Method and system for treating wastewater from production of synthetic steroid hormone drugs

Abstract

Disclosed are a method and system for treating wastewater from the production of synthetic steroid hormone drugs. When polyaluminium chloride is used for coagulation, the method collects the coagulation stirring speed value and the coagulation temperature value through real-time monitoring, and introduces a data processing and analysis algorithm at the back end to perform a time series collaborative analysis of the coagulation stirring speed value and the coagulation temperature value, so as to perform adaptive control of coagulation parameters based on the correlation between the coagulation stirring speed and the temperature. In this way, the wastewater treatment effect can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent wastewater treatment, and more specifically, to a method and system for treating wastewater produced from the production of synthetic steroid hormone drugs. Background Art

[0002] Steroidal compounds are a class of important natural organic compounds widely present in biological tissues. In the past, steroidal compounds were mainly obtained by extraction from organisms. With the sharp increase in demand for these compounds, they have gradually turned to artificial synthesis. In the process of artificial synthesis of steroidal compounds, a large amount of production wastewater is generated, and the composition of the wastewater is very complex. Raw materials such as dipregnane, androstane, estranes, etc. will remain in the synthesis process. They are usually composed of five-membered rings and six-membered rings, with large molecular weight, stable structure, and difficult to biodegrade. In addition, some basic compounds of chemical synthesis will remain, such as DHEA and tribromohydantoin allyl alcohol. At the same time, solvent substances such as DMF (N, N dimethylformamide), HMPA (hexamethylphosphoric acid triamine) and tetrahydrofuran, although recovered by distillation, still remain in the wastewater. This results in the production wastewater of synthetic steroid hormones having the characteristics of high concentration of organic matter, large molecular weight, complex and stable structure, and containing organic phosphorus, which is difficult to be effectively treated by traditional biochemical treatment processes.

[0003] In response to the above technical problems, Chinese patent CN112707598A discloses a treatment process for synthetic steroid hormone drug production wastewater, which uses low-temperature wet oxidation to degrade large molecular steroid compounds into small molecules, thereby improving the biodegradability of the wastewater. At the same time, it can oxidize organic phosphorus compounds that are difficult to degrade by oxidative biological methods into orthophosphates, thereby improving the phosphorus removal effect.

[0004] In the above scheme, polyaluminium chloride coagulation is an important step in the wastewater treatment process. By adding coagulants, the suspended matter and particulate matter in the wastewater are aggregated into flocs, which is convenient for subsequent precipitation and filtration. However, in the process of polyaluminium chloride coagulation, the coagulation temperature will affect the coagulation effect. Generally speaking, the higher the coagulation temperature, the faster the flocculation speed and the better the flocculation effect. However, too high a coagulation temperature will also cause the coagulant to decompose and affect the coagulation effect. In addition, the stirring speed of coagulation will also have an important influence on the flocculation effect. For example, too fast a stirring speed will cause the flocs to break and affect the flocculation effect; too slow a stirring speed will slow down the flocculation reaction speed and affect the flocculation efficiency. Therefore, when using polyaluminium chloride for coagulation, the stirring temperature and stirring speed should be monitored in real time to improve the coagulation effect, thereby further improving the wastewater treatment effect.

[0005] Therefore, an optimized scheme for treating wastewater from the production of synthetic steroid hormone drugs is desired. Summary of the invention

[0006] In order to solve the above technical problems, the present application is proposed. The embodiment of the present application provides a method and system for treating synthetic steroid hormone drug production wastewater, which can adaptively adjust the coagulation stirring speed based on the real-time state of the coagulation temperature to improve the flocculation efficiency and effect, thereby further improving the wastewater treatment effect.

[0007] According to one aspect of the present application, a method for treating synthetic steroid hormone drug production wastewater is provided, comprising: filtering the synthetic steroid hormone drug production wastewater, adjusting the pH value of the filtered production wastewater with sulfuric acid to obtain pH-adjusted wastewater; subjecting the pH-adjusted wastewater to low-temperature wet oxidation and pH adjustment in a low-temperature wet oxidation system, and subjecting the pH-adjusted wastewater to flocculation and precipitation to obtain flocculation and precipitation effluent; subjecting the flocculation and precipitation effluent to anaerobic biological treatment to obtain anaerobic biological treated effluent; subjecting the anaerobic biological treated effluent to aerobic biological treatment to obtain treated wastewater; wherein The aerobic biological treatment adopts Fenton advanced oxidation to treat the effluent from the anaerobic biological treatment to obtain the treated wastewater; wherein, in the low-temperature wet oxidation system, sodium hydroxide is used to adjust the pH value of the wastewater to 6-9, and the effluent after the pH value is adjusted is subjected to polyaluminum chloride coagulation, polyacrylamide coagulant and gravity mud-water separation to obtain the effluent after flocculation and precipitation, wherein, in the low-temperature wet oxidation system, sodium hydroxide is used to adjust the pH value of the wastewater to 6-9, and the effluent after the pH value is adjusted is subjected to polyaluminum chloride coagulation, polyacrylamide coagulant and gravity mud-water separation to obtain the effluent after flocculation and precipitation, including:

[0008] Obtaining coagulation stirring speed values ​​and coagulation temperature values ​​at multiple predetermined time points within a predetermined time period;

[0009] Arrange the coagulation stirring speed values ​​and the coagulation temperature values ​​at the plurality of predetermined time points respectively according to the time dimension to obtain a coagulation stirring speed time series input vector and a coagulation temperature time series input vector;

[0010] Performing vector segmentation and domain conversion on the coagulation temperature time series input vector to obtain a sequence of coagulation temperature local time series images;

[0011] Extracting features from the sequence of local time series images of coagulation temperature by a coagulation temperature time series pattern feature extractor based on a deep neural network model to obtain a coagulation temperature time series feature graph;

[0012] Performing a time series characteristic analysis on the coagulation stirring speed time series input vector to obtain a coagulation stirring speed time series characteristic vector;

[0013] Performing a channel-by-channel response correlation analysis on the coagulation temperature time series characteristic diagram and the coagulation stirring speed time series characteristic vector to obtain a stirring speed-coagulation temperature channel-by-channel response time series characteristic; and

[0014] Based on the channel-by-channel response time series characteristics of the stirring speed-coagulation temperature, it is determined whether the coagulation stirring speed value at the current time point should be increased, decreased, or kept unchanged.

