CN114519261A - Method and system for regulating and controlling pressure of gas system pipe network - Google Patents

Method and system for regulating and controlling pressure of gas system pipe network Download PDF

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CN114519261A
CN114519261A CN202210086823.5A CN202210086823A CN114519261A CN 114519261 A CN114519261 A CN 114519261A CN 202210086823 A CN202210086823 A CN 202210086823A CN 114519261 A CN114519261 A CN 114519261A
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pipe network
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gas
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郑忠
张镠
柴毅
刘余
张开天
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Chongqing University
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Abstract

The invention belongs to the technical field of gas system regulation and control, and particularly discloses a method and a system for regulating and controlling the pressure of a pipe network of a gas system. The method punishments the pressure of the gas conveying or cache pipe network according to different values, further optimizes pressure fluctuation, adjusts the scheduling plan of the gas conveying and cache system, enables the pressure of the pipe network to fluctuate near the expected pressure, can relieve the interference of uncertain fluctuation of the pressure of the pipe network to the maximum extent, and guarantees the pressure safety of the pipe network. By adopting the technical scheme, corresponding penalty weights are configured according to the deviations of different pipe network pressures and expected values, a pipe network pressure fluctuation optimization target with relative penalty strength is designed, a corresponding mixed integer linear programming model is established, the pipe network pressure is optimized to fluctuate near the expected values, and the effectiveness of the method is demonstrated by utilizing a control example of an oxygen pipe network system in the steel industry.

Description

Method and system for regulating and controlling pressure of gas system pipe network
Technical Field
The invention belongs to the technical field of gas system regulation, relates to a gas system pipe network pressure regulation and control method and a system, and particularly relates to a gas system optimal scheduling control method and a pipe network pressure fluctuation control method with uncertain interference in the technical field of metallurgical control.
Background
Industrial gas is widely applied to chemical industry, oil refining, coal gas and steel making industry, when in specific use, unbalanced gas supply and demand can cause pressure fluctuation of a pipe network, uncertain interference and over-small pipe network capacity can cause the pressure fluctuation of the pipe network to be over-limited, and safety and production accidents are caused.
In the following, a steel plant will be taken as an example, in which the material flow represents the dynamic movement and conversion of iron-containing materials in the manufacturing process, and the energy flow drives the material flow as a reactant of the 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 due to the contradiction between the intermittent operation of a converter, the sudden shutdown of a blast furnace and the stable operation of air separation low-temperature air separation plants (ASUs), the imbalance of the gas supply and demand is usually caused.
For a gas energy system of an iron and steel enterprise, the existing scheduling optimization is mainly performed from the side of production gas, and the importance and the function of the buffering capacity of oxygen storage equipment in the aspect of dealing with uncertainty of oxygen demand are not concerned.
Disclosure of Invention
The invention aims to provide a method and a system for regulating and controlling the pressure of a pipe network of a gas system, which can optimize the fluctuation of the pressure of the pipe network near a target value, such as the fluctuation near medium pressure.
In order to achieve the purpose, the basic scheme of the invention is as follows: a pressure regulation and control method for a gas system pipe network comprises the following steps:
s1, performing mathematical model transformation on the gas system, wherein decision variables of the mathematical model comprise gas pressure of a pipeline network of a conveying and caching system, and optimization target independent variables of the mathematical model comprise a pressure deviation value and a corresponding penalty weight;
s2, carrying out optimization solution on the mathematical model;
s3, the optimization solution is applied to the gas system as a scheduling scheme.
The working principle and the beneficial effects of the basic scheme are as follows: and designing pressure fluctuation optimization targets with different relative punishment strengths according to the deviation between the pressure of the pipe network and an expected pressure value. The scheme can optimize the pressure fluctuation of the pipe network to be close to an expected value, improve the buffering capacity of the pipe network, and enhance the robustness of the pipe network so as to better resist uncertain interference.
Further, the mathematical model in step S1 contains mathematical expressions of decision variables, constraints, and a plurality of optimization objectives.
Further, decision variables include, but are not limited to, pressure of the pipe network of the delivery and cache system or linear transformation thereof;
the constraint conditional expression includes but is not limited to an equation or an inequality equation of related equipment performance constraint and material balance;
the plurality of optimization objectives include, but are not limited to, pressure fluctuation optimization objectives of the delivery and buffer subsystem pipe network.
Further, the pressure fluctuation optimization target construction steps of the transmission and buffer subsystem pipe network are as follows:
s4-1, acquiring the deviation of the pressure of the gas delivery and cache subsystem pipe network and an expected value;
s4-2, calculating the corresponding relative punishment weight according to the deviation value and a certain formula;
and S4-3, multiplying the pressure deviation value by the corresponding penalty weight to construct a pressure fluctuation optimization target, and obtaining a scheduling plan of the gas system through optimization solution of the mathematical model, so that the pressure of the pipe network fluctuates around the target pressure.
