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

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, th...

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Main Authors: Huang, Yuhao, Gao, Liang, Yi, Zhang, Tai, Kang, Kalita, Pankaj, Prapainainar, Paweena, Garg, Akhil
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/142332
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1423322020-06-19T04:48:25Z An application of evolutionary system identification algorithm in modelling of energy production system Huang, Yuhao Gao, Liang Yi, Zhang Tai, Kang Kalita, Pankaj Prapainainar, Paweena Garg, Akhil School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering System Identification Modelling Methods 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. 2020-06-19T04:48:25Z 2020-06-19T04:48:25Z 2018 Journal Article 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 0263-2241 https://hdl.handle.net/10356/142332 10.1016/j.measurement.2017.09.009 2-s2.0-85029579957 114 122 131 en Measurement © 2017 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Mechanical engineering
System Identification
Modelling Methods
spellingShingle Engineering::Mechanical engineering
System Identification
Modelling Methods
Huang, Yuhao
Gao, Liang
Yi, Zhang
Tai, Kang
Kalita, Pankaj
Prapainainar, Paweena
Garg, Akhil
An application of evolutionary system identification algorithm in modelling of energy production system
description 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.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Huang, Yuhao
Gao, Liang
Yi, Zhang
Tai, Kang
Kalita, Pankaj
Prapainainar, Paweena
Garg, Akhil
format Article
author Huang, Yuhao
Gao, Liang
Yi, Zhang
Tai, Kang
Kalita, Pankaj
Prapainainar, Paweena
Garg, Akhil
author_sort Huang, Yuhao
title An application of evolutionary system identification algorithm in modelling of energy production system
title_short An application of evolutionary system identification algorithm in modelling of energy production system
title_full An application of evolutionary system identification algorithm in modelling of energy production system
title_fullStr An application of evolutionary system identification algorithm in modelling of energy production system
title_full_unstemmed An application of evolutionary system identification algorithm in modelling of energy production system
title_sort application of evolutionary system identification algorithm in modelling of energy production system
publishDate 2020
url https://hdl.handle.net/10356/142332
_version_ 1681058263481712640