CN114563993A - Energy-saving optimization method and optimization system for electric precipitation system of thermal power generating unit - Google Patents
Energy-saving optimization method and optimization system for electric precipitation system of thermal power generating unit Download PDFInfo
- Publication number
- CN114563993A CN114563993A CN202210262328.5A CN202210262328A CN114563993A CN 114563993 A CN114563993 A CN 114563993A CN 202210262328 A CN202210262328 A CN 202210262328A CN 114563993 A CN114563993 A CN 114563993A
- Authority
- CN
- China
- Prior art keywords
- thermal power
- neural network
- energy
- power generating
- load rate
- 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
- 238000005457 optimization Methods 0.000 title claims abstract description 69
- 238000001556 precipitation Methods 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 title claims abstract description 22
- 239000003245 coal Substances 0.000 claims abstract description 91
- 238000013528 artificial neural network Methods 0.000 claims abstract description 69
- 238000013178 mathematical model Methods 0.000 claims abstract description 42
- 239000000428 dust Substances 0.000 claims abstract description 15
- 210000002569 neuron Anatomy 0.000 claims description 10
- 230000005284 excitation Effects 0.000 claims description 4
- 230000004913 activation Effects 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 3
- 238000005367 electrostatic precipitation Methods 0.000 claims 1
- 238000005265 energy consumption Methods 0.000 abstract description 6
- 230000004044 response Effects 0.000 abstract description 6
- 230000005684 electric field Effects 0.000 abstract description 4
- 230000008859 change Effects 0.000 abstract description 3
- 230000004048 modification Effects 0.000 description 3
- 238000012986 modification Methods 0.000 description 3
- 230000009467 reduction Effects 0.000 description 3
- 229910052799 carbon Inorganic materials 0.000 description 2
- 238000004134 energy conservation Methods 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- YCKRFDGAMUMZLT-UHFFFAOYSA-N Fluorine atom Chemical compound [F] YCKRFDGAMUMZLT-UHFFFAOYSA-N 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 210000003050 axon Anatomy 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 229910052731 fluorine Inorganic materials 0.000 description 1
- 239000011737 fluorine Substances 0.000 description 1
- 238000012552 review Methods 0.000 description 1
- 239000000779 smoke Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000005062 synaptic transmission Effects 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41845—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by system universality, reconfigurability, modularity
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/33—Director till display
- G05B2219/33273—DCS distributed, decentralised controlsystem, multiprocessor
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Regulation And Control Of Combustion (AREA)
Abstract
The invention discloses an energy-saving optimization method and an optimization system for an electric precipitation system of a thermal power generating unit, wherein the method comprises the following steps: the method comprises the steps of obtaining operation characteristic parameter data of a boiler and a coal mill of the unit, establishing a coal quality neural network mathematical model according to the parameter data, establishing a coal quality prediction neural network DCS configuration based on the coal quality neural network mathematical model, and compiling the coal quality prediction neural network DCS configuration into a thermal power unit DCS system to obtain a thermal power unit DCS optimization system, wherein the system comprises an ash load rate prediction basic database, an ash load rate prediction analysis system, the thermal power unit DCS optimization system and an electric precipitation energy-saving module control system. According to the invention, through intelligent optimization control on the electric dust removal energy-saving system, when the unit load and the coal quality change, the ash load rate response changes, and each electric field of the electric dust removal system can be controlled to respond in real time, so that the intelligent optimization control on electric dust removal is realized, and the electric dust removal system can safely operate at the lowest energy consumption level.
Description
Technical Field
The invention belongs to the technical field of energy conservation and environmental protection, and particularly relates to an energy-saving optimization method and an optimization system for an electric precipitation system of a thermal power generating unit.