Further, in step S4-2, the relative penalty strength of the pressure deviation is positively correlated with the corresponding pressure deviation;
in adjusting the dispatch plan for the gas system in step S4-3, a minimization of the amplitude of the pressure fluctuations at the desired value is achieved.
Therefore, the pressure deviation of the pipe network is controlled, the relative punishment intensity is controlled, and subsequent regulation and control are facilitated. The pressure fluctuation range is reduced, and stronger safety is shown when the disturbance of the uncertainty of the demand is faced.
Further, the multi-objective function is:
Figure BDA0003487212510000031
wherein Preflu represents a pipe network pressure fluctuation objective function, WPreRepresents a corresponding penalty weight, greater than 0, ObjnRepresenting the other nth objective function, WnRepresenting the corresponding penalty weight.
And acquiring a corresponding objective function according to the scheduling plan, so as to facilitate subsequent regulation and control.
Further, the pipe network pressure fluctuation objective function Preflu can be expressed in a linear or nonlinear mode.
Further, a nonlinear expression adopted by the pipe network pressure fluctuation target function Preflu is as follows:
f(Δpt)＝Δpt·g(Δpt),Δpt＝|pt-pobj|，
Figure BDA0003487212510000032
Figure BDA0003487212510000033
or
Figure BDA0003487212510000034
Wherein, Δ ptThe pressure of the pipe network at the moment t is ptRelative to the target pressure pmidDeviation of (a), g (Δ p)t) Pressure fluctuation value of delta p for pipe networktThe relative penalty strength of the time of day,
Figure BDA0003487212510000035
for a controlled time range, t is
Figure BDA0003487212510000036
Internal time, pmaxFor maximum pipe network pressure, pminIs the minimum value of the pressure of the pipe network.
Further, a linear expression adopted by the pipe network pressure fluctuation target function Preflu is as follows:
Figure BDA0003487212510000041
will function f (Δ p)t) Approximated as N linear functions, y, connected end to endtAs a function f (Δ p)t) A linearized expression ofkIs a pressure deviation Δ ptValue of the right end point, k, of the segment nth segmentnAnd bnThe slope and the intercept of the nth line segment are respectively;
Figure BDA0003487212510000042
is a Boolean variable, indicating Δ ptWhether it is located in the nth segment;
Figure BDA0003487212510000043
and
Figure BDA0003487212510000044
are respectively Δ ptAnd ytComponent in the nth segment, where knAnd bnIs a preset parameter, and the rest are decision variables, knReflects the relative penalty strength, and kn+1>knN is the number of line segments of the linear function, N is a positive integer, N is 1,2, … …, N;
Figure BDA0003487212510000045
further, the optimization solving method in step S2 involves an operation research optimization algorithm, or an intelligent optimization algorithm, or a heuristic optimization algorithm.
The pressure fluctuation target function Preflu of the pipe network is expressed in a linear or nonlinear mode, so that the pressure fluctuation of the pipe network can be calculated accurately and quickly.
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 expanded.
The invention also provides a system which comprises a controller, wherein the controller executes the method, acquires all gas system parameters including the pressure parameters of a conveying or caching pipe network, determines decision variables, optimization targets and constraint conditions, calculates the corresponding relative punishment intensity according to the deviation of the pipe network pressure and the expected value, constructs a pressure fluctuation optimization target, adds the pressure fluctuation optimization target into a total target through weighting, solves the optimal scheduling scheme through an optimization solving algorithm, acts on an actual gas system, enables the pipe network pressure to fluctuate near the target pressure, and improves the capability of the pipe network pressure to resist uncertain fluctuation interference.
The system designs pressure fluctuation optimization targets with different relative punishment strengths aiming at the deviation between the pipeline pressure and the median pressure, improves the buffer capacity of the pipe network, enhances the robustness of the pipe network, establishes a dispatching model for optimizing the middle of the pipe network pressure fluctuation approximate to the safe pressure range, and simultaneously reduces the energy loss so as to better resist the uncertain interference.
Drawings
FIG. 1 is a schematic flow diagram of a gas system conditioning method of the present invention;
FIG. 2 is a line graph of an objective function f (Δ pt) of a gas system regulation method in a preferred embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating pressure fluctuation and buffering capacity variation of a pipe network in a method for regulating and controlling a gas system in accordance with a preferred embodiment of the present invention;
FIG. 4 is a schematic structural view 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 a preferred embodiment of the present invention;
FIG. 6 is a diagram comparing key indicators of manual scheduling results and model scheduling results of gas systems in a preferred embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
In the description of the present invention, it is to be understood that the terms "longitudinal", "lateral", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, merely to facilitate the description of the invention and to simplify the description, and do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the invention.
