Fluid-gpt (fast learning to understand and investigate dynamics with a generative pre-trained transformer): Efficient predictions of particle trajectories and erosion
The deleterious impact of erosion due to high-velocity particle impingement adversely
affects a variety of engineering and industrial systems, resulting in irreversible mechanical …
affects a variety of engineering and industrial systems, resulting in irreversible mechanical …
[HTML][HTML] Estimating processing cost for the recovery of valuable elements from mine tailings using dimensional analysis
OA Marín, A Kraslawski, LA Cisternas - Minerals Engineering, 2022 - Elsevier
The mining industry generates thousands of tons of waste every day. Some types of waste
pose environmental risks, while others can be considered important sources of valuable …
pose environmental risks, while others can be considered important sources of valuable …
Predicting complex erosion profiles in steam distribution headers with convolutional and recurrent neural networks
The effects of erosion due to particle impingement continue to be of immense concern in
various energy and technology industries. Brute force computational fluid dynamics (CFD) …
various energy and technology industries. Brute force computational fluid dynamics (CFD) …
[HTML][HTML] A novel modeling strategy for the prediction on the concentration of H2 and CH4 in raw coke oven gas
The composition of raw coke oven gas, mainly consisting of H 2 and CH 4, has a significant
impact on its utilization in the industry. To better utilize this product, a novel modeling …
impact on its utilization in the industry. To better utilize this product, a novel modeling …
Equation-based and data-driven modeling strategies for industrial coating processes
Abstract Computational Fluid Dynamics (CFD) and Machine Learning (ML) approaches are
implemented and compared in an industrial Chemical Vapor Deposition process for the …
implemented and compared in an industrial Chemical Vapor Deposition process for the …
Physics-based penalization for hyperparameter estimation in gaussian process regression
J Kim, C Luettgen, K Paynabar, F Boukouvala - Computers & Chemical …, 2023 - Elsevier
Abstract In Gaussian Process Regression (GPR), hyperparameters are often estimated by
maximizing the marginal likelihood function. However, this data-dominant hyperparameter …
maximizing the marginal likelihood function. However, this data-dominant hyperparameter …
A hybrid modeling framework for efficient development of Fischer-Tropsch kinetic models
JH Kim, GB Rhim, N Choi, MH Youn, DH Chun… - Journal of Industrial and …, 2023 - Elsevier
Fischer-Tropsch synthesis (FTS) receives an extensive attention as it can be used to
produce various chemicals and fuels, such as linear alpha olefin, gasoline and jet fuel, in a …
produce various chemicals and fuels, such as linear alpha olefin, gasoline and jet fuel, in a …
A hybrid modeling approach to estimate liquid entrainment fraction and its uncertainty
Liquid entrainment fraction is a key parameter for designing and optimizing downstream oil
and gas production equipment and needs to be reliably estimated. In this study, a hybrid …
and gas production equipment and needs to be reliably estimated. In this study, a hybrid …
Physics-informed neural networks with hard linear equality constraints
Surrogate modeling is used to replace computationally expensive simulations. Neural
networks have been widely applied as surrogate models that enable efficient evaluations …
networks have been widely applied as surrogate models that enable efficient evaluations …
A Gaussian process embedded feature selection method based on automatic relevance determination
Y Deng, M Eden, S Cremaschi - Computers & Chemical Engineering, 2024 - Elsevier
Abstract In Gaussian Process, feature importance is inversely proportional to the
corresponding length scale when applying the Automatic Relevance Determination (ARD) …
corresponding length scale when applying the Automatic Relevance Determination (ARD) …