EP1444510A2 - CAPTEUR LOGICIEL NOx EN SORTIE DES CHEMINEES DES FOURS UTILISANT L'AIR ENRICHI EN OXYGENE OU DE L'OXYGENE PUR COMME COMBURANT - Google Patents
CAPTEUR LOGICIEL NOx EN SORTIE DES CHEMINEES DES FOURS UTILISANT L'AIR ENRICHI EN OXYGENE OU DE L'OXYGENE PUR COMME COMBURANTInfo
- Publication number
- EP1444510A2 EP1444510A2 EP02801930A EP02801930A EP1444510A2 EP 1444510 A2 EP1444510 A2 EP 1444510A2 EP 02801930 A EP02801930 A EP 02801930A EP 02801930 A EP02801930 A EP 02801930A EP 1444510 A2 EP1444510 A2 EP 1444510A2
- Authority
- EP
- European Patent Office
- Prior art keywords
- data
- combustion
- neural network
- fumes
- oven
- 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.)
- Withdrawn
Links
- 238000002485 combustion reaction Methods 0.000 title claims abstract description 44
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 title claims description 18
- 239000001301 oxygen Substances 0.000 title claims description 18
- 229910052760 oxygen Inorganic materials 0.000 title claims description 18
- MYMOFIZGZYHOMD-UHFFFAOYSA-N Dioxygen Chemical compound O=O MYMOFIZGZYHOMD-UHFFFAOYSA-N 0.000 title description 4
- 239000003795 chemical substances by application Substances 0.000 title 1
- 238000013528 artificial neural network Methods 0.000 claims abstract description 52
- 239000003517 fume Substances 0.000 claims abstract description 47
- 238000000034 method Methods 0.000 claims abstract description 43
- IJGRMHOSHXDMSA-UHFFFAOYSA-N Atomic nitrogen Chemical compound N#N IJGRMHOSHXDMSA-UHFFFAOYSA-N 0.000 claims abstract description 28
- 229910052757 nitrogen Inorganic materials 0.000 claims abstract description 14
- 238000005259 measurement Methods 0.000 claims description 19
- 230000008569 process Effects 0.000 claims description 19
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 18
- 238000012545 processing Methods 0.000 claims description 17
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 claims description 12
- 239000003546 flue gas Substances 0.000 claims description 10
- 239000007800 oxidant agent Substances 0.000 claims description 10
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 9
- 239000000446 fuel Substances 0.000 claims description 8
- 239000003345 natural gas Substances 0.000 claims description 6
- 230000001590 oxidative effect Effects 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- 238000002844 melting Methods 0.000 claims description 5
- 230000008018 melting Effects 0.000 claims description 5
- 229910001018 Cast iron Inorganic materials 0.000 claims description 4
- 239000000295 fuel oil Substances 0.000 claims description 4
- 239000011521 glass Substances 0.000 claims description 4
- 230000003068 static effect Effects 0.000 claims description 4
- 239000001569 carbon dioxide Substances 0.000 claims description 3
- 239000000203 mixture Substances 0.000 claims description 3
- 238000007599 discharging Methods 0.000 claims description 2
- MWUXSHHQAYIFBG-UHFFFAOYSA-N nitrogen oxide Inorganic materials O=[N] MWUXSHHQAYIFBG-UHFFFAOYSA-N 0.000 description 103
- 210000000225 synapse Anatomy 0.000 description 13
- 230000006870 function Effects 0.000 description 7
- 230000015654 memory Effects 0.000 description 6
- 230000004913 activation Effects 0.000 description 5
- 150000001875 compounds Chemical class 0.000 description 5
- 238000012544 monitoring process Methods 0.000 description 5
- 239000007789 gas Substances 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000003860 storage Methods 0.000 description 3
- 230000015572 biosynthetic process Effects 0.000 description 2
- 238000004422 calculation algorithm Methods 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 2
- 230000000052 comparative effect Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000001105 regulatory effect Effects 0.000 description 2
- 239000000779 smoke Substances 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 238000009530 blood pressure measurement Methods 0.000 description 1
- 238000009529 body temperature measurement Methods 0.000 description 1
- 230000003750 conditioning effect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 239000000428 dust Substances 0.000 description 1
- 239000012530 fluid Substances 0.000 description 1
- 238000012625 in-situ measurement Methods 0.000 description 1
- 230000002452 interceptive effect Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 239000011159 matrix material Substances 0.000 description 1
- 238000000691 measurement method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000002569 neuron Anatomy 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 238000000053 physical method Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000002123 temporal effect Effects 0.000 description 1
- 238000012549 training Methods 0.000 description 1
- 238000012546 transfer Methods 0.000 description 1
