CN-121778809-B - Integrated industrial wastewater treatment system based on coal mine roadway goaf

Abstract

The invention relates to the technical field of industrial wastewater treatment, and discloses an integrated industrial wastewater treatment system based on a goaf of a coal mine roadway. The waste water input unit of the system introduces industrial waste water into the grading treatment unit in the goaf of the coal mine roadway, the goaf space is utilized for treatment, the occupied land of ground facilities and the construction cost are reduced, and the special environment of the goaf is conducive to waste water treatment reaction. The data acquisition unit continuously collects wastewater treatment sequences and effluent quality indexes of each treatment batch, the intelligent analysis unit analyzes fluctuation characteristics of the wastewater treatment sequences according to the wastewater treatment sequences, calculates influence weights of monitoring moments on effluent quality, evaluates significance levels in combination with similarity measurement among treatment batches, extracts key monitoring moments, builds an adaptive prediction model, predicts treatment effects of the key monitoring moments in new batches of wastewater in real time, provides basis for adjustment and optimization of treatment processes, and achieves efficient and accurate treatment of industrial wastewater.

Inventors

• LIU XIAOJIAN
• Meng Qingzhai
• DONG JUN
• GAO SHUAI
• LUO ZHENJIANG
• MA HAIHUI
• GUO XIANGYI
• LU ZHEN
• ZHAO JING

Assignees

• 山东省水工环地质工程有限公司
• 山东省地矿工程勘察院(山东省地质矿产勘查开发局八〇一水文地质工程地质大队)

Dates

Publication Date

20260508

Application Date

20260304

Claims (3)

1. 1. The integrated industrial wastewater treatment system based on the coal mine roadway goaf is characterized by comprising a wastewater input unit, a plurality of grading treatment units, a data acquisition unit and an intelligent analysis unit; The waste water input unit is used for introducing industrial waste water into the grading treatment unit in the goaf of the coal mine roadway, the data acquisition unit is used for continuously collecting a waste water treatment sequence and a water outlet quality index of each treatment batch, the waste water treatment sequence takes the treatment time as a horizontal axis, takes the concentration of pollutants as a vertical axis to form a curve, and a plurality of monitoring moments are equally divided on the treatment time axis; The intelligent analysis unit calculates the influence weight of each monitoring time on the water quality by analyzing the fluctuation characteristics of the wastewater treatment sequence according to the wastewater treatment sequence and the water quality index and aiming at each treatment batch and each monitoring time, evaluates the significance level of the monitoring time by combining the similarity measurement among the treatment batches, extracts the key monitoring time by utilizing a time sequence pattern recognition technology, and finally constructs an adaptive prediction model for predicting the treatment effect of the key monitoring time in the wastewater of a new batch in real time; The intelligent analysis unit is configured to analyze the fluctuation characteristics of the wastewater treatment sequence by adopting the following steps: Dividing the wastewater treatment sequence into a plurality of overlapping time windows for each treatment batch; Calculating the mean value and standard deviation of the concentration of the pollutants in each time window to form a characteristic vector; Performing cluster analysis on the feature vectors of all the processing batches, and identifying a typical fluctuation mode; Calculating the influence weight of each monitoring moment according to the correlation between the fluctuation mode of the time window to which the monitoring moment belongs and the effluent quality index; the intelligent analysis unit is configured to calculate the influence weight of each monitoring moment on the water quality, and adopts a mutual information method, and specifically comprises the following steps: Calculating a mutual information value between a pollutant concentration value and a water outlet quality index at each monitoring moment to serve as an initial influence weight; Considering the time correlation among processing batches, and obtaining corrected influence weights by constructing a time graph model, wherein nodes represent monitoring moments, edges represent time adjacent relations and a random walk algorithm is utilized to adjust initial influence weights; The intelligent analysis unit is configured to evaluate the significance level at the monitoring time, and the intelligent analysis unit comprises the following steps: Taking the wastewater treatment sequence of each treatment batch as a time sequence sample, and calculating pollutant concentration matrixes at all monitoring moments; extracting a main component by using a main component analysis, and calculating a load value on the main component at each monitoring time; Calculating the significance score of each monitoring moment according to the magnitude of the load value and the effluent quality index; the intelligent analysis unit is configured to adopt an importance propagation algorithm when extracting key monitoring time, and specifically comprises the following steps: constructing the monitoring moment as network nodes, and weighting edges between the nodes based on the time proximity and the pollutant concentration similarity; calculating a centrality score of each node by using a PageRank algorithm; Combining the influence weight and the significance score, and carrying out weighted fusion on the centrality score to obtain a comprehensive significance score of each monitoring moment; Selecting a preset number of monitoring moments with highest comprehensive importance scores as key monitoring moments; The intelligent analysis unit is configured to adopt a recurrent neural network model when constructing an adaptive prediction model, and specifically comprises the following steps: Taking the pollutant concentration sequence at the key monitoring moment as an input characteristic, and taking a water outlet quality index as an output label; in the training process, a time back propagation algorithm is adopted to optimize the weight, and regularization is added to prevent over fitting; the intelligent analysis unit is configured to adopt a dynamic time warping algorithm when identifying a typical fluctuation mode, and specifically comprises the following steps: calculating dynamic time warping distances among wastewater treatment sequences of different treatment batches to form a distance matrix; And hierarchical clustering is carried out based on the distance matrix, the distance matrix is grouped into a plurality of clusters, and the center sequence of each cluster represents a typical fluctuation mode.
2. 2. The integrated industrial wastewater treatment system based on the goaf of the coal mine roadway according to claim 1, further comprising a prediction execution unit, wherein the prediction execution unit is used for collecting a wastewater treatment sequence of wastewater to be treated in real time to extract a pollutant concentration value at a key monitoring moment when the effect prediction is carried out on the wastewater to be treated, inputting the concentration value into an adaptive prediction model to obtain a predicted effluent quality index, and dynamically adjusting model parameters according to the deviation of a prediction result and an actual monitoring value.
3. 3. The integrated industrial wastewater treatment system based on the goaf of the coal mine roadway according to claim 1, wherein the grading treatment unit comprises a physical precipitation module, a chemical reaction module and a biodegradation module, the physical precipitation module is configured in an inlet area of the goaf of the coal mine roadway, the chemical reaction module is configured in an intermediate area, the biodegradation module is configured in an outlet area, wastewater sequentially flows through the modules, and the data acquisition unit is provided with a multi-parameter sensor at an outlet of each module for synchronously acquiring pollutant concentration, temperature and pressure data to form a multi-dimensional wastewater treatment sequence.

