Abstract:
The constant emission of polluting gases is causing
an urgent need for timely detection of harmful gas mixtures in
the atmosphere. A method and algorithm of the determining
spectral composition of gas with a gas analyzer using an
artificial neural network (ANN) were suggested in the article. A
small closed gas dynamic system was designed and used as an
experimental bench for collecting and quantifying gas
concentrations for testing the proposed method. This device was
based on AS7265x and BMP180 sensors connected in parallel to
a 3.3 V compatible Arduino Uno board via QWIIC.
Experimental tests were conducted with air from the laboratory
room, carbon dioxide (CO2), and a mixture of pure oxygen (O2)
with nitrogen (N2) in a 9:1 ratio. Three ANNs with one input,
one hidden and one output layer were built. The ANN had 5, 10,
and 20 hidden neurons, respectively. The dataset was divided
into three parts: 70% for training, 15% for validation, and 15%
for testing. The mean square error (MSE) error and regression
were analyzed during training. Training, testing, and validation
error analysis were performed to find the optimal iteration, and
the MSE versus training iteration was plotted. The best
indicators of training and construction were shown by the ANN
with 5 (five) hidden layers, and 16 iterations are enough to train,
test and verify this neural network. To test the obtained neural
network, the program code was written in the MATLAB. The
proposed scheme of the gas analyzer is operable and has a high
accuracy of gas detection with a given error of 3%. The results
of the study can be used in the development of an industrial gas
analyzer for the detection of harmful gas mixtures.
