The dewatering of dredged sludge is a critical step in the minimization and reutilization of this solid waste. However, there is a lack of available literature on the fundamental drying characteristics of dredged sludge. In this work, two kinds of typical sludge dredged from an urban watercourse were tested by low-field NMR to investigate the water distribution in sludge and it was found that water contained in sludge can be classified into three categories: free water, capillary water and bound water. In addition, a novel model was proposed based on the Lennard-Jones equation and Kelvin law to quantitatively evaluate the binding energy during drying. Further, the model results were experimentally verified by thermogravimetry differential thermal analysis (TG-DTA). Results show that the trends of the model are consistent with the experimental values and the gradient of energy consumption during dehydration can be divided into three main stages. In stage 1, the total energy required for dewatering equals the latent heat of free water. In stage 2, binding energy reaches dozens to hundreds of kJ/kg accounting for capillary action. In stage 3, binding energy increases steeply reaching almost thousands of kJ/kg due to intermolecular interactions. All the discovered aspects could improve the management and disposal of dredged sludge from an energy cost perspective.

The increasingly serious imbalance between the supply and demand of water resources necessitates the establishment of a scientific and reasonable comprehensive evaluation method for industrial water use efficiency (WUE). In this study, a general method for industrial WUE evaluation on an enterprise scale was proposed by combining the analytic hierarchy process (AHP), entropy weight method (EWM), and self-organizing map (SOM), and it was tested in several areas of Zhejiang Province, China. The results show that the composite indexes generated using the AHP and EWM were different and were employed as the input of the SOM to divide enterprises into four categories. Most enterprises were classified as Class A, with a relatively high WUE, accounting for 82.5% of the total, while those in Class D, with a relatively low WUE, only accounted for 0.5% of the total. Furthermore, the differences in WUE for industry classification and spatial distribution were also analyzed. The classification results of several industries were more diverse, especially for those industries in which water plays an important role in production. Moreover, the spatial distribution of WUE classifications also implied that the clustering of enterprises has a positive effect on the improvement in WUE. In other words, it is feasible to improve WUE through industry clustering and sub-industry management. In summary, a comprehensive, detailed evaluation of industrial WUE was conducted on an enterprise scale, which can also be applied to other areas and used as a reference for local water resource managers for formulating targeted policies.