Background
In recent years, due to the proposal of a double-carbon target and the impact of new energy power on traditional thermal power, the number of utilization hours of thermal power generating units is reduced year by year, and the average utilization rate of equipment is reduced to about 50%. At present, the load rate of a thermal power generating unit is not high, a large space is left for full load, and the amount of generated smoke and ash is small. Most downstream electric dust removal equipment keeps running under a full-load design working condition, can only output at the maximum power, has high dust removal energy consumption, and consumes a large amount of electric energy in vain. The power consumption of the thermal power plant is higher, and the overall economy of the thermal power plant is lower. Under the large background of energy conservation, consumption reduction, pollution reduction and carbon reduction in the whole industry, the full-load automatic optimized operation and flexible modification of an electric precipitation system of a thermal power generating unit are urgent.
Disclosure of Invention
In order to solve the problems, the invention provides an energy-saving optimization method and an energy-saving optimization system for an electric precipitation system of a thermal power unit, and aims to solve the problems that the electric precipitation equipment of the thermal power unit has high energy consumption for precipitation, a large amount of electric energy is consumed in vain, the plant power consumption rate of the thermal power unit is high, and the overall economy of the thermal power unit is low.
In order to achieve the aim, the invention provides an energy-saving optimization method for an electric precipitation system of a thermal power generating unit, which comprises the following steps:
acquiring operation characteristic parameter data of a unit boiler and a coal mill;
establishing a coal quality neural network mathematical model according to the parameter data;
building a coal quality prediction neural network DCS configuration based on a coal quality neural network mathematical model;
the coal quality prediction neural network DCS configuration is programmed into a thermal power generating unit DCS system, and a thermal power generating unit DCS optimization system is obtained;
and carrying out self-adaptive control on the electric precipitation energy-saving system according to the ash load rate output by the DCS optimization system of the thermal power generating unit.
According to an embodiment of the invention, the parameter data comprises unit current, wind temperature and coal quantity.
According to an embodiment of the invention, the establishing of the coal quality neural network mathematical model according to the parameter data comprises:
and training the unit current, the warm air and the coal amount in the operation characteristic parameter data of the unit boiler and the coal mill by adopting an artificial neural network algorithm to obtain a coal quality neural network mathematical model.
According to an embodiment of the invention, the calculation formula of the coal quality neural network mathematical model is as follows:
Yi＝f(Ui)
in the formula of UiIs the sum of the activation values of the neurons; x is a radical of a fluorine atomjIs the input of a neuron; w is aijIs input x of a neuronjA corresponding weight coefficient; thetaiIs an offset; y isiIs a predicted value of the boiler ash load rate; f (U)i) Is about UiThe excitation function of (2).
According to an embodiment of the invention, the establishing of the coal quality prediction neural network DCS configuration based on the coal quality neural network mathematical model comprises:
inputting the unit current, the air temperature and the coal quantity which are acquired in real time into a coal neural network mathematical model, and calculating to obtain the ash load rate of a predicted boiler;
and establishing a coal quality prediction neural network DCS configuration according to the ash load rate.
According to a specific embodiment of the invention, the self-adaptive control of the electric dust removal energy-saving system according to the ash load rate output by the DCS optimization system of the thermal power generating unit comprises the following steps:
when the working mode of the electric precipitation energy-saving system is an automatic mode, a control instruction of the electric precipitation energy-saving system is generated according to the ash load rate or the electric load rate output by the DCS optimization system of the thermal power generating unit, and when the ash load rate or the electric load rate of the thermal power generating unit is in middle-low load operation, the working mode of the electric precipitation energy-saving system is switched to an ash load rate control mode according to the control instruction.
According to an embodiment of the invention, the ash load rate is a predicted value of the boiler ash load rate output by the coal neural network mathematical model.
An energy-conserving optimizing system of thermal power unit electric precipitation includes:
the ash load rate prediction basic database is used for acquiring the operation characteristic parameter data of the unit boiler and the coal mill;
the ash load rate prediction analysis system is used for establishing a coal quality neural network mathematical model according to the parameter data and predicting the ash load rate of the boiler based on the coal quality neural network mathematical model;
the thermal power generating unit DCS optimization system is used for establishing a coal quality prediction neural network DCS configuration based on the coal quality neural network mathematical model and compiling the coal quality prediction neural network DCS configuration into the thermal power generating unit DCS optimization system;
and the electric precipitation energy-saving module control system is used for carrying out self-adaptive control on the electric precipitation energy-saving system according to the ash load rate output by the DCS optimization system of the thermal power generating unit.
