Evaporator online cleaning adaptive control method based on fouling risk index

Abstract

The application discloses an evaporator online cleaning self-adaptive control method based on a fouling risk index, which comprises the following steps: collecting vacuum degree, kettle temperature, condensate conductivity, mother liquor density, heat exchange end temperature difference, circulating pump current and filter pressure difference, and then performing pretreatment and trend feature extraction, and finally weighting and synthesizing a fouling risk index SRI; according to a hierarchical threshold and a hysteresis logic, a micro-CIP, a short-CIP and a deep-CIP three-level online cleaning are adaptively triggered, and closed-loop linkage adjustment of vacuum kettle temperature matching control, inoculum amount, evaporation load and anti-fouling agent addition is synchronously performed, early fouling warning, graded cleaning on demand and operation parameter collaborative optimization are realized. The application effectively solves the problems of traditional evaporator fouling judgment lag, unreasonable cleaning intensity and no linkage of operation parameters, significantly improves the system operation stability, reduces energy consumption and chemical consumption, can be directly implemented in PLC / DCS engineering, and is suitable for high-salinity wastewater evaporation concentration and crystallization systems.

Description

Technical Field

[0001] This invention relates to the field of industrial wastewater resource utilization and evaporation crystallization process control technology, specifically to an adaptive control method for online scaling risk monitoring and online cleaning (CIP) of evaporators in high-salt wastewater evaporation concentration-crystallization systems, such as saline wastewater and zinc-containing wastewater. Background Technology

[0002] Evaporation concentration and evaporation crystallization are widely used in scenarios involving zero discharge of high-salt wastewater, salt recovery, and resource utilization. In conditions such as zinc-containing wastewater, pickling wastewater, and high-salt concentrated coking wastewater, evaporation systems are typically operating under high dissolved solids loads and complex ionic systems (Cl...). - SO4 2- Ca 2+ Mg 2+ Fe 2+ / Fe 3+ Zn 2+ Conditions involving organic matter / colloids, such as those described above, can easily lead to the following technical problems: (1) Scaling and heat transfer attenuation: Scale layers such as CaSO4, CaCO3, silicates, iron-zinc composite salts, and chloride crystal nuclei are formed on the heat exchange surface, which leads to a decrease in heat transfer coefficient and a deterioration of the temperature difference ΔT at the heat exchange end; (2) Increased circulation resistance and blockage: Uncontrolled crystallization particle size, wall adhesion nucleation and scale shedding cause the filter pressure difference ΔP to increase and the circulation pump load to increase. In severe cases, it can cause material blockage and unplanned shutdown. (3) Vacuum fluctuations and supersaturation impacts: Fluctuations in evaporation load and vacuum system cause abrupt changes in boiling point and supersaturation, inducing sudden nucleation and wall-attached growth, and accelerating scaling; (4) Coarse control of agents: Anti-scaling agents are often added quantitatively based on experience, and cannot be adjusted in real time according to the degree of concentration, entrainment and heat transfer deterioration, resulting in over-addition (causing high cost, foaming and affecting crystal quality) or under-addition (uncontrolled scaling). (5) Existing CIP strategies are outdated and inflexible: Currently, most devices rely on periodic shutdowns for CIP or manual judgment after alarms. Cleaning is often only performed after scaling has become severe, ΔT has deteriorated significantly, or blockage has occurred. This results in: delayed cleaning triggering and significant downtime losses; fixed cleaning intensity, with deep CIP performed even for minor scaling, leading to high water, chemical, and energy consumption; and a lack of coordinated adjustment of key operating variables such as vacuum, reactor temperature, seeding rate, and cooling rate, which makes it easy for scaling to recur in a short period after cleaning.

[0003] Currently available technologies mainly target alarm triggering based on single process parameters (such as heat exchanger temperature difference or filter pressure drop) or adopt fixed-cycle periodic cleaning strategies. Neither of these technologies achieves online scaling risk quantification and graded adaptive cleaning control through multi-variable fusion, nor does it solve the problem of coordinated linkage between operating parameters (vacuum degree, vessel temperature, seed dosage, anti-scaling agent) before and after cleaning.

[0004] Therefore, there is an urgent need for an adaptive control method that can quantify scaling risk online, trigger CIP in stages, and simultaneously regulate key operating variables to reduce the probability of scaling, reduce unplanned downtime, and improve the stability and economic efficiency of the evaporation system. Summary of the Invention

[0005] This invention aims to solve the following technical problems existing in the operation of evaporators during high-salt concentration and crystallization: (1) Lack of quantifiable scaling risk criteria: Existing methods rely on human experience or a single indicator (such as ΔT, pressure drop or vacuum alarm) to make judgments, which cannot identify and warn of early scaling. (2) CIP triggering is delayed and intensity is mismatched: Traditional periodic or post-cleaning leads to over- or under-cleaning, frequent shutdowns, high chemical and water consumption, and cannot achieve continuous online operation; (3) Operating parameters are not linked to scaling risk: Vacuum degree-cooker temperature mismatch leads to oversaturation shock; Insufficient control of seeding amount / cooling rate leads to sudden nucleation and adhesion to the wall; Anti-scaling agent addition cannot be closed-loop, causing re-scaling; (4) Lack of adaptive control logic that can be implemented in engineering: Existing methods are difficult to implement stable, noise-resistant and reproducible interlocking and operation logic in PLC / DCS control systems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: An adaptive control method for online cleaning of evaporators based on a scaling risk index includes the following steps: Step S1: Online location data collection and status determination During the operation of the evaporation concentration / crystallization system, at least the following online signals should be collected and synchronized in time: ① Vacuum degree / Absolute pressure P vac (kPa); ② Evaporation kettle temperature T k (°C); ③ The electrical conductivity κc (μS / cm) of the condensate characterizes the tendency of entrainment, leakage, and impurity accumulation; ④ Density of mother liquor ρm (g / cm³) 3 ), characterizing the degree of concentration and the risk of supersaturation; ⑤ Temperature difference at the heat exchange end ΔT hx (°C), characterizing heat transfer attenuation and scale growth; ⑥ The circulating pump current Ip (A) or frequency fp (Hz) characterizes the change in circulating resistance; ⑦ Filter pressure difference ΔP f (kPa) represents particle clogging, scale shedding, and filtration load.

