CN106168769A - The modeling of a kind of multiple coupled hybrid flowsheet industrial process and emulation mode - Google Patents
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Abstract
The present invention relates to modeling and the emulation mode of a kind of multiple coupled hybrid flowsheet industrial process, comprise the following steps: S1, set the control program of industrial process, set up multiple coupled hybrid flowsheet industrial process model based on Petri network, control program includes production equipment and mutual relation thereof, and model is top-down includes hierarchical structure submodel HM, composite component submodel CCM and basic building block submodel BCM；S2, the initialization data of input control program, the model utilizing step S1 to set up carries out the emulation of industrial process, obtains the related data of industrial process, including time, energy consumption and yield.Compared with prior art, Petri network modeling is applied to the control program of industrial process by the present invention, it is proposed that the modeling method of basic building block, composite component and multilayered structure, model restructural, can carry out the emulation of multiple coupled industrial process, modeling efficiency is high, and the data obtained are closer to reality.
Description
Technical Field
The invention relates to a process industrial process control method, in particular to a modeling and simulation method of a multi-coupling hybrid process industrial process.
Background
The process industry has the characteristics of various, large-batch, uncertain and nonlinear production and continuous, stable and safe production management and control requirements, so that the research of the process industry process becomes a hot problem in academia and industry. Process industrial processes are a class of multi-coupling hybrid systems. The multi-coupling refers to the strong coupling characteristic of network interaction around the physical connection of process industrial production equipment and the management and control of production processes, and energy flow, logistics and information flow. Three flows interact with each other. Small changes in one flow will affect the operation of the other flows, i.e. pulling the whole body. For example, in a typical process industrial process, the material proportion relationship is a key production element, the proportion of input materials of upstream production equipment changes, the variety of output materials and the output rate thereof change correspondingly, wherein the output materials include reusable secondary energy (waste heat) which can be used as operation resources of downstream production equipment, so that the operation of energy source flow is influenced; meanwhile, the variation of the output material variety and the output thereof affects the supply amount of the input material of the downstream production equipment, resulting in the corresponding change of the operation load control of the equipment, namely, the information flow is changed.
Additionally, process industry processes are a typical type of hybrid system. The product processing is continuous production, and the reaction and separation are continuous processes which cannot be interrupted, but are accompanied by a plurality of discrete events such as equipment starting and stopping, equipment failure, equipment maintenance and the like. Therefore, continuous and discrete elements in the process industrial process need to be coordinated, and continuous, stable and safe operation of production is guaranteed. The multi-coupling hybrid characteristic of the process industrial process makes the management and control of the industrial process more difficult and complicated, and especially the strong coupling behavior between three streams is closely associated with a large number of production elements, so that an analyst is often involved in complicated and tedious details.
The multi-coupling hybrid nature of the process industry has made management and control of industrial processes model-based, and process industry process modeling has become an important research area for system modeling technology. In the field, the prior art mainly comprises a process industrial energy consumption process modeling method and a chemical process simulation method based on Petri net.
Petrinet is a system modeling approach that has been widely studied and applied. The method can describe asynchronous and concurrent characteristics, and has a strict mathematical analysis function and visual graphic expression. The scholars apply the Petri net method to modeling of the process industrial energy consumption system, and provide the process industrial energy consumption process modeling method based on Petri net. An enterprise energy consumption process model is established by using a fuzzy Petri network, and the method can describe energy consumption behaviors in a process industrial system in detail, but lacks complete description of logistics and control information flow; a hybrid Petri network model research facing continuous enterprise energy coupling optimization published in high technology communication adopts a color expansion hybrid Petri net modeling method to establish a continuous enterprise energy coupling optimization model consisting of an energy consumption equipment unit model, a production scheduling rate model, an energy system optimization scheduling model and a production control model, although the model considers the coupling effect of energy source flow, material new and information flow, the model is mainly used for the optimization problem of energy consumption activity, and the dynamic behavior of the process industrial process is not fully described and analyzed from the system level.
Chemical process simulation is a process system simulation method for the chemical industry. It was developed mainly to solve the design, modification and optimization problems of the production process. The chemical process simulation can define the structural composition of the production process and the corresponding material balance, energy balance, production equipment constraint and other production process parameters in detail, and can describe the dynamic characteristics of the production process changing along with time. The concrete modeling method can be further divided into: sequential module method, simultaneous equation method, simultaneous module method and data driving method. The sequential module method is a method for calculating module by module, firstly, according to the basic unit operation of production process, every unit operation module and its calculating method are set up, when the complex process system formed from single equipment is to be set up, these unit operation modules can be combined together to describe correspondent process system, and the outlet material flow information obtained by previous module can be used as inlet material flow data required by next module. The method has good simulation effect on the process system with the sequential transmission structure, but the solving capability of the method is greatly reduced for the design problems of the process system with the return circuit structure and the process system; the simultaneous equation method is that a huge equation set is established to describe the whole complex flow system, and flow simulation is carried out through solving the equation set, the method can be suitable for design type problems and simulation type problems, and is also suitable for complex process systems with loop structures, but the establishment process of the equation set is relatively complex; the simultaneous modular approach combines the advantages of the sequential modular approach and the simultaneous equation approach, and divides the flow system description and simulation into a module level and a flow system level. The former adopts a strict unit model for calculation, and constructs a simplified model of the strict unit model according to the calculation result, the simplified models of all units are connected at a flow system level, the solution is carried out at the system level, and the logistics or energy flow data connecting all the units are mainly obtained. The chemical process simulation method mainly provides detailed description of equipment composition structure and logistics characteristics in the production process system, and only takes the energy flow as the input and output attribute of the production equipment although the energy consumption behavior of the equipment is also considered, and the interaction and coupling relationship among the energy, logistics and control information in the production process system is not accurately described.
