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.

使用传递学习方法，将来自开源模拟模型和另一废水处理厂的知识转移到工业废水处理厂，从而提高预测性能。测试和验证性能分别提高了 27% 和 59%。

一种结合迁移学习和物理准则建模的混合方法：提高工业废水处理厂中溶解氧浓度预测

A Hybrid Approach of Transfer Learning and Physics-Informed Modeling:  Improving Dissolved Oxygen Concentration Prediction in an Industrial  Wastewater Treatment Plant