[0007] Optional data collection parameters: steam valve opening degree, circulating flow rate Q (m³ / s). 3 The SRI incorporates data such as the temperature difference between the inlet and outlet of cooling water (°C) to improve prediction accuracy.

[0008] The evaporator enters risk assessment status under the following trigger conditions: continuous operating time reaches the set value (4-12 hours recommended); ΔT is detected. hx A decrease of more than 3% relative to the baseline; ΔP was detected. f The relative initial value increased by more than 5%; the mother liquor density ρm entered the high concentration range (exceeding 95% of the target density).

[0009] Step S2: Data Preprocessing and Feature Construction The following denoising and normalization processing is performed on the signal acquired in step S1: (1) Filtering: Use moving average filtering with a window length of 3 to 10 points or exponential weighted filtering (EWMA) with a time constant of 10 to 120s. (2) Outlier removal: Using the 3σ criterion or interquartile range (IQR) method, outliers that exceed the normal operating range are replaced with the previous valid values; (3) Standardization: Using the relative baseline degradation caliber, normalize to the [0,1] interval. The specific method is as follows: Standardized value = ,in The baseline temperature difference in a clean environment; Standardized value = ,in This is the initial reference pressure difference. Maximum allowable differential pressure increment; ρm standardized value = ; Standardized value of trend term = Normalize according to the preset maximum rate of change and limit the amplitude to [0,1].

[0010] (4) Construction of trend characteristics: Calculate d(ΔThx) / dt, d(ΔPf) / dt, d(ρm) / dt, preferably using the 5-point difference or exponential weighted difference method to identify the "accelerated scaling period".

[0011] Step S3: Construction of Scaling Risk Index (SRI) The Scaling Risk Index (SRI) is constructed as a weighted, dimensionless composite index: Where xi is the standardized value of each online static variable, vj is the standardized value of the trend term, and wi and βj are weight coefficients, satisfying Σwi+Σβj=1.

[0012] (1) Method for determining weighting coefficients The initial weights are determined using an engineering rule method, which can be validated and corrected using historical data. The engineering rule method divides the feature terms into three groups and assigns weights to each group: Heat transfer and blockage core group: ΔT hx ΔP f and its trend term d(ΔT) hx ) / dt、d(ΔP f ) / dt, group weight 0.65~0.80; Concentration and entrainment risk groups: ρm, κc, group weights 0.15–0.30; Run the verification / constraint group: Ip / fp, P vac T k The group weights are 0.05 to 0.15.

[0013] (2) Range of weights for each variable The ranges of values ​​for the online variable weight coefficients wi and the trend term weight βj are as follows: ΔT hx 0.18~0.30; ΔP f : 0.15~0.28; d(ΔT) hx ) / dt:0.08~0.16;d(ΔP f ) / dt: 0.08~0.16; ρm: 0.08~0.18; κc: 0.04~0.12; Ip / fp: 0.04~0.12; P vac : 0.01~0.06; T k : 0.01~0.05, the sum of each weight coefficient equals 1.

[0014] (3) Example weight configuration (applicable to forced circulation evaporation crystallization system) ΔT hx 0.22; ΔP f : 0.20; d(ΔT) hx ) / dt: 0.12; d(ΔP f ) / dt: 0.12; ρm: 0.14; κc: 0.08; Ip / fp: 0.07; P vac : 0.03; T k : 0.02.

[0015] Step S4: Graded threshold criterion and hysteresis triggering Set three-segment thresholds SRI1<SRI2<SRI3 and a release threshold SRI rel (SRI rel < SRI1), and a confirmation time t confirm and a release time t release to avoid false triggering caused by SRI jitter.

[0016] Threshold determination method — historical data quantile method: collect 7 to 30 days of SRI time series under the condition that the device operates stably without cleaning actions, remove the intervals of start-stop and large-load switching, and then take quantiles: SRI1 takes P75 to P85, SRI2 takes P90 to P95, SRI3 takes P97 to P99, and SRI rel takes P60 to P70; t confirm takes 3 to 10 min, and t release takes 5 to 15 min.

[0017] When calibrating with the weights of the example in Step S3 based on 14-day stable operation data of a typical forced circulation evaporative crystallization device, the typical values are: SRI1=0.60, SRI2=0.72, SRI3=0.85, SRI rel =0.55; t confirm =5 min, t release =10 min.

[0018] Triggering rules: When SRI≥SRI1 and the duration ≥t confirm : trigger micro CIP; When SRI≥SRI2 and the duration ≥t confirm : trigger short CIP (covering micro CIP); When SRI≥SRI3, or SRI≥SRI2 and simultaneously satisfies the composite condition that "the continuous decline rate of ΔThx exceeds the threshold and the continuous rise rate of ΔPf exceeds the threshold": trigger deep CIP; Exit condition: SRI≤SRI rel and the stabilization time ≥t release .[]

[0019] SRI is a dimensionless index, and the threshold is not fixed. It should be recalibrated based on the baseline of the present device according to the above method, and is suitable for different evaporator structures and different water quality systems.

[0020] Step S5: Graded cleaning action library for micro CIP / short CIP / deep CIP (1) Micro CIP (online light intervention) Triggering condition: SRI reaches the SRI_1 level.

