CN120247293A - An integrated treatment method and system for oily wastewater - Google Patents
An integrated treatment method and system for oily wastewater Download PDFInfo
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Abstract
本发明公开了一种含油废水的集成化处理方法及处理系统，涉及水处理相关领域，该方法包括：建立含油废水集成化处理平台；通过废水收集模块获取待处理含油废水，排入污染物检测模块中进行检测分析，获得含油废水处理特性参数；基于集成分析模块获取废水处理关键环节，进行数据挖掘，构建废水集成处理空间；将含油废水处理特性参数作为约束参数，在废水集成处理空间内进行多阶段寻优分析，获得目标废水集成处理方案；通过控制处理模块进行集成化处理控制。解决了现有含油废水处理存在的缺乏系统性和集成性，导致整体处理效率低下，处理效果不稳定的技术问题，达到了通过集成化分析处理，提高处理效率和处理效果稳定性的技术效果。
The present invention discloses an integrated treatment method and treatment system for oily wastewater, which relates to the field of water treatment. The method comprises: establishing an integrated treatment platform for oily wastewater; obtaining the oily wastewater to be treated through a wastewater collection module, discharging it into a pollutant detection module for detection and analysis, and obtaining the characteristic parameters of oily wastewater treatment; obtaining the key links of wastewater treatment based on the integrated analysis module, conducting data mining, and constructing a wastewater integrated treatment space; taking the characteristic parameters of oily wastewater treatment as constraint parameters, conducting multi-stage optimization analysis in the wastewater integrated treatment space, and obtaining a target wastewater integrated treatment plan; and performing integrated treatment control through a control treatment module. The method solves the technical problems of the lack of systematicity and integration in the existing oily wastewater treatment, resulting in low overall treatment efficiency and unstable treatment effect, and achieves the technical effect of improving treatment efficiency and stability of treatment effect through integrated analysis and treatment.
Description
Technical Field
The application relates to the field of water treatment, in particular to an integrated treatment method and system for oily wastewater.
Background
Oily wastewater is a large class of industrial wastewater and mainly comes from multiple industries such as petroleum refining, petrochemical industry, food, leather, metal processing and the like. The waste water has large discharge amount, complex components, various organic matters and toxic and harmful substances, and causes serious damage to the environment and the ecological system. Therefore, the treatment of oily wastewater is an important point and a difficult point of industrial pollution prevention and control. Most of the existing oily wastewater treatment methods adopt single technologies, such as gravity separation, air flotation, electric flocculation, adsorption and the like, and the methods achieve certain effects in respective application ranges, however, as the wastewater components are complex and changeable, the single treatment method is difficult to adapt to treatment requirements under different water quality conditions, and the synergistic effect is lacked among the treatment units, so that the overall treatment efficiency is low and the treatment effect is unstable.
In the related art at the present stage, the oily wastewater treatment has the technical problems of low overall treatment efficiency and unstable treatment effect due to the lack of systematicness and integration.
Disclosure of Invention
The application provides an integrated treatment method and a treatment system for oily wastewater, which are characterized in that an integrated treatment platform for the oily wastewater is established, the integrated treatment platform comprises a wastewater collection module, a pollutant detection module, an integrated analysis module and a control treatment module, the wastewater collection module is used for obtaining oily wastewater to be treated, the pollutant detection module is used for detecting and analyzing the wastewater to obtain oily wastewater treatment characteristic parameters, the integrated analysis module is used for carrying out data mining based on wastewater treatment key links to construct a wastewater integrated treatment space for multi-stage optimizing analysis, and the control treatment module is used for carrying out integrated treatment control on the wastewater according to a target wastewater integrated treatment scheme, so that the technical effects of improving treatment efficiency and treatment effect stability through integrated analysis treatment are achieved.
The application provides an integrated treatment method of oily wastewater, which comprises the steps of establishing an oily wastewater integrated treatment platform, wherein the oily wastewater integrated treatment platform comprises a wastewater collection module, a pollutant detection module, an integrated analysis module and a control treatment module, acquiring oily wastewater to be treated through the wastewater collection module, discharging the oily wastewater to be treated into the pollutant detection module for detection analysis to obtain oily wastewater treatment characteristic parameters, acquiring wastewater treatment key links based on the integrated analysis module, wherein the wastewater treatment key links comprise a pretreatment stage, a main treatment stage and a deep treatment stage, performing data mining according to the wastewater treatment key links to construct a wastewater integrated treatment space, performing multi-stage optimizing analysis in the wastewater integrated treatment space by taking the oily wastewater treatment characteristic parameters as constraint parameters to obtain a target wastewater integrated treatment scheme, and performing integrated treatment control on the oily wastewater to be treated through the control treatment module based on the target wastewater integrated treatment scheme.
In a possible implementation manner, the oil-containing wastewater treatment characteristic parameters are obtained, the treatment is performed by obtaining oil-containing wastewater treatment standards, extracting detection indexes of the oil-containing wastewater to be treated according to the oil-containing wastewater treatment standards to obtain wastewater detection association index sets, obtaining a wastewater detection device list through the pollutant detection module, sequentially carrying out association mapping on the wastewater detection association index sets and the wastewater detection device list, activating the association index wastewater detection device set, carrying out component detection on the oil-containing wastewater to be treated based on the association index wastewater detection device set to obtain wastewater index detection data streams, constructing a wastewater characteristic self-adaptive detector, and analyzing the wastewater index detection data streams based on the wastewater characteristic self-adaptive detector to obtain the oil-containing wastewater treatment characteristic parameters.
