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This work aims at developing a methodology that can provide information of in-flame particle radiation in industrial-scale flames. The method is based on a combination of experimental and modeling work. The experiments have been performed in the high-temperature zone of a 77 kW th swirling lignite flame. Spectral radiation, total radiative intensity, gas temperature, and gas composition were measured, and the radiative intensity in the furnace was modeled with an axisymmetric cylindrical radiation model using Mie theory for the particle properties and a statistical narrow-band model for the gas properties. The in-flame particle radiation was measured with a Fourier transform infrared (FTIR) spectrometer connected to a watercooled probe via fiber optics. In the cross-section of the flame investigated, the particles were found to be the dominating source of radiation. Apart from giving information about particle radiation and temperature, the methodology can also provide estimates of the amount of soot radiation and the maximum contribution from soot radiation compared to the total particle radiation. In the center position in the flame, the maximum contribution from soot radiation was estimated to be less than 40% of the particle radiation. As a validation of the methodology, the modeled total radiative intensity was compared to the total intensity measured with a narrow angle radiometer and the agreement in the results was good, supporting the validity of the used approach.

Metabolomic fingerprinting is a growing strategy for characterizing complex biological samples without detailed prior knowledge about the metabolic system. A two-way analysis system with liquid separation and mass spectrometric detection provides detail-rich data suitable for such fingerprints. As a model study, human urine samples, obtained after intake of coffee, tea, or water, were analyzed with capillary electrophoresis electrospray ionization time-of-flight mass spectrometry (CE-ESI-TOF-MS). In-house-developed software (in Matlab) was utilized to manage and explore the large amount of data acquired (230 CE-MS runs, each with 50-100 million nonzero data points). After baseline and noise reduction, followed by suitable binning in time and m/z, the data sets comprised 9 and 14 million data points in negative and positive ESI mode, respectively. Finally, a signal threshold was applied, further reducing the number to about 100 000 data points per data set. A set of interactive exploratory tools, utilizing principal component analysis (PCA) and analysis of variance (ANOVA) results based on a general linear model, facilitated visual interpretation with score plots (for group assessment) and differential fingerprints (for "hot spot" detection). In the model study highly significant differences due to beverage intake were obtained among the 10 first principal components (p < 10(-6) for two of the components in both ESI modes). Especially, the contrasts between "coffee" and "tea or water" indicated several "hot spots" with highly elevated intensities (e.g., for uncharged masses 93, 94, 109, 119, 123, 132, 148, 169, 178, 187, 190, and 193) suitable for further analysis, for example, with tandem MS.

This work studies the radiative heat transfer in a 580 kWth pilot scale test furnace that resembles a full-scale rotary kiln for iron ore pellet production. The aim is to quantify the radiative heat transfer in coal and cofiring flames and also to study the possibility to model the radiative heat transfer for such combustion conditions. Three combustion cases of coal and cofiring are studied, and an evaluation is made using a detailed radiation model. The test furnace is cylindrical and refractory lined but does not rotate and no iron ore pellet bed material is included. In-flame measurements of temperature, gas composition, particle concentration, radiative intensity, and radiative heat flux are conducted for the different fuels and fuel combinations. Overall, the differences in measured radiative intensities and heat fluxes among the three studied fuel cases are minor, which implies that introduction of renewable fuels by cofiring in a full-scale rotary kiln should be feasible with respect to heat transfer conditions. In the model, the furnace is treated as an axisymmetric and infinitely long cylinder, and gas properties are calculated with a statistical narrow-band model, while particle properties are calculated using Mie theory. The modeling results show reasonable to good predictivity compared to the measured intensity data. This indicates that the experimental data is of good quality but also indicates the potential use of the model in full-scale rotary kiln calculations in future work.

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Today a high degree of "false" appendicitis diagnoses are occurring. In this study, a screening experiment of biomarkers of two different kinds of appendicitis, gangrenous and phlegmonous, were conducted with CE and CEC coupled to MS. Plasma samples were obtained from patients pre- and post-surgery. A large amount of data was generated to be able to compare them, and chemometrics tools were utilized to visualize the differences. Indicative patterns were found for both pre- and post-surgery of the two types of inflammation as well as between them. The divergences were traced back to the MS peaks obtained in the CE- and CEC-MS setups as possible biomarkers for the two forms of appendicitis.