Abstract:

The reaction mechanism of chemical process is complex. There is a modeling error between the mechanism model and the actual reaction system. At the same time, there are complex slow time-varying features, such as catalyst deactivation, fuel coking, etc. Thus there will be a mismatch between the process model and the actual process system. A self-adaptive iterative hybrid model (SAIHM) is established to reflect the dynamic characteristics of the process accurately over a long period. The mechanism model and the data-driven model are effectively combined to improve the prediction accuracy of the model; the data-driven model uses the deep recurrent neural network (DRNN) to fully exploit the timing relationship between adjacent conditions; the data-driven model is automatically updated based on the evaluation indicators to resolve the contradiction between accuracy and efficiency. The simulation and comparison results of the self-adaptive iterative hybrid model and the existing mechanism model established based on the historical operation data of an acetylene hydrogenation adiabatic reactor show that the self-adaptive iterative hybrid model can more effectively track the actual system.

Key words: self-adaptive, iterative, hybrid model, dynamic, acetylene hydrogenation

摘要：

化工过程反应机理复杂，机理模型与实际反应系统之间存在建模误差；同时存在复杂的缓慢时变特征，如催化剂失活、燃料结焦等，难以用确定的机理描述，一般采用简化的关系描述，因此过程模型将与实际过程系统逐渐失配。为了建立能长期精确反映过程动态特性的模型，建立了一种基于过程特性的自适应迭代混合模型（self-adaptive iterative hybrid model，SAIHM）。将机理模型和数据驱动的模型有效融合以提高模型的预测精度；数据驱动的模型采用深度循环神经网络（deep recurrent neural network，DRNN）以充分挖掘相邻工况间的时序关系；基于某工厂碳二加氢绝热反应器的历史运行数据建立的自适应迭代混合模型与现有机理模型的仿真对比结果表明，自适应迭代混合模型能更有效地跟踪实际系统。

关键词: 自适应, 迭代, 混合建模, 动态, 碳二加氢