- 239000013598 vector Substances 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
- G01N33/0032—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array using two or more different physical functioning modes
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
- G01N33/0034—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array comprising neural networks or related mathematical techniques
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/0004—Gaseous mixtures, e.g. polluted air
- G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
- G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
- G01N33/0036—General constructional details of gas analysers, e.g. portable test equipment concerning the detector specially adapted to detect a particular component
- G01N33/0037—NOx
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A50/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE in human health protection, e.g. against extreme weather
- Y02A50/20—Air quality improvement or preservation, e.g. vehicle emission control or emission reduction by using catalytic converters
Definitions
- NOx software sensor at the outlet of the furnace chimneys using oxygen-enriched air or pure oxygen as oxidant.
- the invention relates to the field of measuring NOx type gas emissions, in particular in industrial processes.
- Known measurement methods can be grouped into two categories: on the one hand, in situ measurements, by an appropriate NOx physical sensor, and, on the other hand, measurements or estimations by software sensor.
- the fumes leaving the oven are often at high temperatures (between 1400 ° C. and 1600 ° C.) and are charged with dust. All these conditions therefore affect the functioning of the physical NOx sensors that can be installed at the outlet of an oven.
- the invention relates to a method for measuring the NOx content in the fumes produced by combustion in an oven, characterized in that:
- the measurement does not require any physical NOx sensor, the only measurements carried out being those for the input data of the neural network, or else those from which these input data are calculated.
- Such a sensor using a neural network does not require recalibration or maintenance.
- the estimation of NOx is immediate compared to the measurement of a physical NOx sensor, and the implementation of the method according to the invention is less costly than that of a physical sensor.
- a pressure measurement a temperature measurement and one or two concentration data (carbon dioxide (CO2) and / or the oxygen concentration (02), in the fumes) to obtain information on the quantity or concentration of nitrogen in the fumes.
- CO2 carbon dioxide
- 02 oxygen concentration
- the fuel can be natural gas or else fuel oil or a mixture of natural gas and fuel oil.
- the oxidizer can be oxygen or oxygen-enriched air as the oxidant.
- At least two pressure data in the furnace are measured during the combustion process, these pressure data are processed to calculate the average, and this average pressure data is introduced as input data from the neural network.
- the measured data have a degree of correlation, with data or concentration or NOx content, greater than a predetermined degree.
- Data processing to provide data representative of the NOx content can be carried out continuously, that is to say with a temporal periodicity of the order of a few seconds.
- the invention also relates to a device for measuring the NOx content in the fumes produced by combustion in an oven, characterized in that it comprises:
- - sensors to measure at least one pressure data in the oven, at least one temperature data in the oven and / or in the fumes resulting from combustion, and at least one data representative of the concentration of nitrogen in the fumes , means for, or programmed to: have said data processed by a neural network, or to process at least part of this data to form input data for a neural network and to process said data processed by a neural network,
- the invention also relates to a combustion system comprising a burner, an oven, means for discharging combustion products, and a measuring device as above.
- the furnace is for example a glass furnace, or a rotary furnace for secondary melting of cast iron, or an incineration furnace.
- the invention also relates to a computer program comprising instructions for processing, according to a neural network, at least one piece of pressure data from an oven, at least one piece of temperature data in the same oven and / or in fumes resulting from combustion occurring in said furnace, and at least one datum representative of the nitrogen concentration in the fumes, and to calculate, according to this neural network, at least one output datum representative of the concentration or the NOx content in the fumes resulting from combustion.
