Density functional theory and kinetic Monte Carlo simulation study the strong metal-support interaction of dry reforming of methane reaction over Ni based catalysts

Oxide supports modify electronic structures of supported metal nanoparticles,and then affect the catalytic activity associated with the so-called strong metal-support interaction(SMSI).We herein report the strong influence of SMSI employing Ni_(4)/α-MoC(111) and defective Ni_(4)/MgO(100) catalysts used for dry reforming of methane(DRM,CO_(2)+CH_4→2 CO+2 H_(2)) by using density functional theory(DFT) and kinetic Monte Carlo simulation(KMC).The results show that α-MoC(111) and MgO(100) surface have converse electron and structural effect for Ni_(4) cluster.The electrons transfer from a-MoC(111) surface to Ni atoms,but electrons transfer from Ni atoms to MgO(100) surface;an extensive tensile strain is greatly released in the Ni lattice by MgO,but the extensive tensile strain is introduced in the Ni lattice by α-MoC.As a result,although both catalysts show good stability,H_(2)/CO ratio on Ni_(4)/α-MoC(111) is obviously larger than that on Ni_(4)/MgO(100).The result shows that Ni/α-MoC is a good catalyst for DRM reaction comparing with Ni/MgO catalyst.

Remaining useful life prediction for train bearing based on an ILSTM network with adaptive hyperparameter optimization

Remaining useful life(RUL)prediction for bearing is a significant part of the maintenance of urban rail transit trains.Bearing RUL is closely linked to the reliability and safety of train running,but the current prediction accuracy makes it difficult to meet the re-quirements of high reliability operation.Aiming at the problem,a prediction model based on an improved long short-term memory(ILSTM)network is proposed.Firstly,the variational mode decomposition is used to process the signal,the intrinsic mode function with stronger representation ability is determined according to energy entropy and the degradation feature data is constructed com-bined with the time domain characteristics.Then,to improve learning ability,a rectified linear unit(ReLU)is applied to activate a fully connected layer lying after the long short-term memory(LSTM)network,and the hidden state outputs of the layer are weighted by attention mechanism.The Harris Hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of the LSTM.Finally,the ILSTM is applied to predict bearing RUL.Through experimental cases,the better perfor-mance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated,and its superiority of hyperparameters setting is demonstrated.