[0021] Operation: Under conditions of uninterrupted normal operation or short-term load reduction (to 80%–100% of rated load), introduce cleaning fluid into the heat exchange surface and perform pulse flushing at 100%–120% of the rated frequency of the circulating pump. One pulse cycle consists of 20–60 seconds of high-frequency operation followed by 60–120 seconds of normal operation, with a cumulative duration of 5–15 minutes. The cleaning fluid is process water, condensate, or a low-concentration cleaning fluid with a mass fraction of 0.1%–0.5%. The purpose is to inhibit the initial adhesion of soft scale and crystal nuclei.

[0022] (2) Short CIP (Online Switching Circuit Cleaning) Triggering conditions: SRI reaches SRI_2 level, or SRI does not drop to SRI after microCIP execution. rel the following.

[0023] Operation: Switch the system to the bypass CIP loop. Under conditions of temperature 40–80℃ and flow rate not less than 1.2 times the normal circulation flow rate, circulate the cleaning solution for 15–30 minutes, then rinse with process water or condensate for 5–15 minutes, and then resume normal operation. The cleaning endpoint is determined by ΔT. hx Recovery rate ≥15% and ΔP f The decline is ≥20%.

[0024] (3) Deep CIP (Intensive or Shutdown Cleaning) Triggering conditions: SRI reaches SRI3 level, or the compound deterioration conditions are met.

[0025] Operation: Reduce the evaporation load to 50%–75% of the rated load, and execute in stages: ① Feed-liquid replacement for 5–15 min; ② Enhanced circulation cleaning for 30–90 min (cleaning solution is one or more of the following: 1%–5% by mass acid washing solution, alkaline washing solution, or complexing agent solution with a concentration of 2–10 g / L); ③ Process water rinsing for 10–20 min; ④ Reset and start up. Record the heat transfer recovery baseline after deep CIP, and update the threshold and weights.

[0026] Note: The specific type and concentration of the cleaning medium are determined by the process water quality system and the tolerance of the equipment material; micro CIP and short CIP can be performed online, while deep CIP is preferably performed by reducing the load or shutting down the machine.

[0027] Step S6: Vacuum degree and vessel temperature matching control (suppressing oversaturation shock) During the SRI upward trend and before and after CIP switching, vacuum-reactor temperature coordinated ramp control is implemented: (1) Limiting the rate of change of vacuum degree |dP vac / dt|≤r P r P The value range is 0.5~3kPa / min; (2) Limit the temperature change rate of the reactor |dT k / dt|≤r T , r T The value range is 0.2~2°C / min; (3) In the stages of load switching, CIP switching and restarting, priority shall be given to ensuring smooth boiling point change, and avoid sudden nucleation and wall adhesion caused by sudden change of supersaturation.

[0028] Ramp rate r P and r T can be taken in a wide range according to the device scale and evaporation intensity. For small devices (evaporation capacity <5t / h), take a larger value, and for large devices (evaporation capacity ≥20t / h), take a smaller value.

[0029] Step S7: Closed-loop linkage of seeding amount / cooling rate / antiscalant addition With reference to the benchmark seeding amount / crystal slurry circulation ratio Rs0 and the benchmark scale inhibitor addition amount q0, linked regulation is performed in sections according to SRI, and amplitude limiting and change rate limiting are set to avoid control jitter.

[0030] (1) Regulation of seeding amount / crystal slurry circulation ratio SRI<SRI1: Rs=Rs0; SRI1≤SRI<SRI2: Rs=Rs0·[1+0.30·(SRI- SRI1) / (SRI2-SRI1)] (linearly increasing from Rs0 to 1.30Rs0); SRI2≤SRI< SRI2: Rs=Rs0·[1.30+0.40·(SRI-SRI2) / (SRI3-SRI2)] (linearly increasing from 1.30Rs0 to 1.70Rs0); SRI≥SRI3: Rs=1.70Rs0, and deep CIP is triggered preferentially; Change rate limit: |dRs / dt|≤0.05Rs0 / min.

[0031] (2) Amplitude limiting of cooling rate / evaporation load Set evaporation load amplitude limiting coefficient Rc (mapped to the upper limit of steam valve opening or the upper limit of evaporation setting): SRI< SRI1: Rc=1.00; SRI1≤SRI< SRI2: Rc=1.00-0.10·(SRI-SRI1) / ( SRI2-SRI1) (minimum 0.90) SRI2≤SRI< SRI3: Rc=0.90-0.15·(SRI-SRI2) / ( SRI3-SRI2) (minimum 0.75) SRI≥SRI3: Rc=0.75, and execute deep CIP / dilution protection.

[0032] (3) Piecewise linear control of antiscaling agent dosage q Let q min =0.8q0, q max =2.0q0, rate of change limit |dq / dt|≤0.10q0 / min: SRI < SRI1: q = q0; SRI1≤SRI< SRI2:q=q0·[1+0.25·(SRI-SRI1) / (SRI2-SRI1)](q0 to 1.25q0; SRI2≤SRI< SRI3:q=q0·[1.25+0.35·(SRI-SRI2) / (SRI3-SRI2)](1.25q0 to 1.60q0) SRI≥SRI3: q=min(1.60q0,qmax), and prioritize short / deep CIP, without replacing cleaning with simple chemical dosing; The dosage q of the antiscaling agent can also be smoothly adjusted using the PI method: in , Take a value of 0.55 to 0.60. Take a value of 0.4q0 to 0.8q0. Take a speed of 0.03q0 / min to 0.08q0 / min (example parameters: =0.6q0, =0.05q0 / min); When κc rises abnormally (entrainment / leakage / migration abnormality), the operating condition protection and CIP logic are executed first, and PI regulation is suspended to avoid false control caused by adding chemicals alone.

