CN101276437A - Enterprise energy consumption process model building and emulation method - Google Patents

Abstract

The invention particularly relates to a modelling and emulation method for enterprise energy consumption process, belonging to the technical field of energy consumption control, wherein the modelling method comprises using fuzzy variation of a fuzzy Petri net model to represent each energy consumption activity, using fuzzy warehouse to represent the warehouse of raw materials and finished products relevant to the energy consumption activities; the number of marks in the fuzzy warehouse represents the number of the raw materials and the finished products, the relevant supply and demand relationship is denoted by connection line between crunodes; the connection intensity of the connection line signifies the loss rate when conveying the raw materials and finished products from the start point to the end point. The emulation method comprises an initial network model and an excitation fuzzy variation which determines the enabling fuzzy variation set at the current moment, disposes the fuzzy variation at certain excitation moment into the future event table and finds out the minimum excitation time. The method of the invention can clearly and completely describe all kinds of interaction activities in the process of the energy consumption of the enterprise, and the invention has high computing efficiency, strong versatility, which can reinforce the management of energy consumption and save the energy for the enterprise.

Description

Enterprise energy consumption process model building and emulation mode

Technical field

The invention belongs to energy consumption control technology field, be specifically related to a kind of modeling and simulation method of enterprise energy consumption process.

Background technology

Enterprise is the energy resource consumption main body as the intensive terminal user of energy resource consumption.At energy-saving and cost-reducing developing goal, many big-and-middle-sized energy consumption enterprise implements serial of methods and measure.But in actual applications,, the concrete operating position of the energy and the benefit of generation thereof are lacked accurate cognition, influenced the estimation of cost of products and the effective control and the decision-making of enterprise energy consumption because energy consumption enterprise lacks effective efficiency evaluation measures.Therefore, be evaluated as target with enterprise's efficiency, set up the enterprise energy consumption system model, reflect energy consumption situation and influence factor in the enterprise production process comprehensively, exactly, and the energy uses and the dynamic behaviour that consumes in the simulation analysis enterprise production process, will have important Research Significance.It is not only enterprise and realizes that the energy resource consumption situation quantitatively and the important method of qualitative analysis, also is the effective means that enterprise carries out the efficiency assessment, the while also for enterprise implement comprehensively energy-saving and cost-reducing, efficiently use the development strategy of the energy that certain guidance is provided.

Current, both at home and abroad in the development of flowsheeting software, mainly be based on the aspect of technological design, and mostly be the tailored version simulation softward greatly, as, external Aspen Tech company, the petrochemical process simulation softward that SimSci company is released one after another, these softwares are aspect detachment process, and private processes aspect such as olefin polymerization process is ripe relatively, but because modeling technique is still immature, real unitized flowsheeting business software is actually rare, and it is more rare to be based on the software that enterprise's efficiency analyzes.Compare abroad, through years of researches and tachnical storage, China has obtained some achievements in modeling, emulation and the optimization of unit, but with compare the still bigger gap of existence abroad.The main problem that exists is that (1) integration capability is poor.Though each class model is independent to have advanced level technically, for many devices, many processes, multiple operation have serial, the integrated modeling and simulation of whole process of characteristic such as walk abreast, backflow a little less than; (2) model commonality is poor.The model of being developed is often at the specific device of independent enterprise and develop, and causes the model commonality developed poor, and the adaptability of applying is not strong.

At this situation, growth requirement towards high energy consumption industry energy conservation consumption reduction, angle from system, technology and methods such as system ensemble engineering, infotech, the energy and power engineering, environmental engineering, research is based on modeled enterprise energy consumption appraisal procedure, and set up enterprise energy consumption system integration model around this method, with energy consumption process, the material of enterprise move, digitizings such as resource distribution, complementary energy recycling; Based on institute's established model, utilization simulation analysis technology obtains the scientific and rational efficiency evaluation measures of high energy consumption enterprise simultaneously; For the direct energy consumption of business unit's product, indirectly energy consumption and fully enterprise energy consumption situation such as energy consumption forecast analysis is provided, and provide analysis platform for the efficient utilization of enterprise energy most optimum distribution of resources, balanced supply and demand of energy analysis and the energy.

