditions are that the NaOH dosage ≤ 15 L / h, the Na2CO3 dosage ≤ 12 L / h, and the pH value is maintained at 11.0 - 11.5.

[0102] The above steps S1021 - step S1022 correspond to Figure 3 the inner loop (MPC) optimization: Based on real - time water quality data, update the chemical reaction kinetic parameters (such as k1 / k2), solve the optimal chemical agent dosage sequence with a hardness removal rate ≥ 90% within the next 30 minutes through quadratic programming, and output a control signal to the chemical dosing pump. Specifically, in implementation, the model predictive control algorithm can be used to target a hardness removal rate ≥ 90% within the next 30 minutes, roll - optimize the NaOH / Na2CO3 dosage, with the control time domain N = 60 and the prediction time domain P = 120, to solve the lag problem of traditional control.

[0103] Step S103 (outer - loop membrane system intelligent decision - making): Construct a membrane fouling state - space equation, and combine with the dynamic programming algorithm to optimize the membrane type switching and cleaning strategy, and achieve the Pareto - optimal control of flux maintenance and cost minimization.

[0104] This step corresponds to Figure 2 the outer loop (membrane system control).

[0105] As an optional embodiment, in step S103, the constructed membrane fouling state - space equation is as follows:

[0106] First, define the membrane fouling state variables:

[0107] Membrane pore blockage rate ( , is the clean membrane, is completely blocked);

[0108] The thickness h of the filter cake layer on the membrane surface (unit: ).

[0109] Then, establish the state transition equation:

[0110] ;

[0111] Wherein, is the change rate of the membrane pore blockage rate with time, represents a clean membrane, represents complete blockage;

[0112] is the blockage rate constant, which describes the influence of membrane flux on blockage;

[0113] is the cleaning recovery constant, which describes the alleviating effect of cleaning on blockage;

[0114] u clean : cleaning control variable (0 - 1), 0 means not cleaned, 1 means maximum cleaning intensity;

[0115] is the change rate of the thickness h of the filter cake layer on the membrane surface with time (unit: );

[0116] is the filter cake growth coefficient, which represents the filter cake growth rate under unit flux and solid concentration;

[0117] is the backwashing stripping coefficient, which represents the stripping efficiency of cleaning on the filter cake layer;

[0118] J is the membrane flux (unit: L / (m²·h)), which represents the water production per unit membrane area per unit time;

[0119] C solid is the solid concentration of the mixed liquid, that is, the concentration of solid particles such as Mg(OH)2 and CaCO3 generated by the reaction;

[0120] u clean is the cleaning control variable, and its value range is 0 - 1.

[0121] As another optional embodiment, the step S103 includes:

[0122] Step S1031 (membrane fouling state estimation): Estimate the membrane pore blockage rate and the thickness of the filter cake layer on the membrane surface through real-time data, and update the membrane fouling level (mild / moderate / severe);

[0123] This step corresponds to Figure 3"Estimation of membrane fouling state".

[0124] Step S1032 (membrane type switching strategy): Establish a cost - efficiency matrix and use the Q - learning algorithm to select the optimal membrane type;

[0125] This step corresponds to Figure 3 "Q - learning membrane type decision" in. The established cost - efficiency matrix can be as shown in Table 1:

[0126] Table 1: Membrane type cost - efficiency matrix

[0127]

[0128] In Table 1:

[0129] Design flux: The flux of the BUFF membrane is significantly higher than that of the other two membranes;

[0130] Membrane life: Judging from the corrosion resistance of the membrane material, the BUFF membrane made of ePTFE material has the longest life;

[0131] Cleaning cycle: "The membrane cleaning cycle is increased by 50%" in the embodiment corresponds to a cleaning cycle of 4.5 h for the BUFF membrane (traditionally 3 h);

[0132] Comprehensive cost - efficiency ratio: A dimensionless index integrating chemical agent cost, energy consumption, and flux. The lower the value, the higher the cost - performance ratio. The calculation formula is: Comprehensive cost - efficiency ratio = (cleaning cost + energy consumption cost) / (design flux × membrane life).

[0133] Preferably, in the step S1032, the membrane type switching condition is:

[0134] ;

[0135] where m: membrane type variable, A: BUFF membrane, B: ceramic membrane, C: hollow fiber membrane, : membrane module cost, : energy consumption cost, : cleaning cost.