According to an embodiment of the invention, the parameter data comprises unit current, wind temperature and coal quantity.
According to a specific embodiment of the invention, the ash load rate output by the DCS optimization system of the thermal power generating unit is a predicted value of the boiler ash load rate output by the coal neural network mathematical model.
Compared with the prior art, the energy-saving optimization method and the optimization system for the electric precipitation system of the thermal power generating unit, provided by the invention, have the advantages that firstly, a coal neural network mathematical model is constructed according to the operation characteristic parameters of a boiler and a coal mill of the thermal power generating unit, so that the boiler ash load rate is predicted, then the boiler ash load rate predicted by the neural network mathematical model is used as a control input signal of the electric precipitation energy-saving automatic control system, so that the full-load self-adaptive automatic control of the electric precipitation system is realized, and by the intelligent optimization control of the electric precipitation energy-saving system, when the load of the unit and the coal quality change, the response of the ash load rate changes, and each electric field of the controllable dust removal system can respond in real time, and further, intelligent optimization control of electric precipitation is realized, so that the electric precipitation system can safely operate at the lowest energy consumption level, and the power consumption of a plant is effectively reduced under full load, particularly at the medium-low load stage.
Drawings
Fig. 1 is a flowchart of an energy-saving optimization method for an electric precipitation system of a thermal power generating unit according to an embodiment of the present invention.
Fig. 2 is a flowchart of a coal quality prediction neural network DCS configuration establishing method according to an embodiment of the present invention.
Fig. 3 is a structural diagram of an electric dust removal energy-saving optimization system of a thermal power generating unit according to an embodiment of the present invention.
Reference numerals:
1-ash load rate prediction base database; 2-ash load rate predictive analysis system; 3-a DCS optimization system of the thermal power generating unit; 4-electric precipitation energy-saving module control system.
Detailed Description
The present invention is described in detail below with reference to specific embodiments in order to make the concept and idea of the present invention more clearly understood by those skilled in the art. It is to be understood that the embodiments presented herein are only a few of all embodiments that the present invention may have. Those skilled in the art who review this disclosure will readily appreciate that many modifications, variations, or alterations to the described embodiments, either in whole or in part, are possible and within the scope of the invention as claimed.
As used herein, the terms "first," "second," and the like are not intended to imply any order, quantity, or importance, but rather are used to distinguish one element from another. As used herein, the terms "a," "an," and other similar terms are not intended to mean that there is only one of the things, but rather that the pertinent description is directed to only one of the things, which may have one or more. As used herein, the terms "comprises," "comprising," and other similar words are intended to refer to logical interrelationships, and are not to be construed as referring to spatial structural relationships. For example, "a includes B" is intended to mean that logically B belongs to a, and not that spatially B is located inside a. Furthermore, the terms "comprising," "including," and other similar words are to be construed as open-ended, rather than closed-ended. For example, "a includes B" is intended to mean that B belongs to a, but B does not necessarily constitute all of a, and a may also include C, D, E and other elements.
The terms "embodiment," "present embodiment," "an embodiment," "one embodiment," and "one embodiment" herein do not mean that the pertinent description applies to only one particular embodiment, but rather that the description may apply to yet another embodiment or embodiments. Those skilled in the art will appreciate that any descriptions made in relation to one embodiment may be substituted, combined, or otherwise combined with the descriptions in relation to another embodiment or embodiments, and that the substitution, combination, or otherwise combination of the new embodiments as produced herein may occur to those skilled in the art and are intended to be within the scope of the present invention.
