| dc.description.abstract | This study's object is the process of measuring control
over liquefied petroleum gas components using a multilayer perceptron. The problem considered is insufficient
efficiency of existing methods for measuring control over
liquefied petroleum gas components. It can be partially
solved by remote measurement of components of liquefied
petroleum gas and processing of the received data and,
accordingly, control by a neural network. However, another issue arises, associated with the complexity of using
neural networks in combination with peripheral devices,
in particular, means, sensors, gauges, etc., and the need
for significant computing power.
This paper reports a model for measuring control
over liquefied petroleum gas components, which takes
into account its physical characteristics, using a multilayer perceptron, which provides communication with
gas measurement devices. The mechanism for achieving
these results involves training the model based on performance indicators derived from input data, taking into
account the formed features. High generalization ability
and efficiency are illustrated by the coefficient of determination, which is 0,845. High accuracy is illustrated by
the low overall average value of the mean absolute error,
which is 1,1%. That was made possible by the distinctive
features of the proposed solution, namely the optimized
architecture of the model in accordance with the object of
study and its input features. These features are the areas
of the light streaks, their logarithmic ratios, temperature,
the sum and difference of densities of the components of
liquefied petroleum gas.
The results can be applied practically to problems
involving liquefied gas composition analysis, especially at
gas filling stations, oil and gas processing plants, gas storage facilities, and similar sites
Keywords: liquefied petroleum gas, multilayer perceptron, mean absolute error, coefficient of determination | uk_UA |
