Fully refined paraffin refers to a product obtained by using oil-containing wax as the raw material, degreased by sweating or solvent, and then refined by clay or hydrogenation.
Fully refined paraffin is also called refined white wax. The appearance of fully refined paraffin is white solid, with block and granular products. Its product has a higher melting point, less oil content, no sticking at room temperature, no sweating, no greasy feeling, waterproof, moisture-proof It has good electrical insulation. Because of its high degree of sophistication and low content of fused aromatic hydrocarbons. It has good chemical stability and light, good thermal stability, strong toughness, good plasticity, white color, no mechanical impurities and moisture, and no smell. Fully refined paraffin can be Soluble in: benzene, ether, chloroform, carbon disulfide, carbon tetrachloride, turpentine, petroleum, fixed oils.
Mainly used in high-frequency porcelain, carbon paper, stylus wax paper, precision casting, decorative sound-absorbing board and other products. Also used in packaging, electronics, textile candle crayons, match making and other industries. In addition, manufacturing high-grade candles and various industrial, civilian candle products Raw materials and additives, modifiers, etc.
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On February 25, the world's first integrated intelligent detection pipeline for distribution terminals, combining visual recognition and artificial intelligence technology, was officially completed and put into operation at the State Grid Jiangsu Electric Power Research Institute. This groundbreaking system has an annual detection capacity of 300,000 units, including 180,000 intelligent distribution terminals (TTUs) and 120,000 fault indicators (FIs), filling a critical gap in the intelligent testing of distribution equipment.
This new pipeline represents a major leap forward in the field of smart grid development and the Internet of Things (IoT). Distribution automation plays a key role in building strong smart grids and a ubiquitous power IoT network, with power distribution terminals serving as the core components of these systems. Traditionally, the testing of such equipment relied heavily on manual labor or mechanical arms, which led to inefficiencies, high costs, and safety risks.
The newly developed AI-based detection pipeline by the State Grid Jiangsu Electric Power Research Institute covers the entire lifecycle of distribution terminals—from application and approval to delivery, testing, reporting, and distribution. It offers features like self-adaptation, self-learning, self-diagnosis, and compatibility, ensuring efficient and accurate management during commissioning, operation tracking, and state assessment.
The system includes two specialized detection lines: one for TTUs and another for FIs, along with an intelligent three-dimensional warehouse, a terminal detection system, and a comprehensive software platform for managing the testing process. The TTU detection line uses advanced visual recognition and positioning technology to achieve precise alignment and insertion, overcoming challenges related to multi-shape interfaces and 2.5 mm pinhole precision. With 96 stations and 28 test items, it can handle 300,000 units annually. The FI detection line leverages AI and robotic arms to accurately separate spring pieces and position them, solving issues like multi-shape compatibility and 4 mm slit alignment.
According to reports, this innovative system has boosted detection efficiency by 100 times, eliminated labor costs entirely, and significantly improved safety. It addresses the limitations of traditional manual testing, offering a more efficient, reliable, and secure solution. The successful implementation of this AI-driven detection pipeline will enhance the quality control of distribution terminals entering the network, providing strong technical support for the State Grid Corporation’s efforts to build a "three-type and two-network" infrastructure.
(Sources: Jiangsu Science and Technology News, Jiangsu Electric Power News, China Economic News Network)