CN111392823B - Optimized control method and device for electric flocculation process - Google Patents

Optimized control method and device for electric flocculation process Download PDF

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CN111392823B
CN111392823B CN202010293854.9A CN202010293854A CN111392823B CN 111392823 B CN111392823 B CN 111392823B CN 202010293854 A CN202010293854 A CN 202010293854A CN 111392823 B CN111392823 B CN 111392823B
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electric flocculation
model
current
flocculation process
objective
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CN111392823A (en
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阳春华
张凤雪
李勇刚
黄科科
李繁飙
袁卓异
周灿
朱红求
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Central South University
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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
    • C02F1/46—Treatment of water, waste water, or sewage by electrochemical methods
    • C02F1/461—Treatment of water, waste water, or sewage by electrochemical methods by electrolysis
    • C02F1/463—Treatment of water, waste water, or sewage by electrochemical methods by electrolysis by electrocoagulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • C—CHEMISTRY; METALLURGY
    • C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
    • C02F2101/00—Nature of the contaminant
    • C02F2101/10—Inorganic compounds
    • C02F2101/20—Heavy metals or heavy metal compounds
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A20/00—Water conservation; Efficient water supply; Efficient water use
    • Y02A20/152—Water filtration

Abstract

The invention provides an optimized control method and a device for an electric flocculation process, wherein the method comprises the following steps: establishing a relation model of impurity concentration and current in the electric flocculation process, and collecting industrial data to perform parameter identification on the relation model; establishing a multi-objective optimization model based on two conflict objectives of highest impurity removal rate and lowest processing cost; introducing a self-adjusting parameterization method and a reversing factor to construct an optimization model based on a reversing period; and optimizing the current and the commutation period by adopting a state transfer algorithm with an evaluation criterion to obtain an industrial set value. The optimization control method provided by the invention can obtain the optimized set values of the current and the reversing period, can provide real-time control guidance for the current and the reversing period in the electric flocculation process, and has important significance for reducing power consumption, saving cost and stabilizing the outlet purified water quality in the electric flocculation process.

Description

Optimized control method and device for electric flocculation process
Technical Field
The invention belongs to the field of industrial process optimization control, and particularly relates to an optimization control method and device for an electric flocculation process.
Background
The industrial wastewater is a main source of current water body environmental pollution, and particularly, heavy metal pollutants such as cadmium, cobalt, nickel and the like contained in the wastewater have huge hazards such as carcinogenesis and teratogenesis. The electric flocculation process is an effective green method for treating industrial waste water, and is characterized by that it utilizes the electrification of iron plate to make electrode reaction, and makes the separated electric iron ions diffuse into the solution, and make them produce complex redox reaction, hydrolysis reaction and physical adsorption reaction with pollutant in the interior of reactor to form insoluble floccule and make it precipitate so as to obtain the effect of purifying water.
In the electroflocculation process, the setting of the current plays a critical role in the efficient removal of contaminants. In the actual wastewater treatment process, in order to ensure that the content of pollutants in the outlet wastewater reaches the standard, an operator performs current regulation and control according to manual experience to keep the high-power-consumption operation of the electric flocculation process. But it causes waste of electric energy and unstable quality of outlet purified water. In addition, the long-time unidirectional operation of the current can cause the passivation of the anode and the polarization of the cathode, influence the effective transmission of the current between the cathode and the anode, and cause the dissolution of the electrogenerated flocculant to influence the treatment efficiency. Current commutation is an effective way to keep the electrodes running stably, but uncertainty in the commutation period affects the generation of the above-mentioned additional reactions, as well as the regulation of the current.
Therefore, the relation between the current and the reversing period and the impurity removal efficiency and the relation between the impurity removal efficiency and the impurity removal cost are considered and analyzed, the optimization method of the current and the reversing period is researched, the optimal current set value and the reversing period are determined, and the method plays an important role in stably discharging industrial wastewater up to the standard and reducing the waste of energy sources, so that dynamic adjustment in the actual production of enterprises is guided in a standard mode, and the enterprise benefit is improved to the maximum extent.
