Great Wall Motors (China)

Considering the complex pollution characteristics of high ammonia nitrogen (NH3-N), high chemical oxygen demand(COD) and high salinity in photovoltaic (PV) module production wastewater, this paper focuses on process compatibility and cost-effectiveness optimization. It systematically reviews the technological evolution of PV-related wastewater treatment and summarizes the innovative pathway of a graded treatment approach coupled with dynamic interlocking processes. Demondtration through a case study at a 300MW PV park in Ningxia confirmed the pollutant removal efficiency and economic advantages of the proposed process. The results demonstrated an average NH3-Nremoval rate of 98.5%, COD degradation exceeding 95%, salinity recovery purity of 93%, and a 26.7% reduction in per-ton treatment costs compared to conventional methods. To address technical bottlenecks such as salinity separation precision, directions including AIoT-enabled intelligent operation were proposed. This study provides dual technical and economic solutions to achieve zero-liquid discharge for PV industry wastewater.

In this study, we synthesized β-cyclodextrin nanosponges (β-CDNSs) via crosslinking between β-cyclodextrin and diphenyl carbonate using a solvent method. Furthermore, we analyzed the variation of the invert emulsion drilling fluid properties in terms of the rheology, filtration, and emulsion stability subjected to thermal aging at various temperatures in the presence of β-CDNSs. Experimental results indicated that for temperatures below 160 °C, the drilling fluid properties exhibited a limited improvement following the inclusion of β-CDNSs. When the temperature was above 160 °C, hydrothermal carbonization occurred for an oil/water ratio below 9:1. The generated carbon spheres were adsorbed at the oil/water interface and served as emulsion stabilizers. These particles tended to form network structures and provided the desired rheological properties. They could also plug the micropores and reduce the permeability of the filter cake, thereby significantly reducing the filtration loss. The properties of conventional additives deteriorate gradually with an increase in the temperature; hydrothermal carbonization also changes the structure of β-CDNSs and generates carbon spheres above the critical temperature of 160 °C, which improves the performance of the invert emulsion drilling fluid. This study presents a novel method to adjust the properties of invert emulsion drilling fluid under high-temperature conditions.

ABSTRACT Semi-supervised instance segmentation algorithms are mainly divided into algorithms based on pseudo-label generation and algorithms based on transfer learning. The algorithms based on pseudo-label generation need to design a specific pseudo-label generation process, but the process is not scalable for different types of source tasks. The algorithms based on transfer learning that started late have relatively high scalability, but the algorithm research ideas are relatively simple. To expand the research on semi-supervised instance segmentation based on transfer learning, this paper proposes a feature transfer-based semi-supervised instance segmentation algorithm Feature Transfer Mask R-CNN (FT-Mask). The FT-Mask algorithm is more scalable than algorithms based on pseudo-label generation and can be used to transfer knowledge from different types of source tasks. Compared with other semi-supervised instance segmentation algorithms based on transfer learning, FT-Mask uses the feature transfer method to achieve semi-supervised instance segmentation for the first time. The experimental results show that the FT-Mask model improves the semi-supervised instance segmentation accuracy of the Mask R-CNN benchmark model through the semi-supervised learning process, and can achieve effective transfer learning.