摘要
In chemical process industry, the input-output data of processes display complex nonlinear dynamics and strong influences of unobserved hidden states. Their behavior must be modeled using nonlinear time series with an observer-predictor structure. To address this, a sequence-to-sequence model with a memory layer was proposed for a high-density polyethylene slurry reactor. The memory layer retains the chronological contribution of the observer and predictor and effectively captures the lengthy time response. A physics-guided approach was adopted to ensure the directional consistency between input and output variables in key control loops. In this way, a deep learning model can be obtained with historical data and there is no need for plant tests. The resulting model can be used in a nonlinear model predictive control system that not only quickly navigates different grade transitions but also provides steady-state control even though key internal state variables such as catalyst activity change.