A Hybrid Modeling Framework for Membrane Separation Processes: Application to Lithium-Ion Recovery from Batteries

Regufe, Maria João; Santana, Vinícius V.; Ferreira, Alexandre F. P.; Ribeiro, Ana M.; Loureiro, José M.; Nogueira, Idelfonso B. R.

doi:10.3390/pr9111939

Cited by 7 publications

(1 citation statement)

References 24 publications

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“…Simulations are usually necessary for optimization and control ends. For instance, the literature on PSA optimization and control presents works addressing the optimization and control of these units through simulations performed using ML models [21,22]. Furthermore, generally in the chemical engineering literature, it is challenging to find works that properly use ML models for simulation ends.…”

Section: Simulation Case: Short-term Simulation For Online Applicationsmentioning

confidence: 99%

Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

et al. 2022

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A misusage of machine learning (ML) strategies is usually observed in the process systems engineering literature. This issue is even more evident when dynamic identification is performed. The root of this problem is the gradient explode and vanishing issue related to the recurrent neural networks training. However, after the advent of deep learning, these issues were mitigated. Furthermore, the problem of data structuration is often overlooked during the machine learning model identification in this field. In this scenario, this work proposes a guideline for identifying ML models for the different applications in process systems engineering, which are usually for simulation or prediction purposes. While using the proposed guideline, the work also identifies a virtual analyzer for a pressure swing adsorption unit. In these types of adsorption separations, it is usual that the measurement of the main properties is not done online. Therefore, the virtual analyzer is another contribution of this manuscript. The overall results demonstrate that even though the test provides good performance during the ML model identification, its quality might degenerate over the application domain if the model application is overlooked.

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Section: Simulation Case: Short-term Simulation For Online Applicationsmentioning

confidence: 99%

Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

et al. 2022

Self Cite

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Assessment of data‐driven modeling approaches for chromatographic separation processes

Michalopoulou,

Papathanasiou

2024

AIChE Journal

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Chromatographic separation processes are described by nonlinear partial differential and algebraic equations, which may result in high computational cost, hindering further online applications. To decrease the computational burden, different data‐driven modeling approaches can be implemented. In this work, we investigate different strategies of data‐driven modeling for chromatographic processes, using artificial neural networks to predict pseudo‐dynamic elution profiles, without the use of explicit temporal information. We assess the performance of the surrogates trained on different dataset sizes, achieving good predictions with a minimum of 3400 data points. Different activation functions are used and evaluated against the original high‐fidelity model, using accuracy, interpolation, and simulation time as performance metrics. Based on these metrics, the best performing data‐driven models are implemented in a process optimization framework. The results indicate that data‐driven models can capture the nonlinear profile of the process and that can be considered as reliable surrogates used to aid process development.

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A long short-term memory based Quasi-Virtual Analyzer for dynamic real-time soft sensing of a Simulated Moving Bed unit

Marrocos

Iwakiri

Martins

et al. 2022

Applied Soft Computing

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A Hybrid Modeling Framework for Membrane Separation Processes: Application to Lithium-Ion Recovery from Batteries

Cited by 7 publications

References 24 publications

Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

Machine Learning-Based Dynamic Modeling for Process Engineering Applications: A Guideline for Simulation and Prediction from Perceptron to Deep Learning

Assessment of data‐driven modeling approaches for chromatographic separation processes

A long short-term memory based Quasi-Virtual Analyzer for dynamic real-time soft sensing of a Simulated Moving Bed unit

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