Disclosure of Invention
Technical problem to be solved
In order to at least partially overcome the problems in the prior art, the invention provides an optimized control method and device for an electric flocculation process, which are used for automatically determining more reasonable and accurate current and reversing period in the electric flocculation process according to actual conditions, and avoiding the problems of high power consumption caused by manual adjustment and incapability of stably reaching the standard of purified wastewater.
(II) technical scheme
The invention discloses an optimized control method for an electric flocculation process, which comprises the following steps:
step S1: constructing a mechanism relation model of impurity concentration and current in the electric flocculation process, and collecting industrial data to perform parameter identification and verification on the model; the mechanism relation model is a model constructed according to a material conservation principle, a Langmuir equation and a Faraday law and is expressed as follows:
Figure BDA0002451443550000021
Figure BDA0002451443550000022
Figure BDA0002451443550000031
wherein the upper mark j is 1,2, …, N is the number mark of the multi-stage electroflocculation reactors,
Figure BDA0002451443550000032
Figure BDA0002451443550000033
respectively the concentration of impurities, the concentration of dissolved oxygen and the concentration of hydroxyl in the reactor,
Figure BDA0002451443550000034
is the concentration of impurities at the inlet of the reactor,
Figure BDA0002451443550000035
is the reactor voltage, ijIs the current density, QjIs the flow rate, VjIs the reactor volume, AjIs the plate area, F is the Faraday constant, z is the number of transferred charges, σjIs the electrical conductivity of the water to be treated,
Figure BDA0002451443550000036
is an industrial parameter to be identified, and 'means' is defined as … function;
step S2: constructing a dynamic multi-objective optimization model of the electric flocculation process based on the mechanism relation model of the electric flocculation and two conflict objectives of highest impurity removal rate and lowest processing cost;
step S3: an interval self-adjusting parameterization method and a reversing factor are provided and introduced into the dynamic multi-objective optimization model of the electric flocculation process to construct an optimization model based on a reversing period;
step S4: and optimizing the current and the commutation period by adopting a multi-objective state transfer algorithm to obtain a non-dominated solution set, then operating an evaluation criterion to obtain an evaluation value, and selecting the current and the commutation period corresponding to the minimum evaluation value as industrial set values.
Further, the step S2 specifically includes that, after considering constraints of two conflicting objectives, i.e., the highest impurity removal rate and the lowest processing cost, in the mechanism relationship model of the electric flocculation, the obtained dynamic multi-objective optimization model of the electric flocculation process is represented as follows:
maxJ1＝RE (4)
Figure BDA0002451443550000037
Figure BDA0002451443550000041
wherein L isjIs the number of the cathode plates in the electric flocculation reactor, TfIs the electrocoagulation reaction time, p1Is the electricity price, p2Is an environmental protection tax amount, pMIs the pollution equivalent value, q is the pollutant mass, iminAnd imaxThe superscript N is the total number of electroflocculation reactors and RE is the impurity removal rate for the minimum and maximum values of current density.
Further, step S3 specifically includes the following steps S3.1 to 3.3:
s3.1, parameterizing a control variable (current) by adopting a control parameterization method, namely meeting the following requirements:
Figure BDA0002451443550000042
Figure BDA0002451443550000043
Figure BDA0002451443550000044
wherein the content of the first and second substances,
Figure BDA0002451443550000051
is a control variable on the overall time scale,
Figure BDA0002451443550000052
is that
Figure BDA0002451443550000053
The control variable(s) of (a) above,
Figure BDA0002451443550000054
is the time point, H is the number of control intervals;
s3.2 setting the control interval as a decision variable to be optimized
Figure BDA00024514435500000511
Is represented as follows:
Figure BDA0002451443550000055
s3.3 introducing a reversing factor into the optimization model
Figure BDA0002451443550000056
Obtaining a multi-objective optimization model based on the commutation period, wherein the multi-objective optimization model based on the commutation period is expressed as follows:
maxJ1＝RE (9)
Figure BDA0002451443550000057
Figure BDA0002451443550000058
wherein the commutation factor
Figure BDA0002451443550000059
The value is-1 or 1.
