CN117132976A - An intelligent dosing device for sludge dewatering based on floc fractal dimension - Google Patents
An intelligent dosing device for sludge dewatering based on floc fractal dimension Download PDFInfo
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- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F11/00—Treatment of sludge; Devices therefor
- C02F11/12—Treatment of sludge; Devices therefor by de-watering, drying or thickening
- C02F11/14—Treatment of sludge; Devices therefor by de-watering, drying or thickening with addition of chemical agents
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Abstract
The invention discloses a sludge dewatering intelligent dosing device based on a floc fractal dimension, which comprises a model construction module, a model analysis module and a model analysis module, wherein the model construction module is used for calculating the floc fractal dimension and a model of a floc surface value based on a sludge image of water discharged by a water supply plant; the analysis module acquires a real-time floccule fractal dimension D, substitutes the floccule fractal dimension D into a floccule representation curve in the model construction module, outputs a floccule representation value ZX based on the floccule representation curve, and performs dosing analysis to acquire a dosing signal and a non-dosing signal; the dosing module is used for adding a proper and accurate flocculating agent according to the shape characteristics of sludge flocs when a dosing signal is obtained, and accurately and reasonably outputting the medicine amount according to the sludge concentration condition of the sludge water discharged by a water supply plant so as to ensure the high efficiency of agent treatment; and when no medicine is added, the frequency of collection and monitoring is regulated according to the condition of flocs, so that the times of collection, processing and the like are effectively reduced on the premise of ensuring the timeliness of medicine adding, and the medicine adding efficiency is further improved.
Description
Technical Field
The invention relates to the technical field of floc fractal dimension, in particular to an intelligent sludge dewatering dosing device based on the floc fractal dimension.
Background
Chinese patent CN108830429B discloses a method for optimizing a sewage treatment plant based on fractal dimension, comprising: (1) diluting sludge, and putting the sludge on a glass slide drop by drop; (2) photographing the sludge by adopting a microscope; (3) Processing the shot sludge image by using image processing software, obtaining the geometric parameters of the projection area A and the perimeter P of the single sludge flocs, and obtaining a two-dimensional fractal dimension D of the flocs based on a fractal theory; (4) Deducing the sedimentation performance, adsorption performance, biological activity and physical structure of the sludge including the sludge from the fractal dimension through calculation and analysis, thereby evaluating the running state of the sludge water;
in the prior art, the sludge concentration and dehydration link of the water supply plant mainly depends on manual experience and part of technical parameters to select the addition amount of flocculant and coagulant, so that the medicament is wasted and the medicament cannot play the maximum role. At present, the sludge concentration and dehydration link of a water supply plant is rough, and the effect of intelligent analysis and treatment is lacking, so that the concentration and sedimentation process cannot be known dynamically, and the hysteresis phenomenon exists in medicament addition and adjustment.
Disclosure of Invention
The invention aims to solve the problems of the background technology and provides an intelligent sludge dewatering dosing device based on the fractal dimension of flocs.
The aim of the invention can be achieved by the following technical scheme:
an intelligent sludge dewatering dosing device based on a floc fractal dimension, comprising:
the model construction module is used for calculating to obtain a floc fractal dimension and a model of a floc representation value based on a sludge image of the sludge water discharged by the water supply plant;
the specific working process of the model building module is as follows:
step 1: taking sludge water and sludge discharged from a water treatment plant in a historical period as a sample, and dividing the sample into a plurality of equal parts; diluting the sludge water and sludge, taking a drop of the diluted sludge water and sludge, putting the diluted sludge water and sludge on a glass slide, and covering the glass slide with a cover glass; using a microscope to image the slurry of the slide glass covered with the slide glass, and taking at least 5 photos of different views of each sample; processing the photographed sludge water image by adopting image processing software to obtain a projection area A and a perimeter P of a single activated sludge floc, and obtaining a floc fractal dimension D based on a fractal theory;
step 2: analyzing the running state of the sludge in the dehydration treatment process through the sedimentation representation value and the adsorption representation value to obtain a floc representation value;
step 3: constructing a rectangular coordinate system by taking the fractal dimension of the floccule as an abscissa and the floccule representation value as an ordinate, substituting the fractal dimension of the floccule and the floccule representation value under the same time into the coordinate system, and obtaining a floccule representation curve;
the analysis module acquires a real-time floccule fractal dimension D, substitutes the floccule fractal dimension D into a floccule representation curve in the model construction module, outputs a floccule representation value ZX based on the floccule representation curve, and performs dosing analysis to acquire a dosing signal and a non-dosing signal;
and when a dosing signal is obtained, the dosing module is used for adding a proper and accurate flocculation reagent according to the shape characteristics of the sludge flocs.
