CN114519261B - Gas system pipe network pressure regulation and control method and system - Google Patents
Gas system pipe network pressure regulation and control method and system Download PDFInfo
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- 238000004519 manufacturing process Methods 0.000 description 18
- 230000000875 corresponding effect Effects 0.000 description 14
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 12
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- 238000003860 storage Methods 0.000 description 7
- 229910052742 iron Inorganic materials 0.000 description 6
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Abstract
The invention belongs to the technical field of gas system regulation and control, and particularly discloses a gas system pipe network pressure regulation and control method and system. According to the method, the pressure of the gas conveying or buffering pipe network is punished according to the value, so that the pressure fluctuation is optimized, the scheduling plan of the gas conveying and buffering system is adjusted, the pressure of the pipe network fluctuates near the expected pressure, the interference of the uncertainty fluctuation of the pressure of the pipe network can be relieved to the greatest extent, and the pressure safety of the pipe network is guaranteed. By adopting the technical scheme, corresponding punishment weights are configured according to the deviation of different pipe network pressures and expected values, pipe network pressure fluctuation optimization targets with relative punishment intensity are designed, corresponding mixed integer linear programming models are established, the pipe network pressure is optimized to fluctuation near the expected values, and the effectiveness of the method is illustrated by utilizing management and control examples of an oxygen pipe network system in the steel industry.
Description
Technical Field
The invention belongs to the technical field of gas system regulation and control, relates to a gas system pipe network pressure regulation and control method and system, and particularly relates to a gas system optimal scheduling control method and a pipe network pressure fluctuation control method with uncertain disturbance in the technical field of metallurgical control.
Background
Industrial gas is widely applied to chemical industry, oil refining, coal gas and steelmaking industry, and when the industrial gas is used specifically, the unbalanced supply and demand of the gas can cause the pressure fluctuation of a pipe network, and the uncertainty interference and the excessively small capacity of the pipe network can cause the pressure fluctuation of the pipe network to be overrun, so that safety and production accidents are caused.
In the following, a steel enterprise will be described as an example, in which a material flow is represented by dynamic movement and conversion of iron-containing materials in a manufacturing process, and an energy flow drives the material flow as a reactant of chemical physical conversion. In the steel production process of iron making, steel making, continuous casting and hot rolling processes, gas is an important energy substance, and unbalance of gas supply and demand is usually caused due to contradiction between intermittent operation of a converter, abrupt blowing-out of a blast furnace and stable operation of an air separation low-temperature air separation plant (ASUs).
For a gas energy system of an iron and steel enterprise, the existing dispatching optimization is mainly regulated and controlled from the production gas side, and the importance and the effect of the buffer capacity of the oxygen storage equipment in the aspect of coping with the oxygen demand uncertainty are not paid attention to.
Disclosure of Invention
The invention aims to provide a method and a system for regulating and controlling the pressure of a gas system pipe network, which optimize the fluctuation of the pipe network pressure near a target value, such as near medium pressure.
In order to achieve the above purpose, the basic scheme of the invention is as follows: a method for regulating and controlling the pressure of a gas system pipe network comprises the following steps:
S1, carrying out mathematical model transformation on a gas system, wherein decision variables of the mathematical model comprise gas pressure of a pipeline network of a conveying and buffering system, and optimization target independent variables of the mathematical model comprise pressure deviation values and corresponding punishment weights;
s2, carrying out optimization solution on the data model;
And S3, acting the optimized solution on the gas system as a scheduling scheme.
The working principle and the beneficial effects of the basic scheme are as follows: for the deviation between the pipe network pressure and the expected pressure value, pressure fluctuation optimization targets with different relative punishment intensities are designed. According to the scheme, the pressure fluctuation of the pipe network can be optimized to be close to an expected value, the buffer capacity of the pipe network is improved, the robustness of the pipe network is enhanced, and the uncertainty interference is better resisted.
Further, the mathematical model in step S1 contains decision variables, constraints and mathematical expressions of a plurality of optimization objectives.
Further, decision variables include, but are not limited to, pressure of the network of delivery and buffer systems or their linear transitions;
constraint expressions include, but are not limited to, equations or inequality equations relating device performance constraints and material balances;
the plurality of optimization objectives includes, but is not limited to, pressure fluctuation optimization objectives for the delivery and buffer subsystem piping network.
