CN117764332A - Environment-friendly and efficient textile printing and dyeing wastewater treatment system - Google Patents
Environment-friendly and efficient textile printing and dyeing wastewater treatment system Download PDFInfo
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- CN117764332A CN117764332A CN202311744754.3A CN202311744754A CN117764332A CN 117764332 A CN117764332 A CN 117764332A CN 202311744754 A CN202311744754 A CN 202311744754A CN 117764332 A CN117764332 A CN 117764332A
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- 239000004753 textile Substances 0.000 title claims abstract description 26
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- 238000000746 purification Methods 0.000 claims abstract description 37
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Abstract
The invention relates to the field of wastewater treatment, and discloses an environment-friendly and efficient textile printing and dyeing wastewater treatment system, which comprises: the control module is used for coordinating and controlling the global function module and editing and sending control instructions; the verification module is used for recording the processing capacity and technical parameters of the purification equipment, the current textile production progress and specific pollutants contained in the polluted wastewater; the environment acquisition module is used for collecting environment data in the future preset time of the current region from the Internet end and acquiring temperature, humidity and wind speed data; through the synergistic effect of the prediction module and the allocation module, the system can reasonably schedule sewage treatment according to the predicted purification capacity change, so that the treatment efficiency is improved, the system can be flexibly adjusted according to actual conditions, the maximum utilization rate of the purification equipment is ensured, the weather data in the next purification period is acquired through the environment acquisition module, and the system is adjusted according to the actual conditions so as to adapt to different environmental conditions.
Description
Technical Field
The invention relates to the technical field of wastewater treatment, in particular to an environment-friendly and efficient textile printing and dyeing wastewater treatment system.
Background
The textile wastewater is mainly wastewater containing natural impurities, fat, starch and other organic matters generated in the processes of raw material cooking, rinsing, bleaching, sizing and the like, the printing and dyeing wastewater is generated in a plurality of processes of washing, dyeing, printing, sizing and the like, contains a large amount of organic matters such as dye, starch, cellulose, lignin, detergent and the like, and inorganic matters such as alkali, sulfide, various salts and the like, has strong pollution, and the treatment process is controlled and scheduled through a wastewater treatment system;
however, existing textile printing and dyeing wastewater treatment systems have drawbacks such as:
1. in the actual wastewater treatment process, the treatment capacities of different purifying equipment are different in different time periods, and the treatment capacities of the current purifying equipment are difficult to automatically identify in a preset period in the prior art, so that the allocated polluted water treatment capacity often has deviation, and high-load operation or resource waste is caused;
2. the processing capacity of the purifying equipment is influenced by environmental factors to cause certain deviation, and the adaptability automatic analysis is difficult to carry out according to the real-time environmental factors, so that decision errors are easy to cause, and further, the production loss is caused.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects existing in the prior art, the invention provides an environment-friendly and efficient textile printing and dyeing wastewater treatment system, which can effectively solve the problems that in the prior art, in the actual wastewater treatment process, the treatment capacities of different purifying devices are different in different periods, the treatment capacities of the current purifying devices are difficult to automatically identify in a preset period, so that the allocated polluted water treatment capacity is always deviated, high-load operation or resource waste is caused, the treatment capacities of the purifying devices are influenced by environmental factors to a certain extent, and the adaptive automatic analysis is difficult to carry out according to the real-time environmental factors, so that decision errors are easy to cause, and further production loss is caused.