In a possible implementation manner, the construction of the wastewater characteristic self-adaptive detector is carried out by acquiring a wastewater characteristic detection data set, classifying and identifying the wastewater characteristic detection data set according to the wastewater detection association index set to obtain a wastewater index detection sample set, respectively carrying out equally-divided weight layer training on the wastewater index detection sample set by using a deep learning network to obtain a wastewater index detection branch network set, carrying out accuracy verification on the wastewater index detection branch network set, taking a branch network accuracy verification result as a fusion decision parameter set, and carrying out weighted fusion on the wastewater index detection branch network set based on the fusion decision parameter set to construct the wastewater characteristic self-adaptive detector.
In a possible implementation manner, the target wastewater integrated treatment scheme is obtained by obtaining a wastewater multi-stage treatment target, respectively carrying out multi-stage weighted fitting on the wastewater multi-stage treatment target based on the wastewater treatment key links to establish a multi-stage dynamic treatment effect evaluation function, carrying out space division on the wastewater integrated treatment space according to the wastewater treatment key links to obtain a multi-stage wastewater treatment memory bank, respectively carrying out multi-stage optimization in the multi-stage wastewater treatment memory bank by using the multi-stage dynamic treatment effect evaluation function as constraint parameters to obtain a multi-stage wastewater treatment parameter set, and carrying out integrated summarization analysis on the multi-stage wastewater treatment parameter set to obtain the target wastewater integrated treatment scheme.
In a possible implementation manner, the method comprises the steps of establishing a multi-stage dynamic treatment effect evaluation function, extracting evaluation indexes of the multi-stage treatment targets of the wastewater to obtain a wastewater treatment effect index set, respectively performing evaluation fitting on the wastewater integrated treatment space based on the wastewater treatment effect index set to obtain a multi-index treatment effect evaluation function set, respectively performing criticality evaluation on the wastewater treatment effect index set according to the wastewater treatment key links to obtain a multi-stage index weight factor set, performing weighted fusion on the multi-index treatment effect evaluation function set based on the multi-stage index weight factor set, and establishing the multi-stage dynamic treatment effect evaluation function.
In a possible implementation manner, the obtained multi-stage wastewater treatment parameter set is treated by taking the oily wastewater treatment characteristic parameter as a constraint parameter, respectively carrying out multi-stage optimizing matching in the multi-stage wastewater treatment memory by utilizing the multi-stage dynamic treatment effect evaluation function to obtain a multi-stage treatment parameter threshold set, respectively randomly selecting N multi-stage treatment parameter sets in the multi-stage treatment parameter threshold set, respectively evaluating the N multi-stage treatment parameter sets by utilizing the multi-stage dynamic treatment effect evaluation function to obtain N multi-stage parameter fitness sets, and respectively carrying out iterative optimizing in the multi-stage treatment parameter threshold set based on the N multi-stage parameter fitness sets to obtain the multi-stage wastewater treatment parameter set.
In a possible implementation manner, the multi-stage wastewater treatment parameter set is obtained by performing optimization interval indentation on the multi-stage treatment parameter threshold value set based on the N multi-stage parameter fitness sets respectively to obtain a multi-stage treatment parameter interval, and performing iterative parameter selection and interval indentation optimization in the multi-stage treatment parameter interval by using the multi-stage dynamic treatment effect evaluation function respectively until a preset termination condition is reached to obtain the multi-stage wastewater treatment parameter set.
The application further provides an integrated treatment system of the oily wastewater, which comprises an oily wastewater integrated treatment platform building module, an integrated analysis module and a control treatment module, wherein the oily wastewater integrated treatment platform building module is used for building an oily wastewater integrated treatment platform, the oily wastewater integrated treatment platform comprises a wastewater collection module, a pollutant detection module, an integrated analysis module and a control treatment module, the wastewater collection detection module is used for obtaining oily wastewater to be treated through the wastewater collection module and discharging the oily wastewater to be treated into the pollutant detection module for detection analysis, the integrated analysis module is used for obtaining an oily wastewater treatment characteristic parameter, the wastewater treatment key link comprises a pretreatment stage, a main treatment stage and a deep treatment stage, and data mining is carried out according to the wastewater treatment key link, a multi-stage optimizing analysis module is used for carrying out multi-stage optimizing analysis in the wastewater integrated treatment space by taking the oily wastewater treatment characteristic parameter as a constraint parameter, and a control treatment module is used for carrying out integrated treatment control on the oily wastewater to be treated based on the target integrated treatment scheme.
The application provides an integrated treatment method and a treatment system for oily wastewater, wherein the integrated treatment platform for oily wastewater is firstly established and comprises a wastewater collection module, a pollutant detection module, an integrated analysis module and a control treatment module, oily wastewater to be treated is obtained through the wastewater collection module and is discharged into the pollutant detection module for detection and analysis, so that characteristic parameters of oily wastewater treatment are obtained, then a key link of wastewater treatment is obtained based on the integrated analysis module, the key link of wastewater treatment comprises a pretreatment stage, a main treatment stage and a deep treatment stage, data mining is carried out according to the key link of wastewater treatment, a wastewater integrated treatment space is constructed, the characteristic parameters of oily wastewater treatment are used as constraint parameters, multi-stage optimizing analysis is carried out in the wastewater integrated treatment space, a target wastewater integrated treatment scheme is obtained, and finally integrated treatment control is carried out on the oily wastewater to be treated based on the target wastewater integrated treatment scheme through the control treatment module, so that the technical effects of improving treatment efficiency and treatment effect stability through integrated analysis are achieved.