Further, the construction steps of the pressure fluctuation optimization target of the conveying and buffering subsystem pipe network are as follows:
s4-1, acquiring deviation between the pressure of a pipe network of the gas conveying and buffering subsystem and an expected value;
s4-2, calculating the corresponding relative penalty weight according to a certain formula according to the deviation value;
S4-3, multiplying the pressure deviation value and the corresponding punishment weight to construct a pressure fluctuation optimization target, and obtaining a scheduling plan of the gas system through optimization solution of a mathematical model to enable the pressure of the pipe network to fluctuate near the target pressure.
Further, the pressure deviation relative penalty intensity in step S4-2 is positively correlated with the corresponding pressure deviation;
In step S4-3, a minimization of the fluctuation range of the pressure at the desired value is achieved when the scheduling plan of the gas system is adjusted.
Thus, the pressure deviation of the pipe network is controlled, so that the relative punishment intensity is controlled, and the subsequent regulation and control are facilitated. The pressure fluctuation range is reduced, and stronger safety is shown when the pressure fluctuation range faces to the disturbance of the uncertainty of the demand.
Further, the multiple objective function is:
Wherein PreFlu denotes a pipe network pressure fluctuation objective function, W Pre denotes a corresponding penalty weight, which is greater than 0, obj n denotes other nth objective functions, and W n denotes a corresponding penalty weight.
And acquiring a corresponding objective function according to the scheduling plan, so that subsequent regulation and control are facilitated.
Further, the network pressure fluctuation objective function PreFlu may be expressed in a linear or nonlinear manner.
Further, the nonlinear expression adopted by the pipe network pressure fluctuation objective function PreFlu is:
Or (b)
Where Δp t is the deviation of the pipe network pressure at time t at p t from the target pressure p mid, g (Δp t) is the relative penalty strength for a pipe network pressure fluctuation of Δp t,For the time range of regulation, t isAt the inner time, p max is the maximum value of the pipe network pressure, and p min is the minimum value of the pipe network pressure.
Further, the linear expression adopted by the pipe network pressure fluctuation objective function PreFlu is:
Approximating the function f (Δp t) as N end-to-end linear functions, y t as a linear representation of the function f (Δp t), a k as the value of the right end point of the nth segment of the pressure deviation Δp t, and k n and b n as the slope and intercept of the nth segment, respectively; Is a boolean variable indicating whether Δp t is in the nth segment; And The components of Δp t and y t in the nth segment are respectively, wherein k n and b n are preset parameters, the rest are decision variables, k n reflects the relative penalty strength, k n+1>kn, N is the number of line segments of a linear function, N is a positive integer, n=1, 2, … …, N;
Further, the optimization solution method in step S2 involves an operation-study optimization algorithm, or an intelligent optimization algorithm, or a heuristic optimization algorithm.
The pipe network pressure fluctuation objective function PreFlu is expressed in a linearization or non-linearization mode, so that the pipe network pressure fluctuation can be accurately and rapidly calculated.
Further, the gas system comprises an oxygen system, or a gas system, or a steam system, or an inert gas system.
The method can be used for various gas systems, and the application range is enlarged.
The invention also provides a system, which comprises a controller, wherein the controller executes the method of the invention, acquires all gas system parameters including the pressure parameters of the conveying or buffering pipe network, determines decision variables, optimization targets and constraint conditions, calculates corresponding relative punishment intensity according to the deviation of the pipe network pressure and an expected value, constructs a pressure fluctuation optimization target, adds the pressure fluctuation optimization target into a total target through weighting, solves an optimal scheduling scheme through an optimization solving algorithm, acts on an actual gas system, ensures that the pipe network pressure fluctuates near the target pressure, and improves the capability of the pipe network pressure for resisting uncertain fluctuation interference.
Aiming at the deviation between the pipeline pressure and the median pressure, the system designs a pressure fluctuation optimization target with different relative punishment intensities, improves the buffering capacity of a pipe network, enhances the robustness of the pipe network, establishes a scheduling model for optimizing the pipe network pressure fluctuation to be close to the middle of a safe pressure range and simultaneously reduces energy loss so as to better resist uncertainty interference.
