Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network

Journal Article · Tue Nov 15 00:00:00 EST 2022 · Chemical Engineering Journal

DOI:https://doi.org/10.1016/j.cej.2022.140367· OSTI ID:1900499

Marcato, Agnese  ^[1]; Estrada Santos, Javier Andres  ^[2]; Boccardo, Gianluca ^[1]; Viswanathan, Hari S.  ^[2]; Marchisio, Daniele  ^[1]; Prodanović, Maša  ^[3]

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1. Politecnico di Torino (Italy)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
3. Univ. of Texas, Austin, TX (United States)

The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1900499
• Report Number(s): LA-UR-22-26762
• Journal Information: Chemical Engineering Journal, Vol. 455; ISSN 1385-8947
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
Energy sciences
Material science
Machine learning
Concentration
Multi-scale
Coupled processes
Porous media
Convolutional neural networks
CFD
Reaction
Filtration