[0015] According to another aspect of the present application, a system for treating wastewater from the production of synthetic steroid hormone drugs is provided, comprising:

[0016] A sulfuric acid adjustment filtration module is used to filter the production wastewater of synthetic steroid hormone drugs and adjust the pH value of the filtered production wastewater with sulfuric acid to obtain wastewater after pH value adjustment;

[0017] A flocculation and sedimentation module is used to pass the wastewater after pH value adjustment through a low-temperature wet oxidation system to perform low-temperature wet oxidation on the effluent and adjust the pH value, and to perform flocculation and sedimentation on the effluent after pH value adjustment to obtain effluent after flocculation and sedimentation;

[0018] An anaerobic biological treatment module is used to perform anaerobic biological treatment on the effluent after flocculation and sedimentation to obtain anaerobic biological treated effluent;

[0019] The aerobic biological treatment module is used to aerobic biologically treat the effluent after the anaerobic biological treatment to obtain the treated wastewater; wherein the aerobic biological treatment adopts Fenton advanced oxidation to treat the effluent after the anaerobic biological treatment to obtain the treated wastewater; wherein, in the low-temperature wet oxidation system, sodium hydroxide is used to adjust the pH value of the wastewater to 6-9, and the effluent after the pH value is adjusted is subjected to polyaluminum chloride coagulation, polyacrylamide coagulant and gravity mud-water separation treatment to obtain the effluent after flocculation and precipitation, wherein the flocculation and precipitation module includes:

[0020] A data acquisition unit, used to acquire the coagulation stirring speed value and the coagulation temperature value at a plurality of predetermined time points within a predetermined time period;

[0021] A vectorization unit, used for arranging the coagulation stirring speed values ​​and the coagulation temperature values ​​at the plurality of predetermined time points according to the time dimension to obtain a coagulation stirring speed time series input vector and a coagulation temperature time series input vector;

[0022] A vector segmentation and domain conversion unit, used for performing vector segmentation and domain conversion on the coagulation temperature time series input vector to obtain a sequence of local time series images of coagulation temperature;

[0023] A feature extraction unit, configured to extract features from the sequence of local time-series images of coagulation temperature by using a coagulation temperature time-series pattern feature extractor based on a deep neural network model to obtain a coagulation temperature time-series feature graph;

[0024] A time series characteristic analysis unit, used for performing time series characteristic analysis on the time series input vector of the coagulation stirring speed to obtain a time series characteristic vector of the coagulation stirring speed;

[0025] a channel-by-channel response correlation analysis unit, configured to perform a channel-by-channel response correlation analysis on the coagulation temperature time series characteristic diagram and the coagulation stirring speed time series characteristic vector to obtain a stirring speed-coagulation temperature channel-by-channel response time series characteristic; and

[0026] The speed control unit is used to determine whether the coagulation stirring speed value at the current time point should be increased, decreased or kept unchanged based on the channel-by-channel response timing characteristics of the stirring speed-coagulation temperature.

[0027] Compared with the prior art, the method and system for treating synthetic steroid hormone drug production wastewater provided by the present application, when using polyaluminium chloride for coagulation, collects the coagulation stirring speed value and the coagulation temperature value through real-time monitoring, and introduces data processing and analysis algorithms at the back end to perform time series collaborative analysis of the coagulation stirring speed value and the coagulation temperature value, so as to perform adaptive control of coagulation parameters based on the correlation between the coagulation stirring speed and the temperature. In this way, the wastewater treatment effect can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following briefly introduces the drawings required for use in the description of the embodiments. The following drawings are not intentionally scaled to the actual size, and the focus is on illustrating the main purpose of the present application.

[0029] Figure 1 The present invention is a flow chart of a method for treating wastewater from the production of synthetic steroid hormone drugs according to an embodiment of the present application.

[0030] Figure 2 It is a block diagram of a system for treating wastewater from the production of synthetic steroid hormone drugs according to an embodiment of the present application.

[0031] Figure 3 This is an application scenario diagram of the method for treating synthetic steroid hormone drug production wastewater according to an embodiment of the present application. DETAILED DESCRIPTION

[0032] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work also fall within the scope of protection of the present application.

[0033] As shown in this application and claims, unless the context clearly indicates an exception, the words "a", "an", "an" and / or "the" do not refer to the singular and may also include the plural. Generally speaking, the terms "include" and "comprise" only indicate the inclusion of the steps and elements that have been clearly identified, and these steps and elements do not constitute an exclusive list. The method or device may also include other steps or elements.

[0034] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules can be used and run on the user terminal and / or server. The modules are only illustrative, and different aspects of the system and method can use different modules.

[0035] Flowcharts are used in the present application to illustrate the operations performed by the system according to the embodiments of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps may be processed in reverse order or simultaneously as required. Meanwhile, other operations may also be added to these processes, or a certain step or several steps of operations may be removed from these processes.