Further, in step S4, the evaluation value e (n) is calculated as follows:
Figure BDA00024514435500000510
where M is the number of objective functions, ωmIs a weight coefficient, Jm(n) is an adaptation value of the mth objective function corresponding to the nth non-dominant solution, Jm(optimal) is the optimal adaptation value of the mth objective function.
In addition, the invention also discloses an optimized control device for the electric flocculation process, which comprises:
the first model establishing unit is used for establishing a mechanism relation model of impurity concentration and current in the electric flocculation process, and collecting industrial data to perform parameter identification and verification on the model; wherein the mechanistic relationship model is represented as follows:
Figure BDA0002451443550000061
Figure BDA0002451443550000062
Figure BDA0002451443550000063
wherein the upper mark j is 1,2, …, N is the number mark of the multi-stage electroflocculation reactors,
Figure BDA0002451443550000064
Figure BDA0002451443550000065
respectively the concentration of impurities, the concentration of dissolved oxygen and the concentration of hydroxyl in the reactor,
Figure BDA0002451443550000066
is the concentration of impurities at the inlet of the reactor,
Figure BDA0002451443550000067
is the reactor voltage, ijIs the current density, QjIs the flow rate, VjIs the reactor volume, AjIs the plate area, F is the Faraday constant, z is the number of transferred charges, σjIs the electrical conductivity of the water to be treated,
Figure BDA0002451443550000068
is an industrial parameter to be identified, and 'means' is defined as … function;
the second model establishing unit is used for establishing a dynamic multi-objective optimization model in the electric flocculation process based on the mechanism relation model of the electric flocculation and two conflict objectives of highest impurity removal rate and lowest processing cost;
the third model establishing unit is used for introducing the interval self-adjusting parameterization method and the reversing factor into the dynamic multi-objective optimization model to construct an optimization model based on the reversing period;
and the optimization control unit is used for optimizing the current and the commutation period by adopting a multi-objective state transfer algorithm to obtain a non-dominated solution set, then operating an evaluation criterion to obtain an evaluation value, and selecting the current and the commutation period corresponding to the minimum evaluation value as industrial set values.
Further, the dynamic multi-objective optimization model of the electric flocculation process of the second model establishing unit is specifically
maxJ1＝RE
Figure BDA0002451443550000071
Figure BDA0002451443550000072
Wherein L isjIs the number of the cathode plates in the electric flocculation reactor, TfIs the electrocoagulation reaction time, p1Is the electricity price, p2Is an environmental protection tax amount, pMIs the pollution equivalent value, q is the pollutant mass, iminAnd imaxThe superscript N is the total number of electroflocculation reactors and RE is the impurity removal rate for the minimum and maximum values of current density.
Further, the third model building includes:
the first computing unit parameterizes the control variable (current) by adopting a control parameterization method, namely, the following conditions are met:
Figure BDA0002451443550000081
Figure BDA0002451443550000082
Figure BDA0002451443550000083
wherein the content of the first and second substances,
Figure BDA0002451443550000084
is a control variable on the overall time scale,
Figure BDA0002451443550000085
is that
Figure BDA0002451443550000086
The control variable(s) of (a) above,
Figure BDA0002451443550000087
is the time point, H is the number of control intervals;
a second calculation unit for setting the control interval as a decision variable to be optimized
Figure BDA00024514435500000811
Is represented as follows:
Figure BDA0002451443550000088
a third calculation unit for introducing a commutation factor into the optimized model
Figure BDA0002451443550000089
And obtaining a multi-objective optimization model based on the commutation period. The multi-objective optimization model based on the commutation period is expressed as follows:
maxJ1＝RE
Figure BDA00024514435500000810
Figure BDA0002451443550000091
wherein the commutation factor
Figure BDA0002451443550000092
Value of-1 or 1
Further, the calculation method of the evaluation value e (n) of the optimization control unit is specifically as follows:
Figure BDA0002451443550000093
where M is the number of objective functions, ωmIs a weight coefficient, Jm(n) is an adaptation value of the mth objective function corresponding to the nth non-dominant solution, Jm(optimal) is the optimal adaptation value of the mth objective function。
In addition, the invention also discloses a non-transitory computer readable storage medium, which stores computer instructions for causing the computer to execute the optimized control method of the electric flocculation process.
