CN114997064A - Intelligent control model for high-toxicity sewage dosing treatment and algorithm thereof - Google Patents
Intelligent control model for high-toxicity sewage dosing treatment and algorithm thereof Download PDFInfo
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Abstract
The invention discloses an intelligent control model for high-toxicity sewage dosing treatment and an algorithm thereof, which are used for acquiring data of dosing equipment for high-toxicity sewage, acquiring a dosage parameter of a medicament for sewage treatment, a water inlet pressure parameter and a water outlet pressure parameter in real time, cleaning the data in a background central data storage server to obtain a training set, training a deep learning algorithm in the background central data storage server by using the training set to obtain an output dosing control model for the dosing quantity of the high-toxicity sewage, predicting and adjusting the addition of the medicament dosage in a sewage treatment field by using deep learning during use, automatically adjusting, effectively overcoming the defect that the traditional manual control mode needs to depend on the experience of sewage treatment technicians, and simultaneously uploading the background central data storage server to perform deep learning on the original actual dosing parameters and the actual dosing parameters of a plurality of prediction models to obtain the intelligent control model for the final dosing sewage treatment, the control precision of the dosage is also improved.
Description
Technical Field
The invention relates to the technical field of intelligent control of sewage treatment, in particular to an intelligent control model for dosing treatment of high-toxicity sewage and an algorithm thereof.
Background
At present, the rapid development of economic society in China, the acceleration of industrial process and the continuous aggravation of the pollution of industrial production to water environment are realized, and particularly, highly toxic sewage containing toxic elements has extremely serious influence on human beings, animals, plants and even the whole ecological system.
Therefore, the treatment of high-toxicity sewage is an important research direction at present, and the chemical adding treatment is needed in the process of treating the high-toxicity sewage, and the traditional high-toxicity sewage treatment chemical adding quantity controller is limited by hardware parameters, so that the controller is difficult to treat a large amount of complex high-toxicity sewage treatment field real-time data, and the problems of poor control effect, low precision, large chemical loss, high sewage treatment cost and the like of the chemical adding quantity for treating the high-toxicity sewage are caused.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides an intelligent control model for dosing treatment of high-toxicity sewage and an algorithm thereof.
The technical scheme of the invention is as follows:
the invention provides an intelligent control model and an algorithm thereof for dosing treatment of high-toxicity sewage, comprising the following steps: step S1: establishing a dosing control model of high-toxicity sewage dosing equipment;
the specific implementation manner of step S1 is as follows:
step S11: data acquisition is carried out on high-toxicity sewage dosing equipment, and a dosage parameter, a water inlet pressure parameter and a water outlet pressure parameter of a medicament for sewage treatment are acquired in real time;
s12: sending the dosing parameter, the water inlet parameter and the water outlet parameter to a background central data storage server and carrying out data cleaning in the background central data storage server to obtain a training set;
s13: training a deep learning algorithm in a background central data storage server by using the training set to obtain a first dosage prediction model for high-toxicity sewage;
step S2: and solving the solution of the first-time dosing quantity prediction model of the high-toxicity sewage by adopting an algorithm.
Preferably, the dosage parameters of the medicament for sewage treatment comprise medicament type, medicament concentration and medicament discharge time.
Preferably, in step S1, the training set is used to train a deep learning algorithm in the background central data storage server to obtain a first time dosing prediction model SL of highly toxic wastewater pn ＝A pn AY pn 3 +B pn AY pn 2 +C pn AY pn (ii) a Wherein Pn is a water inlet pressure parameter; (vi) a Vrefpn effluent pressure parameter; AYPn is the dosage parameter of the sewage treatment medicament; SLPn is a controller control amount.
Preferably, the method further comprises step S3: storing the first-time dosing quantity prediction model of the high-toxicity sewage into a dosing equipment PLC controller positioned on a sewage treatment site, so that the PLC controller calculates a sewage treatment reagent and the dosage thereof according to a water inlet pressure parameter and a water outlet pressure parameter, and controls the pipeline flow of a dosing device according to a PID model calculation result to complete dosing;
step S4: the dosing equipment sends the dosage parameter, the water inlet pressure parameter and the water outlet pressure parameter of the sewage treatment agent which is dosed each time to a background central data storage server through a network, and the data is the original actual dosing parameter;
step S5: and (5) training the original actual dosing parameters of the dosing equipment uploaded to the background central data storage server by using the training set in the S12 through a deep learning algorithm to obtain a dosing quantity prediction model for predicting the dosing quantity of the sewage treatment.
