Semi-supervised adaptive PLS soft-sensor with PCA-based drift correction method for online valuation of NOx emission in industrial water-tube boiler

Abstract

The use of soft sensors for the prediction of Nitric Oxides (NOx) emissions to meet quality regulations has become increasingly attractive from the economic point of view. However, implementation of the standard adaptive PLS soft sensors such as the conventional adaptive block-wise recursive PLS (BW-RPLS) and just in time block-wise recursive PLS (JIT-BW-RPLS) to industrial boilers that are not equipped with an in-line hardware analyzer is impractical. This is due to the limited ability of the adaptive soft sensor to recalibrate without feedback from the actual NOx measurement. Hence, in this paper, a PCA-based drift correction method is proposed for an industrial water-tube boiler in which an in-line hardware analyzer is unavailable. The proposed drift correction factor is used to detect when drift happens and subsequently estimate the corrected NOx value to be used in a semi-supervised manner by the conventional BW-RPLS and JIT-BW-RPLS. Both the proposed semi-supervised BW-RPLS and JIT-BW-RPLS with PCA-based drift correction and estimation methods have displayed an additional 10–20% improvement in prediction accuracy relative to the performance of the conventional supervised BW-RPLS method and 50% prediction improvement compared to offline PLS model, during significant drifts in the industrial boiler operation. All the case studies have been performed using actual industrial data of a water-tube boiler.