A sensing method is presented which offers major potential benefits for industrial processes. The method, developed from electrical spectroscopy, aims to discriminate between the component materials in a process and, with calibration data, identify specific materials; for example in batch chemical reactors for the manufacture of pharmaceutical products. The method addresses the key design need to complete a sensing operation within a temporal window that allows for the process dynamics.Two key system requirements are considered. First, a review is included of candidate excitation signals, in terms of the major parameters of interest, for the relevant frequency range. Second, the acquisition of the corresponding response signals and the extraction of the spectroscopic data from which the materials of interest may be identified. This is based upon an algorithm which is introduced based on the wavelet transform. The composite method is illustrated in trials using a process impedance simulation model and experimental tests on a crystallization process. This paper offers conclusions for applications of the fast sensing method to characterize different process (and other) materials.

This paper addresses the augmentation of a conventional single frequency electrical impedance tomography (EIT) system to form a wideband EIT system. This extends the system to provide spectral information, but with the essential capability to match process dynamics. The underlying opportunity for this study is that process materials may show considerable change in their electrical properties in response to an injected signal over a wide frequency range. This concept is used in the paper to demonstrate the construction of tomographic images for a range of frequency bands that can provide a deeper understanding and interpretation of a process under investigation. This paper describes a trial simulation of this approach and an experimental study. To provide measurements over the required frequency range a linear chirp is used as the excitation signal. Corresponding peripheral measurements have been synthesized using a 2D model in association with the EIDORS forward solver. The measurements are then analysed using an algorithm based on the wavelet transform to reveal spectral band datasets. In the presented feasibility trial a single-channel EIT chirp excitation was implemented, in essence simulating a real-time parallel data collection system, through the use of pseudo-static tests on foodstuff materials. The experimental data were then analysed and tomographic images were reconstructed using the frequency-banded data. The qualitative feasibility results illustrate the promise of this composite approach in exploiting sensitivity to variations over a wide frequency range. They indicate that the described method can augment an EIT sensing procedure to support spectroscopic analysis of the process materials.

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