Angle from the enterprise energy consumption method for establishing model, domestic and international currently used modeling method is based on mathematical model, and commonly used have power consumption statistic model, input-output model, linear programming model and system dynamics method and a System Discrimination modeling method etc.These methods have reflected the production scale and the energy consumption situation of enterprise to a certain extent.Especially input-output model is in the enterprise energy consumption cost analysis, the production of the various products that can quantitatively reflect enterprise comprehensively and produced consumes and constitutes, comprising to the consumption of self-made products, to the consumption of outsourcing raw material and fuel and to the consumption of work etc., this model is the good tool of cost accounting and analysis.But input-output model is to be based upon on the basis of historical data analysis to the accounting of energy consumption, when the industrial structure and technical factor change, static input-output model does not then have the ability of energy consumption forecast analysis, input-output model can not reflect process of manufacture intuitively simultaneously, can only reflect quantitatively that production consumption constitutes.Linear programming is an important branch of operational research, research be how some systems keep optimum duty under static state problem, as: the arrangement problem of the production schedule, the selection problem of technological process, produce blending problem, the coordination problem of operation and the assignment problem of Ministry Of Fuel And Power power resource up and down.Though this model analytically has stronger ability optimizing, but and be not suitable for analyzing whole enterprise dynamic, complicated production run.In addition, system dynamics is as the basic skills of socioeconomic systems analysis, can be used for the interaction of internal factor composition and interaction and the system and the external environmental factor of analytic system, and can determine to influence the key factor of system dynamics behavior and restriction relation each other thereof on this basis.This method is suitable for the qualitative analysis between the systematic influence factor having stronger advantage aspect the economic system prediction, and in the changeable energy consumption process of compositing factor, and be not suitable for doing quantitative analysis with this method.The System Discrimination modeling method need not the complicated mechanism of the process of understanding in depth as setting up one of important means of systematic procedure mathematical model, and the system action information that only need utilize the input and output data of system to be provided is set up the mathematical model of systematic procedure.This method is usually used in setting up the model of Business entity energy consumption unit, if but be used for the production of whole, complicated, dynamic enterprise, identification model lacks certain of overall importance and accuracy.Above-mentioned various energy consumption model modeling method, though advantage is respectively arranged, can not be from the overall situation, angle is set up for the enterprise energy consumption process and melted visual, mathematicization in multi-level, the integrated model of one intuitively.In addition, the model of being developed is developed at the specific device of independent enterprise often, and because enterprise's technological process kind is many, causes the model commonality developed poor, and the adaptability of applying is not strong.How fully to inherit on the basis of active cell Research on Equipment Model, To enterprises efficiency assessment set up be independent of specific can Source Type and with energy equipment, and embody that the energy resource consumption process is installed more, the enterprise energy consumption system model of parallel, the serial of many processes, multiple operation, characteristic such as backflow, be one of characteristic innovation of present patent application.

From the angle of flowsheeting method, the process analogy method that current flowsheeting software is adopted mainly contains: sequential modular approach (as Aspen Plus, PRO/II simulation system) and equation solving approach (as HYSYS, SPEEDUP simulation system).Wherein, sequential modular approach is hidden into each model of element equation in each module dispersedly, is that a kind of module of pursuing is carried out Calculation Method.For system architecture just cell by cell rearwards unit transmit, and not only do not had the quick-reading flow sheets situation that logistics but also incompetent stream unit from behind transmit to the unit of front conversely, adopt sequential modular approach to simulate, will be very smooth.But for the flowsheeting problem that has closed circuit, then need could use sequential modular approach by to after flow process piecemeal, cutting and the increase convergence piece, this way is comparatively burdensome, has strengthened the workload of analog computation.Equation solving approach is meant a huge system of equations as whole complicated flow system model of listing, just usually said simultaneous equations, the way of directly finding the solution.This method has been avoided the multi-level iterative computation in the sequential modular approach, so counting yield is higher.But the weak point of equation solving approach is: correctly set up difficulty of huge system of equations; Can not inherit a large amount of unit modules of having developed; Lack large-scale efficiently Nonlinear System of Equations derivation algorithm.

Summary of the invention

The objective of the invention is to propose the modeling and simulation method of the enterprise energy consumption process of a kind of counting yield height, highly versatile.

The enterprise energy consumption process model is the core of enterprise energy consumption system model, at this model, the method that the present invention proposes comprises: " based on the energy consumption process model building method of expansion Fuzzy Petri Net " and " based on the energy consumption process emulation mode of expansion Fuzzy Petri Net ", and particular content is as follows:

(1) based on the energy consumption process model building method of expanding Fuzzy Petri Net

The enterprise energy consumption process model mainly is meant the process logic of enterprise energy consumption and the interaction between the influence factor, comprises all energy consumption activities and the dependence between them of forming energy consumption process.Reflected how energy consumption system compositing factor (energy consumption equipment, energy transmission pipe network etc.) is worked in coordination with and finished Energy Consumption.The foundation of energy consumption process model should be independent of the specific energy and with can equipment, embodies that the energy resource consumption process is installed more, parallel, the serial of many processes, multiple operation, characteristic such as backflow.

At the characteristics of enterprise energy consumption process model, the enterprise energy consumption process model building method should be chosen a kind of processor-oriented modeling method, describes the various interbehaviors of energy consumption process clear, completely; Simultaneously, model should possess certain graphical expressive ability, can reflect production run intuitively, is easy to and user communication.

In the existing process modeling method, fuzzy Bi Teli (Petri) net is as a kind of mathematics and graphical the description and analysis tool of system, not only have and describe parallel, concurrent capacity, and when operation unlike common Petri nets state be discretize, but have successional.Therefore, the present invention selects the ultimate principle based on Fuzzy Petri Net for use, does corresponding expansion according to the characteristics of energy consumption process, thereby proposes " based on the energy consumption process model building method of expansion Fuzzy Petri Net ".

