Estimation of hydrogen flow rate in atmospheric Ar:H2 plasma by using artificial neural network

Atmospheric Ar:H 2 plasma is an eco-friendly option for the reduction of metal oxides. For better reduction performance and safety concern, the hydrogen gas injected into the reactor should be monitored. A hydrogen flow rate estimation system is presented in this paper by using an artificial neural...

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Bibliographic Details
Published in:Neural computing & applications 2020-03, Vol.32 (5), p.1357-1365
Main Authors: Das, Sarita, Das, Debi Prasad, Sarangi, Chinmaya Kumar, Bhoi, Bhagyadhar
Format: Article
Language:English
Subjects:
Argon
Artificial Intelligence
Artificial neural networks
Atmospheric models
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
D lines
Data Mining and Knowledge Discovery
Emission analysis
Emission spectra
Flow velocity
H alpha line
Hydrogen
Image Processing and Computer Vision
Metal oxides
Neural networks
Nuclear engineering
Nuclear safety
Original Article
Probability and Statistics in Computer Science
Reduction
Reduction (metal working)
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Summary:Atmospheric Ar:H 2 plasma is an eco-friendly option for the reduction of metal oxides. For better reduction performance and safety concern, the hydrogen gas injected into the reactor should be monitored. A hydrogen flow rate estimation system is presented in this paper by using an artificial neural network (ANN) model fed with features of optical emission spectra of the plasma. ANN models are studied with two different sets of input, i.e. for the first case the inputs to the model are the three features of H α line such as the peak intensity count, full-width half maximum and area under H α line, while for the second case, the peak intensity count of a group of emission lines like H α , Ar I, O I, K I, Na D lines are considered as the inputs. ANN model is developed for estimating four different sets of hydrogen flow rates 5, 8, 10 and 12 litres per minute (lpm) when the argon flow rate is constant at 10 lpm. For both the input features, the model performances are compared, and it is shown that improved estimation accuracy is observed from the second case, i.e. from peak intensity count of a group of emission lines instead of only hydrogen emission line.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-018-3674-z