Intelligent control method for sewage treatment

Abstract

The invention relates to the field of sewage treatment, in particular to an intelligent control method for sewage treatment, which comprises the following steps of: constructing an intelligent control model by adopting a near-end strategy optimization algorithm, and defining a state space and an action space through the intelligent control model, wherein the state space comprises an inlet pulp concentration parameter, a flow parameter, a dosage parameter, a settling time parameter and an underflow concentration parameter; in a simulation environment, a reward mechanism is introduced into the intelligent control model, different control strategies are continuously tried through interaction with the environment, and an optimal and low-cost pollutant removal strategy is learned; and the trained intelligent control model is applied to sewage treatment, and the intelligent control model is utilized to intelligently adjust the addition amount of the medicament according to the environmental parameters monitored in real time. By means of the mode, the deep reinforcement learning model adapts to environmental changes in real time, intelligently optimizes the dosage of chemicals, reduces chemical waste and reduces cost, meanwhile, accurate control over pollutant removal is achieved, and the sewage treatment effect is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of sewage treatment, and specifically relates to an intelligent control method for sewage treatment. Background Art

[0002] With the rapid development of modern industry, membrane technology has come into being. It combines sewage biological treatment technology with membrane separation technology. First, it uses biochemical technology to degrade organic matter in water, domesticate dominant bacteria and block bacteria, and then uses membrane technology to filter suspended matter and water-soluble macromolecules to reduce water turbidity, thereby meeting emission standards.

[0003] In the current existing technology, sewage is discharged into the sedimentation tank for sedimentation treatment, which is generally divided into a primary sedimentation tank, a secondary sedimentation tank, an anaerobic tank and a sedimentation tank for sewage treatment. When treating sewage in this way, the same standard is used for treatment. However, no matter what treatment method is used, it relies on manual judgment and execution, that is, the addition of chemicals is done manually based on experience. Excessive addition can easily cause waste, and too little addition cannot remove pollutants. The control accuracy is low and the sewage treatment effect is not ideal.

[0004] To this end, the present invention provides an intelligent control method for sewage treatment. Summary of the invention

[0005] In order to make up for the deficiencies of the prior art, at least one technical problem raised in the background technology is solved.

[0006] The technical solution adopted by the present invention to solve its technical problems is: the intelligent control method for sewage treatment described in the present invention is characterized in that: the method comprises the following steps: step S101: adopting the proximal strategy optimization algorithm to construct an intelligent control model, and defining the state space and action space through the intelligent control model, wherein the state space includes slurry concentration parameters, flow parameters, dosage parameters, sedimentation time parameters and underflow concentration parameters, and the action space includes the control behavior of adjusting the dosage of the agent; step S102: in a simulation environment, introducing a reward mechanism for the intelligent control model, and through interaction with the environment, continuously trying different control strategies to learn the optimal and low-cost pollutant removal strategy; step S103: applying the trained intelligent control model to sewage treatment, and using the intelligent control model to intelligently adjust the amount of agent added according to the real-time monitored environmental parameters.

[0007] Furthermore, the step of learning the optimal and low-cost pollutant removal strategy includes: using the intelligent control module to learn and save the sewage case with the lowest pollutant removal strategy, wherein the sewage case has a small amount of added chemicals, low chemical cost and short sedimentation time among the same type.

[0008] Furthermore, the method also includes: step S201: obtaining the most recent successful sewage treatment case through the intelligent control model, and obtaining the treatment parameters of the sewage treatment case, wherein the treatment parameters include sewage inlet concentration, sewage flow parameters, agent dosage information, sedimentation time and underflow concentration; step S202: classifying the sewage treatment case through the intelligent control model and assigning an ID code corresponding to the type, uploading the most recent successful sewage treatment case and its corresponding treatment parameters to the server, and using the intelligent control model to save the ID code corresponding to the sewage treatment case.

[0009] Furthermore, the step of applying the trained intelligent control model to sewage treatment and using the intelligent control model to intelligently adjust the amount of reagent added according to the real-time monitored environmental parameters specifically includes: step S1031: obtaining information about the sewage site that needs to be treated, wherein the sewage site information includes sewage capacity, sewage concentration, sewage underflow concentration, sewage site treatment time, and sewage hazard level; step 1032: inputting the sewage site information into the intelligent control module, and monitoring the parameters of the sewage site that needs to be treated in real time within the next preset time period, and analyzing the polluted particles or main polluting chemicals in the sewage site; step S1033: matching the ID code of the successful sewage treatment case corresponding to the sewage site according to the analyzed results; step S1034: obtaining the treatment parameters of the successful sewage treatment case corresponding to the ID code from the server according to the ID code, and adjusting the amount of reagent added according to the treatment parameters.