The substance of this method is: represent each energy consumption activity with the fuzzy transition of Fuzzy Petri Nets Model, warehouse with fuzzy storehouse corresponding raw material of represented energy consumption activity and finished product, fuzzy storehouse in reference numerals represent the quantity of raw material and finished product, corresponding relation between supply and demand is represented with the line between node, the proportion of goods damageds the when strength of joint of line is represented from origin-to-destination transportation raw material or finished product.After being familiar with the general energy consumption process of enterprise, using this method enterprise energy consumption process can be mapped as the form of Fuzzy Petri Net, and introduce corresponding transformation rule.

The ultimate principle of method is by providing to give a definition.

Definition 1: the definition of enterprise energy consumption process Fuzzy Petri Nets Model (being designated as EEC-FPN)

Be defined as one 9 tuple: EEC-FPN=(P, T, G, Y _I, Y _O, SP, EP, M ₀(p), ∑).

Wherein, P={P ₁, P ₂..., P _nBe fuzzy storehouse finite aggregate, be used to represent the warehouse of enterprise's raw material (comprising materials such as the energy and the non-energy) and finished product;

T={T ₁, T ₂..., T _mBe fuzzy transition finite aggregate ( Be that P collection and T collection are non-intersect), be used to represent energy consumption activity, energy consumption equipment or the production and processing unit of enterprise;

G={G ₁, G ₂..., G _kBe limited non-Buddhism collection, the control of it and transition interrelates, and can be used for representing branch in the energy transmission pipe network valve that converges and shed, and can control the different material stream that inputs or outputs in the production;

Y _IBe Marked Fuzzy relation on P * T, library representation to the connection situation of transition and the rated input i on the connecting line _k, strength of joint α _kAnd corresponding input intensity computing function Y _I(i _k, α _k), in the energy resource consumption process model, Function Y _I(i _k, α _k) and α _kCan adopt different definition as the case may be, for example: as strength of joint α _kDuring expression " maximum transfer rate of an energy transmission pipe network " class physical significance, can make Y _I(i _k, α _k)=min{i _k, α _k, as strength of joint α _kDuring expression " loss rate " and so on implication, available Y _I(i _k, α _k)=i _k* α _kEnergy consumption in the expression energy transmission course;

Y _OBe Marked Fuzzy relation on T * P, expression be transitted towards the storehouse the connection situation and the rated output i on the connecting line _j, strength of joint β _jAnd corresponding mark incremental computations Function Y _O(i _j, β _j), in the energy resource consumption process model, β _jAnd Y _O(i _j, β _j) definition, same α _kAnd Y _I(i _k, α _k) definition similar;

$\mathrm{SP}\⋐P$ By initial fuzzy storehouse is collected, also be the beginning node of EEC-FPN network. $\mathrm{EP}\⋐P$ For stopping that fuzzy storehouse collects also is the end node of EEC-FPN network.They are respectively applied for beginning and the end position of representing production run;

M ₀(P) be defined on the P a value in [0, ∞) in the function of real number, the initial markers state during the operation beginning of library representation place is used to represent initial resource (material) to distribute;

∑=(E, Q Δ ∏) are the additional information set, and wherein E is the external event set; Q is external arithmetic control set, such as: Q can be a System Discrimination algorithm, can identify the mathematical model of certain unit energy consumption equipment in the production through this algorithm, then can calculate under the situation of the specified quantitative input energy energy consumption situation of this equipment and output products amount by this mathematical model; The external tool collection of Δ for supporting to produce; ∏ is personal information set related in the production run.

In the definition 2:EEC-FPN network fuzzy storehouse definition

Right $\∀P_i\∈P,$ P _i=(M ₀, M _i(t), d _i, h _i), wherein,

M ₀Be the fuzzy P of storehouse institute _iInitial marking, promptly initial resource is distributed, if P _iBe continuous, M then ₀Value is arithmetic number and zero, if P _iDisperse, then M ₀Value is positive integer and zero; M _i(t) be the P of storehouse institute _iIn the sign of moment t, the variation of interior resource of the fuzzy over time storehouse of its reflection institute or material; d _iBe and the fuzzy storehouse P of institute _iThe time limit that interrelates.

The definition of fuzzy transition in the definition 3:EEC-FPN network

Right $\∀T_i\∈T,$ T _iBe 10 tuples: T _i=(τ _i, f _i, q _i, e _i, R _i, Δ _i, PO _i, G _i, N _i, h _Ti, d _Ti).