[0033] Step S8 (optional): Evaluation of cleaning effect and self-learning of strategy After each CIP, record and evaluate the following metrics: ΔT hx Recovery rate (%), ΔP f The evaluation results include the percentage decrease in scale level (%), the change in circulating pump load (%), and the rescaling cycle (h). Based on the evaluation results, the weighting coefficients wi and βj, the thresholds SRI1 to SRI3, and the cleaning action parameters are adaptively updated to achieve continuous optimization for different salt systems and different loads.

[0034] The beneficial effects of this invention: Compared with existing periodic CIP or single-index alarm cleaning methods, this invention has at least the following beneficial effects: (1) Quantifiable scaling risk: By integrating multiple points to construct SRI, early signs of scaling can be identified and warned, with an advance warning time of more than 30 minutes compared to a single indicator. (2) CIP grading and on-demand cleaning: Micro CIP / short CIP / deep CIP grading triggering reduces unnecessary deep cleaning, which is expected to reduce downtime by 20% to 40% and reduce cleaning water and chemical consumption by 15% to 30%; (3) Suppressing oversaturation impact and wall-attached nucleation: Vacuum-cooker temperature matching and seeding / cooling rate linkage effectively reduce the probability of sudden nucleation and wall-attached growth, and extend the cleaning cycle; (4) Closed-loop chemical system is more economical: The dosage of antiscaling agent is adjusted according to density / conductivity / ΔT feedback, which reduces the risk of overdosing and improves control robustness; (5) High feasibility of engineering: The method can be implemented in PLC / DCS in the form of logic control and action library, which is easy to replicate and promote. Attached Figure Description

[0035] Figure 1 The flowchart of the adaptive control method for online cleaning (CIP) of evaporators based on the Scaling Risk Index (SRI) shows the steps from S1 to S8 and their logical relationships. Figure 2 This diagram illustrates the layout of key online points in the evaporation system and the connection of controllers / actuators. It shows the connection relationships between the evaporator body, heat exchanger / heating chamber, condenser, vacuum system, circulating pump, filter, CIP tank / chemical tank, CIP pump / valve assembly, antiscaling agent metering pump, and controller (PLC / DCS). Figure 3 The timing diagram for the micro CIP / short CIP / deep CIP motion library shows the time relationship between threshold triggering and linkage control. Detailed Implementation

[0036] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0037] like Figure 1-3 As shown, an adaptive control method for online cleaning of evaporators based on a scaling risk index includes the following steps: Step S1: Online location data collection and status determination During the operation of the evaporation concentration / crystallization system, at least the following online signals should be collected and synchronized in time: ① Vacuum degree / Absolute pressure P vac (kPa); ② Evaporator temperature Tk (°C); ③ The electrical conductivity κc (μS / cm) of the condensate characterizes the tendency of entrainment, leakage, and impurity accumulation; ④ Density of mother liquor ρm (g / cm³) 3 ), characterizing the degree of concentration and the risk of supersaturation; ⑤ Temperature difference at the heat exchange end ΔT hx (°C), characterizing heat transfer attenuation and scale growth; ⑥ Circulating pump current I p (A) or frequency f p (Hz), characterizing the change in cyclic resistance; ⑦ Filter pressure difference ΔP f (kPa) represents particle clogging, scale shedding, and filtration load.

[0038] Optional data collection parameters: steam valve opening degree, circulating flow rate Q (m³ / s). 3 The SRI incorporates data such as the temperature difference between the inlet and outlet of cooling water (°C) to improve prediction accuracy.

[0039] The evaporator enters risk assessment status under the following trigger conditions: continuous operating time reaches the set value (4-12 hours recommended); ΔT is detected. hx A decrease of more than 3% relative to the baseline; ΔP was detected. f The relative initial value increased by more than 5%; the mother liquor density ρm entered the high concentration range (exceeding 95% of the target density).

[0040] Step S2: Data Preprocessing and Feature Construction The following denoising and normalization processing is performed on the signal acquired in step S1: (1) Filtering: Use moving average filtering with a window length of 3 to 10 points or exponential weighted filtering (EWMA) with a time constant of 10 to 120s. (2) Outlier removal: Using the 3σ criterion or interquartile range (IQR) method, outliers that exceed the normal operating range are replaced with the previous valid values; (3) Standardization: Using the relative baseline degradation caliber, normalize to the [0,1] interval. The specific method is as follows: Standardized value = ,in The baseline temperature difference in a clean environment; Standardized value = ,in This is the initial reference pressure difference. Maximum allowable differential pressure increment; ρm standardized value = ; Standardized value of trend term = Normalize according to the preset maximum rate of change and limit the amplitude to [0,1].

[0041] (4) Trend feature construction: Calculate d( ) / dt、d( ) / dt、d( ) / dt, preferably using a 5-point difference or exponentially weighted difference method, is used to identify the "accelerated scaling period".

[0042] Step S3: Construction of Scaling Risk Index (SRI) The Scaling Risk Index (SRI) is constructed as a weighted, dimensionless composite index: Where xi is the standardized value of each online static variable, vj is the standardized value of the trend term, and wi and βj are weight coefficients, satisfying Σwi+Σβj=1.

[0043] (1) Method for determining weighting coefficients The initial weights are determined using an engineering rule method, which can be validated and corrected using historical data. The engineering rule method divides the feature terms into three groups and assigns weights to each group: Heat transfer and blockage core group: ΔT hx ΔP f and its trend term d(ΔT) hx ) / dt、d(ΔP f ) / dt, group weight 0.65~0.80; Concentration and entrainment risk groups: ρm, κc, group weights 0.15–0.30; Run the verification / constraint group: I p / f p P vac T k The group weights are 0.05 to 0.15.