(III) advantageous effects
1) The invention provides an optimal control method and device for an electric flocculation process, which are based on a constructed electric flocculation process mechanism model and aim at two conflicting targets of the electric flocculation process: the impurity removal rate is highest and the cost is minimum, and the current setting problem is converted into a dynamic multi-objective optimization problem. Further provides a self-adjusting parameterization method and introduces a commutation factor to realize the optimization of the current commutation period. And constructing an evaluation criterion, evaluating the multiple groups of current and commutation period optimized values, and obtaining industrial set values of the current and the commutation period. The problems of power consumption waste and incapability of stably operating the electrodes for a long time caused by manual experience operation are solved.
2) Under the same working condition, compared with a manual setting method, the method provided by the invention can reduce the concentration fluctuation of the outlet impurity ions, save the average power consumption, reduce the environmental tax, prolong the service life of the polar plate, and achieve the aims of stabilizing the outlet purified water quality, saving energy and reducing emission.
3) The invention goes deep into the principle of the electric flocculation reaction process, introduces a self-adjusting parameterization method and a reversing factor by combining with the change of the performance of an actual production electrode, is suitable for the optimal control of the current and the reversing period in the electric flocculation process, and further has important significance for stabilizing the concentration of impurities at an outlet in the electric flocculation wastewater treatment process, improving the quality of purified water and reducing the cost.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments will be briefly described as follows:
FIG. 1 is a flow chart of a method for optimizing control of an electroflocculation process according to an embodiment of the present invention;
FIG. 2 is a graph illustrating the outlet heavy metal ion concentration according to an embodiment of the present invention;
FIG. 3 is a setting of No. 1 electroflocculation reactor current and reversal period according to an example of the present invention;
FIG. 4 is a No. 2 electroflocculation reactor current and reversal period setting according to an example of the present invention;
fig. 5 is a block diagram of an optimized control device for an electric flocculation process according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions 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 is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Fig. 1 is a flow chart of an optimized control method of an electric flocculation process according to an embodiment of the present invention, as shown in fig. 1, including:
step S1: constructing a mechanism relation model of impurity concentration and current in the electric flocculation process, and collecting industrial data to perform parameter identification and verification on the model;
preferably, the step S1 specifically includes that the mechanism relation model of impurity concentration and current in the electrocoagulation process is a model constructed according to the principle of conservation of materials, the langmuir equation and faraday' S law, and is expressed as follows:
Figure BDA0002451443550000111
Figure BDA0002451443550000121
Figure BDA0002451443550000122
wherein the upper mark j is 1,2, …, N is the number mark of the multi-stage electroflocculation reactors,
Figure BDA0002451443550000123
Figure BDA0002451443550000124
respectively the concentration of impurities, the concentration of dissolved oxygen and the concentration of hydroxyl in the reactor,
Figure BDA0002451443550000125
is the concentration of impurities at the inlet of the reactor,
Figure BDA0002451443550000126
is the reactor voltage, ijIs the current density, QjIs the flow rate, VjIs the reactor volume, AjIs the plate area, F is the Faraday constant, z is the number of transferred charges, σjIs the electrical conductivity of the water to be treated,
Figure BDA0002451443550000127
is the industrial parameter to be identified, and "means" is defined as … function ".
Step S2: constructing a dynamic multi-objective optimization model of the electric flocculation process based on the mechanism relation model of the electric flocculation and two conflict objectives of highest impurity removal rate and lowest processing cost;
preferably, the step S2 further includes that, after considering constraints of two conflicting objectives, i.e., the highest impurity removal rate and the lowest treatment cost, in the mechanism relationship model of the electric flocculation, the obtained dynamic multi-objective optimization model of the electric flocculation process is represented as follows:
maxJ1＝RE (4)
Figure BDA0002451443550000128
Figure BDA0002451443550000131
wherein L isjIs the number of the cathode plates in the electric flocculation reactor, TfIs the electrocoagulation reaction time, p1Is the electricity price, p2Is an environmental protection tax amount, pMIs the pollution equivalent value, q is the pollutant mass, iminAnd imaxThe superscript N is the total number of electroflocculation reactors and RE is the impurity removal rate for the minimum and maximum values of current density.
