EP2446193B1 - Verfahren zur regelung eines verbrennungsprozesses, insbesondere in einem feuerraum eines fossilbefeuerten dampferzeugers, und verbrennungssystem - Google Patents

Verfahren zur regelung eines verbrennungsprozesses, insbesondere in einem feuerraum eines fossilbefeuerten dampferzeugers, und verbrennungssystem Download PDF

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Publication number
EP2446193B1
EP2446193B1 EP10729831.7A EP10729831A EP2446193B1 EP 2446193 B1 EP2446193 B1 EP 2446193B1 EP 10729831 A EP10729831 A EP 10729831A EP 2446193 B1 EP2446193 B1 EP 2446193B1
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EP
European Patent Office
Prior art keywords
combustion
variables
control
values
different
Prior art date
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Not-in-force
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EP10729831.7A
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German (de)
English (en)
French (fr)
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EP2446193A2 (de
Inventor
Matthias Behmann
Till SPÄTH
Klaus Wendelberger
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Siemens AG
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Siemens AG
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Publication of EP2446193A2 publication Critical patent/EP2446193A2/de
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Publication of EP2446193B1 publication Critical patent/EP2446193B1/de
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Classifications

    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F23COMBUSTION APPARATUS; COMBUSTION PROCESSES
    • F23DBURNERS
    • F23D1/00Burners for combustion of pulverulent fuel
    • F23D1/02Vortex burners, e.g. for cyclone-type combustion apparatus
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F23COMBUSTION APPARATUS; COMBUSTION PROCESSES
    • F23NREGULATING OR CONTROLLING COMBUSTION
    • F23N5/00Systems for controlling combustion
    • F23N5/003Systems for controlling combustion using detectors sensitive to combustion gas properties
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F23COMBUSTION APPARATUS; COMBUSTION PROCESSES
    • F23NREGULATING OR CONTROLLING COMBUSTION
    • F23N5/00Systems for controlling combustion
    • F23N5/02Systems for controlling combustion using devices responsive to thermal changes or to thermal expansion of a medium
    • FMECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F23COMBUSTION APPARATUS; COMBUSTION PROCESSES
    • F23NREGULATING OR CONTROLLING COMBUSTION
    • F23N2900/00Special features of, or arrangements for controlling combustion
    • F23N2900/05006Controlling systems using neuronal networks

