Abstract:

Simulation of naphtha pyrolysis in industrial ethylene cracking furnaces, which usually requires both firebox and reactor models, is non-linear and strongly compounded. The firebox model involves a great number of variables and takes a lot of time to get a solution. An intelligent hybrid model was proposed by first designing an artificial neural network (ANN) from data of the firebox model and then combining ANN with the reactor model. The intelligent hybrid modelling and simulation was developed on an industrial ethylene cracking furnace. By using actual process data, it is demonstrated that the hybrid simulation shows good agreement with industrial production. The hybrid model significantly reduces simulation time and largely meets requirement of industrial modeling.

Key words: simulation, model reduction, pyrolysis, neural network, ethylene, zone method

摘要：

乙烯裂解炉辐射段的过程模拟，一般由管内反应过程模型与管外传热模型两部分组成，具有非线性、强耦合的特点。其中管外传热模型涉及变量参数众多、求解过程耗时长。针对这一问题，提出了一种智能混合建模方法，在构建基于区域法的管外传热计算模型的基础上，利用该模型产生的数据，设计构造了针对管外传热计算的神经网络模型。利用该模型与管内反应过程模型相耦合，实现对乙烯裂解炉辐射段的智能混合建模与模拟。结合工业实际算例，验证了基于机器学习和机理模型的智能混合建模的可行性，裂解产物预测精度良好，且混合模型可以大大缩短计算时间，更加符合工业计算的要求。

关键词: 模拟, 模型简化, 热解, 神经网络, 乙烯, 区域法