[0136] The specific meanings of each item in the formula are shown in Table 2 as follows:

[0137] Table 2: Meanings of each item in the formula

[0138]

[0139] Step S1033 (optimization of cleaning cycle): When the membrane pore blockage rate is greater than or equal to the first preset threshold or the thickness h of the filter cake layer on the membrane surface is greater than or equal to the second preset threshold, trigger chemical cleaning, and the cleaning liquid formula is automatically switched according to the membrane type.

[0140] This step corresponds to Figure 3 "Cleaning cycle optimization" in. Specifically, for example, when θ≥0.6 or h≥50μm, chemical cleaning is triggered, and the cleaning solution formulation is automatically switched according to the membrane type (e.g., 0.1% NaOH for ceramic membranes and 0.05% citric acid for BUFF membranes).

[0141] The above steps S1031 - S1033 correspond to Figure 3 Outer loop optimization / dynamic programming in: Estimate the membrane fouling state using real - time data ( ), select the membrane type with the lowest cost under the current fouling state from Table 1 through the Q - learning algorithm (e.g., preferentially select BUFF membranes under high - hardness working conditions), and at the same time, according to the threshold (such as ), trigger the cleaning program. Specifically, during implementation, the membrane fouling state can be estimated in real - time through the state equations of the membrane pore blockage rate and the filter cake layer thickness h; select the optimal membrane type from the cost - efficiency matrix using the Q - learning algorithm, and determine the cleaning cycle in combination with dynamic programming (e.g., when chemical cleaning is triggered); the input parameters are membrane differential pressure, product water turbidity, and membrane flux; the output control is the membrane module switching instruction, the start - stop of the backwashing pump, and the selection of the cleaning solution formulation.

[0142] Through the above steps S1031 - S1033, the system can automatically balance the treatment effect and operation cost under complex working conditions, significantly improving the intelligent level of leachate concentrated water softening treatment. Compared with the prior art, it has the following advantages:

[0143] Membrane type switching strategy: For the first time, quantify the membrane material characteristics (flux, lifespan, cleaning cost) into a computable cost matrix, and combine Q - learning to achieve intelligent decision - making, solving the low - efficiency problem of traditional manual switching (efficiency increased by 40%, membrane lifespan extended by 25%).

[0144] Implementation example data support:

[0145] Compared with traditional control, the chemical agent consumption of the method of the present invention is reduced by 20% (1.2 kg / ton vs 1.5 kg / ton), the membrane cleaning cycle is extended by 50% (4.5 h vs 3 h), and the comprehensive cost per ton of water is reduced by 15% - 20%.

[0146] As an alternative embodiment, the method further includes:

[0147] Step S104 (dual - closed - loop coordinated control): Use the dynamic programming algorithm to solve the multi - objective optimization problem including hardness removal rate, membrane flux, and operation cost, and achieve the coordinated control of the chemical section and the membrane section.

[0148] In this step, a linkage mechanism between the reaction section and the membrane section is established. The inner loop provides basic conditions for the outer loop, and the outer loop feeds back to affect the inner loop. This step corresponds to Figure 2 the cooperative control module in

[0149] Preferably, in step S104, the objective function constructed to include the hardness removal rate, membrane flux maintenance, and chemical cost is:

[0150] , ;

[0151] where the objective function F: the comprehensive optimization objective, which needs to be minimized;

[0152] : the weight coefficient of the hardness removal rate (dimensionless), giving priority to ensuring the quality of the produced water;

[0153] : the total hardness of the real-time produced water (unit: mg / L);

[0154] y target : the total hardness of the target produced water (design value ≤ 1800 mg / L);

[0155] The square of the norm term represents the deviation between the actual hardness and the target value, ensuring that the quality of the produced water meets the standards;

[0156] : the weight coefficient of the membrane flux (dimensionless), balancing the flux stability;

[0157] : the cumulative value of the membrane flux within the period T (unit: L / m²), reflecting the operation efficiency of the membrane system;

[0158] : the weight coefficient of the chemical cost (dimensionless), controlling the operation cost;

[0159] c i : the unit price of the i-th chemical (unit: yuan / L), such as NaOH, Na2CO3;

[0160] m i : the dosage of the i-th chemical (unit: L);

[0161] n: the number of chemical types, that is, the number of chemical types to be optimized (such as NaOH and Na2CO3, in this case n = 2).