[0010] Furthermore, the method also includes: step S301: before treating sewage in the sewage place, connecting the agent adding machine to the intelligent control module; step S302: during the sewage treatment in the sewage place, controlling the amount of the agent output by the agent adding machine through the intelligent control module according to the treatment parameters of the successful sewage treatment case.

[0011] Furthermore, the method also includes: step S401: during the sewage treatment process at the sewage site, real-time detection of the concentration of polluted particles or the concentration of main polluting chemicals in the sewage site, and gradually controlling the amount of the agent output by the agent adding machine to decrease according to the decrease in the concentration of polluted particles or the concentration of main polluting chemicals in the sewage site; step S402: judging whether the concentration of polluted particles or the concentration of main polluting chemicals in the sewage site is lower than a preset concentration; step S403: if it is judged that the concentration of polluted particles or the concentration of main polluting chemicals in the sewage site is lower than a preset concentration, then judging that the sewage treatment of the sewage site is completed, and treating the sewage in the sewage site by sedimentation method.

[0012] The beneficial effects of the present invention are as follows:

[0013] 1. The intelligent control method for sewage treatment disclosed in the present invention includes: using a proximal strategy optimization algorithm to construct an intelligent control model, and defining a state space and an action space through the intelligent control model, wherein the state space includes a slurry concentration parameter, a flow parameter, a dosage parameter, a sedimentation time parameter, and an underflow concentration parameter; in a simulation environment, introducing a reward mechanism to the intelligent control model, and through interaction with the environment, constantly trying different control strategies to learn the optimal and low-cost pollutant removal strategy; applying the trained intelligent control model to sewage treatment, and using the intelligent control model to intelligently adjust the amount of reagent added according to the real-time monitored environmental parameters. In the above manner, the deep reinforcement learning model of the present invention adapts to environmental changes in real time, and uses an intelligent control module to intelligently optimize the amount of reagent added, which can effectively reduce the waste of reagents, reduce costs, and also achieve precise control of pollutant removal, thereby improving the treatment effect of sewage. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] The present invention will be further described below in conjunction with the accompanying drawings.

[0015] Figure 1 It is a schematic flow chart of a first embodiment of the intelligent control method for sewage treatment of the present invention;

[0016] Figure 2 It is a schematic flow chart of a second embodiment of the intelligent control method for sewage treatment of the present invention;

[0017] Figure 3 1 is a flow chart of a third embodiment of the intelligent control method for sewage treatment of the present invention;

[0018] Figure 4 1 is a flow chart of a fourth embodiment of the intelligent control method for sewage treatment of the present invention;

[0019] Figure 5 It is a flow chart of the fifth embodiment of the intelligent control method for sewage treatment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0021] like Figure 1 As shown, the sewage treatment intelligent control method disclosed in the present invention comprises the following steps:

[0022] Step S101: construct an intelligent control model using a proximal strategy optimization algorithm, and define a state space and an action space through the intelligent control model.

[0023] Preferably, the state space includes slurry concentration parameters, flow parameters, dosage parameters, sedimentation time parameters and underflow concentration parameters, and the action space includes control behaviors for adjusting the dosage of the agent.

[0024] Step S102: In the simulation environment, a reward mechanism is introduced into the intelligent control model. By interacting with the environment, different control strategies are continuously tried to learn the optimal and low-cost pollutant removal strategy.

[0025] Preferably, the step of learning the optimal and low-cost pollutant removal strategy in step S102 includes: using the intelligent control module to learn and save the sewage case with the lowest pollutant removal strategy, wherein the sewage case has a small amount of added medicine, low medicine cost and short sedimentation time among the same type. In other words, the intelligent control model only learns cases with low cost and good treatment effect, and does not learn some cases with high cost and poor treatment effect.

[0026] Step S103: Apply the trained intelligent control model to sewage treatment, and use the intelligent control model to intelligently adjust the amount of reagent added according to the real-time monitored environmental parameters.