In the description of the present invention, unless otherwise specified and limited, it is to be noted that the terms "mounted," "connected," and "connected" are to be interpreted broadly, and may be, for example, a mechanical connection or an electrical connection, a communication between two elements, a direct connection, or an indirect connection via an intermediate medium, and specific meanings of the terms may be understood by those skilled in the art according to specific situations.
The specific symbols are named as follows:
indexing:
time period sequence number t;
i, scheduling equipment serial numbers;
k number of liquid oxygen tanks;
g, L gas and liquid product corner marks;
g belongs to the { G, L } product type;
Figure BDA0003487212510000071
the air separation is in stable operation, and the load increasing and reducing modes are adopted;
Figure BDA0003487212510000072
in the process of closing and starting the liquefier, the operation mode is stabilized;
Figure BDA0003487212510000073
the evaporator is closed, and the operation mode is stabilized;
m,
Figure BDA0003487212510000074
the operating mode of device u;
ASU, Liq, Eva air separation, liquefier and evaporator corner marks;
u belongs to { ASU, Liq, Eva }, and the type of the equipment is scheduled;
collection
Figure BDA0003487212510000075
Scheduling time Range, tfIs the last time period;
Figure BDA0003487212510000076
supporting a historical time range of a scheduling model;
ASU space division set;
a set of Liq liquefiers;
a set of Eva evaporators;
an Inv liquid oxygen tank set;
u { ASU, Liq, Eva }, device type set;
Mudevice u operating mode set
Figure BDA0003487212510000077
Figure BDA0003487212510000078
And (3) transferring a relationship set: the minimum holding time is needed after the operation mode m of the device u I is transferred to the mode m';
Figure BDA0003487212510000079
and (3) transferring a relationship set: after the operation mode m of the device u I is transferred to the mode m', the maximum holding time limit is generated;
Figure BDA00034872125100000710
and (3) transferring a relationship set: the operation mode m of the device i is forbidden to be transferred to the mode m';
parameter(s)
FAi,kConnecting the air separation i and the liquid oxygen tank k to be 1, otherwise, connecting the air separation i and the liquid oxygen tank k to be 0;
FLi,kthe connection between the liquefier i and the liquid oxygen tank k is 1, otherwise, the connection is 0;
FGi,kthe evaporator i is connected with the liquid oxygen tank k to be 1, otherwise, the evaporator i is connected with the liquid oxygen tank k to be 0;
r 0.00143T/Nm3the conversion relation between the volume and the mass of oxygen in a surface state;
R 8.31446m3PaK-1mol-1an ideal gas constant;
t pipe network temperature (K);
total volume (km) of V oxygen pipe network and spherical tank3)；
Δt Time period(h)；
pmax,pminThe safe upper and lower limits of the pressure of the pipe network;
δispace division i maximum variable load rate;
kithe air separation i produces the ratio of liquid oxygen to oxygen;
Figure BDA0003487212510000081
the upper limit and the lower limit of the oxygen production rate of the air separation i;
fispace division i allows the frequency of the load to be adjusted;
Figure BDA0003487212510000082
keeping the upper and lower limits of the time period number after the air separation i operation mode m is transferred to m';
Lirated load of liquefier i;
Girated load of evaporator i;
Figure BDA0003487212510000083
the upper and lower limits of the k capacity of the liquid oxygen tank;
Inv_inikinitial reserve of a liquid oxygen tank k;
Figure BDA0003487212510000084
initializing an ith device u mode m;
Figure BDA0003487212510000085
the length of historical time the ith device u is involved in;
Figure BDA0003487212510000086
mode transition history of the ith device u
Figure BDA0003487212510000087
Length of history time referred to by ith device u
DtThe oxygen demand flow in the time period t;
continuously variable
Figure BDA0003487212510000091
The No. i scheduling equipment produces the load of a product g in a time period t;
Reltoxygen evolution rate for time period t;
ptthe pressure of the oxygen pipe network in the time period t;
Invk,tthe stock of the liquid oxygen tank k in the time period t;
binary variable
Figure BDA0003487212510000092
The scheduling device u No. i is 1 in the mode m in the time period t, otherwise, the mode m is 0;
Figure BDA0003487212510000093
the scheduling device u with the i number has 1 when the mode m is changed to m' in the time period t, otherwise, it is 0. In the present invention, the variables are italics and the constants are orthosomes.
The invention discloses a method for regulating and controlling the pressure of a pipe network of a gas system, which comprises the steps of designing a pipe network pressure fluctuation optimization target with relative punishment intensity, establishing a gas system scheduling mixed integer linear programming model, effectively inhibiting the probability that the pipe network pressure greatly deviates from a target value (such as a median value) through a relative punishment mechanism, ensuring the pressure safety with high probability even if the volume of a pipeline is halved, and optimizing the pipe network pressure fluctuation near the median pressure.