[0162] The constraint conditions can be flexibly set according to the actual production conditions (such as the size of the reaction kettle, the amount of cost) and production requirements. In the embodiments of the present invention, the constraint conditions are:

[0163] The total hardness of the produced water ≤ 1800 mg / L, and the turbidity ≤ 2 NTU;

[0164] The membrane flux J ≥ 0.7J max (J max being the designed flux for each membrane type);

[0165] The cleaning period T clean ≥ 2 h (to avoid over - cleaning).

[0166] In this way, the dynamic programming algorithm is used to solve the optimal control sequence, realizing the collaborative optimization of the chemical section and the membrane section.

[0167] Preferably, in the step S104, when the turbidity of the membrane pool is greater than the third preset threshold, the sedimentation time of the reaction pool is automatically extended; when the membrane flux is continuously lower than 80% of the design value for a certain period of time, the dynamic mass balance equation of the magnesium / calcium removal reaction is corrected in reverse, and the dosage of Na2CO3 is increased.

[0168] In specific implementation, when the turbidity of the membrane pool > 1.5 NTU, the sedimentation time of the reaction pool is automatically extended by 10 min, and the electrostatic adsorption intensity of the crystal catcher is increased (by adjusting the voltage of the PTFE coating on the inner wall of the reactor); when the membrane flux is continuously lower than 80% of the design value for 30 min, the chemical reaction model is corrected in reverse, and the dosage of Na2CO3 is increased by 5% - 10% to promote the formation of large - sized crystals and reduce membrane pore blockage.

[0169] The above step S104 corresponds to Figure 3 the collaborative control and self - correction in: linking the reaction pool and the membrane system, such as adjusting the chemical dosage when the membrane flux drops to promote large - particle crystallization; optimizing the Q / R weight matrix of the MPC and the coefficient of the membrane fouling model every 72 h by the PSO algorithm to adapt to water quality fluctuations (automatically correcting parameters when the total hardness changes by ± 10%).

[0170] As another alternative embodiment, the method further includes:

[0171] Step S105 (adaptive parameter optimization): Dynamically adjusting control parameters based on the particle swarm optimization algorithm to adapt to water quality fluctuations and equipment aging.

[0172] This step corresponds to Figure 3 the "self - correction layer" in. Adaptive parameter optimization: Taking the cost per ton of water as the core objective, dynamically adjusting control parameters by the PSO algorithm to adapt to water quality fluctuations (when the total hardness changes by ± 10%, the parameter adjustment time < 15 min), realizing the unity of "meeting water quality standards" and "economic operation".

[0173] The role of using the PSO algorithm in the optimization mechanism is:

[0174] Optimized variables: weight matrices Q / R of the inner-loop MPC (adjust the priority between hardness removal rate and chemical dosage); parameters of the outer-loop membrane fouling model (blockage rate and cleaning recovery constant);

[0175] Iterative objective: By adjusting the above parameters, minimize f while meeting the constraints of product water quality (total hardness ≤ 1800 mg / L, turbidity ≤ 2 NTU).

[0176] During specific implementation, based on historical operation data (≥ 72 h), the particle swarm optimization (PSO) algorithm can be used to dynamically adjust the control parameters (such as the Q / R weights of MPC, the coefficients of the membrane fouling model), with the objective function of minimizing the comprehensive cost per ton of water. The optimization formula:

[0177] , ;

[0178] where f is the comprehensive cost per ton of water treatment, unit: yuan / ton;

[0179] C chemical is the total cost of chemical agents, including the consumption costs of NaOH and Na2CO3, unit: yuan; s

[0180] C membrane is the cost of membrane module loss, including membrane replacement and maintenance costs, unit: yuan;

[0181] C[[ID=3,3]] energy is the total cost of system energy consumption, including the electricity consumption of equipment such as water pumps and agitators, unit: yuan;

[0182] Q treated is the total treated water volume, unit: ton.

[0183] Implementation case: A pilot-scale system for leachate concentrate softening in a waste incineration power plant in Zhejiang

[0184] 1. Initialization of control parameters

[0185] Control objectives of the reaction tank: pH = 11.5 ± 0.05, calcium hardness removal rate ≥ 91%, magnesium hardness removal rate ≥ 75%;

[0186] Membrane system parameters: Design flux of BUFF membrane is 50 L / h·m², ceramic membrane is 25 L / h·m², hollow fiber membrane is 20 L / h·m²;

[0187] Optimization algorithm parameters: MPC prediction horizon is 120 min, control horizon is 60 min, number of PSO particles is 30, number of iterations is 50.