With the generation of large-scale complex systems in the process industry, the models established by the method become large and complex, the modeling process is complicated and clumsy, and the management and control requirements on the complex process industrial process are difficult to meet.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a modeling and simulation method of a multi-coupling hybrid process industrial process by using hybrid, controlled and colored Petri net. The method adopts a component-based packaging concept, supports a multi-level reconfigurable modeling mode, can completely and accurately describe the energy flow, the logistics and the information flow in the process of the process industry and the coupling relation among the energy flow, the logistics and the information flow, and has the advantages of high calculation efficiency, strong reusability and quick and flexible modeling process.
The purpose of the invention can be realized by the following technical scheme:
a modeling and simulation method for a multi-coupling hybrid process industrial process comprises the following steps:
s1, setting a control scheme of the industrial process, and establishing a Petri network-based multi-coupling hybrid process industrial process model, wherein the control scheme comprises production equipment and the interrelation thereof, and the model comprises from top to bottom:
the hierarchical structure submodel HM is a 3-tuple structure { H, B, L }, wherein H describes the hierarchical structure of the model, B describes a component set contained in each hierarchy, L describes the connection relationship among the components, and the components comprise basic components and composite components formed by connecting the basic components;
the CCM is a 2-tuple structure { B ', L' }, wherein B 'describes basic components contained in the CCM, L' describes the connection relation among the basic components, each basic component is provided with an interface library, and the matched interface libraries among the basic components are connected with each other;
a basic component submodel BCM, wherein the BCM describes the internal structure of a basic component;
in the model, a component corresponds to production equipment, an interface library comprises an energy library place, a material library place and an information library place, and the connection relation among the interface libraries corresponds to energy flow, material flow or information flow among production devices;
and S2, inputting the initialization data of the control scheme, and performing simulation of the industrial process by using the model established in the step S1 to obtain relevant data of the industrial process, including time, energy consumption and yield. The transition enabling rules and the transition exciting rules are evolution rules of model simulation, and different transition functions have different enabling rules and different exciting rules, so that the identification of the connected library is evolved to promote the model simulation.
By setting various different control schemes, the results obtained by each scheme are compared, and the optimal control scheme can be obtained.
The basic component submodel BCM is of an 8-tuple structure { P, T, A, I, O, F, C, M },
wherein P is a finite library set, P ═ Pd∪Pc∪PiWherein P isdIs a subset of a discrete library for describing discrete states of an industrial process, including operation and shutdown, PcIs a subset of a continuous store for describing energy and material objects in the industrial process, including an energy store PeAnd the Material depot Pm，PiThe information base subset is used for describing various control information and monitoring information in the industrial process;
t is finite transition set, T ═ Td∪Tc∪Ti∪TctlWherein, TdIs a discrete transition subset and is used for describing various discrete events in the industrial process, including equipment starting and stopping, equipment failure recovery, equipment starting maintenance and equipment completion, TcFor a continuous transition subset, describing continuous production in a production process, TiIs a subset of information transitions for describing information acquisition, monitoring and processing, TctlFor controlling subsets of transitions, for describing discrete transitionsAnd control of continuous transitions;
a is directed arc set connecting the library and the transition, A is (P × T) ∪ (T × P), A is Ac∪Ai∪ArAccording to the enabling rules, the directed arc contains three types, AcThe corresponding enabling rule is that the identification of the input library is not less than AcThe weight of (A) plays a role of general enabling, AiThe corresponding enabling rule is that the identification of the input library is less than AiThe weight of (A) plays a role in inhibition, ArThe corresponding enabling rule is located at A for the identification of the input libraryrThe value interval defined by the weight value plays a role of interval;
the weights on the directed arc are Boolean (0,1), non-negative integersNon-negative real numberOr a certain real interval valueFor positive real numbers, the different types of I, O and the different effects of the associated directed arcs are as follows:
wherein, the directed arc in the formula (1-1) plays a role in inputting and outputting energy, general enabling between materials and continuous production, the directed arc in the formula (1-2) plays a role in inputting and outputting discrete events and general enabling between discrete states, the directed arc in the formula (1-3) plays a role in controlling continuous production/discrete events, the directed arc in the formula (1-4) represents input information and generates output information through a control strategy, the directed arc in the formula (1-5) represents input information and generates output information through processing, the directed arc in the formula (1-6) plays a role in acquiring and monitoring information of discrete states, input and output energy and materials, the directed arc in the formula (1-7) plays a role in monitoring overflow of the capacity of the storage tank and generating overflow state, and the directed arc in the formula (1-8) plays a role in monitoring ultralow capacity of the storage tank, The function of generating an ultra-low state, and the function of monitoring the capacity of the storage tank without exceeding the limit and generating the state without exceeding the limit is realized by the directional arc in the formula (1-9).
