An adaptive least squares support vector machine model with a novel update for NOx emission prediction

• An adaptive LSSVM model with a novel update is presented.
• The process variations are divided into irreversible and reversible types.
• Sample addition and replacement are used to update the LSSVM model incrementally.
• The method is applied to predict NOx emissions using real operation data.
• The prediction accuracy maintains high even the process characteristics vary.

This paper presents an adaptive least squares support vector machine (LSSVM) model with a novel update to tackle process variations. The key idea of the update is to divide the process variations into two main categories, namely, irreversible and reversible variations. Correspondingly, sample addition and sample replacement are proposed to update the model. The incremental LSSVM algorithm and detailed update procedure are also provided. A benchmark simulation with a time-varying nonlinear function is conducted to evaluate the effectiveness of the update algorithm. Finally, the proposed method is applied to predict the nitrogen oxide (NOx) emissions of a coal-fired boiler using real operation data from a power plant. Results reveal that the LSSVM model with the novel update maintains high prediction accuracy despite different process characteristics. Meanwhile, the time consumed in the update process is decreased because of the incremental form compared with the model reconstruction.