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A sensor system for fast gas composition analysis is presented. Using linear Raman scattering the simultaneous detection of virtually all components of fuel gas mixtures such as natural gas and biogas can be achieved. The system consists of commercially available hardware components, in detail a frequency doubled continuous wave laser at 532 nm and a compact spectrometer with an embedded charge coupled device chip. For the evaluation of the Raman spectra a fast software module based on a contour fit algorithm is developed. Moreover, modules for controlling the hardware components are implemented in the sensor software ensuring simple operability of the entire system. In this paper the sensor is characterized in terms of, e.g., accuracy, reproducibility, detection limits and temporal performance. Finally its application for natural gas analysis in a gas turbine power plant is demonstrated, and the results obtained are compared to gas chromatography results.

A protocol for the estimation of growth kinetics for complex-shaped particles is presented. The estimation is based on multidimensional particle size distribution (nD PSD) and concentration data. While the latter is obtained by an in situ mid-infrared absorption probe, nD PSD data is measured via an imaging based setup presented earlier. The data is fitted to the output of a morphological population balance equation, which is solved by a customized high resolution algorithm. The procedure is first validated in silico using a virtual implementation of the measurement setup before it is applied to seeded desupersaturation experiments of the β polymorph of l-glutamic acid. Prominent broadening of the product PSD is observed and different size (in)­dependent growth models are fitted to the data. Confidence intervals, local identifiability, and correlation of the parameters are studied. Finally, the estimated growth rate is compared to literature results.

A technique for the detection and measurement of the agglomeration of needle-like particles is presented. A novel image analysis routine, based on a supervised machine learning strategy, is used to identify agglomerates that are subsequently characterized by their volume. Through repeated measurement of a large number of agglomerates, a 1D particle size distribution of agglomerates is reconstructed. Concurrently, established tools are used to characterize needle-like primary crystals, whose shape is described by cylinders and whose population can be described by a separate, two-dimensional