[0036] Below, the exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application, and it should be understood that the present application is not limited to the exemplary embodiments described here.

[0037] In view of the above technical problems, the technical concept of the present application is to collect the coagulation stirring speed value and the coagulation temperature value through real-time monitoring when using polyaluminium chloride for coagulation, and introduce data processing and analysis algorithms at the back end to perform time series collaborative analysis of the coagulation stirring speed value and the coagulation temperature value, so as to perform adaptive control of coagulation parameters based on the correlation between the coagulation stirring speed and the temperature. In this way, the coagulation stirring speed can be adaptively adjusted based on the real-time state of the coagulation temperature to improve the flocculation efficiency and effect, thereby further improving the wastewater treatment effect.

[0038] Figure 1 Flow chart of a method for treating synthetic steroid hormone drug production wastewater according to an embodiment of the present application. Figure 1As shown, according to an embodiment of the present application, the method for treating synthetic steroid hormone drug production wastewater comprises the following steps: S110, filtering the synthetic steroid hormone drug production wastewater, and adjusting the pH value of the filtered production wastewater with sulfuric acid to obtain pH-adjusted wastewater; S120, subjecting the pH-adjusted wastewater to low-temperature wet oxidation and pH adjustment in a low-temperature wet oxidation system, and subjecting the pH-adjusted wastewater to flocculation and precipitation to obtain flocculated and precipitated wastewater; S130, subjecting the flocculated and precipitated wastewater to anaerobic sludge treatment. S140, subjecting the anaerobic biological treatment effluent to aerobic biological treatment to obtain treated wastewater; wherein, the aerobic biological treatment uses Fenton advanced oxidation to treat the anaerobic biological treatment effluent to obtain the treated wastewater; wherein, in the low-temperature wet oxidation system, sodium hydroxide is used to adjust the pH value of the wastewater to 6-9, and the effluent after the pH value is adjusted is subjected to polyaluminum chloride coagulation, polyacrylamide coagulant and gravity mud-water separation treatment to obtain the flocculation and sedimentation effluent.

[0039] Among them, in the low-temperature wet oxidation system, sodium hydroxide is used to adjust the pH value of the wastewater to 6-9, and the effluent after the pH value is adjusted is subjected to polyaluminum chloride coagulation, polyacrylamide coagulant and gravity mud-water separation treatment to obtain the effluent after the flocculation precipitation, including: obtaining the coagulation stirring speed value and the coagulation temperature value at multiple predetermined time points within a predetermined time period; arranging the coagulation stirring speed values ​​and the coagulation temperature values ​​at the multiple predetermined time points according to the time dimension to obtain the coagulation stirring speed time series input vector and the coagulation temperature time series input vector; performing vector segmentation and domain conversion on the coagulation temperature time series input vector to obtain a local time series diagram of the coagulation temperature The invention discloses a method for preparing a sequence of local time series images of coagulation temperature; performing feature extraction on the sequence of local time series images of coagulation temperature by using a coagulation temperature time series pattern feature extractor based on a deep neural network model to obtain a coagulation temperature time series feature diagram; performing time series feature analysis on the coagulation stirring speed time series input vector to obtain a coagulation stirring speed time series feature vector; performing channel-by-channel response correlation analysis on the coagulation temperature time series feature diagram and the coagulation stirring speed time series feature vector to obtain a stirring speed-coagulation temperature channel-by-channel response time series feature; and, based on the stirring speed-coagulation temperature channel-by-channel response time series feature, determining whether the coagulation stirring speed value at the current time point should be increased, decreased or kept unchanged.

[0040] Specifically, in the technical solution of the present application, first, the coagulation stirring speed value and the coagulation temperature value at multiple predetermined time points within a predetermined time period are obtained. Then, considering that the coagulation stirring speed value and the coagulation temperature value both have a time series dynamic change law in the time dimension, therefore, in order to be able to analyze and characterize the time series characteristics of the coagulation stirring speed value and the coagulation temperature value, it is necessary to first arrange the coagulation stirring speed value and the coagulation temperature value at the multiple predetermined time points respectively according to the time dimension to obtain the coagulation stirring speed time series input vector and the coagulation temperature time series input vector, so as to respectively integrate the distribution information of the coagulation stirring speed value and the coagulation temperature value in the time series.

[0041] Then, considering that the coagulation temperature has temporal volatility in the time dimension, it may have different temporal changes and trends in different time periods, and considering that the image form can more fully and accurately reflect the temporal changes of the coagulation temperature than the vector. Based on this, in order to represent and process the temporal information of the coagulation temperature in the form of an image, in the technical solution of the present application, the coagulation temperature temporal input vector is vector-cut into a sequence of local temporal input vectors of the coagulation temperature, and then a vector-image converter is used to obtain a sequence of local temporal images of the coagulation temperature. It should be understood that by cutting the temporal input vector into a sequence of local temporal input vectors, the changing trend of the coagulation temperature in different time periods can be captured later. Then, by performing domain conversion through the vector-image converter, the local temporal information of the coagulation temperature can be converted into the corresponding image form, which is conducive to a more comprehensive understanding of the temporal change law of the coagulation temperature and provides a basis for the subsequent adjustment of the coagulation stirring speed.

[0042] Correspondingly, the coagulation temperature time series input vector is vector segmented and domain converted to obtain a sequence of coagulation temperature local time series images, including: vector segmenting the coagulation temperature time series input vector into a sequence of coagulation temperature local time series input vectors and then passing the vector-image converter to obtain the sequence of coagulation temperature local time series images.

