نوع مقاله : مقاله پژوهشی
نویسندگان
1 دانشگاه صنعتی شریف
2 استاد تمام دانشکده مهندسی شیمی و نفت
چکیده
کلیدواژهها
موضوعات
عنوان مقاله [English]
نویسندگان [English]
The global warming and climate change due to the increase in greenhouse gas emissions are the vital and challenging issues in this period of human life. Human’s effort to address this problem has led to provide several solutions including net-zero carbon, i.e., carbon capture and storage and energy transition i.e., reducing or even replacing fossil fuels with renewable energy sources such as solar, wind, hydro etc. However, climate dependence and the highly fluctuating nature of clean energy production from these sources require a large-scale storage system to continuously meet energy demand. Hydrogen as an energy carrier, and underground hydrogen storage (UHS) hold potential for sustainable supply of a large amount of energy in the peak of energy consumption. Hydrogen has different characteristics and dynamical behaviors in the porous media compared to other gases. Thus it is required to create complex compositional models and perform time-consuming simulations to seek for the best gas storage scenario based on operational parameters including and cushion gas and working gas injection/production flowrates, perforations and cushion gas composition. In this study, the process of UHS in a depleted gas reservoir was simulated. Due to the reservoir remaining gas saturation and phase behavior of fluids in porous media, both hydrogen recovery factor and purity are considered as target variables. Next, the design of experiment methods (e.g., Latin hypercube) was utilized to generate the required train and test subsets for artificial neural network model. The feed-forward model with 10 neurons and sigmoid activation function as a smart proxy model with accuracy equal to 0.97 and 0.94, respectively, for training and testing subsets provided the best performance for predicting the hydrogen purity and recovery factor or the target parameters in the multi-objective optimization process of decision variables by genetic algorithm. The optimum solutions, i.e., Pareto front for the decision variables showed the dominant percentage of nitrogen gas with the base gas composition of 75, 20 and 5% respectively for nitrogen, carbon dioxide and methane. Moreover, production from top perforations and hydrogen injection in the lower part of the preformation were determined as optimal conditions. The implemented procedure in this paper can be used for UHS field studies and fast decision making in the large-scale energy storage operations.
کلیدواژهها [English]
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