CN112462603B - Optimal regulation and control method, device, equipment and medium for regional atmosphere heavy pollution emergency - Google Patents
Optimal regulation and control method, device, equipment and medium for regional atmosphere heavy pollution emergency Download PDFInfo
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Abstract
The application discloses an optimal regulation and control method, device, equipment and medium for regional atmosphere heavy pollution emergency. The method comprises the following steps: establishing a regional atmosphere heavy pollution emergency optimization regulation and control model; the model comprises an objective function and a constraint condition; the objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated; the constraint conditions comprise an atmospheric pollutant concentration limit value of a control target area and fixed emission reduction proportions of different treatment plans; the area to be treated comprises a plurality of administrative subareas; solving the model to obtain an optimal regulation plan of the area to be treated; based on the difference between the weather conditions and the air quality constraint limits on a daily basis, a dynamic adjustment optimization day by day during the heavy pollution process can be proposed. The method converts the heavy pollution emergency practical problem into the mathematical problem of the optimization algorithm, can solve the optimal solution of the equation, can better reflect the planning and management target of the practical problem, and can realize the treatment purpose and simultaneously reduce the economic loss of pollution control to the minimum.
Description
Technical Field
The application relates to the technical field of atmospheric pollution treatment, in particular to an optimal regulation and control method, device, electronic equipment and computer readable storage medium for regional heavy pollution emergency.
Background
The development of social economy usually generates certain environmental pollution problem, and effective treatment of environmental pollution also needs certain economic loss as cost, and the two are inseparable. How to find the best balance point between the green environment and the economic development, the environmental manager needs to make the best environmental pollution improvement problem under the premise of meeting the minimum economic loss of the area, and the problem to be solved is also the current urgent problem.
In recent years, a regional heavy pollution event, which is mainly characterized by a wide regional range, a heavy pollution degree and a long duration, has occurred in some cases. Due to geographical location and meteorological conditions, some regions are more frequently polluted events. The current emergency management mode is still mainly a rough and high-cost control mode, and although many scholars develop more researches on heavy pollution treatment plans, few emergency methods capable of meeting the actual requirements of heavy pollution are available in practical application. The normal order of society and the health of public are seriously influenced by heavily polluted weather, at present, a scheme of 'one cutting' is still used in many areas on the emergency treatment of the heavy pollution event of the atmosphere, and a scientific and effective optimization regulation and control scheme is lacked in the aspect of heavy pollution emergency. The regional heavy pollution weather emergency management work is well done, the improvement of the air quality is promoted, the coordinated development between the economic society and the ecological environment is realized, and the method has very important significance.
In the aspect of the previous heavy pollution treatment plan research, the commonly used heavy pollution emergency management and control evaluation and scheme making methods mainly comprise the following steps:
1) and optimizing the control scheme through scene simulation of a plurality of groups of control schemes.
And through heavy pollution process analysis, a plurality of groups of control schemes are provided, scene simulation is carried out based on the analysis result of the control schemes, and the control scheme with the best pollutant concentration improvement condition in the simulation result is screened.
2) And directly judging the adopted pollution treatment plan based on the concentration of the heavy pollution.
And (4) judging the pollutant emission reduction proportion required by improving the concentration to a certain extent according to the concentration characteristics in the heavy pollution period by each city to make a pollution treatment plan.
However, the method 1) needs to perform multiple scenario simulations, needs to consume a large amount of computer computing power and time, and often cannot give an optimal management and control scheme in time when heavy pollution prediction and early warning are handled. The method 2) does not consider the importance of regional transmission on the air quality, the complex meteorological conditions are also dynamically changed, the local pollution emission reduction is not necessarily effective in improving the air quality condition, the heavy pollution problem is considered from the perspective of regional joint defense joint control, and a dynamic time-interval regional heavy pollution treatment plan is formulated. In addition, the method 1) and the method 2) do not consider the influence of heavy pollution emergency management and control on economy, and heavy pollution emergency plans obtained by the two methods may have great influence on regional economy.
Disclosure of Invention
The application aims to provide an optimal regulation and control method and device for regional atmosphere heavy pollution emergency, electronic equipment and a computer readable storage medium. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
According to an aspect of the embodiment of the application, an optimal regulation and control method for regional atmosphere heavy pollution emergency is provided, and comprises the following steps:
establishing an optimal regulation and control model for regional heavy atmospheric pollution emergency; the optimal regulation and control model for regional atmosphere heavy pollution emergency comprises an objective function and constraint conditions; the objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated; the constraint conditions comprise pollutant concentration limit values of a control target area and fixed emission reduction ratios specified by different heavy pollution emergency plans; the control target area and the area to be treated both comprise a plurality of administrative partitions;
and solving the regional atmospheric heavy pollution emergency optimization regulation and control model for controlling the atmospheric heavy pollution emergency optimization regulation and control of the target region.