[0043] Furthermore, since each of the local time-series images of the coagulation temperature contains local time-series variation characteristics and trend information about the coagulation temperature in a local time period, in order to be able to perform correlation analysis on the time-series characteristics of these coagulation temperatures, so as to provide more detailed and comprehensive information to reveal the temperature variation law during the coagulation process, in the technical solution of the present application, the sequence of the local time-series images of the coagulation temperature is further subjected to feature mining in a coagulation temperature time-series pattern feature extractor based on a three-dimensional convolutional neural network model, so as to extract the time-series pattern of the coagulation temperature in each local time period based on the global time-series correlation feature information, thereby obtaining a coagulation temperature time-series feature graph. In other words, through the processing of the feature extractor based on the three-dimensional convolutional neural network model, the global time-series variation pattern and characteristics of the coagulation temperature can be effectively learned and extracted from each of the local time-series images of the coagulation temperature.

[0044] Correspondingly, in the step of extracting features from the sequence of local time series images of coagulation temperature by a coagulation temperature time series pattern feature extractor based on a deep neural network model to obtain a coagulation temperature time series feature graph, the deep neural network model is a three-dimensional convolutional neural network model.

[0045] Next, considering that the coagulation stirring speed has a time-series change pattern and trend in the time dimension, therefore, in the technical solution of the present application, the coagulation stirring speed time-series input vector is subjected to feature extraction in a coagulation stirring speed time-series feature extractor based on a one-dimensional convolutional layer to extract the time-series feature information of the coagulation stirring speed value in the time dimension, thereby obtaining the coagulation stirring speed time-series feature vector.

[0046] Correspondingly, a time series feature analysis is performed on the coagulation stirring speed time series input vector to obtain a coagulation stirring speed time series feature vector, including: passing the coagulation stirring speed time series input vector through a coagulation stirring speed time series feature extractor based on a one-dimensional convolutional layer to obtain the coagulation stirring speed time series feature vector.

[0047] In the coagulation process, the coagulation stirring speed and the coagulation temperature are two key parameters, which interact with each other, and the interaction and response between them have an important influence on the coagulation effect. Therefore, it is necessary to perform correlation and responsiveness analysis on the timing characteristics of the coagulation stirring speed and temperature, so as to better analyze and understand the relationship between them, so as to utilize the correlation between the coagulation stirring speed and the coagulation temperature to perform adaptive control of the coagulation stirring speed value. Based on this, in the technical solution of the present application, the coagulation stirring speed timing characteristic vector and the coagulation temperature timing characteristic diagram are further passed through the MetaNet-based coagulation temperature-coagulation stirring speed channel-by-channel response module to obtain the stirring speed-coagulation temperature channel-by-channel response timing characteristic diagram. In particular, here, the MetaNet-based coagulation temperature-coagulation stirring speed channel-by-channel response module can learn and model the temporal relationship between coagulation stirring speed and coagulation temperature. It can obtain more comprehensive and detailed correlation information by responding to the coagulation stirring speed time-series feature vector and the coagulation temperature time-series feature diagram channel by channel, revealing the temporal relationship between coagulation stirring speed and coagulation temperature for further analysis and adaptive control of the coagulation stirring speed value based on changes in coagulation temperature.

[0048] Correspondingly, a channel-by-channel response correlation analysis is performed on the coagulation temperature timing characteristic diagram and the coagulation stirring speed timing characteristic vector to obtain a stirring speed-coagulation temperature channel-by-channel response timing characteristic, including: passing the coagulation stirring speed timing characteristic vector and the coagulation temperature timing characteristic diagram through a MetaNet-based coagulation temperature-coagulation stirring speed channel-by-channel response module to obtain a stirring speed-coagulation temperature channel-by-channel response timing characteristic diagram as the stirring speed-coagulation temperature channel-by-channel response timing characteristic.

[0049] Specifically, the coagulation stirring speed time series feature vector and the coagulation temperature time series feature diagram are passed through a MetaNet-based coagulation temperature-coagulation stirring speed channel-by-channel response module to obtain a stirring speed-coagulation temperature channel-by-channel response time series feature diagram as the stirring speed-coagulation temperature channel-by-channel response time series feature, including: passing the coagulation stirring speed time series feature vector through a point convolution layer to obtain a first convolution feature vector; passing the first convolution feature vector through a corrected linear unit based on a ReLU function to obtain a first corrected convolution feature vector; passing the first corrected convolution feature vector through a point convolution layer to obtain a second convolution feature vector; passing the second convolution feature vector through a corrected linear unit based on a Sigmoid function to obtain a second corrected convolution feature vector; and, fusing the second corrected convolution feature vector with the coagulation temperature time series feature diagram to obtain the stirring speed-coagulation temperature channel-by-channel response time series feature diagram.

[0050] Then, the channel-by-channel response time series characteristic diagram of the stirring speed-coagulation temperature is passed through a classifier to obtain a classification result, and the classification result is used to indicate whether the coagulation stirring speed value at the current time point should be increased, decreased, or kept unchanged. In other words, the channel-by-channel responsive fusion characteristic information between the time series characteristics of the coagulation stirring speed value and the time series characteristics of the coagulation temperature value is used for classification processing, so as to perform adaptive control of coagulation parameters based on the correlation between the coagulation stirring speed and the temperature, so as to improve the flocculation efficiency and effect.

[0051] Accordingly, based on the channel-by-channel response timing characteristics of the stirring speed-coagulation temperature, it is determined whether the coagulation stirring speed value at the current time point should increase, decrease or remain unchanged, including: passing the stirring speed-coagulation temperature channel-by-channel response timing characteristic diagram through a classifier to obtain a classification result, and the classification result is used to indicate whether the coagulation stirring speed value at the current time point should increase, decrease or remain unchanged.

