Bioinspired Encoder–Decoder Recurrent Neural Network with Attention for Hydroprocessing Unit Modeling,Industrial & Engineering Chemistry Research

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Bioinspired Encoder–Decoder Recurrent Neural Network with Attention for Hydroprocessing Unit Modeling
Industrial & Engineering Chemistry Research ( IF 3.8 ) Pub Date : 2023-10-27 , DOI: 10.1021/acs.iecr.3c01953
Shu-Bo Yang ₁ , Jesús Moreira ₂ , Zukui Li ₁

Affiliation

The hydroprocessing technique is used to refine crude oil and produce lighter, valuable products. Developing models of these units is crucial for predicting the process dynamics and facilitating optimization and control. In this research, we develop attention-based encoder–decoder recurrent neural network (A-ED-RNN) models, employing various RNN cells such as bioinspired neural circuit policies (NCPs), gated recurrent unit (GRU), and long short-term memory (LSTM), to predict diesel and jet production rates within an industrial hydroprocessing unit. A key innovation is integrating the NCP into the A-ED-RNN models, harnessing its advanced computational power to attain enhanced performance with a smaller model size compared to that of GRU and LSTM cells. The developed RNN models effectively capture the dynamics of diesel and jet production, surpassing the traditional data-driven models. Notably, the NCP-based A-ED-RNN model demonstrates superior memory efficiency and predictive ability, standing out among all of the developed RNN models, underscoring its potential for modeling complex processes.

中文翻译：

受仿生启发的编码器-解码器循环神经网络，关注加氢处理装置建模

加氢处理技术用于精炼原油并生产更轻、更有价值的产品。开发这些单元的模型对于预测过程动态并促进优化和控制至关重要。在这项研究中，我们开发了基于注意力的编码器-解码器递归神经网络（A-ED-RNN）模型，采用各种 RNN 单元，例如仿生神经电路策略（NCP）、门控循环单元（GRU）和长期短期内存（LSTM），用于预测工业加氢处理装置内的柴油和喷气机生产率。一项关键创新是将 NCP 集成到 A-ED-RNN 模型中，利用其先进的计算能力，以比 GRU 和 LSTM 单元更小的模型尺寸获得增强的性能。开发的 RNN 模型有效地捕捉了柴油和喷气式飞机生产的动态，超越了传统的数据驱动模型。值得注意的是，基于 NCP 的 A-ED-RNN 模型表现出了卓越的记忆效率和预测能力，在所有已开发的 RNN 模型中脱颖而出，凸显了其对复杂过程进行建模的潜力。

更新日期：2023-10-27

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