An operator training system (OTS) for dynamic load change operation education in air separation processes is developed. A linear parameter varying (LPV) dynamic model based on an iterative optimization strategy is identified to represent the nonlinear characteristics of the air separation process. First, the local models at typical working points are identified. Second, the weighting functions between the local models are designed and estimated. Finally, an iterative optimization strategy optimizes the local models and weighting functions. The identified LPV model is used as the core training model of the OTS. Model validation with actual data shows that the identified OTS model has reasonable simulation accuracy. By designing a model predictive control (MPC) algorithm, a multidimensional skill evaluation algorithm based on MPC operation data is proposed. The developed OTS and skill evaluation algorithm are applied in a vocational skill competition. Results show that the operation skills of different operators can be reflected and distinguished.

开发了用于空分过程中动态负荷变化操作教育的操作员培训系统（OTS）。确定了基于迭代优化策略的线性参数变化（LPV）动态模型来表示空气分离过程的非线性特征。首先，识别典型工作点的局部模型。其次，设计和估计局部模型之间的加权函数。最后，迭代优化策略优化局部模型和权重函数。识别出的LPV模型作为OTS的核心训练模型。用实际数据进行模型验证表明，识别出的OTS模型具有合理的模拟精度。通过设计模型预测控制 (MPC) 算法，提出了一种基于MPC操作数据的多维技能评价算法。开发的OTS和技能评估算法应用于职业技能竞赛。结果表明，不同操作人员的操作技能可以得到体现和区分。