Petrochemical applications of gas chromatography

Crucello, Juliana; Porto, Nathália de Aguiar; Carvalho, Rogério M.; Ferreira, Alexandre de Andrade; Carbonezi, Carlos Alberto; Hantao, Leandro Wang

doi:10.1016/b978-0-12-820675-1.00002-2

Cited by 3 publications

(1 citation statement)

References 71 publications

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“…Hence, two non-conventional settings may be used. The "Fastest AGC" setting uses the previous scan for calculation of the AGC injection time, 32,40 while the "Fastest Scan" uses up to two scans back to estimate the AGC information. The latter conguration effectively unlocks acquisition rates above 23 scans per s. So, the acquisition rate was evaluated according to the parameters AGC target value, mass resolving power, and C-trap ion injection time using the "Fastest Scan" setting.…”

Section: Evaluation Of the Acquisition Ratementioning

confidence: 99%

Advanced tuning of the ion management parameters in GC × GC-HRMS using a Fourier transform Orbitrap mass analyzer for pixel-based data handling and multivariate analysis

2022

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Section: Evaluation Of the Acquisition Ratementioning

confidence: 99%

Advanced tuning of the ion management parameters in GC × GC-HRMS using a Fourier transform Orbitrap mass analyzer for pixel-based data handling and multivariate analysis

2022

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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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