A method and system for removing algal pollutants from water

Abstract

This invention discloses a method and system for removing algal pollutants from water, relating to the fields of water treatment and environmental protection technology. The method includes the following steps: acquiring characteristic parameters of algal flocs in the target raw water to determine the compatibility range between flocs and air bubble attachment; coupling the compatibility range with the real-time hydrolysis state of the flocculant, constructing a nonlinear mapping relationship based on the phase matching of the hydrolysis hysteresis characteristics and the floc bubble-trapping gain response, and generating adjustable control commands; dynamically adjusting the dissolved air pressure and microporous aeration intensity according to the adjustable control commands to maintain a match between the air bubble particle size and the floc structure; and mixing the matched micro-nano bubbles with the flocs to generate a stable scum layer and complete solid-liquid separation. This invention achieves efficient solid-liquid separation by acquiring floc characteristics in real time and dynamically adjusting the parameters of the air flotation system, enabling the bubbles to match the flocs and form a stable scum layer, thus solving the problems of unstable scum and low separation efficiency in traditional air flotation methods.

Description

Technical Field

[0001] This invention relates to the field of water treatment and environmental protection technology, and more specifically, to a method and system for removing algal pollutants from water. Background Technology

[0002] In the field of water purification, efficient removal of algae from water is a key technological requirement. The synergistic treatment of micro / nano-air flotation and flocculants is a common technical approach for achieving efficient algae separation, realizing solid-liquid separation through the collision and adsorption of algae flocs by air bubbles. However, in existing technologies, flocculant dosing and micro / nano-air flotation bubble release are usually controlled independently, lacking real-time coupling adjustment of floc characteristics and bubble parameters. This leads to unstable floc-bubble matching, fluctuating flotation capture efficiency, and difficulty in maintaining constant effluent quality. Furthermore, existing control systems struggle to dynamically adjust flotation parameters based on floc evolution and floc hydrolysis processes, failing to form closed-loop adaptive control, thus limiting the application effectiveness of micro / nano-air flotation in efficient algae removal and stable solid-liquid separation.

[0003] The above-disclosed technical solutions have at least the following technical problems: In existing algae removal systems, the flocculant addition and the release of micro-nano air flotation bubbles lack real-time coupling control, resulting in a mismatch between floc characteristics and bubble parameters, which in turn causes fluctuations in air flotation capture efficiency and unstable effluent water quality. Summary of the Invention

[0004] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method and system for removing algal pollutants from water. By acquiring floc characteristics in real time and dynamically adjusting the parameters of the air flotation system, the air bubbles are matched with the flocs to form a stable scum layer, thereby efficiently completing solid-liquid separation and solving the problems of unstable scum and low separation efficiency in traditional air flotation methods.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] On one hand, a method for removing algal pollutants from water includes the following steps: obtaining characteristic parameters of algal flocs in the target raw water and determining the adaptation range for floc attachment to bubbles; coupling the adaptation range with the real-time hydrolysis state of the flocculant, constructing a nonlinear mapping relationship based on the phase matching of the hydrolysis hysteresis characteristics and the bubble-trapping gain response of the floc, and generating an adjustable control command; dynamically adjusting the dissolved air pressure and microporous aeration intensity according to the adjustable control command to keep the bubble particle size and floc structure matched; mixing the matched micro-nano bubbles with the flocs to generate a stable scum layer and complete solid-liquid separation.

[0007] In a preferred embodiment, obtaining the algal floc characteristic parameters of the target raw water includes: collecting real-time light scattering curves during the flocculation process and decomposing the scattering intensity into characteristic waveforms according to the time series and scattering angle; calculating the structural evolution characteristic quantities of the flocs based on the transition points between the characteristic waveforms; and mapping the structural evolution characteristic quantities to floc characteristic parameters based on the relationship between Mie theory and the scattering scaling of fractal flocs.

[0008] In a preferred embodiment, the mapping of structural evolution characteristics to floc characteristic parameters based on the Mie theory and the scattering scaling relationship of fractal flocs includes: calculating the floc particle size growth rate and average particle size based on the nucleation response characteristics of the low-angle scattering region; calculating the structural density parameters characterizing the overall spatial filling and aggregation degree of the flocs based on the aggregation characteristics of the mid-angle scattering region; and calculating the surface roughness parameters characterizing the surface irregularity of the flocs based on the reconstruction perturbation characteristics of the high-angle scattering region.

