Combustion Control Experimentations at a Pilot Scale Glass Furnace

Keyvan, Shahla; Rossow, Rodney; Vélez, Mariano; Headrick, William L.; Moore, Robert E.; Romero, Carlos E.

doi:10.1002/9781118407004.ch17

Cited by 1 publication

(2 citation statements)

References 7 publications

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“…Burning in power boilers takes place in turbulent flames. 23,24 These turbulences manifest themselves, among other things, as flame flickering. The combustion process, therefore, affects the parameters of flame flickering, such as mean brightness, the amplitude of changes in brightness and their frequency spectrum.…”

Section: Methodsmentioning

confidence: 99%

“…One of the applications in which these networks perform particularly well is the detection of anomalies in the time series of different signals. 32–36 In the study, we utilised three types of DRNNs. They are as follows: LSTM recurrent neural network, one-dimensional (1D) convolutional neural network LSTM (CNN-LSTM) and 1D convolutional LSTM (ConvLSTM).…”

Section: Methodsmentioning

confidence: 99%

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Combustion process monitoring based on flame intensity time series

Omiotek

Smolarz

2020

Proceedings of the Institution of Mechanical Engineers, Part I:

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The pulverised coal and its mixture with biomass are one of the most popular fuels in industrial energy. To ensure, on one hand, minimal greenhouse gas emission and, on the other hand, maximum energy production efficiency, it is necessary to monitor the combustion process of these fuels. One way to do this is to monitor the flame intensity. This is an optical, non-invasive solution, and information on the status of the process is obtained with minimal delay. The article proposes a method for identifying undesired combustion states for which the excess air coefficient is greater or smaller than the value ensuring total combustion. Three deep recurrent neural network architectures for classifying the flame intensity time series were explored. The best results were obtained using the convolutional long short-term memory model, which offered the accuracy of 86.5%–99.8%, depending on the current thermal power. The prediction time of a single data sequence was about 0.6 ms. High accuracy and low time consumption of the proposed method create an opportunity for its use in industrial combustion systems of pulverised coal and its mixture with biomass.

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Section: Methodsmentioning

confidence: 99%