Wherein, τ _iBe be defined on the T a value in [0, ∞) in the function of real number, expression transition T _iIgniting (enabling) threshold, in the energy consumption process model, produce necessary minimum raw material number or the required energy quantity of energy consumption equipment work in order to be expressed as;

f _iBe a map that is defined on the T, it is one to the transition map among the T and is defined in its each input intensity Y _I(i _k, α _k) on a nonnegative function, be called fuzzy transition T _iThe state transitions control function, work as f _i〉=τ _iThe time fuzzy transition have the ability of igniting (enabling);

q _i∈ Q is fuzzy transition T _iS operation control, can be used for representing the dynamics of energy consumption equipment or unit energy consumption activity passing through q _iComputing can obtain the output products that the input energy of specified quantitative produced or the quantity of intermediate product behind certain physics, chemical reaction;

e _i∈ E, e _iBe and T _iThe external event that interrelates, typical external event is as specific message issue, specific incident generation etc.;

R _i=(ε, π _i), ε represents T _iExecutor's information, promptly

And π _i∈ ∏ ∪ null}, if ε=0, π _i=null, otherwise π _i∈ ∏;

Δ _iThe ∈ Δ represents to carry out the external tool of this task; PO _i=IPO _i∪ OPO _iBe fuzzy transition T _iThe inputoutput data object set, ${\mathrm{IPO}}_i\⋐P$ Be T _iThe input object collection, ${\mathrm{OPO}}_i\⋐P$ Be T _iThe object output collection;

G _i∈ G is T _iDoor collection (make G _i=IG _iBe T _iThe input gate collection, G _i=OG _iBe T _iThe out gate collection), it with the management functions such as transmission, reception and route of message token or different material stream from fuzzy transition and fuzzy storehouse separate, make structure of models more clear, reasonable;

N _i=(l, ∫), l represents this T _iNested attribute,

If l=1, ∫ ∈ EEC-FPN _S, (EEC-FPN _SBe the subnet of EEC-FPN);

For discrete T _i, d _TiBe T _iThe time delay that causes, and for continuous T _i, then definable blurs transition T _iMaximum excitation speed be v _Ti, v _Ti=1/d _Ti

Getting in touch between father's net and the subnet among the definition 4:EEC-FPN

Blur transition T if there is nested in the EEC-FPN model _i, i.e. l (T _i)=1, ∫ (T _i)=EEC-FPN _S, and T _iThe inputoutput data object set be respectively IPO _iAnd OPO _i, SP (EEC-FPN then _S)=IPO _i, EP (EEC-FPN _S)=OPO _i

(2) based on the energy consumption process emulation mode of expanding Fuzzy Petri Net

Simulation algorithm has partly been described what use is made of institute established model and has been carried out emulation.The enterprise energy consumption process model is based on the expansion Fuzzy Petri Net, so the research of simulation algorithm should launch based on the analytical approach of expansion Fuzzy Petri Nets Model.The evolutionary process of energy consumption system is to advance by the state of discrete event and continuous dynamic process is common, therefore, must consider these two kinds of evolution modes simultaneously in emulation mechanisms.The emulation of energy consumption process is formed by the quantitative change and qualitative change mixed and alternate, in emulation mechanisms, each class variable and status information all by fuzzy storehouse in sign embody, and all kinds of Energy Consumption and dependent event are all finished by fuzzy exciting of transition.The excitation process of fuzzy transition is equal to the consumption of energy consumption activity to the energy, each fuzzy transition excite beginning all will consume its preposition fuzzy storehouse in relevant holder agree (energy), and in these fuzzy transition, produce new Tuo Ken, and this new Tuo Ken is positioned over its rearmounted bluring in the institute of storehouse.According to such thinking, select the step-length of time minimum in each incident (transition excite beginning and transition to excite end all can regard the incident that occurs in the emulation as) as the system emulation clock, realize the propelling of emulation.Concrete simulation algorithm is as follows:

1, initialization pessimistic concurrency control, and global simulation clock t and emulation concluding time tmax, determine each fuzzy storehouse in the model initial marking M0 (P).

2, determine whether current sign is target identification, if emulation finishes, otherwise, change step 3 over to.(perhaps judge the value of current t, if t 〉=t _Max, emulation finishes, otherwise changes step 3) over to.

3, determine current t constantly enable fuzzy transition collection T ^E

3.1, make A={a ₁, a ₂..., a _n} ^T=0, E={e ₁, e ₂..., e _m} ^T=0.Here, A represent each fuzzy storehouse the sign situation, E represents the situation that enables of each fuzzy transition, its element is 0 or 1.As the fuzzy P of storehouse institute _i(i=1,2 ..., identification number n) greater than 0 or its when being supplied to, a _iBe 1, otherwise a _iBe 0; As fuzzy transition T _j(j=1,2 ..., when m) enabling, e _jBe 1, otherwise e _jBe 0;

3.2, for the fuzzy P of storehouse institute _i, i carries out cycle criterion from 1 to n, if M _i(t)＞0, a then _i=1; Otherwise a _i=0;

3.3, for a that satisfies condition arbitrarily _i=1 the fuzzy storehouse P of institute _iCarry out cycle criterion, whether then to judge the time limit link with it, if, a _i=1, otherwise make a _i=0;

3.4, for fuzzy transition T _j, j carries out cycle criterion from 1 to m, works as T _jInitial conditions be " with " time, $\∀P_i\∈T_j^{*},$ (a _i=1) ∧ (f _j〉=τ _j), e then _j=1, otherwise, e _j=0; Work as T _jInitial conditions be " or " time, $\∃P_i\∈T_j^{*},$ (a _i=1) ∧ (f _j〉=τ _j), e then _j=1, otherwise, e _j=0;

3.5, T ^E={ T _j| e _jThe fuzzy transition collection of=1} for enable this moment.