Drawings
FIG. 1 is a schematic flow diagram of a gas system regulation method of the present invention;
FIG. 2 is a line graph of the objective function f (Δpt) of a gas system tuning method in accordance with a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram of the piping network pressure fluctuation and buffering capacity change of the gas system regulation method according to a preferred embodiment of the present invention;
FIG. 4 is a schematic diagram of the structure of a gas system in a preferred embodiment of the present invention;
FIG. 5 is a graph of pipe network pressure results for manual and model scheduling of gas systems in accordance with a preferred embodiment of the present invention;
FIG. 6 is a graph comparing key indexes of manual scheduling results and model scheduling results of a gas system according to a preferred embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
In the description of the present invention, it should be understood that the terms "longitudinal," "transverse," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
In the description of the present invention, unless otherwise specified and defined, it should be noted that the terms "mounted," "connected," and "coupled" are to be construed broadly, and may be, for example, mechanical or electrical, or may be in communication with each other between two elements, directly or indirectly through intermediaries, as would be understood by those skilled in the art, in view of the specific meaning of the terms described above.
Specific symbols are named as follows:
Index:
t time interval sequence number;
i, scheduling equipment serial numbers;
k liquid oxygen tank number;
g, L gas and liquid product corner marks;
G epsilon { G, L } product type;
space division stable operation, load increasing and load reducing modes;
In the closing and starting of the liquefier, a stable operation mode is realized;
the evaporator is closed, and the operation mode is stabilized;
m, an operation mode of the device u;
ASU, liq, eva space division, liquefier and evaporator corner mark;
u e { ASU, liq, eva }, schedule device type;
Aggregation
Scheduling time range, t f is the last time period;
Supporting a historical time range of a scheduling model;
ASU space division set;
A set of Liq liquefiers;
Eva evaporator set;
inv liquid oxygen tank collection;
U { ASU, li q, eva }, set of device types;
M u device u operating mode set
Transfer relationship set: the ith device u needs the minimum holding time after the operation mode m is transferred to the mode m';
Transfer relationship set: the ith device u has a maximum hold time limit after the operation mode m is transferred to the mode m';
transfer relationship set: the ith device u operates mode m to prohibit transition to mode m';
Parameters (parameters)
FA i,k space division i is connected with liquid oxygen tank k to be 1, otherwise to be 0;
FL i,k liquefier i is connected to liquid oxygen tank k to be 1, whereas 0;
FG i,k evaporator i is connected to liquid oxygen tank k to be 1, otherwise 0;
r0.00143T/Nm 3, the conversion relation between oxygen volume and mass in the form;
R8.314476 m 3PaK-1mol-1, ideal gas constant;
t pipe network temperature (K);
V total volume of oxygen network and spherical tank (km 3);
Δt Time period(h)；
p max,pmin pipe network pressure safety upper and lower limits;
Delta i space division irax load rate;
k i space division i produces the ratio of liquid oxygen to oxygen;
Upper and lower limits of Pr i ASU,G,max,Pri ASU,G,min space division i production oxygen rate;
f i space division i allows adjusting the frequency of the load;
Maintaining the upper and lower limits of the number of time periods after the space division i operation mode m is transferred to m';
Rated load of L i liquefier i;
Rated load of G i evaporator i;
The upper and lower limits of the capacity of the liquid oxygen tank k;
Inv_ini k liquid oxygen tank k initial reserves;
Initializing an ith device u mode m;
The length of the history time involved by the i-th device u;
mode transition history of ith device u Historical time length of ith device u involvement
D t oxygen demand flow for period t;
continuously variable
The ith scheduling equipment produces the load of the product g in the period t;
Oxygen evolution rate for Rel t period t;
p t oxygen network pressure at time t;
The inventory of Inv k,t liquid oxygen tank k during time period t;
Binarized variable
The ith scheduling device u is in a mode m in a period t and is 1, otherwise is 0;
the i-th scheduling apparatus u transitions its mode m to m' at the time period t to 1 and vice versa to 0. In the invention, the variables are italics and the constants are normal.
The invention discloses a gas system pipe network pressure regulation and control method, which is characterized in that a pipe network pressure fluctuation optimization target with changing relative punishment intensity is designed, a gas system dispatching mixed integer linear programming model is established, the probability that the pipe network pressure deviates from a target value (such as a median value) greatly can be effectively restrained by a relative punishment mechanism, the pressure safety of a pipe is guaranteed even if the pipe volume is halved, and the fluctuation of the pipe network pressure near the medium pressure is optimized.