[0052] Furthermore, in the technical solution of the present application, the method for treating synthetic steroid hormone drug production wastewater also includes a training step: for training the coagulation temperature time series pattern feature extractor based on the three-dimensional convolutional neural network model, the coagulation stirring speed time series feature extractor based on the one-dimensional convolutional layer, the MetaNet-based coagulation temperature-coagulation stirring speed channel-by-channel response module and the classifier.

[0053] In a specific example, the training step includes: obtaining training data, the training data including training coagulation stirring speed values ​​and training coagulation temperature values ​​at multiple predetermined time points within a predetermined time period; arranging the training coagulation stirring speed values ​​and training coagulation temperature values ​​at the multiple predetermined time points respectively according to the time dimension to obtain a training coagulation stirring speed time series input vector and a training coagulation temperature time series input vector; performing vector segmentation and domain conversion on the training coagulation temperature time series input vector to obtain a sequence of training coagulation temperature local time series images; performing feature extraction on the sequence of training coagulation temperature local time series images through the coagulation temperature time series pattern feature extractor based on the three-dimensional convolutional neural network model to obtain a training coagulation temperature time series feature map; passing the training coagulation stirring speed time series input vector through the coagulation stirring speed time series feature extractor based on the one-dimensional convolutional layer to obtain a training coagulation stirring speed time series feature vector ; Pass the training coagulation stirring speed time series feature vector and the training coagulation temperature time series feature diagram through the MetaNet-based coagulation temperature-coagulation stirring speed channel-by-channel response module to obtain the training stirring speed-coagulation temperature channel-by-channel response time series feature diagram; pass the training stirring speed-coagulation temperature channel-by-channel response time series feature diagram through the classifier to obtain the classification loss function value; calculate the specific loss function value of the training stirring speed-coagulation temperature channel-by-channel response time series feature diagram and the training coagulation temperature time series feature diagram; and, use the weighted sum of the classification loss function value and the specific loss function value as the loss function value to train the coagulation temperature time series pattern feature extractor based on the three-dimensional convolutional neural network model, the coagulation stirring speed time series feature extractor based on the one-dimensional convolutional layer, the MetaNet-based coagulation temperature-coagulation stirring speed channel-by-channel response module and the classifier.

[0054] In particular, in the above technical solution, the feature matrix of the training coagulation temperature time series feature map represents the local correlation features between the local time series distribution images of the coagulation temperature, and each feature matrix follows the channel distribution of the coagulation temperature time series pattern feature extractor based on the three-dimensional convolutional neural network model. Therefore, after the training coagulation stirring speed time series feature vector and the training coagulation temperature time series feature map pass through the coagulation temperature-coagulation stirring speed channel-by-channel response module based on MetaNet, the local time series of the coagulation stirring speed value expressed by the training coagulation stirring speed time series feature vector will be obtained. The sequence correlation features are used to constrain the channel distribution between the feature matrices of the training coagulation temperature time series feature diagram. As a result, the feature group density representation of the training stirring speed-coagulation temperature channel-by-channel response time series feature diagram relative to the training coagulation temperature time series feature diagram in the overall feature diagram distribution dimension will also be different. In this way, when the model is trained as a whole, there will be an iterative imbalance between the coagulation temperature time series pattern feature extractor based on the three-dimensional convolutional neural network model and the coagulation temperature-coagulation stirring speed channel-by-channel response module based on MetaNet, which will affect the overall training efficiency of the model.

[0055] Therefore, the applicant of the present application considered improving the consistency of the feature group density representation of the training stirring speed-coagulation temperature channel-by-channel response timing characteristic diagram and the training coagulation temperature timing characteristic diagram, and further introduced a specific loss function for the training stirring speed-coagulation temperature channel-by-channel response timing characteristic diagram and the training coagulation temperature timing characteristic diagram.

[0056] Accordingly, in one example, calculating the specific loss function value of the training stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram and the training coagulation temperature time series characteristic diagram includes: calculating the specific loss function value of the training stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram and the training coagulation temperature time series characteristic diagram using the following specific loss calculation formula; wherein the specific loss calculation formula is:

[0057]

[0058] Wherein, V1 is the first eigenvector obtained after the training stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram is expanded, V2 is the second eigenvector obtained after the training coagulation temperature time series characteristic diagram is expanded, L is the length of the eigenvector, and represents the square of the vector's two-norm, represents vector subtraction, v 1i represents the i-th eigenvalue of the first eigenvector, v 2irepresents the i-th eigenvalue of the second eigenvector, exp(·) represents the exponential operation of a value, and the exponential operation of the value represents the calculation of the value of a natural exponential function with the value as a power, is the value of a specific loss function.

[0059] Here, the loss function performs group count attention based on the feature group density, and performs adaptive attention to the different density representation modes between the training stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram and the training coagulation temperature time series characteristic diagram by recursively mapping the group count as the output feature group density. By using it as a loss function to train the model, the model can avoid over-estimation and under-estimation for the different density modes under the feature distribution of the training stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram and the training coagulation temperature time series characteristic diagram, and learn the correspondence between the eigenvalue distribution and the group density distribution, thereby achieving the optimization of the consistency of the feature group density representation between the training stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram and the training coagulation temperature time series characteristic diagram with different feature densities, so as to improve the overall training efficiency of the model. In this way, the coagulation stirring speed can be adaptively adjusted based on the real-time state of the coagulation temperature to improve the flocculation efficiency and effect, thereby further improving the wastewater treatment effect.

[0060] In summary, the method for treating synthetic steroid hormone drug production wastewater based on the embodiments of the present application is explained, which can improve the wastewater treatment effect.

