CN102096741A - Thermodynamic system dynamic model establishing method - Google Patents
Thermodynamic system dynamic model establishing method Download PDFInfo
- Publication number
- CN102096741A CN102096741A CN2011100420273A CN201110042027A CN102096741A CN 102096741 A CN102096741 A CN 102096741A CN 2011100420273 A CN2011100420273 A CN 2011100420273A CN 201110042027 A CN201110042027 A CN 201110042027A CN 102096741 A CN102096741 A CN 102096741A
- Authority
- CN
- China
- Prior art keywords
- node
- output
- input
- parameter
- variable
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Feedback Control In General (AREA)
Abstract
The invention discloses a thermodynamic system dynamic model establishing method which belongs to the technical field of thermodynamic system dynamic characteristic analysis and optimal operation. The method comprises the following steps of: analyzing the causal relation between main parameters of a thermodynamic system to be modeled; determining a plurality of input variables, output variables and intermediate variables; according to the transfer order of each of the variables, determining an input layer, an output layer, an intermediate layer and the number of nodes in each layer; connecting the nodes with a causal relation or a reverse causal transfer relation by using directed line segments with arrows; determining an overall causal transfer topological structure of a system; marking standard data structures on each directed line segment representing the causal relation; and identifying related parameters in each data structure by operating measured data by the system. The model established by the method can effectively describe a causal dynamic transfer relation of each key physical parameters in the thermodynamic system, is suitable for analyzing the relation among substances, energy transfer and balance of the thermodynamic system from a relatively macroscopic perspective, thereby analyzing and optimizing the operating characteristics of the system.
Description
Technical field
The invention belongs to the specificity analysis of therrmodynamic system and optimize the running technology field; Be particularly related to a kind of method for building up of therrmodynamic system dynamic model.
Background technology
The dynamic model of therrmodynamic system is for heat supply, thermal power generation process or analysis on Operating, fault detection and diagnosis, system optimization design, energy-saving and emission-reduction operational mode with industrial system of similar thermal procession selected and emulation platform structure etc. has important significance for theories and using value.
At present, the modeling method of therrmodynamic system moving model is mainly to adopt theoretical analysis, experience to conclude or the two method that combines.The analytic model that theorizes need be understood the basic physics of object, chemical rule in depth, has tighter scientific evidence, and institute's established model mechanism is distinct, but modeling process is strongly professional and can't carry out accurate Analysis to complication system.Experience is concluded the working mechanism that model does not need taking into account system or process, mainly sets up according to the quantitative relationship of measured data, belongs to the black-box modeling category.But this modeling method needs a large amount of measured datas, and the complexity of model is difficult for definite, and relatively poor for the response consistance of different floor datas.
Current another popular therrmodynamic system modeling method is a neural net model establishing.Import neural network by input, output data, allow neural network be in operation and learn automatically, adapt to the time variation of big operating mode range and system's operation characteristic system.The shortcoming of this modeling method is that model parameter does not have physical significance, and travelling speed is slack-off when parameter is more, the convergence variation.
Normal in actual therrmodynamic system modeling process two kinds of tendencies appear: the one, put undue emphasis on the effect of service data, ignore research to system's operation mechanism, by approach, the mode of match obtains the complex mappings between I/O, and then the operation of simulation system.There are problems such as the model variable physical significance is indeterminate, model structure is complicated, debug difficulties in this mode in theory research and engineering application.The 2nd, too value the precision of model, wish internal system, outside various influence factors are completely taken into account, mechanically expand the scale of model, think that the complicated model precision is high more more.This mode not only can not guarantee the quality of model, also can reduce the practicality and the applicability of model to a certain extent.
Summary of the invention
In order simply to characterize quiet, the dynamic perfromance of therrmodynamic system lucidly, set up the causalnexus of key physical parameter, the present invention proposes a kind of method for building up of therrmodynamic system dynamic model, it is characterized in that, the dynamic model of described therrmodynamic system has multilayer from bottom to top, the multinode cause and effect of standard and dynamically transmits topological structure, the orlop of this topological structure is an input layer, comprises 1-m input node, and each input node is represented an input variable; The superiors are output layer, comprise the 1-n output node, and each output node is represented an output variable; Include from the 2nd layer of a plurality of middle layer to the i-1 layer between input node and the output node, each middle layer has a plurality of intermediate nodes that 1-p quantity does not wait, and each intermediate node is represented the intermediate variable of input variable in the output variable transmittance process; Wherein m, n, i and p are positive integer.
