In recent years, AI technologies have been increasingly applied to automate the operation of waste treatment plants.
Examples include automated 3D control of garbage pits and cranes, as well as monitoring combustion conditions and estimating heat generation using deep learning.
To achieve full automation, it is essential to predict future operating conditions.
This research develops AI models for predicting system behavior based on nonlinear system identification using deep learning.
The models are constructed using real operational data, and their effectiveness is validated.