Precise proportioning and closed-loop control system for lime addition

Abstract

The application discloses a lime adding precise proportioning and closed-loop control system and relates to the technical field of industrial reagent adding. The application discloses a lime adding precise proportioning and closed-loop control system and relates to the technical field of industrial reagent adding. The core lies in a feedforward prediction+feedback calibration two-dimension error compensation algorithm and a hardware cooperative innovation insurance mechanism of the control unit. The feedforward module dynamically calculates an activity coefficient through a calcium ion electrode and a temperature sensor, optimizes an adding amount model in combination with a temperature correction function, and compensates for material characteristic drift. The feedback module fuses double-sensor signals, solves a detection lag problem through a lag error prediction model and dynamic PID parameter adjustment. With hardware redundancy, automatic cleaning and algorithm fault-tolerant design, the system concentration fluctuation is ≤±0.002 g / cm 3 , continuous failure-free operation is more than 30 days, the system adapts to the needs of multiple industries, reduces unit consumption and maintenance cost, and improves process stability.

Description

Technical Field

[0001] This invention belongs to the field of industrial reagent dosing technology, and particularly relates to a precise lime dosing ratio and closed-loop control system. Background Technology

[0002] Lime, as a commonly used regulator and neutralizer in industrial production, has its dosage and proportion accuracy directly determining the process effect: In mineral processing flotation, deviations in lime slurry concentration can cause fluctuations in the pH value of the slurry, reducing mineral separation efficiency; in water treatment, excessive lime addition can cause a surge in sludge, while insufficient addition can fail to meet discharge standards; in the building materials industry, deviations in lime dosage can directly affect the strength and durability of the mixture.

[0003] Existing lime dosing systems rely on real-time feedback from sensors such as online densitometers and pH meters. However, the lime hydration reaction has a delay of 3-8 minutes, meaning the sensor readings cannot reflect the true state of the reaction system in real time. This leads to an "overshoot-reverse" cycle in PID control, with concentration fluctuation errors typically ranging from ±0.004 g / cm³. 3 above; Moreover, the purity (Ca(OH)2 content), fineness, and moisture content of lime powder vary from batch to batch. Traditional systems only calculate the dosage according to a fixed ratio formula, without considering the fluctuation of effective components caused by differences in material activity. The actual effective concentration deviation can be more than ±1.0%. Furthermore, factors such as electromagnetic interference, pipeline vibration, and uneven mixing in industrial settings can lead to sensor signal distortion and unstable material delivery, further amplifying the mixing ratio error.

[0004] While existing technologies attempt to improve stability by optimizing sensor accuracy (such as using a radiometric densitometer) and improving the feeding mechanism (such as an arch-breaking scraper feeder), they have not fundamentally solved the systematic errors caused by "detection lag" and "characteristic drift," making it difficult to meet the requirements of high-precision processes. Summary of the Invention

[0005] To address the problems in the prior art, the present invention proposes the following technical solution: A precise lime dosing and closed-loop control system includes a material supply unit, a mixing unit, a detection unit, a control unit, and an execution unit; The control unit includes a feedforward prediction module and a feedback calibration module. It achieves precise control of lime addition ratio through a two-dimensional error compensation algorithm. The detection unit and the execution unit form an innovative insurance mechanism through hardware co-design to jointly solve the systematic errors caused by material characteristic drift and detection lag.

[0006] As a preferred embodiment of the above technical solution, the material supply unit includes a lime silo, a variable frequency screw feeder, a high-precision weighing sensor, and an anti-caking vibrator; the weighing sensor collects lime addition data in real time to provide basic parameters for the feedforward prediction module, and the vibrator is linked with the feeder to ensure the continuity of material supply.

[0007] As a preferred embodiment of the above technical solution, the detection unit includes a radiometric densitometer, a calcium ion selective electrode, and a temperature sensor, with signals from the three sensors being acquired synchronously. The detection unit is equipped with a backup detection channel, forming a hardware redundancy design, which automatically switches when the main sensor fails, ensuring the continuity of detection.

