  In continuous process plants controlled by model predictive control,
model-plant mismatch (MPM), due to the aging of processes, causes degradation
of control performance. We propose a concept called Model Life Extension (MLE)
and its implementation to mitigate this degradation in a non-invasive manner.
The purpose of MLE is to continually update (re-identify) process models by
using routine operating data on the assumption that the timescale of aging is
much larger than the interval of excitation of reference signals. We
implemented MLE by estimating MPM via $\mathcal{L}_1$ regularized regression
and by finding an optimal regularization parameter via cross-validation and
showed through numerical experiments that an optimal parameter can exist and be
found by cross-validation for a pilot-scale distillation column. We then
constructed the updated model based on the found parameter to demonstrate the
possibility of correcting static-gain mismatch and transport-delay mismatch
without injecting excitation signals to process inputs.
