  Five simple soft sensor methodologies with two update conditions were
compared on two experimentally-obtained datasets and one simulated dataset. The
soft sensors investigated were moving window partial least squares regression
(and a recursive variant), moving window random forest regression, the mean
moving window of $y$, and a novel random forest partial least squares
regression ensemble (RF-PLS), all of which can be used with small sample sizes
so that they can be rapidly placed online. It was found that, on two of the
datasets studied, small window sizes led to the lowest prediction errors for
all of the moving window methods studied. On the majority of datasets studied,
the RF-PLS calibration method offered the lowest one-step-ahead prediction
errors compared to those of the other methods, and it demonstrated greater
predictive stability at larger time delays than moving window PLS alone. It was
found that both the random forest and RF-PLS methods most adequately modeled
the datasets that did not feature purely monotonic increases in property
values, but that both methods performed more poorly than moving window PLS
models on one dataset with purely monotonic property values. Other data
dependent findings are presented and discussed.
