Machine learning overview
ML algorithms are widely employed in process engineering and often
hidden behind the terms of AI , soft sensing , data fusion or digital
twin . In the process industry, typically, large amounts of data are
available from the physical sensors in a production plant. This data can
be collected, combined and processed by means of ML algorithms (data
fusion, soft(ware) sensors) to obtain more meaningful data for the
training of data-driven models that capture the real process conditions
, . The virtual model (digital twin) can be further used to optimize the
production process and an implementation in a loop with the physical
world ensures the adaptability of the model . Commonly, one of the
following three goals is pursued when applying ML techniques in the
process industry: (i) online prediction, (ii) process monitoring, (iii)
process fault detection . Some of the most popular algorithms to achieve
these goals are provided in Table 1 . The ML algorithms are classified
into supervised or unsupervised depending on training data being labeled
or unlabeled, respectively. The working principles behind those
algorithms with some example applications are concisely summarized in Geet al. .
Table 1: Examples for popular
unsupervised and supervised ML methods.