[0008] As a preferred embodiment of the above technical solution, the core algorithm of the feedforward prediction module includes: Detection of free Ca in lime slurry using a calcium ion-selective electrode 2+ Concentration, combined with temperature sensor data, to calculate the current effective activity coefficient K of the lime in real time (K = measured Ca). 2+ Concentration / Theoretical Ca 2+ Concentration), dynamically correcting for differences in material purity; Establish a feedforward injection calculation model in: The required mass concentration of lime slurry according to the process specifications; The measured density of lime slurry; This refers to the effective volume of the mixing tank. This represents the real-time activity coefficient. This is a temperature correction function. =1+0.005×(25-T), which compensates for the effect of temperature on the hydration reaction rate.

[0009] As a preferred embodiment of the above technical solution, the core algorithm of the feedback calibration module includes: The true concentration value is obtained by fusing the densitometer signal and the calcium ion electrode signal using the Kalman filter algorithm. in The weighting coefficient is (0.6-0.8). Based on concentration change rate Establish a hysteresis error prediction model: The hydration delay time is 3-8 minutes. This is the error correction term updated in real time by the BP neural network; according to With target concentration To compensate for the deviation, the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller are dynamically adjusted.

[0010] As a preferred embodiment of the above technical solution, the execution unit includes a variable frequency speed controller, a pneumatic ceramic regulating valve, and an automatic cleaning device; the automatic cleaning device is activated every 1.5-2 hours to clean the sensor probe to avoid signal distortion; the variable frequency speed controller and the regulating valve synchronously adjust the lime dosage and the clean water flow rate according to the instructions of the control unit.

[0011] As a preferred embodiment of the above technical solution, the control unit is also equipped with an algorithm fault tolerance mechanism: when the activity coefficient K exceeds the normal range of 0.8-1.2, the EDTA titration calibration program is automatically started, the feedforward formula parameters are corrected, and an audible and visual alarm is triggered.

[0012] As a preferred embodiment of the above technical solution, the mixing unit includes a stirring tank, a high-precision water metering pump, and a static mixer; the stirring tank is equipped with a shock-absorbing base.

[0013] The beneficial effects of this invention are as follows: This invention fundamentally solves the systematic errors caused by material property drift and detection lag in existing technologies through the dual-dimensional error compensation algorithm of the feedforward prediction module and the feedback calibration module, combined with hardware. The feedforward prediction module captures free Ca in real time through a calcium ion selective electrode. 2+ The effective activity coefficient K is dynamically calculated based on the concentration and temperature data. A dosage model incorporating K and a temperature correction function f(T) is then used to pre-compensate for deviations in effective components caused by fluctuations in lime purity and fineness. The feedback calibration module uses a Kalman filter algorithm to fuse signals from both the density meter and calcium ion electrode to obtain the true concentration value. Simultaneously, a hysteresis error prediction model is established based on the concentration change rate and hydration delay time. A BP neural network is used to update the error correction term in real time and dynamically adjust PID parameters to avoid overshoot and integral saturation, achieving accurate calibration of the detection hysteresis. Combined with the hardware redundancy design of the detection unit, the automatic sensor cleaning device of the execution unit, the anti-interference structure of the mixing unit, and the algorithmic fault-tolerant mechanism of the control unit, a dual guarantee is formed, ultimately controlling the lime slurry concentration fluctuation error within ±0.002 g / cm³. 3 Within this range, the accuracy of lime addition ratio and the stability of process parameters have been significantly improved, while lime consumption per unit, equipment maintenance costs and the frequency of manual intervention have been reduced. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments.

[0015] The control system of this invention includes a material supply unit, a proportioning and mixing unit, a detection unit, a control unit, and an execution unit, wherein: Material supply unit: including lime silo, screw feeder, weighing sensor (accuracy ±0.1%), and vibrator (linked with feeder to prevent clumping); Mixing unit: includes a mixing tank (equipped with a slant paddle mixer, speed 30-60 rpm), a water metering pump, and a static mixer; Detection unit: includes a radiometric densitometer (detection accuracy ±0.001 g / cm³). 3 The system uses a calcium ion selective electrode and a temperature sensor, with signals from all three components acquired synchronously. Control unit: Based on PLC controller, a feedforward prediction module + feedback calibration module is built, with a built-in two-dimensional error compensation algorithm; The execution unit includes a frequency converter (to control the feeder speed), a pneumatic ceramic regulating valve (to control the clean water flow), and an automatic cleaning device (to periodically clean the sensor probe).