The method firstly analyzes the relationship between the pressure of the pipe network and the buffering capacity and the interference of uncertainty of the required gas amount under different pipeline volumes. Then, aiming at the deviation between the pipeline pressure and the median pressure, a pressure fluctuation optimization target with different relative penalty intensities is designed and linearized, and a gas system scheduling model for optimizing pipeline network pressure fluctuation and gas diffusion based on Mixed Integer Linear Programming (MILP) is further established. As shown in fig. 1, the method for regulating and controlling the pressure of the pipe network of the gas system comprises the following steps:
and S1, performing mathematical model transformation on a gas system, wherein the gas system comprises an oxygen system, a fuel gas system, a coal gas system, a steam system or an inert gas system.
Preferably, the mathematical model is a mathematical programming model, or a statistical learning model. More preferably, the mathematical model in step S2 contains mathematical expressions of decision variables, constraints, and a plurality of optimization objectives. Decision variables include, but are not limited to, pressure or linear translation of the piping network of the transport and cache system. The constraint conditional expression includes, but is not limited to, an equation or inequality equation for the relevant plant performance constraint and material balance. The plurality of optimization objectives include, but are not limited to, pressure fluctuation optimization objectives of the transport and cache subsystem pipe network.
And 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.
S3, the optimization solution is used as a scheduling scheme to be indicated for the gas system.
The gas system comprises a production subsystem, a conveying and caching subsystem and a consumption subsystem; the production subsystem process gas is eventually delivered to the consumption subsystem by delivery and buffering by the delivery and buffering subsystem. The production subsystem includes a gas production facility, and the process gas and liquid product are delivered to the transport and cache subsystem. The conveying and buffering subsystem mainly comprises an evaporator and a liquefier, and realizes the interconversion of liquid and gas products; the liquid tank is responsible for storing the liquid product; the gas pipe network and the spherical tank are connected with each other and used for temporarily storing and conveying gas, and the consumption subsystem consists of gas users and gas emission.
The gas system pipe network pressure regulating method further comprises the following steps:
the relationship among the carding gas production subsystem, the gas delivery and buffer subsystem and the gas consumption subsystem, wherein, the gas delivery and buffer system pipe network includes but is not limited to the gas delivery and buffer equipment of pipe network and spherical tank.
Physical constraints of the associated equipment in the card gas production subsystem, the gas delivery and buffer subsystem, and the gas consumption subsystem. Specific physical constraints include, but are not limited to, performance conditioning constraints of the plant and mass balance constraints between the plants.
In a preferred scheme of the invention, the construction steps of the pressure fluctuation optimization target of the transmission and buffer subsystem pipe network are as follows:
s4-1, acquiring the deviation of the pressure of the gas delivery and cache subsystem pipe network and an expected value;
and S4-2, calculating the corresponding relative penalty weight according to a certain formula according to the deviation value, wherein the pressure deviation relative penalty strength in the step S4-2 is positively correlated with the corresponding pressure deviation.
And S4-3, multiplying the pressure deviation value and the corresponding penalty weight to construct a pressure fluctuation optimization target, and obtaining a scheduling plan of the gas system through optimization solution of a mathematical model, so that the pressure of the pipe network fluctuates around the target pressure. In adjusting the dispatch plan for the gas system, a minimization of the amplitude of the pressure fluctuations at the desired value is achieved in step S4-3.
And when the dispatching plan of the gas consumption subsystem is adjusted, the minimization of pressure fluctuation of the pipe network is realized.
The multi-objective function is:
Y＝min(PreFlu×WPre+OxyRel×WRel)，
preflu represents a pipe network pressure fluctuation objective function, WPreRepresents the corresponding penalty weight, is more than 0, OxyRel represents the oxygen diffusion optimization target, WRelRepresenting a corresponding penalty weight, greater than 0.
As shown in fig. 3, the black curve visually shows the process of the pipe pressure fluctuation. The three gray-scale triangular arrows vividly describe the relationship between pressure level and the storage, supply and buffering capacity of the pipeline, the arrows pointing in the descending direction. The point B meets the upper pressure limit, the upper limit is exceeded, the safety accident of a pipe network can be caused, the point D reaches the lower pressure limit, if the lower pressure limit is exceeded, the safety of steel production is damaged due to insufficient oxygen, the point A and the point C are at the intermediate pressure, the pressure has larger oxygen storage space and supply margin, and larger pressure rising and reducing buffer space exists. Therefore, the closer the pipeline pressure is to the median value, the larger the buffer capacity is, the stronger the buffering capacity of the pipe network is, and the better the influence of the pipe network on the disturbance of the uncertainty of the oxygen demand and the change of the pipeline volume is processed. To improve the pipe network buffering capacity and safety, the present embodiment optimizes the pressure fluctuations to near the median pressure level by minimizing both the shadow area and Δ pt.