Described cause and effect is dynamically transmitted in the topological structure, and lower level node is to having one-to-many or many-to-one cause and effect transitive relation, by the directed line segment connection of mark arrow between the upper layer node; When pointing to upper layer node by lower level node, arrow represents that the forward of input variable to intermediate variable or intermediate variable to output variable influences; When pointing to lower level node by upper layer node, arrow represents that output variable is to the reverse influence to input variable of middle variable or intermediate variable; The node of every directed line segment initiating terminal is called " because of node ", and the node of Zhongdao end is called " fruit node ", and their constitute " cause and effect node to ".
Dynamic transitive relation between the cause and effect node is by being labeled in standard data structure on the directed line segment [±, v, K, T, τ]
AbcdDefinition, its corresponding mathematical relation is:
Wherein, ± expression cause-effect relationship is positive interaction or negative interaction, and s is a Laplace operator; V is integer and v ∈ [1,0,1,2], " fruit node " characterized the pace of change of " because of node " during v=-1, " fruit node " changes with " because of node " equal proportion during v=0, and " fruit node " has the linearity relation of adding up with " because of node " during v=1, and " fruit node " has the parabolic type relation of adding up with " because of node " during v=2; K is the real number greater than 0, characterizes the static proportionate relationship of " fruit node " and " because of node "; T is the real number greater than 0, and " fruit node " follows the inertial properties of response when characterizing " because of node " variation; τ is the real number greater than 0, and " fruit node " follows the hysteresis characteristic of response when characterizing " because of node " variation; The subscript abcd of standard data structure represents cause and effect node residing position in whole topological structure, a represents " because of node " residing number of plies, b represents " because of node " position in this layer, and c represents the residing number of plies of fruit node, and d represents the position of fruit node in this layer; Described cause and effect is dynamically transmitted in the topological structure, with adjacent a plurality of " because of nodes " dynamic transitive relation is arranged as if " fruit node ", and then the value of " fruit node " is its algebraic sum with relevant " because of node " dynamic transitive relation.
Concrete implementation step is as follows:
Step 1: analyze the cause-effect relationship for the treatment of major parameter in the modeling therrmodynamic system, determine input variable, output variable and the intermediate variable of model;
Step 2: according to input variable, output variable, intermediate variable number and their pass order, determine that cause and effect dynamically transmits node level in the topology diagram and the node number in each layer, directed line segment with the band arrow connects the node with cause and effect transitive relation, determines that the whole cause and effect of system is transmitted topological structure;
Step 3: represent at each bar to mark standard data structure [±, v, K, T, τ] on the causal directed line segment
AbcdWherein in each group standard data structure each parameter really definite sequence be: 1. the corresponding increase of output node parameter value when if certain directed line segment input node parameter value increases, then the data structure that marks on this directed line segment is got "+", if the output node parameter value reduces then to get "-"; 2. selecting system is jumped to one group of actual operating data of another working point by a working point, observe from data, v gets 1 when concerning if the temporal linearity that has the output parameter that this directed line segment connects and input parameter adds up, and gets 2 if the parabola shaped v that adds up when concerning of class is similar to; If do not have the accumulation relation between output parameter and the input parameter, promptly after input parameter departed from former equilibrium point and stablizes, output parameter also settled out no longer very soon and changes, and then selects v=0; If the approximate differential relation is arranged between output parameter and the input parameter, then selects v=-1; 3. the system's actual operating data curve observation from choosing, the time interval that the initial moment of input parameter transition and output parameter responded between the initial moment is τ; 4. the system actual operating data curve observation from choosing when v=-1 or 0, responds from output parameter and to be carved into its used time of tending towards stability when initial and to be about 3.5T; When v=1 or 2, respond from output parameter and to be carved into its radius-of-curvature used time of tending towards stability when initial and to be about 3.5T; 5. v=-1 is worked as in the system actual operating data curve observation from choosing, and responds from output parameter and is carved into K that its peak value between tending towards stability is about its input transition amount when initial doubly; Work as v=0, the steady-state value after the output parameter response tends towards stability is about K times of its input transition amount; When v=1 or 2, the radius-of-curvature after then the output parameter response tends towards stability is about K times of its input transition amount.
