Energy Study of Drying: Using Machine Learning to Predict the Energy Consumption of an Industrial Powder Drying Process

In this thesis, we use data science / statistical techniques to better understand the energy consumption behind a powder drying facility located in Zwolle, as part of Abbott's initiative to better manage its energy consumption. As powder drying is by far the facility's most energy intensive process, this project therefore focuses exclusively on...

Empirical Evaluation of the Performance of CEVAE under Misspecification of the Latent Dimensionality

Causal machine learning deals with the inference of causal relationships between variables in observational datasets. <br/>For certain datasets, it is correct to assume a causal graph where information about unobserved confounders can only be obtained through noisy proxies, and CEVAE aims to address this case. <br/>The number of dimensions of...