A Cost-Efficient Electrical Impedance Tomography System for Adaptive Process Analytics

Abstract

Electrical impedance tomography (EIT) has significant potential as a noninvasive tool for spatially resolved process monitoring, yet most reported systems remain laboratory prototypes and are rarely suitable for integration into chemical engineering environments. We present a fully embedded, low-cost EIT platform designed as a deployable tomographic sensor for in situ monitoring of reactive media in laboratory-scale reactors. The platform can be realized for approximately $442 in prototype quantities and below $310 in small-batch production. The system enables spatially resolved conductivity mapping with 32 measurement channels and is designed for straightforward integration into laboratory and reactor environments. Its compact USB-powered hardware is complemented by microcontroller firmware in C and an object-oriented Python interface for experiment control and data analysis. On-board impedance calculation allows stand-alone operation and supports real-time monitoring of dynamic conductive processes. Hardware characterization includes measurements of total harmonic distortion plus noise for signal excitation and voltage measurement with mean values up to 0.0651%, a signal frequency of 105 kHz and optimized analogue amplification across all gain settings, a variable analogue filter gain ranging from −1.35 to 34.81 dB, and characterization of the current source at excitation currents of 100 and 350 μA. Additionally, the system’s performance is validated for the adjacent and opposed pair pattern, achieving a mean signal-to-noise ratio of up to 76.59 dB. The imaging performance is quantified on circular phantoms in terms of position error, spatial resolution, and detection limit, and the long-term stability is verified over a 20-h measurement with a relative drift below 0.03%. The system achieves frame rates of 7.36 to 13.18 fps depending on the measurement pattern. The system is validated in a chemically relevant scenario by monitoring the dissolution of 5 mol L–1 H2SO4(aq). The resulting spatial conductivity maps resolve transient concentration gradients associated with proton activity, illustrating the potential of the platform for the detection of local process states in adaptive reactor environments. Together, the characterized hardware performance and validation under chemically relevant conditions establish the platform as a scalable in situ imaging sensor suitable for integration into adaptive, data-driven reactor systems.