Inferential sensor for on-line monitoring of ammonium bisulfate formation temperature in coal-fired power plants

As a byproduct of the selective catalytic reduction system, ammonium bisulfate could lead to frequent unit outages by forming sticky deposits on the surface of air preheaters and heat rate deterioration in coal-fired power plants. Field tests were carried out to investigate the variation of ammonium bisulfate formation temperature at a coal-fired unit, retrofit with an on-line ammonium bisulfate probe. Two inferential sensor models are proposed in this paper. One is based on adaptive principle component analysis, to infer the ammonium bisulfate formation temperature from real process variables, using a linear interpolation approach suitable for control schemes. The other approach is a support vector regression based model, implemented to give predicted value directly from the input variables, while on-line data are unavailable. Model results indicate that both models can properly represent the inherent relationships between the selected input variables and ammonium bisulfate formation temperature. The adaptive principle component analysis model can be easily included in a selective catalytic reduction control loop and give high resolution predicted data, especially when the continuous analyzer is available. The support vector regression model can serve as a useful backup and replacement model, when the hard sensor is faulty or unavailable.