[0009] In a preferred embodiment, determining the fit range between floc and bubble adhesion includes:

[0010] The sedimentation distribution of algal flocs in the liquid is calculated based on the characteristic parameters of algal flocs; the rise velocity distribution range of bubbles in water is calculated; the sedimentation velocity distribution of flocs and the rise velocity distribution of bubbles are matched bidirectionally to determine the high adhesion probability matching range; the high adhesion probability matching range is mapped to the bubble diameter range to obtain the bubble adaptation range.

[0011] In a preferred embodiment, the coupling of the adaptation range with the real-time hydrolysis state of the flocculant includes: real-time measurement of the concentration and hydrolysis rate of the flocculant hydrolysis products, quantifying them into a hydrolysis factor; constructing a nonlinear mapping function based on the bubble adaptation range and the hydrolysis factor, outputting real-time optimal bubble parameters, including bubble diameter and volume fraction; sending the real-time optimal bubble parameters to the micro-nano air flotation device, and collecting actual operating data to feed back to the nonlinear mapping function for parameter correction;

[0012] Based on the corrected bubble parameters, the dissolved air pressure and aeration intensity setpoints are recalculated to generate the final adjustable control command.

[0013] In a preferred embodiment, the construction of the nonlinear mapping function based on the bubble adaptation interval and the hydrolysis factor specifically involves: performing monotonic segmentation on the time series changes of the bubble adaptation interval, extracting the expansion and contraction segments of the interval, and calculating the change in the floc bubble trapping rate within each segment to determine the gain response segment; dividing the operating conditions according to the real-time changes in the hydrolysis factor, and constructing the corresponding hydrolysis hysteresis characteristic interval based on historical experimental data; calculating the time overlap length between the gain response segment and the hydrolysis hysteresis characteristic interval to determine the phase difference; and shifting the turning point of the nonlinear mapping function based on the phase difference to establish a time dependency relationship.

[0014] In a preferred embodiment, the step of shifting and adjusting the inflection point of the nonlinear mapping function based on the phase difference further includes: using the gain response segment as a slope reference for the growth segment of the nonlinear function, determining the boundary conditions of the saturation segment based on the hydrolysis hysteresis characteristic interval, and constructing a multi-segment nonlinear gain structure; calibrating the parameters of the multi-segment nonlinear gain structure based on historical bubble-catching efficiency samples; calculating the bubble-catching capacity intensity factor under the current hydrolysis state through the calibrated nonlinear mapping function, and solving the real-time optimal bubble parameters accordingly, which are used as input parameters for system control commands.

[0015] In a preferred embodiment, the step of dynamically adjusting the dissolved air pressure and microporous aeration intensity according to adjustable control commands to maintain a match between bubble particle size and floc structure specifically involves: issuing adjustable control commands to the dissolved air device and the microporous aeration unit, and performing preliminary synchronous adjustment of the dissolved air pressure and aeration intensity according to the commands; monitoring bubble parameters and floc structure indicators in real time and comparing them with target values ​​to determine whether the current parameters meet the bubble-floc adaptation relationship; and performing secondary fine-tuning of the dissolved air pressure and aeration intensity based on the comparison results to achieve dynamic matching between bubble particle size and floc structure.

[0016] In a preferred embodiment, the process of mixing the matched micro / nano bubbles with flocs to generate a stable scum layer and complete solid-liquid separation specifically involves: directional collisions between micro / nano bubbles and flocs within a turbulent gradient-controlled mixing zone to form flocs with multi-point attached bubble clusters; introducing the flocs into a low-shear transition zone to redistribute the attached bubbles along a path of minimum energy consumption, causing the flocs to float as a whole; introducing the floating flocs into an interfacial aggregation zone, and adjusting the rising velocity of the liquid phase interface to allow the flocs to accumulate laterally and form an initial scum layer; and applying a controllable surface compression force to the initial scum layer to improve its support strength.

[0017] The enhanced scum layer is physically separated from the lower clear water zone to complete solid-liquid separation.