The scheme firstly analyzes the relation between the pipe network pressure and the buffer capacity and the interference of the uncertainty of the gas required under different pipeline volumes. Then, for the deviation between the pipeline pressure and the median pressure, a pressure fluctuation optimization target with different relative punishment intensities is designed and linearized, and a gas system scheduling model for optimizing pipe network pressure fluctuation and gas diffusion based on Mixed-integer INTEGER LINEAR programming (MILP) is further established. As shown in fig. 1, the gas system pipe network pressure regulation method comprises the following steps:
s1, carrying out mathematical model transformation on a gas system, wherein the gas system comprises an oxygen system, a gas system, a steam system or an inert gas system.
Preferably, the mathematical model is a mathematical planning model, or a statistical learning model. More preferably, the mathematical model in step S2 comprises a mathematical expression of decision variables, constraints and a plurality of optimization objectives. Decision variables include, but are not limited to, the pressure of the network of the delivery and buffer system or its linear transformation. Constraint expressions include, but are not limited to, equations or inequality equations relating device performance constraints and material balances. The plurality of optimization objectives includes, but is not limited to, pressure fluctuation optimization objectives for the delivery and buffer subsystem piping network.
S2, carrying out optimization solution on the mathematical model, wherein the optimization solution method relates to an operation research optimization algorithm, an intelligent optimization algorithm or a heuristic optimization algorithm.
And S3, optimizing the solution as a scheduling scheme to act on the gas system.
The gas system comprises a production subsystem, a conveying and caching subsystem and a consumption subsystem; the production gas of the production subsystem is finally delivered to the consumption subsystem through the delivery and buffering of the delivery and buffering subsystem. The production subsystem comprises a gas production device for producing gas and liquid products, and the gas production device is used for conveying the gas and the liquid products to the conveying and buffering subsystem. The conveying and buffering subsystem mainly comprises an evaporator and a liquefier, so that the mutual conversion of liquid and gas products is realized; a liquid tank, responsible for the storage of the liquid product; the gas pipe network and the spherical tank are connected with each other and are used for temporarily storing and conveying gas, and the consumption subsystem consists of a gas user and gas discharge.
The gas system pipe network pressure regulation method further comprises the following steps:
And (3) combing the relationship of the gas production subsystem, the gas delivery and caching subsystem and the gas consumption subsystem, wherein the gas delivery and caching system pipe network comprises, but is not limited to, gas delivery and caching equipment of pipe networks and spherical tanks.
Physical constraints of associated equipment in the gas production subsystem, gas delivery and caching subsystem, and gas consumption subsystem are combed. Specific physical constraints include, but are not limited to, performance condition constraints of the devices and material balance constraints between the devices.
In a preferred scheme of the invention, the construction steps of the pressure fluctuation optimization target of the conveying and buffering subsystem pipe network are as follows:
s4-1, acquiring deviation between the pressure of a pipe network of the gas conveying and buffering subsystem and an expected value;
S4-2, calculating the corresponding relative punishment weight according to a certain formula according to the deviation value, wherein the pressure deviation relative punishment intensity in the step S4-2 is positively correlated with the corresponding pressure deviation.
S4-3, multiplying the pressure deviation value and the corresponding punishment weight to construct a pressure fluctuation optimization target, and obtaining a scheduling plan of the gas system through optimization solution of a mathematical model to enable the pressure of the pipe network to fluctuate near the target pressure. In step S4-3, a minimization of the fluctuation range of the pressure at the desired value is achieved when the scheduling plan of the gas system is adjusted.
The minimization of pipe network pressure fluctuations is achieved when adjusting the scheduling plan of the gas consumption subsystem.
The multiple objective functions are:
Y＝min(PreFlu×WPre+OxyRel×WRel)，
PreFlu denotes a pipe network pressure fluctuation objective function, W Pre denotes a corresponding penalty weight which is larger than 0, oxyRel denotes an oxygen evolution optimization objective, and W Rel denotes a corresponding penalty weight which is larger than 0.