As a further scheme of the invention: the sedimentation performance value is obtained by:
setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring a sedimentation value ZCTC of an acquisition initial time Tc, a midpoint sedimentation value ZCTZ of an acquisition midpoint time Tz and an end sedimentation value ZCTj of an acquisition end time Tj;
acquiring a settlement maximum value ZCTCmax and a settlement minimum value ZCTCmin in an acquisition time node, and acquiring time Tmax corresponding to the settlement maximum value ZCTCmax and acquiring time Tmin corresponding to the settlement minimum value ZCTCmin;
by the formulaCalculating to obtain a sedimentation representation value ZXC; wherein, a1 and a2 are proportionality coefficients, a1 takes on a value of 0.51, and a2 takes on a value of 0.63.
As a further scheme of the invention: the adsorption performance value is obtained by:
setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring an adsorption value ZXTc of an acquisition initial time Tc, a midpoint adsorption value ZXTz of an acquisition midpoint time Tz and an end adsorption value ZXTj of an acquisition end time Tj;
acquiring an adsorption maximum value ZXTcmax and an adsorption minimum value ZXTcmin in an acquisition time node, and acquiring time Tmax corresponding to the adsorption maximum value ZXTcmax and acquiring time Tmin corresponding to the adsorption minimum value ZXTcmin;
by the formulaCalculating to obtain an adsorption representation value ZXX; wherein a3 and a4 are proportionality coefficients.
As a further scheme of the invention: by the formulaCalculating to obtain a batting surface value ZX.
As a further scheme of the invention: the specific working process of the analysis module is as follows:
step 1: comparing the obtained floc expression value ZX with a floc expression threshold value;
if the floc expression value ZX is less than or equal to the floc expression threshold value, generating a non-dosing signal;
if the floc expression value ZX > the floc expression threshold, a dosing signal is generated.
As a further scheme of the invention: the specific working process of the dosing module is as follows:
the method comprises the steps of obtaining a maximum floc particle size value in a detection period i, marking the maximum floc particle size value as ZL i, summing the maximum floc particle size value ZL i in the detection period i, averaging to obtain a floc particle size maximum average value ZLp, and comparing the floc particle size maximum average value ZLp with a floc particle size maximum average threshold value:
if the maximum average value ZLp of the floc particle sizes is larger than the maximum average threshold value of the floc particle sizes, determining a floc particle size influence signal, establishing a rectangular coordinate system by taking time as an X axis and taking the floc particle sizes as a Y axis, marking n particle size points in the rectangular coordinate system by taking the starting time of a detection period i and the maximum floc particle size value in the detection period i, marking two particle size points with the maximum ordinate value as high diameter points, marking two particle size points with the minimum ordinate value as low diameter points, sequentially connecting the two high diameter points and the low diameter points to obtain a quadrangle, and marking the area value of the quadrangle as a particle size influence coefficient ZHx.
As a further scheme of the invention: the maximum floc dispersion value in the detection period i is obtained and marked as ZP i, the maximum floc dispersion value ZP i in the detection period i is summed and averaged to obtain a floc dispersion maximum average value ZPp, and the floc dispersion maximum average value ZPp is compared with a floc dispersion maximum average threshold value:
if the maximum average value ZPp of the floc dispersion is greater than the maximum average threshold value of the floc dispersion, determining a material pump influence signal, establishing a rectangular coordinate system by taking time as an X axis and taking the floc dispersion as a Y axis, marking n dispersion points in the rectangular coordinate system by using the starting time of the detection period i and the maximum floc dispersion value in the detection period i, marking two dispersion points with the maximum ordinate value as high dispersion points, marking two dispersion points with the minimum ordinate value as low dispersion points, sequentially connecting the two high dispersion points and the low dispersion points to obtain a quadrangle, and marking the area value of the quadrangle as a dispersion influence coefficient ZBx.
As a further scheme of the invention: obtaining a particle size influence coefficient ZHx and a dispersion influence coefficient ZBx, and correspondingly a particle size influence standard coefficient ZHXb and a dispersion influence standard coefficient ZBXb; by the formulaCalculating to obtain the medicament addition LY; wherein b1 and b2 are proportionality coefficients.