4, to enabling fuzzy transition collection T ^EIn each fuzzy transition T _j, determine its excitation instant t respectively according to time delay _j, and with t _jAnd excite fuzzy transition T accordingly _jPut into event table T in the future ^FIn.

5, the event table T in future of scan model ^F, find out minimum excitation instant min (t _j) and excite fuzzy transition T accordingly _j

6, advance global simulation clock t=min (t _j), excite corresponding fuzzy transition T _j, according to following formula calculate relevant fuzzy storehouse in sign, obtain the sign that next is netted constantly, change step 2 over to.

1. be discrete when bluring transition

Work as T _jExcite the back at t+dt:

$M_i(t+\mathrm{dt})=\left\{\begin{array}{cc}{M}_i\left(t\right)-I(P_i,T_j),& \∀P_i\∈{\mathrm{IOP}}_j\\ M_i\left(t\right)+O(P_i,T_j),& \∀P_i\∈{\mathrm{OOP}}_j\end{array}\right.,$ Here I (P _i, T _j) and O (P _i, T _j) be respectively and blur the P of storehouse institute _iWith fuzzy transition T _jBetween input, output intensity computing function; M _i(t) the fuzzy P of storehouse institute of expression moment t _iResource quantity.

2. be continuous when bluring transition

In moment t → t+dt, P _iSign change:

$M_i(t+\mathrm{dt})=\left\{\begin{array}{cc}{M}_i\left(t\right)-v_j\left(t\right)\·I(P_i,T_j)\·\mathrm{dt},& \∀P_i\∈{\mathrm{IOP}}_j\\ M_i\left(t\right)+v_j\left(t\right)\·O(P_i,T_j)\·\mathrm{dt},& \∀P_i\∈{\mathrm{OOP}}_j\end{array}\right.,$ Here I (P _i, T _j) and O (P _i, T _j) the fuzzy P of storehouse institute of expression equally respectively _iWith fuzzy transition T _jBetween input, output intensity computing function; M _i(t) the fuzzy P of storehouse institute of expression moment t _iResource quantity, and v _j(t) be fuzzy transition T _jExcitation rate.

The inventive method can be described the various interbehaviors of enterprise clear, completely, can react production run intuitively, be easy to and user communication, and, counting yield height, highly versatile, and can instruct enterprise to strengthen managing power consumption, energy savings.

Description of drawings

Fig. 1 is the EEC-FPN model diagram of embodiment.

Among the figure, P ₁---the warehouse of unstripped gas A, P ₂---the warehouse of unstripped gas B, P ₃---by reactor powered can, P ₄---the storage of mixer output, P ₅---storage power supply state sign, P ₆---deposit the input of the required chilled water of heat interchanger, P ₇---the storage of reactor output, P ₈---deposit the output of the required chilled water of heat interchanger, P ₉---the output storage of heat interchanger, P ₁₀---flash evaporator power can, P ₁₁---the storage of products C, P ₁₂---the warehouse of unreacting gas, P ₁₃---inside reactor capacity, P ₁₄---storage power down mode sign, P ₁₅---the storage of waste gas D, T ₁---mixer, T ₂---abstract type activity, expression reactor working portion, T ₃---heat interchanger, T ₄---cooling activity, T ₅---flash evaporator, T ₆---dispenser, promptly complementary energy reclaims link, T ₇---powered operation, T ₈---the operation that has a power failure, G ₁---input and door, the G of mixer ₂---input and door, the G of reactor ₃---input and door, the G of heat interchanger ₄---output and door, the G of heat interchanger ₅---input and door, the G of flash evaporator ₆---output and door, the G of flash evaporator ₇, G ₈---the input and the door of confession, power failure logical operation.

Embodiment

With certain reaction under high pressure flow process is example, further specifies the inventive method.

Reaction under high pressure flow process: enter reactor after two kinds of different unstripped gas A, B and circular flow (with A for leading and containing a small amount of B and C) mix.Reactor carries out A+B → C+D reaction under the electrical heating situation, its working temperature should maintain 80 ~ 100 ℃, and when reactor volume reaches 60L, should stop charging.Enter flash evaporator after the cooling of reactor outlet stream stock-traders' know-how heat interchanger.Major product C flows out bottom flash evaporator, and unreacted A (and a spot of B and C) and waste gas D deliver to dispenser after the flash evaporator vapor phase exit is discharged, make waste gas D discharge, and unreacted A (and a spot of B and C) returns use.This production run is except that the consumption to raw material A, B, and the main usefulness of its production equipment can be electricity and water.

The EEC-FPN model of this reaction under high pressure flow process as shown in Figure 1.

