A robust localized soft sensor for particulate matter modeling in Seoul metro systems

Abstract

Developing accurate soft sensors to predict and monitor the indoor air quality (IAQ) of hazardous pollutants that accumulate in underground metro systems is of key importance. The just-in-time (JIT) learning technique possesses a local feature that can track the variations in the dynamic process more effectively, which is different from the traditional soft sensor modeling methods, such as partial least squares (PLS), which models the process in an offline and global way. In this study, a robust soft sensor that combined the JIT learning technique with a least squares support vector regression (LSSVR) method, named JIT–LSSVR, was derived in order to improve the prediction performance of a PM2.5 soft sensor in a subway station. Additionally, in order to eliminate the adverse effects caused by the outliers in the process variables, an outlier detection step was integrated into the JIT–LSSVR modeling procedure. The performance evaluation results demonstrated that the proposed robust JIT–LSSVR soft sensor has the capability to model nonlinear and dynamic subway systems. The root mean square error of the JIT–LSSVR soft sensor was improved by 55% in comparison with that of the LSSVR soft sensor.