[0016] The core of this invention lies in the dual control of a feedforward prediction module and a feedback calibration module, which, together with hardware collaboration, forms a double insurance, thereby solving the systematic errors caused by "detection lag" and "characteristic drift"; the specific principle is as follows: 1. Feedforward prediction algorithm (to solve the problem of material property drift) Establish a feedforward prediction equation to compensate for errors caused by fluctuations in material properties in advance: Real-time identification of characteristic parameters: Detection of free Ca in lime slurry using a calcium ion selective electrode. 2+ Concentration, combined with temperature sensor data, to calculate the current effective activity coefficient K of the lime in real time (K = measured Ca). 2+ Concentration / Theoretical Ca 2+ Concentration), dynamically correcting for differences in material purity; Dosage prediction formula: The traditional proportioning formula is optimized by introducing the activity coefficient K, and a feedforward dosage calculation model is established: in: The required mass concentration of lime slurry (g / cm³) for the process. 3 ); The measured density of lime milk (g / cm³) 3 ); The effective volume of the mixing tank (cm²) 3 ); The real-time activity coefficient is (0.8-1.2). The temperature correction function (T is the temperature of the lime slurry, °C) is expressed as follows: =1+0.005×(25-T), which compensates for the effect of temperature on the hydration reaction rate.

[0017] 2. Feedback calibration mechanism module (to solve the problem of detection lag) A dual-sensor fusion algorithm combined with delay compensation is used to calibrate the feedforward injection rate in real time. Dual-sensor data fusion: The concentration signal from the radiometric densitometer (high-frequency but lagging) and the activity signal from the calcium ion electrode (real-time but susceptible to interference) are fused using a Kalman filter algorithm to obtain the true concentration value. : in The weighting coefficient (0.6-0.8) is dynamically adjusted based on the signal-to-noise ratio. Delay compensation algorithm: Based on closed-loop transfer function analysis, a hysteresis error prediction model is established. This is achieved by detecting the current concentration change rate. ,predict + ( Concentration value after 3-8 minutes (the hydration reaction delay time). Adjust the implementing agency in advance: in The error correction term is updated in real time using a BP neural network trained on historical data. Finally, according to With target concentration To mitigate the deviation, the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller are dynamically adjusted to avoid the overshoot problem of traditional PID controllers. when When the proportional coefficient Kp is increased (0.8→1.2), the response speed is accelerated. According to the proportional control principle, increasing Kp can accelerate the system response speed, enabling the control system to react more quickly to the concentration deviation. By increasing the adjustment range of lime dosage, the gap between the actual concentration and the target concentration can be quickly narrowed. Alternatively, reduce the integral coefficient Ki (0.1→0.05). The integral coefficient is used to eliminate steady-state error. If Ki is too large, it can easily cause integral saturation, leading to overshoot or lag in the system regulation. Reducing Ki can weaken the accumulation rate of the integral term and avoid controller output saturation due to long-term deviation accumulation, thereby maintaining the stability and control accuracy of the system.

[0018] 3. Co-design with hardware Hardware redundancy design: The radioactivity density meter and the Coriolis mass flow meter (backup) form a dual detection channel. When the main sensor fails, the system automatically switches to the backup channel and calculates the concentration through the mass flow data to ensure continuous detection. Algorithm fault tolerance mechanism: When the activity coefficient K exceeds the normal range (0.8-1.2), the system automatically starts the EDTA titration calibration program (once every 4 hours), corrects the feedforward formula parameters, and triggers an audible and visual alarm to prompt the operator to check the quality of the lime raw materials; Anti-interference hardware collaboration: All sensor signals are transmitted to the PLC through an isolation module (4-20mA current signal), the mixing tank is equipped with a shock-absorbing base, and the pipes are lined with UHMWPE to reduce adhesion, thereby reducing the impact of interference on the algorithm from the hardware level.

