Model-based fault diagnosis of selective catalytic reduction for a smart cogeneration plant running on fast pyrolysis bio-oil

Abstract

The paper puts forward a method for the detection of hydrothermal aging of selective catalytic reduction (SCR) units. First, a dynamic model of SCR performance including heat transfer, ammonia adsorption, NOx reduction, and ammonia oxidation is developed. The model then is parameterized and tuned with respect to published experimental data of two SCR units before and after hydrothermal aging. Such tuning also determines the target parameters of the SCR dynamic model most relevant to hydrothermal aging. The constant terms in SCR reaction and ammonia oxidation rates are the identified target parameters. Next, a model-based detection algorithm is proposed which is based on non-linear parameter estimation techniques. The performance of the proposed detection algorithm is tested via numerical simulation scenarios generated by high fidelity dynamic model of a cogeneration plant running on fast pyrolysis biooil. Satisfactory performance and robustness of fault detection are illustrated when it is subject to measurement noise and varying operating conditions.