CN112759048A - PAC solution intelligent feeding control method - Google Patents
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- CN112759048A CN112759048A CN202011516332.7A CN202011516332A CN112759048A CN 112759048 A CN112759048 A CN 112759048A CN 202011516332 A CN202011516332 A CN 202011516332A CN 112759048 A CN112759048 A CN 112759048A
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- 238000000034 method Methods 0.000 title claims abstract description 25
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 132
- 230000016615 flocculation Effects 0.000 claims abstract description 61
- 238000005189 flocculation Methods 0.000 claims abstract description 61
- 238000012417 linear regression Methods 0.000 claims abstract description 23
- 238000007781 pre-processing Methods 0.000 claims abstract description 6
- 150000001491 aromatic compounds Chemical class 0.000 claims description 4
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 239000000126 substance Substances 0.000 description 4
- 230000008901 benefit Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 3
- 238000012806 monitoring device Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000007405 data analysis Methods 0.000 description 2
- 238000001514 detection method Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 description 1
- 230000001580 bacterial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
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- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000012535 impurity Substances 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 244000005700 microbiome Species 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
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- 239000005416 organic matter Substances 0.000 description 1
- 239000007800 oxidant agent Substances 0.000 description 1
- 230000008092 positive effect Effects 0.000 description 1
- 239000012629 purifying agent Substances 0.000 description 1
- 239000002994 raw material Substances 0.000 description 1
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/52—Treatment of water, waste water, or sewage by flocculation or precipitation of suspended impurities
- C02F1/5209—Regulation methods for flocculation or precipitation
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/52—Treatment of water, waste water, or sewage by flocculation or precipitation of suspended impurities
- C02F1/5236—Treatment of water, waste water, or sewage by flocculation or precipitation of suspended impurities using inorganic agents
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2209/00—Controlling or monitoring parameters in water treatment
- C02F2209/40—Liquid flow rate
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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Abstract
The invention relates to a PAC solution intelligent feeding control method, which comprises the step of obtaining the water inlet flow Q of a flocculation tank1Presetting the addition Q of PAC solution according to the objective function2(ii) a Collecting water quality data in a flocculation tank in a preset period, and preprocessing the collected water quality data to obtain preprocessed data; establishing an influent flow Q for the flocculation basin based on the pretreatment data1And amount Q of PAC solution added2The partial regression coefficient theta of the multiple linear regression model is calculated1And coefficient of reaction theta2Obtaining a polynomial regression relational expression; collecting water quality data in the flocculation tank in the next preset period, and calculating the addition Q of the PAC solution in the next preset period according to the polynomial regression relation2(ii) a Generating a PAC flow change curve to realize the addition of the PAC solution. The invention realizes the implementation of the PAC solution by constructing a multiple linear regression modelTimely and accurate feeding.
Description
Technical Field
The invention relates to the technical field of water supply control, in particular to a PAC solution intelligent feeding control method.
Background
The water supply safety of residents is a big matter related to the county of China, the water quality safety of factory water reaches the standard and is the most important part, and the guarantee of the water quality safety of water supply is the key point of research of expert scholars and water supply equipment companies in various water departments and industries in recent years. The water quality refers to the quality of all water bodies, the sum of types and quantities of all substances in the water bodies except water molecules can obtain various characteristics and components of the water bodies from the water quality, for example, physical characteristics such as color, turbidity and taste of water can be seen, and in addition, the water quality also comprises inorganic substances, organic substance content, various bacterial microorganisms and the like.
In order to ensure the safety of the effluent quality of a water plant, water supply companies generally adopt an extensive and relatively fixed conventional treatment mode aiming at different types of water quality, and under the condition of lacking a treatment measure combining water source quality and an intelligent control technology, impurities and harmful substances in water are usually eliminated by adding a large amount of water purifying agents and oxidizing agents. This kind of artificial and rely on empirical formula construction methods, lead to having wasted a large amount of materials and energy consumption, not only reduce economic benefits, the quality of water risk management and control rises moreover, causes extra burden for the environment simultaneously.
With the continuous development of the internet technology, especially the deep research and application of the intelligent control algorithm and the big data analysis technology, the accurate delivery of the PAC solution is realized on the basis of mastering the water quality of a water source by means of the technical means such as the intelligent algorithm and the big data analysis.