Drawings
In order to more clearly illustrate the technical solution of the embodiments of the present application, the following will briefly describe the drawings of the embodiments of the present application, in which flowcharts are used to illustrate operations performed by a system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed in order precisely. Rather, the various steps may be processed in reverse order or simultaneously, as desired. Also, other operations may be added to or removed from these processes.
Fig. 1 is a schematic flow chart of an integrated treatment method for oily wastewater according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an integrated treatment system for oily wastewater according to an embodiment of the present application.
Reference numerals illustrate the oily wastewater integrated treatment platform establishment module 10, the wastewater collection and detection module 20, the integrated analysis module 30, the multi-stage optimizing analysis module 40 and the control treatment module 50.
Detailed Description
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
The present application will be described in further detail below with reference to the accompanying drawings, in order to make the objects, technical solutions and advantages of the present application more apparent, and the described embodiments should not be construed as limiting the present application, but all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict. The terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or modules that may not be expressly listed or inherent to such process, method, article, or apparatus, and unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used herein is for the purpose of describing embodiments of the application only.
The embodiment of the application provides an integrated treatment method of oily wastewater, as shown in fig. 1, comprising the following steps:
Step S100, an oily wastewater integrated treatment platform is established, and the oily wastewater integrated treatment platform comprises a wastewater collection module, a pollutant detection module, an integrated analysis module and a control treatment module.
Specifically, a complete oily wastewater integrated treatment platform is constructed by integrating hardware equipment (such as sensors, reactors, pumps and the like) and software systems (such as data acquisition and analysis software, control system software and the like) of different functional modules. The platform comprises a wastewater collection module, a pollutant detection module, an integrated analysis module and a control processing module, wherein the wastewater collection module comprises a wastewater collection system which is a part responsible for collecting oily wastewater, the pollutant detection module comprises various detection instruments such as an oil analyzer, a water quality analyzer and the like which are parts used for detecting the types and the concentrations of pollutants in the wastewater, the integrated analysis module is a part for integrating and analyzing detection data, a data analysis platform is constructed by utilizing big data analysis and machine learning technology and is used for processing and analyzing the wastewater detection data, and the control processing module is a part for controlling the wastewater treatment process according to analysis results and comprises an automatic control system which can automatically adjust treatment parameters according to the analysis results and realize intelligent control of wastewater treatment.
And step 200, acquiring oily wastewater to be treated through the wastewater collection module, and discharging the oily wastewater to be treated into the pollutant detection module for detection and analysis to obtain oily wastewater treatment characteristic parameters.
Specifically, oily wastewater to be treated (wastewater containing oil contaminants to be treated) is introduced into a wastewater collection module through a pipe, a pump, or the like. The wastewater is sampled and analyzed by an instrument in the pollutant detection module to obtain wastewater treatment characteristic parameters (parameters describing key characteristics in the wastewater treatment process, such as oil content, chemical oxygen demand, pH value and the like).
In a possible implementation manner, the step S200 further includes a step S210 of obtaining an oily wastewater treatment standard, and extracting a detection index of the oily wastewater to be treated according to the oily wastewater treatment standard to obtain a wastewater detection association index set. In particular, the latest oily wastewater treatment standard, namely the water quality index and the emission standard which are required to be achieved by the specified oily wastewater treatment, is obtained from the related departments or internal files. According to the standard content, detection indexes directly related to oil-containing wastewater treatment, such as oil content, chemical Oxygen Demand (COD), biological Oxygen Demand (BOD), pH value, suspended Substances (SS) and the like, are screened out.
Step S220, obtaining a wastewater detection device list through the pollutant detection module, carrying out association mapping on the wastewater detection association index set and the wastewater detection device list in sequence, and activating the association index wastewater detection device set. Specifically, using information system or database technology, all devices available for wastewater detection are listed, including information on their model, function, accuracy, etc. And (2) matching the detection indexes extracted in the step S210 with detection devices in the device list to ensure that each index has a corresponding detection device. And activating a detection device associated with the detection index according to the matching result, and preparing for detection.
And step S230, carrying out component detection on the oily wastewater to be treated based on the associated index wastewater detection device set to obtain a wastewater index detection data stream. Specifically, a proper amount of samples are collected from the oily wastewater to be treated, the samples are sent into an activated detection device for component detection, qualitative and quantitative analysis is carried out on components in the wastewater, and data in the detection process are recorded in real time to form a wastewater index detection data stream.
And step S240, constructing a wastewater characteristic self-adaptive detector, and analyzing the wastewater index detection data stream based on the wastewater characteristic self-adaptive detector to obtain the oily wastewater treatment characteristic parameter. Specifically, according to historical data, a self-adaptive wastewater characteristic detector is constructed by utilizing machine learning or data analysis technology, and the detector is a detection tool capable of automatically adjusting an analysis model according to wastewater detection data and identifying wastewater characteristics. The wastewater index detection data stream obtained in step S230 is input into a wastewater characteristic adaptive detector, and the input data stream is analyzed by the detector to identify and output key characteristic parameters in the wastewater, such as oil content, COD value, and the like.
In a possible implementation manner, the step S240 further includes a step S241 of acquiring a wastewater characteristic detection data set, and classifying and identifying the wastewater characteristic detection data set according to the wastewater detection association index set to obtain a wastewater index detection sample set. Specifically, various detection data in the wastewater treatment process are collected from sources such as historical wastewater treatment records, laboratory detection data and the like. And (3) carrying out preprocessing operations such as cleaning, de-duplication, formatting and the like on the acquired data, and ensuring the accuracy and consistency of the data. And classifying and identifying the pretreated data according to the wastewater detection associated index set (such as oil content, COD, BOD, pH values and the like) to form a wastewater index detection sample set. Each sample set corresponds to a specific wastewater detection associated index.