As a further scheme of the invention: further comprises:
the monitoring module monitors and judges the batting surface value according to the condition of the batting surface value when the non-dosing signal is obtained;
the specific working process of the monitoring module is as follows:
step 1: calculating the obtained floc expression value ZX through a formula ZG=ln (d 1 x ZX) to obtain a floc supervision value ZG; wherein d1 is a proportionality coefficient, and d1 takes a value of 1.35;
step 2: comparing the obtained floc supervision value ZG with a floc supervision threshold;
when the floc supervision value ZG is more than or equal to X2, generating a low-frequency monitoring signal;
when X2 is more than the floc supervision value ZG and is more than or equal to X1, an intermediate frequency monitoring signal is generated;
when the floc supervision value ZG is smaller than X1, generating a high-frequency monitoring signal; wherein X1 and X2 are set thresholds, and X2 > X1 > 0.
The invention has the beneficial effects that:
according to the intelligent dosing device for sludge dewatering, the treatment data in the sludge water and the sludge of the water supply plant are analyzed in real time based on the fractal dimension of the flocs to obtain the flocs representation value, whether dosing is carried out is judged according to the flocs representation value, and when dosing is carried out, the drug quantity is accurately and reasonably output according to the sludge treatment condition, so that the high efficiency of drug treatment is ensured; and when no medicine is added, the frequency of collection and monitoring is regulated according to the condition of flocs, so that the times of collection, processing and the like are effectively reduced on the premise of ensuring the timeliness of medicine adding, and the medicine adding efficiency is further improved.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a system block diagram of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention discloses an intelligent sludge dewatering dosing device based on a floc fractal dimension, which comprises:
the model construction module is used for calculating and obtaining a floc fractal dimension based on a sludge image of the sludge water discharged by the water supply plant;
the specific working process of the model building module is as follows:
step 1: taking sludge water and sludge discharged from a water treatment plant in a historical period as a sample, and dividing the sample into a plurality of equal parts; diluting the sludge water and sludge, taking a drop of the diluted sludge water and sludge, putting the diluted sludge water and sludge on a glass slide, and covering the glass slide with a cover glass; using a microscope to image the slurry of the slide glass covered with the slide glass, and taking at least 5 photos of different views of each sample; processing the photographed sludge water image by adopting image processing software to obtain a projection area A and a perimeter P of a single activated sludge floc, and obtaining a floc fractal dimension D based on a fractal theory;
step 2: analyzing the running state of the sludge in the dehydration treatment process through the sedimentation representation value and the adsorption representation value to obtain a floc representation value;
the sedimentation performance value is obtained by:
setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring a sedimentation value ZCTC of an acquisition initial time Tc, a midpoint sedimentation value ZCTZ of an acquisition midpoint time Tz and an end sedimentation value ZCTj of an acquisition end time Tj;
acquiring a settlement maximum value ZCTCmax and a settlement minimum value ZCTCmin in an acquisition time node, and acquiring time Tmax corresponding to the settlement maximum value ZCTCmax and acquiring time Tmin corresponding to the settlement minimum value ZCTCmin;
by the formulaCalculating to obtain a sedimentation representation value ZXC; wherein, a1 and a2 are proportionality coefficients, the value of a1 is 0.51, and the value of a2 is 0.63;
the adsorption performance value is obtained by:
setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring an adsorption value ZXTc of an acquisition initial time Tc, a midpoint adsorption value ZXTz of an acquisition midpoint time Tz and an end adsorption value ZXTj of an acquisition end time Tj;
acquiring an adsorption maximum value ZXTcmax and an adsorption minimum value ZXTcmin in an acquisition time node, and acquiring time Tmax corresponding to the adsorption maximum value ZXTcmax and acquiring time Tmin corresponding to the adsorption minimum value ZXTcmin;
by the formulaCalculating to obtain an adsorption representation value ZXX; wherein, a3 and a4 are proportionality coefficients, the value of a3 is 0.85, and the value of a4 is 0.47;
by the formulaCalculating to obtain a batting surface value ZX;
step 3: constructing a rectangular coordinate system by taking the fractal dimension of the floccule as an abscissa and the floccule representation value as an ordinate, substituting the fractal dimension of the floccule and the floccule representation value under the same time into the coordinate system, and obtaining a floccule representation curve;