- the invention also relates to a computer program comprising instructions for:
- FIG. 1 represents a network of neurons
- FIG. 2 represents a schematic diagram of the creation of a network of meurones
- FIG. 3 represents an oven structure
- FIG. 4 represents the neural network of a sensor according to the invention
- - Figures 5 and 6 represent data acquisition and processing means that can be used in the context of the present invention
- - Figure 7 shows comparative results obtained using two sensors, one of which according to the invention.
- DETAILED DESCRD? TION OF EMBODIMENTS OF THE INVENTION The invention uses a neural network in order to measure or estimate the quantities of nitrogen oxides produced by combustion.
- a neural network 32 is shown diagrammatically in FIG. 1.
- the references 20, 22, 24, 26 designate various network layers, including an input layer 20, an output layer 26 and various hidden layers 22, 24.
- the network can also have only one, or more than two.
- the output layer 26 supplies the amount of NOx to the user.
- Each layer k has a certain number of synapses ik (NI for layer 20, N2 for layer 22, N3 for layer 24, and 2 for output layer 26)
- the input data (processed data) are introduced into the synapses of the input layer.
- Each synapse of the neural network corresponds to a nonlinear activation function F, such as a hyperbolic tangent function or a sigmoid function, as well as an activation threshold.
- a nonlinear activation function F such as a hyperbolic tangent function or a sigmoid function
- each synapse i of each layer is linked to the synapses j of the next layer, and a weight Pij weights each link between a synapse i and a synapse j. This weight weighs the influence of the result of each synapse i in the calculation of the result provided by each synapse j to which it is linked.
- the output Sj of a synapse j is equal to the value of the activation function Fj applied to the weighted sum, by the weights Pij of the synapses, of the results Ai of the activation functions of the synapses i which are connected to it.
- Fj the activation function
- the network 32 shown in FIG. 1 is an open network. In a looped network, one of the output data is reused as input data.
- Physical data relating to a combustion process which produces NOx compounds, can be measured during the course of such a process; these data are then presented as input to the network. They can be pre-processed to determine if they are relevant, preferably non-redundant, and provide a complete preference determination of the process.
- Non-redundancy means that the correlations between the selected inputs are weak.
- Completeness means that all the information necessary to produce a neural network is present in the data set.
- Data processing for the creation of a neural network involves the acquisition of raw or physical data on the process. This acquisition preferably covers the entire range over which it is desired that the system can predict. Also, and in accordance with the diagram of FIG. 2, raw data are first of all measured (step 29). These data are then introduced into software 30 for creating neural networks.
- the final choice of the best network actually depends on the objectives set. We are in fact seeking to obtain the smallest relative difference, or a predetermined relative difference, between the measurements (of NOx, these measurements being carried out using NOx sensors) and the network predictions. Various data processing tools can also be used.
- a set of relevant, non-redundant data ensuring the completeness of the system is as follows: - at least one pressure measured in the oven,
- NOx measurements can be obtained with a NOx hardware sensor, for a certain period of time and with operator monitoring.
- This raw data is applied to the input layer 20 of the neural network 32, the assembly constituting a set of data sufficient to produce a neural network whose continuous use is possible.
- an average temperature in the oven instead of or in addition to the temperature in the flue gases. This average temperature then results from several temperature data obtained by several sensors arranged in the oven.
- the network itself can be obtained by implementing neural network production software, such as the NeuroOne software, from the company NETRAL.
- the user or the designer of the network indicates to this algorithm or to the software used the following data:
- the software or algorithm determines the synapses of the neural network and the corresponding weights. More specifically, software in source code or in executable code is produced, which allows the user to obtain NOx concentration data as a function of physical data or of raw data measured directly on the process. If this raw data measurement is carried out continuously or almost continuously (i.e.
- the senor thus produced can supply, continuously or almost continuously (with the same period or frequency), a measurement or a signal representative of a measurement of the NOx content produced.
- a neural network is chosen:
- the calculation times for a network with two or more hidden layers being too long in the case of a desired period of use of around a few seconds, for example 1 to 5 or 15 seconds,
- This furnace uses pure oxygen as an oxidizer, natural gas as fuel, and is equipped with a 1 MW burner.
