A novel standpoint of Pressure Swing Adsorption processes multi-objective optimization: An approach based on feasible operation region mapping

Rebello, Carine M.; Martins, Márcio A. F.; Rodrigues, Alírio E.; Loureiro, José M.; Ribeiro, Ana M.; Nogueira, Idelfonso B. R.

doi:10.1016/j.cherd.2021.12.047

Cited by 18 publications

(13 citation statements)

References 37 publications

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“…PSO was used as the optimization method as it has been demonstrated as an efficient and suitable approach for characterizing confidence regions in optimization problems. ,− …”

Section: Methodsmentioning

confidence: 99%

Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

et al. 2023

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Model-based optimization of simulated moving bed reactors (SMBRs) requires efficient solvers and significant computational power. Over the past years, surrogate models have been considered for such computationally demanding optimization problems. In this sense, artificial neural networks�ANNs�have found applications for modeling the simulated moving bed (SMB) unit but not yet been reported for the reactive SMB (SMBR). Despite ANNs' high accuracy, it is essential to assess its capacity to represent the optimization landscape well. However, a consistent method for optimality assessment using surrogate models is still an open issue in the literature. As such, two main contributions can be highlighted: the SMBR optimization based on deep recurrent neural networks (DRNNs) and the characterization of the feasible operation region. This is done by recycling the data points from a metaheuristic technique�optimality assessment. The results demonstrate that the DRNN-based optimization can address such complex optimization while meeting optimality.

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“…PSO was used as the optimization method as it has been demonstrated as an efficient and suitable approach for characterizing confidence regions in optimization problems. ,− …”

Section: Methodsmentioning

confidence: 99%

Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

et al. 2023

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“…With the particles generated during the PSO implementation, building an FOR containing close-to-optimal points was possible. The evaluation was based on a Fisher–Snedecor test, whose deduction was previously presented in the literature. ,, Given a certain particle with the corresponding calculated value of the OF(s), the said particle will be part of the FOR if the condition in eq is followed. Y [ j , o ] ≤ Y normalb normale normals normalt [ m , o ] + N exp N normalD normalV N exp − N normalD normalV + 1 F α ( N D V , N exp − N D V + 1 ) with N exp being the number of experiments considered, α the confidence interval, Y the matrix containing the global set of particles generated during the PSO algorithm with dimensions of false[ N normali normalt normal_ normalm normala normalx × normalN normalp normala normalr normalt normal_ normalm normala normalx , N normalO normalF false] , and Y best the matrix containing the optimal values of the OFs with dimensions of [number of particles in G best , N OF ].…”

Section: Methodsmentioning

confidence: 99%

Metaheuristic Framework for Material Screening and Operating Optimization of Adsorption-Based Heat Pumps

et al. 2023

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The current methods applied to material screening for adsorption-based heat pumps are based on a fixed set of temperatures or their independent variation, providing a limited, insufficient, and unpractical evaluation of different adsorbents. This work proposes a novel strategy for the simultaneous optimization and material screening in the design of adsorption heat pumps by implementing a meta-heuristic approach, particle swarm optimization (PSO). The proposed framework can effectively evaluate variable and broad operation temperature intervals to search for viable zones of operation for multiple adsorbents at once. The criteria for selecting the adequate material were the maximum performance and the minimum heat supply cost, which were considered the objective functions of the PSO algorithm. First, the performance was assessed individually, followed by a single-objective approximation of the multi-objective problem. Next, a multi-objective approach was also adopted. With the results generated during the optimization, it was possible to find which adsorbents and temperature sets were the most suitable according to the main objective of the operation. The Fisher–Snedecor test was applied to expand the results obtained during PSO application and a feasible operating region built around the optima, enabling the arrangement of close-to-optima data into practical design and control tools. This approach allowed for a fast and intuitive evaluation of multiple design and operation variables.

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“…However, design parameters and operating variables are always highly coupled with purity, recovery rate, productivity, energy consumption and other performance indicators, making it difficult to determine its optimal conditions through empirical methods. In addition, as PSA optimization is a typical multi-objective optimization problem, several objective functions may conflict with each other; for example, purity and recovery vary in the opposite direction [34,59,60]. Due to the mutual influence and tradeoff between different objective functions, a multi-objective optimization problem has a series of optimal solutions Processes 2022, 10, 812 9 of 19 (called the Pareto optimal set) rather than an optimal solution [61,62].…”

Section: Optimization Strategiesmentioning

confidence: 99%

“…If the fit obtained from the training does not meet the requirements, the percentages of the three sets are changed and retrained. Lee et al [60] trained and tested a dynamic-model ANN for H 2 recovery and CO2 capture from the tail gas of hydrogen plants, and they found that the dynamic-model-based ANN could precisely predict the dynamic behavior and optimum performance of an integrated process at a low computational cost. changed and retrained.…”

Section: Surrogate Modelmentioning

confidence: 99%

“…changed and retrained. Lee et al [60] trained and tested a dynamic-model ANN for H2 recovery and CO2 capture from the tail gas of hydrogen plants, and they found that the dynamic-model-based ANN could precisely predict the dynamic behavior and optimum performance of an integrated process at a low computational cost. In addition to ANN, applications of other surrogate models, such as response surface models, Kriging models, polynomial models, orthogonal configuration models, support vector machines, and gaussian processes regression, have been reported as well.…”

Section: Surrogate Modelmentioning

confidence: 99%

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A Review of Numerical Research on the Pressure Swing Adsorption Process

et al. 2022

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The pressure swing adsorption (PSA) process has been considered a promising method for gas separation and purification. However, experimental methods are time-consuming, and it is difficult to obtain the detailed changes in variables in the PSA process. This review focuses on the numerical research developed to realize the modelling, optimization and control of the cyclic PSA process. A complete one-dimensional mathematical model, including adsorption bed, auxiliary devices, boundary conditions and performance indicators, is summarized as a general modelling approach. Key simplified assumptions and special treatments for energy balance are discussed for model reliability. Numerical optimization models and control strategies are reviewed for the PSA process as well. Relevant attention is given to the combination of deep-learning technology with artificial-intelligence-based optimization algorithms and advanced control strategies. Challenges to further improvements in the adsorbent database establishment, multiscale computational mass transfer model, large-scale PSA facility design, numerical computations and algorithm robustness are identified.

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A novel standpoint of Pressure Swing Adsorption processes multi-objective optimization: An approach based on feasible operation region mapping

Cited by 18 publications

References 37 publications

Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

Novel Framework for Simulated Moving Bed Reactor Optimization Based on Deep Neural Network Models and Metaheuristic Optimizers: An Approach with Optimality Guarantee

Metaheuristic Framework for Material Screening and Operating Optimization of Adsorption-Based Heat Pumps

A Review of Numerical Research on the Pressure Swing Adsorption Process

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