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A novel human-machine interactive training method for dynamic load changeoperation in air separation processes (ASPs) is proposed. A shadow operator(SO) is developed in this method to train ASP operators through industrialmodel predictive control (IMPC) and Bloom's taxonomy. First, a nonlineartwo-layer IMPC machine algorithm is developed for dynamic load changeoperation. The IMPC uses a linear parameter varying prediction model and aniterative multi-step linearization algorithm to compute accurate controldecisions. Second, a hierarchical human-machine cooperation model isestablished to improve the effectiveness of operation training. The model isinspired by an educational psychology framework (Bloom's taxonomy) and assistsASP operators in enhancing their dynamic operational skills. Finally, fivedynamic training modes of the SO are designed based on the IMPC algorithm andthe human-machine cooperation model. The practical application resultsdemonstrate that the SO improves the effectiveness of skill acquisition fornovice operators and the safety of dynamic operations.

Shadow operator: Effective dynamic load change operation training in air separation processes based on industrial nonlinear MPC and Bloom's taxonomy