  The steam drum water level is a critical parameter that directly impacts the
safety and efficiency of power plant operations. However, predicting the drum
water level in boilers is challenging due to complex non-linear process
dynamics originating from long-time delays and interrelations, as well as
measurement noise. This paper investigates the application of Transformer-based
models for predicting drum water levels in a steam boiler plant. Leveraging the
capabilities of Transformer architectures, this study aims to develop an
accurate and robust predictive framework to anticipate water level fluctuations
and facilitate proactive control strategies. To this end, a prudent pipeline is
proposed, including 1) data preprocess, 2) causal relation analysis, 3) delay
inference, 4) variable augmentation, and 5) prediction. Through extensive
experimentation and analysis, the effectiveness of Transformer-based approaches
in steam drum water level prediction is evaluated, highlighting their potential
to enhance operational stability and optimize plant performance.