[0019] 4. Work Process Initialization phase: Input the target concentration required by the process. The system automatically loads initial PID parameters and activity coefficient K=1.0 based on parameters such as the volume V of the mixing tank. Feedforward dosing stage: The weighing sensor detects the amount of lime added, the water metering pump injects water into the mixing tank according to the flow rate calculated by the feedforward formula, and the agitator starts stirring (45 rpm). Real-time detection phase: The radiometric densitometer, calcium ion electrode, and temperature sensor simultaneously collect data and transmit it to the control unit; Two-dimensional compensation stage: The control unit corrects the activity coefficient K through a feedforward algorithm and predicts the concentration change trend through a feedback algorithm, and dynamically adjusts the screw feeder speed and clean water flow rate; Closed-loop stable phase: when and The deviation is ≤ ±0.002 g / cm 3 When the system reaches a stable state, it maintains the current execution parameters, and the automatic cleaning device starts every 2 hours to clean the sensor probe. Anomaly Handling Phase: When the deviation persists for more than 5 minutes or the sensor signal is abnormal, the system switches to the backup detection channel, triggers an alarm, and records the fault data.

[0020] Example 1

[0021] Mineral processing flotation technology (copper ore flotation) Process Background: In the copper ore flotation process, lime slurry acts as a pH adjuster, requiring the pulp pH to be maintained at 9.5±0.2. Excessive concentration fluctuations can lead to the ineffectiveness of the xanthate collector, reducing copper recovery. The core challenges in this scenario are the fluctuation in the purity of the lime raw material (85%-95%) and the delay in the hydration reaction (5 minutes), corresponding to the two major problems that this invention aims to solve: material characteristic drift and detection lag.

[0022] Process parameters: Target lime slurry concentration 1.0 g / cm³ 3 The mixing tank has a volume of 5m³. 3 100m³ slurry processing capacity 3 / h, hydration reaction delay time 5 minutes, lime raw material purity fluctuation range 85%-95% (Ca(OH)2 content).

[0023] System Configuration: Material supply unit: 10m 3 Lime silo + variable frequency screw feeder (0-50kg / h) + high-precision weighing sensor (±0.1kg) -- The high-precision design of the weighing sensor provides accurate basic data for lime dosage for the feedforward prediction algorithm, ensuring the accuracy of the calculation of the activity coefficient K; Detection unit: DM500 radiometric densitometer (±0.001g / cm³) 3 +Ca-100 calcium ion electrode (response ≤3s) -- The calcium ion electrode is the core detection element of the feedforward prediction module of this invention, capturing free Ca in real time. 2+ The concentration is dynamically calculated to obtain the activity coefficient K, while the densitometer provides a concentration feedback signal to the feedback calibration module. The two work together to achieve two-dimensional data acquisition. Control unit: Siemens S7-1500 PLC + this invention's dual-dimensional error compensation algorithm -- for a 5-minute hydration delay, the algorithm automatically sets the delay compensation time Δt=5 minutes, and updates the error correction term ε in real time through a BP neural network, avoiding the overshoot problem of traditional PID; Execution unit: Pneumatic ceramic regulating valve (regulation accuracy ±0.5%) + automatic cleaning device (2 hours / time) -- The automatic cleaning device solves the signal distortion problem caused by scaling on the sensor probe, ensures the reliability of the data source of the feedback calibration module, and is an important part of the "algorithm-hardware collaboration" innovation insurance of this invention.

[0024] Implementation results: Lime slurry concentration fluctuation: ±0.0015 g / cm³ 3 --This is attributed to the feedforward algorithm's real-time compensation for purity fluctuations (dynamic adjustment of K value) and the feedback algorithm's prediction and calibration of delay errors, resulting in a significant improvement in accuracy compared to traditional schemes; Slurry pH stability: 9.5±0.15 -- High-precision concentration control is directly translated into stable pH value, verifying the accurate mapping logic of "concentration-parameter" in this invention; Copper flotation recovery rate: 87.5% (85% in the original process) -- Stable pH value ensures consistent activity of xanthate collector, directly improving the recovery rate and demonstrating the positive enhancement of the process effect by the technology of this invention; Lime consumption per unit: 3.2 kg / t ore (original process 3.9 kg / t) -- Feedforward algorithm accurately matches the purity of raw materials, avoiding excessive addition due to low purity, thus saving raw materials; Continuous operation stability: 30 days without failure -- thanks to the dual innovative insurance of "hardware redundancy (automatic sensor cleaning) + algorithm fault tolerance (K-value abnormal alarm)" of this invention, manual intervention is reduced.