[0061] Figure 2 FIG. 1 is a block diagram of a system 100 for treating synthetic steroid hormone drug production wastewater according to an embodiment of the present application. Figure 2As shown, according to an embodiment of the present application, the system 100 for treating the production wastewater of synthetic steroid hormone drugs comprises: a sulfuric acid adjustment filtration module 110, which is used to filter the production wastewater of synthetic steroid hormone drugs, and then adjust the pH value of the filtered production wastewater with sulfuric acid to obtain wastewater after pH value adjustment; a flocculation and sedimentation module 120, which is used to pass the wastewater after pH value adjustment through a low-temperature wet oxidation system to perform low-temperature wet oxidation effluent and pH value adjustment, and to perform flocculation and sedimentation on the effluent after pH value adjustment to obtain effluent after flocculation and sedimentation; an anaerobic biological treatment module 130, which is used to perform anaerobic biological treatment on the effluent after flocculation and sedimentation aerobic biological treatment to obtain effluent after anaerobic biological treatment; an aerobic biological treatment module 140, for aerobic biological treatment of the effluent after anaerobic biological treatment to obtain treated wastewater; wherein the aerobic biological treatment uses Fenton advanced oxidation to treat the effluent after anaerobic biological treatment to obtain the treated wastewater; wherein, in the low-temperature wet oxidation system, sodium hydroxide is used to adjust the pH value of the wastewater to 6-9, and the effluent after the pH value is adjusted is subjected to polyaluminum chloride coagulation, polyacrylamide coagulant and gravity mud-water separation treatment to obtain the effluent after flocculation and precipitation, wherein the flocculation and precipitation module 12 0, including: a data acquisition unit, used to acquire the coagulation stirring speed value and the coagulation temperature value at a plurality of predetermined time points within a predetermined time period; a vectorization unit, used to arrange the coagulation stirring speed value and the coagulation temperature value at the plurality of predetermined time points respectively according to the time dimension to obtain the coagulation stirring speed time series input vector and the coagulation temperature time series input vector; a vector segmentation and domain conversion unit, used to perform vector segmentation and domain conversion on the coagulation temperature time series input vector to obtain a sequence of local time series images of coagulation temperature; a feature extraction unit, used to extract the local time series image of coagulation temperature by a coagulation temperature time series pattern feature extractor based on a deep neural network model; Feature extraction is performed on a sequence of time-series images to obtain a coagulation temperature time-series characteristic diagram; a time-series characteristic analysis unit is used to perform time-series characteristic analysis on the coagulation stirring speed time-series input vector to obtain a coagulation stirring speed time-series characteristic vector; a channel-by-channel response correlation analysis unit is used to perform channel-by-channel response correlation analysis on the coagulation temperature time-series characteristic diagram and the coagulation stirring speed time-series characteristic vector to obtain a stirring speed-coagulation temperature channel-by-channel response time-series characteristic; and a speed control unit is used to determine whether the coagulation stirring speed value at the current time point should be increased, decreased or unchanged based on the stirring speed-coagulation temperature channel-by-channel response time-series characteristic.

[0062] Here, those skilled in the art will appreciate that the specific functions and operations of the various modules in the system 100 for treating synthetic steroid hormone drug production wastewater have been described above with reference to Figure 1 The method for treating synthetic steroid hormone drug production wastewater has been described in detail in the description of the method, and therefore, its repeated description will be omitted.

[0063] As described above, the system 100 for processing synthetic steroid hormone drug production wastewater according to the embodiment of the present application can be implemented in various wireless terminals, such as a server with an algorithm for processing synthetic steroid hormone drug production wastewater, etc. In one example, the system 100 for processing synthetic steroid hormone drug production wastewater according to the embodiment of the present application can be integrated into the wireless terminal as a software module and / or hardware module. For example, the system 100 for processing synthetic steroid hormone drug production wastewater can be a software module in the operating system of the wireless terminal, or can be an application developed for the wireless terminal; of course, the system 100 for processing synthetic steroid hormone drug production wastewater can also be one of the many hardware modules of the wireless terminal.

[0064] Alternatively, in another example, the system 100 for treating synthetic steroid hormone drug production wastewater and the wireless terminal may also be separate devices, and the system 100 for treating synthetic steroid hormone drug production wastewater may be connected to the wireless terminal via a wired and / or wireless network and transmit interactive information in accordance with an agreed data format.

[0065] Figure 3 FIG. 1 is an application scenario diagram of a method for treating synthetic steroid hormone drug production wastewater according to an embodiment of the present application. Figure 3 As shown, in this application scenario, first, the coagulation stirring speed values ​​at multiple predetermined time points within a predetermined time period are obtained (for example, Figure 3 D1) and coagulation temperature values ​​(e.g. Figure 3 D2 as shown in the figure), and then, the coagulation stirring speed values ​​and coagulation temperature values ​​at the plurality of predetermined time points are input to a server (for example, Figure 3 S) as shown in , wherein the server is able to use the algorithm for treating synthetic steroid hormone drug production wastewater to process the coagulation stirring speed values ​​and coagulation temperature values ​​at the multiple predetermined time points to obtain a classification result indicating that the coagulation stirring speed value at the current time point should be increased, decreased or remain unchanged.

[0066] In addition, it will be appreciated by those skilled in the art that various aspects of the present application may be illustrated and described by a number of patentable categories or situations, including any new and useful process, machine, product or combination of substances, or any new and useful improvements thereto. Accordingly, various aspects of the present application may be performed entirely by hardware, entirely by software (including firmware, resident software, microcode, etc.), or by a combination of hardware and software. The above hardware or software may all be referred to as "data blocks", "modules", "engines", "units", "components" or "systems". In addition, various aspects of the present application may be represented as a computer product located in one or more computer-readable media, which includes computer-readable program code.