[0044] (2) Range of weights for each variable The ranges of values ​​for the online variable weight coefficients wi and the trend term weight βj are as follows: ΔT hx 0.18~0.30; ΔP f : 0.15~0.28; d(ΔT) hx ) / dt:0.08~0.16;d(ΔP f ) / dt: 0.08~0.16; ρm: 0.08~0.18; κc: 0.04~0.12; I p / f p : 0.04~0.12; P vac : 0.01~0.06; T k: 0.01 to 0.05, and the sum of each weight coefficient is equal to 1.

[0045] (3) Example weight configuration (for forced circulation evaporative crystallization systems) ΔT hx : 0.22; ΔP f : 0.20; d(ΔT hx ) / dt: 0.12; d(ΔP f ) / dt: 0.12; ρm: 0.14; κc: 0.08; I p / f p : 0.07; P vac : 0.03; T k : 0.02.

[0046] Step S4: Graded threshold criterion and hysteresis triggering Three-section thresholds SRI1 < SRI2 < SRI3 and a release threshold SRI rel (SRI rel < SRI1), and a confirmation time t confirm and a release time t release to avoid false triggering caused by SRI jitter.

[0047] Threshold determination method - historical data quantile method: collect 7 to 30 days of SRI time series under the condition that the device operates stably without cleaning actions, remove the intervals of start-stop and large load switching, and then take quantiles: SRI1 is taken as P75 to P85, SRI2 is taken as P90 to P95, SRI3 is taken as P97 to P99, SRI rel is taken as P60 to P70; t confirm is taken as 3 to 10 min, t release is taken as 5 to 15 min.

[0048] When calibrating with the example weights in step S3 and based on 14 days of stable operation data of a typical forced circulation evaporative crystallization device, the typical values are: SRI1=0.60, SRI2=0.72, SRI3=0.85, SRI rel =0.55; t confirm =5 min, t release =10 min.

[0049] Triggering rules: When SRI ≥ SRI1 and the duration ≥ t confirm : trigger micro CIP; When SRI ≥ SRI2 and the duration ≥ t confirm : trigger short CIP (covers micro CIP); When SRI ≥ SRI3, or SRI ≥ SRI2 and simultaneously satisfy "ΔThx The rate of continuous decline exceeds the threshold and ΔP f The combined condition of "continuously rising rate exceeding threshold" triggers deep CIP; Exit condition: SRI≤SRI rel And the settling time is ≥ t release .

[0050] SRI is a dimensionless index, and its threshold is not fixed. It should be recalibrated based on the baseline of this device using the method described above. It is applicable to different evaporator structures and different water quality systems.

[0051] Step S5: Micro CIP / Short CIP / Deep CIP Graded Cleaning Action Library (1) Micro CIP (Online Light Intervention) Triggering condition: SRI reaches SRI1 level.

[0052] Operation: Under conditions of uninterrupted normal operation or short-term load reduction (to 80%–100% of rated load), introduce cleaning fluid into the heat exchange surface and perform pulse flushing at 100%–120% of the rated frequency of the circulating pump. One pulse cycle consists of 20–60 seconds of high-frequency operation followed by 60–120 seconds of normal operation, with a cumulative duration of 5–15 minutes. The cleaning fluid is process water, condensate, or a low-concentration cleaning fluid with a mass fraction of 0.1%–0.5%. The purpose is to inhibit the initial adhesion of soft scale and crystal nuclei.

[0053] (2) Short CIP (Online Switching Circuit Cleaning) Triggering conditions: SRI reaches SRI2 level, or SRI does not drop to SRI after microCIP execution. rel the following.

[0054] Operation: Switch the system to the bypass CIP loop. Under conditions of temperature 40–80℃ and flow rate not less than 1.2 times the normal circulation flow rate, circulate the cleaning solution for 15–30 minutes, then rinse with process water or condensate for 5–15 minutes, and then resume normal operation. The cleaning endpoint is determined by ΔT. hx Recovery rate ≥15% and ΔP f The decline is ≥20%.

[0055] (3) Deep CIP (Intensive or Shutdown Cleaning) Triggering conditions: SRI reaches SRI3 level, or the compound deterioration conditions are met.

[0056] Operation: Reduce the evaporation load to 50%–75% of the rated load, and execute in stages: ① Feed-liquid replacement for 5–15 min; ② Enhanced circulation cleaning for 30–90 min (cleaning solution is one or more of the following: 1%–5% by mass acid washing solution, alkaline washing solution, or complexing agent solution with a concentration of 2–10 g / L); ③ Process water rinsing for 10–20 min; ④ Reset and start up. Record the heat transfer recovery baseline after deep CIP, and update the threshold and weights.

[0057] Note: The specific type and concentration of the cleaning medium are determined by the process water quality system and the tolerance of the equipment material; micro CIP and short CIP can be performed online, while deep CIP is preferably performed by reducing the load or shutting down the machine.

[0058] Step S6: Vacuum degree and vessel temperature matching control (suppressing oversaturation shock) During the SRI upward trend and before and after CIP switching, vacuum-reactor temperature coordinated ramp control is implemented: (1) Limiting the rate of change of vacuum degree |dP vac / dt|≤r P r P The value range is 0.5~3kPa / min; (2) Limit the rate of change of the vessel temperature |dT k / dt|≤r T r T The value range is 0.2~2℃ / min; (3) During the load switching, CIP switching and restarting stages, priority should be given to ensuring smooth boiling point changes to avoid sudden nucleation and adhesion caused by abrupt changes in supersaturation.

[0059] ramp rate r P r T A wider range can be selected based on the scale of the equipment and the evaporation intensity. Larger values ​​can be selected for small equipment (evaporation capacity <5t / h) and smaller values ​​can be selected for large equipment (evaporation capacity ≥20t / h).

