Machine Learning for Air Flow Characterization : An application of Theory-Guided Data Science for Air Fow characterization in an Industrial Foundry

Abstract: In industrial environments, operators are exposed to polluted air which after constant exposure can cause irreversible lethal diseases such as lung cancer. The current air monitoring techniques are carried out sparely in either a single day annually or at few measurement positions for a few days.In this thesis a theory-guided data science (TGDS) model is presented. This hybrid model combines a steady state Computational Fluid Dynamics (CFD) model with a machine learning model. Both the CFD model and the machine learning algorithm was developed in Matlab. The CFD model serves as a basis for the airflow whereas the machine learning model addresses dynamical features in the foundry. Measurements have previously been made at a foundry where five stationary sensors and one mobile robot were used for data acquisition. An Echo State Network was used as a supervised learning technique for airflow predictions at each robot measurement position and Gaussian Processes (GP) were used as a regression technique to form an Echo State Map (ESM). The stationary sensor data were used as input for the echo state network and the difference between the CFD and robot measurements were used as teacher signal which formed a dynamic correction map that was added to the steady state CFD. The proposed model utilizes the high spatio-temporal resolution of the echo state map whilst making use of the physical consistency of the CFD. The initial applications of the novel hybrid model proves that the best qualities of these two models could come together in symbiosis to give enhanced characterizations.The proposed model could have an important role for future characterization of airflow and more research on this and similar topics are encouraged to make sure we properly understand the potential of this novel model.