[0018] On the other hand, a system for removing algal pollutants from water includes the following modules: a bubble adaptation determination module, used to acquire the characteristic parameters of algal flocs in the target raw water and determine the adaptation range between flocs and bubble attachment; a flocculant hydrolysis state coupling module, used to couple the adaptation range with the real-time hydrolysis state of the flocculant to generate adjustable control commands; an air flotation system dynamic adjustment module, used to dynamically adjust the dissolved air pressure and microporous aeration intensity according to the adjustable control commands to keep the bubble particle size and floc structure matched; and a solid-liquid separation module, used to mix the matched micro-nano bubbles with the flocs to generate a stable scum layer and complete solid-liquid separation.

[0019] The technical effects and advantages of the method and control system for removing algal pollutants from water according to the present invention are as follows:

[0020] This invention acquires the characteristic parameters of algal flocs in raw water in real time, determines the bubble matching range by combining the floc-bubble collaborative discrimination model, and couples this range with the hydrolysis state of the flocculant to generate adjustable control commands, thereby realizing the dynamic adjustment of the micro-nano air flotation system, keeping the bubble particle size and floc structure matched, thereby improving the algae capture efficiency, stabilizing the scum layer structure, and achieving efficient and controllable solid-liquid separation. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the process for removing algal pollutants from water according to the present invention;

[0022] Figure 2 This is a schematic diagram of a system structure for removing algal pollutants from water according to the present invention. Detailed Implementation

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1, Figure 1 This invention discloses a method for removing algal pollutants from water, comprising the following steps:

[0025] S1, Obtain the characteristic parameters of algal flocs in the target raw water and determine the adaptation range for floc attachment and bubble attachment;

[0026] In this embodiment, the floc characteristic parameters are used to characterize the average particle size, structural density, and surface roughness of the floc.

[0027] The adaptation range includes the target bubble particle size range and the target bubble volume fraction, which are used to characterize the optimal flotation capture parameters under the current floc structure.

[0028] The acquisition of algal floc characteristic parameters of the target raw water specifically includes:

[0029] Real-time light scattering curves were collected during the flocculation process, and the scattering intensity was decomposed into three types of characteristic waveforms according to the time series and scattering angle: nucleation segment, aggregation segment, and reconstruction segment. The nucleation segment corresponds to a sudden jump in low-angle scattering, the aggregation segment corresponds to a continuous increase in medium-angle scattering, and the reconstruction segment corresponds to high-frequency fluctuations in high-angle scattering.

[0030] It should be noted that the division of the nucleation segment, the mid-angle aggregation segment, and the high-angle reconstruction segment is based on the joint change of the first and second derivatives of the scattering intensity: when the first derivative of the low-angle scattering intensity with respect to time is greater than the first threshold for three consecutive sampling periods, it is marked as the nucleation segment; when the second derivative of the mid-angle scattering intensity is stable and positive and its absolute value exceeds the second threshold, it is marked as the aggregation segment; when the normalized high-frequency energy of the high-angle scattering signal exceeds the third threshold within the window width, it is marked as the reconstruction segment. The thresholds are obtained from the water plant baseline experiment.

[0031] Based on the transition points between the three types of characteristic waveforms, the structural evolution characteristics of flocs are calculated, including the nucleation response slope, aggregation growth curvature, and reconstruction perturbation factor.

[0032] Nucleation response slope: used to characterize the rate of change in the number of initially generated particles. It is obtained by extracting the original time series of low-angle scattering signals within the nucleation segment, which reflects the generation of initial microparticles; identifying the intervals in the nucleation segment where the scattering signal increases rapidly, i.e., marking the nucleation rise region where the signal shows a continuous increase; and calculating the unit time increase of the signal within this rise region.

[0033] Agglomeration growth curvature: used to characterize the rate of floc size expansion and densification trend. It is used to reflect the floc size expansion and densification process by extracting the time series of mid-angle scattering signals within the agglomeration segment.

[0034] Identify the signal intervals that transition from slow growth to accelerated growth, as the tortuous segments representing the characteristics of the aggregation stage; quantify the curvature trend based on the magnitude of the change in growth rate within this interval, and use the degree of acceleration from slow to fast growth rate as the aggregation growth curvature.

[0035] Reconstruction disturbance factor: used to characterize the frequency and amplitude of floc surface breakage and reorganization. It is obtained by extracting the time series of high-angle scattering signals within the reconstruction segment, which reflects the breakage, peeling and reattachment behavior of the floc surface. Local short-term abrupt changes or high-frequency fluctuation regions are extracted from this time series as the reflection segment of surface reconstruction. The degree of surface disturbance is quantified according to the frequency and amplitude of these fluctuations.

