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Energy efficiency optimization of belt conveyors based on finite-time recurrent neural networks

This paper studies belt conveyor based energy efficiency optimization by using recurrent neural networks. Firstly, two belt conveyor based energy efficiency optimization problems are presented by taking minimum inventory, maximum inventory and material consumption into account. Secondly, in order to solve these problems, we transform them into nonlinear convex programming problems. Finite-time recurrent neural networks are presented to solve these problems. Suboptimal solutions to the problems can be derived in a finite-time interval. Lastly, by numerical simulations, it is shown that the proposed optimization methods achieve desired performance.