Model life extension for continuous process: Non-invasive correction of model-plant mismatch with regularization
Yohe Kono and Minoru Koizumi
在由模型预测控制控制的连续过程工厂中,模型-工厂不匹配(MPM)由于过程的老化而导致控制性能的退化。 我们提出了一个称为模型寿命延长(MLE)的概念及其实现,以非侵入性的方式减轻这种退化。 MLE的目的是通过使用常规操作数据不断更新(重新识别)过程模型,假设老化的时间尺度远大于引用信号的激发间隔。 我们通过L_1正则化回归估计MPM并通过交叉验证找到最佳正则化参数来实现MLE,并通过数值实验表明,通过跨验证可以存在最佳参数,并通过试点尺度蒸馏柱的交叉验证找到。 然后,我们根据找到的参数构建了更新的模型,以证明在不向处理输入注入注入注入的情况下纠正静态增益不匹配和传输延迟不匹配的可能性。
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 excitatio...