Further, the economic loss caused by pollutant emission reduction in the area to be treated is the sum of emission reduction losses of all the partitions in the area to be treated.
Further, the emission reduction loss of each partition is the product of the emission reduction ratio under the pollution control plan level corresponding to the partition and the emission reduction cost of unit proportion.
Further, the formula of the objective function is
Wherein T represents the total time period for which the heavy pollution process lasts, and T represents a specific time period (such as a certain hour, a certain day, a certain number of days, etc.) in the whole heavy pollution process; k represents K areas to be controlled; j represents a partition sequence number, and j is a positive integer; b represents the pollution control plan level, and b is a positive integer; b represents the selectable emergency plan control level of each area to be controlled during heavy pollution; qt,j,bRepresenting the emission reduction proportion of the subarea j in the period t under the pollution control plan level b; pj,bRepresenting the unit proportion emission reduction cost of the partition j under the pollution control plan level b; cost represents the total economic loss caused by pollutant emission reduction of all the subareas in the whole heavy pollution period.
Further, the constraints include:
the pollutant concentration of the control target area is lower than a preset threshold value;
the pollution treatment plans of all levels correspond to fixed emission reduction ratios.
Wherein, the pollutant concentration of the control target area is lower than a preset threshold value, and the formula is as follows:
Ctin order to control the average pollutant concentration of a target administrative district after a control scheme is adopted in the whole heavy pollution period T within the period T,
Otpresetting a threshold value representing the average concentration of pollutants in a management and control target administrative area;
z represents the number of all the control target administrative districts i;
Qt,j,brepresenting the emission reduction proportion of the subarea j to be treated in the period t under the pollution treatment plan level b;
θt,i,jthe contribution proportion of the emission of the subarea j to be treated to the pollutant concentration of the pipe control target area i in the period t,
Pt,irepresenting the total concentration of the control target area i during the period t.
According to another aspect of the embodiments of the present application, there is provided an optimal regulation and control device for emergency of heavy pollution of the atmosphere, including:
the modeling module is used for establishing an optimal regulation and control model for regional atmosphere heavy pollution emergency; the optimal regulation and control model for regional atmosphere heavy pollution emergency comprises an objective function and constraint conditions; the objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated; the constraint conditions comprise pollutant concentration limit values of a control target area and fixed emission reduction ratios of different treatment plans; the control target area and the area to be treated both comprise a plurality of administrative partitions;
and the solving module is used for solving the emergent optimal regulation and control model of the regional heavy pollution and is used for emergent optimal regulation and control of the heavy pollution in the region to be treated.
According to another aspect of the embodiments of the present application, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the program to implement the above-mentioned optimal regulation and control method for regional heavy pollution emergency.
According to another aspect of the embodiments of the present application, there is provided a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the optimal regulation and control method for regional heavy pollution emergency.
The technical scheme provided by one aspect of the embodiment of the application can have the following beneficial effects:
according to the regional atmosphere heavy pollution emergency optimization control method provided by the embodiment of the application, an optimization control mathematical equation is established according to analysis of heavy pollution emergency problems, actual problems are converted into mathematical problems, an equation optimal solution can be solved, the obtained optimal solution can better reflect planning and management targets of the actual problems, the minimum economic cost and the air quality concentration limit value are taken as targets, and compared with the existing rough heavy pollution emergency control strategy, the economic loss of pollution control can be reduced to the minimum while the purpose of treatment is achieved.
Compared with the prior mathematical algorithms in the research and data, the combined mode of the pollution control plans adopted by each city of the area to be controlled omits complicated parameters, simplifies the equation algorithm, omits the development of algorithm functions, directly brings all heavy pollution early warning schemes in the practical situation into the equation to solve the economic loss total cost of each combined mode and the concentration limit value of each city, and screens and judges the economic loss total cost and the concentration limit value to find the optimal solution. By the method, the pollutant concentration improvement conditions under all optional emission reduction scenes can be basically obtained, and the calculation time of air quality mode simulation under multiple emission reduction scenes is saved.
In the heavy pollution emergency scheme, an environment manager does not usually perform targeted analysis on a heavy pollution process and set an optimal emission reduction ratio, but selects optimal time-share different-grade plans from a plurality of pollution treatment plans, seeks an optimal solution between the environment and the economy in the arrangement and combination of the plurality of pollution treatment plan grades, and further meets the requirements of the environment manager on a heavy pollution emergency management method and practical operation.
Additional features and advantages of the application will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application. The objectives and other advantages of the application may be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
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In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 shows a flow chart of an optimal regulation and control method for regional atmosphere heavy pollution emergency according to an embodiment of the present application;
FIG. 2 illustrates a first zone emergency emission reduction cost histogram in an embodiment of the present application;
FIG. 3 shows a schematic diagram of the results expected from the protocol in one embodiment of the present application;
figure 4 shows the superiority of the optimization scheme compared to the actual scheme of an embodiment of the present application.