- FIG. 3 Its structure is given schematically in FIG. 3, in which the reference 40 designates the burner itself, supplied by conduits 42 and 44 respectively with fuel and oxidizer, and the reference 46 the furnace in which combustion takes place.
- a chimney 48 is disposed at the outlet of the oven 46, the opening of a register 50 making it possible to adjust the pressure in the oven.
- a water circuit system (not shown in the figure) makes it possible to transfer energy to a load.
- Thermocouples placed against the wall of the oven, on the outside, make it possible to measure the outside temperature of the oven.
- Temperature sensors are arranged in the vault, inside the oven 46. For example, 11 sensors (only two of which are shown) are arranged along the vault, from the inlet of the oven to its exit. Thus, a sensor 54 makes it possible to measure the temperature in the vault, close to the root of the flame 52, while a sensor 56 makes it possible to measure the temperature in the vault, close to the outlet of the oven 46.
- Two pressure sensors 55, 57 are also arranged in the oven.
- a temperature sensor 58 can also be placed in the chimney 48, in order to measure the temperature of the fumes.
- sensors 60, 62 make it possible to measure concentrations of CO2 and oxygen (preferably dry).
- a neural network for such an oven can be produced using the NeuroOne software from the company NETRAL.
- the network is therefore provided in the form of an executable code.
- the physical data measured or the raw data used are: the 2 oven pressures (measured using sensors 55 and 57), the percentages of CO2 and oxygen in the flue gases (measured using sensor 62) , the vault temperatures measured longitudinally in the furnace, the percentage of nitrogen in the fuel, the purity of the oxygen used, the flow of oxygen introduced through the duct 44, the temperature of the fumes (measured with the sensor 58) , and the fuel flow rate introduced through line 42.
- Data processing makes it possible to:
- each of the data is preferably considered as an average over a certain time interval, for example as a moving average over an interval of 3 minutes, with an acquisition period which may be 15 s.
- a bias is also generated by the software for producing the neural network.
- the output of the network is preferably thresholded, that is to say that the NOx concentrations below a certain threshold or a certain predetermined limit value, for example 200 ppm, are not taken into account. Indeed, a value of NOx lower than such a threshold can correspond to a deficiency of one of the sensors and therefore does not present any interest in modeling.
- the structure of the network obtained is shown diagrammatically in FIG. 4.
- the network only has one hidden layer 22. It further comprises the input 20 and output 26 layers, the reference 21 designating the input bias.
- the data are averaged over time, as indicated by the symbol ⁇ ...>.
- the index f relates to the data measured in the oven. Those for which an average is made between several sensors are surmounted by a bar.
- FIGS. 5 and 6 A system for processing the measurements made is shown in FIGS. 5 and 6.
- Such a system includes a PC microcomputer 70 to which the data measured by the sensors 54 - 62 are transmitted via a link 61.
- the microcomputer 70 comprises (FIG. 6) a microprocessor 82, a set of RAM memories 80 (for the storage of data), a ROM memory 84 (for the storage of program instructions).
- a data acquisition card 89 transforms the analog data supplied by the sensors into digital data and puts this data in the format required by the microcomputer. These various elements are connected to a bus 88.
- Peripheral devices allow interactive dialogue with a user.
- the display means (screen) 74 make it possible to provide a user with a visual indication relating to the calculated NOx content.
- a link 63 makes it possible to control the modification of certain operating parameters of a combustion process.
- the data or the instructions are loaded to implement a processing of the raw or physical data according to the invention, and in particular to carry out the preliminary processing of the raw or directly measured data (see FIG. 2), and to calculate the NOx content using a neural network 32.
- These data or instructions for processing the raw or physical data can be transferred into a memory area of the microcomputer 70 from a floppy disk or any other medium that can be read by a microcomputer or a computer (for example: hard disk, ROM read-only memory, dynamic RAM memory or any other type of RAM memory, compact optical disk, magnetic or optical storage element).
- Curve I represents the results obtained by modeling and curve II those obtained by measurement with NOx sensors placed directly in the stack 48 and constantly monitored. As we can see, the modeling makes it possible to get as close as possible to the NOx content, since we obtain a standard deviation on the relative error of less than 2% between the calculated concentrations and those measured by a physical sensor . In other words, 95.45% of the NOx predicted by the software sensor is within ⁇ 4% of the measured value.