[0188] 2. Comparison of operation effects<,

[0189] The comparison between the method of the present invention and traditional control is shown in Table 3 as follows:

[0190] Table 3: Comparison of operation effects

[0191]

[0192] In summary, the present invention relates to a dual-loop predictive control method for the softening process of leachate nanofiltration / reverse osmosis concentrate. By constructing a chemical reaction kinetics model and a membrane fouling state space equation, the collaborative optimization control of the calcium and magnesium hardness removal rate and the membrane system operation efficiency is realized, which is applicable to the pilot-scale and industrial-scale treatment scenarios of high-salt and high-hardness wastewater.

[0193] Inner loop: Based on the chemical reaction kinetics model, model predictive control (MPC) of the dosing amounts of NaOH / Na2CO3 is realized to solve the problems of dynamic lag of pH value and fluctuation of hardness removal rate;

[0194] Outer loop: Construct a membrane fouling state space equation, and combine with the dynamic programming algorithm to optimize the membrane type switching and cleaning strategy, so as to realize the Pareto optimal control of flux maintenance and cost minimization;

[0195] Dual-loop collaborative control mechanism: The inner loop and the outer loop are linked through real-time data sharing (such as the particle size distribution of solids after reaction, the membrane flux decay rate), and the chemical agent dosing and membrane cleaning strategies are dynamically adjusted.

[0196] The present invention also has the following innovation points:

[0197] 1. Innovation in modeling dimension: For the first time, a dual-scale model of the leachate concentrate softening process is established. At the microscopic level, the crystallization kinetics of Mg(OH)2 / CaCO3 is analyzed, and at the macroscopic level, a membrane fouling state space equation is constructed, breaking through the limitation of traditional control relying on empirical formulas.

[0198] 2. Innovation in control structure: A dual-loop predictive control architecture for the chemical reaction section and the membrane separation section is proposed. Through the cross-scale collaboration of MPC and dynamic programming, the Pareto optimal control of the hardness removal rate and the membrane operation efficiency is realized, and the problem of multi-objective conflict is solved.

[0199] 3. Innovation in decision-making mechanism: The Q-learning algorithm is introduced to construct a multi-membrane type intelligent decision-making system, and the optimal membrane type is dynamically selected based on the real-time fouling state and cost model. Compared with manual switching, the efficiency is increased by 40%, and the membrane life is extended by 25%.

[0200] 4. Innovation in adaptive ability: A parameter self-tuning mechanism based on particle swarm optimization is designed to automatically adapt to water quality fluctuations (for example, when the total hardness changes by ±10%, the control parameter adjustment time < 15 min), significantly improving the system robustness.

[0201] 5. Dual closed-loop collaborative control mechanism: The inner and outer loops are linked through real-time data sharing (such as solid particle size distribution after reaction and membrane flux attenuation rate), dynamically adjusting the reagent addition and membrane cleaning strategies.

[0202] Through the deep integration of theoretical modeling, intelligent algorithms and engineering practice, this invention solves the industry problems of "low control accuracy - high operating costs - poor system robustness" in the softening treatment of high-hardness wastewater, and has significant technological advancement and engineering application value.

[0203] 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 a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A leachate concentrated water softening control method based on double closed-loop predictive control, characterized in that Including: Step S101: Obtain multi-dimensional data collected in real time; Step S102: Construct a dynamic mass balance equation for the magnesium / calcium removal reaction, and based on the model predictive control algorithm, roll-optimize the dosage of NaOH / Na2CO3 to achieve precise control of the pH value and the hardness removal rate; Step S103: Construct a membrane fouling state space equation, and combine with the dynamic programming algorithm to optimize the membrane type switching and cleaning strategy to achieve the Pareto optimal control of flux maintenance and cost minimization; Among them, in the step S102, the constructed dynamic mass balance equation for the magnesium / calcium removal reaction is: Magnesium removal reaction: ； Among them, : The change rate of magnesium ion concentration in the reaction pool with time, k1: Reaction rate constant, [OH - : Hydroxide ion concentration in the reaction pool, V: Volume of the reaction pool, Q: Inlet water flow rate, : Mass flow rate of magnesium ions brought in by the inlet water, : Mass flow rate of magnesium ions carried out by the outlet water; Calcium removal reaction: ； wherein, k2: reaction rate constant, : concentration of carbonate ions in the reaction pool, and the meanings of other terms are the same as those in the magnesium removal reaction.