Step 4: after determining that cause and effect is transmitted the standard data structure of each bar directed line segment in the topological structure, the actual conditions whether input layer of test cause and effect topological structure and quiet, dynamic relationship between the output layer meet therrmodynamic system, emphasis checks whether static gain is accurate, as the big parameter value that then needs suitably to adjust in the relevant criterion data structure of deviation.
Follow four above steps,, can obtain the dynamic causal model of therrmodynamic system in conjunction with software programming.
The invention has the beneficial effects as follows the sign therrmodynamic system causality model of setting up according to the inventive method quiet, dynamic perfromance have simple in structure, relation is distinct, the characteristics that are easy to Project Realization.This model can effectively characterize various factors and the summary change procedure thereof that causes that the key physical parameter changes in the therrmodynamic system, be suitable for from material, NE BY ENERGY TRANSFER and the equilibrium relation of angle analysis therrmodynamic system of macroscopic view relatively, and then the operating economy problems of system, emission problem etc. are analyzed and optimized.
Description of drawings
Fig. 1 is the general structural drawing of therrmodynamic system dynamic model;
Fig. 2 is for using the fired power generating unit main steam pressure system model that the inventive method is set up;
Fig. 3 is for when unit of fuel quantity step increase, the main steam pressure, Boiler Steam generation, the dynamic change of steam turbine throttle flow and the correlation curve of unit actual operating data that are obtained by dynamic causality model;
Fig. 4 is for when main steam control valve aperture step increases by 10%, the main steam pressure, Boiler Steam generation, the dynamic change of steam turbine throttle flow and the correlation curve of unit actual operating data that are obtained by dynamic causality model.
Embodiment
The dynamic model method for building up of the therrmodynamic system that the present invention proposes can be applicable to the thermal power generation process and has the industrial system of similar thermal procession, and is realized by the mode of software programming.Below in conjunction with accompanying drawing the present invention is elaborated:
At certain fuel-burning power plant 600MW unit system unit main steam pressure object, the modeling procedure of using the inventive method is as follows:
1) analyzes and determines to treat the cause-effect relationship of modeling.Analyze as can be known, influence main steam pressure P
TDirect variable be Boiler Steam generation D and steam turbine throttle flow D
T, simultaneously, main steam pressure P
TAlso can oppositely influence Boiler Steam generation D and steam turbine throttle flow D
TBut Boiler Steam generation D and steam turbine throttle flow D
TBut not operational ton, general not as the input variable of model.Further analyze as can be known, the fuel quantity M that enters boiler can directly influence Boiler Steam generation D, and the aperture μ of main steam control valve can influence steam turbine throttle flow D
TTherefore, main steam pressure P
TBe subjected to the remote effect of fuel quantity M and main steam control valve aperture μ;
2) according to the general structural drawing analysis of therrmodynamic system dynamic model shown in Figure 1, this structure has an input layer, an output layer and a middle layer; Input layer comprises two input variables: the aperture μ of fuel quantity M and main steam control valve; Output layer comprises an output variable: main steam pressure P
TThe middle layer comprises two intermediate variables: Boiler Steam generation D and steam turbine throttle flow D
TConnect directed line segment to the middle layer variables D by input layer variable M; Connect directed line segment to the middle layer variables D by input layer variable μ
TConnect directed line segment to output layer variable P by the middle layer variables D
TBy the middle layer variables D
TConnect directed line segment to output layer variable P
TBy output layer variable P
TOppositely connect directed line segment to the middle layer variables D; By output layer variable P
TOppositely connect directed line segment to the middle layer variables D
T
3) represent on the causal directed line segment at each bar and mark standard data structure, and according to the correlation parameter in each standard data structure of measured data identification of unit.For the ease of comparing, the unit measured data that adopts in the present embodiment has all been carried out mark and has been changed processing, and the original state value that is about to each parameter of measured data corresponds to 0, and the stable state final value after changing is corresponded to 1.Use advanced language programming and realize the dynamic causal model of thermal power generation unit main steam pressure shown in Figure 2, wherein standard data structure [±, v, K, T, τ]