In a preferred scheme of the invention, a nonlinear expression adopted by a pipe network pressure fluctuation target function Preflu is as follows:
extreme Δ pt should be avoided while minimizing shadow areas. Due to the fact that
Figure BDA00034872125100001210
Is very low, the average value of the Δ pt is low, but the extreme Δ pt threatens the safety of the pipe network. In the scheme, Δ pt is matched with various relative punishment strengths g (Δ pt), the larger Δ pt is, the larger threat is, g (Δ pt) is also larger, and the product f (Δ pt) of Δ pt and g (Δ pt) is minimized to reduce the occurrence probability of extreme Δ pt;
f(Δpt)＝Δpt·g(Δpt),Δpt＝|pt-pobj|,pobj＝(pmax+pmin)/2，
Figure BDA0003487212510000121
Figure BDA0003487212510000122
or
Figure BDA0003487212510000123
Where the objective function f (Δ pt) will be non-linear and increase faster, as shown in fig. 2.Δ ptThe pressure of the pipe network at the moment t is ptRelative to the target pressure pobjDeviation of (a), g (Δ p)t) For pipe network pressure fluctuation value is delta ptThe relative penalty strength of the time of day,
Figure BDA0003487212510000124
for a controlled time range, t is
Figure BDA0003487212510000125
Internal time, pmaxFor maximum pipe network pressure, pminIs the minimum value of the pressure of the pipe network.
The linear expression adopted by the pressure fluctuation objective function Preflu of the pipe network is as follows: for MILP modeling, the objective function needs to be linearized, including the removal of the absolute value of Δ pt and the piecewise linearization of the function f (). First, by removing the absolute value of Δ pt by adding a constraint, the equivalent objective function is:
Figure BDA0003487212510000126
will function f (Δ p)t) Approximated as N linear functions, y, connected end to endtAs a function f (Δ p)t) A linearized expression ofkIs a pressure deviation Δ ptValue of the right end point, k, of the segment nth segmentnAnd bnThe slope and the intercept of the nth line segment are respectively;
Figure BDA0003487212510000127
is a Boolean variable, indicating Δ ptWhether it is located in the nth segment;
Figure BDA0003487212510000128
and
Figure BDA0003487212510000129
are respectively Δ ptAnd ytComponent in the nth segment, where knAnd bnIs a preset parameter, and the rest are decision variables, knReflects the relative penalty strength, and kn+1>knN is the number of line segments of the linear function, N is a positive integer, N is 1,2, … …, N;
Figure BDA0003487212510000131
the gas release amount objective function is:
Figure BDA0003487212510000132
wherein, ReltRepresenting the gas release rate over the t-th time period, at is the length of each divided period in the scheduling model.
And (3) equipment capacity constraint:
introducing a series of Boolean variables
Figure BDA0003487212510000133
To determine which mode the scheduling device is in and the scheduling device can only be in one of the modes per time period.
Figure BDA0003487212510000134
There is a maximum limit to the rate of change of the Air Separation (ASU) load, so that the operating load of the ASU satisfies the following equation:
Figure BDA0003487212510000135
Figure BDA0003487212510000136
meanwhile, the air separation (air separation) device also generates liquid oxygen with a fixed proportion according to the following formula in the operation process,
Figure BDA0003487212510000141
Figure BDA0003487212510000142
representing the upper and lower limits of the oxygen load of ASU production;
the load on the evaporator and liquefier was varied between a constant load and zero load as follows,
Figure BDA0003487212510000143
Figure BDA0003487212510000144
device transfer relationship constraints;
introducing a set of Boolean variables
Figure BDA0003487212510000145
To indicate whether the scheduled device mode is transferred, the transfer relationship is constrained by the following equation,
Figure BDA0003487212510000146
after the ASUs mode is changed to steady state operation (mode 1) or the liquefier mode is changed, they are maintained for a certain minimum time, as follows,
Figure BDA0003487212510000147
Figure BDA0003487212510000148
Figure BDA0003487212510000149
theta is only the number of the superposition in the formula sigma, m1,m2,m3Modes relating to air separation and liquefiers.
After the ASU switches its mode to the increased or decreased load mode, the hold time is less than the set maximum time,
Figure BDA00034872125100001410
Figure BDA00034872125100001411
to reduce the risk of equipment failure, the number of load adjustments of the ASU should be limited within the scheduling time, as follows:
Figure BDA0003487212510000151
the ASU inhibits direct mode switching between load increase and load decrease modes, to reduce their risk of failure,
Figure BDA0003487212510000152
Figure BDA0003487212510000153
energy balance constraint:
the relation between the input/output flow difference and the pressure change rate of the pipe network can use a Kerbelon equation, and the dynamic balance equation of the pipe network is as follows:
Figure BDA0003487212510000154
in order to ensure the safety and normal oxygen supply of the pipe network, the pressure of the pipe network must be limited within a safety range,
Figure BDA0003487212510000155
the liquid oxygen tank is a storage device of a backup system, and the dynamic balance of the liquid oxygen in the storage tank is as follows:
Figure BDA0003487212510000156
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.