Preferably, the method further comprises step S6: transmitting a dosing quantity prediction model of the sewage treatment dosing quantity calculated by the deep learning algorithm back to PLC controllers of other dosing equipment on a sewage treatment site, so that the dosing quantity value calculated by the dosing quantity prediction model is used as a control parameter for dosing each time, and the dosing equipment transmits a sewage treatment dosing quantity parameter, a water inlet pressure parameter and a water outlet pressure parameter for dosing each time to a background central data storage server through a network, wherein the data are actual dosing parameters of the prediction model;
step S7: and the background central data storage server performs deep learning algorithm training on one original actual dosing parameter and the multiple prediction model actual dosing parameters to obtain an intelligent control model finally used for dosing treatment of high-toxicity sewage.
The invention achieves the following beneficial effects: through the process from the step S1 to the step S7, the dosage of the chemicals on the sewage treatment site can be predicted and adjusted by deep learning, automatic adjustment is carried out, the problem that the traditional manual control mode needs to depend on the experience of sewage treatment technicians is effectively overcome, meanwhile, the background central data storage server is uploaded to carry out deep learning on the original actual dosing parameters and the actual dosing parameters of the multiple prediction models to obtain the intelligent control model finally used for dosing treatment of high-toxicity sewage, and the dosage control precision is also improved.
Drawings
FIG. 1 is a block diagram of the present invention.
Detailed Description
To facilitate an understanding of the present invention by those skilled in the art, specific embodiments thereof are described below with reference to the accompanying drawings.
As shown in FIG. 1, the invention provides an intelligent control model and its algorithm for dosing treatment of highly toxic sewage,
in the present embodiment, it is preferred that,
step S1: establishing a dosing control model of high-toxicity sewage dosing equipment;
the specific implementation manner of step S1 is as follows:
step S11: data acquisition is carried out on high-toxicity sewage dosing equipment, and a dosage parameter, a water inlet pressure parameter and a water outlet pressure parameter of a medicament for sewage treatment are acquired in real time;
s12: sending the dosing parameter, the water inlet parameter and the water outlet parameter to a background central data storage server and carrying out data cleaning in the background central data storage server to obtain a training set;
s13: training a deep learning algorithm in a background central data storage server using the training set to obtain a second for high toxicity sewage dosingOne-time dosing quantity prediction model SL pn ＝A pn AY pn 3 +B pn AY pn 2 +C pn AY pn (ii) a Wherein Pn is a water inlet pressure parameter; (vi) a Vrefpn effluent pressure parameter; AYPn is the dosage parameter of the sewage treatment medicament; SLPn is the controller control quantity;
step S2: solving the optimal solution of the first-time dosing amount prediction model of the high-toxicity sewage by adopting an algorithm;
step S3: storing the first dosing quantity prediction model into a dosing equipment PLC (programmable logic controller) positioned on a sewage treatment site, so that the PLC calculates a sewage treatment medicament and the dosage thereof according to a water inlet pressure parameter and a water outlet pressure parameter, and controls the flow of a dosing device pipeline according to a PID (proportion integration differentiation) model calculation result to complete dosing;
step S4: the dosing equipment sends the dosage parameter, the water inlet pressure parameter and the water outlet pressure parameter of the sewage treatment agent which is dosed each time to a background central data storage server through a network, and the data is the original actual dosing parameter;
step S5: training original actual dosing parameters of dosing equipment uploaded to the background central data storage server by using the training set in S12 through a deep learning algorithm to obtain a dosing quantity prediction model for predicting the dosing quantity of sewage treatment;
step S6: transmitting a dosing quantity prediction model of the sewage treatment dosing quantity calculated by the deep learning algorithm back to PLC controllers of other dosing equipment on a sewage treatment site, so that the dosing quantity value calculated by the dosing quantity prediction model is used as a control parameter for dosing each time, and the dosing equipment transmits a sewage treatment dosing quantity parameter, a water inlet pressure parameter and a water outlet pressure parameter for dosing each time to a background central data storage server through a network, wherein the data are actual dosing parameters of the prediction model;
step S7: and the background central data storage server performs deep learning algorithm training on one original actual dosing parameter and the multiple prediction model actual dosing parameters to obtain an intelligent control model finally used for dosing treatment of high-toxicity sewage.