As shown in fig. 3, the black curve visually shows the piping pressure fluctuation process. Three gray triangle arrows vividly describe the relationship between pressure level and pipe storage, supply and buffer capacity, with the arrows pointing in the direction of descent. Point B meets the upper pressure limit, exceeding which may lead to a network safety accident, point D reaches the lower pressure limit, if exceeding which the safety of steel production will be compromised due to insufficient oxygen, points a and C are at intermediate pressure with larger oxygen storage space and supply margin, and there is a larger pressure rise and fall buffer space. Therefore, the closer the pipeline pressure is to the median, the larger the buffer capacity, the stronger the buffer capacity of the pipe network, and the better the pipe network can handle the interference of uncertainty of oxygen demand and the influence of the pipeline volume change. To improve pipe network buffering capacity and safety, the present embodiment optimizes pressure fluctuations to near median pressure levels by minimizing both shadow area and Δpt.
In a preferred embodiment of the present invention, the nonlinear expression used by the pipe network pressure fluctuation objective function PreFlu is:
extreme deltapt should be avoided while minimizing the shadow area. Due to The deltapt is very low in most of the range, the average value of deltapt is low, but the extreme deltapt poses a threat to the safety of the network. In this scheme, Δpt is matched with various relative penalty intensities g (Δpt), the greater Δpt is, the greater the threat is, the greater g (Δpt) is, and the product f (Δpt) of Δpt and g (Δpt) is minimized to reduce the occurrence probability of extreme Δpt;
Wherein the objective function f (deltapt) will be nonlinear and accelerate the growth, as shown in figure 2.Δp t is the deviation of the pipe network pressure at time t at p t from the target pressure p obj, g (Δp t) is the relative penalty strength for a pipe network pressure fluctuation of Δp t, For the time range of regulation, t isAt the inner time, p max is the maximum value of the pipe network pressure, and p min is the minimum value of the pipe network pressure.
The linear expression adopted by the pipe network pressure fluctuation objective function PreFlu is as follows: modeling MILP requires linearizing the objective function, including piece-wise linearization that removes the absolute value of Δpt and the function f (). First, the absolute value of Δpt is removed by adding a constraint, and the equivalent objective function is:
Approximating the function f (Δp t) as N end-to-end linear functions, y t as a linear representation of the function f (Δp t), a k as the value of the right end point of the nth segment of the pressure deviation Δp t, and k n and b n as the slope and intercept of the nth segment, respectively; Is a boolean variable indicating whether Δp t is in the nth segment; And The components of Δp t and y t in the nth segment are respectively, wherein k n and b n are preset parameters, the rest are decision variables, k n reflects the relative penalty strength, k n+1>kn, N is the number of line segments of a linear function, N is a positive integer, n=1, 2, … …, N;
The gas release objective function is:
where Rel t represents the gas release rate in the t-th time period, Δt is the length of each divided period in the scheduling model.
Device capacity constraints:
introducing a series of Boolean variables To determine which mode the scheduling device is in and the scheduling device can only be in one of the modes for each time period.
There is a maximum limit to the rate of change of the Air Separation (ASU) load, so the ASU operating load satisfies the following equation:
Meanwhile, the air separation device generates liquid oxygen with fixed proportion according to the following formula in the running process,
Representing the upper and lower limits of the oxygen load of ASU production;
the load of the evaporator and liquefier varies between constant load and zero load as follows,
Device transfer relationship constraints;
introducing a set of Boolean variables To indicate whether the mode of the scheduling device is to be transitioned, the transition relationship is constrained by,
ASUs after the mode is switched to steady operation (mode 1) or the liquefier mode is changed, they will remain for a certain minimum time, as shown below,
Θ is simply the number superimposed in the formula Σ, and m 1,m2,m3 relates to the mode of the space division and liquefier.
After the ASU has switched its mode to the increased or decreased load mode, the holding time is less than the set maximum time,
To reduce the risk of equipment failure, the number of load adjustments of the ASU should be limited within the scheduling time range, as follows:
The ASU prohibits direct mode conversion between the load-up and load-down modes, to reduce their risk of failure,
Energy balance constraint:
The relation between the input/output flow difference and the pressure change rate of the pipe network can be represented by a Kelarong equation, and the dynamic balance equation of the pipe network is as follows:
To ensure the safety and normal supply of oxygen to the pipe network, the pressure of the pipe network must be limited within a safe range,
The liquid oxygen tank is a storage device of a backup system, and the dynamic balance of liquid oxygen in the storage tank is as follows:
due to the limited capacity of the liquid oxygen tanks, the liquid oxygen inventory in each tank is limited to a safe range according to the following equation.