I is the set of input functions from the library onto the transitional directional arc,o is the set of output functions that migrate from the transition to all the directed arcs of the library,I. o defines the weight on the directed arc, → representing the mapping of the function;
f is the set of transition excitation functions, F: Tc∪Ti∪Tctl→ f, comprising a continuous transition excitation function f (T)c) Information transition excitation function f (T)i) And controlling the transition excitation function f (T)ctl) Wherein, f (T)c) Describing the rate at which a production facility converts input energy or material to output energy or material, which may be flow rate, temperature rate, or pressure rate, f (T)i) For collecting, monitoring and processing information identified by the input library, f (T)ctl) Optimization and control strategies including time functions, logic rules, and fuzzy rules identified by the input library, → representing the mapping of the functions;
c is the color set of the library and the transition,if p isi∈·t∩Pc，pj∈·t∩PcThen t and pi,pjPresence of color setsEach colorRepresenting a class of attribute parameters, wherein t and t respectively represent an input library place and an output library place of the transition t;
m is the identification set of the library, M ═ M0∪MτTau time identification set MτAnd an initial time identification set M0Respectively, the number of the elements is npSet of (2), nPAnd | P |, | represents the number of elements in the set, and the identification types and meanings thereof corresponding to different types of libraries are respectively as follows:
wherein p is a library, m (p) is an identifier corresponding to the library p, m (p) in formula (2-1) represents a discrete state of production equipment, m (p) in formula (2-2) represents attribute parameters of input and output energy and materials, m (p) in formula (2-3) represents control over discrete transition and is related to time tau, tau is fixed time or random time, m (p) in formula (2-4) represents control over continuous transition and is related to time tau, and tau is fixed time or random time.
pi，pjIs corresponding to oneThe vector is identified and, in response to the identification,| represents the number of elements in the set, i.e.Accordingly, there is a vector of input-output functions, i.e. There is a vector of the excitation function that is present, description of the inventionThe time-varying speed of the represented property parameter.
At the initial identification M0Under the action of (2), the transition in the model is enabled and excited continuously, so that the model mark M is marked0Transition from elapsed time τ to MτThe model identification describes the state of the system. M0，MτThe initial state of the system and the state at time τ, respectively. Therefore, the enabling and triggering rules of the transition are evolution rules of the model simulation. Transition enabling and excitation of the transition are closely related, and different transition effects have different enabling and excitation rules, so that the identification of the connected library is evolved, and model simulation is promoted.
(1) Enabling function
Enabled at time τ, if and only ifAnd isOr mτ(pc) 0 but pcIs in the supply state.
And after t is excited, the time Δ τ elapsed is:
① case T ∈ TdThen, there are:new identity m (p)' ═ mτ(p)-I(p,t)；New identity m (p)' ═ mτ(p)+O(p,t)。
②t∈TcThen, there are:new identity m (p)' ═ mτ(p)-I(p,t)f(t)Δτ；New identity m (p)' ═ mτ(p)+O(p,t)f(t)Δτ。
In particular, when p, t has a color set c, at mark mτNext, transition t is with respect to colorEnable, if and only if mτ(pc) > 0 or mτ(pc) 0 but pcIs in the supply state.
And after t is excited, the time Δ τ elapsed is:
new identification
New identification
(2) Inhibition of
The enable rule of the suppression effect is opposite to the enable effect, the effect is generally used for controlling the storage space of the buffer, and when the storage space is not overflowed, the excitation of the output transition is suppressed; when the storage space overflows, its output transitions are enabled and fired.
Even if the enabling rule is:enabled at time τ, if and only ifAnd after t is excited, a new mark m (p)' ═ mτ(p), i.e., the identity of the input transition of the suppression arc is not changed after it is fired.
(3) Control action
Control action is typically used to control the continuous transition TcOr controlling discrete transition TdThe excitation of (a) is carried out,enabled at time τ if and only if p ∈ · t ∩ pi,mτ(p)＞0,I(p,tc) When t is excited, a new mark m (p)' (m)τ(p)，f(tc)'＝mτ(p)。Enabled at time τ if and only if p ∈ · t ∩ pi,mτ(p)＝1,I(p,td) When t is equal to 1cAfter excitation, a new mark m (p)' (0).
(4) Information monitoring and processing
Information monitoringWith processing for monitoring the input information store piAnd the state is changed to T via informationiIs converted into an appropriate form to be output as an output information base pjIs represented by the identifier of (a).Enabled at time τ, if and only if I (p)i,t)＝O(piAnd t) is 1. When t is excited, a new mark m (p)i)’＝mτ(pi)，m(pj)’＝mτ(pi)f(t)。
(5) Reasoning effect
Inference action is generally used for information control, controlling transition TctlThe excitation function of (a) being an inference rule or control algorithm, TctlWill input information base piAs input to the inference rules or control algorithms, generates output information, expressed as output libraries p, from the inference and controljIs detected.Enabled at time τ, if and only if I (p)i,t)＝O(piAnd t) is 1. When t is excited, a new mark m (p)i)'＝mτ(pi)，m(pj)'＝mτ(pi)f(t)。
Compared with the prior art, the invention has the following advantages:
(1) the Petri net modeling is applied to a control scheme of an industrial process, a modeling method of a basic component, a composite component and a multilayer structure is provided based on a component-based packaging concept, a model is reconfigurable, simulation of a multi-coupling industrial process can be performed, modeling efficiency is high, energy flow, logistics and information flow and coupling relations among the energy flow, the logistics and the information flow in the process industrial process can be completely and accurately described, and obtained data are closer to reality.
(2) The HM defines a hierarchical structure of a modeling method, components contained in each hierarchy and connection relations of the components, the CCM defines basic components contained in a composite component and connection relations of the basic components, and the BCM defines an internal structure of the basic components; the BCM is an 8-tuple structure and describes the internal structures of basic components in the HM and the CCM; the above structure enables a detailed description of the multi-coupling hybridization characteristics of the process industry.