[0036] Based on the Mie theory of light scattering and the scattering scaling relationship of fractal flocs, structural evolution characteristics are mapped to floc characteristic parameters.

[0037] The relationship between the Mie theory based on light scattering and the scattering scaling of fractal flocs maps structural evolution characteristics to floc characteristic parameters, specifically:

[0038] Based on Mie scattering theory, the rate of change of the nucleation response slope in the low-angle scattering region is correlated with the particle size growth rate. According to the cubic dependence of scattering intensity on particle size, the instantaneous particle size increment is solved, and the continuous increment is integrated to obtain the average particle size parameter of the flocs.

[0039] Based on the scattering scaling relationship of fractal flocs, a correspondence is established between the scattering scaling index of the agglomeration growth curvature in the mid-angle scattering region and the fractal dimension of the flocs. The structural density of the flocs is calculated according to the analytical expression between the fractal dimension and the space filling coefficient.

[0040] Based on the surface roughness sensitivity of high-angle scattering, a correspondence is established between the attenuation ratio of the reconstruction perturbation factor in the high-angle scattering region and the surface undulation amplitude. By differentiating the high-angle normalized power spectrum of the scattering signal, the surface roughness parameter characterizing the irregularity of the floc surface is obtained.

[0041] In this embodiment, determining the fit range between flocculent and bubble adhesion specifically means:

[0042] Based on the characteristic parameters of algal flocs, the settling velocity of the flocs in the liquid is calculated using the Stokes formula to obtain the current settling velocity distribution of the flocs;

[0043] Based on the diameter, surface tension, and liquid viscosity of the micro- and nano-bubbles, the distribution range of the bubble's rising rate in water is calculated, thus obtaining the bubble's rising rate range.

[0044] Plot a bidirectional matching curve corresponding to the settling velocity distribution and the rising velocity range. Mark the area on the curve where the settling velocity and rising velocity are close and can achieve stable collision and adhesion, forming a matching interval with a high adhesion probability. The horizontal axis is the bubble rising rate and the vertical axis is the floc settling rate.

[0045] Mapping the region on the matching curve where the ratio of settling to rising rate meets the adhesion requirements back to the bubble diameter range yields the bubble adaptation range of the current floc. The adhesion requirement refers to maintaining the ratio between the settling velocity of the floc and the rising velocity of the bubble within a stable collision range. That is, the velocity difference between the two should not be so large that the bubble will skim over the surface of the floc, nor so small that it is insufficient to overcome the interfacial tension and make it difficult to form an effective contact, so that the floc and the bubble can maintain adhesion and enter the floating process after the collision.

[0046] During the mapping process, areas where flocs alternately rise and fall due to excessively fast settling or rising speeds can be eliminated, ensuring stable bubble adhesion.

[0047] The specific formula for calculating the settling rate is as follows:

[0048]

[0049] The specific formula for calculating the rate of ascent is as follows:

[0050]

[0051] in, For the settling rate, The density of the flocs, For water density, For the dynamic viscosity of water, It is the acceleration due to gravity. The equivalent radius of the floc is... For the rate of increase, This represents the effective density of the bubbles (approximately 0). The correction factor is determined by surface tension. Bubble equivalent radius.

[0052] S2, the adaptation range is coupled with the real-time hydrolysis state of the flocculant, and a nonlinear mapping relationship is constructed based on the phase matching of the hydrolysis hysteresis characteristics and the bubble trapping gain response of the floc, so as to generate adjustable control commands;

[0053] In this embodiment, the coupling of the adaptation range with the real-time hydrolysis state of the flocculant specifically refers to:

[0054] During the flocculation process, the concentration and hydrolysis rate of flocculant hydrolysis products in the raw water are measured in real time by online sensors, and the measurement results are quantified into hydrolysis factors.

[0055] A nonlinear mapping function is constructed based on the bubble adaptation range and hydrolysis factor to obtain the real-time optimal bubble diameter and volume fraction.

[0056] The real-time optimal bubble diameter and volume fraction, along with the corresponding air flotation system parameters, are sent to the micro-nano air flotation device to achieve dynamic matching of bubble size and volume fraction. This enables micro-nano bubbles to achieve a high adhesion probability with flocs during collisions, while maintaining the structural stability of the scum layer.

