Prediction of geomechanical bearing capacity using autoregressive deep neural network in carbon capture and storage systems.

Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation.

Investigation of catalytic activity of metal doped nanocages (Ni-C72 and Ni-Al36P36) for ozone decomposition to oxygen molecules.

CONTEXT: The potential of Ni-C72 and Ni-Al36P36 as effective catalysts for O3 decomposition is examined by LH and ER mechanisms. The activation barrier energy and Gibbs free energy of reaction steps for O3 decomposition on Ni-C72 and Ni-Al36P36 are calculated. The ∆Eformation of Ni-C72 and Ni-Al36P36 are negative values and these structures are stable nano-catalysts. The Ni atoms are catalytic positions to adsorb the O3 and other important species of O3 decomposition by LH and ER mechanisms. The Ni-Al36P36 for O3 decomposition has lower Eacivation and more negative ∆Greaction than Ni-C72. The Eacivation value of rate-determining step for O3 decomposition by LH mechanism is lower than ER mechanism. The Ni-C72 and Ni-Al36P36 can catalyze the reaction steps of O3 decomposition by LH and ER mechanisms. METHODS: The structures of Ni-C72 and Ni-Al36P36 nanocages and their complexes with O3 and other important species of are optimized by PW91PW91/6-311 + G (2d, 2p) model and M06-2X/cc-pVQZ model in GAMESS software. The strcutures of nanocages and their complexes with important species of O3 decomposition by LH and ER mechanisms are optimized and their frequencies are calculated in order to demonstrate that these structures are real minima on the potential energy surface.