Robust optimization of test miner considering deep-sea environmental variables

Abstract

A tracked vehicle is a part of a deep-sea manganese nodule miner and it is important to maintain robustness of the performances that can be influenced by noise variables including deep-sea environmental variables. Thus it is necessary to adopt robust optimization that improves the mean of performances as well as minimizes the variance of performances due to noise variables. In this paper, we use multiplicative decomposition (MD) method to perform the robust optimization for a test miner tracked vehicle considering noise variables in the KODOS area of CCFZ of the northeastern equatorial Pacific. MD method accurately calculates the means and variances of responses caused by deep-sea environmental variables which are obtained from in-situ experiments in the KODOS area. Finally, the robust optimization of the test miner tracked vehicle for collecting deep-sea manganese nodules has been achieved.