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In continuous process plants controlled by model predictive control,model-plant mismatch (MPM), due to the aging of processes, causes degradationof 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 byusing routine operating data on the assumption that the timescale of aging ismuch larger than the interval of excitation of reference signals. Weimplemented MLE by estimating MPM via $\mathcal{L}_1$ regularized regressionand by finding an optimal regularization parameter via cross-validation andshowed through numerical experiments that an optimal parameter can exist and befound by cross-validation for a pilot-scale distillation column. We thenconstructed the updated model based on the found parameter to demonstrate thepossibility of correcting static-gain mismatch and transport-delay mismatchwithout injecting excitation signals to process inputs.

Model life extension for continuous process: Non-invasive correction of model-plant mismatch with regularization