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Freeze desalination is an emerging technique since it uses much less energy than most other thermal technologies. As a portion of zero/minimum liquid discharge technologies, crystallization is being commercially used, however, it is probably the most expensive section of the desalination plant. Several freeze desalination techniques are being developed including progressive layer, falling film, suspension freeze, and gas hydrate desalination. The emphasis of most of these methods is to improve the desalination efficiency. Developing a complete freeze desalination plant requires recognizing the critical importance of both recovery rate and desalination efficiency. In a study, a comprehensive freeze desalination plant was designed with a 50% recovery rate and 50% desalination efficiency. To achieve proper salt rejection from 78% of incoming seawater, the plant needs to undergo 46 stages of desalination. The plant is then redesigned with the recovery rate of 90% and the desalination efficiency of 90%. It is shown that in only 6 stages of desalination, 89% of the whole seawater can be desalinated which is a cost reduction of at least 87%. [ABSTRACT FROM AUTHOR]

To ensure the safety and stability of the shield tunnel construction process, the ground settlement induced by the shield construction needs to be effectively predicted. In this paper, a prediction method combining empirical mode decomposition (EMD), chaotic adaptive sparrow search algorithm (CASSA), and extreme learning machine (ELM) is proposed. First, the EMD is used to decompose the settlement sequence into trend vectors and fluctuation vectors to fully extract the effective information of the sequence; Second, the sparrow search algorithm is improved by introducing Cubic chaotic mapping to initialize the population and adaptive factor to optimize the searcher's position formula, and the chaotic adaptive sparrow search algorithm is proposed; Finally, the CASSA-ELM prediction model is constructed by using CASSA to find the optimal values of weights and thresholds in the extreme learning machine. The fluctuation components and trend components decomposed by EMD are predicted one by one, and the prediction results are superimposed and reconstructed to obtain the predicted final settlement. Taking a shield interval in Jiangsu, China as an example, the meta-heuristic algorithm-optimized ELM model improves the prediction accuracy by 10.70% compared with the traditional ELM model. The combined EMD-CASSA-ELM prediction model can greatly improve the accuracy and speed of surface settlement prediction, and provide a new means for safety monitoring in shield tunnel construction. Intelligent prediction methods can predict surface subsidence more automatically and quickly, becoming a new development trend. [ABSTRACT FROM AUTHOR]