CN-122022064-A - Electrolysis sewage treatment effect prediction system based on big data model

CN122022064ACN 122022064 ACN122022064 ACN 122022064ACN-122022064-A

Abstract

The invention discloses an electrolytic sewage treatment effect prediction system based on a big data model, which relates to the technical field of electrolytic sewage treatment and aims to solve the technical problem of low prediction accuracy caused by the lack of comprehensive analysis of multidimensional interference factors, if the current efficiency attenuation interference analysis unit realizes abnormal judgment of the apparent electric power efficiency of the electrode, prejudgment of running stability and tracing of efficiency influence, scientific basis is provided for operation and maintenance of the electrode, if the external interference effect evaluation unit realizes accurate evaluation of the external interference effect, the electrolysis process is adjusted pertinently so as to reduce the external interference influence, if the bubble behavior recognition detection unit builds a correlation model of the bubble behavior and the gas absorption efficiency, abnormal early warning and pertinence treatment of the gas absorption efficiency are realized, and the risk of harmful gas leakage is avoided.

Inventors

ZHAO BIN
LI MINGFANG
WU QINGYU
YU YUE
ZHANG LIWEI
LI XIN
YAN LINGYUN

Assignees

山东同其万疆科技创新有限公司

Dates

Publication Date: 20260512
Application Date: 20260225

Claims (9)

