Prediction, parametric analysis and bi-objective optimization of waste heat utilization in sinter cooling bed using evolutionary algorithm

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@Article{Liu:2015:Energy,
  author =       "Yan Liu and Jian Yang and Jing-yu Wang and 
                 Xu-gang Ding and Zhi-long Cheng and Qiu-wang Wang",
  title =        "Prediction, parametric analysis and bi-objective
                 optimization of waste heat utilization in sinter
                 cooling bed using evolutionary algorithm",
  journal =      "Energy",
  volume =       "90, Part 1",
  pages =        "24--35",
  year =         "2015",
  ISSN =         "0360-5442",
  DOI =          "doi:10.1016/j.energy.2015.05.120",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0360544215007422",
  abstract =     "Based on our previous work, the AEGs (annual energy
                 gains) could be obtained on energy and exergy analysis
                 for a sinter cooling bed. In the present study, a
                 method synthesizing both economic cost and energy
                 benefit aspects of the sinter cooling bed is proposed.
                 Firstly, the GP (genetic programming) is employed to
                 derive accurate correlations between the AEGs and
                 operational parameters. Then, the economic cost model
                 is established to evaluate effects of operational and
                 economic parameters on the EAOC (equivalent annual
                 operational cost). Finally, bi-objective optimization
                 of the sinter cooling bed is performed to achieve the
                 optimal operational conditions from both waste heat use
                 and economic cost aspects using NSGA-II (non-dominated
                 sorting genetic algorithm-II). In order to maximize the
                 AEGs and minimize the EAOC, the EAOC and the AEGs based
                 on the first and second laws of thermodynamics are
                 selected as two objective functions. A Pareto frontier
                 obtained shows that an increase in the AEGs can
                 increase the EAOC of the sinter cooling bed. Under the
                 given operational conditions, the optimum solutions
                 with their corresponding decision variables are
                 obtained. After considering both two Pareto frontiers
                 curves, a set of suggested operational parameters for
                 the decision-makers is also obtained.",
  keywords =     "genetic algorithms, genetic programming, Sinter
                 cooling bed, Waste heat recovery, Bi-objective
                 optimization",
}

Genetic Programming entries for Fiona Yan Liu Jian Yang Jing-yu Wang Xu-gang Ding Zhi-long Cheng Qiu-wang Wang

Citations