[0025] Example 2: Municipal wastewater treatment (sludge dewatering pretreatment) Process background: Before sludge dewatering, lime slurry needs to be added to adjust the pH to 12±0.3. The core pain point is that the large fluctuation of the moisture content of the influent sludge (80%-90%) leads to the instability of the mixing system, and the traditional fixed ratio cannot cope with load changes. The adaptive closed-loop control of this invention is just right to meet the dynamic adjustment needs of this scenario.

[0026] Process parameters: Target lime slurry concentration 0.8 g / cm³ 3 The mixing tank has a volume of 10m³. 3 sludge treatment capacity 20m³ 3 / h, hydration reaction delay time 6 minutes, influent sludge moisture content 80%-90% (with large fluctuations).

[0027] System Configuration: Mixing and proportioning unit: Clean water metering pump (0-5m) 3 / h, ±0.01m 3 / h) + slant paddle mixer (40rpm) -- high-precision clean water metering pump with feedforward formula It provides accurate V (volume) and ρ (density) correlation data, and the optimized stirrer speed ensures uniform mixing of lime slurry, reducing the impact of local concentration deviations on the detection signal; Detection Unit: A new online pH monitor (for auxiliary calibration) and temperature sensor (0-50℃) have been added. The pH meter serves as an auxiliary signal source for feedback calibration, and its data is fused with that of the density meter (using Kalman filtering) to improve accuracy. The reliability of the (true concentration) calculation depends on the temperature sensor and the temperature correction function. Provide data support; Execution unit: Variable frequency speed controller (controls feeder speed) + anti-caking vibrator -- The vibrator solves the clumping problem during the mixing process of sludge and lime, ensures effective dissolution of lime, and guarantees the accuracy of the calculation of the activity coefficient K. This is a synergistic effect of the material supply unit and algorithm of this invention.

[0028] Implementation results: Lime slurry concentration fluctuation: ±0.0018 g / cm³ 3--The algorithm of this invention dynamically adjusts the feeder speed and clean water flow rate by identifying system changes caused by fluctuations in sludge moisture content in real time, thereby achieving adaptive control of concentration; Sludge pH stability: 12±0.2 -- Precise concentration control + pH-assisted calibration significantly reduces pH deviation compared to traditional methods (±0.8); The moisture content of the sludge after dewatering is 65% (72% in the original process). The pH stability more fully disrupts the colloidal structure of the sludge, improving the dewatering effect and directly demonstrating the role of the technology of this invention in ensuring the process objectives. Lime consumption per unit: 4.5 kg / t sludge (original process 5.6 kg / t) -- closed-loop control avoids excessive addition, while feedforward algorithm compensates for differences in lime activity, thus reducing the unit consumption; Sludge treatment cost reduction: 15% -- This is the result of the combined effect of reduced unit consumption and improved dewatering effect, demonstrating the economic benefits of this invention.

[0029] Example 3: Building Materials Industry (Aerated Concrete Block Production) Process background: As the core raw material for the hydration reaction of aerated concrete, the concentration stability of lime directly affects the strength and porosity distribution of the blocks. The core pain point is the distortion of the detection signal caused by the high viscosity of the slurry and the fluctuation of lime fineness (200-300 mesh). This invention addresses this scenario through a dual design of hardware structure optimization and algorithm parameter adaptation.

[0030] Process parameters: Target lime slurry concentration 1.2 g / cm³ 3 The mixing tank has a volume of 8m³. 3 5m³ of slurry can be processed. 3 / h, hydration reaction delay time 4 minutes, lime fineness fluctuation range 200-300 mesh.

[0031] Explanation of the relationship between system configuration and the core technology of this invention: Material supply unit: Arch-breaking lime silo + twin screw feeder (anti-clogging) -- In response to the characteristics of large lime consumption and easy arching in the building materials industry, the twin screw feeder ensures the continuity of material supply, provides stable feeding data for the feedforward algorithm, and avoids algorithm calculation deviations caused by material interruption; Detection Unit: Coriolis mass flow meter (backup channel) + density meter dual-probe design -- The dual-probe density meter solves the problem of high-viscosity slurry adhering to a single probe, and the backup mass flow meter constitutes a hardware redundancy design. This is the core hardware configuration of the invention with dual innovation insurance, ensuring that the detection signal is not interrupted. Control Unit: Algorithm weight coefficient α=0.7 (adapting to the high viscosity characteristics of the slurry) -- This invention's algorithm supports dynamic adjustment of the weight coefficient. In high viscosity scenarios, increasing the density meter signal weight (α=0.7) reduces signal interference from the calcium ion electrode due to viscosity, thus optimizing... Calculation accuracy.

