Impedance-based estimation of process parameters in electrolytic systems via circuit-embedded neural network (CENN)

We present a physics-aware machine learning framework for real-time estimation of electrolyte concentration and temperature from electrochemical impedance spectroscopy (EIS) measurements. Using nanoporous gold electrodes in aqueous H₂SO₄ as a model system, impedance spectra were acquired across concentrations of 1−20 mM and temperatures of 26−50 °C. The spectra were interpreted using a ZARC + transmission-line equivalent circuit and globally fitted over the full (c, T) domain, revealing two characteristic time scales associated with interfacial charge transfer and porous transport. The resulting physically consistent parameter maps were embedded into a circuit-informed neural network, enabling high-fidelity forward prediction of full impedance spectra directly from state variables (RMSE = 0.06 Ω). Inversion of this model allowed accurate estimation of electrolyte properties from measured spectra, achieving mean absolute errors of 0.10 mM in concentration and 1.0 °C in temperature. To further ccelerate sensing, Jacobian-based sensitivity analysis identified three informative frequencies that preserved essential spectral information. This reduced acquisition time by more than 95%-from minutes to under 6 s-while maintaining high accuracy (0.12mM, 1.1 °C). Although demonstrated for a specific electrochemical system, the proposed framework provides an interpretable and transferable workflow for rapid multiparameter sensing, provided that the equivalent-circuit structure and calibration domain are adapted to the target system.

DDC Class

541.37: Electrochemistry

621.38: Electronics, Communications Engineering

620.11: Engineering Materials

Funding(s)

SFB 1615 - SMARTe Reaktoren für die Verfahrenstechnik der Zukunft

SFB 1615 - Teilprojekt A02: Quantitative elektrische 3D-Impedanztomographie in Echtzeit für Mehrphasenreaktoren

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https://creativecommons.org/licenses/by/4.0/

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impedance-based-estimation-of-process-parameters-in-electrolytic-systems-via-circuit-embedded-neural-network-(cenn).pdf

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Impedance-based estimation of process parameters in electrolytic systems via circuit-embedded neural network (CENN)