Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning

Flow, transport, mechanical, and fracture properties of porous media depend on their morphology and are usually estimated by experimental and/or computational methods. The precision of the computational approaches depends on the accuracy of the model that represents the morphology. If high accuracy is required, the computations and even experiments can be quite time-consuming. At the same time, linking the morphology directly to the permeability, as well as other important flow and transport properties, has been a long-standing problem. In this paper, we develop a new network that utilizes a deep learning (DL) algorithm to link the morphology of porous media to their permeability. The network is neither a purely traditional artificial neural network (ANN), nor is it a purely DL algorithm, but, rather, it is a hybrid of both. The input data include three-dimensional images of sandstones, hundreds of their stochastic realizations generated by a reconstruction method, and synthetic unconsolidated porous media produced by a Boolean method. To develop the network, we first extract important features of the images using a DL algorithm and then feed them to an ANN to estimate the permeabilities. We demonstrate that the network is successfully trained, such that it can develop accurate correlations between the morphology of porous media and their effective permeability. The high accuracy of the network is demonstrated by its predictions for the permeability of a variety of porous media.

     

Synthesis of molecularly imprinted polymer as a sorbent for solid phase extraction of bovine albumin from whey,milk, urine and serum

This study describes the synthesis of molecularly imprinted polymers (MIPs) using bovine albumin as a template, 2-VP as a functional monomer, EGDMA as a cross-linker and AIBN as an initiator by radical polymerization. Non-imprinted polymers (NIPs) were prepared and treated with the same method, but in the absence of bovine albumin. The synthesized MIPs and NIPs were characterized on the basis of FTIR, TGA and DTA. An adsorption process (solid phase extraction, SPE) for the removal of bovine albumin using the fabricated MIPs and NIPs was evaluated under various conditions. Effective parameters on bovine albumin retention for example, pH, flow rate, nature of the eluent, the ionic strength, selectivity coefficient, and retention capacity were studied. Competition test implicates that the MIP adsorbents have the strongest specific retention and enrichment for bovine albumin respect to NIPs. The maximum adsorption of bovine albumin by the fabricated MIPs was 24 mg/g. The calibration curves were linear in the range of 20–200 mg/L of bovine albumin. The limit of detection (LOD), the calibration sensitivity, the relative standard deviation (RSD) and preconcentration factor under optimal experimental conditions were 2.44 and 25, respectively. The extraction of bovine albumin from blood serum, urine, whey and milk samples had a selectivity and enrichment property. In the actual experiment for real samples, recovery of ~ 80% was achieved.     

A comparative study of electrocatalytic performance of the M@Pt (M = Fe<Subscript>3</Subscript>O<Subscript>4</Subscript>, Co and Ni) nanoparticles for direct ethanol fuel cells

In this study, we have successfully synthesized the M@Pt (M = Fe₃O₄, Co and Ni) nanoparticles catalysts through the sodium borohydride reduction method for a comparative studies of their electrocatalytic performance towards ethanol oxidation in acidic media. After the structure, surface morphology and chemical composition characterization of the synthesized core–shell nanoparticles, their electrocatalytic activities towards oxidation of ethanol in acidic media were studied in detail. We investigated the effect of the core element (Fe₃O₄, Co and Ni) on the electrochemical behaviour as well as the enhancement of the electrocatalytic activity for ethanol oxidation reaction. Overall, the obtained results show that the all of these electrocatalysts exhibited an enhanced activity than Pt-alone nanoparticles towards ethanol oxidation reaction. On the other hand, the comparative studies of their electrocatalytic performance show that the Ni@Pt nanoparticles present the best performance with the maximum electrocatalytic activity and stability.     

A Multiscale Approach for Geologically and Flow Consistent Modeling

Subsurface geological models are usually constructed on high-resolution grids in a way that various complexities and heterogeneities are depicted properly. Such models, however, cannot be used directly in the current flow simulators, as they are tied with high computational cost. Thus, using upscaling, by which one can produce flow consistent models that can alleviate the computational burden of flow simulators, is inevitable. Although the upscaling methods are able to reproduce the flow responses, they might not retain the initial geological assumptions. The reservoir models are initially constructed on uniform and high-resolution grids and then, if necessary, are upscaled to be used for flow simulations. A subsurface modeling approach that not only preserves the geological heterogeneity but also provides models that can be used, straight or with a small level of upscaling, in the flow simulators is desirable. In this paper, a new multiresolution method based on (1) the importance of conditioning well data and (2) being geologically and flow consistent is presented. This method discretizes the initial model into several regions based on the available data. Then, the initial assumed geological model is converted into, for example, various high- and low-resolution models. Next, the high-resolution model is used for regions with high-quality data (e.g., well locations), while the low-resolution model is used for the remaining areas. Finally, the patterns of these areas are interlocked, which result in a multiresolution geologically and flow consistent subsurface model. The accuracy of this method is demonstrated using two-phase flow simulation on four complex subsurface systems. The results indicate that the same flow responses, in a much less time, are reproduced using the multiscale models. The speed-up factor gained using the proposed method is also several orders of magnitude.