Caiyuan Xiao; Guiju Zhang
Volume 1, Issue 1 , February 2022, , Pages 15-29
Abstract
This paper presents an analysis of improving the efficiency of a hybrid solid oxide fuel cell (SOFC) and a micro gas turbine (mGT) system. The main reason for using SOFC technology is the generation of its less harmful products with higher performance compared to the traditional power generation systems. ...
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This paper presents an analysis of improving the efficiency of a hybrid solid oxide fuel cell (SOFC) and a micro gas turbine (mGT) system. The main reason for using SOFC technology is the generation of its less harmful products with higher performance compared to the traditional power generation systems. In addition, the combination of the gas turbine can improve the SOFC system’s reliability. Due to the importance of SOFC systems degradation in the industry, using the optimized hybrid system to reduce SOFC degradation is a proper process. This study presents a new developed bio-inspired optimization technique based on the rhino herd algorithm. After validation of the method with some different bio-inspired methods, it is employed to optimal size selection of the gas turbine for the fuel cell system reliability. Simulation results show that using a larger size of the turbine gives a higher level of power to the SOFC. It also decreases the efficiency of the initial turbine and increases the initial capital investment.
Haoran Fu; Huahui Li; T. Ramayah
Volume 1, Issue 1 , February 2022, , Pages 30-45
Abstract
A new methodology is suggested in this study for providing an optimum energy demand forecasting for the future projections. The paper presents an improved version of manta ray foraging optimizer (iMRFO) for giving an optimum and suitable forecasting model. The model designing has been done on Taiwan ...
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A new methodology is suggested in this study for providing an optimum energy demand forecasting for the future projections. The paper presents an improved version of manta ray foraging optimizer (iMRFO) for giving an optimum and suitable forecasting model. The model designing has been done on Taiwan as the case study. The optimized forecasting is performed based on three models, including linear, exponential, and quadratic models where their coefficients are optimized by the suggested iMRFO algorithm based on different affective factors containing yearly growth rate of the real GDP, yearly growth rate of the population, annual industry share in growth rate of GDP, annual rate of urbanization, and annual coal consumption. Simulation results showed that using the proposed -energy demand prediction technique based on iMRFO has higher accuracy and reliability prediction in the direction of the other compared methods from the literature, such as ACO, GA/PSO, basic MRFO-based, and multiple linear regression models. Two different scenarios have been measured for more analyzing the suggested method. The results finally show that energy intensity in Taiwan will decline in varying degrees based on both scenarios which indicates that additional growth of efficient strategies and actions is needed for ensuring that the target is accomplished.
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