And step S242, respectively performing equal weight layer training on the wastewater index detection sample set by using a deep learning network to obtain a wastewater index detection branch network set. Specifically, a deep learning branch network is constructed for each wastewater detection associated index, and an equal weight layer is set for training. The equal weight layer is used for ensuring that all the characteristics of the input data (namely all the components of the wastewater detection associated index) can be equally focused in the training process. The wastewater index detection sample set is input into a corresponding branch network for training, so that the network can accurately identify and predict various characteristic indexes in the wastewater.
Step S243, performing accuracy verification on the wastewater index detection branch network set, and taking the branch network accuracy verification result as a fusion decision parameter set. Specifically, the accuracy of the branch network is verified by using methods such as cross verification, a wastewater index detection sample set is divided into a training set and a verification set, the branch network is trained by using the training set, the accuracy of the network is verified by using the verification set, and the accuracy of each branch network on the verification set is calculated and used as an evaluation index of the accuracy of the branch network. And taking the accuracy rates as fusion weights for subsequent network weighted fusion, wherein the fusion weights are weights of different branch network prediction results aiming at the same wastewater detection association index.
And step S244, carrying out weighted fusion on the wastewater index detection branch network set based on the fusion decision parameter set, and constructing the wastewater characteristic self-adaptive detector. Specifically, for each wastewater detection associated index, there are multiple branch networks giving the predicted results. And (3) carrying out weighted fusion on the prediction results according to the fusion weights obtained in the step S243 to obtain the final prediction result. The final prediction result is the output of the wastewater characteristic adaptive detector on the wastewater detection correlation index. For example, if for a certain wastewater detection correlation indicator (e.g., oil content), there are three branch networks A, B, C that give predictions a, b, c, respectively, and their fusion weights are w 1、w2、w3 (meeting w 1+w2+w3 =1), respectively, the output of the wastewater characteristic adaptive detector for that indicator is output=w 1a+w2b+w3 c. Thus, for each wastewater detection correlation indicator, the wastewater characteristic adaptive detector can provide a final output which integrates the predicted results of a plurality of branch networks. Together, these outputs constitute the overall output of the wastewater characteristics adaptive detector for subsequent wastewater treatment decisions. This implementation improves the accuracy and robustness of the detection by exploiting the predictive capabilities of multiple branch networks.
And step S300, acquiring a wastewater treatment key link based on the integrated analysis module, wherein the wastewater treatment key link comprises a pretreatment stage, a main treatment stage and a deep treatment stage, and performing data mining according to the wastewater treatment key link to construct a wastewater integrated treatment space.
Specifically, according to the wastewater treatment process, it is divided into a pretreatment stage, a main treatment stage, and a deep treatment stage, which are stages having a critical influence in the wastewater treatment process. And excavating the historical data of wastewater treatment by using a machine learning algorithm, and extracting key features. And constructing a wastewater integrated treatment space containing different treatment stages and parameters according to the data mining result.
And step S400, taking the characteristic parameters of the oily wastewater treatment as constraint parameters, and carrying out multi-stage optimizing analysis in the wastewater integrated treatment space to obtain a target wastewater integrated treatment scheme.
Specifically, the oil-containing wastewater treatment characteristic parameters are used as constraint conditions, optimization analysis is carried out one by one in the wastewater integrated treatment space according to the sequence of a pretreatment stage, a main treatment stage and a deep treatment stage, and an optimal treatment scheme is found.
In one possible implementation manner, the step S400 further includes a step S410 of obtaining a wastewater multi-stage treatment target, and performing multi-stage weighted fitting on the wastewater multi-stage treatment target based on the wastewater treatment key links, so as to establish a multi-stage dynamic treatment effect evaluation function. Specifically, the wastewater multi-stage treatment targets are set according to the standards and requirements of wastewater treatment, and include treatment effects, such as oil removal ratio, chemical Oxygen Demand (COD) reduction, biological Oxygen Demand (BOD) and other values, which are required to be achieved by each of the pretreatment stage, the main treatment stage and the advanced treatment stage. Because of the importance and interaction of the different processing stages, the processing targets of each stage are weighted, and a mathematical method (such as linear weighted sum, nonlinear function, etc.) is used to construct a comprehensive evaluation function. This function can reflect the expected effect of the entire process flow. For example, in the pretreatment stage, the weight of the treatment efficiency index is increased, in the advanced treatment stage, the weight of the pollutant content index is increased, and the evaluation function fitting is respectively carried out according to different weights of the indexes, so as to obtain the dynamic effect evaluation function applicable to different requirements of each stage.
And step S420, dividing the wastewater integrated treatment space according to the wastewater treatment key links to obtain a multi-stage wastewater treatment memory bank. Specifically, according to the key links of wastewater treatment, the wastewater integrated treatment space is divided into different subspaces, and each subspace corresponds to one treatment stage. And (3) sorting historical treatment data, including treatment conditions, treatment parameters and treatment effects, classifying the data according to treatment stages, and storing the data in corresponding subspaces to obtain a multi-stage wastewater treatment memory bank.
And S430, taking the characteristic parameters of the oily wastewater treatment as constraint parameters, and respectively carrying out multi-stage optimization in the multi-stage wastewater treatment memory by utilizing the multi-stage dynamic treatment effect evaluation function to obtain a multi-stage wastewater treatment parameter set. Specifically, the parameters of the oily wastewater treatment characteristics, such as the initial oil content, COD value, pH value, etc., of the wastewater limit the scope of treatment options. Within the memory of each processing stage, an optimization algorithm (e.g., genetic algorithm, particle swarm algorithm, etc.) is used to find a set of processing parameters that satisfy the constraint conditions and evaluate the optimal function values.
