Investigation and Improvement of Machine Learning Models Applied to the Optimization of Gas Adsorption Processes

Richard, Klaus Feio Soares; Azevedo, Diana Cristina Silva de; Bastos‐Neto, Moises

doi:10.1021/acs.iecr.2c04374

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…It is possible to suppress the term A in minimizing the cost function Equation (19), as it does not depend on the variable decision. This can help to simplify the optimization process and improve the efficiency of the predictive control system.…”

Section: Mpcmentioning

confidence: 99%

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Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

Ramos-Martinez,

Torres-Cantero,

Ortiz-Torres

et al. 2023

Mathematics

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This paper introduces a new approach to controlling Pressure Swing Adsorption (PSA) using a neural network controller based on a Model Predictive Control (MPC) process. We use a Hammerstein–Wiener (HW) model representing the real PSA process data. Then, we design an MPC-controlled model based on the HW model to maintain the bioethanol purity near 99% molar fraction. This work proposes an Artificial Neural Network (ANN) that captures the dynamics of the PSA model controlled by the MPC strategy. Both controllers are validated using the HW model of the PSA process, showing great performance and robustness against disturbances. The results show that we can follow the desired trajectory and attenuate disturbances, achieving the purity of bioethanol at a molar fraction value of 0.99 using the ANN based on the MPC strategy with 94% of fit in the control signal and a 97% fit in the purity signal, so we can conclude that our ANN can be used to attenuate disturbances and maintain purity in the PSA process.

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Section: Mpcmentioning

confidence: 99%

“…One of the works found on PSA using ANN was presented in [19]; the research focused on different learning methods to implement an optimal model of a PSA plant. An improvement of the automatic learning model was explored to optimize the purity and recovery parameters of CO 2 .…”

Section: Introductionmentioning

confidence: 99%

Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

Ramos-Martinez,

Torres-Cantero,

Ortiz-Torres

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

Assuring optimality in surrogate‐based optimization: A novel theorem and its practical implementation in pressure swing adsorption optimization

Rebello,

Costa,

Sánchez

et al. 2024

Can J Chem Eng

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Surrogate‐based optimization has gained significant traction in several engineering fields, given its ability to handle complex systems without significant computational burdens. However, surrogate models are approximations and may have limitations, including the possibility of artificial minima. The main contribution of this work is the derivation of a robustness test that guarantees the optimality of surrogate‐based optimization. The derivation of this metric is based on the universal approximation theorem. The full framework proposed in this work is also composed by a sampling sizing methodology to randomly select samples within a feasible operating region (FOR) resulting from the optimization population, reducing the computational cost of the analysis and avoiding biases in the robustness calculation. The applicability and importance of this methodology are demonstrated through a case study of a complex chemical process—a pressure swing adsorption (PSA) unit—which presents a high computational cost to solve optimization problems. The results highlight the need and importance of evaluating the optimality of surrogate‐based optimization schemes.

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Use of machine learning to identify key factors regulating volatilization of semi-volatile organic chemicals from soil to air

Wang,

Zhang,

Wang

et al. 2024

Science of The Total Environment

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Investigation and Improvement of Machine Learning Models Applied to the Optimization of Gas Adsorption Processes

Cited by 5 publications

References 25 publications

Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

Control for Bioethanol Production in a Pressure Swing Adsorption Process Using an Artificial Neural Network

Assuring optimality in surrogate‐based optimization: A novel theorem and its practical implementation in pressure swing adsorption optimization

Use of machine learning to identify key factors regulating volatilization of semi-volatile organic chemicals from soil to air

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