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《工程(英文)》 >> 2019年 第5卷 第6期 doi: 10.1016/j.eng.2019.02.013

蒸汽裂解建模中的人工智能——详细流出物预测深度学习算法

a Laboratory for Chemical Technology, Department of Materials, Textiles and Chemical Engineering, Ghent University, Gent 9052, Belgium
b SynBioC Research Group, Department of Green Chemistry and Technology, Faculty of Bioscience Engineering, Ghent University, Gent 9000,
Belgium

收稿日期: 2018-09-14 修回日期: 2019-01-07 录用日期: 2019-02-11 发布日期: 2019-10-09

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摘要

化工过程可以从快速准确的流出物成分预测中获益良多,以进行工厂设计、控制和优化。工业4.0革命宣称,通过将机器学习引入这些领域,就可实现可观的经济收益和环境收益。高频优化和工艺控制的瓶颈往往是按要求对原料和产品等进行详细分析所需的时间。为解决这些问题,已为最大的化工品生产工艺——蒸汽裂解——建立由四个深度学习人工神经网络(DL ANNs)组成的框架。所提出的方法可根据有限数量的石脑油商业指标和可快速获得的工艺特性,确定石脑油原料的详细特性和蒸汽裂解炉流出物的详细组成。根据沸点曲线上的三个点和PIONA(烷烃、异链烷烃、烯烃、环烷烃和芳香烃)特性预测石脑油的详细特性。若沸点不可用,则同时对沸点进行估计。即使在估计沸点的情况下,所建立的深度学习人工神经网络仍优于已有的香农信息熵最大化和传统人工神经网络等方法。对于原料重构,在测试集得到的平均绝对误差(MAE)为0.3 wt%,流出物预测的平均绝对误差为0.1 wt%。结合所有网络时——使用前一网络的输出作为下一网络的输入——流出物平均绝对误差增大至0.19 wt%。除这些网络具有高精度外,主要好处是获得预测值所需的计算成本可忽略不计。在标准的英特尔i7处理器上,预测值以毫秒为单位。COILSIM1D等商业软件在精度方面表现稍好一些,但每个反应所需的中央处理器时间以秒为单位。速度大大提高,精度损失极小,使所提出的框架非常适用于连续监控难以获取的工艺参数,且非常适用于预想的高频实时优化(RTO)策略或工艺控制。然而,缺乏基本依据意味着几乎完全丧失基本的理解,而这并不总是被工程界广泛接受。此外,对于那些与训练集内石脑油非常不同的石脑油,所建立网络的性能有明显下降。

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