[0032] Implementation results: Lime slurry concentration fluctuation: ±0.002 g / cm³ 3 --Algorithm parameter adaptation + dual-probe detection effectively offsets the effects of high viscosity and fineness fluctuations, achieving the accuracy target preset by this invention; Block compressive strength: 3.5MPa (national standard 3.0MPa) -- Stable concentration ensures sufficient hydration reaction and uniform pore distribution, directly improving strength and verifying the core guarantee role of the invention technology in product quality; Pore ​​qualification rate: 92% (80% in the original process) -- concentration deviation controlled within ±0.002g / cm 3 Within this range, avoid excessive or insufficient local reactions and improve the uniformity of stomatal formation; Lime consumption per cubic meter: 6.8 kg / m³ 3 Blocks (original process 8.0kg / m) 3 -- The feedforward algorithm accurately compensates for activity differences caused by fineness fluctuations, avoiding ineffective dosing; Product qualification rate improvement: 10% -- stable concentration + strength meets the standard, which directly translates into an improved product qualification rate, demonstrating the industrial application value of this invention.

[0033] Example 4: Flue gas desulfurization process (limestone-gypsum method for coal-fired power plants) Process background: The core challenge of using lime slurry as a desulfurization absorbent is the large fluctuation in flue gas SO2 concentration (800-1200 mg / m³). 3 Due to the high temperature of the slurry (40-60℃), the system needs to have rapid response and high temperature resistance detection capabilities. The dynamic PID parameter adjustment and high temperature resistance hardware adaptation of this invention perfectly meet this requirement.

[0034] Process parameters: Target lime slurry concentration 0.6 g / cm³ 3 The mixing tank has a volume of 15m³. 3 Flue gas treatment capacity 10000m³ 3 / h, hydration reaction delay time 7 minutes, flue gas SO2 concentration fluctuation range 800-1200 mg / m³ 3 .

[0035] System Configuration: Actuating unit: High-flow-rate clean water metering pump (0-10m) 3 / h) + Wear-resistant regulating valve -- Large flow configuration is suitable for the large-scale processing needs of power plants. The wear-resistant regulating valve can cope with the scouring of high-concentration slurry, ensure the long-term stability of the actuator, and form hardware-algorithm synergy with the fast response of the algorithm. Detection Unit: High-Temperature Densitometer (suitable for slurry temperatures of 40-60℃) + SO2 Online Monitor (linked calibration) -- The high-temperature densitometer ensures detection accuracy under high-temperature conditions, and the SO2 monitor data is linked with the concentration data to provide process effect correlation signals to the feedback calibration module and optimize the adjustment logic; Control unit: Delay compensation time Δt = 7 minutes, PID parameter dynamic adjustment range expanded -- In response to large fluctuations in SO2 concentration, the algorithm automatically expands the PID parameter adjustment range (Kp = 0.6-1.4) to speed up the response and avoid a decrease in desulfurization efficiency.

[0036] Implementation effects and technical causal explanation: Lime slurry concentration fluctuation: ±0.0016 g / cm³ 3 --Dynamic PID parameter adjustment to cope with SO2 fluctuations, and delay compensation algorithm to offset the effect of 7-minute hydration lag; Desulfurization efficiency: 98.5% (original process 95%) -- stable concentration ensures consistent absorbent activity, rapid response to SO2 fluctuations, and avoids insufficient absorption; Slurry scaling frequency: 1 time / 30 days (original process 3 times / 30 days) -- stable concentration reduces local supersaturation crystallization, reduces scaling risk, and demonstrates the positive impact of the invention on equipment maintenance; Lime consumption per unit area: 5.2 kg / kg SO2 (original process 6.5 kg / kg SO2) -- precise proportioning avoids over-addition, while feedforward algorithm compensates for activity decay at high temperature; Equipment maintenance costs reduced by 25% – reduced scaling frequency + lower unit consumption, resulting in an overall reduction in maintenance costs.