[0060] Step S7: Closed-loop linkage of seed dosage / cooling rate / anti-scaling agent addition Using the baseline seeding rate / crystal slurry circulation ratio Rs0 and the baseline scale inhibitor dosage q0 as references, the scale slurry is adjusted in stages according to the SRI, and the amplitude and rate of change are set to avoid control fluctuations.

[0061] (1) Adjustment of seeding rate / crystal slurry circulation ratio SRI <SRI1:Rs=Rs0; SRI1≤SRI< SRI2:Rs=Rs0·[1+0.30·(SRI-SRI1) / (SRI2-SRI1)](Rs0 increases linearly to 1.30Rs0) SRI2≤SRI<SRI3: Rs=Rs0·[1.30+0.40·(SRI- SRI2) / ( SRI3- SRI2)] (linearly increasing from 1.30Rs0 to 1.70Rs0); SRI≥SRI3: Rs=1.70Rs0, and deep CIP is triggered preferentially; Change rate limit: |dRs / dt|≤0.05Rs0 / min.

[0062] (2) Cooling rate / evaporation load limiting Define Rc as the evaporation load limiting coefficient (mapped to the upper limit of steam valve opening or the upper limit of evaporation setting): SRI< SRI1: Rc=1.00; SRI1≤SRI< SRI2: Rc=1.00-0.10·(SRI-SRI2) / ( SRI2-SRI1) (minimum 0.90) SRI2≤SRI< SRI3: Rc=0.90-0.15·(SRI-SRI2) / ( SRI3-SRI2) (minimum 0.75) SRI≥SRI3: Rc=0.75, and deep CIP / load reduction protection is performed.

[0063] (3) Piecewise linear control of antifouling agent dosage q Set qmin=0.8q0, qmax=2.0q0, change rate limit |dq / dt|≤0.10q0 / min: SRI< SRI1: q=q0; SRI1≤SRI< SRI2: q=q0·[1+0.25·(SRI-SRI1) / ( SRI2-SRI1)] (from q0 to 1.25q0); SRI2≤SRI<SRI3: q=q0·[1.25+0.35·(SRI-SRI2) / ( SRI3-SRI2)] (from 1.25q0 to 1.60q0) SRI≥SRI3: q=min(1.60q0,qmax), and short / deep CIP is performed preferentially, and cleaning shall not be replaced by simply adding chemicals; The antifouling agent dosage q can also be smoothly adjusted by PI mode: wherein , takes 0.55 to 0.60, takes 0.4q0 to 0.8q0, Take a value of 0.03q0 / min to 0.08q0 / min (example parameters: Kp=0.6q0, Ki=0.05q0 / min); when κc rises abnormally (entrainment / leakage / migration abnormality), prioritize the execution of operating condition protection and CIP logic, suspend PI regulation, and avoid false control caused by relying solely on chemical addition.

[0064] Step S8 (optional): Evaluation of cleaning effect and self-learning of strategy After each CIP, record and evaluate the following metrics: ΔT hx Recovery rate (%), ΔP f The evaluation results include the percentage decrease in scale level (%), the change in circulating pump load (%), and the rescaling cycle (h). Based on the evaluation results, the weighting coefficients wi and βj, the thresholds SRI1 to SRI3, and the cleaning action parameters are adaptively updated to achieve continuous optimization for different salt systems and different loads.

[0065] Example 1: Constructing SRIs at online locations and triggering adaptive control of "micro CIPs and short CIPs" Operating conditions and equipment: One vacuum evaporation concentration-crystallization system with an evaporation capacity of approximately 8 t / h; the evaporator is a forced circulation type; online points include P vac T k ,κc,ρm,ΔT hx I p ΔP f The PLC sampling period is 30 seconds.

[0066] step: (1) Data collection and preprocessing: The above points were collected with a sampling period of 30 seconds, and outliers were removed using a 5-point moving average and 3σ. The trend term d(ΔT) was calculated. hx ) / dt、d(ΔP f ) / dt (using 5-point difference).

[0067] (2) SRI calculation: ρm, κc, ΔT hx I p ΔP f After standardizing the trend term according to the relative baseline degradation metric, the SRI is obtained by weighting and summing the terms as follows: ΔT hx Weight 0.22, ΔP f Weight 0.20, d(ΔT) hx ) / dt weight 0.12, d(ΔP f Weights of ) / dt are 0.12, ρm is 0.14, κc is 0.08, and I p Weight 0.07, P vac Weight 0.03, T k Weight 0.02.

[0068] (3) Threshold setting: Based on the quantile method of 14-day stable operation data of the device, SRI1=0.60, SRI2=0.72, SRI3=0.85, t confirm =5min, SRI rel =0.55, t release =10min; Vacuum ramp rate r P =1.5kPa / min, boiler temperature ramp rate r T =0.8℃ / min.

[0069] (4) Trigger micro CIP: When SRI≥SRI1 (0.60) and lasts for 5 minutes, execute micro CIP: Increase the frequency of the circulating pump by 10% and run it in pulse mode (one cycle is 30 seconds of high-frequency operation followed by 60 seconds of normal operation) for 8 minutes. Vacuum and autoclave temperature are controlled by ramp matching (|dP) vac / dt|≤1.5kPa / min,|dT k / dt|≤0.8℃ / min); Antiscaling agent dosage according to ρm and ΔT hx The feedback is adjusted upwards according to a piecewise linear rule, not exceeding qmax = 2.0q0.

[0070] (5) Upgrade to short CIP: If SRI is still ≥ SRI2 (0.72) after micro CIP and lasts for 5 minutes, then perform short CIP: Switching to the CIP loop reduces the load to 85% of the rated load. The system was circulated and cleaned for 20 minutes at a flow rate of 1.3 times the normal flow rate and a temperature of 60°C, and then rinsed with process water for 10 minutes. After cleaning, restore the load and record ΔT. hx Recovery rate and ΔP f The extent of the decline.

