In this paper, we propose an Elman recurrent neural network to predict and analyze a time series of gas energy consumption in an air handling unit. To this end, we consider the volatility of the time series and demonstrate that there exists a correlation in the time series of the volatilities, which suggests that the gas consumption time series contain a non-negligible amount of the non-linear correlation. Based on this finding, we adopt the Elman recurrent neural network as the model for the prediction of the gas consumption. As the simplest form of the recurrent network, the Elman network is designed to learn sequential or time-varying pattern and could predict learned series of values. The Elman network has a layer of “context units” in addition to a standard feedforward network. By adjusting two parameters in the model and performing the cross validation, we demonstrated that the proposed model predicts the gas consumption with the relative errors and the average errors in the range of 2%~5% and 3kWh~8kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric and the gas energies.

Gas hydrate desalination process is based on a liquid to solid (Gas Hydrate, GH) phase change followed by a physical process to separate the GH from the remaining salty water. The GH based desalination process show 60.5-90% of salt rejection, post treatment like reverse osmosis (RO) process is needed to finally meet the product water quality. In this study, the energy consumption of the GH and RO hybrid system was investigated. The energy consumption of the GH process is based on the cooling and heating of seawater and the heat of GH formation reaction while RO energy consumption is calculated using the product of pressure and flow rate of high pressure pumps used in the process. The relation between minimum energy consumption of RO process and RO recovery depending on GH salt rejection, and (2) energy consumption of electric based GH process can be calculated from the simulation. As a result, energy consumption of GH-RO hybrid system and conventional seawater RO process (with/without enregy recovery device) is compared. Since the energy consumption of GH process is too high, other solution used seawater heat and heat exchanger instead of electric energy is suggested.

Recently, various studies for reducing GHG and energy consumption are increasing in the world. In this study, the previous studies were analyzed to improve reduction methods and propose alternatives in terms of global competitiveness and environmental protection. The result shows systematic research and policy support are necessary to reduce GHG and energy consumption in maintenance field of infrastructure