In addition, T1 represents mixer and satisfies τ ₁=2, $f_1=\frac{I(P_1,T_1)}{I(P_2,T_1)},$ q ₁: O (P ₃, T ₁)=I (P ₁, T ₁)+I (P ₂, T ₁), R ₁=0, IPO ₁={ P ₁, P ₂, OPO ₁={ P ₃, l ₁=0, h _T1=1, V ₁=1; T2 is abstract type activity, expression reactor working portion, and satisfy τ ₂=0, f ₂=(I (P ₃, T ₂)＞0) ∧ (I (P ₄, T ₂)＞0), q ₂: O (P ₇, T ₂)=60, R ₂=0, IPO ₂={ P ₃, P ₄, P ₅, OPO ₂={ P ₇, P ₁₃, l ₂=1, ∫ ₂: slightly, h _T2=1, V ₂=1/3; M ₀(P ₁)=49, M ₀(P ₂)=30, M ₀(P ₂)=80, M ₀(P ₁₄)=1, M ₀(P ₆)=60, M ₀(P ₁₀)=50, other each fuzzy storehouse initial marking be 0.

Provide emulation below and advance 30s, the sampling period is when being 1s, and the number change of the part energy (or material) and equipment state switch instances are as shown in table 1 below:

Number change of each energy of table 1 (or material) and equipment state switch instances

Time	Unstripped gas A	Unstripped gas B	Potpourri	Electricity	Power supply state	Reactant	Unreacting gas	The reaction output	Waste gas	Power down mode
											Initial value	49	30	0	80	0	0	0	0	0	1
1	47	29	3	80	1	0	0	0	0	0
											2	47	29	3	80	1	0	0	0	0	0
3	45	28	2.94	79.99529	1	30.6	0	0	0	0
											4	45	28	2.94	79.99529	1	30.6	0	0	0	0
5	43	27	5.94	79.99529	1	30.6	0	0	0	0
											6	43	27	5.94	79.99529	1	30.6	0	0	0	0
7	41	26	5.880001	79.99057	1	61.2	0	0	0	0

8	41	26	5.880001	79.99057	0	1.200001	1	60	0	1
											9	39.3	25	8.880001	79.99057	1	1.200001	0	60	0.7	0
10	39.3	25	8.880001	79.99057	1	1.200001	0	60	0.7	0
											11	37.3	24	8.820001	79.98586	1	31.8	0	60	0.7	0
12	37.3	24	8.820001	79.98586	1	31.8	0	60	0.7	0
											13	35.3	23	11.82	79.98586	1	31.8	0	60	0.7	0
14	35.3	23	11.82	79.98586	1	31.8	0	60	0.7	0
											15	33.3	22	11.76	79.98114	1	62.4	0	60	0.7	0
16	33.3	22	11.76	79.98114	0	2.400002	1	120	0.7	1
											17	31.6	21	14.76	79.98114	1	2.400002	0	120	1.4	0
18	31.6	21	14.76	79.98114	1	2.400002	0	120	1.4	0
											19	29.6	20	14.7	79.97643	1	33	0	120	1.4	0
20	29.6	20	14.7	79.97643	1	33	0	120	1.4	0
											21	27.6	19	17.7	79.97643	1	33	0	120	1.4	0
22	27.6	19	17.7	79.97643	1	33	0	120	1.4	0
											23	25.6	18	17.64	79.97171	1	63.6	0	120	1.4	0
24	25.6	18	17.64	79.97171	0	3.599998	1	180	1.4	1
											25	23.9	17	20.64	79.97171	1	3.599998	0	180	2.1	0
26	23.9	17	20.64	79.97171	1	3.599998	0	180	2.1	0
											27	21.9	16	20.58	79.967	1	34.2	0	180	2.1	0
28	21.9	16	20.58	79.967	1	34.2	0	180	2.1	0
											29	19.9	15	23.58	79.967	1	34.2	0	180	2.1	0
30	19.9	15	23.58	79.967	1	34.2	0	180	2.1	0

Claims (1)

1, a kind of enterprise energy consumption process model building and emulation mode is characterized in that comprising " based on the energy consumption process model building method of expansion Fuzzy Petri Net " and " based on the energy consumption process emulation mode of expansion Fuzzy Petri Net ", and particular content is as follows:

(1) based on the energy consumption process model building method of expanding Fuzzy Petri Net

The substance of this method is: represent each energy consumption activity with the fuzzy transition of enterprise energy consumption process Fuzzy Petri Nets Model, warehouse with fuzzy storehouse corresponding raw material of represented energy consumption activity and finished product, fuzzy storehouse in reference numerals represent the quantity of raw material and finished product, corresponding relation between supply and demand is represented with the line between node, the proportion of goods damageds the when strength of joint of line is represented from origin-to-destination transportation raw material or finished product; Wherein:

Definition 1: the enterprise energy consumption process Fuzzy Petri Nets Model, be designated as EEC-FPN, it is defined as:

One 9 tuple: EEC-FPN=(P, T, G, Y _I, Y _O, SP, EP, M ₀(p), ∑);

Wherein, P={P ₁, P ₂..., P _nBe fuzzy storehouse finite aggregate, be used to represent the warehouse of enterprise's raw material and finished product;

T={T ₁, T ₂..., T _mBe the finite aggregate of fuzzy transition,

Be that P collection and T collection are non-intersect, be used to represent energy consumption activity, energy consumption equipment or the production and processing unit of enterprise;

G={G ₁, G ₂..., G _kBe limited non-Buddhism collection, the control of it and transition interrelates, and be used for representing the branch of the energy transmission pipe network valve that converges and shed, and control inputs or outputs the different material stream in the production;

Y _IBe Marked Fuzzy relation on P * T, library representation to the connection situation of transition and the rated input i on the connecting line _k, strength of joint α _kAnd corresponding input intensity computing function Y _I(i _k, α _k), in the energy resource consumption process model, Function Y _I(i _k, α _k) and α _kCan adopt different definition as the case may be: as strength of joint α _kDuring expression " maximum transfer rate of an energy transmission pipe network " class physical significance, make Y _I(i _k, α _k)=min{i _k, α _k, as strength of joint α _kDuring expression " loss rate " and so on implication, use Y _I(i _k, α _k)=i _k* α _kEnergy consumption in the expression energy transmission course;

Y _OBe Marked Fuzzy relation on T * P, expression be transitted towards the storehouse the connection situation and the rated output i on the connecting line _j, strength of joint β _jAnd corresponding mark incremental computations Function Y _O(i _j, β _j), in the energy resource consumption process model, β _jAnd Y _O(i _j, β _j) definition, same α _kAnd Y _I(i _k, α _k) definition similar;

$\mathrm{SP}\⋐P$ For initial fuzzy storehouse collects is the beginning node of EEC-FPN network, $\mathrm{EP}\⋐P$ For stopping that fuzzy storehouse collects is the end node of EEC-FPN network, and they are respectively applied for beginning and the end position of representing production run;

M ₀(P) be defined on the P a value in [0, ∞) in the function of real number, the initial markers state during the operation beginning of library representation place is used to represent initial resource to distribute;

∑=(Δ ∏) is the additional information set for E, Q, and wherein E is the external event set, and Q is external arithmetic control set, and the external tool collection of Δ for supporting to produce, ∏ are personal information set related in the production run;

In the definition 2:EEC-FPN network fuzzy storehouse definition

Right $\∀P_i\∈P,$ P _i=(M ₀, M _i(t), d _i, h _i), wherein,

M ₀Be the fuzzy P of storehouse institute _iInitial marking, promptly initial resource is distributed, if P _iBe continuous, M then ₀Value is arithmetic number and zero, if P _iDisperse, then M ₀Value is positive integer and zero; M _i(t) be the P of storehouse institute _iIn the sign of moment t, the variation of interior resource of the fuzzy over time storehouse of its reflection institute or material; d _iBe and the fuzzy storehouse P of institute _iThe time limit that interrelates;

The definition of fuzzy transition in the definition 3:EEC-FPN network

Right $\∀T_i\∈T,$ T _iBe 10 tuples: T _i=(τ _i, f _i, q _i, e _i, R _i, Δ _i, PO _i, G _i, N _i, h _Ti, d _Ti);

Wherein, τ _iBe be defined on the T a value in [0, ∞) in the function of real number, expression transition T _iThe igniting threshold, in the energy consumption process model, produce necessary minimum raw material number or the required energy quantity of energy consumption equipment work in order to be expressed as;

f _iBe a map that is defined on the T, it is one to the transition map among the T and is defined in its each input intensity Y _I(i _k, α _k) on a nonnegative function, be called fuzzy transition T _iThe state transitions control function, work as f _i〉=τ _iThe time fuzzy transition have the ability of igniting;

q _i∈ Q is fuzzy transition T _iS operation control, be used to represent the dynamics of energy consumption equipment or unit energy consumption activity pass through q _iComputing obtain the output products that the input energy of specified quantitative produced or the quantity of intermediate product behind certain physics, chemical reaction;

e _i∈ E, e _iBe and T _iThe external event that interrelates;

R _i=(ε, π _i), ε represents T _iExecutor's information, promptly

And π _i∈ ∏ ∪ null}, if ε=0, π _i=null, otherwise π _i∈ ∏;

Δ _iThe ∈ Δ represents to carry out the external tool of this task; PO _i=IPO _i∪ OPO _iBe fuzzy transition T _iThe inputoutput data object set, ${\mathrm{IPO}}_i\⋐P$ Be T _iThe input object collection, ${\mathrm{OPO}}_i\⋐P$ Be T _iThe object output collection;

G _i∈ G is T _iDoor collection, make G _i=IG _iBe T _iThe input gate collection, G _i=OG _iBe T _iThe out gate collection, it with the management function of transmission, reception and the route of message token or different material stream from fuzzy transition and fuzzy storehouse separate, make structure of models more clear, reasonable;