(II) technical scheme
In order to achieve the above object, the present invention is realized by the following technical scheme,
the invention discloses an environment-friendly and efficient textile printing and dyeing wastewater treatment system, which comprises:
the control module is used for coordinating and controlling the global function module and editing and sending control instructions;
the verification module is used for recording the processing capacity and technical parameters of the purification equipment, the current textile production progress and specific pollutants contained in the polluted wastewater;
the environment acquisition module is used for collecting environment data in the future preset time of the current region from the Internet end and acquiring temperature, humidity and wind speed data;
the processing acquisition module is used for acquiring real-time operation parameters of the purification equipment in the current period;
the summarizing module is used for integrating the weather data with the operation parameters of the purifying equipment and outputting the weather data into a machine-readable language as a data base required by analysis;
the prediction module is used for analyzing and predicting the capacity change of the purifying equipment in the next period through the machine learning algorithm by processing the data collected by the acquisition module and the summarization module;
the allocation module is used for carrying out corresponding sewage treatment scheduling according to the predicted data and issuing a wastewater flow allocation instruction to the purification equipment;
the available simulation module is used for calling the processing acquisition module to acquire the processed water body and the data of the purification equipment after the purification period is finished, evaluating the performance of the current purification equipment and the availability of the purification capacity of the current purification equipment, and outputting the actual purification data of the purification equipment;
the comparison module is used for comparing the predicted data with the actual purified data and judging whether the deviation value exceeds a preset threshold value or not;
and the tracing judgment module is used for tracing and tracing the abnormal situation when the deviation value of the predicted data and the actual purified data exceeds a preset threshold value, and analyzing and distinguishing the cause of the problem.
Still further, the prediction process of the prediction module includes the following steps:
step 61: acquiring historical wastewater treatment data, current environmental weather, temperature, humidity, wind speed, real-time operation parameters of purifying equipment and textile production progress data through a treatment acquisition module and a summarizing module;
step 62: cleaning the collected data, removing abnormal data, filling the missing value, and integrating the data from different sources;
step 63: selecting and extracting characteristics aiming at purifying equipment, environmental conditions and pollution source types to serve as input variables of a prediction model;
step 64: selecting a machine learning prediction model according to the data characteristics, and training the selected model by using historical data to establish the prediction model;
step 65: the trained model predicts and analyzes various parameters in a future wastewater treatment period, obtains the wastewater treatment capacity, the required chemical addition amount, the energy consumption condition and the generated waste amount in the next period, outputs the analyzed result, presents the analyzed result in the form of a numerical value or a chart, and provides an expected value for the capacity change of the purifying equipment.
Further, in the step 64, the machine learning prediction model trains a plurality of individual learners through ensemble learning, combines the individual learners through a combination strategy, weights and sums the prediction results of each learner, and substitutes a step function to obtain a final prediction classification result, wherein the calculation formula is as follows:
wherein F is a classification prediction coefficient, sign (·) is a step function, a is a weight value assigned to the weak learner, h is the weak learner, y is a designated iteration number, and T represents a training number.
Still further, the processing the real-time operating parameters in the acquisition module includes: discharge flow, water pH value, water temperature, water pollutant and purified matter concentration.
Still further, the capability variation attribute of the purification apparatus in the prediction module includes: processing power, energy consumed and chemicals consumed.
Further, the operation flow of the traceability judging module comprises the following steps:
step 1, a management and control module: comparing and analyzing the actual purifying data and the predicted data collected in the system to detect whether an abnormal condition exists;
step 1, a verification module: once an abnormality is detected, starting to trace back data of the abnormality, and tracking operation records, parameter setting, types of chemicals and quantity data of the purifying equipment;
step 1, an environment acquisition module: and (3) carrying out root cause analysis on the basis of the data backtracking of the verification module in the step (1) so as to determine the specific cause of the problem.
Still further, the abnormal situation in the step 1 management and control module includes: improper adjustment of operation parameters, reduced processing capacity, unqualified water quality, equipment failure, abnormal external environment and abnormal collected data sources.
Furthermore, the traceability judging module is interactively connected with a correction module through a wireless network, and the correction module is used for making a scheme based on the specific cause of the determined problem according to the diagnosis result of the traceability judging module, feeding back the scheme to the management and control module and related operators, and correspondingly adjusting the operation setting of the problem equipment according to the feedback.
Still further, the solution formulation includes: device parameter adjustment, device fault repair, operation flow change and monitoring point increase.