Drawings
FIG. 1 is a basic component model of the reaction apparatus based on the component-based hybrid Petri net modeling semantics according to the embodiment;
FIG. 2 is a basic component model of the storage device based on the component-based hybrid Petri net modeling semantics according to the embodiment;
FIG. 3 is an import direction control basic component model based on component-based hybrid Petri net modeling semantics according to the embodiment;
fig. 4 is a model of a shunting direction control basic component based on a componentized hybrid petrinet modeling semantic in the present embodiment;
FIG. 5 is a basic component model of the throttling device based on the component-based hybrid Petri net modeling semantics according to the embodiment;
FIG. 6 is a schematic diagram of a chlorine product production process in the chlor-alkali industry of this embodiment;
FIG. 7 is a process model of the present embodiment;
reference numerals:
Pm1、Pm2、Pm3-Material depot, Pe1、Pe2、Pe3Energy repository, Pi1-Pi21-information repository, Pd1-device stopped state discrete depot, Pd2-the operating state of the apparatus is in a discrete depot, Pd3The apparatus being in an overflow condition from the storage, Pd4The apparatus being in an ultra-low state at a discrete depot, Pd5The device is in an unconfined state and is separated from the deviceWarehouse facility, Tc1、Tc2Continuous change of processing scheme, Td4-the device initiates a discrete transition, Td3-discrete transition of equipment shutdown, Td2-equipment failure or start of service discrete transition, Td1-discrete transition after recovery or overhaul of equipment failure, Tctl1、Tctl2Control of the transition, Ti1-Ti8-information transition;
m1-basic component of electric storage device, M2-basic component of electric shunt device, M3-basic component of refined brine storage device, M4-basic component of refined brine shunt device, M5-basic component of reaction device of electrolytic cell 1, M6-basic component of reaction device of electrolytic cell 2, M7-basic component of steam storage device, M8-composite component of chlorine influx shunt device, M9-basic component of chlorine shunt device, M10-basic component of chlorine influx device, M11-basic component of steam influx device, M12-basic component of condenser reaction device, M13-basic component of reactor device, M14-basic component of liquid chlorine storage device, M15-basic component of hydrochloric acid storage device.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. The present embodiment is implemented on the premise of the technical solution of the present invention, and a detailed implementation manner and a specific operation process are given, but the scope of the present invention is not limited to the following embodiments.
Examples
The invention relates to a modeling and simulation method of a multi-coupling hybrid process industrial process, which comprises the following two steps:
s1, setting a control scheme of the industrial process, and establishing a Petri network-based multi-coupling hybrid process industrial process model, wherein the control scheme comprises production equipment and the mutual relation of the production equipment;
and S2, inputting the initialization data of the control scheme, and performing simulation of the industrial process by using the model established in the step S1 to obtain relevant data of the industrial process, including time, energy consumption and yield.
The modeling method of the step S1 comprises the following steps:
1) based on a component packaging concept, a modeling concept of a basic component and a composite component is provided;
although production equipment related to the process industrial process has various varieties and specifications according to different specific industries, the production equipment can be basically divided into three categories, namely reaction devices (physical and chemical reaction devices such as distillation, separation, reforming, electrolysis and the like), storage devices (storage tanks) and direction control and throttling devices (valves and pumps) according to functional division. Each type of device has some basic functions in common use. Based on the common analysis of the production equipment, component models of the three types of production equipment are established, and the models provide basic structures and functions of corresponding production equipment types and can be used as basic frameworks for modeling of specific production equipment. Structurally, the structural members are classified into two major categories, basic members and composite members. A unit description, such as a chemical reaction of a certain reactor, based on the components providing the production facility functions. For production equipment with complex functions, such as a rectifying tower, the process structure is complex, the production equipment comprises a rectifying section, each tower plate of a stripping section, a feeding plate, a tower top condenser, a reflux tank, a tower kettle, a reboiler and the like, the dynamic characteristics of each component are greatly different, and the functions of the whole rectifying tower are difficult to describe by using one basic component. For complex process equipment, assembly can be carried out based on basic components, and more complex composite components can be constructed. The input and output interfaces of the basic component provide energy, material and information exchange, and can describe the coupling relation of energy flow, material flow and information flow in the industrial process in detail.
The componentization packaging concept packages the specific details of the production equipment, only provides the equipment functions and the input and output interface description, and has a basic framework for realizing the equipment functions inside, so that a user can customize and modify the equipment according to specific requirements during specific use. By the definition of the basic component and the composite component, the management and control of the process industrial process can be positioned on different levels, energy source flow, material flow and information flow can be extracted from fussy equipment details according to requirements, only the input-output relation of the three flows on an equipment interface needs to be concerned, and the system complexity caused by the strong coupling characteristic is reduced to a certain extent. Meanwhile, the introduction of the component can effectively reduce the complexity of the process industrial process established by adopting Petri net, accelerate the modeling process and improve the modeling efficiency.
2) A multi-level reconfigurable modeling framework supporting a unit layer, a subsystem layer and a system layer;
the unit layer provides production equipment level modeling, embodies a component packaging concept and can provide a basic modeling module for the subsystem layer and the system layer; the subsystem layer describes a subsystem, such as a production line, and is formed by connecting a plurality of production devices, and the model consists of basic components or composite components; the system layer describes the whole process industrial process system and is formed by connecting subsystems.
3) Based on the hybrid, controlled and colored Petri net, the basic semantics of a component hybrid Petri net modeling method are provided;
definition 1: three-layer modeling framework
According to the three-layer modeling framework, the Basic semantics of the componentized hybrid Petri net modeling method comprise three parts, namely CpnHPN { HM, CCM, BCM }, a Hierarchical Model (HM), a Composite Component Model (CCM), and a Basic Component Model (BCM), from top to bottom. The HM defines the hierarchical structure of the modeling method, the components contained in each hierarchy and the connection relationship of the components, the CCM defines the basic components contained in the composite component and the connection relationship of the basic components, and the BCM defines the internal structure of the basic components.