And step S440, carrying out integrated summarization analysis on the multi-stage wastewater treatment parameter set to obtain the target wastewater integrated treatment scheme. Specifically, the optimal processing parameter sets of all stages are integrated together, whether the processing parameters of all stages are mutually coordinated or not is checked, whether conflict or unreasonable exists or not is judged, the parameters are adjusted according to the needs, the processing scheme is optimized, and finally a complete processing scheme is formed. According to the realization mode, the complexity and the diversity of wastewater treatment can be reflected more accurately through multi-stage treatment targets and weighted fitting, the refinement treatment is realized, the oily wastewater can be treated more effectively, and the treatment efficiency and the treatment effect are improved.
In one possible implementation manner, the step S410 further includes a step S411 of establishing a multi-stage dynamic treatment effect evaluation function, and performing evaluation index extraction on the multi-stage treatment target of the wastewater to obtain a wastewater treatment effect index set. Specifically, the multi-stage wastewater treatment targets are deeply analyzed in a literature investigation mode and the like, and key indexes capable of reflecting the wastewater treatment effect, namely specific parameters or indexes for measuring the wastewater treatment effect, including oil content, suspended matter content, chemical Oxygen Demand (COD), biological Oxygen Demand (BOD) and the like, are extracted from the targets.
And step S412, respectively carrying out evaluation fitting on the wastewater integrated treatment space based on the wastewater treatment effect index set to obtain a multi-index treatment effect evaluation function set. Specifically, based on the extracted wastewater treatment effect index set, the wastewater integrated treatment space is subjected to evaluation fitting, namely, the behavior or performance of an actual system is matched or approximated with theoretical expectation through a mathematical model or a simulation model, and a mathematical relationship between the wastewater treatment effect index and wastewater treatment process parameters is established by utilizing tools such as a statistical method, a machine learning algorithm and the like.
And S413, respectively carrying out criticality evaluation on the wastewater treatment effect index sets according to the wastewater treatment key links to obtain a multi-stage index weight factor set. Specifically, the importance of each wastewater treatment effect index in different treatment stages is determined by scoring or sorting the importance of each index in the wastewater treatment process according to the key links (such as a pretreatment stage, a main treatment stage and a deep treatment stage) of wastewater treatment by expert scoring, delphi and other methods.
Step S414, performing weighted fusion on the multi-index processing effect evaluation function set based on the multi-index weight factor set, and establishing the multi-stage dynamic processing effect evaluation function. Specifically, the index evaluation functions of each stage are weighted and combined by using a mathematical method (such as a weighted average method) to form a final multi-stage dynamic processing effect evaluation function. The function comprehensively considers a plurality of key indexes in the wastewater treatment process and the importance of different treatment stages, and provides a basis for subsequent optimizing analysis. The implementation method fully considers the importance of different processing stages and different indexes through the criticality evaluation and the weighted fusion, and improves the accuracy and the practicability of the evaluation function.
In a possible implementation manner, the step S430 further includes a step S431 of using the oily wastewater treatment characteristic parameter as a constraint parameter, and performing multi-stage optimization matching in the multi-stage wastewater treatment memory by using the multi-stage dynamic treatment effect evaluation function to obtain a multi-stage treatment parameter threshold set. Specifically, the search range is limited by taking the characteristic parameters of the treatment of the oily wastewater as input. In a multi-stage wastewater treatment memory bank, similar historical treatment cases are searched according to constraint parameters. Based on the searched historical cases, a set of process parameter thresholds for each process stage is determined, the sets of thresholds defining a range of possible process parameters.
Step S432, randomly selecting N multi-stage processing parameter sets from the multi-stage processing parameter threshold sets, and evaluating the N multi-stage processing parameter sets by using the multi-stage dynamic processing effect evaluation function to obtain N multi-stage parameter fitness sets. Specifically, within the parameter threshold set for each processing stage, N processing parameter sets are randomly generated. And simulating or calculating each multi-stage processing parameter set by utilizing the multi-stage dynamic processing effect evaluation function to obtain a corresponding processing effect evaluation value (namely, fitness).
Step S433, performing iterative optimization in the multi-stage treatment parameter threshold set based on the N multi-stage parameter fitness sets, to obtain the multi-stage wastewater treatment parameter set. Specifically, an optimization algorithm is employed to continually iteratively update a set of processing parameters within a set of multi-stage processing parameter thresholds. In each iteration, excellent processing parameter sets are selected according to the fitness value to perform operations such as crossing, mutation and the like, a new processing parameter set is generated, and the fitness of the processing parameter sets is re-evaluated. Setting termination conditions such as iteration times, fitness threshold and the like, stopping iteration when the conditions are met, and outputting an optimal or near-optimal multi-stage wastewater treatment parameter set. According to the implementation mode, through multi-stage optimizing matching and random selection and evaluation, the searching range is rapidly reduced, unnecessary calculated amount is reduced, and optimizing efficiency is improved.
In a possible implementation manner, the step S433 further includes a step S4331 of obtaining the multi-stage wastewater treatment parameter set, and the optimizing interval is set back to the multi-stage treatment parameter threshold set based on the N multi-stage parameter fitness sets, so as to obtain a multi-stage treatment parameter interval. Specifically, N multi-stage parameter fitness sets are analyzed to find out the interval where the parameter set with higher fitness is located. Based on the intervals in which these high fitness parameter sets are located, new, smaller search intervals, i.e., multi-stage processing parameter intervals, are determined.
