A Hybrid Approach of Transfer Learning and Physics-Informed Modeling:
  Improving Dissolved Oxygen Concentration Prediction in an Industrial
  Wastewater Treatment Plant
Ece S. Koksal Erdal Aydin
Constructing first principles models is a challenging task for nonlinear and
complex systems such as a wastewater treatment unit. In recent years,
data-driven models are widely used to overcome the complexity. However, they
often suffer from issues such as missing, low quality or noisy data. Transfer
learning is a solution for this issue where knowledge from another task is
transferred to target one to increase the prediction performance. In this work,
the objective is increasing the prediction performance of an industrial
wastewater treatment plant by transferring the knowledge of (i) an open-source
simulation model that captures the underlying physics of the process, albeit
with dissimilarities to the target plant, (ii) another industrial plant
characterized by noisy and limited data but located in the same refinery, and
(iii) the model in (ii) and making the objective function of the training
problem physics informed where the physics information derived from the
open-source model in (ii). The results have shown that test and validation
performance are improved up to 27% and 59%, respectively.