FIG. 5 is a block diagram illustrating a regional atmosphere heavy pollution emergency optimization control device according to an embodiment of the present application;
fig. 6 shows a block diagram of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It will be understood by those within the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
The optimization control model can provide scientific theoretical basis and method route for heavy pollution emergency, the basic principle is that under the condition of known system characteristics, certain parameter control is met, the system is enabled to reach the optimal operation or balance state, the scientific theory for searching the optimal solution from all possible control schemes is emphasized, and the optimization control model is divided into a linear programming model and a nonlinear programming model. The optimization method for solving the actual engineering problem is generally divided into three steps: firstly, establishing an optimized mathematical model according to a proposed optimization problem, determining variables, and listing constraint conditions and objective functions; analyzing and solving the established mathematical model, and selecting an optimized solving method; listing a program diagram and writing a program according to the algorithm of the optimization method, solving the program diagram by using a computer, and evaluating and analyzing the convergence, the universality, the simplicity, the calculation efficiency and the error of the algorithm. An optimal result for meeting the requirements of the target solution can be determined among a plurality of alternative solutions by an optimization method.
The general expression of the linear programming model is:
maximum (minimum) objective equation Max/Min Q ═ C1x1+C2x2+C3x3+...+Cnxn
Multiple constraints
a11x1+a12x2+a13x3+...+a1nxn≤b1
a21x1+a22x2+a23x3+...+a2nxn≤b2
...
an1x1+an2x2+an3x3+...+annxn≤bn
Constraint x of the variable's own features1,x2,x3,...,xn≥0
The formation and the persistence of the heavy pollution process have regional characteristics, the optimization control method takes the air quality improvement during the regional heavy pollution process as a core target, considers the emission reduction cost of each industry in heavy pollution emergency, takes a plurality of cities and surrounding areas of an area to be treated as pollution source body marks for optimizing the area to be treated, takes a subarea with the improved air quality in the area to be treated as a receptor mark, and applies a third-generation air quality numerical mode and a coupling pollution source tracking module or a sensitivity analysis module (NAQPMS-OSAM, CAMx-PSAT/OSAT or CAMx-DDM/HDDM)Etc.) to simulate or predict the heavy pollution process, quantitatively analyze the particulate matters (such as PM) of pollution emission pairs of receptor cities in various regions2.5、PM10Etc.) concentration contribution rate, further calculating the change response relation of emission reduction to the pollutant concentration of the simulation area according to the area difference of the emission list of the pollution sources in the area, and carrying out primary PM (particulate matter) treatment on the main pollutants2.5And its precursor SO2And optimally distributing the reduction amount of NOx and VOCs, establishing an optimal regulation mathematical model suitable for heavy pollution emergency based on a linear programming theory according to the total emission reduction cost of the region and the weight analysis of the pollutant concentration limit value of each subarea, introducing all pollution treatment plan combinations of each city time interval of the region to be treated, solving and obtaining an optimal dynamic early warning regulation scheme of typical pollutants of each city during the heavy pollution process, and finding an optimal balance point of environment and economy.
As shown in fig. 1, an embodiment of the present application provides an optimal regulation and control method for regional heavy pollution emergency, including:
s10, establishing an optimal regulation and control model for regional heavy atmospheric pollution emergency; the atmosphere heavy pollution emergency optimization regulation and control model comprises a target function and constraint conditions; the objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated; the constraint conditions comprise pollutant concentration limit values of a control target area and fixed emission reduction ratios of different treatment plans; the control target area and the area to be treated both comprise a plurality of administrative partitions;
and S20, solving the regional atmosphere heavy pollution emergency optimization regulation and control model, and using the regional atmosphere heavy pollution emergency optimization regulation and control model to perform emergency optimization regulation and control on atmospheric heavy pollution in the region to be treated.
In addition, according to the difference of weather conditions and air quality constraint limits on a daily basis, daily dynamic adjustment optimization during the heavy pollution process is provided.
The objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated.
And calculating the total emission reduction cost of the region according to the emission reduction ratio in the unit emission reduction cost and the pollution control plan combination. The method comprises the steps of firstly, counting the emission reduction proportion and the emission reduction cost of each pollutant under each early warning level, further obtaining the emission reduction economic loss of each city under each pollution treatment plan combination, and adding the emission reduction cost of each city under each pollution treatment plan combination in each time period to obtain the total emission reduction cost of the region in the time period.