Step S4332, respectively performing iterative parameter selection and interval indentation optimization in the multi-stage treatment parameter interval by using the multi-stage dynamic treatment effect evaluation function until a preset termination condition is reached, so as to obtain the multi-stage wastewater treatment parameter set. Specifically, the initial parameter set is randomly generated within the multi-stage process parameter interval determined in step S4331. And evaluating the parameter sets by utilizing a multi-stage dynamic processing effect evaluation function to obtain the fitness value of the parameter sets. And updating the parameter set according to the fitness value by combining the interval indentation technology. And as the iteration is carried out, the search interval is further reduced according to the fitness value of the newly generated parameter set. Setting termination conditions such as iteration times, fitness threshold and the like, stopping iteration when the conditions are met, and outputting an optimal multi-stage wastewater treatment parameter set. By means of interval indentation, the implementation mode obviously reduces the search space and unnecessary calculation amount, and therefore the search efficiency is improved.
And S500, carrying out integrated treatment control on the oily wastewater to be treated based on the target wastewater integrated treatment scheme through the control treatment module.
Specifically, according to parameters and steps in the target wastewater integrated treatment scheme, wastewater is subjected to integrated treatment through an automatic device (such as a reactor, a pump and the like) and a control system (such as a PLC (programmable logic controller), a DCS (distributed control system) and the like), namely, a plurality of treatment steps and parameters are integrated together to be controlled, the wastewater treatment effect is monitored in real time in the treatment process, and the wastewater treatment effect is adjusted as required. The embodiment of the application adopts the technical means of establishing an oil-containing wastewater integrated treatment platform, comprising a wastewater collection module, a pollutant detection module, an integrated analysis module and a control treatment module, wherein the wastewater collection module is used for obtaining oil-containing wastewater to be treated, the pollutant detection module is used for detecting and analyzing the wastewater to obtain oil-containing wastewater treatment characteristic parameters, the integrated analysis module is used for carrying out data mining based on key links of wastewater treatment, constructing a wastewater integrated treatment space and carrying out multi-stage optimizing analysis, and the control treatment module is used for carrying out integrated treatment control on the wastewater according to a target wastewater integrated treatment scheme, so that the technical effects of improving treatment efficiency and treatment effect stability through integrated analysis treatment are achieved.
In the above, an integrated treatment method of oily wastewater according to an embodiment of the present invention is described in detail with reference to fig. 1. Next, an integrated treatment system for oily wastewater according to an embodiment of the present invention will be described with reference to fig. 2.
The integrated treatment system for the oily wastewater is used for solving the technical problems of low overall treatment efficiency and unstable treatment effect caused by the lack of systematicness and integration in the existing oily wastewater treatment, and achieves the technical effects of improving the treatment efficiency and the stability of the treatment effect through integrated analysis treatment. An integrated treatment system for oily wastewater comprises an oily wastewater integrated treatment platform establishment module 10, a wastewater collection and detection module 20, an integrated analysis module 30, a multi-stage optimizing analysis module 40 and a control treatment module 50.
The system comprises an oily wastewater integrated treatment platform building module 10, an oily wastewater integrated treatment platform, an integrated analysis module 30 and a control treatment module 50, wherein the oily wastewater integrated treatment platform comprises a wastewater collection module, a pollutant detection module, an integrated analysis module and a control treatment module, the wastewater collection detection module 20 is used for obtaining oily wastewater to be treated through the wastewater collection module and discharging the oily wastewater to be treated into the pollutant detection module for detection analysis to obtain oily wastewater treatment characteristic parameters, the integrated analysis module 30 is used for obtaining wastewater treatment key links, the wastewater treatment key links comprise a pretreatment stage, a main treatment stage and a deep treatment stage, data mining is carried out according to the wastewater treatment key links to construct a wastewater integrated treatment space, the multi-stage optimization analysis module 40 is used for carrying out optimizing analysis in the wastewater integrated treatment space by taking the oily wastewater treatment characteristic parameters as constraint parameters to obtain a target wastewater integrated treatment scheme, and the control treatment module 50 is used for carrying out integrated treatment control on the oily wastewater to be treated based on the target wastewater integrated treatment scheme.
Next, a specific configuration of the wastewater collection detection module 20 will be described in detail. As described above, the wastewater collection and detection module 20 may further include a detection index extraction unit configured to obtain an oily wastewater treatment standard, extract a detection index of the oily wastewater to be treated according to the oily wastewater treatment standard, obtain a wastewater detection association index set, an association mapping unit configured to obtain a wastewater detection device list through the contaminant detection module, sequentially perform association mapping on the wastewater detection association index set and the wastewater detection device list, activate an association index wastewater detection device set, a component detection unit configured to perform component detection on the oily wastewater to be treated based on the association index wastewater detection device set, obtain a wastewater index detection data stream, and a wastewater characteristic adaptive detection unit configured to construct a wastewater characteristic adaptive detector, analyze the wastewater index detection data stream based on the wastewater characteristic adaptive detector, and obtain the oily wastewater treatment characteristic parameter.
