  In coal-fired power plants, it is critical to improve the operational
efficiency of boilers for sustainability. In this work, we formulate real-time
boiler control as an optimization problem that looks for the best distribution
of temperature in different zones and oxygen content from the flue to improve
the boiler's stability and energy efficiency. We employ an efficient algorithm
by integrating appropriate machine learning and optimization techniques. We
obtain a large dataset collected from a real boiler for more than two months
from our industry partner, and conduct extensive experiments to demonstrate the
effectiveness and efficiency of the proposed algorithm.
