The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previ...The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.展开更多
采用递增式学习策略优化条件随机域(conditional random fields,CRF)的特征模板以提高中文地名的识别效果,结合语言学相关知识构建规则库,以弥补机器学习模型获取知识不够全面导致召回率偏低的不足,最终实现了CRF与规则相结合的中文地...采用递增式学习策略优化条件随机域(conditional random fields,CRF)的特征模板以提高中文地名的识别效果,结合语言学相关知识构建规则库,以弥补机器学习模型获取知识不够全面导致召回率偏低的不足,最终实现了CRF与规则相结合的中文地名识别系统.实验结果表明,采用CRF与规则相结合的方法识别中文文本中的地名是有效的,对Bakeoff2007NER任务的MSRA语料进行开放测试,召回率、精确率和F值分别为94.67%、92.35%和93.50%.展开更多
基金Project(62073342)supported by the National Natural Science Foundation of ChinaProject(2014 AA 041803)supported by the Hi-tech Research and Development Program of China。
文摘The operation variables,including feed rate of ore slurry,caustic solution and live steams in the double-stream alumina digestion process,determine the product quality,process costs and the environment pollution.Previously,they were set by the technical workers according to the offline analysis results and an empirical formula,which leads to unstable process indices and high consumption frequently.So,a multi-objective optimization model is built to maintain the balance between resource consumptions and process indices by taking technical indices and energy efficiency as objectives,where the key technical indices are predicted based on the digestion kinetics of diaspore.A multi-objective state transition algorithm(MOSTA)is improved to solve the problem,in which a self-adaptive strategy is applied to dynamically adjust the operator factors of the MOSTA and dynamic infeasible threshold is used to handle constraints to enhance searching efficiency and ability of the algorithm.Then a rule based strategy is designed to make the final decision from the Pareto frontiers.The method is integrated into an optimal control system for the industrial digestion process and tested in the actual production.Results show that the proposed method can achieve the technical target while reducing the energy consumption.
文摘采用递增式学习策略优化条件随机域(conditional random fields,CRF)的特征模板以提高中文地名的识别效果,结合语言学相关知识构建规则库,以弥补机器学习模型获取知识不够全面导致召回率偏低的不足,最终实现了CRF与规则相结合的中文地名识别系统.实验结果表明,采用CRF与规则相结合的方法识别中文文本中的地名是有效的,对Bakeoff2007NER任务的MSRA语料进行开放测试,召回率、精确率和F值分别为94.67%、92.35%和93.50%.