Still further, the management and control module is interactively connected with the verification module through a wireless network, the verification module is interactively connected with the environment acquisition module and the processing acquisition module through the wireless network, the summarizing module is interactively connected with the environment acquisition module, the processing acquisition module and the prediction module through the wireless network, the prediction module is interactively connected with the allocation module through the wireless network, the allocation module is interactively connected with the available simulation module through the wireless network, the available simulation module is interactively connected with the comparison module through the wireless network, the comparison module is interactively connected with the tracing judgment module through the wireless network, the tracing judgment module is interactively connected with the correction module through the wireless network, and the correction module is interactively connected with the management and control module through the wireless network.
(III) beneficial effects
Compared with the known public technology, the technical scheme provided by the invention has the following beneficial effects;
1. through the synergistic effect of prediction module and allotment module, the system can rationally schedule sewage treatment according to the purification ability change of prediction to improve treatment effeciency, the system can carry out nimble adjustment according to the actual conditions, ensures the maximum utilization ratio of clarification plant, acquires the weather data in the next purification cycle through environment acquisition module, and these data can influence clarification plant's operation effect, through considering external factors such as weather, the system can adjust according to the actual conditions, in order to adapt to different environmental conditions, improves the treatment effect.
2. The system can find deviation and correspondingly adjust through comparison of the predicted data and the actual purified data, and the tracing judging module can trace the cause of abnormality, thereby being beneficial to improving and optimizing the operation setting of the system.
3. Through reasonable sewage treatment scheduling and operation optimization of the purification equipment, the system can reduce energy consumption and adverse effect on environment, and the high-efficiency treatment capacity and data analysis optimization of the system can improve the effect of wastewater treatment to the greatest extent, so that environmental protection and resource conservation are realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic diagram of a frame of an environmentally friendly and efficient textile printing and dyeing wastewater treatment system;
reference numerals in the drawings represent respectively, 1, a management and control module; 2. a verification module; 3. an environment collection module; 4. a processing and collecting module; 5. a summarizing module; 6. a prediction module; 7. a deployment module; 8. an analog module may be used; 9. comparison module; 10. a tracing judgment module; 11. and a correction module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention is further described below with reference to examples.
Example 1
An environment-friendly and efficient textile printing and dyeing wastewater treatment system of the embodiment, as shown in fig. 1, comprises:
the management and control module 1 is used for coordinating and controlling the global function module and editing and sending control instructions;
the verification module 2 is used for recording the processing capacity and technical parameters of the purification equipment, the current textile production progress and specific pollutants contained in the polluted wastewater;
the environment acquisition module 3 is used for collecting environment data in the future preset time of the current region from the internet end and acquiring temperature, humidity and wind speed data;
the processing and collecting module 4 is configured to obtain real-time operation parameters of the purifying device in the current period, where the real-time operation parameters include: discharge flow, water pH value, water temperature, water pollutant and purified matter concentration;
the summarizing module 5 is used for integrating the weather data with the operation parameters of the purifying equipment and outputting the weather data into a machine-readable language as a data base required by analysis;
the prediction module 6 is configured to analyze and predict, through the machine learning algorithm, the capability change of the purifying apparatus in the next period by processing the data collected by the collection module 4 and the summarization module 5, where the capability change attribute of the purifying apparatus includes: processing power, energy consumed, and chemicals consumed;
the allocation module 7 is used for carrying out corresponding sewage treatment scheduling according to the predicted data and issuing a wastewater flow allocation instruction to the purification equipment;
the available simulation module 8 is used for calling the processing acquisition module 4 to acquire the processed water body and the data of the purifying equipment after the purifying period is finished, evaluating the performance of the current purifying equipment and the availability of the purifying capacity of the current purifying equipment, and outputting the actual purifying data of the purifying equipment;
a comparison module 9 for comparing the predicted data with the actual purified data to determine whether the deviation value exceeds a preset threshold;
the tracing judgment module 10 is configured to trace and trace the abnormal situation when the deviation value of the predicted data and the actual purified data exceeds a preset threshold value, and analyze and identify the cause of the problem;
the operation flow of the traceability judgment module 10 comprises the following steps:
step 1, control module 1: comparing and analyzing the actual purifying data and the predicted data collected in the system to detect whether an abnormal condition exists, wherein the abnormal condition comprises the following steps: improper adjustment of operation parameters, reduced processing capacity, unqualified water quality, equipment failure, abnormal external environment and abnormal collected data sources;
step 1, verification module 2: once an abnormality is detected, starting to trace back data of the abnormality, and tracking operation records, parameter setting, types of chemicals and quantity data of the purifying equipment;
step 1, an environment acquisition module 3: and (3) carrying out root cause analysis on the basis of the data backtracking of the verification module 2 in the step (1) so as to determine the specific cause of the problem.
