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Analyzing large trajectory sets enables deeper insights into multiple real-world problems. For example, animal migration data, multi-agent analysis, and virtual entertainment can all benefit from deriving conclusions from large sets of trajectory data. However, the analysis is complicated by several factors when using traditional analytic techniques. For example, directly visualizing the trajectory set results in a multitude of lines that cannot be easily understood. Statistical analysis methods and non-direct visualization techniques (e.g., parallel coordinates) produce conclusions that are non-intuitive and difficult to understand. By using two complementary processes---clustering and visualization---a new approach is developed to analyzing large trajectory sets. First, clustering techniques are developed and refined to group related trajectories together. From these similar sets, a trajectory composition visualization is created and implemented that clearly depicts the cluster characteristics including application-specific attributes. The effectiveness of the approach is demonstrated on two separate and distinct types of data sets resulting in actionable conclusions. The first application, multi-agent analysis, represents a rich, spatial data set. When analyzed using this approach, deficiencies in the underlying artificial intelligence algorithms can be determined leading to improved agent performance. Student course-grade history analysis, the second application, requires adapting the approach for a non-spatial data set. However, the results enable a clear understanding of which courses are most critical in a student's career and which student groups require assistance to succeed. This research contributes to methods for trajectory clustering, techniques for large-scale visualization of trajectory data, and processes for analyzing student data.

Comparison is drawn between two small reactor systems in the context of production of methanol from synthesis gas (syngas) and of ethylene by the non-oxidative dehydrogenation of ethane using computer-based modelling techniques. The production of methanol requires syngas containing hydrogen and carbon monoxide at controlled temperatures. The supply of syngas from a combination of steam reforming and catalytic combustion of methane was modelled for a parallel microchannel array and for a cascade reactor system in which reaction occurs in a series of beds, heat exchange in interconnecting microchannel heat exchangers being used to maintain the desired temperature. Although conversion was slightly higher in the microchannel systems, the advantages of better temperature control and easy replacement of deactivated catalyst favoured the cascade array. The use of the two reactor systems was also modelled for the ethane dehydrogenation, a reaction in which temperature control is essential to minimise coking. In this case catalytic combustion of methane provided direct heat to the dehydrogenation chamber. Again the microchannels gave slightly better conversion but temperature control was superior in the cascade system. The size of equipment needed to produce unit amount of product was smaller for the cascade systems in both cases, favouring their use for remote location operations. (C) 2009 Elsevier B.V. All rights reserved.