Step S3: an interval self-adjusting parameterization method and a reversing factor are provided and introduced into the dynamic multi-objective optimization model of the electric flocculation process to construct an optimization model based on a reversing period;
preferably, the step S3 specifically includes the following steps S3.1 to S3.3:
s3.1 control parameterization method for control variable (current)
Figure BDA0002451443550000132
) Carrying out parameterization, namely:
Figure BDA0002451443550000133
Figure BDA0002451443550000134
Figure BDA0002451443550000141
wherein the content of the first and second substances,
Figure BDA0002451443550000142
is a control variable on the overall time scale,
Figure BDA0002451443550000143
is that
Figure BDA0002451443550000144
The control variable(s) of (a) above,
Figure BDA0002451443550000145
is the time point, H is the number of control intervals;
s3.2 setting the control interval as a decision variable to be optimized
Figure BDA0002451443550000146
The decision variables are represented as follows:
Figure BDA0002451443550000147
s3.3 introducing a reversing factor into the optimization model
Figure BDA0002451443550000148
Obtaining a multi-objective optimization model based on the commutation period, wherein the multi-objective optimization model based on the commutation period is expressed as follows:
maxJ1＝RE (9)
Figure BDA0002451443550000149
Figure BDA00024514435500001410
step S4: and optimizing the current and the commutation period by adopting a multi-objective state transfer algorithm to obtain a non-dominated solution set, then operating an evaluation criterion to obtain an evaluation value, and selecting the current and the commutation period corresponding to the minimum evaluation value as industrial set values. After the reactor is controlled in real time by using the industrial set values (including current and reversing period) obtained by optimization, more reasonable and accurate current and reversing period in the electric flocculation process can be determined according to actual conditions, so that the problems that high power consumption is caused by manual adjustment and purified wastewater cannot stably reach the standard are solved.
In step S4, the evaluation value calculation method is as follows:
Figure BDA0002451443550000151
where M is the number of objective functions, ωmIs a weight coefficient, Jm(n) is an adaptation value of the mth objective function corresponding to the nth non-dominant solution, Jm(optimal) is the optimal adaptation value of the mth objective function.
To clarify the superiority of the optimization control method of the present invention, the method provided in the above embodiment is explained below by using a specific example.
Taking the removal of heavy metal lead in a certain electric flocculation wastewater treatment plant as an example, firstly, establishing a relation model of lead ion concentration and current in an electric flocculation process, and collecting industrial field data to perform parameter identification on the model; secondly, establishing a dynamic multi-objective optimization model of the electric flocculation process aiming at two conflict objectives of highest impurity removal rate and lowest processing cost; introducing a self-adjusting parameterization method and a reversing factor into the model, and establishing an optimization model based on a reversing period; and fourthly, optimizing the current and the commutation period by adopting a multi-target state transfer algorithm, and then obtaining the industrial set values of the current and the commutation period by using an evaluation criterion.
The method for optimally setting the current and the reversing period in the electric flocculation process is adopted, and the outlet lead ion concentration ratio in the one-day wastewater treatment process is shown in figure 2. As can be seen from fig. 2, the lead ion concentration in the outlet purified water is far lower than that in the manual control by adopting the optimal setting method provided by the present invention, and the fluctuation frequency of the ion concentration is reduced, and the standard deviation comparison before and after use is shown in table 1. The setting of the current and the control period of the No. 1 electric flocculation reactor and the No. 2 electric flocculation reactor is shown in FIGS. 3-4 (wherein the dotted line represents manual setting, and the solid line represents optimization setting), and the production index pairs are shown in Table 2, after the optimization setting method provided by the invention is used, the average power consumption is reduced by 115.4 kWh/day, and the average cost is saved by 642.94 yuan/day.