the analysis module acquires a real-time floccule fractal dimension D, substitutes the floccule fractal dimension D into a floccule representation curve in the model construction module, outputs a floccule representation value ZX based on the floccule representation curve, and performs dosing analysis or not;
the specific working process of the analysis module is as follows:
step 1: comparing the obtained floc expression value ZX with a floc expression threshold value;
if the floc expression value ZX is less than or equal to the floc expression threshold value, generating a non-dosing signal;
if the floc expression value ZX is greater than the floc expression threshold, generating a dosing signal;
the non-dosing signal in the invention shows that the sewage treatment device has better sedimentation effect and adsorption effect in the current sewage treatment process, and has good sewage purification effect, so that no dosing is needed; the dosing signal indicates that the sewage treatment device does not have better sedimentation effect and adsorption effect in the current sewage treatment process, and has poor sewage purification effect, and dosing is needed;
the dosing module is used for adding a proper and accurate flocculation reagent aiming at the shape characteristics of the sludge flocs when a dosing signal is obtained;
the specific working process of the dosing module is as follows:
step 1: the method comprises the steps of obtaining a maximum floc particle size value in a detection period i, marking the maximum floc particle size value as ZL i, summing the maximum floc particle size value ZL i in the detection period i, averaging to obtain a floc particle size maximum average value ZLp, and comparing the floc particle size maximum average value ZLp with a floc particle size maximum average threshold value:
if the maximum average value ZLp of the floc particle sizes is larger than the maximum average threshold value of the floc particle sizes, determining a floc particle size influence signal, establishing a rectangular coordinate system by taking time as an X axis and taking the floc particle sizes as a Y axis, marking n particle size points in the rectangular coordinate system by taking the starting time of a detection period i and the maximum floc particle size value in the detection period i, marking two particle size points with the maximum ordinate value as high diameter points, marking two particle size points with the minimum ordinate value as low diameter points, sequentially connecting the two high diameter points and the low diameter points to obtain a quadrangle, and marking the area value of the quadrangle as a particle size influence coefficient ZHx;
step 2: the maximum floc dispersion value in the detection period i is obtained and marked as ZP i, the maximum floc dispersion value ZP i in the detection period i is summed and averaged to obtain a floc dispersion maximum average value ZPp, and the floc dispersion maximum average value ZPp is compared with a floc dispersion maximum average threshold value:
if the maximum average value ZPp of the floc dispersion is larger than the maximum average threshold value of the floc dispersion, determining a material pump influence signal, establishing a rectangular coordinate system by taking time as an X axis and taking the floc dispersion as a Y axis, marking n dispersion points in the rectangular coordinate system by taking the starting time of a detection period i and the maximum floc dispersion value in the detection period i, marking two dispersion points with the maximum ordinate value as high dispersion points, marking two dispersion points with the minimum ordinate value as low dispersion points, sequentially connecting the two high dispersion points and the low dispersion points to obtain a quadrangle, and marking the area value of the quadrangle as a dispersion influence coefficient ZBx;
step 3: obtaining a particle size influence coefficient ZHx and a dispersion influence coefficient ZBx, and correspondingly a particle size influence standard coefficient ZHXb and a dispersion influence standard coefficient ZBXb; by the formulaCalculating to obtain the medicament addition LY; wherein, b1 and b2 are proportionality coefficients, b1 takes a value of 0.84, and b2 takes a value of 0.84; LYb is the standard value added in the pharmaceutical process;
the monitoring module monitors and judges the batting surface value according to the condition of the batting surface value when the non-dosing signal is obtained;
the specific working process of the monitoring module is as follows:
step 1: calculating the obtained floc expression value ZX through a formula ZG=ln (d 1 x ZX) to obtain a floc supervision value ZG; wherein d1 is a proportionality coefficient, and d1 takes a value of 1.35;
step 2: comparing the obtained floc supervision value ZG with a floc supervision threshold;
when the floc supervision value ZG is more than or equal to X2, generating a low-frequency monitoring signal;
when X2 is more than the floc supervision value ZG and is more than or equal to X1, an intermediate frequency monitoring signal is generated;
when the floc supervision value ZG is smaller than X1, generating a high-frequency monitoring signal; wherein X1 and X2 are set thresholds, and X2 > X1 > 0;
in the invention, the intervals of the nodes of the acquisition time indicated by the low-frequency monitoring signal, the intermediate-frequency monitoring signal and the high-frequency monitoring signal are sequentially reduced.