2. The leachate concentrated water softening control method based on double closed-loop predictive control according to claim 1, wherein, The step S102 includes: Step S1021: According to the real-time water quality data, through the dynamic mass balance equation of the magnesium / calcium removal reaction, optimize the dosage of NaOH / Na2CO3 in real time, control the pH value and the calcium and magnesium hardness removal rate, and dynamically correct the reaction kinetic parameters to correct the water quality fluctuation; Step S1022: In each control cycle, perform quadratic programming to solve and generate the optimal chemical dosage sequence in the future preset time period.

3. The leachate concentrated water softening control method based on double closed-loop predictive control according to claim 2, wherein In the step S1021, the formula for dynamically correcting the reaction kinetic parameters is: ； Among them, : The reaction rate constant at time t, : The reaction rate constant at the previous moment, : The adaptive learning rate, : The measured hardness of the produced water at the previous moment, : The predicted hardness of the produced water by the model at the previous moment, : The target hardness of the produced water; And / or, in the step S1022, the formula for quadratic programming is: ； wherein, : the predicted water production hardness at time t + k at time t, : the target water production hardness at time t + k, Q: hardness error weight matrix, : the chemical dosage at time t + k calculated at time t, R: control input weight matrix, N: control time domain.

4. The leachate concentrated water softening control method based on double closed-loop predictive control according to claim 1, characterized in that In the step S103, the constructed membrane fouling state space equation is: ； Among them, : Membrane pore blockage rate, : Membrane pore blockage rate : Rate of change with time, : Blockage rate constant, : Cleaning recovery constant, u clean : Cleaning control variable, h: Membrane surface cake layer thickness, : Rate of change of membrane surface cake layer thickness h with time, : Cake growth coefficient, : Backwashing stripping coefficient, J: Membrane flux, C solid : Mixed liquor solid concentration.

5. The leachate concentrated water softening control method based on double closed-loop predictive control according to claim 1, wherein, The step S103 includes: Step S1031: Estimate the membrane pore blockage rate and the thickness of the filter cake layer on the membrane surface through real-time data, and update the membrane fouling level; Step S1032: Establish a cost-efficiency matrix and use the Q-learning algorithm to select the optimal membrane type; Step S1033: When the membrane pore blockage rate is greater than or equal to the first preset threshold or the thickness of the filter cake layer on the membrane surface is greater than or equal to the second preset threshold, trigger chemical cleaning, and the cleaning solution formula is automatically switched according to the membrane type.

6. The leachate concentrate softening control method based on double closed-loop predictive control according to claim 5, characterized in that In the step S1032, the membrane type switching condition is: ； Among them, m: membrane type variable, A: BUFF membrane, B: ceramic membrane, C: hollow fiber membrane, : membrane module cost, : energy consumption cost, : cleaning cost.

7. The leachate concentrated water softening control method based on double closed-loop predictive control according to any one of claims 1-6, characterized in that, The method further includes: Step S104: Use the dynamic programming algorithm to solve the multi-objective optimization problem including the hardness removal rate, membrane flux, and operating cost to achieve the coordinated control of the chemical section and the membrane section.

8. The leachate concentrated water softening control method based on double closed-loop predictive control according to claim 7, characterized in that, In the step S104, the constructed objective function including the hardness removal rate, membrane flux maintenance, and chemical cost is: ， ； Among them, F: objective function, : hardness removal rate weight coefficient, : total hardness of real-time produced water, y target : target total hardness of produced water, : membrane flux weight coefficient, : cumulative value of membrane flux within period T, : chemical cost weight coefficient, c i : unit price of the i-th chemical, m i : dosage of the i-th chemical, n: number of chemical types; And / or, in the step S104, when the turbidity of the membrane pool is greater than the third preset threshold, automatically extend the sedimentation time of the reaction pool; And / or, in the step S104, when the membrane flux is continuously lower than 80% of the design value for a certain period of time, reversely correct the dynamic mass balance equation of the magnesium / calcium removal reaction and increase the dosage of Na2CO3.

9. The leachate concentrated water softening control method based on double closed-loop predictive control according to claim 7, characterized in that, The method further includes: Step S105: Dynamically adjust the control parameters based on the particle swarm optimization algorithm to adapt to water quality fluctuations and equipment aging.

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