At the end of the dispatch time range, the stock level in the liquid oxygen tank should be greater than its initial level, ensuring that the profit of liquid oxygen is not lost, achieving maximum profit,
Initial conditions include initial liquid oxygen reserves, initial pipe network pressure, and initial plant modes (space division apparatus, liquefiers, and evaporators) as shown in equations (1) - (3). Considering the maximum/minimum hold time constraints of ASU and liquefier modes, their mode transfer history will be taken as an initialization condition, as shown in equation (4):
p0＝p_ini 2)
where p_ini is the initial pipe network pressure.
In a preferred embodiment of the present invention, experimental tests were performed on actual production data of a certain iron and steel enterprise, and the structure of the oxygen system is shown in fig. 4. The oxygen production system comprises 4 variable-load air separation devices for respectively producing high-pressure oxygen of 3.1MPa and liquid oxygen with a certain proportion. The buffer system mainly comprises a high-pressure pipe network and 8 oxygen spherical tanks. The backup system comprises two liquid oxygen tanks, 1 liquefier and 2 evaporators. The oxygen users mainly comprise 8 steel-making converters and 3 iron-making blast furnaces. The oxygen consumption of the small users is small and can be ignored. The aerobic flow comprises a blast furnace discharging process (a shadow part) and a scene that a plurality of converters simultaneously blow oxygen. The experimental case contains a complete conventional component of an oxygen system of a certain iron and steel enterprise and a typical scene of unbalanced oxygen supply and demand, and provides reliable test conditions for verifying the performance of a model.
The scheduling time range of the model corresponds to the oxygen demand and is divided into 72 time periods on average. The pipe network pressure fluctuation optimization objective function is linearized in three sections, a1=0.7/3, a2=0.7x2/3, and q=3. The experimental environment is windows10, which is provided with a 1-core CPU and an 8GB RAM. MILP was built in the python programming language according to the formula. Solving by a branch-and-bound algorithm in a python-based ortools solver takes about 3 minutes, and meets the real-time requirement of a scheduling site.
In order to quantify the pressure fluctuations and oxygen emissions of the pipe network, several key indicators are defined, such as mean pressure median deviation (APMD), maximum Pressure Median Deviation (MPMD), and oxygen emission rate (ORR), as shown in equations (5) - (7).
Where D t represents the oxygen demand flow for period t, ave () and max () are functions of average and minimum values, respectively, APMD and MPMD represent that |Δpt| is atAverage and minimum values within. APMD reflect the overall pressure fluctuations. And MPMD represents extreme fluctuations, which are important for safety. ORR isThe ratio of the total oxygen emissions to the total demand reflects the total emissions. The smaller the APMD and MPMD values, the stronger the buffering capacity of the pipe network. The smaller the ORR, the less energy is lost.
To illustrate the effectiveness of the optimization Model (Model-CPF) taking into account pipe network pressure fluctuations, the optimization objective is equation (8) with respect to the Model (Model-IPF) that ignores the optimization of pressure fluctuations.
Y＝min(OxyRel×WRel) 8)
The pipe network pressure results of the manual scheduling and the model scheduling are shown in fig. 5. Most of the pressure fluctuations based on manual scheduling are below the median pressure level. In particular, at the boxes, the pressure is close to the lower limit, the pressure based on Model-IPF is most of the time deviated from the median pressure level, and the steel production is likely to be affected by insufficient oxygen supply, even reaching the upper and lower limits at the circles. Therefore, once the user's demand changes, causing pressure overrun, accidents are highly likely to occur. The Model-CPF based pressure fluctuates slightly around the neutral pressure level, exhibiting better buffering against demand changes.
The effectiveness of the method is illustrated by the control example of the oxygen pipe network system in the steel industry, the oxygen system of the steel enterprise is mostly manually scheduled, and key index pairs of the manual scheduling result and the model scheduling result are shown in fig. 6. The manually scheduled APMD and MPMD indexes are 0.2MPa and 0.162MPa better than the Model-IPF, but 0.095MPa and 0.305MPa worse than the Model CPF. This is because the Model-IPF completely ignores optimization of pressure fluctuations, and the human controls pressure through scheduling experience, the Model-CPF has the ability to quickly optimize pressure fluctuations. For the ORR index, 0.8% oxygen emissions were only in manual scheduling, which suggests that the model has a significant optimizing effect on oxygen emissions. In the face of complex and changeable field environments, manual scheduling may meet feasibility, but multi-objective optimization is difficult to achieve. With the help of powerful computers, the model can be optimized quickly and multi-objective. The Model-CPF not only achieves zero oxygen emissions, but also reduces the pressure fluctuation range, and exhibits greater safety in the face of demand uncertainty disturbances.