Figure BDA0003487212510000157
When the scheduling time range is over, the stock level in the liquid oxygen tank is higher than the initial level, the profit of the liquid oxygen is not lost, the maximum profit is realized,
Figure BDA0003487212510000158
the initial conditions include the initial liquid oxygen storage, the initial pipe network pressure, and the initial plant mode (air separation unit, liquefier, and evaporator), as shown in equations (1) to (3). Considering the maximum/minimum hold time constraints of the ASU and liquefier modes, their mode transition history will be the initialization condition, as shown in equation (4):
Figure BDA0003487212510000161
p0＝p_ini 2)
Figure BDA0003487212510000162
Figure BDA0003487212510000163
wherein p _ ini is the initial pipe network pressure.
In a preferred embodiment of the present invention, the actual production data of a certain iron and steel enterprise is experimentally tested, and the structure of the oxygen system is shown in fig. 4. The oxygen generation system comprises 4 variable-load air separation units which respectively produce high-pressure oxygen of 3.1MPa and liquid oxygen in a certain proportion. The buffer system mainly comprises a high-pressure pipe network and 8 oxygen spherical tanks. The backup system includes 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 small users is very small and can be ignored. The aerobic flow rate includes a blast furnace descending process (shaded part) and a scene of simultaneously blowing oxygen by a plurality of converters. The experimental case contains complete conventional components of an oxygen system of a certain steel enterprise and a typical scene of unbalanced oxygen supply and demand, and provides reliable test conditions for verifying the performance of the model.
The scheduling time range of the model corresponds to the oxygen demand, and is divided into 72 periods on average. The optimization objective function of the pressure fluctuation of the pipe network is linearized in three sections, wherein a1 is 0.7/3, a2 is 0.7 2/3, and q is 3. The experimental environment is windows 10, and 1-core CPU and 8GB RAM are provided. MILP is built in the python programming language according to the formula. Solving is carried out through a branch-and-bound algorithm in an ortools solver based on python, the time is consumed for about 3 minutes, and the real-time requirement of a scheduling site is met.
In order to quantify the pressure fluctuation and the oxygen discharge of the pipe network, several key indexes are defined, such as Average Pressure Median Deviation (APMD), Maximum Pressure Median Deviation (MPMD) and oxygen discharge rate (ORR), which are shown in formulas (5) to (7).
Figure BDA0003487212510000171
Figure BDA0003487212510000172
Figure BDA0003487212510000173
Wherein D istRepresenting the oxygen demand flow for time period t, ave () and max () are functions of the mean and minimum values, respectively, APMD and MPMD represent | Δ pt | at
Figure BDA0003487212510000174
Mean and minimum values within. APMD reflects global pressure fluctuations. And MPMD represents extreme fluctuations, which is important for safety. ORR is
Figure BDA0003487212510000175
The ratio of total internal oxygen emissions to total demand reflects the total emissions. The smaller the APMD and MPMD values are, the stronger the buffering capacity of the pipe network is. The smaller the ORR, the less energy loss.
In order to illustrate the effectiveness of the Model (Model-CPF) for optimizing the pressure fluctuation of the pipe network, the Model (Model-IPF) for optimizing the pressure fluctuation is ignored as a comparison, and the optimization target is the formula (8).
Y＝min(OxyRel×WRel) 8)
The results of pipe network pressure for both manual and model dispatch are shown in fig. 5. Most of the pressure fluctuations based on manual scheduling are below the median pressure level. In particular, at the box, where the pressure is near the lower limit, steel production is likely to be affected by insufficient oxygen supply and Model-IPF based pressures are most of the time shifted away from the median pressure level, even reaching the upper and lower limits at the circle. Thus, once the pressure exceeds the limit due to changes in the user's demand, accidents are most likely to occur. The Model-CPF based pressure fluctuates slightly around the neutral pressure level, showing better cushioning ability to cope with changes in demand.
The effectiveness of the method is illustrated by a control example of an oxygen pipe network system in the steel industry, the oxygen system of the steel enterprise mostly adopts manual scheduling, and the key index pair of the manual scheduling result and the model scheduling result is shown in fig. 6. The indexes of the APMD and the MPMD which are manually scheduled are 0.2MPa and 0.162MPa better than those of the Model-IPF, but are 0.095MPa and 0.305MPa worse than those of the Model CPF. This is because the Model-IPF ignores optimization of pressure fluctuations completely, human control of pressure through scheduling experience, and the Model-CPF has the ability to quickly optimize pressure fluctuations. For the ORR index, 0.8% of oxygen emission is only in manual scheduling, which indicates that the model has a significant optimization effect on oxygen emission. In the face of complex and variable field environments, manual scheduling may meet feasibility, but multi-objective optimization is difficult to achieve. With the help of a powerful computer, the model can be optimized in multiple targets rapidly. 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.