The working principle of the invention is as follows: according to the intelligent dosing device for sludge dewatering, the treatment data in the sludge water of the water supply plant is analyzed in real time based on the fractal dimension of the flocs to obtain the flocs representation value, whether dosing is carried out or not is judged according to the flocs representation value, and when dosing is carried out, the drug quantity is accurately and reasonably output according to the sludge treatment condition, so that the high efficiency of drug treatment is ensured; and when no medicine is added, the frequency of collection and monitoring is regulated according to the condition of flocs, so that the times of collection, processing and the like are effectively reduced on the premise of ensuring the timeliness of medicine adding, and the medicine adding efficiency is further improved.
The foregoing describes one embodiment of the present invention in detail, but the description is only a preferred embodiment of the present invention and should not be construed as limiting the scope of the invention. All equivalent changes and modifications within the scope of the present invention are intended to be covered by the present invention.
Claims (9)
1. Intelligent sludge dewatering dosing device based on flocculation fractal dimension, which is characterized by comprising:
the model construction module is used for calculating to obtain a floc fractal dimension and a model of a floc representation value based on a sludge image of the sludge water discharged by the water supply plant;
the specific working process of the model building module is as follows:
step 1: taking sludge water and sludge discharged from a water treatment plant in a historical period as a sample, and dividing the sample into a plurality of equal parts; diluting the sludge water and sludge, taking a drop of the diluted sludge water and sludge, putting the diluted sludge water and sludge on a glass slide, and covering the glass slide with a cover glass; using a microscope to image the slurry of the slide glass covered with the slide glass, and taking at least 5 photos of different views of each sample; processing the photographed sludge water image by adopting image processing software to obtain a projection area A and a perimeter P of a single activated sludge floc, and obtaining a floc fractal dimension D based on a fractal theory;
step 2: analyzing the running state of the sludge in the dehydration treatment process through the sedimentation representation value and the adsorption representation value to obtain a floc representation value;
step 3: constructing a rectangular coordinate system by taking the fractal dimension of the floccule as an abscissa and the floccule representation value as an ordinate, substituting the fractal dimension of the floccule and the floccule representation value under the same time into the coordinate system, and obtaining a floccule representation curve;
the analysis module acquires a real-time floccule fractal dimension D, substitutes the floccule fractal dimension D into a floccule representation curve in the model construction module, outputs a floccule representation value ZX based on the floccule representation curve, and performs dosing analysis to acquire a dosing signal and a non-dosing signal;
and when a dosing signal is obtained, the dosing module is used for adding a proper and accurate flocculation reagent according to the shape characteristics of the sludge flocs.
2. The intelligent sludge dewatering and dosing device based on the fractal dimension of flocs according to claim 1, wherein the sedimentation representation value is obtained by the following means:
setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring a sedimentation value ZCTC of an acquisition initial time Tc, a midpoint sedimentation value ZCTZ of an acquisition midpoint time Tz and an end sedimentation value ZCTj of an acquisition end time Tj;
acquiring a settlement maximum value ZCTCmax and a settlement minimum value ZCTCmin in an acquisition time node, and acquiring time Tmax corresponding to the settlement maximum value ZCTCmax and acquiring time Tmin corresponding to the settlement minimum value ZCTCmin;
by the formulaCalculating to obtain sedimentation performance valueZXC; wherein, a1 and a2 are proportionality coefficients, a1 takes on a value of 0.51, and a2 takes on a value of 0.63.
3. The intelligent sludge dewatering and dosing device based on the fractal dimension of flocs according to claim 2, wherein the adsorption performance value is obtained by the following method:
setting an acquisition time node as T, wherein the acquisition time node T comprises an acquisition initial time Tc, an acquisition midpoint time Tz and an acquisition end time Tj;
acquiring an adsorption value ZXTc of an acquisition initial time Tc, a midpoint adsorption value ZXTz of an acquisition midpoint time Tz and an end adsorption value ZXTj of an acquisition end time Tj;
acquiring an adsorption maximum value ZXTcmax and an adsorption minimum value ZXTcmin in an acquisition time node, and acquiring time Tmax corresponding to the adsorption maximum value ZXTcmax and acquiring time Tmin corresponding to the adsorption minimum value ZXTcmin;
by the formulaCalculating to obtain an adsorption representation value ZXX; wherein a3 and a4 are proportionality coefficients.
