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Fouling is a challenge in many processes, especially in heat exchangers and evaporators. Even though many treatments have been developed to mitigate fouling on metallic surfaces, no standardized method exists to compare equipment based on its antifouling performance. In this work, a screening apparatus is presented, which allows to compare samples of treated metallic surfaces based on thermal fouling resistance. A parameter screening and a ranking of three differently treated metallic surfaces using two model substances, namely, whey protein concentrate (WPC) and calcium sulfate, are demonstrated. [ABSTRACT FROM AUTHOR]

Fouling mitigation provides many challenges in process equipment, especially in heat exchangers. In this work, the fouling behaviors of two polymers are compared to 1.4301 stainless steel (SS) in calcium sulfate solutions. An experimental setup that allows to classify the materials quickly and under varying process conditions is used to determine the fouling behavior. Results are compared to a screening apparatus which can test up to three materials simultaneously. Finally, the cleaning behavior of all three materials is investigated. Results show that polyether ether ketone (PEEK) is superior to SS in mitigating fouling and is easier to clean; however, polyether ether ketone with 30 % talcum (TKT) is not. This is attributed to the surface roughness of the materials. [ABSTRACT FROM AUTHOR]

One major focus in forensics is the identification of individuals based on different kinds of evidence found at a crime scene and in the digital domain. Here, we assess the potential of using in-vehicle digital data to capture the natural driving behavior of individuals in order to identify them. We formulate a forensic scenario of a hit-and-run car accident with a known and an unknown suspect being the actual driver during the accident. Specific aims of this study are (i) to further develop a workflow for driver identification in digital forensics considering a scenario with an unknown suspect, and (ii) to assess the potential of one-class compared to multi-class classification for this task. The developed workflow demonstrates that in the application of machine learning in digital forensics it is important to decide on the statistical application, data mining or hypothesis testing in advance. Further, multi-class classification is superior to one-class classification in terms of statistical model quality. Using multi-class classification it is possible to contribute to the identification of the driver in the hit-and-run accident in both types of application, data mining and hypothesis testing. Model quality is in the range of already employed methods for forensic identification of individuals.