The management and control module 1 is in interactive connection with the verification module 2 through a wireless network, the verification module 2 is in interactive connection with the environment acquisition module 3 and the processing acquisition module 4 through the wireless network, the summarizing module 5 is in interactive connection with the environment acquisition module 3, the processing acquisition module 4 and the prediction module 6 through the wireless network, the prediction module 6 is in interactive connection with the allocation module 7 through the wireless network, the allocation module 7 is in interactive connection with the available simulation module 8 through the wireless network, the available simulation module 8 is in interactive connection with the comparison module 9 through the wireless network, the comparison module 9 is in interactive connection with the tracing judgment module 10 through the wireless network, the tracing judgment module 10 is in interactive connection with the correction module 11 through the wireless network, and the correction module 11 is in interactive connection with the management and control module 1 through the wireless network.
When the method is specifically implemented, sewage treatment can be reasonably scheduled according to predicted purification capacity fluctuation, so that treatment efficiency is improved, the system can be flexibly adjusted according to actual conditions, the maximum utilization rate of the purification equipment is ensured, weather data in the next purification period are acquired through the environment acquisition module 3, the running effect of the purification equipment can be influenced by the data, external factors such as weather are considered, and the system can be adjusted according to the actual conditions so as to adapt to different environmental conditions and improve treatment effect.
Example 2
The embodiment also provides a prediction process, which comprises the following steps:
step 61: acquiring historical wastewater treatment data, current environmental weather, temperature, humidity, wind speed, real-time purification equipment operation parameters and textile production progress data through a treatment acquisition module 4 and a summarization module 5;
step 62: cleaning the collected data, removing abnormal data, filling the missing value, and integrating the data from different sources;
step 63: selecting and extracting characteristics aiming at purifying equipment, environmental conditions and pollution source types to serve as input variables of a prediction model;
step 64: selecting a machine learning prediction model according to the data characteristics, and training the selected model by using historical data to establish the prediction model;
step 65: the trained model predicts and analyzes various parameters in a future wastewater treatment period, obtains the wastewater treatment capacity, the required chemical addition amount, the energy consumption condition and the generated waste amount in the next period, outputs the analyzed result, presents the analyzed result in a numerical value or chart form, and provides an expected value for the capacity change of the purifying equipment;
the machine learning prediction model trains a plurality of individual learners through integrated learning, combines the individual learners through a combination strategy, uses a serialization method generated in series, calculates the weight value of each sample, then enters the next iteration, after a learning algorithm is appointed, T times of training are carried out to obtain a appointed number of weak learners h, at the moment, each learner has corresponding weight, the prediction results of each learner are weighted and summed, and a step function is substituted to obtain a final prediction classification result, wherein the calculation formula is as follows:
wherein F is a classification prediction coefficient, sign (·) is a step function, a is a weight value assigned to the weak learner, h is the weak learner, y is a designated iteration number, and T represents a training number.
In this embodiment, the operation parameters of the current purifying device are obtained through the processing and collecting module 4, and the whole system is monitored and regulated in real time through the management and control module 1, so that the possible problems can be found and solved in time, the stable operation of the system is ensured, the data are analyzed and optimized through the prediction module 6, the comparison module 9 and the tracing judgment module 10, the deviation can be found and the corresponding adjustment can be carried out through the comparison of the prediction data and the actual purifying data, and the tracing judgment module 10 can trace the abnormal cause, thereby being helpful for improving and optimizing the operation setting of the system.