TABLE 1
Manual setting Optimized settings
Standard deviation (mg/L) 5.37×10-3 2.76×10-3
TABLE 2
Index (I) Manual setting Optimized settings
Electric flocculation power consumption (kWh/day) 384.9 269.5
Average removal rate (%) 83.72 96.82
Total cost (Yuan/Tian) 1137.36 494.42
Therefore, the optimal control method for the electric flocculation process can optimally control the current and the reversing period of the electric flocculation process, and has important significance for the optimal guidance of the electric flocculation wastewater treatment process.
FIG. 5 is a block diagram of an apparatus for optimizing and controlling an electrocoagulation process according to an embodiment of the present invention, as shown in FIG. 5, including a first model building unit, a second model building unit, a third model building unit, and an optimizing and controlling unit; wherein, the above units are respectively functional modules corresponding to steps S1-S4, specifically:
(1) the first model establishing unit is used for establishing a mechanism relation model of impurity concentration and current in the electric flocculation process and collecting industrial data for verification.
The model of the mechanism relation between the impurity concentration and the current in the electric flocculation process is a model constructed according to the principle of conservation of materials, the Langmuir equation and the Faraday's law and is expressed as follows:
Figure BDA0002451443550000171
Figure BDA0002451443550000172
Figure BDA0002451443550000173
wherein the upper mark j is 1,2, …, N is the number mark of the multi-stage electroflocculation reactors,
Figure BDA0002451443550000174
Figure BDA0002451443550000175
respectively the concentration of impurities in the reactor, dissolutionThe concentration of oxygen, the concentration of hydroxyl,
Figure BDA0002451443550000176
is the concentration of impurities at the inlet of the reactor,
Figure BDA0002451443550000177
is the reactor voltage, ijIs the current density, QjIs the flow rate, VjIs the reactor volume, AjIs the plate area, F is the Faraday constant, z is the number of transferred charges, σjIs the electrical conductivity of the water to be treated,
Figure BDA0002451443550000178
is the industrial parameter to be identified, and "means" is defined as … function ".
(2) The second model building unit is used for an electric flocculation process model and two conflict targets: the impurity removal rate is highest, the treatment cost is lowest, and a dynamic multi-objective optimization model in the electric flocculation process is established.
Preferably, after constraints of two conflicting objectives of highest impurity removal rate and lowest treatment cost are considered in the mechanism relation model of the electric flocculation, an obtained dynamic multi-objective optimization model of the electric flocculation process is represented as follows:
maxJ1＝RE
Figure BDA0002451443550000179
Figure BDA0002451443550000181
wherein L isjIs the number of the cathode plates in the electric flocculation reactor, TfIs the electrocoagulation reaction time, p1Is the electricity price, p2Is an environmental protection tax amount, pMIs the pollution equivalent value, q is the pollutant mass, iminAnd imaxThe superscript N is the total number of electroflocculation reactors and RE is the impurity removal rate for the minimum and maximum values of current density.
(3) And the third model establishing unit is used for introducing the interval self-adjusting parameterization method and the reversing factor into the dynamic multi-objective optimization model to construct an optimization model based on the reversing period.
Preferably, the modeling unit comprises a first calculation unit, parameterizing the control variable (current) using a control parameterization method, namely:
Figure BDA0002451443550000182
Figure BDA0002451443550000183
Figure BDA0002451443550000184
wherein the content of the first and second substances,
Figure BDA0002451443550000191
is a control variable on the overall time scale,
Figure BDA0002451443550000192
is that
Figure BDA0002451443550000193
The control variable(s) of (a) above,
Figure BDA0002451443550000194
is the time point, H is the number of control intervals;
the second calculation unit sets the control interval as a decision variable to be optimized as follows:
Figure BDA0002451443550000195
a third calculation unit for introducing a commutation factor into the optimized model
Figure BDA0002451443550000196
And obtaining a multi-objective optimization model based on the commutation period as follows:
maxJ1＝RE
Figure BDA0002451443550000197
Figure BDA0002451443550000198
(4) the optimization control unit is used for optimizing the current and the commutation period by a multi-objective state transfer algorithm to obtain a non-dominated solution set, then operating an evaluation criterion to obtain an evaluation value, and selecting the current and the commutation period corresponding to the minimum evaluation value as industrial set values.