Definitions

  • the invention relates to a method for controlling a combustion process, in particular in a combustion chamber of a fossil-fired steam generator, in which spatially resolved measured values are determined in the combustion chamber.
  • the invention further relates to a corresponding combustion system.
  • the fuel is first processed (eg grinding the coal in the coal mill, preheating the fuel oil or the like) and then fed controlled with the combustion air to the combustion chamber according to the current heat demand of the system.
  • the introduction of the fuel into the furnace takes place at different points of the steam generator to the so-called burners.
  • the supply of air takes place at various points. At the burners themselves always takes place an air supply.
  • the invention thus utilizes an improved detection of the current state of Feuerungsluien by the use of at least one measurement technique with spatially resolving detection range for the quantitative determination of the combustion products after combustion inside the technical furnace for a differentiated and faster process control.
  • An essential advantage of the invention is that the complex measured value distributions of the spatially resolving measurement technique can be processed by the transformation to simple state or controlled variables using conventional controllers. Furthermore, it is achieved by the inverse transformation that the output signals of the conventional controllers are distributed according to a predetermined optimization target to the existing control variables. Thus, an optimal interaction between the newly defined control concepts and the installed complex measurement technology is achieved. In particular, however, an as efficient as possible, low-wear and running with the lowest possible emissions combustion process is realized by the thus improved control structures.
  • the state variables are determined on the basis of statistical information of the spatially resolved measured values. This has the advantage that here the enormous diversity of information about the existing example, temperature or concentration distributions can be compacted. Weighting can be introduced and other image processing methods used. Another advantage is that in this way process variables arise with which the combustion process can be described and regulated.
  • the distribution of the controller outputs to the actuators is optimized in one embodiment using a neural network.
  • the control interventions can also be finely adjusted using the neural network. This achieves a particularly intelligent and exact control which is robust against the variation of external influences, e.g. variable fuel quality.
  • Decisive in the selection of the measurement technique is that it is suitable for determining essential properties of the combustion with spatial resolution. Measurements are carried out, for example, on a cross section of the combustion chamber near the combustion process. The measured values characterize combustion based on properties such as local concentrations (CO, 02, C02, H20, ...) and temperature.
  • variable transformation VT In the context of a variable transformation VT, these data, which are identified by M measured values MW in the figure, are converted in a first step into state variables that can be used in terms of control technology.
  • the spatial information about the combustion chamber is mapped to individual key figures and thus condensed.
  • an optimization target can be defined as a setpoint.
  • these state variables in combination with conventional, process technology available measurement and process information characterize the current operating state of the combustion process.
  • variable transformation VT converts any desired number of M measured values MW into an arbitrary number of N controlled variables RG, where M and N represent natural numbers and N is usually smaller than M.
  • the control variables RG are state variables which are then used as actual values for individual controllers.
  • the N controlled variables are fed to N regulators R.
  • the control module which contains a subtractor and further control technology components such as a PI controller.
  • This is a conventional control module that may already be present in the technical system to be controlled. It can also be a multi-variable control module, depending on the design variant.
  • the control block considered here also has an input ESW for the desired value of the derived state variable. This is either specified manually, is given constant or load-dependent and should characterize the desired operating behavior. Still exists In addition to the input ERG for the controlled variable RG another input EPG for any other process variables PG, which are detected outside of the spatially resolving measuring system.
  • the control difference between the setpoint and actual value is formed, the control difference by the other process variables varies, for example, to adjust the controller gain as a function of the current load situation, and the existing controller (here PI) supplied, which determines the necessary manipulated variable changes , This signal is present at the output ARA of the controller.
  • control outputs RA there are N controllers, there are N values for the control outputs RA (see figure). It now applies, in a back transformation RT, to convert these signals RA, designated as control outputs, of the number N in such a way that a certain number of K actuators each receive the actuating signal necessary to achieve the control target.
  • the control outputs RA of the N controllers R must now be used to derive control interventions for various actuators with which the combustion process can be favorably influenced. In this case, a control intervention can be made on a plurality of actuators in differentiated strength.
  • Actuators are, for example, the openings of the combustion chamber arranged louvers.
  • the distribution of N control outputs to K actuators takes place (N, K each natural numbers).
  • process variables PG are taken into account, which are recorded outside the spatially resolving measuring system.
  • the distribution of the controller outputs is performed on the actuators in an optimal manner, so that, for example, a minimization of emissions can take place and at the same time the highest possible efficiency of the system becomes.
  • the calculation unit RT also has optimization values OW from the optimizer OPT be supplied. The optimizer receives information from different areas.
  • the optimizer can also obtain measurement results of spatially resolving measuring devices arranged in the combustion chamber.
  • a number M' of the spatially resolved measured values is converted into any number N 'of state variables which are fed to the optimizer OPT.
  • N 'of state variables which are fed to the optimizer OPT.
  • the optimizer OPT may be connected to a neural network NN.
  • a hybrid control structure of conventional control blocks and neural networks is achieved.
  • the neural network is trained with process measures and serves as a specific model for predicting the behavior of the furnace.
  • An iterative optimization algorithm determines the optimum distribution of the control actions on the actuators and correction values for the actuators based on the firing reaction predicted by the neural network. This optimizes the process according to a given target function.
  • the optimization values OW can, for example, also be trim factors.
  • trim factors By means of the trimming factors, the results of the back transformation RT are weighted, shifted and adjusted in accordance with the optimization process in accordance with the desired control target.
  • a total manipulated variable calculation GSB for the existing K actuators takes place.
  • the different control interventions on different actuators from different identified setpoint deviations are superimposed additively to a total control intervention for each actuator.
  • K manipulated variable changes ST are forwarded to the individual actuators such as air dampers or fuel supply devices.

Landscapes

  • Engineering & Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Mechanical Engineering (AREA)
  • General Engineering & Computer Science (AREA)
  • Regulation And Control Of Combustion (AREA)
  • Incineration Of Waste (AREA)
  • Control Of Steam Boilers And Waste-Gas Boilers (AREA)
EP10729831.7A 2009-06-24 2010-06-23 Verfahren zur regelung eines verbrennungsprozesses, insbesondere in einem feuerraum eines fossilbefeuerten dampferzeugers, und verbrennungssystem Not-in-force EP2446193B1 (de)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102009030322A DE102009030322A1 (de) 2009-06-24 2009-06-24 Konzept zur Regelung und Optimierung der Verbrennung eines Dampferzeugers auf der Basis von räumlich auflösender Messinformation aus dem Feuerraum
PCT/EP2010/058878 WO2010149687A2 (de) 2009-06-24 2010-06-23 Verfahren zur regelung eines verbrennungsprozesses, insbesondere in einem feuerraum eines fossilbefeuerten dampferzeugers, und verbrennungssystem