[0057] During the operation of the micro-nano air flotation system, the floc settling rate, bubble rising rate, and scum layer stability are collected in real time. The actual measurement results are fed back to the nonlinear mapping function to correct the real-time optimal bubble diameter and volume fraction. The correction is made by comparing the actual deviation with the output of the nonlinear mapping function and adjusting the function gain, inflection point, or saturation segment boundary to correct the real-time optimal bubble diameter and volume fraction, so that the calculation results are consistent with the actual operating state.

[0058] The dissolved air pressure setpoint and aeration intensity setpoint are recalculated based on the updated real-time optimal bubble diameter and volume fraction, and the recalculated setpoints are integrated into the final adjustable control command.

[0059] The nonlinear mapping function constructed based on the bubble adaptation range and hydrolysis factor yields the real-time optimal bubble diameter and volume fraction, specifically as follows:

[0060] Based on the time series changes of the bubble adaptation interval, the adaptation interval is segmented monotonically, and the interval expansion segment and interval contraction segment are extracted respectively. The change in floc bubble trapping rate in the two segments is calculated to obtain a gain response segment that describes the degree of influence of the adaptation interval change on the bubble trapping efficiency.

[0061] Based on the changing trend of real-time hydrolysis factors, the hydrolysis process is divided into three stages: insufficient activity, active release, and excessive hydrolysis. Based on the differences in bubble capture response under the three working conditions in historical water sample experiments, corresponding hydrolysis hysteresis characteristic intervals are constructed.

[0062] Based on the gain response segment and the hydrolysis hysteresis characteristic range, the time overlap length of the two is calculated to determine their phase difference, and the turning point of the nonlinear mapping function is shifted and adjusted according to the phase difference to establish the time dependence between the change of the adaptation range and the change of the hydrolysis factor.

[0063] Based on the phase alignment results, the gain response segment is used as the slope reference of the nonlinear function growth segment, and the hydrolysis hysteresis characteristic interval is used as the boundary condition of the nonlinear function saturation segment. Based on this, a multi-segment nonlinear gain structure with an early suppression segment, a rapid growth segment and a hysteresis plateau segment is constructed.

[0064] Based on historical bubble-catching efficiency samples, the parameters of the multi-segment nonlinear gain structure are calibrated. The slope value, inflection point position and saturation upper limit of the gain structure are determined by the minimum deviation criterion, so that the nonlinear mapping function can truly reflect the coupling relationship between the bubble adaptation range and the hydrolysis factor.

[0065] The bubble trapping capacity intensity factor under the current hydrolysis state is calculated based on the nonlinear mapping function after parameter calibration. The real-time optimal bubble diameter and bubble volume fraction are then calculated based on this intensity factor and used as input parameters for the subsequent generation of control commands for the micro-nano air flotation system.

[0066] In this embodiment, the output real-time optimal bubble diameter and volume fraction correspond to the adjustable parameters of the air flotation system, including gas flow rate, dissolved gas pressure and micropore aeration intensity, providing closed-loop control input for subsequent micro-nano bubble generation.

[0067] The hydrolysis factor is specifically:

[0068]

[0069] in, As a hydrolysis factor, This refers to the concentration of flocculant hydrolysis products. The hydrolysis rate, , The empirical weights are used to characterize the influence of hydrolysis state on floc formation rate and structural evolution.

[0070] The specific calculation formula for the nonlinear mapping function is as follows:

[0071]

[0072] in, To achieve the optimal bubble diameter in real time, This represents the real-time optimal bubble volume fraction.

[0073] S3 dynamically adjusts the dissolved air pressure and microporous aeration intensity according to the adjustable control command, so that the bubble particle size and floc structure are matched.

[0074] In this embodiment, the step of dynamically adjusting the dissolved gas pressure and microporous aeration intensity according to adjustable control commands to keep the bubble particle size and floc structure matched specifically involves:

[0075] Adjustable control commands are sent to the dissolved gas device and the microporous aeration unit, and the dissolved gas pressure regulating component and the aeration intensity regulating component are synchronously adjusted by a preset range based on the commands, so that the dissolved gas pressure and aeration intensity initially approach the target gas flow rate, the target dissolved gas pressure and the target aeration intensity.

[0076] After the initial adjustment is completed, the bubble size distribution, bubble formation rate per unit volume and floc structure index are monitored in real time. The monitoring results are compared with the real-time optimal bubble diameter and volume fraction to determine whether the current dissolved air pressure and aeration intensity meet the matching relationship between bubbles and flocs.