When the intelligent control model is used, through the processes from the step S1 to the step S7, the dosage of the chemicals on the sewage treatment site can be predicted and adjusted by deep learning, automatic adjustment is achieved, the problem that the traditional manual control mode needs to depend on the experience of sewage treatment technicians is effectively overcome, meanwhile, the background central data storage server is uploaded to perform deep learning on the original actual dosing parameters and the actual dosing parameters of the multiple prediction models to obtain the intelligent control model finally used for dosing treatment of high-toxicity sewage, and the dosage control precision is also improved.
The above-described embodiments of the present invention do not limit the scope of the present invention. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (5)
1. An intelligent control model and algorithm for high toxicity sewage adds medicine and handles, its characterized in that includes:
step S1: establishing a dosing control model of high-toxicity sewage dosing equipment;
the specific implementation manner of step S1 is as follows:
step S11: data acquisition is carried out on high-toxicity sewage dosing equipment, and a dosage parameter, a water inlet pressure parameter and a water outlet pressure parameter of a medicament for sewage treatment are acquired in real time;
s12: sending the dosing parameter, the water inlet parameter and the water outlet parameter to a background central data storage server and carrying out data cleaning in the background central data storage server to obtain a training set;
s13: training a deep learning algorithm in a background central data storage server by using the training set to obtain a first drug adding amount prediction model for the drug adding amount of the high-toxicity sewage;
step S2: and solving the solution of the first-time dosing quantity prediction model of the high-toxicity sewage by adopting an algorithm.
2. The intelligent control model and algorithm for dosing treatment of highly toxic sewage according to claim 1, wherein the dosage parameters of the chemicals for sewage treatment include chemical type, chemical concentration and chemical discharge time.
3. The intelligent control model and algorithm for drug-adding treatment of high-toxicity sewage according to claim 1, wherein the training set is used in step S1 to train the deep learning algorithm in the background central data storage server to obtain the first time dosing prediction model SL for high-toxicity sewage pn ＝A pn AY pn 3 +B pn AY pn 2 +C pn AY pn (ii) a Wherein Pn is a water inlet pressure parameter; (vi) a Vrefpn effluent pressure parameter; AYPn is the dosage parameter of the sewage treatment medicament; SLPn is a controller control amount.
4. The intelligent control model and the algorithm thereof for dosing treatment of highly toxic sewage according to claim 1, further comprising step S3: storing the first-time dosing quantity prediction model of the high-toxicity sewage into a dosing equipment PLC controller positioned on a sewage treatment site, so that the PLC controller calculates a sewage treatment reagent and the dosage thereof according to a water inlet pressure parameter and a water outlet pressure parameter, and controls the pipeline flow of a dosing device according to a PID model calculation result to complete dosing;
step S4: the dosing equipment sends the dosage parameter, the water inlet pressure parameter and the water outlet pressure parameter of the sewage treatment agent which is dosed each time to a background central data storage server through a network, and the data is the original actual dosing parameter;
step S5: and (5) training the original actual dosing parameters of the dosing equipment uploaded to the background central data storage server by using the training set in the S12 through a deep learning algorithm to obtain a dosing quantity prediction model for predicting the dosing quantity of the sewage treatment.
5. The intelligent control model and the algorithm thereof for dosing treatment of highly toxic sewage according to claim 4, further comprising step S6: transmitting a dosing quantity prediction model of the sewage treatment dosing quantity calculated by the deep learning algorithm back to PLC controllers of other dosing equipment on a sewage treatment site, so that the dosing quantity value calculated by the dosing quantity prediction model is used as a control parameter for dosing each time, and the dosing equipment transmits a sewage treatment dosing quantity parameter, a water inlet pressure parameter and a water outlet pressure parameter for dosing each time to a background central data storage server through a network, wherein the data are actual dosing parameters of the prediction model;
step S7: and the background central data storage server performs deep learning algorithm training on one original actual dosing parameter and the multiple prediction model actual dosing parameters to obtain an intelligent control model finally used for dosing treatment of high-toxicity sewage.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117291404A (en) * | 2023-11-24 | 2023-12-26 | 今创集团股份有限公司 | Centralized management system and method for toilet wastewater of high-speed railway passenger train |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117291404A (en) * | 2023-11-24 | 2023-12-26 | 今创集团股份有限公司 | Centralized management system and method for toilet wastewater of high-speed railway passenger train |
| CN117291404B (en) * | 2023-11-24 | 2024-01-30 | 今创集团股份有限公司 | Centralized management system and method for toilet wastewater of high-speed railway passenger train |
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