Process Control via Electrical Impedance Tomography for Energy-Aware Industrial Systems

Krzysztof Król (), Grzegorz Kłosowski, Tomasz Rymarczyk, Konrad Gauda, Monika Kulisz, Ewa Golec and Agnieszka Surowiec
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Krzysztof Król: WSEI University, Faculty of Transportation and Information Technology, Projektowa 4, 20-209 Lublin, Poland
Grzegorz Kłosowski: Lublin University of Technology, Faculty of Management, Nadbystrzycka 38, 20-618 Lublin, Poland
Tomasz Rymarczyk: WSEI University, Faculty of Transportation and Information Technology, Projektowa 4, 20-209 Lublin, Poland
Konrad Gauda: WSEI University, Faculty of Transportation and Information Technology, Projektowa 4, 20-209 Lublin, Poland
Monika Kulisz: Lublin University of Technology, Faculty of Management, Nadbystrzycka 38, 20-618 Lublin, Poland
Ewa Golec: WSEI University, Faculty of Administration and Social Sciences, Projektowa 4, 20-209 Lublin, Poland
Agnieszka Surowiec: Lublin University of Technology, Faculty of Management, Nadbystrzycka 38, 20-618 Lublin, Poland

Energies, 2025, vol. 18, issue 22, 1-25

Abstract: Conventionally, tomography is an inspection technique in which tomographic images are intended for human perception and interpretation. In this work, we shift this paradigm by transforming tomography into an autonomous estimator of industrial reactor states, enabling fully automated process control. Alcoholic fermentation was employed as an example of a controlled process in the current study. The work presents an original concept utilizing transfer learning in conjunction with a ResNet-type artificial neural network, which converts electrical measurements into a sequence of values correlated with the conductivity of pixels constituting the cross-section of the examined biochemical reactor. The conductivity vector is transformed into a parameter determining substrate concentration, enabling dynamic process regulation in response to signals generated from EIT (Electrical Impedance Tomography). Within the scope of the described research, calibration of the conductivity vector against substrate concentrations was performed, and a Matlab/Simulink-based dynamic Monod kinetics model was developed. The obtained results demonstrate high accuracy in substrate concentration estimation relative to reference values throughout a forty-six-hour process. The same signals enable energy-efficient process control, in which cooling and mixing intensity are regulated according to energy prices and renewable energy availability. This strategy may possess particular application in facilities where fermentation installations are co-located with bioenergy production units.

Keywords: electrical impedance tomography (EIT); adaptive control; energy-aware industrial systems; neural networks (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: 2025
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