Disclosure of Invention
The invention aims to solve the technical problem of providing an intelligent PAC solution adding control method, which can realize real-time and accurate PAC solution adding.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for controlling intelligent addition of the PAC solution comprises the following steps:
step (1): obtaining the water inlet flow Q of the flocculation tank1Presetting the addition Q of PAC solution according to the objective function2；
Step (2): collecting water quality data in a flocculation tank in a preset period, and preprocessing the collected water quality data to obtain preprocessed data;
and (3): establishing an influent flow Q for the flocculation basin based on the pretreatment data1And amount Q of PAC solution added2The partial regression coefficient theta of the multiple linear regression model is calculated1And coefficient of reaction theta2Obtaining a polynomial regression relational expression;
and (4): collecting water quality data in the flocculation tank in the next preset period, and calculating the addition Q of the PAC solution in the next preset period according to the polynomial regression relation2；
And (5): and generating a PAC flow change curve with time as an independent variable to realize the addition of the PAC solution.
The objective function in the step (1) is specifically as follows: COD, turbidity and UV of the water in the flocculation basin by adding minimal PAC solution254The content of (a) falls within a preset range.
Preprocessing the acquired data in the step (2) to obtain preprocessed data, which specifically comprises the following steps: and deleting abnormal values and supplementing zero points for the acquired data to obtain preprocessed data.
The addition amount Q of PAC solution in the step (3)2The formula of (1) is:
wherein F (,) is a multiple linear regression model, Q2The addition amount of the PAC solution is the addition amount of the PAC solution; q1The water inflow of the flocculation tank; theta1At a flow rate Q2The amount of feed of (1) and1partial regression coefficient of water inflow of (a);
is a random error.
The formula of the constraint condition of the multiple linear regression model in the step (3) is as follows:
wherein c is the removal rate of organic matters in the flocculation tank and
C1for COD in water at the water inlet of the flocculation tankAmount, C2COD content in water at the water outlet of the flocculation tank; m is turbidity removal rate in the flocculation tank and
M1is turbidity in water at the water inlet of the flocculation tank, M2Is turbidity in water quality at the water outlet of the flocculation tank; u is the aromatic compound removal rate in the flocculation tank and
U1is UV in water quality of the water inlet254Content, U2Is UV in water quality of the water outlet254Content (c);
values representing c, m and u respectively tend to 1; theta2Is the reaction coefficient of the multiple linear regression model.
The step (3) calculates a partial regression coefficient theta of the multiple linear regression model by a polynomial fitting method1And coefficient of reaction theta2。
The step (4) further comprises: respectively calculating the COD content C of the water outlet of the flocculation tank by introducing a grey correlation analysis algorithm2Turbidity M2And UV254Content U2Amount Q added to PAC solution2The degree of correlation of (a) is optimized for the multiple linear regression model, and the formula is as follows:
wherein λ iscIs the COD content C of the water outlet of the flocculation tank2Amount Q added to PAC solution2Degree of association of (A)mIs turbidity M of the water outlet of the flocculation tank2Amount Q added to PAC solution2Degree of association of (A)uIs UV of the water outlet of the flocculation tank254Content U2Amount Q added to PAC solution2Degree of association of (C)eFor a set outlet water quality COD parameter, MeSet outlet water turbidity parameter, UeFor a set outlet water quality UV254Parameter, Q2The addition amount of the PAC solution is the addition amount of the PAC solution; q1The water inflow of the flocculation tank; theta1At a flow rate Q2The amount of feed of (1) and1partial regression coefficient of water inflow.
Advantageous effects
Due to the adoption of the technical scheme, compared with the prior art, the invention has the following advantages and positive effects: according to the method, intelligent control over PAC solution addition is realized by constructing a multiple linear regression model, raw materials can be effectively saved under the condition that the effluent quality is ensured to reach the standard regulation, the energy-saving target is reached, and the economic benefit is improved; according to the method, the correlation degree of the water quality parameters and the addition amount of the PAC solution can be obtained by introducing a grey correlation degree analysis algorithm, so that the optimization of a multiple regression relation model is realized, and the accurate and intelligent addition of the PAC solution is further realized.
Drawings
FIG. 1 is a schematic diagram of water treatment in a water plant according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of an embodiment of the present invention.
Detailed Description
The invention will be further illustrated with reference to the following specific examples. It should be understood that these examples are for illustrative purposes only and are not intended to limit the scope of the present invention. Further, it should be understood that various changes or modifications of the present invention may be made by those skilled in the art after reading the teaching of the present invention, and such equivalents may fall within the scope of the present invention as defined in the appended claims.