[0071] (6) Effect evaluation: ΔT after short CIP hx Recovery ≥15%, ΔP f When the current drops by ≥20%, the circulating pump current decreases, the SRI drops below SRI_rel (0.55) and remains stable for 10 minutes, and the system returns to steady-state operation.

[0072] Example 2: SRI triggers deep CIP and executes a coordinated scale inhibition process involving "vacuum-boiler temperature-seeding-cooling-dosing". Operating condition: Operating in the high concentration zone, ρm continuously increases, ΔT hx Rapid decline and ΔP f rise.

[0073] (1) The acquisition and SRI calculation method of Example 1 were adopted; the threshold setting was the same as that of Example 1.

[0074] (2) When SRI ≥ SRI_3 (0.85), or when “ΔT” is satisfied hx The rate of continuous decrease exceeds 0.5℃ / h and ΔP f Deep CIP is triggered when the combined condition of a sustained rate of increase exceeding 0.2 kPa / h for 10 minutes is met. The load is reduced to 65% of the rated load, and a smooth transition from vacuum to autoclave temperature is performed (|dP) vac / dt|≤1.5kPa / min,|dT k / dt|≤0.8℃ / min), to avoid oversaturation impact; Deep CIP segmented execution: material replacement for 10 min → enhanced circulation cleaning with 2% citric acid solution for 45 min → process water rinsing for 15 min → reset and start-up; During the start-up phase, the seeding rate is increased to 1.70Rs0, and the cooling rate is limited (load limit Rc=0.75). The anti-scaling agent is applied according to ρm, κc, and ΔT. hx The closed-loop adjustment was set to 1.60q0.

[0075] (3) Record the heat transfer recovery baseline and rescaling cycle after deep CIP, and update the threshold and weight for subsequent operation.

[0076] ΔT after deep CIP hx Recovery to more than 85% of baseline, ΔP f When the value drops back to below 120% of the initial value, the rescaling cycle is extended by more than 30% compared to the previous one.

[0077] It should be noted that the above description of the technical solutions is exemplary, and this specification may be embodied in different forms and should not be construed as limiting it to the technical solutions set forth herein. Rather, providing these descriptions will ensure that the disclosure of this invention is thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Furthermore, the technical solutions of this invention are defined only by the scope of the claims.

[0078] Finally, it should be noted that the above embodiments are merely representative examples of the present invention. Obviously, the present invention is not limited to the above embodiments and many variations are possible. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention should be considered within the protection scope of the present invention.

Claims

1. An adaptive control method for online cleaning of evaporators based on a scaling risk index, characterized in that, Comprising the following steps: S1. Online Point Data Acquisition and Status Criteria: During the operation of the evaporation concentration / crystallization system, at least the following online signals should be acquired and synchronized in real time: Vacuum degree P vac evaporator temperature T k Condensate conductivity κc, mother liquor density ρm, heat exchanger end temperature difference ΔT hx Circulating pump current or frequency Ip / fp, filter pressure difference ΔP f ; S2. Data Preprocessing and Feature Construction: The signals collected in step S1 are subjected to moving average or exponential filtering for noise reduction, outlier removal, and standardization to the [0, 1] interval, and normalized using the relative baseline degradation caliber; trend features d(ΔT) are constructed. hx ) / dt、d(ΔP f ) / dt, d(ρm) / dt; S3, construction of fouling risk index SRI: the dimensionless fouling risk index SRI is obtained by weighted summation of the standardized variable value xi obtained in S2 and the standardized trend value vj according to the following formula: Where: wi is the weight coefficient of the static variable, βj is the weight coefficient of the trend term, and the sum of all weight coefficients equals 1; ΔT hx The weight wi is taken as 0.18~0.30, ΔP f The weights wi and βj are taken as follows: wi = 0.15–0.28; βj = 0.18–0.30; ρm = 0.08–0.18; κc = 0.04–0.12; Ip / fp = 0.04–0.12; and P = 0.04–0.

12. vac With the temperature T k The total weights range from 0.03 to 0.

10. S4, Graded threshold criterion and hysteresis triggering: set three-level thresholds SRI1<SRI2<SRI3 and a release threshold SRI rel < SRI1, as well as a confirmation time t confirm and a release time t release ; when SRI≥SRI1 and the duration is not less than t confirm , micro CIP is triggered; when SRI≥SRI2 and the duration is not less than t confirm , short CIP is triggered; when SRI≥SRI3, or SRI≥SRI2 and simultaneously satisfies the composite condition that ΔT hx keeps decreasing and ΔP f keeps increasing, deep CIP is triggered; when SRI drops below SRI rel and the duration is not less than t release , the cleaning state is exited; S5, graded cleaning execution: performing the following cleaning actions in a graded manner according to the criterion in S4: S51, micro CIP: without interrupting normal operation or reducing the load for a short time, introducing low-concentration cleaning solution or process water with a mass fraction of 0.1% to 0.5% into the heat exchange surface, performing pulse flushing at 100% to 120% of the rated frequency of the circulating pump, wherein the pulse cycle is high-frequency operation for 20 to 60s, then returning to normal operation for 60 to 120s, and the cumulative duration is 5 to 15min; S52, short CIP: switching to a bypass CIP loop, performing cyclic cleaning with low-concentration cleaning solution for 15 to 30min under the conditions of a temperature of 40 to 80°C and a flow rate not less than 1.2 times the normal circulating flow rate, then performing displacement rinsing with process water or condensed water for 5 to 15min, and then returning to operation; S53, deep CIP: after reducing the evaporation load to 50% to 75% of the rated load, performing four stages in sections: material liquid displacement for 5 to 15min, enhanced circulating cleaning for 30 to 90min, process water rinsing for 10 to 20min, and resetting and starting up, wherein the cleaning solution is one or a combination of more of low-concentration acid pickling solution, alkaline cleaning solution or complexing agent solution; S6. Vacuum-cure temperature matching control: During the SRI rising trend and before and after CIP switching, limit the rate of change of vacuum level |dP. vac / dt|Does not exceed the preset rate limit r P Limit the rate of change of the vessel temperature |dT k / dt|Does not exceed the preset rate limit r T To suppress supersaturation shocks caused by sudden changes in boiling point; r P The value range is 0.5~3kPa / min, r T The value range is 0.2~2℃ / min; S7, linked control of seeding dosage, cooling rate and anti-fouling agent dosage: taking the reference seeding dosage Rs0, the reference evaporation load coefficient Rc=1.00 and the reference anti-fouling agent dosage q0 as references, performing segmented linear adjustment according to SRI: when SRI < SRI1, the reference values are maintained; when SRI1 ≤ SRI < SRI2, the seeding dosage is linearly increased by 0% to 30% on the basis of Rs0, the load coefficient is linearly decreased by 0% to 10%, and the anti-fouling agent dosage is linearly increased to 1.00q0 to 1.25q0; when SRI2 ≤ SRI < SRI3, the seeding dosage is increased by 30% to 70% on the basis of Rs0, the load coefficient is decreased to 0.75 to 0.90, and the anti-fouling agent dosage is increased to 1.25q0 to 1.60q0; when SRI ≥ SRI3, the seeding dosage is increased to 1.70Rs0, the load coefficient is decreased to 0.75, and deep CIP is triggered preferentially; the minimum value of the anti-fouling agent dosage is set as qmin=0.8q0, and the maximum value is set as qmax=2.0q0; the rate-of-change limiting of each adjustment amount does not exceed 5% to 10% / min of the reference value.

