Optimizing the Neural Network Loss Function in Electrical Tomography to Increase Energy Efficiency in Industrial Reactors

Monika Kulisz (), Grzegorz Kłosowski, Tomasz Rymarczyk, Jolanta Słoniec, Konrad Gauda and Wiktor Cwynar
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Monika Kulisz: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Grzegorz Kłosowski: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Tomasz Rymarczyk: Institute of Computer Science and Innovative Technologies, WSEI University, 20-209 Lublin, Poland
Jolanta Słoniec: Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland
Konrad Gauda: Institute of Computer Science and Innovative Technologies, WSEI University, 20-209 Lublin, Poland
Wiktor Cwynar: Institute of Public Administration and Business, WSEI University, 20-209 Lublin, Poland

Energies, 2024, vol. 17, issue 3, 1-17

Abstract: 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.

Keywords: electrical tomography; energy efficiency; loss function; industrial reactors; LSTM networks; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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