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This paper presents innovative machine-learning solutions to enhance energy efficiency in
electrical tomography for industrial reactors. Addressing the key challenge of optimizing the neural
model’s loss function, a classifier tailored to precisely recommend optimal loss functions based on the
measurement data is designed. This classifier recommends which model, equipped with given loss
functions, should be used to ensure the best reconstruction quality. The novelty of this study lies in
the optimal adjustment of the loss function to a specific measurement vector, which allows for better
reconstructions than that by traditional models trained based on a constant loss function. This study
presents a methodology enabling the development of an optimal loss function classifier to determine
the optimal model and loss function for specific datasets. The approach eliminates the randomness
inherent in traditional methods, leading to more accurate and reliable reconstructions. In order
to achieve the set goal, four models based on a simple LSTM network structure were first trained,
each connected with various loss functions: HMSE (half mean squared error), Huber, l1loss (L1 loss
for regression tasks—mean absolute error), and l2loss (L2 loss for regression tasks—mean squared
error). The best classifier training results were obtained for support vector machines. The quality of
the obtained reconstructions was evaluated using three image quality indicators: PSNR, ICC, and
MSE. When applied to simulated cases and real measurements from the Netrix S.A. laboratory, the
classifier demonstrated effective performance, consistently recommending models that produced
reconstructions that closely resembled the real objects. Such a classifier can significantly optimize the
use of EIT in industrial reactors by increasing the accuracy and efficiency of imaging, resulting in
improved energy management and efficiency.
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