1. The electrolytic sewage treatment effect prediction system based on the big data model is characterized by comprising a sewage treatment prediction platform, wherein the sewage treatment prediction platform is in communication connection with: The current efficiency attenuation interference analysis unit is used for detecting the current efficiency in the sewage electrolysis treatment stage and carrying out electrolysis treatment interference analysis when the current efficiency is attenuated; The external interference effect evaluation unit is used for evaluating the external interference effect of the sewage electrolytic treatment stage; And the bubble behavior recognition and detection unit is used for recognizing and detecting the bubble behavior of the electrolytic sewage treatment process.
2. The electrolytic sewage treatment effect prediction system based on the big data model according to claim 1, wherein the process of the current efficiency attenuation interference analysis unit is as follows: Collecting the inlet/outlet water key pollutant concentration of each batch of sewage to be treated according to the electrolytic sewage treatment stage, and marking the inlet/outlet water key pollutant concentration as a treatment task index; The method comprises the steps of carrying out image acquisition on electrodes used for electrolytic sewage treatment, obtaining the corresponding pollutant removal amount deviation and the corresponding charge consumption amount deviation of adjacent single treatment stages of the same batch through continuous single treatment amount numerical value acquisition analysis of the current batch treatment sewage, carrying out dimensionalization treatment on deviation numerical values to obtain the sum of the same batch deviation, synchronously obtaining the corresponding pollutant removal average value deviation and the corresponding charge consumption average value deviation of the single treatment stages of the non-same batch, and carrying out dimensionalization treatment on deviation numerical values to obtain the sum of the non-same batch deviation.
3. The big data model-based electrolytic sewage treatment effect prediction system according to claim 2, wherein the same batch deviation sum and the non-same batch deviation sum are respectively compared with thresholds, and the specific thresholds include a same batch deviation threshold and a non-same batch deviation threshold; If the same batch deviation sum exceeds the same batch deviation threshold, or the non-same batch deviation sum exceeds the non-same batch deviation threshold, the apparent power efficiency of the currently used electrode is deduced to be abnormal, and the current period is marked as an efficiency abnormal period; and respectively marking the electrode images as an efficiency abnormal image and an efficiency normal image according to the time period type.
4. The electrolytic sewage treatment effect prediction system based on the big data model according to claim 3 is characterized in that the percentage of the surface dirt coverage area of the electrode corresponding to the efficiency abnormal image and the efficiency normal image is obtained, the percentage is marked as a characterization parameter, and the statistics of the characterization parameter is carried out according to each time stamp of the corresponding type image; selecting a curve segment of the characteristic parameter floating curve with a growing trend and marking the curve segment as a growing curve segment; If the overlapping time of the corresponding time period of the growing curve section and the normal efficiency time period exceeds a set overlapping time threshold value, and the deviation parameter growing span in the overlapping time period of the corresponding time period of the growing curve section and the normal efficiency time period is lower than the growing span threshold value, the external interference risk of the corresponding electrode in the current growing curve section is deduced to be high, an external interference signal is generated and sent to a sewage treatment prediction platform, after the external interference signal is received by the sewage treatment prediction platform, the electrolytic process executing process tracing detection is carried out, and if the overlapping time of the corresponding time period of the growing curve section and the normal efficiency time period does not exceed the set overlapping time threshold value, or the deviation parameter growing span in the overlapping time period of the corresponding time period of the growing curve section and the normal efficiency time period is deduced to be continuous, the electric efficiency attenuation of the corresponding current in the current growing curve section is deduced to be continuous, the electric efficiency interference signal is generated and sent to the sewage treatment prediction platform, and after the electric efficiency interference signal is received by the sewage treatment prediction platform, the corresponding electrode of the electrolytic process is operated and maintained.
5. The electrolytic sewage treatment effect prediction system based on the big data model according to claim 1, wherein the process of the external disturbance effect evaluation unit is as follows: The method comprises the steps of recording suspended matter concentration of corresponding treated sewage through a suspended matter concentration meter, collecting an electrolyzer light transmittance image, setting an external interference effect evaluation period, obtaining a corresponding electrolyzer light transmittance image according to a starting time point and a finishing time point corresponding to the external interference effect evaluation period, and carrying out data acquisition and analysis on the electrolyzer light transmittance image, wherein the suspended matter concentration of each region in the electrolyzer light transmittance image is obtained, the suspended matter concentration range is set, adjacent regions corresponding to the suspended matter concentration in the suspended matter concentration range are divided into the same region, and a suspended matter concentration distribution map of each region in the electrolyzer light transmittance image is obtained through division; and obtaining the visibility of the surface of the corresponding electrode of the sewage treatment in the external interference effect evaluation period through the floating of the concentration distribution diagram of the suspended matters corresponding to each moment.
6. The big data model based electrolytic sewage treatment effect prediction system according to claim 5, wherein the suspended matter concentration distribution map and the electrode surface visibility are analyzed: Acquiring the areas of concentration peaks and areas of areas where corresponding concentrations are located in a suspended matter concentration distribution diagram, obtaining concentration peak value values and area values of the areas after dimensionality removal treatment, and obtaining distribution diagram reflecting coefficients according to summation of the values; If the distribution diagram showing coefficient is not over the showing coefficient threshold and the electrode surface visibility is lower than the visibility threshold, the outside interference analysis is deduced to be abnormal in the outside interference effect evaluation period, a process optimization signal is generated and sent to the sewage treatment prediction platform, if the distribution diagram showing coefficient is not over the showing coefficient threshold and the electrode surface visibility is lower than the visibility threshold, the outside interference analysis is deduced to be normal in the outside interference effect evaluation period, a low interference signal is generated and sent to the sewage treatment prediction platform, and the sewage treatment prediction platform carries out self-inspection and adaptation adjustment on the current electrolytic sewage treatment process after receiving the low interference signal.
7. The electrolytic sewage treatment effect prediction system based on the big data model according to claim 1, wherein the process of the bubble behavior recognition detection unit is as follows: The method comprises the steps of acquiring an execution stage of an electrolytic sewage treatment process, acquiring a bubble generation stage, a separation stage and a rising stage in the execution stage, acquiring a bubble generation speed increase span in the bubble generation stage and a bubble separation speed increase span in the separation stage, uniformly marking the span as a bubble cycle characteristic, and acquiring a generation speed average value and a size average value of bubbles according to each stage and marking the bubble cycle characteristic.
8. The electrolytic sewage treatment effect prediction system based on the big data model according to claim 7, wherein if the parameters in the bubble cycle characteristics all show a growing trend or the surface characteristics of the bubbles all show a growing trend, the abnormal gas absorption efficiency in the current electrolytic sewage treatment stage is inferred, and a signal of abnormal gas absorption efficiency is generated and sent to the sewage treatment prediction platform; if the parameters in the bubble period characteristics do not all show a growing trend, and the bubble surface characteristics do not all show a growing trend, the gas absorption efficiency in the current electrolysis sewage treatment stage is deduced to be normal, and a signal with normal gas absorption efficiency is generated and sent to a sewage treatment prediction platform.
9. The electrolytic sewage treatment effect prediction system based on the big data model according to claim 1, wherein after the sewage treatment prediction platform receives the abnormal signal of the gas absorption efficiency, the type of the sewage currently treated by the electrolytic sewage treatment process is determined corresponding to the type of the gas product, if the gas product contains harmful substances, process treatment early warning is performed, the environment of the area is controlled, and meanwhile, the process adjustment, the treatment mode replacement or the electrode specification replacement are performed according to the type of the gas product.

