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Artificial Neural Network Optimized by Modified Particle Swarm Optimization for Predicting Peak Particle Velocity Induced by Blasting Operations in Open Pit Mines

Bui Xuan‑Nam, Nguyen Hoang, Nguyen Truc Anh

Inżynieria Mineralna

2021

no. 2

79--90

Blasting is an indispensable part of the open pit mining operations. It plays a vital role in preparing the rock mass for subsequent operations, such as loading/unloading, transporting, crushing, and dumping. However, adverse effects, especially blast-induced ground vibrations, are considered one of the most dangerous problems. In this study, artificial intelligence was supposed to predict the intensity of blast-induced ground vibration, which is represented by the peak particle velocity (PPV). Accordingly, an artificial neural network was designed to predict PPV at the Coc Sau open pit coal mine with 137 blasting events were collected. Aiming to optimize the ANN model, the modified version of the particle swarm optimization (MPSO) algorithm was applied to optimize the ANN model for predicting PPV, called the MPSO-ANN model. For the comparison purposes, two forms of empirical equations, namely United States Bureau of Mining (USBM) and U Langefors - Kihlstrom, were also developed to predict PPV and compared with the proposed MPSO-ANN model. The results showed that the proposed MPSO-ANN model provided a better performance with a mean absolute error (MAE) of 1.217, root-mean-squared error (RMSE) of 1.456, and coefficient of determination (R2) of 0.956. Meanwhile, the empirical models only provided poorer performances with an MAE of 1.830 and 2.012, RMSE of 2.268 and 2.464, and R2 of 0.874 and 0.852 for the USBM and U Langefors – Kihlstrom empirical models, respectively.

Adaptive protection scheme for optimally coordinated relay setting using modified PSO algorithm

Atteya A. I., El Zonkoly A. M., Ashour H. A.

Journal of Power Technologies

2017

Vol. 97, nr 5

463--469

With growing power demand and heightened concern about the use of fossil fuels in conventional power plants, the integration of distributed energy resources into power networks is gaining attention due to their ability to cater for localized energy needs, putting the concept of the Smart grid center stage. Network protection systems, faced with a gradual increase in complexity, will have to develop responses to the changes brought about by ever greater penetration by distributed generation and sophisticated network topologies. The main goal of this paper is to provide optimal relay coordination of an adaptive protection scheme. Designed software based on a Modified Particle Swarm Optimization (MPSO) algorithm is implemented to solve the relay coordination problem. In this study, the 14 IEEE bus system is tested across a range of power system scenarios to validate the suggested technique. The results obtained show that optimal relay settings are achieved by the proposed algorithm regardless of the prevailing network topology.