Optimal control of cobalt crust seabedmining parameters based on simulated annealing genetic algorithm

Under the condition of the designated collection ratio and the interfused ratio of mullock, to ensure the least energy consumption, the parameters of collecting head （the feed speed, the axes height of collecting head, and the rotate speed） are chosen as the optimized parameters. According to the force on the cutting pick, the collecting size of the cobalt crust and bedrock and the optimized energy consumption of the collecting head, the optimized design model of collecting head is built. Taking two hundred groups seabed microtopography for grand in the range of depth displacement from 4.5 to 5.5 era, then making use of the improved simulated annealing genetic algorithm （SAGA）, the corresponding optimized result can be obtained. At the same time, in order to speed up the controlling of collecting head, the optimization results are analyzed using the regression analysis method, and the conclusion of the second parameter of the seabed microtopography is drawn.

Cobalt crust recognition based on kernel Fisher discriminant analysis and genetic algorithm in reverberation environment

Recognition of substrates in cobalt crust mining areas can improve mining efficiency.Aiming at the problem of unsatisfactory performance of using single feature to recognize the seabed material of the cobalt crust mining area,a method based on multiple-feature sets is proposed.Features of the target echoes are extracted by linear prediction method and wavelet analysis methods,and the linear prediction coefficient and linear prediction cepstrum coefficient are also extracted.Meanwhile,the characteristic matrices of modulus maxima,sub-band energy and multi-resolution singular spectrum entropy are obtained,respectively.The resulting features are subsequently compressed by kernel Fisher discriminant analysis(KFDA),the output features are selected using genetic algorithm(GA)to obtain optimal feature subsets,and recognition results of classifier are chosen as genetic fitness function.The advantages of this method are that it can describe the signal features more comprehensively and select the favorable features and remove the redundant features to the greatest extent.The experimental results show the better performance of the proposed method in comparison with only using KFDA or GA.

Distribution and Classification of Cobalt-Rich Crust

Based on the on the spot investigation and related data, cobalt rich crust is mainly distributed in the low latitude area near the equator, mostly within 20°S to 20°N, especially 5°-15°(S and N). The analysis of the microtopographic and microphysicognomy features shows that crusts are often present in the complicated topographic regions such as seamount slopes, convex parts of seamounts and joint faults, of which the ideal region is seamount slopes in water depth of 1 500-2 500 m. The authors analyze the relation of the crusts and their bedrock, bedrock type, crust thickness and occurrence, and then attempt to classify the crusts as different types.

China Enters a New Stage of Cobalt Crust Exploration

Recently,the'Ocean No.Six'expedition team of the Guangzhou Marine Geological Survey under the China Geological Survey successfully completed the 1.5 m shallow drilling operations at 44 stations set up in the Weijia Guyot mine area,Western Pacific,marking a milestone in the transition of China’s cobalt crust exploration from a resource survey stage to a general exploration stage.