[0067] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined as such herein.

[0068] The above is an explanation of the present application and should not be considered as a limitation thereof. Although several exemplary embodiments of the present application are described, it will be readily understood by those skilled in the art that many modifications may be made to the exemplary embodiments without departing from the novel teachings and advantages of the present application. Therefore, all of these modifications are intended to be included within the scope of the present application as defined by the claims. It should be understood that the above is an explanation of the present application and should not be considered to be limited to the specific embodiments disclosed, and modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present application is defined by the claims and their equivalents.

Claims

1. A method for treating wastewater from the production of synthetic steroid hormone drugs, comprising: After filtering the production wastewater of synthetic steroid hormone drugs, adjusting the pH value of the filtered production wastewater with sulfuric acid to obtain wastewater after pH adjustment; The wastewater after pH adjustment is passed through a low-temperature wet oxidation system for low-temperature wet oxidation effluent and pH adjustment, and the effluent after pH adjustment is subjected to flocculation and sedimentation to obtain effluent after flocculation and sedimentation; the effluent after flocculation and sedimentation is subjected to anaerobic biological treatment to obtain effluent after anaerobic biological treatment; the effluent after anaerobic biological treatment is subjected to aerobic biological treatment to obtain treated wastewater; wherein the aerobic biological treatment adopts Fenton advanced oxidation to treat the effluent after anaerobic biological treatment to obtain the treated wastewater; wherein, in the low-temperature wet oxidation system, sodium hydroxide is used to adjust the pH value of the wastewater to 6-9, and the effluent after pH adjustment is subjected to polyaluminum chloride coagulation, polyacrylamide coagulant and gravity mud-water separation treatment to obtain the effluent after flocculation and sedimentation, characterized in that it includes: Obtaining coagulation stirring speed values ​​and coagulation temperature values ​​at multiple predetermined time points within a predetermined time period; Arrange the coagulation stirring speed values ​​and the coagulation temperature values ​​at the plurality of predetermined time points respectively according to the time dimension to obtain a coagulation stirring speed time series input vector and a coagulation temperature time series input vector; Performing vector segmentation and domain conversion on the coagulation temperature time series input vector to obtain a sequence of coagulation temperature local time series images; Extracting features from the sequence of local time series images of coagulation temperature by a coagulation temperature time series pattern feature extractor based on a deep neural network model to obtain a coagulation temperature time series feature graph; Performing a time series characteristic analysis on the coagulation stirring speed time series input vector to obtain a coagulation stirring speed time series characteristic vector; Performing a channel-by-channel response correlation analysis on the coagulation temperature time series characteristic diagram and the coagulation stirring speed time series characteristic vector to obtain a stirring speed-coagulation temperature channel-by-channel response time series characteristic; and Based on the channel-by-channel response time series characteristics of the stirring speed-coagulation temperature, it is determined whether the coagulation stirring speed value at the current time point should be increased, decreased, or kept unchanged.

2. The method for treating wastewater from the production of synthetic steroid hormone drugs according to claim 1, characterized in that: The coagulation temperature time series input vector is vector segmented and domain converted to obtain a sequence of coagulation temperature local time series images, including: vector segmenting the coagulation temperature time series input vector into a sequence of coagulation temperature local time series input vectors and then passing the vector-image converter to obtain the sequence of coagulation temperature local time series images.

3. The method for treating wastewater from the production of synthetic steroid hormone drugs according to claim 2, characterized in that: The deep neural network model is a three-dimensional convolutional neural network model.

4. The method for treating wastewater from the production of synthetic steroid hormone drugs according to claim 3, characterized in that: Performing time series feature analysis on the coagulation stirring speed time series input vector to obtain a coagulation stirring speed time series feature vector, including: passing the coagulation stirring speed time series input vector through a coagulation stirring speed time series feature extractor based on a one-dimensional convolutional layer to obtain the coagulation stirring speed time series feature vector.

5. The method for treating wastewater from the production of synthetic steroid hormone drugs according to claim 4, characterized in that: The coagulation temperature timing characteristic diagram and the coagulation stirring speed timing characteristic vector are subjected to a channel-by-channel response correlation analysis to obtain a stirring speed-coagulation temperature channel-by-channel response timing characteristic, including: the coagulation stirring speed timing characteristic vector and the coagulation temperature timing characteristic diagram are subjected to a coagulation temperature-coagulation stirring speed channel-by-channel response module based on MetaNet to obtain a stirring speed-coagulation temperature channel-by-channel response timing characteristic diagram as the stirring speed-coagulation temperature channel-by-channel response timing characteristic.

6. The method for treating wastewater from the production of synthetic steroid hormone drugs according to claim 5, characterized in that: The coagulation stirring speed time series feature vector and the coagulation temperature time series feature diagram are passed through a coagulation temperature-coagulation stirring speed channel-by-channel response module based on MetaNet to obtain a stirring speed-coagulation temperature channel-by-channel response time series feature diagram as the stirring speed-coagulation temperature channel-by-channel response time series feature, including: Passing the time series feature vector of the coagulation stirring speed through a point convolution layer to obtain a first convolution feature vector; Passing the first convolution feature vector through a rectified linear unit based on a ReLU function to obtain a first rectified convolution feature vector; Passing the first modified convolutional feature vector through a point convolution layer to obtain a second convolutional feature vector; Passing the second convolution feature vector through a rectified linear unit based on a Sigmoid function to obtain a second rectified convolution feature vector; and The second modified convolution feature vector is fused with the coagulation temperature time series characteristic diagram to obtain the stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram.