Preferably, the method for calculating the evaluation value of the optimization control unit specifically includes:
Figure BDA0002451443550000201
where M is the number of objective functions, ωmIs a weight coefficient, Jm(n) is an adaptation value of the mth objective function corresponding to the nth non-dominant solution, Jm(optimal) is the optimal adaptation value of the mth objective function.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit. The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
Finally, the description is as follows: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (3)

1. An optimized control method for an electric flocculation process is characterized by comprising the following steps:
step S1: constructing a mechanism relation model of impurity concentration and current in the electric flocculation process, and collecting industrial data to perform parameter identification and verification on the model; the mechanism relation model is a model constructed according to a material conservation principle, a Langmuir equation and a Faraday law and is expressed as follows:
Figure FDA0002961987840000011
Figure FDA0002961987840000012
Figure FDA0002961987840000013
wherein the upper mark j is 1,2, …, N is the number mark of the multi-stage electroflocculation reactors,
Figure FDA0002961987840000014
Figure FDA0002961987840000015
respectively the concentration of impurities, the concentration of dissolved oxygen and the concentration of hydroxyl in the reactor,
Figure FDA0002961987840000016
is the concentration of impurities at the inlet of the reactor,
Figure FDA0002961987840000017
is the reactor voltage, ijIs the current density, QjIs the flow rate, VjIs the reactor volume, AjIs the plate area, F is the Faraday constant, z is the number of transferred charges, σjIs the electrical conductivity of the water to be treated,
Figure FDA0002961987840000018
is an industrial parameter to be identified, and 'means' is defined as … function;
step S2: constructing a dynamic multi-objective optimization model of the electric flocculation process based on the mechanism relation model of the electric flocculation and two conflict objectives of highest impurity removal rate and lowest processing cost;
the step S2 specifically includes that, after considering constraints of two conflicting objectives of the highest impurity removal rate and the lowest processing cost in the mechanism relationship model of the electric flocculation, the obtained dynamic multi-objective optimization model of the electric flocculation process is represented as follows:
maxJ1＝RE (4)
Figure FDA0002961987840000021
Figure FDA0002961987840000022
wherein L isjIs the number of the cathode plates in the electric flocculation reactor, TfIs the electrocoagulation reaction time, p1Is the electricity price, p2Is an environmental protection tax amount, pMIs the pollution equivalent value, q is the pollutant mass, iminAnd imaxThe minimum and maximum values of the current density, the superscript N being the total number of electroflocculation reactors and RE being the impurity removal rate;
step S3: an interval self-adjusting parameterization method and a reversing factor are provided and introduced into the dynamic multi-objective optimization model of the electric flocculation process to construct an optimization model based on a reversing period; step S3 further includes the following steps S3.1 to 3.3:
s3.1, parameterizing the control variable by adopting a control parameterization method,
namely, the following conditions are satisfied:
Figure FDA0002961987840000031
Figure FDA0002961987840000032
Figure FDA0002961987840000033
wherein the content of the first and second substances,
Figure FDA0002961987840000034
is a control variable on the overall time scale,
Figure FDA0002961987840000035
is that
Figure FDA0002961987840000036
The control variable(s) of (a) above,
Figure FDA0002961987840000037
is the time point, H is the number of control intervals;
s3.2 setting the control interval as a decision variable to be optimized
Figure FDA0002961987840000038
Is represented as follows:
Figure FDA0002961987840000039
s3.3 introducing a reversing factor into the optimization model
Figure FDA00029619878400000310
Obtaining a multi-objective optimization model based on the commutation period, wherein the multi-objective optimization model based on the commutation period is expressed as follows:
maxJ1＝RE (9)
Figure FDA00029619878400000311
Figure FDA0002961987840000041
wherein the commutation factor
Figure FDA0002961987840000042
The value is-1 or 1;
step S4: optimizing the current and the commutation period by adopting a multi-objective state transfer algorithm to obtain a non-dominated solution set, then operating an evaluation criterion to obtain an evaluation value, and selecting the current and the commutation period corresponding to the minimum evaluation value as industrial set values; in step S4, the evaluation value e (n) is calculated as follows:
Figure FDA0002961987840000043
where M is the number of objective functions, ωmIs a weight coefficient, Jm(n) is an adaptation value of the mth objective function corresponding to the nth non-dominant solution, Jm(optimal) is the optimal adaptation value of the mth objective function.
2. An optimal control device for an electric flocculation process, which is characterized by comprising:
the first model establishing unit is used for establishing a mechanism relation model of impurity concentration and current in the electric flocculation process, and collecting industrial data to perform parameter identification and verification on the model; wherein the mechanistic relationship model is represented as follows:
Figure FDA0002961987840000051
Figure FDA0002961987840000052
Figure FDA0002961987840000053
wherein the upper mark j is 1,2, …, N is the number mark of the multi-stage electroflocculation reactors,
Figure FDA0002961987840000054
Figure FDA0002961987840000055
respectively the concentration of impurities, the concentration of dissolved oxygen and the concentration of hydroxyl in the reactor,
Figure FDA0002961987840000056
is the concentration of impurities at the inlet of the reactor,
Figure FDA0002961987840000057
is the reactor voltage, ijIs the current density, QjIs the flow rate, VjIs the reactor volume, AjIs the plate area, F is the Faraday constant, z is the number of transferred charges, σjIs the electrical conductivity of the water to be treated,
Figure FDA0002961987840000058
is an industrial parameter to be identified, and 'means' is defined as … function;
the second model establishing unit is used for establishing a dynamic multi-objective optimization model in the electric flocculation process based on the mechanism relation model of the electric flocculation and two conflict objectives of highest impurity removal rate and lowest processing cost; the electric flocculation process dynamic multi-objective optimization model of the second model establishing unit specifically comprises the following steps:
maxJ1＝RE
Figure FDA0002961987840000059
Figure FDA0002961987840000061
wherein L isjIs the number of the cathode plates in the electric flocculation reactor, TfIs the electrocoagulation reaction time, p1Is the electricity price, p2Is an environmental protection tax amount, pMIs the pollution equivalent value, q is the pollutant mass, iminAnd imaxFor the minimum and maximum values of the current density, the superscript N is the total number of electroflocculation reactors and RE is the impurity removal rate
The third model establishing unit is used for introducing the interval self-adjusting parameterization method and the reversing factor into the dynamic multi-objective optimization model to construct an optimization model based on the reversing period; the third model building comprises:
the first computing unit parameterizes the control variable by adopting a control parameterization method, namely, the following conditions are met:
Figure FDA0002961987840000062
Figure FDA0002961987840000063
Figure FDA0002961987840000071
wherein the content of the first and second substances,
Figure FDA0002961987840000072
is a control variable on the overall time scale,
Figure FDA0002961987840000073
is that
Figure FDA0002961987840000074
The control variable(s) of (a) above,
Figure FDA0002961987840000075
is the time point, H is the number of control intervals;
a second calculation unit for setting the control interval as a decision variable to be optimized
Figure FDA0002961987840000076
Is represented as follows:
Figure FDA0002961987840000077
a third calculation unit for introducing a commutation factor into the optimized model
Figure FDA0002961987840000078
To obtain a radicalA multi-objective optimization model in the commutation period; the multi-objective optimization model based on the commutation period is expressed as follows:
maxJ1＝RE
Figure FDA0002961987840000079
Figure FDA00029619878400000710
wherein the commutation factor
Figure FDA00029619878400000711
The value is-1 or 1;
the optimization control unit is used for optimizing the current and the commutation period by adopting a multi-objective state transfer algorithm to obtain a non-dominated solution set, then operating an evaluation criterion to obtain an evaluation value, and selecting the current and the commutation period corresponding to the minimum evaluation value as industrial set values; the calculation method of the evaluation value e (n) of the optimization control unit specifically includes:
Figure FDA0002961987840000081
where M is the number of objective functions, ωmIs a weight coefficient, Jm(n) is an adaptation value of the mth objective function corresponding to the nth non-dominant solution, Jm(optimal) is the optimal adaptation value of the mth objective function.
3. A non-transitory computer readable storage medium storing computer instructions that cause a computer to perform the method of optimally controlling an electroflocculation process of claim 1.
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