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

TL;DR

This paper introduces a novel training method for air separation process operators using a shadow operator that combines industrial nonlinear MPC and Bloom's taxonomy to enhance skill acquisition and operational safety.

Contribution

It develops a nonlinear two-layer IMPC algorithm and a hierarchical human-machine cooperation model based on Bloom's taxonomy for effective dynamic operation training.

Findings

Improves novice operators' skill acquisition

Enhances safety of dynamic operations

Demonstrates effectiveness through practical application

Abstract

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.