The construction wastewater characteristic self-adaptive detector comprises a wastewater index detection sample set acquisition subunit, an equally-divided weight layer training subunit, an accuracy verification subunit and a weighting fusion subunit, wherein the wastewater index detection sample set acquisition subunit is used for acquiring a wastewater characteristic detection data set, classifying and identifying the wastewater characteristic detection data set according to the wastewater detection association index set to obtain a wastewater index detection sample set, the equally-divided weight layer training subunit is used for respectively carrying out equally-divided weight layer training on the wastewater index detection sample set by utilizing a deep learning network to obtain a wastewater index detection branch network set, the accuracy verification subunit is used for carrying out accuracy verification on the wastewater index detection branch network set and taking a branch network accuracy verification result as a fusion decision parameter set, and the weighting fusion subunit is used for carrying out weighted fusion on the wastewater index detection branch network set based on the fusion decision parameter set to construct the wastewater characteristic self-adaptive detector.
Next, the specific configuration of the multi-stage optimization analysis module 40 will be described in detail. As described above, the multi-stage optimizing analysis module 40 may further include a multi-stage dynamic treatment effect evaluation function establishing unit configured to obtain a multi-stage wastewater treatment target, perform multi-stage weighted fitting on the multi-stage wastewater treatment target based on the wastewater treatment key links, respectively, to establish a multi-stage dynamic treatment effect evaluation function, a space dividing unit configured to divide the wastewater integrated treatment space according to the wastewater treatment key links to obtain a multi-stage wastewater treatment memory, and a multi-stage optimizing unit configured to perform multi-stage optimizing in the multi-stage wastewater treatment memory by using the multi-stage dynamic treatment effect evaluation function as a constraint parameter, respectively, to obtain a multi-stage wastewater treatment parameter set, and an integrated summary analysis unit configured to perform integrated summary analysis on the multi-stage wastewater treatment parameter set, to obtain the target wastewater integrated treatment scheme.
The multi-stage dynamic treatment effect evaluation function establishment unit can further comprise an evaluation index extraction subunit for extracting evaluation indexes of the wastewater multi-stage treatment targets to obtain a wastewater treatment effect index set, an evaluation fitting subunit for respectively performing evaluation fitting on the wastewater integrated treatment space based on the wastewater treatment effect index set to obtain a multi-index treatment effect evaluation function set, a criticality evaluation subunit for respectively performing criticality evaluation on the wastewater treatment effect index set according to the wastewater treatment key link to obtain a multi-stage index weight factor set, and a weighting fusion subunit for performing weighted fusion on the multi-index treatment effect evaluation function set based on the multi-stage index weight factor set to establish the multi-stage dynamic treatment effect evaluation function.
The multi-stage optimizing unit can further comprise a multi-stage optimizing matching subunit, an evaluating subunit and an iterative optimizing subunit, wherein the multi-stage optimizing matching subunit is used for taking the oil-containing wastewater treatment characteristic parameters as constraint parameters, respectively performing multi-stage optimizing matching in the multi-stage wastewater treatment memory library by utilizing the multi-stage dynamic treatment effect evaluating function to obtain multi-stage treatment parameter threshold sets, respectively randomly selecting N multi-stage treatment parameter sets in the multi-stage treatment parameter threshold sets, respectively evaluating the N multi-stage treatment parameter sets by utilizing the multi-stage dynamic treatment effect evaluating function to obtain N multi-stage parameter adaptation degree sets, and the iterative optimizing subunit is used for respectively performing iterative optimizing in the multi-stage treatment parameter threshold sets based on the N multi-stage parameter adaptation degree sets to obtain the multi-stage wastewater treatment parameter sets.
The iterative optimizing subunit may further include an optimizing interval retracting subunit configured to respectively perform optimizing interval retracting on the multi-stage processing parameter threshold set based on the N multi-stage parameter fitness sets to obtain a multi-stage processing parameter interval, and a multi-stage wastewater processing parameter set obtaining subunit configured to respectively perform iterative parameter selection and interval retracting optimizing within the multi-stage processing parameter interval by using the multi-stage dynamic processing effect evaluation function until a preset termination condition is reached, to obtain the multi-stage wastewater processing parameter set.
The integrated treatment system for the oily wastewater provided by the embodiment of the invention can execute the integrated treatment method for the oily wastewater provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Although the present application makes various references to certain modules in a system according to an embodiment of the present application, any number of different modules may be used and run on a user terminal and/or a server, and each unit and module included are merely divided according to functional logic, but are not limited to the above-described division, so long as the corresponding functions can be implemented, and in addition, specific names of each functional unit are only for convenience of distinguishing from each other, and are not intended to limit the scope of protection of the present application.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application. In some cases, the acts or steps recited in the present application may be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Claims (8)
1. An integrated treatment method for oily wastewater, which is characterized by comprising the following steps:
establishing an oily wastewater integrated treatment platform, wherein the oily wastewater integrated treatment platform comprises a wastewater collection module, a pollutant detection module, an integrated analysis module and a control treatment module;
The oily wastewater to be treated is obtained through the wastewater collection module, and is discharged into the pollutant detection module for detection and analysis, so that oily wastewater treatment characteristic parameters are obtained;
Acquiring a wastewater treatment key link based on the integrated analysis module, wherein the wastewater treatment key link comprises a pretreatment stage, a main treatment stage and a deep treatment stage, and performing data mining according to the wastewater treatment key link to construct a wastewater integrated treatment space;
Taking the oily wastewater treatment characteristic parameter as a constraint parameter, and carrying out multi-stage optimizing analysis in the wastewater integrated treatment space to obtain a target wastewater integrated treatment scheme;
and carrying out integrated treatment control on the oily wastewater to be treated based on the target wastewater integrated treatment scheme through the control treatment module.
