Engineering Optimization Approaches of Nonferrous Metallurgical Processes

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InCollection{Chen:2014:OCMIEC,
  author =       "Xiaofang Chen and Honglei Xu",
  title =        "Engineering Optimization Approaches of Nonferrous
                 Metallurgical Processes",
  booktitle =    "Optimization and Control Methods in Industrial
                 Engineering and Construction",
  publisher =    "Springer",
  year =         "2014",
  editor =       "Honglei Xu and Xiangyu Wang",
  volume =       "72",
  series =       "Intelligent Systems, Control and Automation: Science
                 and Engineering",
  pages =        "107--124",
  keywords =     "genetic algorithms, genetic programming, engineering
                 optimisation, nonferrous metallurgical processes,
                 sequential operating, imperial smelting furnace",
  isbn13 =       "978-94-017-8043-8",
  language =     "English",
  DOI =          "doi:10.1007/978-94-017-8044-5_7",
  abstract =     "The engineering optimisation approaches arising in
                 nonferrous metallurgical processes are developed to
                 deal with the challenges in current nonferrous
                 metallurgical industry including resource shortage,
                 energy crisis and environmental pollution. The great
                 difficulties in engineering optimisation for nonferrous
                 metallurgical process operation lie in variety of
                 mineral resources, complexity of reactions, strong
                 coupling and measurement disadvantages. Some
                 engineering optimisation approaches are discussed,
                 including operational-pattern optimisation,
                 satisfactory optimisation with soft constraints
                 adjustment and multi-objective intelligent satisfactory
                 optimisation. As an engineering optimisation case, an
                 intelligent sequential operating method for a practical
                 Imperial Smelting Process is illustrated. Considering
                 the complex operating optimisation for the Imperial
                 Smelting Process, with the operating stability
                 concerned, an intelligent sequential operating strategy
                 is proposed on the basis of genetic programming (GP)
                 adaptively designed, implemented as a multi-step state
                 transferring procedure. The individuals in GP are
                 constructed as a chain linked by a few relation
                 operators of time sequence for a facilitated evolution
                 with compact individuals. The optimal solution gained
                 by evolution is a sequential operating program of
                 process control, which not only ensures the tendency to
                 optimisation but also avoids violent variation by
                 operating the parameters in ordered sequences.
                 Industrial application data are given as
                 verifications.",
}

Genetic Programming entries for Xiaofang Chen Honglei Xu

Citations