.png){kind=icon}
Return
Data augmentation driven by optimization for membrane separation process synthesis
.png){kind=icon}
OA
AI
DOI：10.1016/j.compchemeng.2023.108342.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper proposes a new hybrid strategy to optimally design membrane separation problems. We formulate the problem as a Non-Linear Programming (NLP) model. A common approach to represent the physical behavior of the membrane is to discretize the system of differential equations that govern the separation process. Instead, we represent the input/output behavior of the single membrane by an artificial neural network (ANN) predictor. The ANN is trained on a dataset obtained through the MEMSIC simulator. The equation form of the trained predictor (shape and weights) is then inserted in the NLP model at the place of the discretized system of differential equations. To improve the ANN accuracy without an excessive computational burden, we propose data augmentation strategies to target the regions where densify the dataset. We compare a data augmentation strategy from the literature with a novel one that densifies the dataset around the stationary points visited by a global optimization algorithm. Our approach was validated using a relevant industrial case study: hydrogen purification. Validation by simulation is performed on the obtained solutions. The computational results show that a data augmentation smartly coupled with optimization can produce a robust and reliable design tool.
Keywords:
Neural network
Data augmentation
Global optimization
Membrane gas separation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
3.9
Papers:
8.1K
Citations:
1.7W
Organization
Cited Papers
ShareSaveShareSaveShareSavePerceived and Individual Ideals of Gender in Swedish Adolescents with and without an Eating Disorder
PREAI
ShareSaveShareSaveArtificial intelligence-incorporated membrane fouling prediction for membrane-based processes in the past 20 years: A critical review.png)
WATER RESEARCH
WATER RESEARCHIF12.4
PREAI
ShareSave
View more