4. A sludge dewatering intelligent dosing device based on a floc fractal dimension according to claim 3, wherein the formula is adoptedCalculating to obtain a batting surface value ZX.
5. The intelligent sludge dewatering and dosing device based on the fractal dimension of flocs according to claim 4, wherein the specific working process of the analysis module is as follows:
step 1: comparing the obtained floc expression value ZX with a floc expression threshold value;
if the floc expression value ZX is less than or equal to the floc expression threshold value, generating a non-dosing signal;
if the floc expression value ZX > the floc expression threshold, a dosing signal is generated.
6. The intelligent sludge dewatering and dosing device based on the fractal dimension of flocs according to claim 1, wherein the specific working process of the dosing module is as follows:
the method comprises the steps of obtaining a maximum floc particle size value in a detection period i, marking the maximum floc particle size value as ZLI, summing the maximum floc particle size value ZLI in the detection period i, taking an average to obtain a floc particle size maximum average value ZLp, and comparing the floc particle size maximum average value ZLp with a floc particle size maximum average threshold value:
if the maximum average value ZLp of the floc particle sizes is larger than the maximum average threshold value of the floc particle sizes, determining a floc particle size influence signal, establishing a rectangular coordinate system by taking time as an X axis and taking the floc particle sizes as a Y axis, marking n particle size points in the rectangular coordinate system by taking the starting time of a detection period i and the maximum floc particle size value in the detection period i, marking two particle size points with the maximum ordinate value as high diameter points, marking two particle size points with the minimum ordinate value as low diameter points, sequentially connecting the two high diameter points and the low diameter points to obtain a quadrangle, and marking the area value of the quadrangle as a particle size influence coefficient ZHx.
7. The intelligent sludge dewatering and dosing device based on the fractal dimension of flocs according to claim 6, wherein the maximum floc dispersion value in the detection period i is obtained and marked as ZP i, the maximum floc dispersion value ZP i in the detection period i is summed and averaged to obtain a floc dispersion maximum average value ZPp, and the floc dispersion maximum average value ZPp is compared with a floc dispersion maximum average threshold value:
if the maximum average value ZPp of the floc dispersion is greater than the maximum average threshold value of the floc dispersion, determining a material pump influence signal, establishing a rectangular coordinate system by taking time as an X axis and taking the floc dispersion as a Y axis, marking n dispersion points in the rectangular coordinate system by using the starting time of the detection period i and the maximum floc dispersion value in the detection period i, marking two dispersion points with the maximum ordinate value as high dispersion points, marking two dispersion points with the minimum ordinate value as low dispersion points, sequentially connecting the two high dispersion points and the low dispersion points to obtain a quadrangle, and marking the area value of the quadrangle as a dispersion influence coefficient ZBx.
8. The intelligent sludge dewatering and dosing device based on the fractal dimension of flocs according to claim 7, wherein a particle size influence coefficient ZHx and a dispersion influence coefficient ZBx and a particle size influence standard coefficient ZHxb and a dispersion influence standard coefficient ZBxb respectively are obtained; by the formulaCalculating to obtain the medicament addition LY; wherein b1 and b2 are proportionality coefficients.
9. The intelligent sludge dewatering and dosing device based on the fractal dimension of flocs according to claim 1, further comprising:
the monitoring module monitors and judges the batting surface value according to the condition of the batting surface value when the non-dosing signal is obtained;
the specific working process of the monitoring module is as follows:
step 1: calculating the obtained floc expression value ZX through a formula ZG=ln (d 1 x ZX) to obtain a floc supervision value ZG; wherein d1 is a proportionality coefficient, and d1 takes a value of 1.35;
step 2: comparing the obtained floc supervision value ZG with a floc supervision threshold;
when the floc supervision value ZG is more than or equal to X2, generating a low-frequency monitoring signal;
when X2 is more than the floc supervision value ZG and is more than or equal to X1, an intermediate frequency monitoring signal is generated;
when the floc supervision value ZG is smaller than X1, generating a high-frequency monitoring signal; wherein X1 and X2 are set thresholds, and X2 > X1 > 0.
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| CN202311088836.7A CN117132976A (en) | 2023-08-28 | 2023-08-28 | An intelligent dosing device for sludge dewatering based on floc fractal dimension |
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