Definition 2: HM basic semantics
HM is a 3-tuple structure, HM ═ H, B, L. Wherein H describesHaving a hierarchical structure of HM, { H ═ H1,...,hi,...,hn}，hiCorresponding to the ith layer of the model, the model layer number n is | H |. B describes the component set contained in each hierarchy, which is a subset of the power set of the component set, is hiThe set of components that a layer contains. Wherein,either a composite component CCM (shown as a solid rectangular box with dotted fill) or a base component BCM (shown as a solid rectangular box). L describes the connection relation between the components, the basic components are connected through an interface library, and the interface library can be an energy library place, a material library place and an information library place. The interface libraries matched between the basic components describe the representation of the same energy, material and information object in different production equipment, and the connection of the interface libraries between the upstream and downstream basic components constitutes the energy flow, material flow and information flow in the system. Matching pairs of L-purpose interface libraryDenotes the connection relationship between the members, and has the meaning of hkThe jth member in a layerIncluding a library piAnd hk'the j' th member in a layerIncluding a library pi' is the matching interface library. There is a directional arc between them pointing from the former to the latter. The matched interface library can be an energy library (the directed arcs are represented as solid one-way arrows) and a material library (directed arc table)Shown as a dashed one-way arrow) and a library owner (directed arcs are shown as dotted one-way arrows). The connection relation between each component is expressed by a set of matched pairs of the interface library, i.e.
Definition 3: CCM basic semantics
The CCM is a 2-tuple that describes the composite building blocks contained in the HM architecture. For example for composite structures in HM Wherein B ═ B1,...,bi,., describeA set of basic building blocks, L',<pi|bi,pj|bj>,..) describe the connection relationship between these basic building blocks, i.e. the matching pairs of the interface library.
Definition 4: BCM basic semantics
The BCM is an 8-tuple structure, which describes the internal structure of the basic components in the HM and CCM, i.e., BCM ═ { P, T, a, I, O, F, C, M }, where P is a finite library set, T is a finite transition set, a is a directed arc set connecting the library and the transition, I is an input function from the library to the directed arc of the transition, O is an output function from the transition to all the directed arcs of the library, F is a firing function of the transition, C is a color set of the library and the transition, and M is the identity of the library. The detailed description of each component is as follows.
The limited library set P defines: including three types of library subsets P ═ Pd∪Pc∪Pi. Wherein, PdIs a subset of discrete libraries (shown as solid circular boxes) and is used to describe the industryVarious discrete states of the process (e.g., running, stopped); pcIs a subset of a continuum used to describe energy and material objects in an industrial process. Further divided into energy depots Pe(shown as a solid annular frame) and a Material warehouse Pm(shown as dotted annular boxes), i.e. Pc＝Pe∪Pm；PiIs a subset of the information library (shown as dotted circle boxes) used to describe various control and monitoring information in the industrial process.
The finite transition set T defines: containing four types of transition subsets, T ═ Td∪Tc∪Ti∪Tctl. Wherein, TdDescribing various discrete events (equipment starting and stopping, equipment failure recovery, equipment starting maintenance and equipment maintenance completion) in the industrial process for a discrete transition subset (shown as a solid line vertical rectangular frame); t iscFor a continuous transition subset (shown as solid standing rectangular rings), continuous production during production is described; t isiRepresenting information acquisition, monitoring and processing for the information transition subset (represented as a dotted vertical rectangular frame); t isctlTo control the subset of transitions (shown as black filled upright rectangular boxes), control of discrete transitions and continuous transitions is described.
The directed arc set A is defined as A (P × T) ∪ (T × P), and the directed arc contains three types A (A) according to the enabling rulec∪Ai∪Ar。AcThe corresponding enabling rule is that the identification of the input library is not less than AcIs a general enabling function (represented as a solid one-way arrow); a. theiThe corresponding enabling rule is that the identification of the input library is less than AiThe weight of (c) is a suppression (represented as a solid line segment with a circular frame at the end); a. therThe corresponding enabling rule is located at A for the identification of the input libraryrThe interval (shown as a solid line segment with circular frames at both ends) is within the numerical range defined by the weight of (2).
The input function I and the output function O define:is the input function from the library onto the transitional directional arc,is the output function on all the directed arcs from the transition to the library. I and O define the weight on the directed arc, and the weight can be a Boolean value (0,1) or a non-negative integerNon-negative real numberOr a certain real interval value (Is positive and real, and). The weight types are closely related to the effect of the directed arcs, and equation (1) and table 1 describe I, O for different types and associated different effects of directed arcs, respectively. In the formula (1), t and t represent the input library site and the output library site of the transition t, respectively.
TABLE 1 directed arc Effect associated with I, O types in equation (1)
Definition of the excitation function F: f is Tc∪Ti∪Tctl→ f is the excitation function for continuous transition, information transition and control transition. Wherein, f (T)c) May be a function of time identified with respect to the input library, which describes that the production facility is to beThe speed of converting input energy (material) into output energy (material) can be flow speed, temperature speed or pressure speed; f (T)i) Can be any function for collecting, monitoring and processing information identified by the input library; f (T)ctl) The control function can be an optimization and control strategy related to a time function, a logic rule, a fuzzy rule and the like identified by the input library.