[0027] Further, such as Figure 2 As shown, the sewage treatment intelligent control method also includes the following steps:

[0028] Step S201: obtaining the most recent successful sewage treatment case through the intelligent control model, and obtaining the treatment parameters of the sewage treatment case.

[0029] Preferably, the treatment parameters include sewage inlet concentration, sewage flow parameters, agent dosing information, sedimentation time and underflow concentration.

[0030] Step S202: Classify the sewage treatment case and assign an ID code corresponding to the type through the intelligent control model, upload the most recent successful sewage treatment case and its corresponding treatment parameters to the server, and use the intelligent control model to save the ID code corresponding to the sewage treatment case.

[0031] It should be understood that in step S202, the relevant parameters of the most recent successful sewage treatment case (such as the dosage of the agent, the auxiliary materials used in the agent, and the detailed steps for using the agent) will be uploaded to the server. The intelligent control model does not save these contents. The intelligent control model only stores the ID code corresponding to the case. When the case needs to be found, you only need to find the ID code from the intelligent control model to match the content of the case (such as the composition of the agent and its control parameters) on the server, and then import these contents to realize intelligent control. In addition, after downloading the relevant content of the case from the server, you need to start the execution instruction so that the intelligent control model will automatically import the relevant content of the case to implement the corresponding execution action.

[0032] That is to say, the intelligent control model will search for cases related to sewage treatment companies on the Internet for study, or go to professional websites to search for sewage treatment cases recorded in relevant papers or other professional documents for study.

[0033] Further, such as Figure 3 As shown, in step S103, the trained intelligent control model is applied to sewage treatment, and the steps of using the intelligent control model to intelligently adjust the amount of the agent added according to the real-time monitored environmental parameters specifically include:

[0034] Step S1031: Obtain information on the sewage site that needs to be treated.

[0035] Preferably, the sewage site information includes sewage capacity, sewage concentration, sewage underflow concentration, sewage site treatment time, and sewage hazard level.

[0036] Step S1032: Input the sewage site information into the intelligent control module, and monitor the parameters of the sewage site to be treated in real time within the next preset time period, and analyze the polluted particles or main polluting chemicals in the sewage site.

[0037] It should be understood that identifying the polluting particles or major polluting chemicals in sewage makes it possible to accurately locate the pollution source, making sewage treatment more efficient.

[0038] Step S1033: According to the parsed result, the ID code of the successful sewage treatment case corresponding to the sewage site is matched.

[0039] Step S1034: According to the ID code, the server is used to obtain the processing parameters of the successful sewage treatment case corresponding to the ID code, and the amount of the reagent added is adjusted according to the processing parameters.

[0040] It should be understood that in addition to obtaining treatment agents corresponding to sewage pollution particles or major polluting chemical substances, treatment agents corresponding to secondary pollutants will also be further obtained to achieve better sewage treatment effects.

[0041] Further, such as Figure 4 As shown, the intelligent control method for sewage treatment also includes:

[0042] Step S301: Before treating sewage in a sewage site, connect the reagent adding machine to the intelligent control module.

[0043] Step S302: During the sewage treatment process at the sewage site, the amount of the agent output by the agent adding machine is controlled by the intelligent control module according to the treatment parameters of the successful sewage treatment case.

[0044] Further, such as Figure 5 As shown, the intelligent control method for sewage treatment also includes:

[0045] Step S401: During sewage treatment at a sewage site, the concentration of polluted particles or the concentration of major polluting chemicals at the sewage site is detected in real time, and the amount of the agent output by the agent adding machine is gradually controlled to decrease according to the decrease in the concentration of polluted particles or the concentration of major polluting chemicals at the sewage site.

[0046] Step S402: determining whether the concentration of polluted particles or the concentration of major polluting chemical substances in the sewage site is lower than a preset concentration;

[0047] Step S403: If it is determined that the concentration of polluted particles or the concentration of major polluting chemicals in the sewage site is lower than the preset concentration, it is determined that the sewage treatment of the sewage site is completed, and the sewage in the sewage site is treated by a sedimentation method.

[0048] It should be understood that if the concentration of polluted particulate matter or the concentration of major polluting chemicals in the sewage site is determined to be not lower than the preset concentration, it is determined that the sewage treatment of the sewage site is not completed and further chemical treatment is required, then the process returns to step S401.