Description

Integrated industrial wastewater treatment system based on coal mine roadway goaf Technical Field The invention relates to the technical field of industrial wastewater treatment, in particular to an integrated industrial wastewater treatment system based on a goaf of a coal mine roadway. Background In the prior art, the monitoring of the wastewater treatment process is generally based on the preset time interval for water quality detection, and the monitoring strategy cannot be adjusted according to the dynamic change characteristics of the treatment process. The selection of monitoring points often depends on experience setting, and a quantitative evaluation method for the importance of the monitoring moment is lacking. The data processing method mostly adopts static threshold alarming or simple trend analysis, and is difficult to capture complex nonlinear characteristics in the processing process. The predictive model is typically built using all of the monitored data, without consideration of the differential contributions of the different time points to the treatment effect. The system needs to solve key problems of monitoring point optimization selection, dynamic characteristic extraction in a processing process, accurate construction of a prediction model and the like. The traditional wastewater treatment monitoring system has obvious defects in a data acquisition link. Equally spaced sampling approaches may miss critical changing nodes during processing, resulting in loss of important information. The monitoring data analysis method is single, and the characteristic change rules of different processing stages cannot be effectively identified. The prediction model lacks pertinence and cannot adapt to the special requirements of different water qualities and treatment processes. The prior art needs a novel processing system capable of intelligently identifying key monitoring time, accurately analyzing dynamic characteristics of a processing process and establishing a self-adaptive prediction model. The wastewater treatment environment of the goaf of the coal mine roadway is complex, and more intelligent monitoring and prediction means are needed to ensure the treatment effect. Disclosure of Invention The invention aims to provide an integrated industrial wastewater treatment system based on a goaf of a coal mine roadway, so as to solve the problems in the background technology. To achieve the above object, the present invention provides an integrated industrial wastewater treatment system based on goaf of coal mine roadway, the system comprising: The device comprises a wastewater input unit, a plurality of grading processing units, a data acquisition unit and an intelligent analysis unit; The waste water input unit is used for introducing industrial waste water into the grading treatment unit in the goaf of the coal mine roadway, the data acquisition unit is used for continuously collecting a waste water treatment sequence and a water outlet quality index of each treatment batch, the waste water treatment sequence takes the treatment time as a horizontal axis, takes the concentration of pollutants as a vertical axis to form a curve, and a plurality of monitoring moments are equally divided on the treatment time axis; The intelligent analysis unit calculates the influence weight of each monitoring time on the water quality by analyzing the fluctuation characteristics of the wastewater treatment sequence according to the wastewater treatment sequence and the water quality index, and evaluates the significance level of the monitoring time by combining the similarity measurement among the treatment batches, further extracts the key monitoring time by utilizing a time sequence pattern recognition technology, and finally builds an adaptive prediction model for predicting the treatment effect of the key monitoring time in the wastewater of the new batch in real time. Preferably, when the intelligent analysis unit is configured to analyze the fluctuation characteristics of the wastewater treatment sequence, the following steps are adopted: Dividing the wastewater treatment sequence into a plurality of overlapping time windows for each treatment batch; Calculating the mean value and standard deviation of the concentration of the pollutants in each time window to form a characteristic vector; Performing cluster analysis on the feature vectors of all the processing batches, and identifying a typical fluctuation mode; And calculating the influence weight of each monitoring moment according to the correlation between the fluctuation mode of the time window to which the monitoring moment belongs and the effluent quality index. Preferably, the intelligent analysis unit is configured to calculate the influence weight of each monitoring moment on the water quality, and adopts a mutual information method, which specifically includes: Calculating a mutual information value between a pollutant concentration value and a wa