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.

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 cm,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.