CN-122022511-A - Intelligent control system and method for industrial wastewater treatment of park

CN122022511ACN 122022511 ACN122022511 ACN 122022511ACN-122022511-A

Abstract

The invention belongs to the technical field of wastewater treatment, and discloses an intelligent control system and method for industrial wastewater treatment of a park; the method comprises the steps of obtaining pre-discharge data of enterprises, deploying key nodes outside a wastewater treatment plant, monitoring in real time at the key nodes to obtain monitoring data, quantifying the load impact strength of wastewater discharged by the enterprises through a water inflow load impact index, modeling water inflow fluctuation of a treatment plant as a load event track, defining a self-healing event chain twin simulator, pre-modeling a water inflow multi-scene according to the load event track, pre-constructing a feedforward rule base, judging the impact strength of the wastewater according to the water inflow load impact index, triggering rules in the feedforward rule base according to the output of a multi-source information sensing module and a pre-modeling evaluation module and the impact strength, further outputting an adjustment report, controlling an actuating mechanism to act according to the adjustment report, treating the wastewater, and dynamically adjusting a target interval according to the feedback data.

Inventors

GU YANPING
LIU PENG
HUANG JIAN
CHEN YING
XIANG JUNHONG
WU JINBO
WU CHANGHUAI
ZHENG BAOXIAN
SU LONGQIANG
QIAN HAO
GAO SHANG
QIAN YIMIN
SU FEI
CHEN YAN
SHI YI

Assignees

浙江省水利河口研究院(浙江省海洋规划设计研究院)

Dates

Publication Date: 20260512
Application Date: 20251230

Claims (10)