Relative penalty strength k in pressure fluctuation optimization objectivekAnd the difference rate q is an important parameter, q is respectively set to be 0,1,2 and 3, and the influence of the uncertainty of the evaluation requirement on the pressure fluctuation index and the safety of the pipe network is shown in table 1. When q is 0, i.e., k1 k2 k3 is 1, the pressure fluctuation optimization objective does not consider the relative penalty strength as a comparison baseline. All oxygen emission rates were 0 for all four of these sets of conditions, indicating that they are all very effective in optimizing oxygen emissions. For each row of indices in Table 1, q ∈ {1,2,3} is much higher (10% to 63%) than the baseline index, with APMDs and APMDs being the best when q is 3. The greater q, the closer the pressure is to the intermediate pressure. The larger the difference rate, the stronger the ability to suppress the intermediate pressure deviation. In an oxygen demand uncertainty test, MPMDs in the table 1 are reduced along with the increase of q, and the safety rate is increased along with the increase of q, which shows that the larger the difference rate is, the more beneficial to inhibiting extreme pressure is, and the more beneficial to improving the safety of a pipe network is. The level of security rate increase for q ∈ {1,2,3} relative to baseline increases with a, indicating that the effect of improving the relative penalty strength is more pronounced with increasing demand uncertainty. Therefore, different relative punishment strengths are set, so that the method plays an important role in restraining extreme pressure fluctuation and better resisting disturbance of demand uncertainty, thereby improving the safety of the pipeline.
TABLE 1 statistics of pipeline pressure fluctuation test indicators under relative penalty strength settings
Figure BDA0003487212510000191
The invention also provides a system which comprises a controller, wherein the controller executes the method, acquires all gas system parameters including the pressure parameters of the conveying or cache pipe network, determines decision variables, optimization targets and constraint conditions, calculates the corresponding relative punishment intensity according to the deviation of the pressure and the expected value of the pipe network, constructs a pressure fluctuation optimization target, adds the pressure fluctuation optimization target into a total target by weighting, solves an optimal scheduling scheme by an optimization solving algorithm, acts on an actual gas system, enables the pressure of the pipe network to fluctuate near the target pressure, and improves the capability of the pipe network for resisting uncertain fluctuation interference.
The low pressure oxygen demand is stable during normal operation of the iron works blast furnace. However, the high pressure oxygen demand of the converter in the steel plant fluctuates dramatically, which may cause the pressure fluctuation of the high pressure pipe network to be large. As shown in fig. 6, ASUs generate high pressure oxygen and deliver it to a high pressure network (i.e., oxygen network) connected to an oxygen balloon, and output it to the user in three ways. The first is supplied directly to the steel plant, the second and third are supplied to the steel plant and to the small users through regulators and low-pressure pipe networks, respectively. The pressure regulator maintains the pressure in the low pressure pipe network stable through feedback regulation, and therefore, the oxygen output of the pipe network can be assumed to be the oxygen consumption and the oxygen release of users. When the oxygen supply is over, the pipe network needs to store the redundant oxygen. However, when the oxygen demand exceeds the supply, the demand gap is temporarily supplied by the oxygen in the pipe network, the pressure fluctuation of the pipe network is caused by the unbalance of the supply and demand, and the pipe network has the oxygen storage, supply and buffering capacities.
The scheme can ensure the safety of the pipe network under 2% of uncertain demand deviation disturbance. Even if the uncertainty deviation reaches 6%, the safety probability of the pipe network also exceeds 90%. And by setting different relative punishment strengths, the extreme pressure deviation of the pipe network is effectively inhibited, and the safety of the pipe network is improved. In addition, the capacity reduction of the pipe network can accelerate and weaken the capacity of resisting the uncertainty of the demand, but when the uncertainty degree of the demand is lower, the safety can still be guaranteed to reach more than 90% under the condition that the volume of the pipe network is reduced by half.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean 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 invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. 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 invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (12)

1. A method for regulating and controlling the pressure of a pipe network of a gas system is characterized by comprising the following steps:
s1, performing mathematical model conversion on the gas system, wherein decision variables of the mathematical model comprise gas pressure of a pipeline network of a conveying and caching system, and optimization target independent variables of the mathematical model comprise a pressure deviation value and a corresponding penalty weight;
s2, carrying out optimization solution on the mathematical model;
s3, the optimization solution is applied to the gas system as a scheduling scheme.
