Effective and mechanistic insights into shale gas wastewater reverse osmosis concentrate treatment using ozonation-biological activated carbon process.

Reverse osmosis (RO) plays a pivotal role in shale gas wastewater resource utilization. However, managing the reverse osmosis concentrate (ROC) characterized by high salinity and increased concentrations of organic matter is challenging. In this study, we aimed to elucidate the enhancement effects and mechanisms of pre-ozonation on organic matter removal efficacy in ROC using a biological activated carbon (BAC) system. Our findings revealed that during the stable operation phase, the ozonation (O3 and O3/granular activated carbon)-BAC system removes 43.6-72.2 % of dissolved organic carbon, achieving a 4-7 fold increase in efficiency compared with that in the BAC system alone. Through dynamic analysis of influent and effluent water quality, biofilm performance, and microbial community structure, succession, and function prediction, we elucidated the following primary enhancement mechanisms: 1) pre-ozonation significantly enhances the biodegradability of ROC by 4.5-6 times and diminishes the organic load on the BAC system; 2) pre-ozonation facilitates the selective enrichment of microbes capable of degrading organic compounds in the BAC system, thereby enhancing the biodegradation capacity and stability of the microbial community; and 3) pre-ozonation accelerates the regeneration rate of the granular activated carbon adsorption sites. Collectively, our findings provide valuable insights into treating ROC through pre-oxidation combined with biotreatment.

QNet: An Adaptive Quantization Table Generator Based on Convolutional Neural Network.

The JPEG is one of the most widely used lossy image-compression standards, whose compression performance depends largely on a quantization table. In this work, we utilize a Convolutional Neural Network (CNN) to generate an image-adaptive quantization table in a standard-compliant way. We first build an image set containing more than 10,000 images and generate their optimal quantization tables through a classical genetic algorithm, and then propose a method that can efficiently extract and fuse the frequency and spatial domain information of each image to train a regression network to directly generate adaptive quantization tables. In addition, we extract several representative quantization tables from the dataset and train a classification network to indicate the optimal one for each image, which further improves compression performance and computational efficiency. Tests on diverse images show that the proposed method clearly outperforms the state-of-the-art method. Compared with the standard table at the compression rate of 1.0 bpp, the regression and classification network provide average Peak Signal-to-Noise Ratio (PSNR) gains of nearly 1.2 and 1.4 dB. For the experiment under Structural Similarity Index Measurement (SSIM), the improvements are 0.4% and 0.54%, respectively. The proposed method also has competitive computational efficiency, as the regression and classification network only take 15 and 6.25 milliseconds, respectively, to process a 768 W 512 image on a single CPU core at 3.20 GHz.