摘要
在6km水下采集富钴结壳,其能耗是一个突出的问题.以采集头的行走速度、滚筒切割速度、切削深度及截齿的截线距为设计变量,以采集头最小切削能耗为目标,建立了采集头工作参数优化设计模型.设计模糊控制器嵌入到遗传算法中控制交叉率、变异率的自动调整,利用模糊遗传算法对模型进行优化,得到最优切削深度、截齿的截线距、采矿车行走速度、采集头切割速度及最小功率分别为3.0993cm、5.5979cm、0.2889m/s、0.3535m/s和149.6229kW.水池试验证明优化结果令人满意.图6,表2,参8.
Energy consumption is an important problem when cobalt-rich crusts are mined under 6 000 meters seabed. Using cutting depth, distance of picks, feeding speed and rotation speed of mining head as design variants, aiming at minimum cutting energy consumption, a process parameters optimizing model of mining head was established. Fuzzy controllers were embedded to the genetic algorithms to control the crossover probability and the mutation probability. The fuzzy genetic algorithms ( FGA ) were used to solve the optimization model. The optimal results are as follows: cutting depth,distance of cutting teeth,marching speed,rotation speed and cutting power are 3.099 3 cm、 5.597 9 cm,0.288 9 m/s,0.353 5m/s and 149.622 9 kW respectively. The experiments which were completed under a water pool of 8 m depth indicate that the optimized resuh is satisfactory, and the optimized results provide theoretical reference for selecting work parameters of mining head. 6figs., 2tabs., 8refs.
出处
《湖南科技大学学报(自然科学版)》
CAS
北大核心
2006年第1期29-32,共4页
Journal of Hunan University of Science And Technology:Natural Science Edition
基金
国家海洋技术发展项目(DY105-03-02-1)
国家自然科学基金资助项目(50474052)
关键词
钴结壳
遗传算法
模糊控制
优化设计
cobalt crust
genetic algorithms
fuzzy control
optimized design