[0077] Based on the comparison results, the dissolved gas pressure and micropore aeration intensity are fine-tuned a second time. Specifically, the second fine-tuning is as follows: when the actual bubble particle size detected deviates from the real-time optimal bubble diameter, the dissolved gas pressure is automatically increased or decreased; when the effective bubble volume fraction detected deviates from the real-time optimal volume fraction, the micropore aeration intensity is automatically increased or decreased, so that the actual bubble particle size distribution and floc structure are dynamically matched.

[0078] The results of the secondary fine-tuning are returned to the update module of the adjustable control command to correct the target gas flow rate, dissolved gas pressure and aeration intensity for the next control cycle, so that the adjustable control command forms a closed-loop adaptive adjustment capability, thereby maintaining the optimal adhesion probability of micro-nano bubbles and flocs and the stability of the scum layer in the long term.

[0079] The process of comparing the monitoring results with the real-time optimal bubble diameter and volume fraction item by item is used to determine whether the current dissolved air pressure and aeration intensity meet the compatibility relationship between bubbles and flocs. Specifically:

[0080] The dynamic observations of the relative motion between flocs and bubbles are obtained, including the floc settling velocity time series, the bubble rising rate time series, and the floc surface deformation frequency. The above observations are then fused to generate an instantaneous dynamic parameter set of the relative motion between flocs and bubbles.

[0081] Based on the real-time optimal bubble diameter and volume fraction, the target dynamic consistency window is calculated, where the consistency window is defined as the allowable dynamic difference interval between the floc settling velocity and the bubble rising rate, and the time duration threshold of the adhesion window is further determined by combining the floc surface deformation frequency.

[0082] The instantaneous dynamic parameter set monitored in real time is projected onto the target dynamic consistency window to obtain the dynamic matching degree function. This function simultaneously considers the influence of the settling velocity-rising rate difference, floc deformation frequency and bubble density distribution on the adhesion persistence.

[0083] The continuity and stability of the dynamic matching degree function are used to determine whether the current system parameters satisfy the bubble-floc fit relationship.

[0084] The judgment results are converted into target correction direction parameters for dissolved air pressure and aeration intensity, which are used to guide the subsequent adjustment module to perform secondary system adjustment according to the dynamic path of "increasing pressure - decreasing aeration intensity" or "decreasing pressure - increasing aeration intensity", so that the system can once again approach the target dynamic consistency window.

[0085] The determination of whether the current system parameters satisfy the bubble-flocculation adaptation relationship specifically involves:

[0086] When the dynamic matching degree remains continuously higher than the matching threshold within the preset time window, it is determined that the current dissolved air pressure and aeration intensity parameters can stably support the attachment of bubbles and flocs.

[0087] If the dynamic matching degree experiences periodic collapses, oscillations, or interruptions below the threshold, it is determined that the current system parameters are insufficient to maintain the adhesion dynamics process.

[0088] The target dynamic consistency window is specifically calculated using the following formula:

[0089]

[0090] The dynamic matching degree function is specifically calculated using the following formula:

[0091]

[0092]

[0093] in, For the target dynamic consistency window, The settling velocity of the flocs. The rising speed of the bubble. To set the allowable rate deviation threshold, The frequency of floc deformation. To evaluate the length of time, For dynamic matching degree, The weighting is the activity weight of the flocs.

[0094] S4, the matched micro-nano bubbles are mixed with flocs to generate a stable scum layer and complete solid-liquid separation.

[0095] In this embodiment, the process of mixing the matched micro-nano bubbles with flocs to generate a stable scum layer and complete solid-liquid separation specifically involves:

[0096] Based on the bubble size and volume fraction after dynamic matching control, micro-nano bubbles and target flocs are directionally collided in a turbulent gradient-controlled mixing zone, so that stable multi-point attached bubble clusters are formed on the surface of the flocs.

[0097] The flocs that form bubble clusters are introduced into a low-shear transition zone, which causes the attached micro- and nano-bubbles to redistribute along the path of minimum energy consumption on the surface of the flocs, thereby improving the stability of the local buoyancy center of the flocs and causing the flocs as a whole to have an upward tendency.