Example 3
In this embodiment, as shown in fig. 1, the traceability judgment module 10 is interactively connected with a correction module 11 through a wireless network, and the correction module 11 is configured to perform, according to a diagnosis result of the traceability judgment module 10, a solution formulation based on a specific cause of the determined problem, where a solution formulation content includes: the equipment parameters are adjusted, equipment faults are repaired, the operation flow is changed, monitoring points are increased, the scheme is fed back to the management and control module 1 and related operators, the management and control module 1 carries out corresponding adjustment according to the feedback, and the operation setting of the problem equipment is correspondingly adjusted.
In summary, the invention aims to reasonably schedule sewage treatment according to the predicted change of the purifying capacity of the purifying equipment, so as to improve the treatment efficiency, the system can be flexibly adjusted to adapt to the actual situation, ensure the maximum utilization rate of the purifying equipment, and the system can consider external factors such as weather and the like by acquiring the weather data in the next purifying period, and adjust according to the actual situation so as to adapt to different environmental conditions and improve the treatment effect;
meanwhile, by acquiring the operation parameters of the current purifying equipment, the system can monitor and regulate the whole system in real time, discover and solve problems in time, ensure the stable operation of the system, and compare predicted data with actual purified data, discover deviation and adjust correspondingly through data analysis and optimization. In addition, the system can also track the cause of the anomaly, helping to improve and optimize the operational settings of the system.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; while the invention has been described in detail with reference to the foregoing embodiments, it will be appreciated by those skilled in the art that variations may be made in the techniques described in the foregoing embodiments, or equivalents may be substituted for elements thereof; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. An environment-friendly and efficient textile printing and dyeing wastewater treatment system, which is characterized by comprising:
the control module (1) is used for coordinating and controlling the global function module and editing and sending control instructions;
the verification module (2) is used for recording the processing capacity and technical parameters of the purification equipment, the current textile production progress and specific pollutants contained in the polluted wastewater;
the environment acquisition module (3) is used for collecting environment data in the future preset time of the current region from the internet end and acquiring temperature, humidity and wind speed data;
the processing acquisition module (4) is used for acquiring real-time operation parameters of the purification equipment in the current period;
the summarizing module (5) is used for integrating the weather data with the operation parameters of the purifying equipment, outputting the weather data into a machine-readable language and taking the machine-readable language as a data base required by analysis;
the prediction module (6) is used for analyzing and predicting the capacity change of the purifying equipment in the next period through a machine learning algorithm by processing the data collected by the acquisition module (4) and the summarization module (5);
the allocation module (7) is used for carrying out corresponding sewage treatment scheduling according to the predicted data and issuing a wastewater flow allocation instruction to the purification equipment;
the available simulation module (8) is used for calling the processing acquisition module (4) to acquire the data of the processed water body and the purification equipment after the purification period is finished, evaluating the performance of the current purification equipment and the availability of the purification capacity of the current purification equipment, and outputting the actual purification data of the purification equipment;
a comparison module (9) for comparing the predicted data with the actual purified data and judging whether the deviation value exceeds a preset threshold value;
and the tracing judgment module (10) is used for tracing and tracing the abnormal condition when the deviation value of the predicted data and the actual purified data exceeds a preset threshold value, and analyzing and distinguishing the cause of the problem.
2. An environmentally friendly and efficient textile printing and dyeing wastewater treatment system according to claim 1, characterized in that the prediction process of the prediction module (6) comprises the following steps:
step 61: acquiring historical wastewater treatment data, current environmental weather, temperature, humidity, wind speed, real-time purification equipment operation parameters and textile production progress data through a treatment acquisition module (4) and a summarizing module (5);
step 62: cleaning the collected data, removing abnormal data, filling the missing value, and integrating the data from different sources;
step 63: selecting and extracting characteristics aiming at purifying equipment, environmental conditions and pollution source types to serve as input variables of a prediction model;
step 64: selecting a machine learning prediction model according to the data characteristics, and training the selected model by using historical data to establish the prediction model;
step 65: the trained model predicts and analyzes various parameters in a future wastewater treatment period, obtains the wastewater treatment capacity, the required chemical addition amount, the energy consumption condition and the generated waste amount in the next period, outputs the analyzed result, presents the analyzed result in the form of a numerical value or a chart, and provides an expected value for the capacity change of the purifying equipment.
