Electrical impedance tomography (EIT) is used to determine the spatial conductivity distribution of a measurement environment that involves several applications in the areas of process engineering and medical diagnostics. In common EIT systems, the measurements are performed by means of current patterns which are injected into the measurement environment using a serial time-division-multiplexing (TDM) scheme for the excitation of different electrodes. The measurement rate can be increased by performing parallel excitation using orthogonal signals. In this paper, a code-divisionmultiplexing (CDM) measurement procedure is presented. To optimize the separation between different measurement channels and the dynamic range, orthogonal Walsh-Hadamard codes are used. The measurements are conducted with a fast EIT system with nine parallel excitation sources and 18 parallel measurement channels. The measured crosstalk rejection between different channels is larger than 98 dB. The maximum absolute deviation between different measurement sets for repeated measurements is less than 24 µV with a mean standard deviation of less than 8.2 µV. The dynamic range for impedance measurements based on different excitation procedures (TDM, frequencydivision-multiplexing, and CDM) has been determined. Furthermore, reconstructed conductivity distributions based on measurements with the different excitation procedures have been compared with each other for different measurement scenarios (a root mean square difference of less than 1.2%). Finally, the influences of frequency-dependent measurement objects on the excitation procedures have been discussed.
This paper reports a design of an exchangeable miniaturized mass spectrometry chip using spring-loaded pins and O-rings for electrical and fluidic connections. This planar microelectromechanical system (MEMS)-chip works with 300 µm high silicon structures between two borosilicate glasses and has a size of 13 mm × 7 mm. Because of its small size a small vacuum pump is sufficient, and it is suitable for mobile measurements and portable applications. Because of exchangeability of MEMS-chips with fluidic and electrical high voltage connections a direct comparison between chips is possible.
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are valuable analytical and quality control methods for most industrial chemical processes as they provide information on the concentrations of individual compounds and by-products. These processes are traditionally carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been realized to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra, to train an ANN with better prediction performance and speed than state-of-the-art analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
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