Work

Multi-strategy modeling integrating kinetics mechanism of cracking and pyrolysis and unsupervised dual-stage attention long and short-term memory network

Year: 2025

Type: article

Abstract: The fluid catalytic cracking process utilizing the dual-riser reactors (MIP-LTAG) holds significant importance in the development of petrochemical enterprises. It aims to reduce fuel consumption while increasing output. Consequently, modeling for the production process is an essential task. However, traditional methods struggle to accurately describe the complex reaction mechanisms involved in the cracking/pyrolysis dual reaction pathways. Additionally, due to the coupling of variables and insufficiency of dynamic characteristics, capturing multi-variable spatio-temporal dependencies remains challenging. This paper focuses on key indicators such as product yield and carbon emissions within the core reaction-regeneration unit of the target technological process. A lumped kinetic mechanism model is constructed to balance the reaction pathway. Variational mode decomposition (VMD) is employed to perform decomposition of the coupled variables. The unsupervised dual-stage attentional long short term memory model (UDA-LSTM) is utilized to capture multi-scale characteristics. To leverage these advantages, this paper designs three hybrid model for collaborative optimization of multi-objective predictions. Finally, the effectiveness of the proposed hybrid modeling framework is validated through an actual industrial production case. The predicted mean squared error (MSE) of the main product yield does not exceed 0.2, and the constructed process model supports real-time monitoring of the production process by refineries. • A novel lumped kinetics model based on FCC process products is proposed. • VMD-UDA-LSTM model is proposed to solve the data coupling problem in FCC process. • Establish a multi-strategy of hybrid model for predicting the FCC process. • Validity of the proposed approach is demonstrated by case studies of an industrial refinery.

Source: Fuel Processing Technology

DOI: 10.1016/j.fuproc.2025.108349

Authors: Bin Wang, Kai Luo, Xiangming Chen, Kai Deng, Jian Long, Wenze Guo

Institutions: East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education

Language: English

Topic: Fault Detection and Control Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Responsible consumption and production, Affordable and clean energy

Open Access status: gold

Estimated APC paid: $3,180