1. An intelligent control system for industrial wastewater treatment of a campus, the intelligent control system for industrial wastewater treatment of a campus comprising: The multi-source information sensing module is used for acquiring pre-arrangement data of enterprises, deploying key nodes outside the wastewater treatment plant, and monitoring at the key nodes in real time to acquire monitoring data; the pre-modeling evaluation module is used for carrying out priori detection and temperature correction on the monitoring data, quantifying the load impact strength of the wastewater discharged by the enterprise through the inflow load impact index, modeling the inflow fluctuation of the treatment plant as a load event track, defining a self-healing event chain twin simulator, and pre-modeling the multiple scenes of inflow according to the load event track, wherein the generated self-healing correction factor provides support for the inflow load impact index; The intelligent rule decision center is used for pre-constructing a feedforward rule base, judging the impact strength of the wastewater according to the impact index of the inflow load, triggering rules in the feedforward rule base according to the output of the multi-source information sensing module and the previewing evaluation module and the impact strength, and further outputting an adjustment report; and the execution feedback module is used for controlling the action of the execution mechanism according to the adjustment report, treating the wastewater and dynamically adjusting the target interval according to the feedback data.
2. The intelligent control system for industrial wastewater treatment on a campus of claim 1, wherein the method for acquiring the pre-discharge data of the enterprise, deploying a key node outside the wastewater treatment plant, monitoring at the key node in real time, and acquiring the monitoring data comprises: The wastewater treatment plant is in butt joint with ERP or drainage scheduling ports of all enterprises in the park, and the read pre-drainage data comprise planned drainage time periods, pre-estimated drainage amount, pre-drainage wastewater concentration and reporting information of main pollutant types in future periods of the enterprises; taking a main pipe network and a key enterprise outlet outside a wastewater treatment plant as key nodes, and arranging sensors at the key nodes, wherein monitoring data of real-time monitoring comprises pollutant types, flow, conductivity, pH value, ORP value, temperature, turbidity and toxicity indexes; And a standard on-line water quality analyzer is arranged at the tail end of a biochemical pool in the wastewater treatment plant and is used for monitoring the wastewater discharged by treatment, and feedback data are readings of the water quality analyzer.
3. An intelligent control system for industrial wastewater treatment on a campus according to claim 2 wherein the prior detection and temperature correction of the monitored data and quantification of the load impact strength of the wastewater discharged by the enterprise by the influent load impact index comprises: Applying priori knowledge inspection to the monitored data, cleaning the data which does not accord with the priori knowledge standard, and replacing; introducing temperature correction, outputting corrected monitoring data, and recording the corrected monitoring data as original data; calculating the change rate of each parameter in the original data, performing deviation calculation by comparing the current value with the average value of the previous period, aligning all data according to time stamps, and generating a current load snapshot; A quantization index LI_tw is predefined for evaluating the impact strength of the upcoming wastewater load, and the inflow load impact index LI_tw=alpha×DeltaQ+beta×DeltaEC+gamma× (Q_w/Q_av) +epsilon×Re is calculated, wherein alpha, beta and gamma represent weight coefficients, deltaQ represents the flow rate of the current pipe network, deltaEC represents the conductivity rate of the current pipe network, Q_w represents the estimated total drainage, Q_av represents the designed average value, epsilon represents the correction weight, and the self-healing correction factor Re is extracted from the self-healing event chain twinning simulator.
4. An intelligent control system for industrial wastewater treatment on a campus according to claim 3, wherein the method for extracting the self-healing correction factor Re from the self-healing event chain twinning simulator comprises: Firstly modeling a load event track, counting the number of early-warning pollutant categories in a starting point, generating 4 scenes if the number of the early-warning pollutant categories is more than 2, otherwise, generating 3 scenes by default, wherein the scenes refer to random combination of the early-warning pollutant categories, adjusting the numerical value of the load event track according to pollution effects in each scene, and secondly, running a self-healing event chain twin simulator for each possible scene, pre-modeling the full-chain process of the load event track, and obtaining the complete-chain track of each scene; Extracting the load event track value of the complete chain track in each scene, and then self-healing correction factors , wherein, Representing the peak concentration of the i-th scene, Representing the expected value, vv represents the number of scenes.
5. An intelligent control system for industrial wastewater treatment on a campus as claimed in claim 4, wherein the modeling of the influent wave of the treatment plant as a load event trace, the method comprises: the load event trajectory includes a starting point, arrival time of wastewater, peak concentration and duration, and a load profile; The early warning list is predefined and comprises early warning ranges of various pollutant categories and the change rate of the pollutant categories, if the pollutant categories which are the same as those in the early warning list exist in the current load snapshot and the change rate is in the early warning range, a scene is generated according to the load event track, the early warning pollutant categories are output, the starting point is marked as the current timestamp, and otherwise, the scene is not generated; And acquiring the arrival time, the peak concentration and the duration of the wastewater based on the estimated drainage amount, the pipe network flow rate and the regulating tank volume, calculating the water quantity intensity and the time period ratio, predefining the water quantity threshold value and the time period threshold value, judging the load profile to be of a peak type if the water quantity intensity is greater than the water quantity threshold value and judging the load profile to be of a platform type if the time period ratio is less than the time period threshold value, otherwise.
6. The intelligent control system for industrial wastewater treatment in a park according to claim 5, wherein before the feed-forward rule base is scanned, firstly, primarily judging an inflow load impact index LI_tw, if LI_tw is less than 0.4, judging that the impact is low, preferentially triggering an energy efficiency rule in the feed-forward rule base, and if a matching rule exists, outputting an adjustment report, otherwise, regulating and controlling by using a basic target interval; If LI_tw is more than or equal to 0.4 and less than or equal to 0.7, judging that the impact is medium, preferentially triggering quality rules in a feedforward rule base, if matching rules exist, outputting an adjustment report, otherwise, regulating and controlling by using a preset medium target interval; if 0.7< LI_tw, judging that the impact is high, preferentially triggering the safety rules in the feedforward rule base, if the matching rules exist, outputting an adjustment report, otherwise, regulating and controlling by using a preset excitation target interval; in addition, a supplementary judgment is added, and the purpose of the supplementary judgment is to combine the load event track and the data in the current load snapshot to assist LI_tw in impact judgment.
7. The intelligent control system for industrial wastewater treatment on a campus of claim 6, wherein the feed forward rule base includes safety rules, quality rules, and energy efficiency rules; The regulation of the safety rule comprises a toxic impact early warning rule, a high-concentration wastewater buffering rule and an extreme uncertainty self-healing rule, wherein the toxic impact early warning rule judges whether emergency buffering is triggered based on a toxicity index in a current load snapshot and a polymerization chain track, if so, the regulating tank is switched to a small-flow progressive water inlet mode, and meanwhile, the adding amount of an antidote is adjusted to enable an ORP value to be in a target interval; The quality rule regulation comprises a high-load aeration regulation rule, a pollutant type targeted dosing rule and a denitrification strengthening rule, wherein the high-load aeration regulation rule regulates DO values in a target interval according to the flow change rate in the current load snapshot and the peak concentration of the polymerization chain track, starts a high-load coping mode and increases the internal reflux ratio; aiming at the pollution type pre-warning in the load event track and the pollution synergistic effect existing in the complete chain track, the pollutant type targeted dosing rule dynamically adjusts the coagulant dosing interval and the pH value E [6.8,7.2]; The regulation of the energy efficiency rule comprises a low-load energy saving rule and an event ending recovery rule, wherein the low-load energy saving rule regulates that when LI_tw is low and the load profile of a load event track is a platform type, the target interval of the regulated aeration intensity is reduced, the carbon source addition amount is gradually decreased, the event ending recovery rule is ended based on the duration of the load event track, the regulating tank mode is switched back to the full mixing mode, and all parameters are recovered to a base line threshold value.
8. An intelligent control system for industrial wastewater treatment on a campus as claimed in claim 7, wherein said method for controlling actuator action based on adjustment reports comprises: The execution mechanism comprises an aeration fan, a dosing pump, an internal reflux pump and an adjusting tank valve; The execution feedback module analyzes the target interval and the specific instruction in the adjustment report, and issues the target interval and the specific instruction to the execution mechanism for real-time regulation and control.
9. The intelligent control system for industrial wastewater treatment on a campus of claim 8, wherein the method for dynamically adjusting the target interval according to the feedback data comprises: And the execution feedback module receives feedback data, corrects a target interval generated by the feedforward rule base, and simultaneously updates the self-healing event chain twin simulator and the self-healing correction factor.
10. An intelligent control method for industrial wastewater treatment of a campus, for implementing the intelligent control system for industrial wastewater treatment of a campus according to any one of claims 1 to 9, comprising: Step S1, collecting pre-arranged data, monitoring data and feedback data through a multi-source information sensing module, and carrying out priori detection and temperature correction on the monitoring data to generate a current load snapshot; s2, calculating an inflow load impact index, modeling a load event track, and generating a self-healing correction factor according to a self-healing event chain twinning simulator; S3, the intelligent rule decision center performs impact judgment according to the impact index of the water inlet load, scans a feedforward rule base, triggers a matching rule and generates an adjustment report; s4, controlling the action of an executing mechanism according to the adjustment report to treat the wastewater; and S5, updating the target interval and the self-healing correction factor according to the feedback data.