Example 1
Additional aspects and advantages of embodiments of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of embodiments of the invention. With reference to fig. 1 to 2, an embodiment of the present invention provides an energy saving optimization method for an electric precipitation system of a thermal power generating unit, including:
s1: and acquiring operation characteristic parameter data of the unit boiler and the coal mill, wherein the parameter data comprises unit current, air temperature and coal quantity.
S2: establishing a coal quality neural network mathematical model according to the parameter data, which specifically comprises the following steps:
training the unit current, the warm air and the coal amount in the operation characteristic parameter data of the unit boiler and the coal mill by adopting an artificial neural network algorithm to obtain a coal quality neural network mathematical model, wherein the calculation formula of the coal quality neural network mathematical model is as follows:
Yi＝f(Ui)
in the formula of UiIs the sum of the activation values of the neurons; x is the number ofjIs the input of a neuron, i.e. x1，x2，……，xnIs the input of a neuron, is the information from the axons of the preceding n neurons; w is aijIs input x of a neuronjCorresponding weight coefficient, wi1，wi2……，winAre i neuron pairs x respectively1，x2，……，xnThe weight coefficient of (a), i.e., the synaptic transmission efficiency; thetaiIs an offset; y isiIs the output of the i neuron, namely the predicted value of the boiler ash load rate; f (x) is the excitation function that determines the input x to i neurons1，x2，……，xnThe mode in which the co-stimulus is output when it reaches the threshold, the invention f (U)i) Is about UiThe excitation function of (2).
S3: establishing a coal quality prediction neural network DCS configuration based on a coal quality neural network mathematical model, which specifically comprises the following steps:
s31: inputting the unit current, the air temperature and the coal quantity which are acquired in real time into a coal neural network mathematical model, and calculating to obtain the ash load rate of a predicted boiler;
s32: and establishing a coal quality prediction neural network DCS configuration according to the ash load rate.
The ash load rate Y of the boiler can be predicted according to the coal quality neural network mathematical model by inputting parameters such as unit current, air temperature, coal quantity and the like into the coal quality neural network mathematical modeliAnd constructing the coal quality prediction neural network DCS configuration.
S4: and (4) compiling the coal quality prediction neural network DCS configuration into a thermal power generating unit DCS system to obtain a thermal power generating unit DCS optimization system.
S5: and carrying out self-adaptive control on the electric precipitation energy-saving system according to the ash load rate output by the DCS optimization system of the thermal power generating unit.
The method comprises the steps of adding program logic of an intelligent electric precipitation energy-saving system in a DCS (distributed control system) optimization system of a thermal power generating unit, generating a control instruction of the electric precipitation energy-saving system according to an ash load rate or an electric load rate output by the DCS optimization system of the thermal power generating unit when the working mode of the electric precipitation energy-saving system is an automatic mode, and switching the working mode of the electric precipitation energy-saving system into an ash load rate control mode according to the control instruction when the ash load rate or the electric load rate of the thermal power generating unit is in medium-low load operation, wherein the ash load rate is a predicted value of a boiler ash load rate output by a coal quality neural network mathematical model.
Example 2
The embodiment of the invention provides an electric precipitation energy-saving optimization system for a thermal power generating unit, as shown in fig. 3, comprising:
and the ash load rate prediction basic database 1 is used for acquiring operation characteristic parameter data of a unit boiler and a coal mill, wherein the parameter data comprises unit current, air temperature and coal quantity.
And the ash load rate prediction analysis system 2 is used for establishing a coal quality neural network mathematical model according to the parameter data and predicting the ash load rate of the boiler based on the coal quality neural network mathematical model.
And the thermal power generating unit DCS optimization system 3 is used for establishing the coal quality prediction neural network DCS configuration based on the coal quality neural network mathematical model and programming the coal quality prediction neural network DCS configuration into the thermal power generating unit DCS optimization system.
And the electric precipitation energy-saving module control system 4 is used for carrying out self-adaptive control on the electric precipitation energy-saving system according to the ash load rate output by the DCS optimization system of the thermal power generating unit. The ash load rate output by the DCS optimization system of the thermal power generating unit is a predicted value of the boiler ash load rate output by the coal neural network mathematical model.