7. The method for treating wastewater from the production of synthetic steroid hormone drugs according to claim 6, characterized in that: Based on the channel-by-channel response time series characteristics of the stirring speed-coagulation temperature, determine whether the coagulation stirring speed value at the current time point should increase, decrease or remain unchanged, including: passing the stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram through a classifier to obtain a classification result, and the classification result is used to indicate whether the coagulation stirring speed value at the current time point should increase, decrease or remain unchanged.

8. The method for treating wastewater from the production of synthetic steroid hormone drugs according to claim 7, characterized in that: It also includes a training step: for training the coagulation temperature time series pattern feature extractor based on the three-dimensional convolutional neural network model, the coagulation stirring speed time series feature extractor based on the one-dimensional convolutional layer, the coagulation temperature-coagulation stirring speed channel-by-channel response module based on MetaNet, and the classifier.

9. The method for treating wastewater from the production of synthetic steroid hormone drugs according to claim 8, characterized in that: The training step comprises: Acquire training data, wherein the training data includes training coagulation stirring speed values ​​and training coagulation temperature values ​​at multiple predetermined time points within a predetermined time period; Arranging the training coagulation stirring speed values ​​and the training coagulation temperature values ​​at the plurality of predetermined time points respectively according to the time dimension to obtain a training coagulation stirring speed time series input vector and a training coagulation temperature time series input vector; Performing vector segmentation and domain conversion on the training coagulation temperature time series input vector to obtain a sequence of training coagulation temperature local time series images; The coagulation temperature time series pattern feature extractor based on the three-dimensional convolutional neural network model performs feature extraction on the sequence of the training coagulation temperature local time series images to obtain a training coagulation temperature time series feature graph; Passing the training coagulation stirring speed time series input vector through the coagulation stirring speed time series feature extractor based on the one-dimensional convolution layer to obtain a training coagulation stirring speed time series feature vector; The training coagulation stirring speed time series feature vector and the training coagulation temperature time series feature diagram are passed through the MetaNet-based coagulation temperature-coagulation stirring speed channel-by-channel response module to obtain a training stirring speed-coagulation temperature channel-by-channel response time series feature diagram; Passing the training stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram through the classifier to obtain a classification loss function value; Calculating the specific loss function value of the training stirring speed-coagulation temperature channel-by-channel response time series characteristic diagram and the training coagulation temperature time series characteristic diagram; and The weighted sum of the classification loss function value and the specific loss function value is used as the loss function value to train the coagulation temperature time series pattern feature extractor based on the three-dimensional convolutional neural network model, the coagulation stirring speed time series feature extractor based on the one-dimensional convolutional layer, the MetaNet-based coagulation temperature-coagulation stirring speed channel-by-channel response module and the classifier.

10. A system for treating wastewater from the production of synthetic steroid hormone drugs, comprising: A sulfuric acid adjustment filtration module is used to filter the production wastewater of synthetic steroid hormone drugs and adjust the pH value of the filtered production wastewater with sulfuric acid to obtain wastewater after pH adjustment; A flocculation and sedimentation module is used to pass the wastewater after pH adjustment through a low-temperature wet oxidation system to perform low-temperature wet oxidation on the effluent and adjust the pH value, and to perform flocculation and sedimentation on the effluent after pH adjustment to obtain effluent after flocculation and sedimentation; An anaerobic biological treatment module is used to perform anaerobic biological treatment on the effluent after flocculation and sedimentation to obtain anaerobic biological treated effluent; The aerobic biological treatment module is used to aerobically treat the effluent after the anaerobic biological treatment to obtain treated wastewater; wherein the aerobic biological treatment adopts Fenton advanced oxidation to treat the effluent after the anaerobic biological treatment to obtain the treated wastewater; wherein, in the low-temperature wet oxidation system, sodium hydroxide is used to adjust the pH value of the wastewater to 6-9, and the effluent after the pH value is adjusted is subjected to polyaluminum chloride coagulation, polyacrylamide coagulant and gravity mud-water separation treatment to obtain the effluent after flocculation and precipitation, characterized in that the flocculation and precipitation module comprises: A data acquisition unit, used to acquire the coagulation stirring speed value and the coagulation temperature value at a plurality of predetermined time points within a predetermined time period; A vectorization unit, used for arranging the coagulation stirring speed values ​​and the coagulation temperature values ​​at the plurality of predetermined time points according to the time dimension to obtain a coagulation stirring speed time series input vector and a coagulation temperature time series input vector; A vector segmentation and domain conversion unit, used for performing vector segmentation and domain conversion on the coagulation temperature time series input vector to obtain a sequence of local time series images of coagulation temperature; A feature extraction unit, configured to extract features from the sequence of local time-series images of coagulation temperature by using a coagulation temperature time-series pattern feature extractor based on a deep neural network model to obtain a coagulation temperature time-series feature graph; A time series characteristic analysis unit, used for performing time series characteristic analysis on the time series input vector of the coagulation stirring speed to obtain a time series characteristic vector of the coagulation stirring speed; a channel-by-channel response correlation analysis unit, configured to perform a channel-by-channel response correlation analysis on the coagulation temperature time series characteristic diagram and the coagulation stirring speed time series characteristic vector to obtain a stirring speed-coagulation temperature channel-by-channel response time series characteristic; and The speed control unit is used to determine whether the coagulation stirring speed value at the current time point should be increased, decreased or kept unchanged based on the channel-by-channel response timing characteristics of the stirring speed-coagulation temperature.

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