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In this work, a novel concept in freeze desalination (FD) was introduced. Nowadays the total liquefied natural gas (LNG) production capacity has reached 290 Megatons per year. Its enormous cold energy released from re-gasification can be used in the freeze desalination process to minimize the overall energy consumption. A process of FD on flake ice maker utilizing LNG cold energy was designed and simulated by HYSYS software. An ice bucket on flake ice maker was chosen as seawater crystallizer mainly due to its continuous ice making and removing ice without heat source. A dynamic model of the freezing section has been developed and simulated through gPROMS software. The results show that the consumption of 1 kg equivalent LNG cold energy can obtain about 2 kg of ice melt water. In addition, it is shown that the power consumption of this LNG/FD hybrid process is negligible. Keywords:Freeze desalination; Flake ice maker; LNG cold energy; Dynamic modeling; Ice growth

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In this work, an artificial neural network (ANN) model based on the experimental data was developed to study the performance of vacuum membrane distillation (VMD) desalination process under different operating parameters such as the feed inlet temperature, the vacuum pressure, the feed flow rate and the feed salt concentration.The proposed model was found to be capable of predicting accurately the unseen data of the VMD desalination process. The correlation coefficient of the overall agreement between the ANN predictions and experimental data was found to be more than 0.994.The calculation value of the coefficient of variation (CV) was 0.02622, and there was coincident overlap between the target and the output data from the 3D generalization diagrams. The optimal operating conditions of the VMD process can be obtained from the performance analysis of the ANN model with a maximum permeate flux and an acceptable CV value based on the experiment.

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