The electric precipitation energy-saving optimization system of the thermal power generating unit comprises a boiler and a coal mill of the thermal power generating unit, an ash load rate prediction basic database 1, a coal quality neural network DCS optimization system 3, an ash load rate response control unit, an ash load rate real-time response control unit, an ash load rate prediction base database, an ash load rate prediction analysis system 2, an ash load rate prediction analysis system, an ash load rate neural network DCS optimization system, an ash load rate prediction neural network DCS optimization system, an ash load rate self-adaptive control unit, an ash load rate control unit, an electric field in the electric precipitation energy-saving optimization system, an ash load rate real-time response control unit, an intelligent optimization system, an intelligent optimization control unit, an intelligent control unit control, the intelligent electric dust removal energy-saving system can safely operate at the lowest energy consumption level.
In summary, the energy-saving optimization method and the optimization system for the electric precipitation system of the thermal power generating unit provided by the invention are characterized in that firstly, a coal quality neural network mathematical model is constructed according to the operation characteristic parameters of a boiler and a coal mill of the thermal power generating unit so as to predict the boiler ash load rate, then the boiler ash load rate predicted by the neural network mathematical model is used as a control input signal of the electric precipitation energy-saving automatic control system, so that the full-load self-adaptive automatic control of the electric precipitation system is realized, and by the intelligent optimization control of the electric precipitation energy-saving system, when the load of the unit and the coal quality change, the response of the ash load rate changes, and each electric field of the controllable dust removal system can respond in real time, and further, intelligent optimization control of electric precipitation is realized, so that the electric precipitation system can safely operate at the lowest energy consumption level, and the electricity consumption rate of a unit plant is effectively reduced under full load, particularly at the medium-low load stage.
The concepts, principles and concepts of the invention have been described above in detail in connection with specific embodiments (including examples and illustrations). Those skilled in the art will appreciate that the embodiments of the present invention are capable of other than the several forms described above and that the steps, methods, systems, and components of the embodiments described herein are capable of further modifications, permutations and equivalents after reading the present specification, which should be considered as falling within the scope of the present invention, which is limited only by the claims.
Claims (10)
1. An energy-saving optimization method for an electric precipitation system of a thermal power generating unit is characterized by comprising the following steps:
acquiring operation characteristic parameter data of a unit boiler and a coal mill;
establishing a coal quality neural network mathematical model according to the parameter data;
building a coal quality prediction neural network DCS configuration based on the coal quality neural network mathematical model;
the coal quality prediction neural network DCS configuration is programmed into a thermal power generating unit DCS system, and a thermal power generating unit DCS optimization system is obtained;
and performing self-adaptive control on the electric dust removal energy-saving system according to the ash load rate output by the DCS optimization system of the thermal power generating unit.
2. The energy-saving optimization method for the thermal power generating unit electric precipitation system according to claim 1, wherein the parameter data comprise unit current, wind temperature and coal amount.
3. The energy-saving optimization method for the thermal power generating unit electric precipitation system according to claim 1, wherein the establishing of the coal neural network mathematical model according to the parameter data comprises:
and training the unit current, the warm air and the coal amount in the operation characteristic parameter data of the unit boiler and the coal mill by adopting an artificial neural network algorithm to obtain a coal quality neural network mathematical model.
4. The energy-saving optimization method for the thermal power generating unit electric precipitation system according to claim 3, wherein the calculation formula of the coal neural network mathematical model is as follows:
Yi＝f(Ui)
in the formula of UiIs the sum of the activation values of the neurons; x is the number ofjIs the input of a neuron; w is aijIs input x of a neuronjA corresponding weight coefficient; thetaiIs an offset; y isiIs a predicted value of the boiler ash load rate; f (U)i) Is about UiThe excitation function of (2).