[0098] Based on the upward trend, the gas-containing flocs are introduced into the interface aggregation zone. In this zone, by adjusting the slow upward speed of the liquid phase interface, multiple gas-containing flocs are laterally accumulated at the interface to form an initial algal scum layer.

[0099] A controllable surface compressive force is applied to the initial algal scum layer. By adjusting the interfacial tension gradient, the micro-nano bubbles inside the scum layer are kept in a uniform support state, thereby improving the overall support strength of the scum layer structure and preventing local collapse.

[0100] Based on the structural support strength and interface stability of the scum layer, the scum layer is physically separated from the lower clear water zone, so that the flocs and suspended bubbles are kept in the scum layer, thereby completing stable solid-liquid separation and obtaining clean effluent.

[0101] The formation of the stable scum layer is based on the trend of scum optical density change to determine the timing of scum scraping, so as to keep the scum moisture content within a controllable range.

[0102] Example 2, Figure 2 The present invention discloses a system for removing algal contaminants from water, comprising the following modules:

[0103] Bubble Adaptation Determination Module: Used to obtain the characteristic parameters of algal flocs in the target raw water and determine the adaptation range between floc attachment and bubble attachment;

[0104] Flocculant hydrolysis state coupling module: used to couple the adaptation range with the real-time hydrolysis state of the flocculant, and to construct a nonlinear mapping relationship based on the phase matching of the hydrolysis hysteresis characteristics and the floc bubble trapping gain response to generate adjustable control commands;

[0105] The air flotation system dynamic adjustment module is used to dynamically adjust the dissolved air pressure and microporous aeration intensity according to adjustable control commands, so that the bubble particle size and floc structure are matched.

[0106] Solid-liquid separation module: used to mix the matched micro-nano bubbles with flocs to generate a stable scum layer and complete solid-liquid separation.

[0107] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0108] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0109] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0110] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0111] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0112] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for removing algal pollutants from water, characterized in that, Includes the following steps: To obtain the characteristic parameters of algal flocs in the target raw water and determine the suitable range for floc and bubble attachment, the following steps are taken: Real-time light scattering curves during flocculation are collected, and the scattering intensity is decomposed into characteristic waveforms according to time series and scattering angle; based on the transition points between characteristic waveforms, the structural evolution characteristic quantities of the flocs are calculated; based on the Mie theory and the scattering scaling relationship of fractal flocs, the structural evolution characteristic quantities are mapped to floc characteristic parameters, including: based on the nucleation response characteristics of the low-angle scattering region, the floc particle size growth rate and average particle size are calculated; based on the aggregation characteristics of the mid-angle scattering region, the structural density parameters characterizing the overall spatial filling and aggregation degree of the flocs are calculated; based on the reconstruction perturbation characteristics of the high-angle scattering region, the surface roughness parameters characterizing the surface irregularity of the flocs are calculated. The determination of the fit range between floc and bubble adhesion is specifically as follows: Based on the characteristic parameters of algal flocs, the settling velocity of the flocs in the liquid is calculated using the Stokes formula to obtain the current settling velocity distribution of the flocs. According to the diameter, surface tension, and liquid viscosity of the micro / nano bubbles, the distribution range of the bubble rising velocity in the water is calculated to obtain the bubble rising velocity range. A bidirectional matching curve is plotted corresponding to the settling velocity distribution and the rising velocity range. Regions on the curve where the settling velocity and rising velocity are close and can achieve stable collision adhesion are marked, forming a matching interval with a high adhesion probability. The regions on the matching curve where the ratio of settling to rising velocity meets the adhesion requirements are mapped back to the bubble diameter range to obtain the bubble fitting interval of the current flocs. The adhesion requirement refers to the ratio between the settling velocity of the flocs and the rising velocity of the bubbles remaining within a stable collision range. The process couples the adaptation range with the real-time hydrolysis state of the flocculant, including: real-time measurement of the concentration and hydrolysis rate of flocculant hydrolysis products, quantifying them as hydrolysis factors; constructing a nonlinear mapping function based on the bubble adaptation range and the hydrolysis factors, outputting real-time optimal bubble parameters, including bubble diameter and volume fraction; sending the real-time optimal bubble parameters to the micro-nano air flotation device, and collecting actual operating data to feed back to the nonlinear mapping function for parameter correction; recalculating the dissolved air pressure and aeration intensity setpoints based on the corrected bubble parameters, generating the final adjustable control command. The construction of the nonlinear mapping function based on the bubble adaptation interval and the hydrolysis factor specifically involves: performing monotonic segmentation on the time series changes of the bubble adaptation interval, extracting the expansion and contraction segments of the interval, and calculating the change in the floc bubble trapping rate within each segment to determine the gain response segment; dividing the working conditions according to the real-time changes in the hydrolysis factor, and constructing the corresponding hydrolysis hysteresis characteristic interval based on historical experimental data; calculating the time overlap length between the gain response segment and the hydrolysis hysteresis characteristic interval to determine the phase difference; and shifting the turning point of the nonlinear mapping function based on the phase difference to establish a time dependency relationship. The dissolved air pressure and microporous aeration intensity are dynamically adjusted according to the adjustable control command to keep the bubble particle size and floc structure matched. The matched micro-nano bubbles are mixed with flocs to generate a stable scum layer and complete solid-liquid separation; The specific formula for calculating the settling rate is as follows: The specific formula for calculating the rate of ascent is as follows: in, For the settling rate, The density of the flocs, For water density, For the dynamic viscosity of water, It is the acceleration due to gravity. The equivalent radius of the floc is... For the rate of increase, This represents the effective density of the bubbles (approximately 0). The correction factor is determined by surface tension. Bubble equivalent radius.