Abcd, corresponding mathematical relation is:
As shown in Figure 2, fuel quantity M has the cause and effect transitive relation of forward to Boiler Steam generation D, and its corresponding standard data structure is [+, 0,1,16,20]
1121, show from the M D that changes to begin to respond 20 seconds time delay that begin to respond from D and enter new stable state and have inertia propagation process about 3.5 * 16 seconds, the steady-state gain from M to D is 1; The aperture μ of main steam control valve changes steam turbine throttle flow D
TInfluence quick, can think in proportion directly to change that both not free delay does not have Inertial Processing yet, so its standard data structure is recognized as [+, 0,1,0,0]
1222Boiler Steam generation D is to main steam pressure P
TInfluence also have the cause and effect transitive relation of forward, its corresponding standard data structure is [+, 1,1,0,0]
2131, show if Boiler Steam generation D step increases and steam turbine throttle flow D
TConstant, P then
TCan constantly add up with speed 1, response process does not have inertia and delay substantially; Steam turbine throttle flow D
TTo main steam pressure P
TInfluence be reverse, be [, 1,1,0,0]
2231, if D
TStep increases and Boiler Steam generation D is constant, then P
TCan constantly reduce with speed 1, obviously, if make main steam pressure P
TStable, steam turbine throttle flow D
TD should have matching relationship with the Boiler Steam generation; Further analyze as can be known main steam pressure P
TTo steam turbine throttle flow D
TD also has acting in opposition with the Boiler Steam generation, main steam pressure P
TRaise and can suppress D on the one hand, can make on the other hand increases D
TTherefore, two interactivelies are opposite, can obtain [,-1,1,16,0] according to system's actual measurement service data
3121[+, 0,1, and 0,0]
3122, P
TThe influence of D is had the characteristics of negative sense differential, and the response of D have the inertia about 3.5 * 16 seconds, P
TTo D
TInfluence quick, be approximately simple proportionate relationship.
4) model of setting up is tested.Make μ=0, fuel quantity M step is increased a unit, observe main steam pressure P
T, Boiler Steam generation D and steam turbine throttle flow D
TSituation of change, and The model calculation and measured data compared, as shown in Figure 3; Make M=0, main steam control valve aperture μ step is opened big by 10%, observe main steam pressure P
T, Boiler Steam generation D and steam turbine throttle flow D
TSituation of change, and The model calculation and measured data compared, as shown in Figure 4.The consistance of diagram model response result and unit measured data is better, and the validity of the inventive method has been described.
The above is the preferred embodiments of the present invention only, is not limited to the present invention, for a person skilled in the art, only needs can realize heat supply, thermal power generation process or modeling with similar thermal procession industrial system according to above-mentioned implementation step.Within the spirit and principles in the present invention all, any modification of being done, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (4)
1. the dynamic model of a therrmodynamic system, it is characterized in that, the dynamic model of described therrmodynamic system has multilayer from bottom to top, the multinode cause and effect of standard and dynamically transmits topological structure, the orlop of this topological structure is an input layer, comprise 1-m input node, each input node is represented an input variable; The superiors are output layer, comprise the 1-n output node, and each output node is represented an output variable; Include from the 2nd layer of a plurality of middle layer to the i-1 layer between input node and the output node, each middle layer has a plurality of intermediate nodes that 1-p quantity does not wait, and each intermediate node is represented the intermediate variable of input variable in the output variable transmittance process; Wherein m, n, i and p are positive integer.
2. according to the dynamic model of the described therrmodynamic system of claim 1, it is characterized in that described cause and effect is dynamically transmitted in the topological structure, lower level node is to having one-to-many or many-to-one cause and effect transitive relation, by the directed line segment connection of mark arrow between the upper layer node; When pointing to upper layer node by lower level node, arrow represents that the forward of input variable to intermediate variable or intermediate variable to output variable influences; When pointing to lower level node by upper layer node, arrow represents that output variable is to the reverse influence to input variable of middle variable or intermediate variable; The node of every directed line segment initiating terminal is called " because of node ", and the node of Zhongdao end is called " fruit node ", and their constitute " cause and effect node to ".