2. The integrated treatment process for oily wastewater of claim 1, wherein said obtaining oily wastewater treatment parameters comprises:
Acquiring an oily wastewater treatment standard, and extracting detection indexes of the oily wastewater to be treated according to the oily wastewater treatment standard to obtain a wastewater detection association index set;
Acquiring a wastewater detection device list through the pollutant detection module, sequentially carrying out association mapping on the wastewater detection association index set and the wastewater detection device list, and activating the association index wastewater detection device set;
Performing component detection on the oily wastewater to be treated based on the associated index wastewater detection device set to obtain wastewater index detection data flow;
And constructing a wastewater characteristic self-adaptive detector, and analyzing the wastewater index detection data stream based on the wastewater characteristic self-adaptive detector to obtain the oily wastewater treatment characteristic parameter.
3. The integrated treatment process of oily wastewater of claim 2, wherein said constructing an adaptive wastewater property detector comprises:
Acquiring a wastewater characteristic detection data set, and classifying and identifying the wastewater characteristic detection data set according to the wastewater detection associated index set to obtain a wastewater index detection sample set;
respectively carrying out equal weight layer training on the wastewater index detection sample set by using a deep learning network to obtain a wastewater index detection branch network set;
Performing accuracy verification on the wastewater index detection branch network set, and taking a branch network accuracy verification result as a fusion decision parameter set;
And carrying out weighted fusion on the wastewater index detection branch network set based on the fusion decision parameter set to construct the wastewater characteristic self-adaptive detector.
4. The integrated treatment process for oily wastewater of claim 1, wherein said obtaining the targeted integrated wastewater treatment regimen comprises:
acquiring a wastewater multi-stage treatment target, respectively carrying out multi-stage weighted fitting on the wastewater multi-stage treatment target based on the wastewater treatment key links, and establishing a multi-stage dynamic treatment effect evaluation function;
Performing space division on the wastewater integrated treatment space according to the wastewater treatment key links to obtain a multi-stage wastewater treatment memory bank;
Taking the characteristic parameters of oily wastewater treatment as constraint parameters, and respectively carrying out multi-stage optimization in the multi-stage wastewater treatment memory by utilizing the multi-stage dynamic treatment effect evaluation function to obtain a multi-stage wastewater treatment parameter set;
And carrying out integrated summarization analysis on the multi-stage wastewater treatment parameter set to obtain the target wastewater integrated treatment scheme.
5. The integrated treatment process of oily wastewater of claim 4, wherein said establishing a multi-stage dynamic treatment effect evaluation function comprises:
Extracting evaluation indexes of the wastewater multi-stage treatment targets to obtain a wastewater treatment effect index set;
respectively carrying out evaluation fitting on the wastewater integrated treatment space based on the wastewater treatment effect index set to obtain a multi-index treatment effect evaluation function set;
Respectively carrying out criticality evaluation on the wastewater treatment effect index set according to the wastewater treatment key links to obtain a multi-stage index weight factor set;
And carrying out weighted fusion on the multi-index processing effect evaluation function set based on the multi-index weight factor set, and establishing the multi-stage dynamic processing effect evaluation function.
6. The integrated treatment process of oily wastewater of claim 4, wherein said obtaining a multi-stage wastewater treatment parameter set comprises:
Taking the oily wastewater treatment characteristic parameters as constraint parameters, and respectively carrying out multi-stage optimizing matching in the multi-stage wastewater treatment memory by utilizing the multi-stage dynamic treatment effect evaluation function to obtain a multi-stage treatment parameter threshold set;
Randomly selecting N multi-stage processing parameter sets in the multi-stage processing parameter threshold set respectively, and evaluating the N multi-stage processing parameter sets by utilizing the multi-stage dynamic processing effect evaluation function respectively to obtain N multi-stage parameter fitness sets;
And respectively carrying out iterative optimization in the multi-stage treatment parameter threshold value set based on the N multi-stage parameter fitness sets to obtain the multi-stage wastewater treatment parameter set.
7. The integrated treatment process of oily wastewater of claim 6, wherein said obtaining said set of multi-stage wastewater treatment parameters comprises:
Respectively retracting the optimizing interval of the multi-stage processing parameter threshold value set based on the N multi-stage parameter fitness sets to obtain a multi-stage processing parameter interval;
And respectively carrying out iterative parameter selection and interval indentation optimizing in the multi-stage treatment parameter interval by utilizing the multi-stage dynamic treatment effect evaluation function until a preset termination condition is reached, so as to obtain the multi-stage wastewater treatment parameter set.
8. An integrated treatment system for oily wastewater, said system for carrying out the integrated treatment process of oily wastewater of any of claims 1-7, said system comprising:
The system comprises an oily wastewater integrated treatment platform building module, a control treatment module and a control treatment module, wherein the oily wastewater integrated treatment platform building module is used for building an oily wastewater integrated treatment platform, and the oily wastewater integrated treatment platform comprises a wastewater collection module, a pollutant detection module, an integrated analysis module and a control treatment module;
The wastewater collection and detection module is used for obtaining oily wastewater to be treated through the wastewater collection module, and discharging the oily wastewater to be treated into the pollutant detection module for detection and analysis to obtain oily wastewater treatment characteristic parameters;
The integrated analysis module is used for acquiring a wastewater treatment key link, wherein the wastewater treatment key link comprises a pretreatment stage, a main treatment stage and a deep treatment stage, and data mining is carried out according to the wastewater treatment key link to construct a wastewater integrated treatment space;
The multi-stage optimizing analysis module is used for taking the oily wastewater treatment characteristic parameter as a constraint parameter, and carrying out multi-stage optimizing analysis in the wastewater integrated treatment space to obtain a target wastewater integrated treatment scheme;
and the control processing module is used for carrying out integrated processing control on the oily wastewater to be processed based on the target wastewater integrated processing scheme.
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