Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/142332
Title: An application of evolutionary system identification algorithm in modelling of energy production system
Authors: Huang, Yuhao
Gao, Liang
Yi, Zhang
Tai, Kang
Kalita, Pankaj
Prapainainar, Paweena
Garg, Akhil
Keywords: Engineering::Mechanical engineering
Issue Date: 2018
Source: Huang, Y., Gao, L., Yi, Z., Tai, K., Kalita, P., Prapainainar, P., & Garg, A. (2018). An application of evolutionary system identification algorithm in modelling of energy production system. Measurement, 114, 122-131. doi:10.1016/j.measurement.2017.09.009
Journal: Measurement
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 modeling 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 °C, methanol flow rate of 28.04 mL/min and methanol concentration of 0.29 M are the optimum settings for achieving the maximum power density of 7.36 mW/cm2 for DMFC.
URI: https://hdl.handle.net/10356/142332
ISSN: 0263-2241
DOI: 10.1016/j.measurement.2017.09.009
Rights: © 2017 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:MAE Journal Articles

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