The relative penalty intensity k k and the difference rate q thereof in the pressure fluctuation optimization target are important parameters, q is respectively set to 0,1,2 and 3, and the influence of the uncertainty of the demand on the pressure fluctuation index and the safety of the pipe network is evaluated as shown in table 1. When q=0, i.e., k1=k2=k3=1, the pressure fluctuation optimization objective does not consider the relative penalty intensity as a comparison baseline. Under these four sets of conditions, all oxygen emission rates were 0, indicating that they have good effect in optimizing oxygen emission. For each row of the indices in table 1, q e {1,2,3} is greatly elevated (10% to 63%) compared to the baseline index, APMDs and APMDs being best when q=3. The greater q, the closer the pressure is to the intermediate pressure. The larger the table difference rate, the greater the ability to suppress the intermediate pressure deviation. In the oxygen demand uncertainty test, MPMDs in table 1 decreases with increasing q, and the safety rate increases with increasing q, which indicates that the larger the difference rate is, the more favorable the extreme pressure is to be suppressed, and the more favorable the safety of the pipe network is to be improved. The level of improvement in the safety rate of q e {1,2,3} over the baseline increases with increasing a, indicating that the improvement in relative penalty strength is more pronounced with increasing demand uncertainty. Therefore, different relative punishment intensities are set, and the method has an important effect on suppressing extreme pressure fluctuation and better resisting interference of uncertainty of demands, so that the safety of the pipeline is improved.
Table 1 statistics of pipeline pressure fluctuation test index at relative penalty intensity settings
The invention also provides a system, which comprises a controller, wherein the controller executes the method of the invention to obtain all gas system parameters including the pressure parameters of the conveying or buffer pipe network, determine decision variables, optimization targets and constraint conditions, calculate the corresponding relative punishment intensity according to the deviation between the pipe network pressure and the expected value, construct a pressure fluctuation optimization target, add the pressure fluctuation optimization target into the overall target through weighting, solve the optimal scheduling scheme through an optimization solution algorithm, act on an actual gas system, enable the pipe network pressure to fluctuate near the target pressure, and improve the capability of the pipe network pressure for resisting uncertain fluctuation interference.
The low pressure oxygen demand is stable during normal operation of the blast furnace in the iron works. However, the high-pressure oxygen demand of the converter in the steel mill fluctuates severely, which may result in large pressure fluctuation of the high-pressure pipe network. As shown in fig. 6, ASUs generates high-pressure oxygen and delivers it to a high-pressure pipe network (i.e., an oxygen pipe network) connected to an oxygen spherical tank, and outputs it to a user in three ways. The first is directly supplied to the steelworks, and the second and third are supplied to the steelworks and small users, respectively, through regulators and low-pressure pipe networks. The pressure regulator maintains pressure in the low pressure pipe network stable through feedback regulation, so the oxygen output of the pipe network can be assumed to be the oxygen consumption and the oxygen release of the user. When oxygen is supplied and required, the network is required to store excess oxygen. However, when the oxygen demand exceeds the supply, the demand gap is temporarily supplied by oxygen in the pipe network, and the unbalance of the supply and the demand can cause the pressure fluctuation of the pipe network, and the pipe network has the oxygen storage, oxygen supply and buffering capabilities.
The scheme can ensure the safety of the pipe network under the condition of 2% of uncertain demand deviation disturbance. Even if uncertainty deviation reaches 6%, the pipe network safety probability exceeds 90%. And by setting different relative punishment intensities, the extreme pressure deviation of the pipe network is effectively restrained, and the safety of the pipe network is improved. In addition, the capacity reduction of the pipe network accelerates and weakens the capability of resisting the uncertainty of the demand, but when the uncertainty degree of the demand is lower, the safety can still be ensured to be more than 90% under the condition of halving the volume of the pipe network.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the invention, the scope of which is defined by the claims and their equivalents.