[0037] Example 5: Paper Industry (Pulp Bleaching Pretreatment) Process background: Before pulp bleaching, the pH needs to be adjusted to 10.0±0.2. The core problem is that the pulp has many impurities, which leads to sensor contamination and pulp concentration fluctuations (10%-15%). This invention addresses this problem by using a high-frequency cleaning device and an adaptive algorithm.

[0038] Process parameters: Target lime slurry concentration 0.7 g / cm³ 3 The mixing tank has a volume of 6m³. 3 Pulp processing capacity 8m³ 3 / h, hydration reaction delay time 3 minutes, pulp concentration fluctuation range 10%-15%.

[0039] Explanation of the relationship between system configuration and the core technology of this invention: Mixing Unit: Static Mixer (Enhances the uniformity of pulp and lime slurry mixing) -- Solves local concentration deviations caused by uneven mixing of pulp and lime slurry, provides the detection unit with a true overall concentration signal, and avoids the algorithm from being adjusted based on distorted data; Detection Unit: Calcium Ion Electrode + Densitometer Signal Fusion Weight α=0.65 -- The presence of many impurities in the pulp makes the calcium ion electrode signal susceptible to interference. The algorithm reduces its weight (α=0.65) and increases the weight of the densitometer signal to optimize the data fusion effect. Execution unit: High-frequency automatic cleaning device (1.5 hours / cycle) -- This device addresses the problem of pulp impurity adhesion, shortens the cleaning cycle, ensures the cleanliness of the sensor probe, and represents an optimized design for the "hardware adaptation scenario" of this invention, ensuring the reliability of the algorithm data source.

[0040] Implementation results: Lime slurry concentration fluctuation: ±0.0017 g / cm³ 3 --High-frequency cleaning + weight optimization to offset the effects of impurity interference and concentration fluctuations; Pulp pH stability: 10.0±0.18 -- Precise concentration control leads to pH stability, meeting the requirements of the bleaching process; Paper whiteness deviation: ≤1% (original process ≤3%) -- pH stability ensures uniform bleaching agent reaction, reducing whiteness deviation; Reduced bleaching agent consumption: 8% -- pH stability reduces ineffective bleaching agent consumption, demonstrating the synergistic energy-saving effect of this invention; Paper qualification rate: 99% (original process 95%) -- reduced whiteness deviation + pH stability, directly improving product qualification rate.

[0041] Example 6: Chemical Industry (Neutralization Treatment of Acidic Wastewater) Process background: The acidity of chemical wastewater fluctuates greatly (500-1000mg / L), requiring the system to have extremely strong self-adaptive capabilities. The core pain points are high acidity fluctuations and high lime impurity content. This invention addresses these issues by optimizing the material supply unit through a BP neural network.

[0042] Process parameters: Target lime slurry concentration 0.9 g / cm³ 3 The mixing tank has a volume of 12m³. 3 Wastewater treatment capacity 50m³ 3 / h, hydration reaction delay time 8 minutes, wastewater acidity fluctuation range 500-1000 mg / L (in H+). + count).

[0043] Explanation of the relationship between system configuration and the core technology of this invention: Control Unit: BP Neural Network Error Correction Term ε Optimization (Adapted to High Acidity Fluctuation Scenarios) -- To address large acidity fluctuations, the algorithm increases the update frequency of ε (updating once every 0.5 seconds) to improve prediction accuracy and avoid lag in concentration adjustment; Detection Unit: Three-probe density meter (improves signal stability) + pH online monitor (real-time feedback) -- The three-probe density meter takes the average value to reduce signal fluctuations, and the pH meter provides real-time feedback on the neutralization effect. It is calibrated in conjunction with concentration data and the algorithm adjustment logic is optimized. Material supply unit: Increase the power of the vibrator (to adapt to the characteristics of high impurity content in chemical lime) -- many impurities can easily lead to clumping. Strengthening the vibrator ensures uniform lime addition, provides stable addition data for the feedforward algorithm, and avoids deviation in the calculation of the activity coefficient K.