2. The adaptive control method for online cleaning of evaporators based on scaling risk index according to claim 1, characterized in that: In step S3, the weight coefficients are determined by combining the historical data quantile method and the engineering rule method: the characteristic items are divided into three groups, namely the core group of heat transfer and blockage, the group of concentration and entrainment risk, and the group of operation check constraint, the group weights are 0.65 to 0.80, 0.15 to 0.30, and 0.05 to 0.15 respectively, and the weights within the group are distributed according to the sensitivity of each variable to fouling and normalized; when there is not less than 7 days of historical data of stable operation of the device, the data is used to check and correct the weights determined by the engineering rule method.

3. The adaptive control method for online cleaning of evaporators based on scaling risk index according to claim 1, characterized in that: In step S4, the thresholds SRI1, SRI2, SRI3, and SRI rel Determined using the historical data quantile method: SRI sequences were collected for 7–30 days during stable operation of the unit without cleaning operations. After excluding start-up / shutdown and significant load switching periods, SRI1 was taken as the P75–P85 quantile, SRI2 as the P90–P95 quantile, and SRI3 as the P97–P99 quantile. rel For P60 to P70 quantiles; t confirm Take 3-10 minutes, t release Take for 5 to 15 minutes.

4. The adaptive control method for online cleaning of evaporators based on scaling risk index according to claim 1, characterized in that: In step S7, the anti-fouling agent dosage q is adjusted by a proportional-integral PI mode: in , Take a value of 0.55 to 0.

60. Take a value of 0.4q0 to 0.8q0. Take a value of 0.03q0 / min to 0.08q0 / min; when κc rises abnormally, prioritize the operation condition protection and CIP logic, and suspend PI regulation.

5. The adaptive control method for online cleaning of evaporators based on scaling risk index according to claim 1, characterized in that: In step S2, the standardization processing mode is: in: ; This refers to the temperature difference at the heat exchange ends. This refers to the filter pressure differential. This is the initial reference pressure difference. To allow the maximum differential pressure increment; This represents the baseline temperature difference in a clean environment.

6. The adaptive control method for online cleaning of evaporators based on scaling risk index according to claim 1, characterized in that: In step S1, one or more of the following can be selected as extended signals: steam valve opening degree, circulating flow rate Q, and cooling water inlet and outlet temperature difference, and incorporated into the SRI construction; the triggering condition for the evaporator to enter the risk assessment state is at least one of the following: continuous operating time reaches a set value; ΔT is detected. hx Downward trend; ΔP detected f Upward trend; The density ρm of the mother liquor enters the high concentration range.

7. The adaptive control method for online cleaning of evaporators based on scaling risk index according to claim 1, characterized in that: It also includes step S8, cleaning effect evaluation and parameter self-learning: after each CIP execution, ΔT is recorded. hx Recovery rate, ΔP f The weighting coefficients wi and βj, the classification thresholds SRI1 to SRI3, and the cleaning action parameters are adaptively updated based on the evaluation results, taking into account the drop amplitude, changes in the circulating pump load, and the rescaling cycle, in order to achieve continuous optimization for different salt systems and different load conditions.

8. The adaptive control method for online cleaning of evaporators based on scaling risk index according to claim 1, characterized in that: In step S5, the cleaning medium for micro CIP and short CIP is one or more of the following: process water, condensate, pickling solution with a mass fraction of 0.1% to 2%, alkaline pickling solution with a mass fraction of 0.1% to 2%, or complexing agent solution with a concentration of 0.5 to 5 g / L; the cleaning medium for deep CIP is one or more of the following: pickling solution with a mass fraction of 1% to 5%, alkaline pickling solution with a mass fraction of 1% to 5%, or complexing agent solution with a concentration of 2 to 10 g / L.

9. The adaptive control method for online cleaning of evaporators based on scaling risk index according to claim 1, characterized in that: Steps S1 to S7 are implemented in the form of logic control and motion library in PLC or DCS control system. The sampling period is 10 to 60 seconds. The data processing and SRI calculation period is the same as the sampling period. The CIP trigger logic is implemented through interlocking condition judgment.

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