  A novel human-machine interactive training method for dynamic load change
operation in air separation processes (ASPs) is proposed. A shadow operator
(SO) is developed in this method to train ASP operators through industrial
model predictive control (IMPC) and Bloom's taxonomy. First, a nonlinear
two-layer IMPC machine algorithm is developed for dynamic load change
operation. The IMPC uses a linear parameter varying prediction model and an
iterative multi-step linearization algorithm to compute accurate control
decisions. Second, a hierarchical human-machine cooperation model is
established to improve the effectiveness of operation training. The model is
inspired by an educational psychology framework (Bloom's taxonomy) and assists
ASP operators in enhancing their dynamic operational skills. Finally, five
dynamic training modes of the SO are designed based on the IMPC algorithm and
the human-machine cooperation model. The practical application results
demonstrate that the SO improves the effectiveness of skill acquisition for
novice operators and the safety of dynamic operations.