Definition of color set C: in a continuous industrial process, energy and materials are treated as objects for continuous production, and there are several attribute parameters with time-varying characteristics, including flow rate, temperature, pressure, etc. To describe the time-varying nature of these attribute parameters, the concept of color aggregation is introduced.If p isi∈·t∩Pc,pj∈·t∩PcThen t and pi,pjPresence of color collectionsEach colorRepresenting a certain attribute parameter. p is a radical ofi，pjIs corresponding to oneThe vector is identified and, in response to the identification,namely, it isAccordingly, there is a vector of input-output functions, i.e. There is a vector of the excitation function that is present,describeThe time-varying speed of the represented property parameter.
Definition of library-identified M: m is M0∪Mτ。m0(P) is an identification of the initial time of the depot P, mτ(P) is the identification of tau time of the storehouse P, tau time identification set MτAnd an initial time identification set M0Respectively, the number of the elements is npSet of (2), nPP. The identification types corresponding to the different types of libraries and the meanings thereof are respectively shown in formula (2) and table 2.
TABLE 2 meanings of the identifiers in the library of equation (2)
4) And (3) providing basic component definition for modeling the process industrial process by applying component-based hybrid Petri net modeling semantics.
Aiming at a typical process industrial production device and a reaction process, three basic components for process industrial process modeling are defined by applying component-based hybrid Petri net modeling semantics: reaction device basic component, storage device basic component, direction control and throttling device basic component.
Definition 1: basic structural component of reaction device
As shown in figure 1. The reaction device is used for inputting the material Pm1By physical or chemical reaction Tc1Or Tc2To form a semi-finished product (finished product) Pm2And Pm3Certain energy P needs to be consumed in the product processing processe1And Pe3While simultaneously producing secondary energy P capable of being recyclede2. The device may have a variety of processing schemes, T being listed in the figurec1And Tc2The energy and material used in each of the two processing schemes may be different. The production load for each processing scheme can be determined by Pi8(Pi15) And Pi9(Pi16) And controlling the running speed of the device in the former mode and controlling the start and stop of the processing scheme in the latter mode. The control information of the running speed can be Pi8Given by human, and may be as Pi15By monitoring information P from other production equipmenti19And Pi20By controlling transition Pctl2And (4) automatic generation. At various discrete events (device start-up T)d4And shutdown Td3Equipment failure or start to overhaul Td2T for recovering or repairing equipment failured1) In case of occurrence, the device may be in an operating state Pd2Or a stop state Pd1. The occurrence and time of various discrete events can be controlled by corresponding information base, and the information base can receive artificial information, and can also output information P through other production equipmenti21By controlling transition Tctl1And (4) automatic generation.
Definition 2: storage device base member
As shown in fig. 2. The storage device stores energy (material) P from upstream production equipmente1And storing the product while supplying the product to downstream production equipment for production. The reserve is limited to a range in which the device is in an overflow condition P when the maximum capacity is exceededd1(ii) a When the capacity is lower than the minimum capacity, the device is in an ultra-low state Pd2(ii) a When not exceeding the limit, the device is in the state of not exceeding the limit Pd3。
Definition 3: basic component of direction control and throttling device
Direction controlThe device is divided into a confluence device and a diversion device, and the basic components of the device are respectively shown in the attached figures 3 and 4. The merging device is used for merging energy (material) P from upstream production equipmente1And Pe2Are combined to form an energy source (material) Pe3(ii) a The shunting device is used for shunting energy (material) P from upstream production equipmente1Splitting to form energy (material) P for downstream production equipmente2And Pe3Opening of the device by Pi4Controlling; basic components of a throttling device for regulating the energy (material) flow P are shown in figure 5e1Flow rate of (T)c1The excitation speed of (2) is the flow speed, from Pi4And (5) controlling. Pi1Controlling the opening of the device.
After the modeling of the multi-coupling hybrid process industrial process is finished, simulation is carried out, namely the evolution rule of the component hybrid Petri net model is marked M initially0Under the action of (2), the transition in the model is enabled and excited continuously, so that the model mark M is marked0Transition from elapsed time τ to MτThe model identification describes the state of the system. M0，MτThe initial state of the system and the state at time τ, respectively. Therefore, the enabling and triggering rules of the transition are evolution rules of the model simulation. Transition enabling and excitation of the transition are closely related, and different transition effects have different enabling and excitation rules, so that the identification of the connected library is evolved, and model simulation is promoted. The method specifically comprises the following steps:
(1) enabling function
Enabled at time τ, if and only ifAnd isOr mτ(pc) 0 but pcTo supply forGiving the state.
And after t is excited, the time Δ τ elapsed is:
① case T ∈ TdThen, there are:new identity m (p)' ═ mτ(p)-I(p,t)；New identity m (p)' ═ mτ(p)+O(p,t)。
②t∈TcThen, there are:new identity m (p)' ═ mτ(p)-I(p,t)f(t)Δτ；New identity m (p)' ═ mτ(p)+O(p,t)f(t)Δτ。
In particular, when p, t has a color set c, at mark mτNext, transition t is with respect to colorEnable, if and only if mτ(pc) > 0 or mτ(pc) 0 but pcIs in the supply state.
And after t is excited, the time Δ τ elapsed is:
new identification
New identification
(2) Inhibition of
The enable rule of the suppression effect is opposite to the enable effect, the effect is generally used for controlling the storage space of the buffer, and when the storage space is not overflowed, the excitation of the output transition is suppressed; when the storage space overflows, its output transitions are enabled and fired.
Even if the enabling rule is:enabled at time τ, if and only ifAnd after t is excited, a new mark m (p)' ═ mτ(p), i.e., the identity of the input transition of the suppression arc is not changed after it is fired.
