Abstract
In common industrial application, the use of a linear and static PLS soft sensor for online prediction and monitoring of industrial boiler is often preferred due to its simple and intuitive framework. However, process dynamics and time-variant factors can negatively affect the accuracy and reliability of PLS soft sensor over its long-term application in process industries. In this paper, development of adaptive soft sensor based on dynamic PLS method has been applied to an industrial water-tube boiler for continuous online prediction of Nitric Oxides emission. In the case study, it is found that the adaptive PLS soft sensor which includes lagged measurements of NOx emission in the model input can significantly improve the prediction accuracy and reliability by 72.7% relative to the performance of linear and static PLS soft sensor when tested on online dataset containing gradual and abrupt changes in the process operating conditions.
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