- Table I are collected the standard deviations of the errors on the NOx concentrations obtained on the training and validation data.
- the software sensor according to the invention is adaptable to all types of ovens using air enriched with oxygen as oxidant or pure oxygen.
- the invention is independent of the control system and adapts to all computer languages, which allows it to be integrated into any control system for existing industrial combustion processes.
- a NOx measurement carried out in accordance with the invention can be used in monitoring mode, for example to trigger an alarm as soon as the NOx content exceeds a certain threshold.
- all the input parameters are fixed, except one and the non-fixed parameter is regulated so as to maintain the NOx content at a constant value or between two values defining a range of variation.
- the regulation is carried out for example using the link 63 (see FIG. 5) which transmits the regulation order to the process.
- the fields of use of the invention are numerous.
- the invention applies in particular to glass furnaces, rotary furnaces for secondary melting of cast iron, incineration furnaces, chemical reactors requiring the presence of a flame and whose oxidant is air enriched with oxygen.
- a static model is therefore implemented to calculate the NOx emissions in various industrial processes, and in particular in the fumes leaving the furnaces using, as oxidizer, air enriched in oxygen or oxygen. .
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- Chemical & Material Sciences (AREA)
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- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Immunology (AREA)
- Food Science & Technology (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- Combustion & Propulsion (AREA)
- General Physics & Mathematics (AREA)
- Medicinal Chemistry (AREA)
- Pathology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Mathematical Physics (AREA)
- Regulation And Control Of Combustion (AREA)
- Measuring Oxygen Concentration In Cells (AREA)
- Investigating Or Analyzing Materials Using Thermal Means (AREA)
Abstract
Description
Claims
Applications Claiming Priority (3)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
FR0113809A FR2831666B1 (fr) | 2001-10-25 | 2001-10-25 | Capteur logiciel nox en sortie des cheminees des fours utilisant l'air enrichi en oxygene ou de l'oxygene pur comme comburant |
FR0113809 | 2001-10-25 | ||
PCT/FR2002/003393 WO2003034819A2 (fr) | 2001-10-25 | 2002-10-04 | CAPTEUR LOGICIEL NOx |
Publications (1)
Publication Number | Publication Date |
---|---|
EP1444510A2 true EP1444510A2 (fr) | 2004-08-11 |
Family
ID=8868715
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
EP02801930A Withdrawn EP1444510A2 (fr) | 2001-10-25 | 2002-10-04 | CAPTEUR LOGICIEL NOx EN SORTIE DES CHEMINEES DES FOURS UTILISANT L'AIR ENRICHI EN OXYGENE OU DE L'OXYGENE PUR COMME COMBURANT |
Country Status (6)
Country | Link |
---|---|
US (1) | US7266460B2 (fr) |
EP (1) | EP1444510A2 (fr) |
JP (1) | JP2005506540A (fr) |
AU (1) | AU2002356209A1 (fr) |
FR (1) | FR2831666B1 (fr) |
WO (1) | WO2003034819A2 (fr) |
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US20060106501A1 (en) * | 2004-11-12 | 2006-05-18 | General Electric Company | NEURAL MODELING FOR NOx GENERATION CURVES |
US7765795B2 (en) * | 2006-04-28 | 2010-08-03 | Caterpillar Inc | NOx control using a neural network |
CN101718769B (zh) * | 2009-11-17 | 2012-12-26 | 重庆大学 | 一种基于并联型遗传Elman神经网络的源驱动式235U浓度识别方法 |