2. The method of claim 1, wherein the mathematical model in step S1 includes mathematical expressions for decision variables, constraints, and a plurality of optimization objectives.
3. The method of claim 2, wherein the decision variables comprise pressure or linear transformation of a pipe network of the delivery and buffer system;
the constraint conditional expression comprises an equation or an inequality equation of related equipment performance constraint and material balance;
the multiple optimization objectives include pressure fluctuation optimization objectives of a delivery and buffer subsystem pipe network.
4. The method for regulating and controlling the pressure of the pipe network of the gas system as claimed in claim 3, wherein the steps of constructing the pressure fluctuation optimization target of the pipe network of the transport and cache subsystems are as follows:
s4-1, acquiring the deviation of the pressure of the gas delivery and cache subsystem pipe network and an expected value;
s4-2, calculating the corresponding relative penalty weight according to the deviation value;
and S4-3, multiplying the pressure deviation value by the corresponding penalty weight to construct a pressure fluctuation optimization target, and obtaining a scheduling plan of the gas system through optimization solution of the mathematical model, so that the pressure of the pipe network fluctuates around the target pressure.
5. The method for regulating pressure of a piping network of a gas system according to claim 4, wherein the relative penalty strength of the pressure deviation is positively correlated with the corresponding pressure deviation in step S4-2;
in adjusting the dispatch plan for the gas system, a minimization of the amplitude of the pressure fluctuation at the desired value is achieved in step S4-3.
6. The method of claim 2, wherein the multi-objective function is:
Figure FDA0003487212500000021
wherein Preflu represents a pipe network pressure fluctuation objective function, WPreRepresents a corresponding penalty weight, greater than 0, ObjnRepresenting the other nth objective function, WnRepresenting the corresponding penalty weight.
7. The method for regulating and controlling the pressure of the pipe network of the gas system according to claim 6, wherein the pipe network pressure fluctuation objective function Preflu can be expressed in a linear or nonlinear manner.
8. The method for regulating and controlling the pressure of the pipe network of the gas system as claimed in claim 7, wherein the pipe network pressure fluctuation objective function Preflu adopts a nonlinear expression as follows:
f(Δpt)＝Δpt·g(Δpt),Δpt＝|pt-pobj|，
Figure FDA0003487212500000022
Figure FDA0003487212500000023
or
Figure FDA0003487212500000024
Wherein, Δ ptThe pressure of the pipe network at the moment t is ptRelative to the target pressure pobjDeviation of (a), g (Δ p)t) Pressure fluctuation value of delta p for pipe networktThe relative penalty strength of the time of day,
Figure FDA0003487212500000025
for a controlled time range, t is
Figure FDA0003487212500000026
The time of day.
9. The method for regulating and controlling the pressure of the pipe network of the gas system as claimed in claim 7, wherein the pipe network pressure fluctuation objective function Preflu adopts a linear expression as follows:
Figure FDA0003487212500000027
will function f (Δ p)t) Approximated as N linear functions, y, connected end to endtAs a function f (Δ p)t) A linearized expression ofkIs the pressure deviation Δ ptValue of the right end point, k, of the segment nth segmentnAnd bnRespectively the slope of the nth line segmentRate and intercept;
Figure FDA0003487212500000031
is a Boolean variable, indicating Δ ptWhether it is located in the nth segment;
Figure FDA0003487212500000032
and
Figure FDA0003487212500000033
are respectively Δ ptAnd ytComponent in the nth segment, where knAnd bnIs a preset parameter, and the rest are decision variables, knReflects the relative penalty strength, and kn+1>knN is the number of line segments of the linear function, N is a positive integer, N is 1,2, … …, N;
Figure FDA0003487212500000034
10. the method of claim 1, wherein the gas system comprises an oxygen system, or a gas system, or a steam system, or an inert gas system.
11. The gas system pipe network pressure regulating method according to claim 1, wherein the optimization solving method in step S2 relates to an operational research optimization algorithm, or an intelligent optimization algorithm, or a heuristic optimization algorithm.
12. A system, characterized by comprising a controller, wherein the controller executes the method of any one of claims 1 to 11, acquires all gas system parameters including pressure parameters of a transmission or cache pipe network, determines decision variables, optimization objectives and constraint conditions, calculates the corresponding relative penalty strength according to the deviation of the pipe network pressure and an expected value, constructs a pressure fluctuation optimization objective, adds the pressure fluctuation optimization objective into a total objective by weighting, solves an optimal scheduling scheme by an optimization solution algorithm, and applies the optimal scheduling scheme to an actual gas system to enable the pipe network pressure to fluctuate near the target pressure, thereby improving the capability of the pipe network pressure to resist uncertain fluctuation interference.
CN202210086823.5A 2021-12-31 2022-01-25 Method and system for regulating and controlling pressure of gas system pipe network Pending CN114519261A (en)

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