An application of evolutionary system identification algorithm in modelling of energy production system. (January 2018) - Huang, Yuhao; Gao, Liang; Yi, Zhang; Tai, Kang; Kalita, P.; Prapainainar, Paweena; Garg, Akhil
Highlights: Survey on System Identification (SI) field is undertaken in this study. Evolutionary SI approach of GP is found to be appropriate method for modeling. An application of GP in modeling of energy system such as fuel cell is illustrated. Experimental data validates the GP model and its performance is found satisfactory. 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/cm 2 for DMFC.