Robust data reconciliation of combustion variables in multi-fuel fired industrial boilers

• Simultaneous data reconciliation and gross errors detection.
• Complex first principle combustion model combining separate process measurements.
• Utilization of nonlinear optimization enabled by computationally light model.
• Identification of process disturbances and sensor failures in industrial power plant.
• High generalization ability of the proposed method.

This paper introduces an application of simultaneous nonlinear data reconciliation and gross error detection for power plants utilizing a complex but computationally light first principle combustion model. Element and energy balances and robust techniques introduce nonlinearity and the consequent optimization problem is solved using nonlinear optimization. Data reconciliation improves estimation of process variables and enables improved sensor quality control and identification of process anomalies. The approach was applied to an industrial 200 MWth fluidized bed boiler combusting wood, peat, bark, and slurry. The results indicate that the approach is valid and is able to perform in various process conditions. As the combustion model is generic, the method is applicable in any boiler environment.