Modeling and optimization of ammonia process: Effect of hydrogen unit performance on the ammonia yield

Farsi, M.; Chabi, Negar; Rahimpour, Mohammad Reza

doi:10.1016/j.ijhydene.2021.09.160

Cited by 7 publications

(1 citation statement)

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“…A high-fidelity model of ammonia synthesis relies on predicting the dynamic interplay between elementary surface reactions at the molecular scale and thermodynamics and transport phenomena at meso and macroscopic scales. − Therefore, a multiscale model becomes indispensable to bridge microscopic features to macroscopic process variables that can be measured and manipulated. Although multiscale modeling approaches have been explored for various catalytic chemical processes, − there is an evident lack of studies on ammonia reactors, − with only a few directly analyzing process dynamics. − …”

Section: Introductionmentioning

confidence: 99%

Learning-Based Model Reduction and Predictive Control of an Ammonia Synthesis Process

Oliveira Cabral,

Bagheri,

Pourkargar

2024

Ind. Eng. Chem. Res.

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This work focuses on developing a learning-based model predictive control (MPC) approach for regulating an ammonia synthesis reactor’s hotspot temperature and outlet concentration dynamics. We create a detailed multiscale-multiphysics dynamic model of the reactor, integrating thermodynamics, kinetics, and transport phenomena to characterize its spatiotemporal behavior at different scales. Despite its accuracy, this model is computationally expensive, rendering it unsuitable for real-time optimization-based decision-making methods like MPC. To address this limitation, we investigate the advantages of deep recurrent neural networks in alleviating the existing computational bottleneck through model reduction. Specifically, we employ a long short-term memory (LSTM) network to approximate the system dynamics and reduce the computational load of the multiscale model. A large-scale time-series simulation data set is generated for LSTM-based model training using a multivariate parallel time-series approach. The LSTM-based model is then utilized for MPC design, while the high-fidelity multiscale model represents the reactor’s spatiotemporal dynamics. Using the LSTM-based model comes at the cost of reduced prediction accuracy. However, frequent feedback from the high-fidelity model at each sampling time enables resetting the LSTM-based model and mitigates the prediction errors.

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