3. The system for treating textile printing and dyeing wastewater according to claim 2, wherein the machine learning prediction model in step 64 trains a plurality of individual learners through integrated learning, combines the individual learners through a combination strategy, weights and sums the prediction result of each learner, substitutes a step function to obtain a final prediction classification result, and the calculation formula is as follows:
wherein F is a classification prediction coefficient, sign (·) is a step function, a is a weight value assigned to the weak learner, h is the weak learner, y is a designated iteration number, and T represents a training number.
4. An environmentally friendly and efficient textile printing and dyeing wastewater treatment system according to claim 1, characterized in that the real-time operating parameters in the treatment acquisition module (4) comprise: discharge flow, water pH value, water temperature, water pollutant and purified matter concentration.
5. An environmentally friendly and efficient textile printing and dyeing wastewater treatment system according to claim 1, characterized in that the capability variation attribute of the purification equipment in the prediction module (6) comprises: processing power, energy consumed and chemicals consumed.
6. The environment-friendly and efficient textile printing and dyeing wastewater treatment system according to claim 1, wherein the operation flow of the traceability judgment module (10) comprises the following steps:
step 1, a management and control module (1): comparing and analyzing the actual purifying data and the predicted data collected in the system to detect whether an abnormal condition exists;
step 1, verifying a module (2): once an abnormality is detected, starting to trace back data of the abnormality, and tracking operation records, parameter setting, types of chemicals and quantity data of the purifying equipment;
step 1, an environment acquisition module (3): and (3) carrying out root cause analysis on the basis of the data backtracking of the verification module (2) in the step (1) so as to determine the specific cause of the problem.
7. The environment-friendly and efficient textile printing and dyeing wastewater treatment system according to claim 6, wherein the abnormal condition in the step 1 control module (1) comprises: improper adjustment of operation parameters, reduced processing capacity, unqualified water quality, equipment failure, abnormal external environment and abnormal collected data sources.
8. The environment-friendly and efficient textile printing and dyeing wastewater treatment system according to claim 1, wherein the traceability judgment module (10) is interactively connected with the correction module (11) through a wireless network, the correction module (11) is used for making a scheme based on the specific cause of the determined problem according to the diagnosis result of the traceability judgment module (10), and feeding back the scheme to the management and control module (1) and related operators, and the management and control module (1) can correspondingly adjust the operation setting of the problem equipment according to the feedback.
9. The environment-friendly and efficient textile printing and dyeing wastewater treatment system according to claim 8, wherein the scheme formulation comprises: device parameter adjustment, device fault repair, operation flow change and monitoring point increase.
10. The environment-friendly and efficient textile printing and dyeing wastewater treatment system according to claim 1, wherein the management and control module (1) is in interactive connection with the verification module (2) through a wireless network, the verification module (2) is in interactive connection with the environment acquisition module (3) and the processing acquisition module (4) through the wireless network, the summarizing module (5) is in interactive connection with the environment acquisition module (3), the processing acquisition module (4) and the prediction module (6) through the wireless network, the prediction module (6) is in interactive connection with the allocation module (7) through the wireless network, the allocation module (7) is in interactive connection with the available simulation module (8) through the wireless network, the available simulation module (8) is in interactive connection with the comparison module (9) through the wireless network, the comparison module (9) is in interactive connection with the source judgment module (10) through the wireless network, the tracing judgment module (10) is in interactive connection with the correction module (11) through the wireless network, and the correction module (11) is in interactive connection with the management and control module (1) through the wireless network.
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