In the refinery process, a vast amount of data is generated in daily production. How to make full use of these data to improve the simulation’s accuracy is crucial to enhancing the refinery operating level. In this paper, a novel deep learning framework integrating the self-organizing map (SOM) and the convolutional neural network (CNN) is developed for modeling the industrial hydrocracking process. The SOM is used to map input variables into two-dimensional maps to extract process features. Then, these maps are fed into the CNN to predict the outputs of the hydrocracking process. The SOM adopted is free of training, which reduces the computational complexity, simplifies the application, and improves the prediction accuracy. Practical guidance on the application of the proposed framework is provided by comparing and analyzing different structures and parameters. Finally, an online modeling scheme is developed and applied in an actual hydrocracking process. Experimental results demonstrate that the proposed framework has great performance in modeling the hydrocracking process and provides a good reference for process optimization.

中文翻译：

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用深层神经网络对加氢裂化过程进行建模

在精炼过程中，日常生产中会生成大量数据。如何充分利用这些数据来提高模拟的准确性，对于提高炼油厂的运行水平至关重要。在本文中，开发了一种集成了自组织图（SOM）和卷积神经网络（CNN）的新型深度学习框架，用于对工业加氢裂化过程进行建模。SOM用于将输入变量映射到二维映射中以提取过程特征。然后，将这些图输入到CNN中以预测加氢裂化过程的输出。采用的SOM无需培训，可以降低计算复杂度，简化应用程序，并提高预测精度。通过比较和分析不同的结构和参数，为所提出的框架的应用提供了实践指导。最后，开发了在线建模方案并将其应用于实际的加氢裂化过程。实验结果表明，该框架在加氢裂化过程建模中具有良好的性能，为工艺优化提供了很好的参考。