Description

Electrolysis sewage treatment effect prediction system based on big data model Technical Field The invention relates to the technical field of electrolytic sewage treatment, in particular to an electrolytic sewage treatment effect prediction system based on a big data model. Background In the field of industrial wastewater and domestic wastewater purification treatment, the electrolytic treatment technology is widely applied to the treatment process of refractory wastewater such as high-concentration organic wastewater, heavy metal wastewater and the like due to the advantages of strong oxidation-reduction capability, high treatment efficiency, no need of adding a large amount of chemical agents and the like, and the current electrolytic wastewater treatment technology is influenced by a plurality of factors such as electrode states, external environment interference, bubble behaviors and the like in the actual operation process, so that the problems of unstable treatment effect, blind operation and maintenance, difficult fault tracing and the like exist, and the popularization and the application of the electrolytic treatment technology and the full play of the treatment efficiency are seriously restricted. Nowadays, prediction of electrolytic sewage treatment effect is mostly dependent on a single operation parameter (such as operation time, voltage, current and the like), and comprehensive analysis on multidimensional interference factors is lacked, so that prediction accuracy is low, in the aspect of electrode state judgment, maintenance decision is carried out only through operation time length or simple voltage rising condition, and real states such as electrode passivation, pollution and the like cannot be accurately identified, so that double dilemma of insufficient maintenance (caused by reduction of treatment effect and substandard pollutant emission) or excessive maintenance (increased equipment shutdown loss and operation and maintenance cost) is caused; Meanwhile, the influence of external interference (such as fluctuation of suspended matter concentration, impurity mixing and the like in sewage) on the electrolytic treatment effect lacks scientific and effective evaluation means, technological abnormality caused by external interference cannot be found timely, and the correlation research of the actions such as bubble generation, separation, rising and the like and the gas absorption efficiency in the electrolytic process is insufficient, so that the running state of the process is difficult to judge through the bubble action, when the gas absorption efficiency is abnormal, early warning and treatment cannot be performed timely, the sewage treatment effect is influenced, and environmental safety risks are possibly caused by leakage of harmful gas products. Aiming at the technical defects, a solution is provided, the collaborative analysis capability of multi-unit data is attached, the linkage traceability and targeted treatment of factors such as electrode efficiency attenuation, external interference, bubble behavior and the like are realized, and the stability and reliability of the electrolytic sewage treatment process are ensured. Disclosure of Invention The invention aims to solve the problems and provide an electrolytic sewage treatment effect prediction system based on a big data model. The invention aims at realizing the technical scheme that the electrolytic sewage treatment effect prediction system based on the big data model comprises a sewage treatment prediction platform, wherein the sewage treatment prediction platform is in communication connection with: The current efficiency attenuation interference analysis unit is used for detecting the current efficiency in the sewage electrolysis treatment stage and carrying out electrolysis treatment interference analysis when the current efficiency is attenuated; The external interference effect evaluation unit is used for evaluating the external interference effect of the sewage electrolytic treatment stage; And the bubble behavior recognition and detection unit is used for recognizing and detecting the bubble behavior of the electrolytic sewage treatment process. Further, the current efficiency attenuation interference analysis unit is as follows: Collecting the inlet/outlet water key pollutant concentration of each batch of sewage to be treated according to the electrolytic sewage treatment stage, and marking the inlet/outlet water key pollutant concentration as a treatment task index; The method comprises the steps of carrying out image acquisition on electrodes used for electrolytic sewage treatment, specifically, carrying out periodic/on-line microscopic camera acquisition and analysis on the values of continuous single treatment capacity corresponding to the current batch of treated sewage, obtaining the deviation of pollutant removal capacity and the deviation of charge consumption capacity corresponding to the adjacent single tr