The Exoplanet Climate Infrared TElescope (EXCITE)

Although there are a large number of known exoplanets, there is little data on their global atmospheric properties. Phase-resolved spectroscopy of transiting planets – continuous spectroscopic observation of planets during their full orbits – probes varied depths and longitudes in the atmospheres thus measuring their three-dimensional thermal and chemical structure and contributing to our understanding of their global circulation. Planets with characteristics suitable for atmospheric characterization have orbits of several days, so phase curve observations are highly resource intensive, especially for shared use facilities. The Exoplanet Climate Infrared TElescope (EXCITE) is a balloon-borne near-infrared spectrometer designed to observe from 1 to 5 μm to perform phaseresolved spectroscopy of hot Jupiters. Flying from a long duration balloon (LDB) platform, EXCITE will have the stability to continuously stare at targets for days at a time and the sensitivity to produce data of the quality and quantity needed to significantly advance our understanding of exoplanet atmospheres. We describe the EXCITE design and show results of analytic and numerical calculations of the instrument sensitivity. We show that an instrument like EXCITE will produce a wealth of quality data, both complementing and serving as a critical bridge between current and future space-based near infrared spectroscopic instruments

Experimental methods in chemical engineering: Artificial neural networks–ANNs

Artificial neural networks (ANNs) are one of the most powerful and versatile tools provided by artificial intelligence and they have now been exploited by chemical engineers for several decades in countless applications. ANNs are computational tools providing a minimalistic mathematical model of neural functions. Coupled with raw data and a learning algorithm, they can be applied to tasks such as modelling, classification, and prediction. Recently, their popularity has grown remarkably and they now constitute one of the most relevant research areas within the fields of artificial intelligence and machine learning. ANNs are large collections of simple classifiers called neurons. Chemical engineers apply them to model complex relationships, predict reactor performance, and to automate process controllers. ANNs can leverage their ability to learn and exploit large data sets, but they can also get stuck in local minima or overfit and are difficult to reverse engineer. In 2016 and 2017, ANNs were cited in 13 245 Web of Science (WoS) articles, 538 of which were in chemical engineering; the top WoS categories were electrical & electronic engineering (1615 occurrences) artificial intelligence (1253), and energy & fuels (980). The top 4 journals mentioning ANNs were Neural Computing & Applications (117), Neurocomputing (84), Energies (76), and Renewable & Sustainable Energy Reviews (76). In the near future, as larger data sets become available (and arduous to analyze), chemical engineers will be able to apply and leverage more sophisticated ANN architectures

Process intensification education contributes to sustainable development goals. Part 1

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