Publications (2)

Publication Number Publication Date
EP2446193A2 EP2446193A2 (de) 2012-05-02
EP2446193B1 true EP2446193B1 (de) 2014-05-07

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EP10729831.7A Not-in-force EP2446193B1 (de) 2009-06-24 2010-06-23 Verfahren zur regelung eines verbrennungsprozesses, insbesondere in einem feuerraum eines fossilbefeuerten dampferzeugers, und verbrennungssystem

Country Status (11)

Country Link
US (1) US9360209B2 (es)
EP (1) EP2446193B1 (es)
CN (1) CN102460018B (es)
AU (1) AU2010264723B2 (es)
BR (1) BRPI1012684A2 (es)
CA (1) CA2766458C (es)
DE (1) DE102009030322A1 (es)
ES (1) ES2465068T3 (es)
MX (1) MX2012000184A (es)
RU (1) RU2523931C2 (es)
WO (1) WO2010149687A2 (es)

Cited By (1)

* Cited by examiner, † Cited by third party
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DE102022106628A1 (de) 2022-03-22 2023-09-28 Uniper Technologies GmbH Verfahren zur Prädiktion verfahrenstechnischer Prozesswerte einer Verbrennungsanlage mittels eines trainierten neuronalen Netzes

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CN103032887B (zh) * 2012-12-31 2015-02-04 河南省电力公司电力科学研究院 一种实现燃煤锅炉节能运行的方法
CN103615736A (zh) * 2013-11-27 2014-03-05 广东电网公司电力科学研究院 泡沫陶瓷燃烧器的火焰区厚度模拟监测方法
CN103615735B (zh) * 2013-11-27 2017-02-01 广东电网公司电力科学研究院 泡沫陶瓷燃烧器的预混燃烧模拟监测方法
DE102015203978A1 (de) * 2015-03-05 2016-09-08 Stg Combustion Control Gmbh & Co. Kg Verfahren zum geregelten Betrieb eines, insbesondere regenerativ, beheizten Industrieofens, Steuer- und Regeleinrichtung und beheizbarer Industrieofen
US10920982B2 (en) * 2015-09-28 2021-02-16 Schlumberger Technology Corporation Burner monitoring and control systems
RU2713850C1 (ru) * 2018-12-10 2020-02-07 Федеральное государственное бюджетное учреждение науки Институт теплофизики им. С.С. Кутателадзе Сибирского отделения Российской академии наук (ИТ СО РАН) Система мониторинга режимов горения топлива путем анализа изображений факела при помощи классификатора на основе свёрточной нейронной сети
RU2715302C1 (ru) * 2018-12-10 2020-02-26 Федеральное государственное бюджетное учреждение науки Институт теплофизики им. С.С. Кутателадзе Сибирского отделения Российской академии наук (ИТ СО РАН) Автоматическая система диагностики процесса сжигания пылеугольного топлива в камере сгорания

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DE102022106628A1 (de) 2022-03-22 2023-09-28 Uniper Technologies GmbH Verfahren zur Prädiktion verfahrenstechnischer Prozesswerte einer Verbrennungsanlage mittels eines trainierten neuronalen Netzes

Also Published As

Publication number Publication date
US9360209B2 (en) 2016-06-07
RU2523931C2 (ru) 2014-07-27
AU2010264723B2 (en) 2013-02-21
ES2465068T3 (es) 2014-06-05
MX2012000184A (es) 2012-02-28
AU2010264723A1 (en) 2012-01-19
DE102009030322A1 (de) 2010-12-30
EP2446193A2 (de) 2012-05-02
CN102460018B (zh) 2016-03-09
CA2766458C (en) 2014-10-14
BRPI1012684A2 (pt) 2016-03-29
US20120125003A1 (en) 2012-05-24
WO2010149687A2 (de) 2010-12-29
WO2010149687A3 (de) 2011-03-03
CN102460018A (zh) 2012-05-16
CA2766458A1 (en) 2010-12-29
RU2012102271A (ru) 2013-07-27

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