5. The energy-saving optimization method for the electric precipitation system of the thermal power generating unit according to claim 1, wherein the establishing of the coal quality prediction neural network DCS configuration based on the coal quality neural network mathematical model comprises:
inputting the unit current, the air temperature and the coal quantity which are acquired in real time into the coal neural network mathematical model, and calculating to obtain the ash load rate of the predicted boiler;
and establishing a coal quality prediction neural network DCS configuration according to the ash load rate.
6. The energy-saving optimization method for the electric precipitation system of the thermal power generating unit according to claim 1, wherein the adaptively controlling the electric precipitation energy-saving system according to the ash load rate output by the DCS optimization system of the thermal power generating unit comprises the following steps:
when the working mode of the electric dust removal energy-saving system is an automatic mode, a control instruction of the electric dust removal energy-saving system is generated according to the ash load rate or the electric load rate output by the DCS optimization system of the thermal power generating unit, and when the ash load rate or the electric load rate of the thermal power generating unit is in medium-low load operation, the working mode of the electric dust removal energy-saving system is switched to an ash load rate control mode according to the control instruction.
7. The energy-saving optimization method for the electric precipitation system of the thermal power generating unit as claimed in claim 6, wherein the ash load rate is a predicted value of a boiler ash load rate output by the coal neural network mathematical model.
8. The utility model provides a thermal power generating unit electrostatic precipitation energy-saving optimization system which characterized in that includes:
the ash load rate prediction basic database is used for acquiring the operation characteristic parameter data of the unit boiler and the coal mill;
the ash load rate prediction analysis system is used for establishing a coal quality neural network mathematical model according to the parameter data and predicting the ash load rate of the boiler based on the coal quality neural network mathematical model;
the thermal power generating unit DCS optimization system is used for establishing a coal quality prediction neural network DCS configuration based on the coal quality neural network mathematical model and compiling the coal quality prediction neural network DCS configuration into the thermal power generating unit DCS optimization system;
and the electric precipitation energy-saving module control system is used for carrying out self-adaptive control on the electric precipitation energy-saving system according to the ash load rate output by the DCS optimization system of the thermal power generating unit.
9. The thermal power generating unit electric precipitation energy-saving optimization system as claimed in claim 8, wherein the parameter data comprises unit current, wind temperature and coal amount.
10. The electric precipitation energy-saving optimization system of claim 8, wherein the ash load rate output by the DCS optimization system of the thermal power generating unit is a predicted value of the boiler ash load rate output by the coal neural network mathematical model.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210262328.5A CN114563993A (en) | 2022-03-17 | 2022-03-17 | Energy-saving optimization method and optimization system for electric precipitation system of thermal power generating unit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210262328.5A CN114563993A (en) | 2022-03-17 | 2022-03-17 | Energy-saving optimization method and optimization system for electric precipitation system of thermal power generating unit |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114563993A true CN114563993A (en) | 2022-05-31 |
Family
ID=81720096
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210262328.5A Pending CN114563993A (en) | 2022-03-17 | 2022-03-17 | Energy-saving optimization method and optimization system for electric precipitation system of thermal power generating unit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114563993A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110619353A (en) * | 2019-08-22 | 2019-12-27 | 中国石油天然气集团有限公司 | Multi-scale logging curve automatic identification method based on deep learning |
| CN111570093A (en) * | 2020-05-22 | 2020-08-25 | 华能平凉发电有限责任公司 | Electric precipitation energy-saving control method and system based on boiler coal quantity and air quantity |
