摘要
水煤浆(CWM)制造过程中,生产成本的降低和水煤浆性能的提高之间存在着矛盾.文中利用最小二乘支持向量机(LSSVM)对球磨机电流和水煤浆浓度进行多目标建模,并采用基于Pareto最优概念的多目标微分进化(MODE)算法对运行工况进行寻优,然后根据模糊集理论在Pareto解集中求得满意解,获得了水煤浆浓度的优化调整方式和提高水煤浆生产效益的策略.
In the production of coal water mixture （ CWM）, there exists an inconsistency between the production cost and the product performance. In order to solve this problem, the least-square support vector machine is em- ployed to establish a muhi-objective optimization model for CWM concentration and ball mill current, and the multi- objective differential evolution algorithm based on Pareto optimal concept is used to optimize the operation condi- tions, Moreover, the fuzzy set theory is introduced to obtain the satisfactory solutions in Pareto solution set. An op- timized adjustment mode of CWM concentration and some strategies to improve the CWM production benefit are fi- nally proposed in the paper.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2009年第2期158-162,共5页
Journal of South China University of Technology(Natural Science Edition)
关键词
水煤浆
优化运行
最小二乘支持向量机
多目标微分进化算法
coal water mixture
optimal operation
least-square support vector machine
multi-objective differential evolution algorithm
