Recent research advances in enhanced CO2 mineralization and geologic CO2 storage

Enhanced CO2 mineralization and geologic CO2 storage have received increasing attention as two prominent approaches in combating climate change and fostering sustainable development of human society. This paper aims to explore three emerging areas of research within the realm of enhanced CO2 mineralization and geologic CO2 storage, including enhanced rock weathering, numerical modeling and validation of CO2 storage accounting for the interplay of various trapping mechanisms, and the examination of how reservoir heterogeneity influences the migration of CO2-brine multiphase flow. Discussions highlight the effectiveness of the spectrum induced polarization for monitoring changes in petrophysical and geochemical properties of rocks during enhanced rock weathering. Additionally, the multi-scale heterogeneity of geological formations needs to be carefully characterized, due to the fact that it plays a vital role in CO2 migration. Further research is required to achieve accurate and reliable simulations of convective mixing for field-scale applications.Document Type: PerspectiveCited as: Zhang, C., Wang, Y., Kou, Z., Zhang, L. Recent research advances in enhanced CO2 mineralization and geologic CO2 storage. Advances in Geo-Energy Research, 2023, 10(3): 141-145. https://doi.org/10.46690/ager.2023.12.0

FEATURE SELECTION VIA LEAST SQUARES SUPPORT FEATURE MACHINE

In many applications such as credit risk management, data are represented as high-dimensional feature vectors. It makes the feature selection necessary to reduce the computational complexity, improve the generalization ability and the interpretability. In this paper, we present a novel feature selection method — "Least Squares Support Feature Machine" (LS-SFM). The proposed method has two advantages comparing with conventional Support Vector Machine (SVM) and LS-SVM. First, the convex combinations of basic kernels are used as the kernel and each basic kernel makes use of a single feature. It transforms the feature selection problem that cannot be solved in the context of SVM to an ordinary multiple-parameter learning problem. Second, all parameters are learned by a two stage iterative algorithm. A 1-norm based regularized cost function is used to enforce sparseness of the feature parameters. The "support features" refer to the respective features with nonzero feature parameters. Experimental study on some of the UCI datasets and a commercial credit card dataset demonstrates the effectiveness and efficiency of the proposed approach.Feature selection, Support Vector Machine, credit assessment