Description

Intelligent control system and method for industrial wastewater treatment of park Technical Field The invention relates to the technical field of wastewater treatment, in particular to an intelligent control system and method for industrial wastewater treatment of a park. Background Industrial park wastewater treatment plants are key environmental protection facilities for centralized treatment of wastewater discharged by various industrial enterprises in the park. The most notable features of park wastewater are the extremely strong complexity, uncertainty and severe fluctuations in both water quality and water quantity. Such volatility is mainly due to the intermittence of the production, the periodicity of the drainage and the diversity of the product types in the campus. The wastewater treatment process and its control system pose serious challenges, specifically: The existing control strategies mostly rely on online detection results of the quality of incoming water to adjust core process parameters. However, accurate detection of critical water quality parameters takes several hours to complete. Even relatively rapid COD, ammonia nitrogen online analyzers, there is a detection delay. When high-concentration and high-toxicity wastewater enters and is pretreated, the control system sends an adjustment command according to the lagged detection data, but the adjustment command is too late, so that environmental protection risks are caused. Conventional automatic control systems (e.g., PID controllers) typically control around a fixed process parameter set point based on the design average water quality. However, when the water inflow load fluctuates severely, especially when the water inflow load is far lower than the design value, the high-strength aeration is maintained to cause huge energy waste, and when the ultra-high load wastewater is impacted, the fixed aeration quantity cannot meet the oxygen demand of the microbial degradation pollutants, so that the treatment is incomplete, and the water outflow exceeds the standard. Therefore, the dual objectives of processing up to standard and energy consumption optimization cannot be simultaneously achieved under the fluctuation working condition. In summary, in the existing intelligent control system of the industrial park wastewater treatment plant, due to the problems of inherent detection lag, inaccurate prediction, stiff set point and the like, when the fluctuation of inflow water facing the core is selected, the stability of the treatment process is poor, the quality of the effluent water is difficult to continuously and stably reach the standard, and the operation energy consumption and the material consumption are high. In view of the above, the present invention proposes an intelligent control system for industrial wastewater treatment in a campus to solve the above-mentioned problems. Disclosure of Invention In order to overcome the defects in the prior art and achieve the purposes, the invention provides the following technical scheme that the intelligent control system for industrial wastewater treatment of a park comprises: The multi-source information sensing module is used for acquiring pre-arrangement data of enterprises, deploying key nodes outside the wastewater treatment plant, and monitoring at the key nodes in real time to acquire monitoring data; the pre-modeling evaluation module is used for carrying out priori detection and temperature correction on the monitoring data, quantifying the load impact strength of the wastewater discharged by the enterprise through the inflow load impact index, modeling the inflow fluctuation of the treatment plant as a load event track, defining a self-healing event chain twin simulator, and pre-modeling the multiple scenes of inflow according to the load event track, wherein the generated self-healing correction factor provides support for the inflow load impact index; The intelligent rule decision center is used for pre-constructing a feedforward rule base, judging the impact strength of the wastewater according to the impact index of the inflow load, triggering rules in the feedforward rule base according to the output of the multi-source information sensing module and the previewing evaluation module and the impact strength, and further outputting an adjustment report; and the execution feedback module is used for controlling the action of the execution mechanism according to the adjustment report, treating the wastewater and dynamically adjusting the target interval according to the feedback data. Preferably, the implementation method of the multi-source information sensing module includes: The wastewater treatment plant is in butt joint with ERP or drainage scheduling ports of all enterprises in the park, and the read pre-drainage data comprise planned drainage time periods, pre-estimated drainage amount, pre-drainage wastewater concentration and reporting information of main pollutant types in fut