| CN112099343A (en) * | 2020-07-29 | 2020-12-18 | 福建龙净环保股份有限公司 | Intelligent energy-saving optimization method of electric precipitation system based on neural network |
| CN112381210A (en) * | 2020-11-15 | 2021-02-19 | 西安热工研究院有限公司 | Coal-fired unit water-cooling wall temperature prediction neural network model |
| CN113742069A (en) * | 2021-08-31 | 2021-12-03 | 平安国际智慧城市科技股份有限公司 | Capacity prediction method and device based on artificial intelligence and storage medium |
-
2022
- 2022-03-17 CN CN202210262328.5A patent/CN114563993A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110619353A (en) * | 2019-08-22 | 2019-12-27 | 中国石油天然气集团有限公司 | Multi-scale logging curve automatic identification method based on deep learning |
| CN111570093A (en) * | 2020-05-22 | 2020-08-25 | 华能平凉发电有限责任公司 | Electric precipitation energy-saving control method and system based on boiler coal quantity and air quantity |
| CN112099343A (en) * | 2020-07-29 | 2020-12-18 | 福建龙净环保股份有限公司 | Intelligent energy-saving optimization method of electric precipitation system based on neural network |
| CN112381210A (en) * | 2020-11-15 | 2021-02-19 | 西安热工研究院有限公司 | Coal-fired unit water-cooling wall temperature prediction neural network model |
| CN113742069A (en) * | 2021-08-31 | 2021-12-03 | 平安国际智慧城市科技股份有限公司 | Capacity prediction method and device based on artificial intelligence and storage medium |
Non-Patent Citations (3)
| Title |
|---|
| 尚福海;: "基于人工神经网络的锅炉控制研究", 黑龙江科技信息, no. 16 * |
| 李树江,高宪文,柴天佑: "智能控制及其在电弧炉除尘系统中的应用", 控制与决策, no. 05, pages 594 * |
| 田莉;: "电除尘器智能控制系统的设计与仿真", 江苏电机工程, no. 04 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110458443B (en) | Smart home energy management method and system based on deep reinforcement learning | |
| Lissa et al. | Deep reinforcement learning for home energy management system control | |
| CN110007595B (en) | Real-time load optimization scheduling method, unit model, unit and system for heat supply system | |
| CN116187601B (en) | Comprehensive energy system operation optimization method based on load prediction | |
| CN112415924A (en) | Energy-saving optimization method and system for air conditioning system | |
| CN111598225B (en) | Air conditioner cold load prediction method based on self-adaptive deep confidence network | |
| CN113902582A (en) | Building comprehensive energy load prediction method and system | |
| CN115062869B (en) | Comprehensive energy scheduling method and system considering carbon emission | |
| Georgiou et al. | Implementing artificial neural networks in energy building applications—A review | |
| CN116451947A (en) | Optimal scheduling method for electric heating gas comprehensive energy system, terminal equipment and storage medium | |
| Ku et al. | Building electric energy prediction modeling for BEMS using easily obtainable weather factors with Kriging model and data mining | |
| CN107871157B (en) | Data prediction method, system and related device based on BP and PSO | |
| Fu et al. | Predictive control of power demand peak regulation based on deep reinforcement learning | |
| CN113723793A (en) | Method, device, equipment and medium for realizing park comprehensive energy system | |
| Ruano et al. | Improving a neural networks based HVAC predictive control approach | |
| CN114563993A (en) | Energy-saving optimization method and optimization system for electric precipitation system of thermal power generating unit | |
| Welch et al. | Comparison of two optimal control strategies for a grid independent photovoltaic system | |
| CN116227883A (en) | Intelligent household energy management system prediction decision-making integrated scheduling method based on deep reinforcement learning | |
| CN115983492A (en) | Hydrogen-containing multi-energy system operation carbon emission optimization decision method, system and medium | |
| CN114647190A (en) | Ash conveying energy-saving optimization method and system for thermal power generating unit | |
| CN115169839A (en) | Heating load scheduling method based on data-physics-knowledge combined drive | |
| CN113435042A (en) | Reinforced learning modeling method for demand response of building air conditioning system | |
| CN112217198A (en) | Photovoltaic power generation management method for multi-step graph neural network self-adaptive dynamic planning | |
| CN113609768A (en) | Bidirectional LSTM network-based distribution room line loss rate calculation method | |
| Lin et al. | Optimizing for Large Time Delay Systems by BP Neural Network and Evolutionary Algorithm Improving. |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |