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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]

In this paper, gangue Coal biochar GCC was developed to absorb heavy metals from an aqueous solution. There is currently insufficient information on the identification of the best biochar modifiers for the purpose of removing heavy metals as well as changes in physicochemical and morphological compositions after modification. For this purpose, after physical treatment GCC ware modified with 5 different offending chemical methods (GCCacidic), (GCCalkaline), (GCCoxidic), (GCCMnOx), and (GCCFeOx). using (XRF), (FT-IR), (XRD), (SEM-EDX), and (BET) assortment. Adsorption tests of 3 heavy metals (Cu, Pb, and Cd) ions under different conditions, ion concentration, pH, Adsorbent dosage, and contact time were investigated in batch experiments. Improved post-modification GCC properties, especially (GCCalkaline) and (GCCMnOx) Significant changes in its surface area as well as functional groups (hydroxyl group, and Manganese oxide group). To will know adsorption capacity of every modified GCC, experimental data were placed in Langmuir and Freundlich's isotherms models. The GCC process follows pseudo-second-order and pseudo-first-order kinetic curves with linear regression. GCC modified with Manganese oxide shows it has the best adsorbent for adsorption of Pb and GCC modified with alkaline is the best adsorbent for adsorption of Cd and Cu metals. For adsorption of mixed metals solution (Pb, Cd, and Cu) GCCMnOx was more suitable than other GCC and modified GCCs biochars. The results show that GCC modified with an acidic component is the worst attraction for removing heavy metals from an aqueous solution. [ABSTRACT FROM AUTHOR]