2. The method for removing algal pollutants from water according to claim 1, characterized in that, The method of shifting and adjusting the inflection point of the nonlinear mapping function based on the phase difference also includes: Using the gain response segment as a slope reference for the growth segment of the nonlinear function, the boundary conditions of the saturation segment are determined based on the hydrolysis hysteresis characteristic range, and a multi-segment nonlinear gain structure is constructed. The parameters of the multi-segment nonlinear gain structure are calibrated based on historical bubble-catching efficiency samples. The bubble trapping capacity intensity factor under the current hydrolysis state is calculated using the calibrated nonlinear mapping function, and the real-time optimal bubble parameters are calculated accordingly, serving as input parameters for system control commands.

3. The method for removing algal pollutants from water according to claim 2, characterized in that, The process of dynamically adjusting the dissolved gas pressure and microporous aeration intensity according to adjustable control commands to maintain a match between bubble particle size and floc structure specifically involves: Adjustable control commands are sent to the dissolved air device and the microporous aeration unit, and the dissolved air pressure and aeration intensity are initially synchronized and adjusted according to the commands. Real-time monitoring of bubble parameters and floc structure indicators, and comparison with target values ​​to determine whether the current parameters meet the bubble-floc adaptation relationship; Based on the comparison results, the dissolved air pressure and aeration intensity are finely adjusted to achieve dynamic matching between bubble particle size and floc structure.

4. The method for removing algal pollutants from water according to claim 3, characterized in that, The process of mixing the matched micro-nano bubbles with flocs to generate a stable scum layer and complete solid-liquid separation is as follows: Within a turbulent gradient-controlled mixing region, micro- and nano-bubbles are directionally collided with flocs to form flocs with multi-point attached bubble clusters. The flocs are introduced into the low-shear transition zone, causing the attached bubbles to redistribute along the path of minimum energy consumption, which promotes the overall floating of the flocs. The floating flocs are introduced into the interface aggregation zone, and the rising speed of the liquid phase interface is adjusted to make the flocs accumulate laterally to form an initial scum layer. Apply a controllable surface compressive force to the initial scum layer to improve the support strength of the scum layer; The enhanced scum layer is physically separated from the lower clear water zone to complete solid-liquid separation.

5. The method for removing algal contaminants from water according to claim 1, wherein the system used in the method comprises the following modules: Bubble Adaptation Determination Module: Used to obtain the characteristic parameters of algal flocs in the target raw water and determine the adaptation range between floc attachment and bubble attachment; Flocculant hydrolysis state coupling module: used to couple the adaptation range with the real-time hydrolysis state of the flocculant, and to construct a nonlinear mapping relationship based on the phase matching of the hydrolysis hysteresis characteristics and the floc bubble trapping gain response to generate adjustable control commands; The air flotation system dynamic adjustment module is used to dynamically adjust the dissolved air pressure and microporous aeration intensity according to adjustable control commands, so that the bubble particle size and floc structure are matched. Solid-liquid separation module: used to mix the matched micro-nano bubbles with flocs to generate a stable scum layer and complete solid-liquid separation.

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