An application of evolutionary system identification algorithm in modelling of energy production system

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@Article{HUANG:2018:Measurement,

• author = "Yuhao Huang and Liang Gao and Zhang Yi and Kang Tai and P. Kalita and Paweena Prapainainar and Akhil Garg",
• title = "An application of evolutionary system identification algorithm in modelling of energy production system",
• journal = "Measurement",
• volume = "114",
• pages = "122--131",
• year = "2018",
• keywords = "genetic algorithms, genetic programming, System identification, Modelling methods, Fuel cell, Energy system",
• ISSN = "0263-2241",
• DOI = "doi:10.1016/j.measurement.2017.09.009",
• URL = "http://www.sciencedirect.com/science/article/pii/S0263224117305742",
• abstract = "The present work introduces the literature review on System Identification (SI) by classifying it into several fields. The review summarizes the need of evolutionary SI method that automates the model structure selection and its parameter evaluation based on only the system data. In this context, the evolutionary SI approach of genetic programming (GP) is applied in modelling and optimization of cleaner energy system such as direct methanol fuel cell. The functional response of the power density of the fuel cell with respect to input conditions is selected based on the minimum training error. Further, an experimental data is used to validate the robustness of the formulated GP model. The analysis based on 2-D and 3-D parametric procedure is further conducted to reveals insights into functioning of the fuel cell. The Pareto front obtained from optimization of model reveals that the operating temperature of 64.5 degree C, methanol flow rate of 28.04mL/min and methanol concentration of 0.29M are the optimum settings for achieving the maximum power density of 7.36mW/cm2 for DMFC",

}

Genetic Programming entries for Yuhao Huang Liang Gao Zhang Yi Kang Tai P Kalita Paweena Prapainainar Akhil Garg

Citations