基于交叉迭代BLSTM网络的乙烯裂解炉建模

数据驱动的乙烯裂解炉模型通常忽视了裂解炉结构和反应机理,存在预测误差偏大的缺点,为此提出基于知识和数据融合驱动的乙烯裂解炉乙烯收率等关键参数的双向长短时间记忆网络(BLSTM)预测模型。为了解决BLSTM建模缺少可用数据的问题,提出了一种采用交叉迭代的BLSTM(CIBLSTM)模型。所提CIBLSTM模型采用了正反向交叉迭代方法,逐步逼近所缺数据的真实值,进而建立乙烯裂解炉的预测模型。为了验证所提CIBLSTM模型的有效性,选取9种工业实际原料与分析数据进行仿真测试,仿真结果验证了所提的CIBLSTM模型的有效性与实用性,所提方法也可应用于其他复杂化工过程建模。

Modeling and application of ethylene cracking furnace based on cross-iterative BLSTM network

The ethylene cracking furnace model based on BP and RBF ignores the reaction mechanism of cracking furnace and has the disadvantage of large prediction error. Therefore, a two-way long-term time-memory network (BLSTM) model based on the reaction mechanism of ethylene cracking furnace to forecast key parameters such as ethylene yield is proposed. To solve the problem that BLSTM modeling lacks available data, a BLSTM model using cross iteration (CIBLSTM) is provided. The CIBLSTM model uses a forward-reverse cross-iteration method to gradually approximate the true value of the lacks available data, and then a prediction model is established for the ethylene cracking furnace. To verify the validity of the proposed CIBLSTM model, nine industrial actual raw materials and analytical data were selected for simulation test. The simulation results verify the validity and practicability of the proposed CIBLSTM model. The proposed method can also be applied to other complex chemical processes modeling.