3. according to the dynamic model of the described therrmodynamic system of claim 2, it is characterized in that the dynamic transitive relation between the cause and effect node is by being labeled in standard data structure on the directed line segment [±, v, K, T, τ]
AbcdDefinition, its corresponding mathematical relation is:
Wherein, ± expression cause-effect relationship is positive interaction or negative interaction, and s is a Laplace operator; V is integer and v ∈ [1,0,1,2], " fruit node " characterized the pace of change of " because of node " during v=-1, " fruit node " changes with " because of node " equal proportion during v=0, and " fruit node " has the linearity relation of adding up with " because of node " during v=1, and " fruit node " has the parabolic type relation of adding up with " because of node " during v=2; K is the real number greater than 0, characterizes the static proportionate relationship of " fruit node " and " because of node "; T is the real number greater than 0, and " fruit node " follows the inertial properties of response when characterizing " because of node " variation; τ is the real number greater than 0, and " fruit node " follows the hysteresis characteristic of response when characterizing " because of node " variation; The subscript abcd of standard data structure represents cause and effect node residing position in whole topological structure, a represents " because of node " residing number of plies, b represents " because of node " position in this layer, and c represents the residing number of plies of fruit node, and d represents the position of fruit node in this layer; Described cause and effect is dynamically transmitted in the topological structure, with adjacent a plurality of " because of nodes " dynamic transitive relation is arranged as if " fruit node ", and then the value of " fruit node " is its algebraic sum with relevant " because of node " dynamic transitive relation.
4. the dynamic model method for building up of a therrmodynamic system is characterized in that, concrete implementation step is as follows:
Step 1: analyze the cause-effect relationship for the treatment of major parameter in the modeling therrmodynamic system, determine input variable, output variable and the intermediate variable of model;
Step 2: according to input variable, output variable, intermediate variable number and their pass order, determine that cause and effect dynamically transmits node level in the topology diagram and the node number in each layer, directed line segment with the band arrow connects the node with cause and effect transitive relation, determines that the whole cause and effect of system is transmitted topological structure;
Step 3: represent at each bar to mark standard data structure [±, v, K, T, τ] on the causal directed line segment
AbcdWherein in each group standard data structure each parameter really definite sequence be: 1. the corresponding increase of output node parameter value when if certain directed line segment input node parameter value increases, then the data structure that marks on this directed line segment is got "+", if the output node parameter value reduces then to get "-"; 2. selecting system is jumped to one group of actual operating data of another working point by a working point, observe from data, v gets 1 when concerning if the temporal linearity that has the output parameter that this directed line segment connects and input parameter adds up, and gets 2 if the parabola shaped v that adds up when concerning of class is similar to; If do not have the accumulation relation between output parameter and the input parameter, promptly after input parameter departed from former equilibrium point and stablizes, output parameter also settled out no longer very soon and changes, and then selects v=0; If the approximate differential relation is arranged between output parameter and the input parameter, then selects v=-1; 3. the system's actual operating data curve observation from choosing, the time interval that the initial moment of input parameter transition and output parameter responded between the initial moment is τ; 4. the system actual operating data curve observation from choosing when v=-1 or 0, responds from output parameter and to be carved into its used time of tending towards stability when initial and to be about 3.5T; When v=1 or 2, respond from output parameter and to be carved into its radius-of-curvature used time of tending towards stability when initial and to be about 3.5T; 5. v=-1 is worked as in the system actual operating data curve observation from choosing, and responds from output parameter and is carved into K that its peak value between tending towards stability is about its input transition amount when initial doubly; Work as v=0, the steady-state value after the output parameter response tends towards stability is about K times of its input transition amount; When v=1 or 2, the radius-of-curvature after then the output parameter response tends towards stability is about K times of its input transition amount;