N _i=(l, ∫), l represents this T _iNested attribute,

If l=1, ∫ ∈ EEC-FPN _S, EEC-FPN _SIt is the subnet of EEC-FPN;

For discrete T _i, d _TiBe T _iThe time delay that causes, and for continuous T _i, then definable blurs transition T _iMaximum excitation speed be v _Ti, v _Ti=1/d _Ti

Getting in touch between father's net and the subnet among the definition 4:EEC-FPN

Blur transition T if there is nested in the EEC-FPN model _i, i.e. l (T _i)=1, ∫ (T _i)=EEC-FPN _S, and T _iThe inputoutput data object set be respectively IPO _iAnd OPO _i, SP (EEC-FPN then _S)=IPO _i, EP (EEC-FPN _S)=OPO _i

(2) based on the energy consumption process emulation mode of expanding Fuzzy Petri Net

Concrete steps are as follows:

Step 1, initialization pessimistic concurrency control, and global simulation clock t and emulation concluding time tmax, determine each fuzzy storehouse in the model initial marking M0 (P);

Step 2, determine whether current sign is target identification, if emulation finishes, otherwise, change step 3 over to.(perhaps judge the value of current t, if t 〉=t _Max, emulation finishes, otherwise changes step 3 over to;

Step 3, determine current t constantly enable fuzzy transition collection T ^E:

3.1, make A={a ₁, a ₂..., a _n} ^T=0, E={e ₁, e ₂..., e _m} ^T=0; Here, A represent each fuzzy storehouse the sign situation, E represents the situation that enables of each fuzzy transition, its element is 0 or 1; As the fuzzy P of storehouse institute _i, i=1,2 ..., n, identification number greater than 0 or its when being supplied to, a _iBe 1, otherwise a _iBe 0; As fuzzy transition T _j(j=1,2 ..., when m) enabling, e _jBe 1, otherwise e _jBe 0;

3.2, for the fuzzy P of storehouse institute _i, i carries out cycle criterion from 1 to n, if M _i(t)＞0, a then _i=1; Otherwise a _i=0;

3.3, for a that satisfies condition arbitrarily _i=1 the fuzzy storehouse P of institute _iCarry out cycle criterion, whether then to judge the time limit link with it, if, a _i=1, otherwise make a _i=0;

3.4, for fuzzy transition T _j, j carries out cycle criterion from 1 to m, works as T _jInitial conditions be " with " time, $\∀P_i\∈T_j^{*},$ (a _i=1) ∧ (f _j〉=τ _j), e then _j=1, otherwise, e _j=0; Work as T _jInitial conditions be " or " time, $\∃P_i\∈T_j^{*},$ (a _i=1) ∧ (f _j〉=τ _j), e then _j=1, otherwise, e _j=0;

3.5, T ^E={ T _j| e _jThe fuzzy transition collection of=1} for enable this moment;

Step 4, to enabling fuzzy transition collection T ^EIn each fuzzy transition T _j, determine its excitation instant t respectively according to time delay _j, and with t _jAnd excite fuzzy transition T accordingly _jPut into event table T in the future ^FIn;

The event table T in future of step 5, scan model ^F, find out minimum excitation instant min (t _j) and excite fuzzy transition T accordingly _j

Step 6, propelling global simulation clock t=min (t _j), excite corresponding fuzzy transition T _j, according to following formula calculate relevant fuzzy storehouse in sign, obtain the sign that next is netted constantly, change step 2 over to:

1. be discrete when bluring transition

Work as T _jExcite the back at t+dt:

$M_i(t+\mathrm{dt})=\left\{\begin{array}{cc}{M}_i\left(t\right)-I(P_i,T_j),& \∀P_i\∈{\mathrm{IOP}}_j\\ M_i\left(t\right)+O(P_i,T_j),& \∀P_i\∈{\mathrm{OOP}}_j\end{array}\right.,$ Here I (P _i, T _j) and O (P _i, T _j) be respectively and blur the P of storehouse institute _iWith fuzzy transition T _jBetween input, output intensity computing function; M _i(t) the fuzzy P of storehouse institute of expression moment t _iResource quantity;

2. be continuous when bluring transition

In moment t → t+dt, P _iSign change:

$M_i(t+\mathrm{dt})=\left\{\begin{array}{cc}{M}_i\left(t\right)-v_j\left(t\right)\·I(P_i,T_j)\·\mathrm{dt},& \∀P_i\∈{\mathrm{IOP}}_j\\ M_i\left(t\right)+v_j\left(t\right)\·O(P_i,T_j)\·\mathrm{dt},& \∀P_i\∈{\mathrm{OOP}}_j\end{array}\right.,$ Here I (P _i, T _j) and O (P _i, T _j) the fuzzy P of storehouse institute of expression equally respectively _iWith fuzzy transition T _jBetween input, output intensity computing function; M _i(t) the fuzzy P of storehouse institute of expression moment t _iResource quantity, and v _j(t) be fuzzy transition T _jExcitation rate.

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