[0049] In addition, in order to further improve the sewage treatment effect, when it is determined that the concentration of polluted particulate matter or the concentration of major polluting chemicals in the sewage site is lower than the preset concentration, the concentration of secondary pollutants is further detected, and it is determined whether the concentration of secondary pollutants is lower than the preset standard value. If so, the sewage treatment of the sewage site is determined to be completed, and the sewage in the sewage site is treated by sedimentation method. After treatment, it can be reused or directly discharged.

[0050] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A sewage treatment intelligent control method, characterized in that: The method comprises the following steps: Step S101: constructing an intelligent control model by using a proximal strategy optimization algorithm, and defining a state space and an action space by using the intelligent control model, wherein the state space includes a slurry concentration parameter, a flow parameter, a dosage parameter, a sedimentation time parameter, and an underflow concentration parameter, and the action space includes a control behavior for adjusting the dosage of the agent; Step S102: In the simulation environment, a reward mechanism is introduced into the intelligent control model, and different control strategies are continuously tried through interaction with the environment to learn the optimal and low-cost pollutant removal strategy; Step S103: Apply the trained intelligent control model to sewage treatment, and use the intelligent control model to intelligently adjust the amount of reagent added according to the real-time monitored environmental parameters.

2. The intelligent control method for sewage treatment according to claim 1, characterized in that: The step of learning the optimal and low-cost pollutant removal strategy includes: using the intelligent control module to learn and save the sewage case with the lowest pollutant removal strategy, wherein the sewage case has a small amount of added chemicals, low chemical cost and short sedimentation time among the same type.

3. The intelligent control method for sewage treatment according to claim 1 is characterized in that: The method further includes: Step S201: obtaining the most recent successful sewage treatment case through the intelligent control model, and obtaining the treatment parameters of the sewage treatment case, wherein the treatment parameters include sewage inlet concentration, sewage flow parameters, information on the dosage of chemicals, sedimentation time and underflow concentration; Step S202: Classify the sewage treatment case and assign an ID code corresponding to the type through the intelligent control model, upload the most recent successful sewage treatment case and its corresponding treatment parameters to the server, and use the intelligent control model to save the ID code corresponding to the sewage treatment case.

4. The intelligent control method for sewage treatment according to claim 3 is characterized in that: The step of applying the trained intelligent control model to sewage treatment and using the intelligent control model to intelligently adjust the amount of the added agent according to the real-time monitored environmental parameters specifically includes: Step S1031: Obtaining information about sewage sites that need to be treated, wherein the sewage site information includes sewage capacity, sewage concentration, sewage underflow concentration, sewage site treatment time, and sewage hazard level; Step S1032: inputting the sewage site information into the intelligent control module, and monitoring the parameters of the sewage site to be treated in real time within the next preset time period, and analyzing the polluted particles or main polluting chemicals in the sewage site; Step S1033: According to the parsed result, the ID code of the successful sewage treatment case corresponding to the sewage site is matched; Step S1034: According to the ID code, the server is used to obtain the processing parameters of the successful sewage treatment case corresponding to the ID code, and the amount of the reagent added is adjusted according to the processing parameters.

5. The intelligent control method for sewage treatment according to claim 4 is characterized in that: The method further includes: Step S301: before treating the sewage in the sewage site, connecting the reagent adding machine to the intelligent control module; Step S302: During the sewage treatment process at the sewage site, the amount of the agent output by the agent adding machine is controlled by the intelligent control module according to the treatment parameters of a successful sewage treatment case.

6. The intelligent control method for sewage treatment according to claim 5 is characterized in that: The method further includes: Step S401: during the sewage treatment process of the sewage site, the concentration of polluted particles or the concentration of main polluting chemical substances in the sewage site is detected in real time, and the amount of the agent output by the agent adding machine is gradually controlled to decrease according to the decrease in the concentration of polluted particles or the concentration of main polluting chemical substances in the sewage site; Step S402: determining whether the concentration of polluted particles or the concentration of major polluting chemical substances in the sewage site is lower than a preset concentration; Step S403: If it is determined that the concentration of polluted particles or the concentration of major polluting chemicals in the sewage site is lower than a preset concentration, it is determined that the sewage treatment of the sewage site is completed, and the sewage in the sewage site is treated by a sedimentation method.

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