(3) Control action
Control action is typically used to control the continuous transition TcOr controlling discrete transition TdThe excitation of (a) is carried out,enabled at time τ if and only if p ∈ · t ∩ pi,mτ(p)＞0,I(p,tc) When t is excited, a new mark m (p)' (m)τ(p)，f(tc)'＝mτ(p)。Enabled at time τ if and only if p ∈ · t ∩ pi,mτ(p)＝1,I(p,td) When t is equal to 1cAfter excitation, a new mark m (p)' (0).
(4) Information monitoring and processing
Information monitoring and processing function for monitoring input information base piAnd the state is changed to T via informationiIs converted into an appropriate form to be output as an output information base pjIs represented by the identifier of (a).Enabled at time τ, if and only if I (p)i,t)＝O(piAnd t) is 1. When t is excited, a new mark m (p)i)’＝mτ(pi)，m(pj)’＝mτ(pi)f(t)。
(5) Reasoning effect
Inference action is generally used for information control, controlling transition TctlThe excitation function of (a) being an inference rule or control algorithm, TctlWill input information base piAs input to the inference rules or control algorithms, generates output information, expressed as output libraries p, from the inference and controljIs detected.Enabled at time τ, if and only if I (p)i,t)＝O(piAnd t) is 1. When t is excited, a new mark m (p)i)'＝mτ(pi)，m(pj)'＝mτ(pi)f(t)。
m0(P) is an identification of the initial time of the depot P, mτ(P) is an identification of the time instance T of the pool P,
the production process of chlorine products in the chlor-alkali industry shown in figure 6 is taken as an example. The refined brine is electrolyzed by the electrolytic cell 1 and the electrolytic cell 2 to produce chlorine. Wherein, the electrolytic bath 1 adopts an ion exchange membrane processing scheme, and the electrolytic bath 2 can adopt two processing schemes of a diaphragm method and an ion exchange membrane method. After the chlorine gas is controlled in the direction of afflux and shunt, one part of the chlorine gas flows into the reactor to generate hydrochloric acid, and the other part of the chlorine gas is condensed by a condenser to form liquid chlorine for storage. The energy consumed by the electrolytic cell, the reactor and the condenser is electric energy. In the case of the medium-pressure or low-pressure method, the generated liquid chlorine needs to be pressurized with steam to be introduced into the storage tank, and thus the condenser needs to consume a part of the steam. The reactor can generate a large amount of recyclable waste heat in the hydrochloric acid production process, and the waste heat is supplied to a condenser for use.
The process model of the embodiment was built from three basic building blocks defined by the componentized hybrid petrinet modeling semantics, as shown in fig. 7. The electrolytic bath, the condenser and the reactor belong to a reaction device, a storage device is needed for the supply and the output of refined brine, liquid chlorine, hydrochloric acid, electricity and steam, and the direction control of the flow division, the influx and the chlorine transmission belongs to a direction control and throttling device. The chlorine transmission is complicated, and can be described by a composite member comprising an influx and shunt basic member, and the composite member corresponds to a multi-input multi-output direction control device in an actual system.
The simulation time was set to 30 hours and the production parameters of the electrolyzer, condenser and reactor are shown in table 3, including the capacity and specific energy consumption of the various process schemes of the plant. The control schemes adopted three schemes shown in Table 4. In the simulation process, the condenser and the reactor are always in a starting state, equipment maintenance events can occur in the electrolytic cell 1, and the electrolytic cell 2 is switched between two schemes of an ion exchange membrane method and a diaphragm method. In different schemes, the start-stop of each device and the conversion time of the processing scheme thereof are different, and the chlorine gas supplied to the condenser and the reactor has different distribution rates. According to the parameter configuration and the control scheme of the model, the model is simulated by adopting the evolution rule of the component hybrid Petri net model provided by the invention, and the simulation result is shown in Table 5. The simulation result describes specific values of liquid chlorine, hydrochloric acid and other material flows and energy source flows such as power consumption, steam consumption and the like at different moments under different situations of the control scheme 1 and the control scheme 2. From the simulation results, it can be seen that the information flow influences the dynamic behavior of the energy source flow and the material flow by controlling the opening of the device. In the control scheme 1, a large amount of waste heat generated in the hydrochloric acid production process can be used for the liquid chlorine pressurization process through recovery, and balance is also generated, so that the steam dosage is a negative value; in the control scheme 2, the waste heat recovery is not enough to meet the steam consumption requirement of liquid chlorine pressurization, and additional amount needs to be supplemented, so that the steam amount is positive.
TABLE 3 Main plant production parameters
TABLE 4 control scheme
TABLE 5 simulation results
While the invention has been described in connection with specific embodiments thereof, it will be understood that these should not be construed as limiting the scope of the invention, which is defined in the following claims, and any variations which fall within the scope of the claims are intended to be embraced thereby.
Claims (6)
1. A modeling and simulation method for a multi-coupling hybrid process industrial process is characterized by comprising the following steps:
s1, setting a control scheme of the industrial process, and establishing a Petri network-based multi-coupling hybrid process industrial process model, wherein the control scheme comprises production equipment and the interrelation thereof, and the model comprises from top to bottom:
the hierarchical structure submodel HM is a 3-tuple structure { H, B, L }, wherein H describes the hierarchical structure of the model, B describes a component set contained in each hierarchy, L describes the connection relationship among the components, and the components comprise basic components and composite components formed by connecting the basic components;
the CCM is a 2-tuple structure { B ', L' }, wherein B 'describes basic components contained in the CCM, L' describes the connection relation among the basic components, each basic component is provided with an interface library, and the matched interface libraries among the basic components are connected with each other;
a basic component submodel BCM, wherein the BCM describes the internal structure of a basic component;
in the model, a component corresponds to production equipment, an interface library comprises an energy library place, a material library place and an information library place, and the connection relation among the interface libraries corresponds to energy flow, material flow or information flow among production devices;
and S2, inputting the initialization data of the control scheme, and performing simulation of the industrial process by using the model established in the step S1 to obtain relevant data of the industrial process, including time, energy consumption and yield.