The embodiment of the invention relates to a PAC solution intelligent feeding control method, wherein a PAC (polyaluminium chloride) solution adding process of a water plant can be regarded as a multiple linear regression model, and various types of constraint conditions exist, as shown in figures 1 and 2, the method specifically comprises the following steps:
step (1): a water source water quality monitoring device is added in a water source area, the water quality of the water source is divided into 5 types, the best type 1 is, the worst type 5 is, PAC solutions with different concentrations are required to be prepared according to different water quality types, and the concentration range is 5% -30%.
Obtaining the water inlet flow Q of the flocculation tank1Presetting the addition Q of PAC solution according to the objective function2。
The objective function in the step (1) is specifically as follows: COD, turbidity and UV of the water in the flocculation basin by adding minimal PAC solution254The content of (a) is reduced to within a preset range, thereby achieving the aims of saving materials and energy.
Step (2): detection devices such as flow and water quality monitors are added in a water intake pipeline of a water plant, a PAC solution adding pipeline is additionally provided with a monitoring device, water quality detection devices are respectively added at water outlets of a flocculation tank, and the installation positions of the monitoring devices are shown in figure 1.
The method comprises the steps of collecting water quality data in a flocculation tank within a preset period, wherein the collection frequency is 1Hz, the collection time is 30s, and preprocessing the collected water quality data, namely deleting abnormal values and supplementing zero points of the collected water quality data to obtain preprocessed data, so that the preprocessed data are obtained.
And (3): establishing an influent flow Q for the flocculation basin based on the pretreatment data1And amount Q of PAC solution added2The partial regression coefficient theta of the multiple linear regression model is calculated by a polynomial fitting method1And coefficient of reaction theta2And obtaining a polynomial regression relational expression.
The addition amount Q of PAC solution in the step (3)2The formula of (1) is:
wherein F (,) is a model of a multiple linear regression relationship, Q2The addition amount of the PAC solution is the addition amount of the PAC solution; q1The water inflow of the flocculation tank; theta1At a flow rate Q2The amount of feed of (1) and1partial regression coefficient of water inflow of (a);
random errors are negligible.
Assuming that the environment of the flocculation tank is a closed space and is not influenced by environmental factors, and the water quantity of the flocculation tank before the whole method is started is 0, the formula of the constraint condition of the multiple linear regression model in the step (3) is as follows:
wherein c is the removal rate of organic matters in the flocculation tank and
C1is the COD content, C, in the water at the water inlet of the flocculation tank2COD content in water at the water outlet of the flocculation tank; m is turbidity removal rate in the flocculation tank and
M1is turbidity in water at the water inlet of the flocculation tank, M2Is turbidity in water quality at the water outlet of the flocculation tank; u is the aromatic compound removal rate in the flocculation tank and
U1is UV in water quality of the water inlet254Content, U2Is UV in water quality of the water outlet254Content (c);
values representing c, m and u respectively tend to 1; theta2Is the reaction coefficient of the multiple linear regression model.
The constraint condition of the multiple linear regression model shows that the flow rate Q is used2The amount of feed of (1) and1the water inflow of (3) can maximize the removal rate of organic matter, the removal rate of turbidity, and the removal rate of aromatic compounds.
And (4): collecting water quality data in the flocculation tank in the next preset period, and calculating the addition Q of PAC solution in the next period according to the polynomial regression relation2。
To ensure the addition amount Q of PAC solution2The step (4) further comprises:respectively calculating the COD content C of the water outlet of the flocculation tank by introducing a grey correlation analysis algorithm2Turbidity M2And UV254Content U2Amount Q added to PAC solution2The degree of correlation of (a) is optimized for the multiple linear regression model, and the formula is as follows:
wherein λ iscIs the COD content C of the water outlet of the flocculation tank2Amount Q added to PAC solution2Degree of association of (A)mIs turbidity M of the water outlet of the flocculation tank2Amount Q added to PAC solution2Degree of association of (A)uIs UV of the water outlet of the flocculation tank254Content U2Amount Q added to PAC solution2Degree of association of (C)eFor a set outlet water quality COD parameter, MeSet outlet water turbidity parameter, UeFor a set outlet water quality UV254Parameter, Q2The addition amount of the PAC solution is the addition amount of the PAC solution; q1The water inflow of the flocculation tank; theta1At a flow rate Q2The amount of feed of (1) and1partial regression coefficient of water inflow.
And (5): generating a PAC flow change curve taking time as an independent variable, issuing an instruction to PAC solution adding equipment according to the PAC flow change curve, and completing the addition of the PAC solution according to the curve so as to realize the real-time and accurate control of the addition of the PAC solution.