US10466221B2 (en) * | 2013-12-20 | 2019-11-05 | Industrial Scientific Corporation | Systems and methods for predicting gas concentration values |
CN104132959B (zh) * | 2014-07-01 | 2016-01-13 | 哈尔滨工业大学 | 一种基于神经网络的严寒地区建筑外墙传热性能预测方法 |
US10690344B2 (en) | 2016-04-26 | 2020-06-23 | Cleaver-Brooks, Inc. | Boiler system and method of operating same |
JP6673799B2 (ja) * | 2016-10-21 | 2020-03-25 | 株式会社神戸製鋼所 | ガス化溶融炉プラントの排ガス制御装置及び排ガス制御方法 |
CN110063326A (zh) * | 2019-04-30 | 2019-07-30 | 济南浪潮高新科技投资发展有限公司 | 基于卷积神经网络的智能驱鸟方法 |
CN110334452B (zh) * | 2019-07-09 | 2021-03-16 | 中南大学 | 一种智慧农业大气污染物浓度分层次预警方法 |
EP4118439A4 (fr) * | 2020-03-10 | 2024-03-13 | Stratuscent Inc. | Systèmes et procédés de détection d'un événement de détection dans un flux de données de mesure de capteur chimique |
CN111589302A (zh) * | 2020-05-29 | 2020-08-28 | 广东电科院能源技术有限责任公司 | 燃煤电厂scr脱硝性能预测方法、装置、设备和存储介质 |
CN111972394B (zh) * | 2020-06-11 | 2022-01-21 | 广东电网有限责任公司 | 一种基于dqn的超声波驱鸟最优频率的选择方法 |
CN116954058B (zh) * | 2023-07-13 | 2024-02-23 | 淮阴工学院 | 一种锅炉NOx浓度预测与智能控制方法及系统 |
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US5249954A (en) * | 1992-07-07 | 1993-10-05 | Electric Power Research Institute, Inc. | Integrated imaging sensor/neural network controller for combustion systems |
US5386373A (en) * | 1993-08-05 | 1995-01-31 | Pavilion Technologies, Inc. | Virtual continuous emission monitoring system with sensor validation |
GB9512929D0 (en) * | 1995-06-24 | 1995-08-30 | Sun Electric Uk Ltd | Multi-gas sensor systems for automatic emissions measurement |
US6045353A (en) * | 1996-05-29 | 2000-04-04 | American Air Liquide, Inc. | Method and apparatus for optical flame control of combustion burners |
DE19808197C2 (de) * | 1998-02-27 | 2001-08-09 | Mtu Aero Engines Gmbh | System und Verfahren zur Diagnose von Triebwerkszuständen |
US6289328B2 (en) * | 1998-04-17 | 2001-09-11 | The United States Of America As Represented By The Secretary Of The Navy | Chemical sensor pattern recognition system and method using a self-training neural network classifier with automated outlier detection |
FR2781039B1 (fr) * | 1998-07-08 | 2000-09-22 | Air Liquide | Procede de combustion d'un combustible avec un comburant riche en oxygene |
US7099836B2 (en) * | 2000-04-24 | 2006-08-29 | Cichanowicz J Edward | Automated method for conducting buy/sell transactions for non-commodity materials or devices |
FR2808592B1 (fr) * | 2000-05-03 | 2002-10-11 | Air Liquide | Capteur logiciel nox |
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2001
- 2001-10-25 FR FR0113809A patent/FR2831666B1/fr not_active Expired - Fee Related
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2002
- 2002-10-04 JP JP2003537398A patent/JP2005506540A/ja not_active Ceased
- 2002-10-04 AU AU2002356209A patent/AU2002356209A1/en not_active Abandoned
- 2002-10-04 US US10/493,642 patent/US7266460B2/en not_active Expired - Lifetime
- 2002-10-04 EP EP02801930A patent/EP1444510A2/fr not_active Withdrawn
- 2002-10-04 WO PCT/FR2002/003393 patent/WO2003034819A2/fr active Application Filing
Non-Patent Citations (1)
Title |
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See references of WO03034819A2 * |
Also Published As
Publication number | Publication date |
---|---|
FR2831666A1 (fr) | 2003-05-02 |
WO2003034819A2 (fr) | 2003-05-01 |
US20040249578A1 (en) | 2004-12-09 |
AU2002356209A1 (en) | 2003-05-06 |
US7266460B2 (en) | 2007-09-04 |
WO2003034819A8 (fr) | 2004-04-01 |
JP2005506540A (ja) | 2005-03-03 |
FR2831666B1 (fr) | 2004-03-12 |
WO2003034819A3 (fr) | 2003-10-16 |
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