An objective function: the economic loss caused by pollutant emission reduction in the area to be treated is minimized,
wherein T represents the total time period for which the heavy contamination process lasts,
t represents a specific period (e.g., an hour, a day, etc.) in the whole heavy pollution process;
k represents K areas to be controlled;
j represents a partition sequence number, and j is a positive integer;
b represents the pollution control plan level, and b is a positive integer;
b represents the selectable emergency plan control level of each area to be controlled during heavy pollution;
Qt,j,brepresenting the emission reduction proportion of the subarea j in the period t under the pollution control plan level b;
Pj,brepresenting the unit proportion emission reduction cost of the partition j under the pollution control plan level b;
cost represents the total economic loss caused by pollutant emission reduction of all the subareas in the whole heavy pollution period.
Constraint conditions are as follows:
1. pollutant concentration of a control target area is lower than a preset threshold value
According to the pollution concentration of the subareas under each pollution level, the emission reduction proportion of the subareas required for improving the pollutant concentration of the subareas can be calculated, and then all pollution treatment plans which can realize that the pollutant concentration of the subareas is reduced to a limit value are screened.
CtIn order to control the average pollutant concentration of a target administrative district after a control scheme is adopted in the whole heavy pollution period T within the period T,
Otpresetting a threshold value representing the average concentration of pollutants in a management and control target administrative area;
z represents the number of all the control target administrative districts i;
Qt,j,brepresenting the emission reduction proportion of the subarea j to be treated in the period t under the pollution treatment plan level b;
θt,i,jthe contribution proportion of the emission of the subarea j to be treated to the pollutant concentration of the pipe control target area i in the period t,
Pt,irepresenting the total concentration of the control target area i during the period t.
In the embodiment, the emission reduction proportion of each city in the scheme can only be selected from the regulations of the emergency plan.
2. The pollution control plans correspond to fixed emission reduction ratios
For example, nonnegative constraints, emission reduction potential constraints, overall constraints under early warning sources, and enterprise pollution control plan constraints are simplified to form fixed emission reduction ratios at various pollution control plan levels, that is, emission reduction ratios of total amounts of pollutants in various cities at a certain pollution early warning level.
In practical problems, the emission reduction cost and the emission reduction proportion are in a linear relation, namely, the emission reduction cost is equivalently and linearly increased under the early warning levels of no emission reduction, no yellow early warning, no orange early warning and no red early warning. The actual problem belongs to the problem of solving the optimal solution in discrete variables of multiple early warning levels of non-emission reduction, yellow early warning, orange early warning and red early warning, in order to realize equation solvable, the emission reduction proportion of each early warning level in the actual problem is basically consistent with the step-shaped equivalent linear increase, and the method limits the emission reduction proportion of the early warning level to belong to the equivalent linear relation (for example, the emission reduction proportion of non-emission reduction, yellow early warning, orange early warning and red early warning is respectively 0,0.1,0.2 and 0.3) on the basis of the linear programming theory.
For example, B represents a selectable emergency plan control level of each area to be controlled during heavy pollution, and B is a positive integer; setting the value range of B as [1,2,3,4], and respectively using 1,2,3 and 4 to represent no early warning, yellow early warning, orange early warning and red early warning, wherein the emission reduction ratios corresponding to the four pollution control plan levels are 0, 10%, 20% and 30%.
Solving the standard scene (non-emission reduction scene) of the air pollutant to the target area to improve the concentration as the constraint condition, and obtaining the pollution control plan combination with all the subareas capable of reaching the corresponding target concentration and the lowest emission reduction cost.
If the equation is not solved, and all the plan combinations cannot meet the requirement of realizing corresponding pollutant concentration improvement in all the subareas, the algorithm solves the maximum improvement amplitude to obtain the area pollution condition with the lowest pollutant concentration.
Equation solution method 1 (iterative algorithm):
the iterative algorithm is a basic method for solving the problem by using a computer, and utilizes the characteristics of high operation speed and suitability for repetitive operation of the computer to make the computer repeatedly execute a group of instructions (or certain steps), and when the group of instructions (or the steps) are executed each time, a new value of the instructions is deduced from an original value of a variable. The method uses an iterative algorithm to introduce multiple groups of variables into a formula and an algorithm to solve the optimal solution of the problem.
In the area to be treated, M cities to be reduced in emission (each city is equivalent to a subarea) in total have 4 response schemes of no early warning, yellow early warning, orange early warning and red early warning, each city adopts a pollution treatment plan, and all combination modes of the pollution treatment plans adopted by all the cities in the area to be treated are calculated to be 4MAnd calculating the concentration improvement effect of each target improvement city in each combination mode by introducing a response relation of source pollution emission on the influence of receptor air quality, calculating the total regional emission reduction cost in each combination mode by introducing the emission reduction cost, and screening an optimal pollution control plan according to the target with the minimum emission reduction cost and the constraint of concentration improvement. And arranging pollution treatment plans day by day, and counting and classifying the whole pollution process. Consider a preliminary knotIf a plan combination with yellow early warning exists between two similar orange early warning days, the method introduces amplified adverse plan change factors to obtain the plan combination with the same or similar plan grades in each pollution time period. The protocol is expected to yield the results shown in figure 3. Equation solution method 2 (integer linear programming):
the number of pollutant emission cities in a region is N, the alternatives of each city are M, for example, 16 cities (respectively A, B, C, D, E, F, G, H, I, J, K, L, M, N, O, P) in the area A, and 4 alternatives of each city including pre-warning, yellow pre-warning, orange pre-warning and red pre-warning are provided, so that the total number of the alternative cities is 416The optimal solution (the air quality reaches the limit value and the emission reduction cost is minimum) is solved in the scheme, and the optimal solution can be realized by traversing and analyzing one by using a computer, but the required time is long, and the timeliness cannot be met. The integer programming problem is considered to be one of the most difficult problems of mathematical programming, and the combination of schemes is exponentially and explosively increased. Therefore, the iterative algorithm is not applicable in the situation of more pollution source partitions or more alternatives.
The optimization algorithm can also be solved using a linear programming method. Here, it is assumed that the atmospheric environmental particulate matter concentration (PM)2.5、PM10) And the emission of the source pollution is in a linear relation with the emission of the source pollution, so that the improvement degree of the air quality of the receptor is in a linear relation with the emission reduction of the source pollution, and the pollution emission reduction and the emission reduction cost are also in a linear relation. For example, the emission reduction of the source urban pollution is 10%, the air quality of the receptor is improved by 10%, and the emission reduction cost needs to be increased by 10%.
On the basis of the original objective function and constraint conditions, the integral linear programming increases the constraint on the emission reduction proportion and the emission reduction cost of the early warning scheme. In practical problems, emission reduction ratios of the early warning levels of no emission reduction, yellow early warning, orange early warning and red early warning basically accord with step-type equivalent linear increase, so that the emission reduction ratios of the early warning levels limited by the integral linear programming accord with the equivalent linear increase. And the emission reduction cost and the emission reduction proportion are in a linear relation, namely under the early warning level from low to high, the emission reduction cost is also increased in an equivalent linear way.
And establishing a linear mathematical equation, and solving by using a pulp integer linear programming function library of Python software to obtain emission reduction proportions of all source cities and form an emergency early warning plan for pollution emission reduction of each city in the region.
Case analysis (taking area a as an example):
an objective function:
1. minimal total economic loss required for pollutant emission reduction in region A
Referring to fig. 2, the total economic loss is calculated according to the emission reduction cost and the emission reduction ratio in the pollution control plan combination.
Constraint conditions are as follows:
1. regional pollutant concentration limit
According to the contribution response relation of the pollution discharge of each city and the surrounding area in the area A to the pollutant concentration of each city in the area A, the pollution discharge of each area to be treated in the pollution treatment plan is required to realize the average PM of each city in the area A2.5The concentration is reduced to the monitoring concentration of actual emission reduction.
2. Fixed emission reduction ratio of different pollution treatment plans
And (4) the emission reduction proportion of the total amount of pollutants in each city under the plan of each early warning grade.
TABLE 1 pollution control plan emission reduction ratio
The method of the embodiment is based on a linear programming theory, an optimized regulation and control equation is established, the minimum total emission reduction cost of a plurality of cities in a region is taken as a target, the constraint condition sets an air quality improvement target of each city and an emission reduction ratio limited by each pollution control plan, the pulp integer linear programming of python is used for solving, the emission reduction ratio of all source cities is obtained, an emergency early warning plan (figure 3) for pollution emission reduction of each city in the region is formed, and compared with an actual scheme (figure 4), the method can achieve the same air quality improvement range as the actual emission reduction and lower emission reduction total cost. Referring to fig. 3 and 4, the latest case evaluation result in the first area represents the advantages of the method of the present application compared with the actual control scheme: under the same air quality improvement range, the economic loss caused by pollution and emission reduction is lower.
The embodiment of the application provides an emergent optimal regulation and control method of regional atmosphere heavy pollution, which comprises the following steps:
establishing an optimal regulation and control model for regional heavy atmospheric pollution emergency; the optimal regulation and control model for regional atmosphere heavy pollution emergency comprises an objective function and constraint conditions; the objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated; the constraint conditions comprise pollutant concentration limit values of a control target area and fixed emission reduction ratios specified by different heavy pollution emergency plans; the control target area and the area to be treated both comprise a plurality of administrative partitions;
and solving the regional atmospheric heavy pollution emergency optimization regulation and control model for controlling the atmospheric heavy pollution emergency optimization regulation and control of the target region.
The objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated.
The present embodiment also takes into account the influence factors of the atmospheric quality conditions of other zones on the present zone. The air pollution has regional characteristics, for example, a first area is used as an area, a certain basin is located, and pollution emission in the area is collected in the area and is not easy to diffuse. All cities in the area have pollutant emission, pollutants have different degrees of influence on the pollutant concentration of each city in the area in the processes of emission, conversion, transmission and the like, and the pollutant emission outside the area also has certain contribution to the atmospheric environment pollutant concentration of each city in the area. In atmospheric environment planning, the pollution degree of a certain city is high, and if emission reduction is unreasonable only for the pollution emission of the city, tracing should be performed on the pollution source of the city, and scientific and reasonable emission reduction, namely area joint defense joint control, is performed. On the basis of tracing the pollution sources, the method comprehensively considers the emission reduction cost of each city and how to distribute the emission reduction proportion requirement of the pollution emission sources to effectively improve the air quality of the area to be treated, and the generated total economic loss of the area emission reduction is minimum.
Source city: the pollution emission source is city. The receptor city: atmospheric ambient air quality improves the target city. The relationship is as follows: the pollutant emissions (industrial emissions, fugitive dust, etc.) of multiple source cities will contribute to the atmospheric environmental air quality (e.g., PM) of the recipient city2.5) Contributing and affecting. In order to improve the air quality of a receptor city, emission reduction of pollutant emission of a source city is needed.
The heavy pollution emergency optimization algorithm is solved by using a linear programming method. Here, it is assumed that the atmospheric environmental particulate matter concentration (PM)2.5、PM10) And the emission of the source pollution is in a linear relation, so that the improvement degree of the air quality of the receptor is in a linear relation due to the emission reduction of the source pollution. And the pollution emission reduction and the emission reduction cost are in a linear relation. For example, the emission reduction of the source urban pollution is 10%, the air quality of the receptor is improved by 10%, and the emission reduction cost needs to be increased by 10%.
According to a typical heavy pollution process from 12 months 16 days in 2017 to 1 month 3 days in 2018 in the first region, converting an actual problem into a mathematical problem, and establishing a linear programming equation:
wherein, T-19 represents that the total time period of the whole heavy pollution process is each day from 12 months 16 days in 2017 to 1 month 3 days in 2018, the total time is 19 days, and a target equation is set for the total control cost of the area in the heavy pollution period;
t represents each day of the whole heavy pollution process;
k is 16 to represent 16 cities to be controlled in the area A;
j represents a partition sequence number, and j is a positive integer;
b represents the pollution control plan level, and b is a positive integer;
b ═ 1,2,3 and 4 represents the selectable emergency plan control level of each area to be controlled during heavy pollution, and represents no early warning, yellow early warning, orange early warning and red early warning respectively;
Qt,j,brepresents daily and minutesThe emission reduction ratio of the area j under the pollution control plan level b corresponds to 0,0.1,0.2 and 0.3 respectively under the levels of no early warning, yellow early warning, orange early warning and red early warning;
Pj,brepresenting the unit proportion emission reduction cost of the partition j under the pollution control plan level b, and meeting a linear relation with the emission reduction proportion;
cost represents the total economic loss caused by pollutant emission reduction of all the subareas in the whole heavy pollution period.
CtIn order to obtain the average pollutant concentration in the area A after the management and control scheme is adopted every day in the whole heavy pollution period T,
Otsetting a day-to-day dynamic concentration threshold value representing the average concentration preset threshold value of the pollutants in the area A;
z-16 represents a total 16 target control cities in the area A;
k is 16 to represent 16 areas to be treated in the area A;
Qt,j,brepresenting the emission reduction ratio of the subarea j (a specific one of 16 cities in the first area) to be treated under the pollution treatment plan level b every day;
θt,i,jthe pollutant concentration contribution proportion of the emission of a subarea j (a specific city of 16 cities in the area A) to a pipe control target area i (a specific city of 16 cities in the area A) to be treated every day;
Pt,irepresenting the total concentration of the control target area i during the period t.
And establishing a linear programming equation, solving by using a pulp integer linear programming function library of Python software, obtaining emission reduction proportions of all source cities, and forming an emergency early warning plan for pollution emission reduction of each city in the region.
The method for optimally regulating and controlling regional atmosphere heavy pollution emergency provided by the embodiment of the application can achieve the following beneficial technical effects:
1. according to analysis of the heavy pollution emergency problem, an optimization control equation based on linear programming is established, the actual problem is converted into a mathematical problem, the optimal solution of the equation can be solved, and the optimal solution can better reflect the planning and management target of the actual problem.
2. The pollution treatment plan combinations of various cities in different time periods are more in line with the actual operation requirements of real environment managers for coping with the heavy pollution process.
3. The minimum economic cost and the air quality concentration limit value are taken as targets, and compared with the existing rough heavy pollution emergency control strategy, the economic loss of pollution control can be reduced to the lowest;
4. the response relation of emission-concentration is established in an air quality source analysis mode, air quality scene simulation is avoided from running for many times, and calculation time of numerical simulation is greatly saved.
The method of the embodiment of the application takes the improvement of the air quality in the air pollution process of the area to be treated as a core target, considers the emission reduction cost of each area in heavy pollution emergency, establishes a multi-target optimization control mathematical model suitable for the heavy pollution emergency, and solves the optimal heavy pollution treatment plan for realizing the minimum economic loss and the maximum environmental benefit. The method saves the time for establishing a plurality of scene simulation to find the optimal scene in the past, has a result superior to 'one-time cutting' and a countermeasure for directly judging a pollution treatment plan according to the heavy pollution concentration, embodies the importance of regional joint defense joint control, provides scientific and reasonable decision basis for regional heavy pollution emergency management and control, and provides scientific theoretical reference and decision support for the regional joint defense joint control to treat atmospheric pollution and fine management and control of air quality.
As shown in fig. 5, another embodiment of the present application further provides an optimized regulation and control device for regional heavy pollution emergency, including:
the modeling module 100 is used for establishing an optimal regulation and control model for regional atmosphere heavy pollution emergency; the atmosphere heavy pollution emergency optimization regulation and control model comprises a target function and constraint conditions; the objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated; the constraint conditions comprise pollutant concentration limit values of a control target area and fixed emission reduction ratios of different treatment plans; the control target area and the area to be treated both comprise a plurality of administrative partitions;
and the solving module 200 is used for solving the emergent optimal regulation and control model of the regional heavy pollution and is used for emergent optimal regulation and control of the heavy pollution of the atmosphere in the region to be treated.
Another embodiment of the present application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the above-mentioned method for optimally regulating and controlling the regional heavy pollution emergency.
Please refer to fig. 6, which illustrates a schematic diagram of an electronic device according to some embodiments of the present application. As shown in fig. 6, the electronic device 20 may include: the system comprises a processor 200, a memory 201, a bus 202 and a communication interface 203, wherein the processor 200, the communication interface 203 and the memory 201 are connected through the bus 202; the memory 201 stores a computer program that can be executed on the processor 200, and when the processor 200 executes the computer program, the processor 200 executes the optimal regulation and control method for the emergency of heavy pollution of the atmosphere provided by any one of the foregoing embodiments of the present application.
The Memory 201 may include a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 203 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The processor 200 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 200. The Processor 200 may be a general-purpose Processor, and may include a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 201, and the processor 200 reads the information in the memory 201 and completes the steps of the method in combination with the hardware thereof.
The electronic equipment provided by the embodiment of the application and the regional atmosphere heavy pollution emergency optimal regulation and control method provided by the embodiment of the application have the same inventive concept and have the same beneficial effects as the method adopted, operated or realized by the electronic equipment.
Another embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the above-mentioned multi-objective optimization regulation and control method for atmospheric pollution abatement.
It should be noted that:
the term "module" is not intended to be limited to a particular physical form. Depending on the particular application, a module may be implemented as hardware, firmware, software, and/or combinations thereof. Furthermore, different modules may share common components or even be implemented by the same component. There may or may not be clear boundaries between the various modules.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. In addition, this application is not directed to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the present application as described herein, and any descriptions of specific languages are provided above to disclose the best modes of the present application.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the application, various features of the application are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: this application is intended to cover such departures from the present disclosure as come within known or customary practice in the art to which this invention pertains. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this application.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The above-mentioned embodiments only express the embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (7)
1. An optimal regulation and control method for regional atmosphere heavy pollution emergency is characterized by comprising the following steps:
establishing an optimal regulation and control model for regional heavy atmospheric pollution emergency; the optimal regulation and control model for regional atmosphere heavy pollution emergency comprises an objective function and constraint conditions; the objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated; the constraint conditions comprise pollutant concentration limit values of a control target area and fixed emission reduction ratios corresponding to pollution treatment plan levels; the control target area and the area to be treated both comprise a plurality of administrative partitions;
solving the regional atmospheric heavy pollution emergency optimal regulation and control model for controlling the atmospheric heavy pollution emergency optimal regulation and control of the target region; the emission reduction loss of each subarea is the product of the fixed emission reduction proportion corresponding to the pollution control plan level corresponding to the subarea and the emission reduction cost of unit proportion;
the formula of the objective function is
Wherein T represents the total time period of the heavy pollution process, and T represents a specific time period in the whole heavy pollution process; k represents K areas to be controlled; j represents a partition sequence number, and j is a positive integer; b represents the pollution control plan level, and b is a positive integer; b represents that each area to be managed is inOptional pollution abatement protocol levels during heavy pollution periods; qt,j,bRepresenting the emission reduction proportion of the subarea j in the period t under the pollution control plan level b; pj,bRepresenting the unit proportion emission reduction cost of the partition j under the pollution control plan level b; cost represents the total economic loss caused by pollutant emission reduction of all the subareas in the whole heavy pollution period;
the constraint conditions include:
the concentration of pollutants in the target area is lower than a preset threshold value;
and (4) fixing emission reduction proportion corresponding to each pollution treatment plan level of the area to be treated.
2. The method of claim 1, wherein the economic loss resulting from abatement of pollutants in the area to be abated is the sum of the abatement losses of all zones within the area to be abated.
3. The method of claim 1, wherein the pollutant concentration in the area to be treated is lower than a preset threshold value, and the formula is as follows:
Ctin order to control the average pollutant concentration, O, of a target administrative district after a control scheme is adopted in the whole heavy pollution period T and within the period TtPresetting a threshold value representing the average concentration of pollutants in a management and control target administrative area; z represents the number of all the control target administrative districts i; qt,j,bRepresenting the emission reduction proportion of the subarea j to be treated in the period t under the pollution treatment plan level b; thetat,i,jThe contribution proportion P of the emission of the subarea j to be treated to the pollutant concentration of the pipe control target area i in the period tt,iRepresenting the total concentration of the control target area i during the period t.
4. The method of claim 3, wherein the contribution of the emission of the treatment zone to the pollutant concentration in the control target area is calculated by an air quality source analysis numerical model.
5. An emergent regulation and control device that optimizes of regional atmosphere heavy pollution, its characterized in that includes:
the modeling module is used for establishing an optimal regulation and control model for regional atmosphere heavy pollution emergency; the atmosphere heavy pollution emergency optimization regulation and control model comprises a target function and constraint conditions; the objective function is the minimization of economic loss caused by pollutant emission reduction in the area to be treated; the constraint conditions comprise pollutant concentration limit values of a control target area and fixed emission reduction ratios corresponding to pollution treatment plan levels; the control target area and the area to be treated both comprise a plurality of administrative partitions;
the solving module is used for solving the emergent optimal regulation and control model of the regional heavy pollution and is used for the emergent optimal regulation and control of the regional heavy pollution; the emission reduction loss of each subarea is the product of the fixed emission reduction proportion corresponding to the pollution control plan level corresponding to the subarea and the emission reduction cost of unit proportion;
the formula of the objective function is
Wherein T represents the total time period of the heavy pollution process, and T represents a specific time period in the whole heavy pollution process; k represents K areas to be controlled; j represents a partition sequence number, and j is a positive integer; b represents the pollution control plan level, and b is a positive integer; b represents the selectable pollution control plan level of each area to be controlled in the heavy pollution period; qt,j,bRepresenting the emission reduction proportion of the subarea j in the period t under the pollution control plan level b; pj,bRepresenting the unit proportion emission reduction cost of the partition j under the pollution control plan level b; cost represents the total economic loss caused by pollutant emission reduction of all the subareas in the whole heavy pollution period;
the constraint conditions include:
the concentration of pollutants in the target area is lower than a preset threshold value;
and (4) fixing emission reduction proportion corresponding to each pollution treatment plan level of the area to be treated.
6. An electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the method for optimally regulating regional atmosphere heavy pollution emergency according to any one of claims 1 to 4.
7. A computer-readable storage medium, on which a computer program is stored, wherein the program is executed by a processor to implement the method for optimally regulating regional atmosphere heavy pollution emergency as claimed in any one of claims 1 to 4.
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| CN113420071A (en) * | 2021-06-24 | 2021-09-21 | 天津大学 | Optimal selection method for joint defense joint control emergency regulation and control scheme of atmosphere pollution area |
| CN113419449B (en) * | 2021-06-28 | 2022-04-22 | 中科三清科技有限公司 | Control method and device for cooperative control of fine particulate matters and ozone |
| CN114199736B (en) * | 2021-12-13 | 2023-12-01 | 北京市生态环境监测中心 | Method for obtaining regional transmission contribution rate of PM2.5 and components thereof |
| CN115271257B (en) * | 2022-09-22 | 2023-01-10 | 中科三清科技有限公司 | Multi-zone atmospheric pollution cooperative regulation and control method and device and electronic equipment |
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Application publication date: 20210309 Assignee: Beijing Zhongke Sanqing Environmental Technology Co.,Ltd. Assignor: 3CLEAR TECHNOLOGY Co.,Ltd. Contract record no.: X2022980012305 Denomination of invention: Optimal control method, device, equipment and medium for emergency response to heavy air pollution in the region Granted publication date: 20211116 License type: Common License Record date: 20220815 |