Therefore, the intelligent control of PAC solution adding is realized by constructing a multiple linear regression model, and the energy-saving target is achieved under the condition that the effluent quality is ensured to reach the standard regulation; according to the method, the correlation degree of the water quality parameters and the addition amount of the PAC solution can be obtained by introducing a grey correlation degree analysis algorithm, so that the optimization of a multiple regression relation model is realized, and the accurate addition of the PAC solution is further realized.
Claims (7)
1. A PAC solution intelligent feeding control method is characterized by comprising the following steps:
step (1): obtaining the water inlet flow Q of the flocculation tank1Presetting the addition Q of PAC solution according to the objective function2；
Step (2): collecting water quality data in a flocculation tank in a preset period, and preprocessing the collected water quality data to obtain preprocessed data;
and (3): establishing an influent flow Q for the flocculation basin based on the pretreatment data1And amount Q of PAC solution added2The partial regression coefficient theta of the multiple linear regression model is calculated1And coefficient of reaction theta2Obtaining a polynomial regression relational expression;
and (4): collecting water quality data in the flocculation tank in the next preset period, and calculating the addition Q of the PAC solution in the next preset period according to the polynomial regression relation2；
And (5): and generating a PAC flow change curve with time as an independent variable to realize the addition of the PAC solution.
2. The PAC solution intelligent dosing control method of claim 1, wherein the objective function in step (1) is specifically: COD, turbidity and UV of the water in the flocculation basin by adding minimal PAC solution254The content of (a) falls within a preset range.
3. The intelligent PAC solution dosing control method according to claim 1, wherein the acquired data is preprocessed in step (2) to obtain preprocessed data, and the preprocessing data specifically comprises: and deleting abnormal values and supplementing zero points for the acquired data to obtain preprocessed data.
4. The intelligent PAC solution adding control method according to claim 1, wherein the adding amount Q of the PAC solution in the step (3)2The formula of (1) is: q2＝F(Q1,θ1) + θ, where F (,) is a multiple linear regression model, Q2The addition amount of the PAC solution is the addition amount of the PAC solution; q1The water inflow of the flocculation tank; theta1At a flow rate Q2The amount of feed of (1) and1partial regression coefficient of water inflow of (a); θ is the random error.
5. The PAC solution intelligent dosing control method according to claim 4, wherein the formula of the constraint condition of the multiple linear regression model in step (3) is as follows:
wherein c is the removal rate of organic matters in the flocculation tank and
C1is the COD content, C, in the water at the water inlet of the flocculation tank2COD content in water at the water outlet of the flocculation tank; m is turbidity removal rate in the flocculation tank and
M1is turbidity in water at the water inlet of the flocculation tank, M2Is turbidity in water quality at the water outlet of the flocculation tank; u is the aromatic compound removal rate in the flocculation tank and
U1is UV in water quality of the water inlet254Content, U2Is UV in water quality of the water outlet254Content (c);
values representing c, m and u respectively tend to 1; theta2Is the reaction coefficient of the multiple linear regression model.
6. The PAC solution smart dosing control method of claim 1, wherein the step (3) calculates the partial regression coefficient θ of the multiple linear regression model by a polynomial fitting method1And coefficient of reaction theta2。
7. The PAC solution smart dosing control method of claim 1, wherein the step (4) further comprises: respectively calculating the COD content C of the water outlet of the flocculation tank by introducing a grey correlation analysis algorithm2Turbidity M2And UV254Content U2Amount Q added to PAC solution2The degree of correlation of (a) is optimized for the multiple linear regression model, and the formula is as follows:
wherein λ iscIs the COD content C of the water outlet of the flocculation tank2Amount Q added to PAC solution2Degree of association of (A)mIs turbidity M of the water outlet of the flocculation tank2Amount Q added to PAC solution2Degree of association of (A)uIs UV of the water outlet of the flocculation tank254Content U2Amount Q added to PAC solution2Degree of association of (C)eFor a set outlet water quality COD parameter, MeSet outlet water turbidity parameter, UeFor a set outlet water quality UV254Parameter, Q2The addition amount of the PAC solution is the addition amount of the PAC solution; q1The water inflow of the flocculation tank; theta1At a flow rate Q2The amount of feed of (1) and1partial regression coefficient of water inflow.
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| CN115140918A (en) * | 2022-08-30 | 2022-10-04 | 聊城集众环保科技有限公司 | Efficient control method for sewage treatment |
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