2. The method of claim 1, wherein the basic building block submodel BCM has an 8-tuple structure { P, T, a, I, O, F, C, M }, wherein P is a finite library site, T is a finite set of transitions, a is a set of directed arcs connecting the library site and the transitions, I is a set of input functions on directed arcs from the library site to the transitions, F is a set of excitation functions of the transitions, C is a set of colors of the library site and the transitions, and M is a set of identifications of the library site.
3. The method of claim 2, wherein the finite library set is P ═ Pd∪Pc∪PiWherein P isdIs a subset of a discrete library for describing discrete states of an industrial process, including operation and shutdown, PcIs a subset of a continuous store for describing energy and material objects in the industrial process, including an energy store PeAnd the Material depot Pm，PiThe information base subset is used for describing various control information and monitoring information in the industrial process;
the limited transition set T is T ═ Td∪Tc∪Ti∪TctlWherein, TdIs a discrete transition subset and is used for describing various discrete events in the industrial process, including equipment starting and stopping, equipment failure recovery, equipment starting maintenance and equipment completion, TcFor a continuous transition subset, describing continuous production in a production process, TiIs a subset of information transitions for describing information acquisition, monitoring and processing, TctlIs a subset of control transitions used to describe the control of discrete transitions and continuous transitions;
the directed arc set A (P × T) ∪ (T × P) of the connection library and the transition is A (A)c∪Ai∪Ar，AcThe corresponding enabling rule is that the identification of the input library is not less than AcThe weight of (A) plays a role of general enabling, AiThe corresponding enabling rule is that the identification of the input library is less than AiThe weight of (A) plays a role in inhibition, ArThe corresponding enabling rule is located at A for the identification of the input libraryrThe value interval defined by the weight value plays a role of interval;
the input function set I on the directed arcs from the library to the transition and the output function set O on the directed arcs from the transition to the library respectively define the weights on the directed arcs, → represents the mapping of functions;
t is the excitation function set of the transitionc∪Ti∪Tctl→ f, comprising a continuous transition excitation function f (T)c) Information transition excitation function f (T)i) And controlling the transition excitation function f (T)ctl) Wherein, f (T)c) Describing the speed at which a production facility converts input energy or material into output energy or material, f (T)i) For collecting, monitoring and processing information identified by the input library, f(Tctl) Optimization and control strategies including time functions, logic rules, and fuzzy rules identified by the input library, → representing the mapping of the functions;
the color set C of the library and the transition specifically comprises:if p isi∈·t∩Pc，pj∈·t∩PcThen t and pi,pjPresence of color setsEach colorRepresenting a class of attribute parameters, wherein t and t respectively represent an input library place and an output library place of the transition t;
the identification set M of the library is M0∪MτTau time identification set MτAnd an initial time identification set M0Respectively, the number of the elements is npSet of (2), nP| P |, | represents the number of elements in the set,
pi，pjis corresponding to oneThe vector is identified and, in response to the identification,| represents the number of elements in the set, i.e.Accordingly, there is a vector of input-output functions, i.e. There is a vector of the excitation function that is present, description of the inventionThe time-varying speed of the represented property parameter.
4. The method of claim 3, wherein the weights on the directed arcs are Boolean (0,1) and non-negative integersNon-negative real numberOr a certain real interval value For positive real numbers, the different types of I, O and the different effects of the associated directed arcs are as follows:
wherein, the directed arc in the formula (1-1) plays a role in inputting and outputting energy, general enabling between materials and continuous production, the directed arc in the formula (1-2) plays a role in inputting and outputting discrete events and general enabling between discrete states, the directed arc in the formula (1-3) plays a role in controlling continuous production/discrete events, the directed arc in the formula (1-4) represents input information and generates output information through a control strategy, the directed arc in the formula (1-5) represents input information and generates output information through processing, the directed arc in the formula (1-6) plays a role in acquiring and monitoring information of discrete states, input and output energy and materials, the directed arc in the formula (1-7) plays a role in monitoring overflow of the capacity of the storage tank and generating overflow state, and the directed arc in the formula (1-8) plays a role in monitoring ultralow capacity of the storage tank, The function of generating an ultra-low state, and the function of monitoring the capacity of the storage tank without exceeding the limit and generating the state without exceeding the limit is realized by the directional arc in the formula (1-9).
5. The method of claim 3, wherein the identification types and their meanings corresponding to different types of libraries are respectively as follows:
wherein p is a library, m (p) is an identifier corresponding to the library p, m (p) in formula (2-1) represents a discrete state of production equipment, m (p) in formula (2-2) represents attribute parameters of input and output energy and materials, m (p) in formula (2-3) represents control over discrete transition and is related to time tau, tau is fixed time or random time, m (p) in formula (2-4) represents control over continuous transition and is related to time tau, and tau is fixed time or random time.
6. The method for modeling and simulating multi-coupling hybrid process industrial process according to claim 3, wherein in step S2, the transition enabling and triggering rules are evolution rules of model simulation, and different transition actions have different enabling and triggering rules, so as to make the linked library identified evolve and promote model simulation.
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