Step 4: after determining that cause and effect is transmitted the standard data structure of each bar directed line segment in the topological structure, the actual conditions whether input layer of test cause and effect topological structure and quiet, dynamic relationship between the output layer meet therrmodynamic system, emphasis checks whether static gain is accurate, as the big parameter value that then needs suitably to adjust in the relevant criterion data structure of deviation.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100420273A CN102096741A (en) | 2011-02-21 | 2011-02-21 | Thermodynamic system dynamic model establishing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2011100420273A CN102096741A (en) | 2011-02-21 | 2011-02-21 | Thermodynamic system dynamic model establishing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN102096741A true CN102096741A (en) | 2011-06-15 |
Family
ID=44129836
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2011100420273A Pending CN102096741A (en) | 2011-02-21 | 2011-02-21 | Thermodynamic system dynamic model establishing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102096741A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109145425A (en) * | 2018-08-10 | 2019-01-04 | 清华大学 | A kind of construction method and device of therrmodynamic system physical model |
| CN109376499A (en) * | 2018-12-20 | 2019-02-22 | 华润电力技术研究院有限公司 | The modeling method and model of fired power generating unit therrmodynamic system |
| CN111931298A (en) * | 2020-10-19 | 2020-11-13 | 国网江西省电力有限公司电力科学研究院 | Simulation calculation method for steam turbine steam distribution end |
| CN112434483A (en) * | 2020-12-18 | 2021-03-02 | 国微集团(深圳)有限公司 | Data transmission system and generation method thereof |
-
2011
- 2011-02-21 CN CN2011100420273A patent/CN102096741A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109145425A (en) * | 2018-08-10 | 2019-01-04 | 清华大学 | A kind of construction method and device of therrmodynamic system physical model |
| CN109145425B (en) * | 2018-08-10 | 2020-08-04 | 清华大学 | Method and device for constructing physical model of thermodynamic system |
| CN109376499A (en) * | 2018-12-20 | 2019-02-22 | 华润电力技术研究院有限公司 | The modeling method and model of fired power generating unit therrmodynamic system |
| CN111931298A (en) * | 2020-10-19 | 2020-11-13 | 国网江西省电力有限公司电力科学研究院 | Simulation calculation method for steam turbine steam distribution end |
| CN111931298B (en) * | 2020-10-19 | 2021-03-02 | 国网江西省电力有限公司电力科学研究院 | Simulation calculation method for steam turbine steam distribution end |
| CN112434483A (en) * | 2020-12-18 | 2021-03-02 | 国微集团(深圳)有限公司 | Data transmission system and generation method thereof |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105512388B (en) | A kind of subcritical fired power generating unit enhancing excitation emulation modelling method based on LABVIEW | |
| CN106931453A (en) | The forecasting system and method for circulating fluid bed domestic garbage burning emission of NOx of boiler | |
| CN104504292A (en) | Method for predicting optimum working temperature of circulating fluidized bed boiler based on BP neural network | |
| CN104333048B (en) | The dispatching method of fired power generating unit and system | |
| CN107831652A (en) | A kind of unit load intelligent optimized control method based on cold end system energy storage | |
| Niu et al. | Case-based reasoning based on grey-relational theory for the optimization of boiler combustion systems | |
| CN108224446A (en) | A kind of automatic combustion Study on Decision-making Method for Optimization of Refuse Incineration Process | |
| CN104539601B (en) | Dynamic network attack process analysis method for reliability and system | |
| CN102096741A (en) | Thermodynamic system dynamic model establishing method | |
| Liu et al. | An overview of modelling and simulation of thermal power plant | |
| CN110579962A (en) | Turbofan engine thrust prediction method based on neural network and controller | |
| Zhao et al. | Optimization of thermal efficiency and unburned carbon in fly ash of coal-fired utility boiler via grey wolf optimizer algorithm | |
| CN104966156B (en) | A kind of double-deck optimization method of iron and steel enterprise's Integrated Energy scheduling problem | |
| CN105718717A (en) | Method and device for establishing boiler combustion process model by utilizing Bayesian network algorithm | |
| Altuntas et al. | Exergoeconomic environmental optimization of piston-prop aircraft engines | |
| JP4989421B2 (en) | Plant control device and thermal power plant control device | |
| CN106610589A (en) | Online hardware closed-loop network source coordination linear active-disturbance-rejection control method | |
| CN105528470B (en) | The lower flow industrial energy consumption process modeling and simulation method of uncertain factor effect | |
| CN106371321A (en) | PID control method for fuzzy network optimization of coking-furnace hearth pressure system | |
| CN116542164A (en) | Combustion system optimization design method based on physical driving parameterized proxy model | |
| CN116796633A (en) | Power grid monitoring model correction method based on digital twin technology | |
| Zha et al. | An improved extreme learning machine with self-recurrent hidden layer | |
| CN106444389A (en) | Method for optimizing PI control by fuzzy RBF neural network based on system of pyrolysis of waste plastic temperature | |
| CN112036091A (en) | Method for predicting air quantity of nozzle of four-corner tangential burner by using regression algorithm | |
| Li et al. | Modeling thermal efficiency of a 300 MW coal-fired boiler by online least square fast learning network |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C02 | Deemed withdrawal of patent application after publication (patent law 2001) | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20110615 |