[0044] Implementation effects and technical causal explanation: Lime slurry concentration fluctuation: ±0.0019 g / cm³ 3 --BP neural network optimization + three-probe detection to cope with high acidity fluctuations and impurity interference; Effluent pH stability: 7.0±0.25 -- Precise concentration control + pH-linked calibration ensures neutralization meets standards; Sludge production: 1.2 kg / m³ 3 Wastewater (original process 1.5kg / m³) 3 --Precise proportioning avoids overdosing and reduces sludge formation; Lime consumption per cubic meter: 4.8 kg / m³ 3 Wastewater (original process 6.0 kg / m³) 3 --Feedforward algorithm compensates for insufficient activity caused by impurities and avoids ineffective addition; Effluent compliance rate: 100% (original process 92%) -- stable concentration + precise pH control ensure that effluent indicators meet standards.

[0045] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it.

Claims

1. A precise lime dosing ratio and closed-loop control system, characterized in that, It includes a material supply unit, a proportioning and mixing unit, a detection unit, a control unit, and an execution unit; The control unit includes a feedforward prediction module and a feedback calibration module. It achieves precise control of lime addition ratio through a two-dimensional error compensation algorithm. The detection unit and the execution unit form an innovative insurance mechanism through hardware co-design to jointly solve the systematic errors caused by material characteristic drift and detection lag.

2. The precise lime dosing and closed-loop control system according to claim 1, characterized in that, The material supply unit includes a lime silo, a variable frequency screw feeder, a high-precision weighing sensor, and an anti-caking vibrator. The weighing sensor collects lime addition data in real time to provide basic parameters for the feedforward prediction module. The vibrator is linked with the feeder to ensure continuous material supply.

3. The precise lime dosing and closed-loop control system according to claim 1, characterized in that, The detection unit includes a radiometric densitometer, a calcium ion selective electrode, and a temperature sensor, all of which acquire signals synchronously. The detection unit is equipped with a backup detection channel, forming a hardware redundancy design. When the main sensor fails, it automatically switches to ensure continuous detection.

4. The precise lime dosing and closed-loop control system according to claim 1, characterized in that, The core algorithm of the feedforward prediction module includes: Detection of free Ca in lime slurry using a calcium ion-selective electrode 2+ Concentration, combined with temperature sensor data, to calculate the current effective activity coefficient K of the lime in real time (K = measured Ca). 2+ Concentration / Theoretical Ca 2+ Concentration), dynamically correcting for differences in material purity; Establish a feedforward injection calculation model in: The required mass concentration of lime slurry according to the process specifications; The measured density of lime slurry; This refers to the effective volume of the mixing tank. This represents the real-time activity coefficient. This is a temperature correction function. =1+0.005×(25-T), which compensates for the effect of temperature on the hydration reaction rate.

5. The precise lime dosing and closed-loop control system according to claim 1, characterized in that, The core algorithm of the feedback calibration module includes: The true concentration value is obtained by fusing the densitometer signal and the calcium ion electrode signal using the Kalman filter algorithm. in The weighting coefficient is (0.6-0.8). Based on concentration change rate Establish a hysteresis error prediction model: The hydration delay time is 3-8 minutes. This is the error correction term updated in real time by the BP neural network; according to With target concentration To compensate for the deviation, the proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd of the PID controller are dynamically adjusted.

6. The precise lime dosing and closed-loop control system according to claim 1, characterized in that, The execution unit includes a variable frequency drive, a pneumatic ceramic regulating valve, and an automatic cleaning device. The automatic cleaning device is activated every 1.5-2 hours to clean the sensor probe to avoid signal distortion. The variable frequency drive and regulating valve synchronously adjust the lime dosage and water flow rate according to the control unit instructions.

7. The precise lime dosing and closed-loop control system according to claim 1, characterized in that, The control unit is also equipped with an algorithm fault tolerance mechanism: when the activity coefficient K exceeds the normal range of 0.8-1.2, the EDTA titration calibration program is automatically started, the feedforward formula parameters are corrected and an audible and visual alarm is triggered.

8. The precise lime dosing and closed-loop control system according to claim 1, characterized in that, The mixing unit includes a stirring tank, a high-precision water metering pump, and a static mixer; the stirring tank is equipped with a shock-absorbing base.

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