Claims (7)
1. The method for regulating and controlling the pressure of the gas system pipe network is characterized by comprising the following steps of:
S1, carrying out mathematical model transformation on a gas system, wherein decision variables of the mathematical model comprise gas pressure of a pipeline network of a conveying and buffering system, and optimization target independent variables of the mathematical model comprise pressure deviation values and corresponding punishment weights;
s2, carrying out optimization solution on the data model;
S3, acting the optimized solution as a scheduling scheme on a gas system;
The mathematical model in step S1 comprises a decision variable, constraint conditions and mathematical expressions of a plurality of optimization targets;
The decision variables comprise the pressure of a pipe network of the conveying and buffering system or the linear conversion of the pressure;
The constraint condition expression comprises an equation or inequality equation of the related device performance constraint and the material balance;
the plurality of optimization objectives includes pressure fluctuation optimization objectives for the transport and buffer subsystem piping network;
The construction steps of the pressure fluctuation optimization target of the conveying and buffering subsystem pipe network are as follows:
s4-1, acquiring deviation between the pressure of a pipe network of the gas conveying and buffering subsystem and an expected value;
s4-2, calculating the corresponding relative penalty weight according to the deviation value;
S4-3, multiplying the pressure deviation value and the corresponding punishment weight to construct a pressure fluctuation optimization target, and obtaining a scheduling plan of the gas system through optimization solution of a mathematical model to enable the pressure of the pipe network to fluctuate near the target pressure;
s4-2, positively correlating the pressure deviation relative punishment intensity with the corresponding pressure deviation;
In the step S4-3, when the scheduling plan of the gas system is adjusted, the fluctuation amplitude of the pressure at the expected value is minimized;
The multiple objective functions are:
Wherein PreFlu denotes a pipe network pressure fluctuation objective function, W Pre denotes a corresponding penalty weight, which is greater than 0, obj n denotes other nth objective functions, and W n denotes a corresponding penalty weight.
2. The method for regulating and controlling the pressure of a pipe network of a gas system according to claim 1, wherein the pipe network pressure fluctuation objective function PreFlu can be expressed in a linear or nonlinear manner.
3. The method for regulating and controlling the pressure of a pipe network of a gas system according to claim 2, wherein the nonlinear expression adopted by the pipe network pressure fluctuation objective function PreFlu is:
Or (b)
Where Δp t is the deviation of the pipe network pressure at time t at p t from the target pressure p obj, g (Δp t) is the relative penalty strength for a pipe network pressure fluctuation of Δp t,For the time range of regulation, t isTime of day.
4. The method for regulating and controlling the pressure of a pipe network of a gas system according to claim 2, wherein the linear expression adopted by the pipe network pressure fluctuation objective function PreFlu is:
Approximating the function f (Δp t) as N end-to-end linear functions, y t as a linear representation of the function f (Δp t), a k as the value of the right end point of the nth segment of the pressure deviation Δp t, and k n and b n as the slope and intercept of the nth segment, respectively; Is a boolean variable indicating whether Δp t is in the nth segment; And The components of Δp t and y t in the nth segment are respectively, wherein k n and b n are preset parameters, the rest are decision variables, k n reflects the relative penalty strength, k n+1>kn, N is the number of line segments of a linear function, N is a positive integer, n=1, 2, … …, N;
5. the method for regulating and controlling the pressure of a pipe network of a gas system according to claim 1, wherein the gas system comprises an oxygen system, a gas system, a steam system, or an inert gas system.
6. The gas system pipe network pressure regulation method according to claim 1, wherein the optimization solution method in step S2 involves an operational optimization algorithm, or an intelligent optimization algorithm, or a heuristic optimization algorithm.
7. A system, characterized by comprising a controller, wherein the controller executes the method of one of claims 1-6, obtains all gas system parameters including pipeline network pressure parameters, determines decision variables, optimization targets and constraint conditions, calculates corresponding relative punishment intensities according to deviation of pipeline network pressure and expected values, constructs a pressure fluctuation optimization target, adds the pressure fluctuation optimization target into a total target through weighting, solves an optimal scheduling scheme through an optimization solving algorithm, acts on an actual gas system, enables pipeline network pressure to fluctuate around the target pressure, and improves the capability of the pipeline network pressure for resisting uncertain fluctuation interference.
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