Real-time process modeling of particleboard manufacture using variable selection and regression methods ensemble
Andre, N. O., and T. M. Young.  2013.  European Journal of Wood and Wood Products (Eur. J. Wood Prod. Holz als Roh- und Werkstoff), 71(3): 361-370.

This study focuses on the real-time prediction of mechanical properties such as internal bond strength (IB) and modulus of rupture (MOR) for a wood composite panels manufacturing process. As wood composite panel plants periodically test their products, a real time data fusion application was developed to align laboratory mechanical test results and their corresponding process data. Fused data were employed to build regression models that yield real-time predicted mechanical property values when new process data become available. The modeling algorithm core uses genetic algorithm to preselect a meaningful subset of process variables. Calibration models are then built using several regression methods: multiple linear regression, ridge regression, neural networks, and partial least squares regression (PLS). Four different predicted response values were generated for each new record of real time process variables. On-line validation results showed good performance of the ridge regression method with a 0.89 correlation coefficient between actual and predicted MOR values, a root mean square error (RMSEP) of 1.05 MPa and a mean normalized error of 9 %. IB was best predicted by PLS with a 0.81 correlation coefficient between actual IB and PLS predicted IB values, a RMSEP of 75.1 kPa, and a mean normalized error of 15 %.