Combination Of Artificial Neural Network Models Research Articles

Numerical study on smart sloped rolling-type seismic isolators integrated with early prediction of peak velocity

The effectiveness of sloped rolling-type seismic isolators (SRI) in seismically protecting critical equipment from malfunction and damage has already been extensively demonstrated. To prevent the isolation displacement response of SRI from reaching a threshold, passively designing a large and conservative damping force for SRI is usually required, which accordingly leads to enlarged and even unacceptable transmitted acceleration responses. That is, for seismic isolation, there is always a trade-off between minimizing acceleration and displacement responses. Previous studies have indicated that by determining the damping force applied to SRI based on the possible information provided by an earthquake early warning system and adjusting it in a semi-active control manner, its acceleration and displacement responses can be controlled more satisfactorily. However, this is based on the premise that the parameters of input excitation needed for determining the damping force are predicted promptly and accurately. Besides, among the discussed parameters, the peak velocity (PV) was most recommended. To further improve this, in this study, for the first few seconds after the arrival of primary waves (P-wave), the prediction models of PV are developed using the artificial neural network (ANN) approach. Based on the early prediction of PV as well as the proposed control law, the required damping force for SRI can be determined merely several seconds after P-wave arrival. The control performances of SRI, whose damping forces are determined using the predictions of PV at different times after P-wave arrival, are numerically examined. Through studies under a large number of conditions based on different earthquake records together with the ground motions recorded in an independent damaging earthquake event, a combination of ANN models at different, suitable times after P-wave arrival is recommended to determine the damping force applied to SRI.

Optimization of OCM reactions over Na–W–Mn/ SiO2 catalyst at elevated pressure using artificial neural network and response surface methodology

In this study, Response Surface Methodology (RSM) and Artificial Neural Network (ANN) predictive models are developed, based on experimental data of the Oxidative Coupling of Methane (OCM) over Na–W–Mn/ SiO2 at 0.4 MPa, which was obtained in an isothermal fixed bed reactor. Results show that the simulation and prediction accuracy of ANN was apparently higher compared to RSM. Thus, the Hybrid Genetic Algorithm (HGA), based on developed ANN models, was used for simultaneous maximization of CH4 conversion and C2+ selectivity. The pareto optimal solutions show that at a reaction temperature of 987 K, feed GHSV of 15790 h−1, diluents amounts of 20 mole%, and methane to oxygen molar ratio of 3.5, the maximum C2+ yield obtained from ANN-HGA was 23.91% (CH4 conversion of 34.6% and C2+ selectivity of 69%), as compared to 22.81% from the experimental measurements (CH4 conversion of 34.0% and C2+ selectivity of 67.1%). The predicted error in optimum yield by ANN-HGA was 4.81%, suggesting that the combination of ANN models with the hybrid genetic algorithm could be used to find a suitable operating condition for the OCM process at elevated pressures.

Optimization of OCM reaction conditions over Na–W–Mn/SiO 2 catalyst at elevated pressure

Performance of the oxidative coupling of methane (OCM) at elevated pressures has been simulated by a set of supervised Artificial Neural Network (ANN) models using reaction data gathered in a microreactor device. Accuracy of the developed models were evaluated by comparing the predicted results with the test data set showing a good agreement. In order to enhance the performance of OCM process at 0.4 MPa as a desired operating pressure for commercial application of OCM, the Hybrid Genetic Algorithm (HGA) was used to obtain the optimal values of the operating conditions. Nondominated Pareto optimal solutions were obtained and additional experiments were carried out at two different optimum conditions in order to verify the optimums. The results show that combination of ANN models with HGA could be used in finding the suitable operating conditions for OCM process at elevated pressures. It was shown that the C 2+ yield of above 23% can be achieved at 0.4 MPa by using Na–W–Mn/SiO 2 as the OCM catalyst.